diff --git a/llmeval-env/lib/python3.10/site-packages/transformers/models/conditional_detr/__init__.py b/llmeval-env/lib/python3.10/site-packages/transformers/models/conditional_detr/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..565323321160ff80e3abbd120dd591dcc43d0f6c --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/transformers/models/conditional_detr/__init__.py @@ -0,0 +1,85 @@ +# Copyright 2022 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from typing import TYPE_CHECKING + +from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available + + +_import_structure = { + "configuration_conditional_detr": [ + "CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", + "ConditionalDetrConfig", + "ConditionalDetrOnnxConfig", + ] +} + +try: + if not is_vision_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["feature_extraction_conditional_detr"] = ["ConditionalDetrFeatureExtractor"] + _import_structure["image_processing_conditional_detr"] = ["ConditionalDetrImageProcessor"] + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_conditional_detr"] = [ + "CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST", + "ConditionalDetrForObjectDetection", + "ConditionalDetrForSegmentation", + "ConditionalDetrModel", + "ConditionalDetrPreTrainedModel", + ] + + +if TYPE_CHECKING: + from .configuration_conditional_detr import ( + CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, + ConditionalDetrConfig, + ConditionalDetrOnnxConfig, + ) + + try: + if not is_vision_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor + from .image_processing_conditional_detr import ConditionalDetrImageProcessor + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_conditional_detr import ( + CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST, + ConditionalDetrForObjectDetection, + ConditionalDetrForSegmentation, + ConditionalDetrModel, + ConditionalDetrPreTrainedModel, + ) + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/llmeval-env/lib/python3.10/site-packages/transformers/models/conditional_detr/__pycache__/__init__.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/transformers/models/conditional_detr/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..2c24af92cb59554bcb10f791681cf5f6de74877a Binary files /dev/null and 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0000000000000000000000000000000000000000..945e5edb32ad30a801d45e6de65edff1bfd2eae4 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/transformers/models/conditional_detr/configuration_conditional_detr.py @@ -0,0 +1,273 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" Conditional DETR model configuration""" +from collections import OrderedDict +from typing import Mapping + +from packaging import version + +from ...configuration_utils import PretrainedConfig +from ...onnx import OnnxConfig +from ...utils import logging +from ..auto import CONFIG_MAPPING + + +logger = logging.get_logger(__name__) + + +from ..deprecated._archive_maps import CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402 + + +class ConditionalDetrConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`ConditionalDetrModel`]. It is used to instantiate + a Conditional DETR model according to the specified arguments, defining the model architecture. Instantiating a + configuration with the defaults will yield a similar configuration to that of the Conditional DETR + [microsoft/conditional-detr-resnet-50](https://huggingface.co/microsoft/conditional-detr-resnet-50) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + use_timm_backbone (`bool`, *optional*, defaults to `True`): + Whether or not to use the `timm` library for the backbone. If set to `False`, will use the [`AutoBackbone`] + API. + backbone_config (`PretrainedConfig` or `dict`, *optional*): + The configuration of the backbone model. Only used in case `use_timm_backbone` is set to `False` in which + case it will default to `ResNetConfig()`. + num_channels (`int`, *optional*, defaults to 3): + The number of input channels. + num_queries (`int`, *optional*, defaults to 100): + Number of object queries, i.e. detection slots. This is the maximal number of objects + [`ConditionalDetrModel`] can detect in a single image. For COCO, we recommend 100 queries. + d_model (`int`, *optional*, defaults to 256): + Dimension of the layers. + encoder_layers (`int`, *optional*, defaults to 6): + Number of encoder layers. + decoder_layers (`int`, *optional*, defaults to 6): + Number of decoder layers. + encoder_attention_heads (`int`, *optional*, defaults to 8): + Number of attention heads for each attention layer in the Transformer encoder. + decoder_attention_heads (`int`, *optional*, defaults to 8): + Number of attention heads for each attention layer in the Transformer decoder. + decoder_ffn_dim (`int`, *optional*, defaults to 2048): + Dimension of the "intermediate" (often named feed-forward) layer in decoder. + encoder_ffn_dim (`int`, *optional*, defaults to 2048): + Dimension of the "intermediate" (often named feed-forward) layer in decoder. + activation_function (`str` or `function`, *optional*, defaults to `"relu"`): + The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, + `"relu"`, `"silu"` and `"gelu_new"` are supported. + dropout (`float`, *optional*, defaults to 0.1): + The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. + attention_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for the attention probabilities. + activation_dropout (`float`, *optional*, defaults to 0.0): + The dropout ratio for activations inside the fully connected layer. + init_std (`float`, *optional*, defaults to 0.02): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + init_xavier_std (`float`, *optional*, defaults to 1): + The scaling factor used for the Xavier initialization gain in the HM Attention map module. + encoder_layerdrop (`float`, *optional*, defaults to 0.0): + The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) + for more details. + decoder_layerdrop (`float`, *optional*, defaults to 0.0): + The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) + for more details. + auxiliary_loss (`bool`, *optional*, defaults to `False`): + Whether auxiliary decoding losses (loss at each decoder layer) are to be used. + position_embedding_type (`str`, *optional*, defaults to `"sine"`): + Type of position embeddings to be used on top of the image features. One of `"sine"` or `"learned"`. + backbone (`str`, *optional*, defaults to `"resnet50"`): + Name of backbone to use when `backbone_config` is `None`. If `use_pretrained_backbone` is `True`, this + will load the corresponding pretrained weights from the timm or transformers library. If `use_pretrained_backbone` + is `False`, this loads the backbone's config and uses that to initialize the backbone with random weights. + use_pretrained_backbone (`bool`, *optional*, defaults to `True`): + Whether to use pretrained weights for the backbone. + backbone_kwargs (`dict`, *optional*): + Keyword arguments to be passed to AutoBackbone when loading from a checkpoint + e.g. `{'out_indices': (0, 1, 2, 3)}`. Cannot be specified if `backbone_config` is set. + dilation (`bool`, *optional*, defaults to `False`): + Whether to replace stride with dilation in the last convolutional block (DC5). Only supported when + `use_timm_backbone` = `True`. + class_cost (`float`, *optional*, defaults to 1): + Relative weight of the classification error in the Hungarian matching cost. + bbox_cost (`float`, *optional*, defaults to 5): + Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost. + giou_cost (`float`, *optional*, defaults to 2): + Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost. + mask_loss_coefficient (`float`, *optional*, defaults to 1): + Relative weight of the Focal loss in the panoptic segmentation loss. + dice_loss_coefficient (`float`, *optional*, defaults to 1): + Relative weight of the DICE/F-1 loss in the panoptic segmentation loss. + bbox_loss_coefficient (`float`, *optional*, defaults to 5): + Relative weight of the L1 bounding box loss in the object detection loss. + giou_loss_coefficient (`float`, *optional*, defaults to 2): + Relative weight of the generalized IoU loss in the object detection loss. + eos_coefficient (`float`, *optional*, defaults to 0.1): + Relative classification weight of the 'no-object' class in the object detection loss. + focal_alpha (`float`, *optional*, defaults to 0.25): + Alpha parameter in the focal loss. + + Examples: + + ```python + >>> from transformers import ConditionalDetrConfig, ConditionalDetrModel + + >>> # Initializing a Conditional DETR microsoft/conditional-detr-resnet-50 style configuration + >>> configuration = ConditionalDetrConfig() + + >>> # Initializing a model (with random weights) from the microsoft/conditional-detr-resnet-50 style configuration + >>> model = ConditionalDetrModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "conditional_detr" + keys_to_ignore_at_inference = ["past_key_values"] + attribute_map = { + "hidden_size": "d_model", + "num_attention_heads": "encoder_attention_heads", + } + + def __init__( + self, + use_timm_backbone=True, + backbone_config=None, + num_channels=3, + num_queries=300, + encoder_layers=6, + encoder_ffn_dim=2048, + encoder_attention_heads=8, + decoder_layers=6, + decoder_ffn_dim=2048, + decoder_attention_heads=8, + encoder_layerdrop=0.0, + decoder_layerdrop=0.0, + is_encoder_decoder=True, + activation_function="relu", + d_model=256, + dropout=0.1, + attention_dropout=0.0, + activation_dropout=0.0, + init_std=0.02, + init_xavier_std=1.0, + auxiliary_loss=False, + position_embedding_type="sine", + backbone="resnet50", + use_pretrained_backbone=True, + backbone_kwargs=None, + dilation=False, + class_cost=2, + bbox_cost=5, + giou_cost=2, + mask_loss_coefficient=1, + dice_loss_coefficient=1, + cls_loss_coefficient=2, + bbox_loss_coefficient=5, + giou_loss_coefficient=2, + focal_alpha=0.25, + **kwargs, + ): + if not use_timm_backbone and use_pretrained_backbone: + raise ValueError( + "Loading pretrained backbone weights from the transformers library is not supported yet. `use_timm_backbone` must be set to `True` when `use_pretrained_backbone=True`" + ) + + if backbone_config is not None and backbone is not None: + raise ValueError("You can't specify both `backbone` and `backbone_config`.") + + if backbone_config is not None and use_timm_backbone: + raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`.") + + if backbone_kwargs is not None and backbone_kwargs and backbone_config is not None: + raise ValueError("You can't specify both `backbone_kwargs` and `backbone_config`.") + + if not use_timm_backbone: + if backbone_config is None: + logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.") + backbone_config = CONFIG_MAPPING["resnet"](out_features=["stage4"]) + elif isinstance(backbone_config, dict): + backbone_model_type = backbone_config.get("model_type") + config_class = CONFIG_MAPPING[backbone_model_type] + backbone_config = config_class.from_dict(backbone_config) + + self.use_timm_backbone = use_timm_backbone + self.backbone_config = backbone_config + self.num_channels = num_channels + self.num_queries = num_queries + self.d_model = d_model + self.encoder_ffn_dim = encoder_ffn_dim + self.encoder_layers = encoder_layers + self.encoder_attention_heads = encoder_attention_heads + self.decoder_ffn_dim = decoder_ffn_dim + self.decoder_layers = decoder_layers + self.decoder_attention_heads = decoder_attention_heads + self.dropout = dropout + self.attention_dropout = attention_dropout + self.activation_dropout = activation_dropout + self.activation_function = activation_function + self.init_std = init_std + self.init_xavier_std = init_xavier_std + self.encoder_layerdrop = encoder_layerdrop + self.decoder_layerdrop = decoder_layerdrop + self.num_hidden_layers = encoder_layers + self.auxiliary_loss = auxiliary_loss + self.position_embedding_type = position_embedding_type + self.backbone = backbone + self.use_pretrained_backbone = use_pretrained_backbone + self.backbone_kwargs = backbone_kwargs + self.dilation = dilation + # Hungarian matcher + self.class_cost = class_cost + self.bbox_cost = bbox_cost + self.giou_cost = giou_cost + # Loss coefficients + self.mask_loss_coefficient = mask_loss_coefficient + self.dice_loss_coefficient = dice_loss_coefficient + self.cls_loss_coefficient = cls_loss_coefficient + self.bbox_loss_coefficient = bbox_loss_coefficient + self.giou_loss_coefficient = giou_loss_coefficient + self.focal_alpha = focal_alpha + super().__init__(is_encoder_decoder=is_encoder_decoder, **kwargs) + + @property + def num_attention_heads(self) -> int: + return self.encoder_attention_heads + + @property + def hidden_size(self) -> int: + return self.d_model + + +class ConditionalDetrOnnxConfig(OnnxConfig): + torch_onnx_minimum_version = version.parse("1.11") + + @property + def inputs(self) -> Mapping[str, Mapping[int, str]]: + return OrderedDict( + [ + ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}), + ("pixel_mask", {0: "batch"}), + ] + ) + + @property + def atol_for_validation(self) -> float: + return 1e-5 + + @property + def default_onnx_opset(self) -> int: + return 12 diff --git a/llmeval-env/lib/python3.10/site-packages/transformers/models/conditional_detr/convert_conditional_detr_original_pytorch_checkpoint_to_pytorch.py b/llmeval-env/lib/python3.10/site-packages/transformers/models/conditional_detr/convert_conditional_detr_original_pytorch_checkpoint_to_pytorch.py new file mode 100644 index 0000000000000000000000000000000000000000..b1a1b1c817ae702ac8f99513ebbe4c90eefdece6 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/transformers/models/conditional_detr/convert_conditional_detr_original_pytorch_checkpoint_to_pytorch.py @@ -0,0 +1,325 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert Conditional DETR checkpoints.""" + + +import argparse +import json +from collections import OrderedDict +from pathlib import Path + +import requests +import torch +from huggingface_hub import hf_hub_download +from PIL import Image + +from transformers import ( + ConditionalDetrConfig, + ConditionalDetrForObjectDetection, + ConditionalDetrForSegmentation, + ConditionalDetrImageProcessor, +) +from transformers.utils import logging + + +logging.set_verbosity_info() +logger = logging.get_logger(__name__) + +# here we list all keys to be renamed (original name on the left, our name on the right) +rename_keys = [] +for i in range(6): + # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms + rename_keys.append( + (f"transformer.encoder.layers.{i}.self_attn.out_proj.weight", f"encoder.layers.{i}.self_attn.out_proj.weight") + ) + rename_keys.append( + (f"transformer.encoder.layers.{i}.self_attn.out_proj.bias", f"encoder.layers.{i}.self_attn.out_proj.bias") + ) + rename_keys.append((f"transformer.encoder.layers.{i}.linear1.weight", f"encoder.layers.{i}.fc1.weight")) + rename_keys.append((f"transformer.encoder.layers.{i}.linear1.bias", f"encoder.layers.{i}.fc1.bias")) + rename_keys.append((f"transformer.encoder.layers.{i}.linear2.weight", f"encoder.layers.{i}.fc2.weight")) + rename_keys.append((f"transformer.encoder.layers.{i}.linear2.bias", f"encoder.layers.{i}.fc2.bias")) + rename_keys.append( + (f"transformer.encoder.layers.{i}.norm1.weight", f"encoder.layers.{i}.self_attn_layer_norm.weight") + ) + rename_keys.append((f"transformer.encoder.layers.{i}.norm1.bias", f"encoder.layers.{i}.self_attn_layer_norm.bias")) + rename_keys.append((f"transformer.encoder.layers.{i}.norm2.weight", f"encoder.layers.{i}.final_layer_norm.weight")) + rename_keys.append((f"transformer.encoder.layers.{i}.norm2.bias", f"encoder.layers.{i}.final_layer_norm.bias")) + # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms + rename_keys.append( + (f"transformer.decoder.layers.{i}.self_attn.out_proj.weight", f"decoder.layers.{i}.self_attn.out_proj.weight") + ) + rename_keys.append( + (f"transformer.decoder.layers.{i}.self_attn.out_proj.bias", f"decoder.layers.{i}.self_attn.out_proj.bias") + ) + rename_keys.append( + ( + f"transformer.decoder.layers.{i}.cross_attn.out_proj.weight", + f"decoder.layers.{i}.encoder_attn.out_proj.weight", + ) + ) + rename_keys.append( + ( + f"transformer.decoder.layers.{i}.cross_attn.out_proj.bias", + f"decoder.layers.{i}.encoder_attn.out_proj.bias", + ) + ) + rename_keys.append((f"transformer.decoder.layers.{i}.linear1.weight", f"decoder.layers.{i}.fc1.weight")) + rename_keys.append((f"transformer.decoder.layers.{i}.linear1.bias", f"decoder.layers.{i}.fc1.bias")) + rename_keys.append((f"transformer.decoder.layers.{i}.linear2.weight", f"decoder.layers.{i}.fc2.weight")) + rename_keys.append((f"transformer.decoder.layers.{i}.linear2.bias", f"decoder.layers.{i}.fc2.bias")) + rename_keys.append( + (f"transformer.decoder.layers.{i}.norm1.weight", f"decoder.layers.{i}.self_attn_layer_norm.weight") + ) + rename_keys.append((f"transformer.decoder.layers.{i}.norm1.bias", f"decoder.layers.{i}.self_attn_layer_norm.bias")) + rename_keys.append( + (f"transformer.decoder.layers.{i}.norm2.weight", f"decoder.layers.{i}.encoder_attn_layer_norm.weight") + ) + rename_keys.append( + (f"transformer.decoder.layers.{i}.norm2.bias", f"decoder.layers.{i}.encoder_attn_layer_norm.bias") + ) + rename_keys.append((f"transformer.decoder.layers.{i}.norm3.weight", f"decoder.layers.{i}.final_layer_norm.weight")) + rename_keys.append((f"transformer.decoder.layers.{i}.norm3.bias", f"decoder.layers.{i}.final_layer_norm.bias")) + + # q, k, v projections in self/cross-attention in decoder for conditional DETR + rename_keys.append( + (f"transformer.decoder.layers.{i}.sa_qcontent_proj.weight", f"decoder.layers.{i}.sa_qcontent_proj.weight") + ) + rename_keys.append( + (f"transformer.decoder.layers.{i}.sa_kcontent_proj.weight", f"decoder.layers.{i}.sa_kcontent_proj.weight") + ) + rename_keys.append( + (f"transformer.decoder.layers.{i}.sa_qpos_proj.weight", f"decoder.layers.{i}.sa_qpos_proj.weight") + ) + rename_keys.append( + (f"transformer.decoder.layers.{i}.sa_kpos_proj.weight", f"decoder.layers.{i}.sa_kpos_proj.weight") + ) + rename_keys.append((f"transformer.decoder.layers.{i}.sa_v_proj.weight", f"decoder.layers.{i}.sa_v_proj.weight")) + rename_keys.append( + (f"transformer.decoder.layers.{i}.ca_qcontent_proj.weight", f"decoder.layers.{i}.ca_qcontent_proj.weight") + ) + # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight")) + rename_keys.append( + (f"transformer.decoder.layers.{i}.ca_kcontent_proj.weight", f"decoder.layers.{i}.ca_kcontent_proj.weight") + ) + rename_keys.append( + (f"transformer.decoder.layers.{i}.ca_kpos_proj.weight", f"decoder.layers.{i}.ca_kpos_proj.weight") + ) + rename_keys.append((f"transformer.decoder.layers.{i}.ca_v_proj.weight", f"decoder.layers.{i}.ca_v_proj.weight")) + rename_keys.append( + (f"transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight", f"decoder.layers.{i}.ca_qpos_sine_proj.weight") + ) + + rename_keys.append( + (f"transformer.decoder.layers.{i}.sa_qcontent_proj.bias", f"decoder.layers.{i}.sa_qcontent_proj.bias") + ) + rename_keys.append( + (f"transformer.decoder.layers.{i}.sa_kcontent_proj.bias", f"decoder.layers.{i}.sa_kcontent_proj.bias") + ) + rename_keys.append((f"transformer.decoder.layers.{i}.sa_qpos_proj.bias", f"decoder.layers.{i}.sa_qpos_proj.bias")) + rename_keys.append((f"transformer.decoder.layers.{i}.sa_kpos_proj.bias", f"decoder.layers.{i}.sa_kpos_proj.bias")) + rename_keys.append((f"transformer.decoder.layers.{i}.sa_v_proj.bias", f"decoder.layers.{i}.sa_v_proj.bias")) + rename_keys.append( + (f"transformer.decoder.layers.{i}.ca_qcontent_proj.bias", f"decoder.layers.{i}.ca_qcontent_proj.bias") + ) + # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias")) + rename_keys.append( + (f"transformer.decoder.layers.{i}.ca_kcontent_proj.bias", f"decoder.layers.{i}.ca_kcontent_proj.bias") + ) + rename_keys.append((f"transformer.decoder.layers.{i}.ca_kpos_proj.bias", f"decoder.layers.{i}.ca_kpos_proj.bias")) + rename_keys.append((f"transformer.decoder.layers.{i}.ca_v_proj.bias", f"decoder.layers.{i}.ca_v_proj.bias")) + rename_keys.append( + (f"transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias", f"decoder.layers.{i}.ca_qpos_sine_proj.bias") + ) + +# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads +# for conditional DETR, also convert reference point head and query scale MLP +rename_keys.extend( + [ + ("input_proj.weight", "input_projection.weight"), + ("input_proj.bias", "input_projection.bias"), + ("query_embed.weight", "query_position_embeddings.weight"), + ("transformer.decoder.norm.weight", "decoder.layernorm.weight"), + ("transformer.decoder.norm.bias", "decoder.layernorm.bias"), + ("class_embed.weight", "class_labels_classifier.weight"), + ("class_embed.bias", "class_labels_classifier.bias"), + ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"), + ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"), + ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"), + ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"), + ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"), + ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"), + ("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"), + ("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"), + ("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"), + ("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"), + ("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"), + ("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"), + ("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"), + ("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"), + ("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"), + ("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"), + ] +) + + +def rename_key(state_dict, old, new): + val = state_dict.pop(old) + state_dict[new] = val + + +def rename_backbone_keys(state_dict): + new_state_dict = OrderedDict() + for key, value in state_dict.items(): + if "backbone.0.body" in key: + new_key = key.replace("backbone.0.body", "backbone.conv_encoder.model") + new_state_dict[new_key] = value + else: + new_state_dict[key] = value + + return new_state_dict + + +def read_in_q_k_v(state_dict, is_panoptic=False): + prefix = "" + if is_panoptic: + prefix = "conditional_detr." + + # first: transformer encoder + for i in range(6): + # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias) + in_proj_weight = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight") + in_proj_bias = state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias") + # next, add query, keys and values (in that order) to the state dict + state_dict[f"encoder.layers.{i}.self_attn.q_proj.weight"] = in_proj_weight[:256, :] + state_dict[f"encoder.layers.{i}.self_attn.q_proj.bias"] = in_proj_bias[:256] + state_dict[f"encoder.layers.{i}.self_attn.k_proj.weight"] = in_proj_weight[256:512, :] + state_dict[f"encoder.layers.{i}.self_attn.k_proj.bias"] = in_proj_bias[256:512] + state_dict[f"encoder.layers.{i}.self_attn.v_proj.weight"] = in_proj_weight[-256:, :] + state_dict[f"encoder.layers.{i}.self_attn.v_proj.bias"] = in_proj_bias[-256:] + + +# We will verify our results on an image of cute cats +def prepare_img(): + url = "http://images.cocodataset.org/val2017/000000039769.jpg" + im = Image.open(requests.get(url, stream=True).raw) + + return im + + +@torch.no_grad() +def convert_conditional_detr_checkpoint(model_name, pytorch_dump_folder_path): + """ + Copy/paste/tweak model's weights to our CONDITIONAL_DETR structure. + """ + + # load default config + config = ConditionalDetrConfig() + # set backbone and dilation attributes + if "resnet101" in model_name: + config.backbone = "resnet101" + if "dc5" in model_name: + config.dilation = True + is_panoptic = "panoptic" in model_name + if is_panoptic: + config.num_labels = 250 + else: + config.num_labels = 91 + repo_id = "huggingface/label-files" + filename = "coco-detection-id2label.json" + id2label = json.load(open(hf_hub_download(repo_id, filename, repo_type="dataset"), "r")) + id2label = {int(k): v for k, v in id2label.items()} + config.id2label = id2label + config.label2id = {v: k for k, v in id2label.items()} + + # load image processor + format = "coco_panoptic" if is_panoptic else "coco_detection" + image_processor = ConditionalDetrImageProcessor(format=format) + + # prepare image + img = prepare_img() + encoding = image_processor(images=img, return_tensors="pt") + pixel_values = encoding["pixel_values"] + + logger.info(f"Converting model {model_name}...") + + # load original model from torch hub + conditional_detr = torch.hub.load("DeppMeng/ConditionalDETR", model_name, pretrained=True).eval() + state_dict = conditional_detr.state_dict() + # rename keys + for src, dest in rename_keys: + if is_panoptic: + src = "conditional_detr." + src + rename_key(state_dict, src, dest) + state_dict = rename_backbone_keys(state_dict) + # query, key and value matrices need special treatment + read_in_q_k_v(state_dict, is_panoptic=is_panoptic) + # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them + prefix = "conditional_detr.model." if is_panoptic else "model." + for key in state_dict.copy().keys(): + if is_panoptic: + if ( + key.startswith("conditional_detr") + and not key.startswith("class_labels_classifier") + and not key.startswith("bbox_predictor") + ): + val = state_dict.pop(key) + state_dict["conditional_detr.model" + key[4:]] = val + elif "class_labels_classifier" in key or "bbox_predictor" in key: + val = state_dict.pop(key) + state_dict["conditional_detr." + key] = val + elif key.startswith("bbox_attention") or key.startswith("mask_head"): + continue + else: + val = state_dict.pop(key) + state_dict[prefix + key] = val + else: + if not key.startswith("class_labels_classifier") and not key.startswith("bbox_predictor"): + val = state_dict.pop(key) + state_dict[prefix + key] = val + # finally, create HuggingFace model and load state dict + model = ConditionalDetrForSegmentation(config) if is_panoptic else ConditionalDetrForObjectDetection(config) + model.load_state_dict(state_dict) + model.eval() + model.push_to_hub(repo_id=model_name, organization="DepuMeng", commit_message="Add model") + # verify our conversion + original_outputs = conditional_detr(pixel_values) + outputs = model(pixel_values) + assert torch.allclose(outputs.logits, original_outputs["pred_logits"], atol=1e-4) + assert torch.allclose(outputs.pred_boxes, original_outputs["pred_boxes"], atol=1e-4) + if is_panoptic: + assert torch.allclose(outputs.pred_masks, original_outputs["pred_masks"], atol=1e-4) + + # Save model and image processor + logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}...") + Path(pytorch_dump_folder_path).mkdir(exist_ok=True) + model.save_pretrained(pytorch_dump_folder_path) + image_processor.save_pretrained(pytorch_dump_folder_path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + parser.add_argument( + "--model_name", + default="conditional_detr_resnet50", + type=str, + help="Name of the CONDITIONAL_DETR model you'd like to convert.", + ) + parser.add_argument( + "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model." + ) + args = parser.parse_args() + convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path) diff --git a/llmeval-env/lib/python3.10/site-packages/transformers/models/conditional_detr/feature_extraction_conditional_detr.py b/llmeval-env/lib/python3.10/site-packages/transformers/models/conditional_detr/feature_extraction_conditional_detr.py new file mode 100644 index 0000000000000000000000000000000000000000..bfdec373f865c5fcbaccfd6b3c906eb690942ddc --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/transformers/models/conditional_detr/feature_extraction_conditional_detr.py @@ -0,0 +1,43 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Feature extractor class for Conditional DETR.""" + +import warnings + +from ...image_transforms import rgb_to_id as _rgb_to_id +from ...utils import logging +from .image_processing_conditional_detr import ConditionalDetrImageProcessor + + +logger = logging.get_logger(__name__) + + +def rgb_to_id(x): + warnings.warn( + "rgb_to_id has moved and will not be importable from this module from v5. " + "Please import from transformers.image_transforms instead.", + FutureWarning, + ) + return _rgb_to_id(x) + + +class ConditionalDetrFeatureExtractor(ConditionalDetrImageProcessor): + def __init__(self, *args, **kwargs) -> None: + warnings.warn( + "The class ConditionalDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers." + " Please use ConditionalDetrImageProcessor instead.", + FutureWarning, + ) + super().__init__(*args, **kwargs) diff --git a/llmeval-env/lib/python3.10/site-packages/transformers/models/conditional_detr/image_processing_conditional_detr.py b/llmeval-env/lib/python3.10/site-packages/transformers/models/conditional_detr/image_processing_conditional_detr.py new file mode 100644 index 0000000000000000000000000000000000000000..e88bfc8fe230dffeb9d21e4057f15398e90c0222 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/transformers/models/conditional_detr/image_processing_conditional_detr.py @@ -0,0 +1,1777 @@ +# coding=utf-8 +# Copyright 2022 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Image processor class for Conditional DETR.""" + +import io +import pathlib +from collections import defaultdict +from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Union + +import numpy as np + +from ...feature_extraction_utils import BatchFeature +from ...image_processing_utils import BaseImageProcessor, get_size_dict +from ...image_transforms import ( + PaddingMode, + center_to_corners_format, + corners_to_center_format, + id_to_rgb, + pad, + rescale, + resize, + rgb_to_id, + to_channel_dimension_format, +) +from ...image_utils import ( + IMAGENET_DEFAULT_MEAN, + IMAGENET_DEFAULT_STD, + AnnotationFormat, + AnnotationType, + ChannelDimension, + ImageInput, + PILImageResampling, + get_image_size, + infer_channel_dimension_format, + is_scaled_image, + make_list_of_images, + to_numpy_array, + valid_images, + validate_annotations, + validate_kwargs, + validate_preprocess_arguments, +) +from ...utils import ( + TensorType, + is_flax_available, + is_jax_tensor, + is_scipy_available, + is_tf_available, + is_tf_tensor, + is_torch_available, + is_torch_tensor, + is_vision_available, + logging, +) + + +if is_torch_available(): + import torch + from torch import nn + + +if is_vision_available(): + import PIL + + +if is_scipy_available(): + import scipy.special + import scipy.stats + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +SUPPORTED_ANNOTATION_FORMATS = (AnnotationFormat.COCO_DETECTION, AnnotationFormat.COCO_PANOPTIC) + + +# Copied from transformers.models.detr.image_processing_detr.get_size_with_aspect_ratio +def get_size_with_aspect_ratio(image_size, size, max_size=None) -> Tuple[int, int]: + """ + Computes the output image size given the input image size and the desired output size. + + Args: + image_size (`Tuple[int, int]`): + The input image size. + size (`int`): + The desired output size. + max_size (`int`, *optional*): + The maximum allowed output size. + """ + height, width = image_size + if max_size is not None: + min_original_size = float(min((height, width))) + max_original_size = float(max((height, width))) + if max_original_size / min_original_size * size > max_size: + size = int(round(max_size * min_original_size / max_original_size)) + + if (height <= width and height == size) or (width <= height and width == size): + return height, width + + if width < height: + ow = size + oh = int(size * height / width) + else: + oh = size + ow = int(size * width / height) + return (oh, ow) + + +# Copied from transformers.models.detr.image_processing_detr.get_resize_output_image_size +def get_resize_output_image_size( + input_image: np.ndarray, + size: Union[int, Tuple[int, int], List[int]], + max_size: Optional[int] = None, + input_data_format: Optional[Union[str, ChannelDimension]] = None, +) -> Tuple[int, int]: + """ + Computes the output image size given the input image size and the desired output size. If the desired output size + is a tuple or list, the output image size is returned as is. If the desired output size is an integer, the output + image size is computed by keeping the aspect ratio of the input image size. + + Args: + input_image (`np.ndarray`): + The image to resize. + size (`int` or `Tuple[int, int]` or `List[int]`): + The desired output size. + max_size (`int`, *optional*): + The maximum allowed output size. + input_data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format of the input image. If not provided, it will be inferred from the input image. + """ + image_size = get_image_size(input_image, input_data_format) + if isinstance(size, (list, tuple)): + return size + + return get_size_with_aspect_ratio(image_size, size, max_size) + + +# Copied from transformers.models.detr.image_processing_detr.get_numpy_to_framework_fn +def get_numpy_to_framework_fn(arr) -> Callable: + """ + Returns a function that converts a numpy array to the framework of the input array. + + Args: + arr (`np.ndarray`): The array to convert. + """ + if isinstance(arr, np.ndarray): + return np.array + if is_tf_available() and is_tf_tensor(arr): + import tensorflow as tf + + return tf.convert_to_tensor + if is_torch_available() and is_torch_tensor(arr): + import torch + + return torch.tensor + if is_flax_available() and is_jax_tensor(arr): + import jax.numpy as jnp + + return jnp.array + raise ValueError(f"Cannot convert arrays of type {type(arr)}") + + +# Copied from transformers.models.detr.image_processing_detr.safe_squeeze +def safe_squeeze(arr: np.ndarray, axis: Optional[int] = None) -> np.ndarray: + """ + Squeezes an array, but only if the axis specified has dim 1. + """ + if axis is None: + return arr.squeeze() + + try: + return arr.squeeze(axis=axis) + except ValueError: + return arr + + +# Copied from transformers.models.detr.image_processing_detr.normalize_annotation +def normalize_annotation(annotation: Dict, image_size: Tuple[int, int]) -> Dict: + image_height, image_width = image_size + norm_annotation = {} + for key, value in annotation.items(): + if key == "boxes": + boxes = value + boxes = corners_to_center_format(boxes) + boxes /= np.asarray([image_width, image_height, image_width, image_height], dtype=np.float32) + norm_annotation[key] = boxes + else: + norm_annotation[key] = value + return norm_annotation + + +# Copied from transformers.models.detr.image_processing_detr.max_across_indices +def max_across_indices(values: Iterable[Any]) -> List[Any]: + """ + Return the maximum value across all indices of an iterable of values. + """ + return [max(values_i) for values_i in zip(*values)] + + +# Copied from transformers.models.detr.image_processing_detr.get_max_height_width +def get_max_height_width( + images: List[np.ndarray], input_data_format: Optional[Union[str, ChannelDimension]] = None +) -> List[int]: + """ + Get the maximum height and width across all images in a batch. + """ + if input_data_format is None: + input_data_format = infer_channel_dimension_format(images[0]) + + if input_data_format == ChannelDimension.FIRST: + _, max_height, max_width = max_across_indices([img.shape for img in images]) + elif input_data_format == ChannelDimension.LAST: + max_height, max_width, _ = max_across_indices([img.shape for img in images]) + else: + raise ValueError(f"Invalid channel dimension format: {input_data_format}") + return (max_height, max_width) + + +# Copied from transformers.models.detr.image_processing_detr.make_pixel_mask +def make_pixel_mask( + image: np.ndarray, output_size: Tuple[int, int], input_data_format: Optional[Union[str, ChannelDimension]] = None +) -> np.ndarray: + """ + Make a pixel mask for the image, where 1 indicates a valid pixel and 0 indicates padding. + + Args: + image (`np.ndarray`): + Image to make the pixel mask for. + output_size (`Tuple[int, int]`): + Output size of the mask. + """ + input_height, input_width = get_image_size(image, channel_dim=input_data_format) + mask = np.zeros(output_size, dtype=np.int64) + mask[:input_height, :input_width] = 1 + return mask + + +# Copied from transformers.models.detr.image_processing_detr.convert_coco_poly_to_mask +def convert_coco_poly_to_mask(segmentations, height: int, width: int) -> np.ndarray: + """ + Convert a COCO polygon annotation to a mask. + + Args: + segmentations (`List[List[float]]`): + List of polygons, each polygon represented by a list of x-y coordinates. + height (`int`): + Height of the mask. + width (`int`): + Width of the mask. + """ + try: + from pycocotools import mask as coco_mask + except ImportError: + raise ImportError("Pycocotools is not installed in your environment.") + + masks = [] + for polygons in segmentations: + rles = coco_mask.frPyObjects(polygons, height, width) + mask = coco_mask.decode(rles) + if len(mask.shape) < 3: + mask = mask[..., None] + mask = np.asarray(mask, dtype=np.uint8) + mask = np.any(mask, axis=2) + masks.append(mask) + if masks: + masks = np.stack(masks, axis=0) + else: + masks = np.zeros((0, height, width), dtype=np.uint8) + + return masks + + +# Copied from transformers.models.detr.image_processing_detr.prepare_coco_detection_annotation with DETR->ConditionalDetr +def prepare_coco_detection_annotation( + image, + target, + return_segmentation_masks: bool = False, + input_data_format: Optional[Union[ChannelDimension, str]] = None, +): + """ + Convert the target in COCO format into the format expected by ConditionalDetr. + """ + image_height, image_width = get_image_size(image, channel_dim=input_data_format) + + image_id = target["image_id"] + image_id = np.asarray([image_id], dtype=np.int64) + + # Get all COCO annotations for the given image. + annotations = target["annotations"] + annotations = [obj for obj in annotations if "iscrowd" not in obj or obj["iscrowd"] == 0] + + classes = [obj["category_id"] for obj in annotations] + classes = np.asarray(classes, dtype=np.int64) + + # for conversion to coco api + area = np.asarray([obj["area"] for obj in annotations], dtype=np.float32) + iscrowd = np.asarray([obj["iscrowd"] if "iscrowd" in obj else 0 for obj in annotations], dtype=np.int64) + + boxes = [obj["bbox"] for obj in annotations] + # guard against no boxes via resizing + boxes = np.asarray(boxes, dtype=np.float32).reshape(-1, 4) + boxes[:, 2:] += boxes[:, :2] + boxes[:, 0::2] = boxes[:, 0::2].clip(min=0, max=image_width) + boxes[:, 1::2] = boxes[:, 1::2].clip(min=0, max=image_height) + + keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0]) + + new_target = {} + new_target["image_id"] = image_id + new_target["class_labels"] = classes[keep] + new_target["boxes"] = boxes[keep] + new_target["area"] = area[keep] + new_target["iscrowd"] = iscrowd[keep] + new_target["orig_size"] = np.asarray([int(image_height), int(image_width)], dtype=np.int64) + + if annotations and "keypoints" in annotations[0]: + keypoints = [obj["keypoints"] for obj in annotations] + # Converting the filtered keypoints list to a numpy array + keypoints = np.asarray(keypoints, dtype=np.float32) + # Apply the keep mask here to filter the relevant annotations + keypoints = keypoints[keep] + num_keypoints = keypoints.shape[0] + keypoints = keypoints.reshape((-1, 3)) if num_keypoints else keypoints + new_target["keypoints"] = keypoints + + if return_segmentation_masks: + segmentation_masks = [obj["segmentation"] for obj in annotations] + masks = convert_coco_poly_to_mask(segmentation_masks, image_height, image_width) + new_target["masks"] = masks[keep] + + return new_target + + +# Copied from transformers.models.detr.image_processing_detr.masks_to_boxes +def masks_to_boxes(masks: np.ndarray) -> np.ndarray: + """ + Compute the bounding boxes around the provided panoptic segmentation masks. + + Args: + masks: masks in format `[number_masks, height, width]` where N is the number of masks + + Returns: + boxes: bounding boxes in format `[number_masks, 4]` in xyxy format + """ + if masks.size == 0: + return np.zeros((0, 4)) + + h, w = masks.shape[-2:] + y = np.arange(0, h, dtype=np.float32) + x = np.arange(0, w, dtype=np.float32) + # see https://github.com/pytorch/pytorch/issues/50276 + y, x = np.meshgrid(y, x, indexing="ij") + + x_mask = masks * np.expand_dims(x, axis=0) + x_max = x_mask.reshape(x_mask.shape[0], -1).max(-1) + x = np.ma.array(x_mask, mask=~(np.array(masks, dtype=bool))) + x_min = x.filled(fill_value=1e8) + x_min = x_min.reshape(x_min.shape[0], -1).min(-1) + + y_mask = masks * np.expand_dims(y, axis=0) + y_max = y_mask.reshape(x_mask.shape[0], -1).max(-1) + y = np.ma.array(y_mask, mask=~(np.array(masks, dtype=bool))) + y_min = y.filled(fill_value=1e8) + y_min = y_min.reshape(y_min.shape[0], -1).min(-1) + + return np.stack([x_min, y_min, x_max, y_max], 1) + + +# Copied from transformers.models.detr.image_processing_detr.prepare_coco_panoptic_annotation with DETR->ConditionalDetr +def prepare_coco_panoptic_annotation( + image: np.ndarray, + target: Dict, + masks_path: Union[str, pathlib.Path], + return_masks: bool = True, + input_data_format: Union[ChannelDimension, str] = None, +) -> Dict: + """ + Prepare a coco panoptic annotation for ConditionalDetr. + """ + image_height, image_width = get_image_size(image, channel_dim=input_data_format) + annotation_path = pathlib.Path(masks_path) / target["file_name"] + + new_target = {} + new_target["image_id"] = np.asarray([target["image_id"] if "image_id" in target else target["id"]], dtype=np.int64) + new_target["size"] = np.asarray([image_height, image_width], dtype=np.int64) + new_target["orig_size"] = np.asarray([image_height, image_width], dtype=np.int64) + + if "segments_info" in target: + masks = np.asarray(PIL.Image.open(annotation_path), dtype=np.uint32) + masks = rgb_to_id(masks) + + ids = np.array([segment_info["id"] for segment_info in target["segments_info"]]) + masks = masks == ids[:, None, None] + masks = masks.astype(np.uint8) + if return_masks: + new_target["masks"] = masks + new_target["boxes"] = masks_to_boxes(masks) + new_target["class_labels"] = np.array( + [segment_info["category_id"] for segment_info in target["segments_info"]], dtype=np.int64 + ) + new_target["iscrowd"] = np.asarray( + [segment_info["iscrowd"] for segment_info in target["segments_info"]], dtype=np.int64 + ) + new_target["area"] = np.asarray( + [segment_info["area"] for segment_info in target["segments_info"]], dtype=np.float32 + ) + + return new_target + + +# Copied from transformers.models.detr.image_processing_detr.get_segmentation_image +def get_segmentation_image( + masks: np.ndarray, input_size: Tuple, target_size: Tuple, stuff_equiv_classes, deduplicate=False +): + h, w = input_size + final_h, final_w = target_size + + m_id = scipy.special.softmax(masks.transpose(0, 1), -1) + + if m_id.shape[-1] == 0: + # We didn't detect any mask :( + m_id = np.zeros((h, w), dtype=np.int64) + else: + m_id = m_id.argmax(-1).reshape(h, w) + + if deduplicate: + # Merge the masks corresponding to the same stuff class + for equiv in stuff_equiv_classes.values(): + for eq_id in equiv: + m_id[m_id == eq_id] = equiv[0] + + seg_img = id_to_rgb(m_id) + seg_img = resize(seg_img, (final_w, final_h), resample=PILImageResampling.NEAREST) + return seg_img + + +# Copied from transformers.models.detr.image_processing_detr.get_mask_area +def get_mask_area(seg_img: np.ndarray, target_size: Tuple[int, int], n_classes: int) -> np.ndarray: + final_h, final_w = target_size + np_seg_img = seg_img.astype(np.uint8) + np_seg_img = np_seg_img.reshape(final_h, final_w, 3) + m_id = rgb_to_id(np_seg_img) + area = [(m_id == i).sum() for i in range(n_classes)] + return area + + +# Copied from transformers.models.detr.image_processing_detr.score_labels_from_class_probabilities +def score_labels_from_class_probabilities(logits: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + probs = scipy.special.softmax(logits, axis=-1) + labels = probs.argmax(-1, keepdims=True) + scores = np.take_along_axis(probs, labels, axis=-1) + scores, labels = scores.squeeze(-1), labels.squeeze(-1) + return scores, labels + + +# Copied from transformers.models.detr.image_processing_detr.post_process_panoptic_sample with DetrForSegmentation->ConditionalDetrForSegmentation +def post_process_panoptic_sample( + out_logits: np.ndarray, + masks: np.ndarray, + boxes: np.ndarray, + processed_size: Tuple[int, int], + target_size: Tuple[int, int], + is_thing_map: Dict, + threshold=0.85, +) -> Dict: + """ + Converts the output of [`ConditionalDetrForSegmentation`] into panoptic segmentation predictions for a single sample. + + Args: + out_logits (`torch.Tensor`): + The logits for this sample. + masks (`torch.Tensor`): + The predicted segmentation masks for this sample. + boxes (`torch.Tensor`): + The prediced bounding boxes for this sample. The boxes are in the normalized format `(center_x, center_y, + width, height)` and values between `[0, 1]`, relative to the size the image (disregarding padding). + processed_size (`Tuple[int, int]`): + The processed size of the image `(height, width)`, as returned by the preprocessing step i.e. the size + after data augmentation but before batching. + target_size (`Tuple[int, int]`): + The target size of the image, `(height, width)` corresponding to the requested final size of the + prediction. + is_thing_map (`Dict`): + A dictionary mapping class indices to a boolean value indicating whether the class is a thing or not. + threshold (`float`, *optional*, defaults to 0.85): + The threshold used to binarize the segmentation masks. + """ + # we filter empty queries and detection below threshold + scores, labels = score_labels_from_class_probabilities(out_logits) + keep = (labels != out_logits.shape[-1] - 1) & (scores > threshold) + + cur_scores = scores[keep] + cur_classes = labels[keep] + cur_boxes = center_to_corners_format(boxes[keep]) + + if len(cur_boxes) != len(cur_classes): + raise ValueError("Not as many boxes as there are classes") + + cur_masks = masks[keep] + cur_masks = resize(cur_masks[:, None], processed_size, resample=PILImageResampling.BILINEAR) + cur_masks = safe_squeeze(cur_masks, 1) + b, h, w = cur_masks.shape + + # It may be that we have several predicted masks for the same stuff class. + # In the following, we track the list of masks ids for each stuff class (they are merged later on) + cur_masks = cur_masks.reshape(b, -1) + stuff_equiv_classes = defaultdict(list) + for k, label in enumerate(cur_classes): + if not is_thing_map[label]: + stuff_equiv_classes[label].append(k) + + seg_img = get_segmentation_image(cur_masks, processed_size, target_size, stuff_equiv_classes, deduplicate=True) + area = get_mask_area(cur_masks, processed_size, n_classes=len(cur_scores)) + + # We filter out any mask that is too small + if cur_classes.size() > 0: + # We know filter empty masks as long as we find some + filtered_small = np.array([a <= 4 for a in area], dtype=bool) + while filtered_small.any(): + cur_masks = cur_masks[~filtered_small] + cur_scores = cur_scores[~filtered_small] + cur_classes = cur_classes[~filtered_small] + seg_img = get_segmentation_image(cur_masks, (h, w), target_size, stuff_equiv_classes, deduplicate=True) + area = get_mask_area(seg_img, target_size, n_classes=len(cur_scores)) + filtered_small = np.array([a <= 4 for a in area], dtype=bool) + else: + cur_classes = np.ones((1, 1), dtype=np.int64) + + segments_info = [ + {"id": i, "isthing": is_thing_map[cat], "category_id": int(cat), "area": a} + for i, (cat, a) in enumerate(zip(cur_classes, area)) + ] + del cur_classes + + with io.BytesIO() as out: + PIL.Image.fromarray(seg_img).save(out, format="PNG") + predictions = {"png_string": out.getvalue(), "segments_info": segments_info} + + return predictions + + +# Copied from transformers.models.detr.image_processing_detr.resize_annotation +def resize_annotation( + annotation: Dict[str, Any], + orig_size: Tuple[int, int], + target_size: Tuple[int, int], + threshold: float = 0.5, + resample: PILImageResampling = PILImageResampling.NEAREST, +): + """ + Resizes an annotation to a target size. + + Args: + annotation (`Dict[str, Any]`): + The annotation dictionary. + orig_size (`Tuple[int, int]`): + The original size of the input image. + target_size (`Tuple[int, int]`): + The target size of the image, as returned by the preprocessing `resize` step. + threshold (`float`, *optional*, defaults to 0.5): + The threshold used to binarize the segmentation masks. + resample (`PILImageResampling`, defaults to `PILImageResampling.NEAREST`): + The resampling filter to use when resizing the masks. + """ + ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(target_size, orig_size)) + ratio_height, ratio_width = ratios + + new_annotation = {} + new_annotation["size"] = target_size + + for key, value in annotation.items(): + if key == "boxes": + boxes = value + scaled_boxes = boxes * np.asarray([ratio_width, ratio_height, ratio_width, ratio_height], dtype=np.float32) + new_annotation["boxes"] = scaled_boxes + elif key == "area": + area = value + scaled_area = area * (ratio_width * ratio_height) + new_annotation["area"] = scaled_area + elif key == "masks": + masks = value[:, None] + masks = np.array([resize(mask, target_size, resample=resample) for mask in masks]) + masks = masks.astype(np.float32) + masks = masks[:, 0] > threshold + new_annotation["masks"] = masks + elif key == "size": + new_annotation["size"] = target_size + else: + new_annotation[key] = value + + return new_annotation + + +# Copied from transformers.models.detr.image_processing_detr.binary_mask_to_rle +def binary_mask_to_rle(mask): + """ + Converts given binary mask of shape `(height, width)` to the run-length encoding (RLE) format. + + Args: + mask (`torch.Tensor` or `numpy.array`): + A binary mask tensor of shape `(height, width)` where 0 denotes background and 1 denotes the target + segment_id or class_id. + Returns: + `List`: Run-length encoded list of the binary mask. Refer to COCO API for more information about the RLE + format. + """ + if is_torch_tensor(mask): + mask = mask.numpy() + + pixels = mask.flatten() + pixels = np.concatenate([[0], pixels, [0]]) + runs = np.where(pixels[1:] != pixels[:-1])[0] + 1 + runs[1::2] -= runs[::2] + return list(runs) + + +# Copied from transformers.models.detr.image_processing_detr.convert_segmentation_to_rle +def convert_segmentation_to_rle(segmentation): + """ + Converts given segmentation map of shape `(height, width)` to the run-length encoding (RLE) format. + + Args: + segmentation (`torch.Tensor` or `numpy.array`): + A segmentation map of shape `(height, width)` where each value denotes a segment or class id. + Returns: + `List[List]`: A list of lists, where each list is the run-length encoding of a segment / class id. + """ + segment_ids = torch.unique(segmentation) + + run_length_encodings = [] + for idx in segment_ids: + mask = torch.where(segmentation == idx, 1, 0) + rle = binary_mask_to_rle(mask) + run_length_encodings.append(rle) + + return run_length_encodings + + +# Copied from transformers.models.detr.image_processing_detr.remove_low_and_no_objects +def remove_low_and_no_objects(masks, scores, labels, object_mask_threshold, num_labels): + """ + Binarize the given masks using `object_mask_threshold`, it returns the associated values of `masks`, `scores` and + `labels`. + + Args: + masks (`torch.Tensor`): + A tensor of shape `(num_queries, height, width)`. + scores (`torch.Tensor`): + A tensor of shape `(num_queries)`. + labels (`torch.Tensor`): + A tensor of shape `(num_queries)`. + object_mask_threshold (`float`): + A number between 0 and 1 used to binarize the masks. + Raises: + `ValueError`: Raised when the first dimension doesn't match in all input tensors. + Returns: + `Tuple[`torch.Tensor`, `torch.Tensor`, `torch.Tensor`]`: The `masks`, `scores` and `labels` without the region + < `object_mask_threshold`. + """ + if not (masks.shape[0] == scores.shape[0] == labels.shape[0]): + raise ValueError("mask, scores and labels must have the same shape!") + + to_keep = labels.ne(num_labels) & (scores > object_mask_threshold) + + return masks[to_keep], scores[to_keep], labels[to_keep] + + +# Copied from transformers.models.detr.image_processing_detr.check_segment_validity +def check_segment_validity(mask_labels, mask_probs, k, mask_threshold=0.5, overlap_mask_area_threshold=0.8): + # Get the mask associated with the k class + mask_k = mask_labels == k + mask_k_area = mask_k.sum() + + # Compute the area of all the stuff in query k + original_area = (mask_probs[k] >= mask_threshold).sum() + mask_exists = mask_k_area > 0 and original_area > 0 + + # Eliminate disconnected tiny segments + if mask_exists: + area_ratio = mask_k_area / original_area + if not area_ratio.item() > overlap_mask_area_threshold: + mask_exists = False + + return mask_exists, mask_k + + +# Copied from transformers.models.detr.image_processing_detr.compute_segments +def compute_segments( + mask_probs, + pred_scores, + pred_labels, + mask_threshold: float = 0.5, + overlap_mask_area_threshold: float = 0.8, + label_ids_to_fuse: Optional[Set[int]] = None, + target_size: Tuple[int, int] = None, +): + height = mask_probs.shape[1] if target_size is None else target_size[0] + width = mask_probs.shape[2] if target_size is None else target_size[1] + + segmentation = torch.zeros((height, width), dtype=torch.int32, device=mask_probs.device) + segments: List[Dict] = [] + + if target_size is not None: + mask_probs = nn.functional.interpolate( + mask_probs.unsqueeze(0), size=target_size, mode="bilinear", align_corners=False + )[0] + + current_segment_id = 0 + + # Weigh each mask by its prediction score + mask_probs *= pred_scores.view(-1, 1, 1) + mask_labels = mask_probs.argmax(0) # [height, width] + + # Keep track of instances of each class + stuff_memory_list: Dict[str, int] = {} + for k in range(pred_labels.shape[0]): + pred_class = pred_labels[k].item() + should_fuse = pred_class in label_ids_to_fuse + + # Check if mask exists and large enough to be a segment + mask_exists, mask_k = check_segment_validity( + mask_labels, mask_probs, k, mask_threshold, overlap_mask_area_threshold + ) + + if mask_exists: + if pred_class in stuff_memory_list: + current_segment_id = stuff_memory_list[pred_class] + else: + current_segment_id += 1 + + # Add current object segment to final segmentation map + segmentation[mask_k] = current_segment_id + segment_score = round(pred_scores[k].item(), 6) + segments.append( + { + "id": current_segment_id, + "label_id": pred_class, + "was_fused": should_fuse, + "score": segment_score, + } + ) + if should_fuse: + stuff_memory_list[pred_class] = current_segment_id + + return segmentation, segments + + +class ConditionalDetrImageProcessor(BaseImageProcessor): + r""" + Constructs a Conditional Detr image processor. + + Args: + format (`str`, *optional*, defaults to `"coco_detection"`): + Data format of the annotations. One of "coco_detection" or "coco_panoptic". + do_resize (`bool`, *optional*, defaults to `True`): + Controls whether to resize the image's (height, width) dimensions to the specified `size`. Can be + overridden by the `do_resize` parameter in the `preprocess` method. + size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 800, "longest_edge": 1333}`): + Size of the image's (height, width) dimensions after resizing. Can be overridden by the `size` parameter in + the `preprocess` method. + resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`): + Resampling filter to use if resizing the image. + do_rescale (`bool`, *optional*, defaults to `True`): + Controls whether to rescale the image by the specified scale `rescale_factor`. Can be overridden by the + `do_rescale` parameter in the `preprocess` method. + rescale_factor (`int` or `float`, *optional*, defaults to `1/255`): + Scale factor to use if rescaling the image. Can be overridden by the `rescale_factor` parameter in the + `preprocess` method. + do_normalize: + Controls whether to normalize the image. Can be overridden by the `do_normalize` parameter in the + `preprocess` method. + image_mean (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_MEAN`): + Mean values to use when normalizing the image. Can be a single value or a list of values, one for each + channel. Can be overridden by the `image_mean` parameter in the `preprocess` method. + image_std (`float` or `List[float]`, *optional*, defaults to `IMAGENET_DEFAULT_STD`): + Standard deviation values to use when normalizing the image. Can be a single value or a list of values, one + for each channel. Can be overridden by the `image_std` parameter in the `preprocess` method. + do_convert_annotations (`bool`, *optional*, defaults to `True`): + Controls whether to convert the annotations to the format expected by the DETR model. Converts the + bounding boxes to the format `(center_x, center_y, width, height)` and in the range `[0, 1]`. + Can be overridden by the `do_convert_annotations` parameter in the `preprocess` method. + do_pad (`bool`, *optional*, defaults to `True`): + Controls whether to pad the image. Can be overridden by the `do_pad` parameter in the `preprocess` + method. If `True` will pad the images in the batch to the largest height and width in the batch. + Padding will be applied to the bottom and right of the image with zeros. + """ + + model_input_names = ["pixel_values", "pixel_mask"] + + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.__init__ + def __init__( + self, + format: Union[str, AnnotationFormat] = AnnotationFormat.COCO_DETECTION, + do_resize: bool = True, + size: Dict[str, int] = None, + resample: PILImageResampling = PILImageResampling.BILINEAR, + do_rescale: bool = True, + rescale_factor: Union[int, float] = 1 / 255, + do_normalize: bool = True, + image_mean: Union[float, List[float]] = None, + image_std: Union[float, List[float]] = None, + do_convert_annotations: Optional[bool] = None, + do_pad: bool = True, + **kwargs, + ) -> None: + if "pad_and_return_pixel_mask" in kwargs: + do_pad = kwargs.pop("pad_and_return_pixel_mask") + + if "max_size" in kwargs: + logger.warning_once( + "The `max_size` parameter is deprecated and will be removed in v4.26. " + "Please specify in `size['longest_edge'] instead`.", + ) + max_size = kwargs.pop("max_size") + else: + max_size = None if size is None else 1333 + + size = size if size is not None else {"shortest_edge": 800, "longest_edge": 1333} + size = get_size_dict(size, max_size=max_size, default_to_square=False) + + # Backwards compatibility + if do_convert_annotations is None: + do_convert_annotations = do_normalize + + super().__init__(**kwargs) + self.format = format + self.do_resize = do_resize + self.size = size + self.resample = resample + self.do_rescale = do_rescale + self.rescale_factor = rescale_factor + self.do_normalize = do_normalize + self.do_convert_annotations = do_convert_annotations + self.image_mean = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN + self.image_std = image_std if image_std is not None else IMAGENET_DEFAULT_STD + self.do_pad = do_pad + self._valid_processor_keys = [ + "images", + "annotations", + "return_segmentation_masks", + "masks_path", + "do_resize", + "size", + "resample", + "do_rescale", + "rescale_factor", + "do_normalize", + "do_convert_annotations", + "image_mean", + "image_std", + "do_pad", + "format", + "return_tensors", + "data_format", + "input_data_format", + ] + + @classmethod + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.from_dict with Detr->ConditionalDetr + def from_dict(cls, image_processor_dict: Dict[str, Any], **kwargs): + """ + Overrides the `from_dict` method from the base class to make sure parameters are updated if image processor is + created using from_dict and kwargs e.g. `ConditionalDetrImageProcessor.from_pretrained(checkpoint, size=600, + max_size=800)` + """ + image_processor_dict = image_processor_dict.copy() + if "max_size" in kwargs: + image_processor_dict["max_size"] = kwargs.pop("max_size") + if "pad_and_return_pixel_mask" in kwargs: + image_processor_dict["pad_and_return_pixel_mask"] = kwargs.pop("pad_and_return_pixel_mask") + return super().from_dict(image_processor_dict, **kwargs) + + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_annotation with DETR->ConditionalDetr + def prepare_annotation( + self, + image: np.ndarray, + target: Dict, + format: Optional[AnnotationFormat] = None, + return_segmentation_masks: bool = None, + masks_path: Optional[Union[str, pathlib.Path]] = None, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + ) -> Dict: + """ + Prepare an annotation for feeding into ConditionalDetr model. + """ + format = format if format is not None else self.format + + if format == AnnotationFormat.COCO_DETECTION: + return_segmentation_masks = False if return_segmentation_masks is None else return_segmentation_masks + target = prepare_coco_detection_annotation( + image, target, return_segmentation_masks, input_data_format=input_data_format + ) + elif format == AnnotationFormat.COCO_PANOPTIC: + return_segmentation_masks = True if return_segmentation_masks is None else return_segmentation_masks + target = prepare_coco_panoptic_annotation( + image, + target, + masks_path=masks_path, + return_masks=return_segmentation_masks, + input_data_format=input_data_format, + ) + else: + raise ValueError(f"Format {format} is not supported.") + return target + + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare + def prepare(self, image, target, return_segmentation_masks=None, masks_path=None): + logger.warning_once( + "The `prepare` method is deprecated and will be removed in a v4.33. " + "Please use `prepare_annotation` instead. Note: the `prepare_annotation` method " + "does not return the image anymore.", + ) + target = self.prepare_annotation(image, target, return_segmentation_masks, masks_path, self.format) + return image, target + + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.convert_coco_poly_to_mask + def convert_coco_poly_to_mask(self, *args, **kwargs): + logger.warning_once("The `convert_coco_poly_to_mask` method is deprecated and will be removed in v4.33. ") + return convert_coco_poly_to_mask(*args, **kwargs) + + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_detection with DETR->ConditionalDetr + def prepare_coco_detection(self, *args, **kwargs): + logger.warning_once("The `prepare_coco_detection` method is deprecated and will be removed in v4.33. ") + return prepare_coco_detection_annotation(*args, **kwargs) + + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.prepare_coco_panoptic + def prepare_coco_panoptic(self, *args, **kwargs): + logger.warning_once("The `prepare_coco_panoptic` method is deprecated and will be removed in v4.33. ") + return prepare_coco_panoptic_annotation(*args, **kwargs) + + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.resize + def resize( + self, + image: np.ndarray, + size: Dict[str, int], + resample: PILImageResampling = PILImageResampling.BILINEAR, + data_format: Optional[ChannelDimension] = None, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + **kwargs, + ) -> np.ndarray: + """ + Resize the image to the given size. Size can be `min_size` (scalar) or `(height, width)` tuple. If size is an + int, smaller edge of the image will be matched to this number. + + Args: + image (`np.ndarray`): + Image to resize. + size (`Dict[str, int]`): + Dictionary containing the size to resize to. Can contain the keys `shortest_edge` and `longest_edge` or + `height` and `width`. + resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`): + Resampling filter to use if resizing the image. + data_format (`str` or `ChannelDimension`, *optional*): + The channel dimension format for the output image. If unset, the channel dimension format of the input + image is used. + input_data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format of the input image. If not provided, it will be inferred. + """ + if "max_size" in kwargs: + logger.warning_once( + "The `max_size` parameter is deprecated and will be removed in v4.26. " + "Please specify in `size['longest_edge'] instead`.", + ) + max_size = kwargs.pop("max_size") + else: + max_size = None + size = get_size_dict(size, max_size=max_size, default_to_square=False) + if "shortest_edge" in size and "longest_edge" in size: + size = get_resize_output_image_size( + image, size["shortest_edge"], size["longest_edge"], input_data_format=input_data_format + ) + elif "height" in size and "width" in size: + size = (size["height"], size["width"]) + else: + raise ValueError( + "Size must contain 'height' and 'width' keys or 'shortest_edge' and 'longest_edge' keys. Got" + f" {size.keys()}." + ) + image = resize( + image, size=size, resample=resample, data_format=data_format, input_data_format=input_data_format, **kwargs + ) + return image + + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.resize_annotation + def resize_annotation( + self, + annotation, + orig_size, + size, + resample: PILImageResampling = PILImageResampling.NEAREST, + ) -> Dict: + """ + Resize the annotation to match the resized image. If size is an int, smaller edge of the mask will be matched + to this number. + """ + return resize_annotation(annotation, orig_size=orig_size, target_size=size, resample=resample) + + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.rescale + def rescale( + self, + image: np.ndarray, + rescale_factor: float, + data_format: Optional[Union[str, ChannelDimension]] = None, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + ) -> np.ndarray: + """ + Rescale the image by the given factor. image = image * rescale_factor. + + Args: + image (`np.ndarray`): + Image to rescale. + rescale_factor (`float`): + The value to use for rescaling. + data_format (`str` or `ChannelDimension`, *optional*): + The channel dimension format for the output image. If unset, the channel dimension format of the input + image is used. Can be one of: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + input_data_format (`str` or `ChannelDimension`, *optional*): + The channel dimension format for the input image. If unset, is inferred from the input image. Can be + one of: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + """ + return rescale(image, rescale_factor, data_format=data_format, input_data_format=input_data_format) + + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.normalize_annotation + def normalize_annotation(self, annotation: Dict, image_size: Tuple[int, int]) -> Dict: + """ + Normalize the boxes in the annotation from `[top_left_x, top_left_y, bottom_right_x, bottom_right_y]` to + `[center_x, center_y, width, height]` format and from absolute to relative pixel values. + """ + return normalize_annotation(annotation, image_size=image_size) + + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor._update_annotation_for_padded_image + def _update_annotation_for_padded_image( + self, + annotation: Dict, + input_image_size: Tuple[int, int], + output_image_size: Tuple[int, int], + padding, + update_bboxes, + ) -> Dict: + """ + Update the annotation for a padded image. + """ + new_annotation = {} + new_annotation["size"] = output_image_size + + for key, value in annotation.items(): + if key == "masks": + masks = value + masks = pad( + masks, + padding, + mode=PaddingMode.CONSTANT, + constant_values=0, + input_data_format=ChannelDimension.FIRST, + ) + masks = safe_squeeze(masks, 1) + new_annotation["masks"] = masks + elif key == "boxes" and update_bboxes: + boxes = value + boxes *= np.asarray( + [ + input_image_size[1] / output_image_size[1], + input_image_size[0] / output_image_size[0], + input_image_size[1] / output_image_size[1], + input_image_size[0] / output_image_size[0], + ] + ) + new_annotation["boxes"] = boxes + elif key == "size": + new_annotation["size"] = output_image_size + else: + new_annotation[key] = value + return new_annotation + + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor._pad_image + def _pad_image( + self, + image: np.ndarray, + output_size: Tuple[int, int], + annotation: Optional[Dict[str, Any]] = None, + constant_values: Union[float, Iterable[float]] = 0, + data_format: Optional[ChannelDimension] = None, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + update_bboxes: bool = True, + ) -> np.ndarray: + """ + Pad an image with zeros to the given size. + """ + input_height, input_width = get_image_size(image, channel_dim=input_data_format) + output_height, output_width = output_size + + pad_bottom = output_height - input_height + pad_right = output_width - input_width + padding = ((0, pad_bottom), (0, pad_right)) + padded_image = pad( + image, + padding, + mode=PaddingMode.CONSTANT, + constant_values=constant_values, + data_format=data_format, + input_data_format=input_data_format, + ) + if annotation is not None: + annotation = self._update_annotation_for_padded_image( + annotation, (input_height, input_width), (output_height, output_width), padding, update_bboxes + ) + return padded_image, annotation + + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.pad + def pad( + self, + images: List[np.ndarray], + annotations: Optional[Union[AnnotationType, List[AnnotationType]]] = None, + constant_values: Union[float, Iterable[float]] = 0, + return_pixel_mask: bool = True, + return_tensors: Optional[Union[str, TensorType]] = None, + data_format: Optional[ChannelDimension] = None, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + update_bboxes: bool = True, + ) -> BatchFeature: + """ + Pads a batch of images to the bottom and right of the image with zeros to the size of largest height and width + in the batch and optionally returns their corresponding pixel mask. + + Args: + images (List[`np.ndarray`]): + Images to pad. + annotations (`AnnotationType` or `List[AnnotationType]`, *optional*): + Annotations to transform according to the padding that is applied to the images. + constant_values (`float` or `Iterable[float]`, *optional*): + The value to use for the padding if `mode` is `"constant"`. + return_pixel_mask (`bool`, *optional*, defaults to `True`): + Whether to return a pixel mask. + return_tensors (`str` or `TensorType`, *optional*): + The type of tensors to return. Can be one of: + - Unset: Return a list of `np.ndarray`. + - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`. + - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`. + - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`. + - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`. + data_format (`str` or `ChannelDimension`, *optional*): + The channel dimension format of the image. If not provided, it will be the same as the input image. + input_data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format of the input image. If not provided, it will be inferred. + update_bboxes (`bool`, *optional*, defaults to `True`): + Whether to update the bounding boxes in the annotations to match the padded images. If the + bounding boxes have not been converted to relative coordinates and `(centre_x, centre_y, width, height)` + format, the bounding boxes will not be updated. + """ + pad_size = get_max_height_width(images, input_data_format=input_data_format) + + annotation_list = annotations if annotations is not None else [None] * len(images) + padded_images = [] + padded_annotations = [] + for image, annotation in zip(images, annotation_list): + padded_image, padded_annotation = self._pad_image( + image, + pad_size, + annotation, + constant_values=constant_values, + data_format=data_format, + input_data_format=input_data_format, + update_bboxes=update_bboxes, + ) + padded_images.append(padded_image) + padded_annotations.append(padded_annotation) + + data = {"pixel_values": padded_images} + + if return_pixel_mask: + masks = [ + make_pixel_mask(image=image, output_size=pad_size, input_data_format=input_data_format) + for image in images + ] + data["pixel_mask"] = masks + + encoded_inputs = BatchFeature(data=data, tensor_type=return_tensors) + + if annotations is not None: + encoded_inputs["labels"] = [ + BatchFeature(annotation, tensor_type=return_tensors) for annotation in padded_annotations + ] + + return encoded_inputs + + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.preprocess + def preprocess( + self, + images: ImageInput, + annotations: Optional[Union[AnnotationType, List[AnnotationType]]] = None, + return_segmentation_masks: bool = None, + masks_path: Optional[Union[str, pathlib.Path]] = None, + do_resize: Optional[bool] = None, + size: Optional[Dict[str, int]] = None, + resample=None, # PILImageResampling + do_rescale: Optional[bool] = None, + rescale_factor: Optional[Union[int, float]] = None, + do_normalize: Optional[bool] = None, + do_convert_annotations: Optional[bool] = None, + image_mean: Optional[Union[float, List[float]]] = None, + image_std: Optional[Union[float, List[float]]] = None, + do_pad: Optional[bool] = None, + format: Optional[Union[str, AnnotationFormat]] = None, + return_tensors: Optional[Union[TensorType, str]] = None, + data_format: Union[str, ChannelDimension] = ChannelDimension.FIRST, + input_data_format: Optional[Union[str, ChannelDimension]] = None, + **kwargs, + ) -> BatchFeature: + """ + Preprocess an image or a batch of images so that it can be used by the model. + + Args: + images (`ImageInput`): + Image or batch of images to preprocess. Expects a single or batch of images with pixel values ranging + from 0 to 255. If passing in images with pixel values between 0 and 1, set `do_rescale=False`. + annotations (`AnnotationType` or `List[AnnotationType]`, *optional*): + List of annotations associated with the image or batch of images. If annotation is for object + detection, the annotations should be a dictionary with the following keys: + - "image_id" (`int`): The image id. + - "annotations" (`List[Dict]`): List of annotations for an image. Each annotation should be a + dictionary. An image can have no annotations, in which case the list should be empty. + If annotation is for segmentation, the annotations should be a dictionary with the following keys: + - "image_id" (`int`): The image id. + - "segments_info" (`List[Dict]`): List of segments for an image. Each segment should be a dictionary. + An image can have no segments, in which case the list should be empty. + - "file_name" (`str`): The file name of the image. + return_segmentation_masks (`bool`, *optional*, defaults to self.return_segmentation_masks): + Whether to return segmentation masks. + masks_path (`str` or `pathlib.Path`, *optional*): + Path to the directory containing the segmentation masks. + do_resize (`bool`, *optional*, defaults to self.do_resize): + Whether to resize the image. + size (`Dict[str, int]`, *optional*, defaults to self.size): + Size of the image after resizing. + resample (`PILImageResampling`, *optional*, defaults to self.resample): + Resampling filter to use when resizing the image. + do_rescale (`bool`, *optional*, defaults to self.do_rescale): + Whether to rescale the image. + rescale_factor (`float`, *optional*, defaults to self.rescale_factor): + Rescale factor to use when rescaling the image. + do_normalize (`bool`, *optional*, defaults to self.do_normalize): + Whether to normalize the image. + do_convert_annotations (`bool`, *optional*, defaults to self.do_convert_annotations): + Whether to convert the annotations to the format expected by the model. Converts the bounding + boxes from the format `(top_left_x, top_left_y, width, height)` to `(center_x, center_y, width, height)` + and in relative coordinates. + image_mean (`float` or `List[float]`, *optional*, defaults to self.image_mean): + Mean to use when normalizing the image. + image_std (`float` or `List[float]`, *optional*, defaults to self.image_std): + Standard deviation to use when normalizing the image. + do_pad (`bool`, *optional*, defaults to self.do_pad): + Whether to pad the image. If `True` will pad the images in the batch to the largest image in the batch + and create a pixel mask. Padding will be applied to the bottom and right of the image with zeros. + format (`str` or `AnnotationFormat`, *optional*, defaults to self.format): + Format of the annotations. + return_tensors (`str` or `TensorType`, *optional*, defaults to self.return_tensors): + Type of tensors to return. If `None`, will return the list of images. + data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`): + The channel dimension format for the output image. Can be one of: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - Unset: Use the channel dimension format of the input image. + input_data_format (`ChannelDimension` or `str`, *optional*): + The channel dimension format for the input image. If unset, the channel dimension format is inferred + from the input image. Can be one of: + - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format. + - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format. + - `"none"` or `ChannelDimension.NONE`: image in (height, width) format. + """ + if "pad_and_return_pixel_mask" in kwargs: + logger.warning_once( + "The `pad_and_return_pixel_mask` argument is deprecated and will be removed in a future version, " + "use `do_pad` instead." + ) + do_pad = kwargs.pop("pad_and_return_pixel_mask") + + max_size = None + if "max_size" in kwargs: + logger.warning_once( + "The `max_size` argument is deprecated and will be removed in a future version, use" + " `size['longest_edge']` instead." + ) + size = kwargs.pop("max_size") + + do_resize = self.do_resize if do_resize is None else do_resize + size = self.size if size is None else size + size = get_size_dict(size=size, max_size=max_size, default_to_square=False) + resample = self.resample if resample is None else resample + do_rescale = self.do_rescale if do_rescale is None else do_rescale + rescale_factor = self.rescale_factor if rescale_factor is None else rescale_factor + do_normalize = self.do_normalize if do_normalize is None else do_normalize + image_mean = self.image_mean if image_mean is None else image_mean + image_std = self.image_std if image_std is None else image_std + do_convert_annotations = ( + self.do_convert_annotations if do_convert_annotations is None else do_convert_annotations + ) + do_pad = self.do_pad if do_pad is None else do_pad + format = self.format if format is None else format + + images = make_list_of_images(images) + + if not valid_images(images): + raise ValueError( + "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, " + "torch.Tensor, tf.Tensor or jax.ndarray." + ) + validate_kwargs(captured_kwargs=kwargs.keys(), valid_processor_keys=self._valid_processor_keys) + + # Here, the pad() method pads to the maximum of (width, height). It does not need to be validated. + validate_preprocess_arguments( + do_rescale=do_rescale, + rescale_factor=rescale_factor, + do_normalize=do_normalize, + image_mean=image_mean, + image_std=image_std, + do_resize=do_resize, + size=size, + resample=resample, + ) + + if annotations is not None and isinstance(annotations, dict): + annotations = [annotations] + + if annotations is not None and len(images) != len(annotations): + raise ValueError( + f"The number of images ({len(images)}) and annotations ({len(annotations)}) do not match." + ) + + format = AnnotationFormat(format) + if annotations is not None: + validate_annotations(format, SUPPORTED_ANNOTATION_FORMATS, annotations) + + if ( + masks_path is not None + and format == AnnotationFormat.COCO_PANOPTIC + and not isinstance(masks_path, (pathlib.Path, str)) + ): + raise ValueError( + "The path to the directory containing the mask PNG files should be provided as a" + f" `pathlib.Path` or string object, but is {type(masks_path)} instead." + ) + + # All transformations expect numpy arrays + images = [to_numpy_array(image) for image in images] + + if is_scaled_image(images[0]) and do_rescale: + logger.warning_once( + "It looks like you are trying to rescale already rescaled images. If the input" + " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again." + ) + + if input_data_format is None: + # We assume that all images have the same channel dimension format. + input_data_format = infer_channel_dimension_format(images[0]) + + # prepare (COCO annotations as a list of Dict -> DETR target as a single Dict per image) + if annotations is not None: + prepared_images = [] + prepared_annotations = [] + for image, target in zip(images, annotations): + target = self.prepare_annotation( + image, + target, + format, + return_segmentation_masks=return_segmentation_masks, + masks_path=masks_path, + input_data_format=input_data_format, + ) + prepared_images.append(image) + prepared_annotations.append(target) + images = prepared_images + annotations = prepared_annotations + del prepared_images, prepared_annotations + + # transformations + if do_resize: + if annotations is not None: + resized_images, resized_annotations = [], [] + for image, target in zip(images, annotations): + orig_size = get_image_size(image, input_data_format) + resized_image = self.resize( + image, size=size, max_size=max_size, resample=resample, input_data_format=input_data_format + ) + resized_annotation = self.resize_annotation( + target, orig_size, get_image_size(resized_image, input_data_format) + ) + resized_images.append(resized_image) + resized_annotations.append(resized_annotation) + images = resized_images + annotations = resized_annotations + del resized_images, resized_annotations + else: + images = [ + self.resize(image, size=size, resample=resample, input_data_format=input_data_format) + for image in images + ] + + if do_rescale: + images = [self.rescale(image, rescale_factor, input_data_format=input_data_format) for image in images] + + if do_normalize: + images = [ + self.normalize(image, image_mean, image_std, input_data_format=input_data_format) for image in images + ] + + if do_convert_annotations and annotations is not None: + annotations = [ + self.normalize_annotation(annotation, get_image_size(image, input_data_format)) + for annotation, image in zip(annotations, images) + ] + + if do_pad: + # Pads images and returns their mask: {'pixel_values': ..., 'pixel_mask': ...} + encoded_inputs = self.pad( + images, + annotations=annotations, + return_pixel_mask=True, + data_format=data_format, + input_data_format=input_data_format, + update_bboxes=do_convert_annotations, + return_tensors=return_tensors, + ) + else: + images = [ + to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) + for image in images + ] + encoded_inputs = BatchFeature(data={"pixel_values": images}, tensor_type=return_tensors) + if annotations is not None: + encoded_inputs["labels"] = [ + BatchFeature(annotation, tensor_type=return_tensors) for annotation in annotations + ] + + return encoded_inputs + + # POSTPROCESSING METHODS - TODO: add support for other frameworks + def post_process(self, outputs, target_sizes): + """ + Converts the output of [`ConditionalDetrForObjectDetection`] into the format expected by the Pascal VOC format (xmin, ymin, xmax, ymax). + Only supports PyTorch. + + Args: + outputs ([`ConditionalDetrObjectDetectionOutput`]): + Raw outputs of the model. + target_sizes (`torch.Tensor` of shape `(batch_size, 2)`): + Tensor containing the size (h, w) of each image of the batch. For evaluation, this must be the original + image size (before any data augmentation). For visualization, this should be the image size after data + augment, but before padding. + Returns: + `List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image + in the batch as predicted by the model. + """ + logging.warning_once( + "`post_process` is deprecated and will be removed in v5 of Transformers, please use" + " `post_process_object_detection` instead, with `threshold=0.` for equivalent results.", + ) + + out_logits, out_bbox = outputs.logits, outputs.pred_boxes + + if len(out_logits) != len(target_sizes): + raise ValueError("Make sure that you pass in as many target sizes as the batch dimension of the logits") + if target_sizes.shape[1] != 2: + raise ValueError("Each element of target_sizes must contain the size (h, w) of each image of the batch") + + prob = out_logits.sigmoid() + topk_values, topk_indexes = torch.topk(prob.view(out_logits.shape[0], -1), 300, dim=1) + scores = topk_values + topk_boxes = torch.div(topk_indexes, out_logits.shape[2], rounding_mode="floor") + labels = topk_indexes % out_logits.shape[2] + boxes = center_to_corners_format(out_bbox) + boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4)) + + # and from relative [0, 1] to absolute [0, height] coordinates + img_h, img_w = target_sizes.unbind(1) + scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1) + boxes = boxes * scale_fct[:, None, :] + + results = [{"scores": s, "labels": l, "boxes": b} for s, l, b in zip(scores, labels, boxes)] + + return results + + # Copied from transformers.models.deformable_detr.image_processing_deformable_detr.DeformableDetrImageProcessor.post_process_object_detection with DeformableDetr->ConditionalDetr + def post_process_object_detection( + self, outputs, threshold: float = 0.5, target_sizes: Union[TensorType, List[Tuple]] = None, top_k: int = 100 + ): + """ + Converts the raw output of [`ConditionalDetrForObjectDetection`] into final bounding boxes in (top_left_x, + top_left_y, bottom_right_x, bottom_right_y) format. Only supports PyTorch. + + Args: + outputs ([`DetrObjectDetectionOutput`]): + Raw outputs of the model. + threshold (`float`, *optional*): + Score threshold to keep object detection predictions. + target_sizes (`torch.Tensor` or `List[Tuple[int, int]]`, *optional*): + Tensor of shape `(batch_size, 2)` or list of tuples (`Tuple[int, int]`) containing the target size + (height, width) of each image in the batch. If left to None, predictions will not be resized. + top_k (`int`, *optional*, defaults to 100): + Keep only top k bounding boxes before filtering by thresholding. + + Returns: + `List[Dict]`: A list of dictionaries, each dictionary containing the scores, labels and boxes for an image + in the batch as predicted by the model. + """ + out_logits, out_bbox = outputs.logits, outputs.pred_boxes + + if target_sizes is not None: + if len(out_logits) != len(target_sizes): + raise ValueError( + "Make sure that you pass in as many target sizes as the batch dimension of the logits" + ) + + prob = out_logits.sigmoid() + prob = prob.view(out_logits.shape[0], -1) + k_value = min(top_k, prob.size(1)) + topk_values, topk_indexes = torch.topk(prob, k_value, dim=1) + scores = topk_values + topk_boxes = torch.div(topk_indexes, out_logits.shape[2], rounding_mode="floor") + labels = topk_indexes % out_logits.shape[2] + boxes = center_to_corners_format(out_bbox) + boxes = torch.gather(boxes, 1, topk_boxes.unsqueeze(-1).repeat(1, 1, 4)) + + # and from relative [0, 1] to absolute [0, height] coordinates + if target_sizes is not None: + if isinstance(target_sizes, List): + img_h = torch.Tensor([i[0] for i in target_sizes]) + img_w = torch.Tensor([i[1] for i in target_sizes]) + else: + img_h, img_w = target_sizes.unbind(1) + scale_fct = torch.stack([img_w, img_h, img_w, img_h], dim=1).to(boxes.device) + boxes = boxes * scale_fct[:, None, :] + + results = [] + for s, l, b in zip(scores, labels, boxes): + score = s[s > threshold] + label = l[s > threshold] + box = b[s > threshold] + results.append({"scores": score, "labels": label, "boxes": box}) + + return results + + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.post_process_semantic_segmentation with Detr->ConditionalDetr + def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple[int, int]] = None): + """ + Converts the output of [`ConditionalDetrForSegmentation`] into semantic segmentation maps. Only supports PyTorch. + + Args: + outputs ([`ConditionalDetrForSegmentation`]): + Raw outputs of the model. + target_sizes (`List[Tuple[int, int]]`, *optional*): + A list of tuples (`Tuple[int, int]`) containing the target size (height, width) of each image in the + batch. If unset, predictions will not be resized. + Returns: + `List[torch.Tensor]`: + A list of length `batch_size`, where each item is a semantic segmentation map of shape (height, width) + corresponding to the target_sizes entry (if `target_sizes` is specified). Each entry of each + `torch.Tensor` correspond to a semantic class id. + """ + class_queries_logits = outputs.logits # [batch_size, num_queries, num_classes+1] + masks_queries_logits = outputs.pred_masks # [batch_size, num_queries, height, width] + + # Remove the null class `[..., :-1]` + masks_classes = class_queries_logits.softmax(dim=-1)[..., :-1] + masks_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width] + + # Semantic segmentation logits of shape (batch_size, num_classes, height, width) + segmentation = torch.einsum("bqc, bqhw -> bchw", masks_classes, masks_probs) + batch_size = class_queries_logits.shape[0] + + # Resize logits and compute semantic segmentation maps + if target_sizes is not None: + if batch_size != len(target_sizes): + raise ValueError( + "Make sure that you pass in as many target sizes as the batch dimension of the logits" + ) + + semantic_segmentation = [] + for idx in range(batch_size): + resized_logits = nn.functional.interpolate( + segmentation[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=False + ) + semantic_map = resized_logits[0].argmax(dim=0) + semantic_segmentation.append(semantic_map) + else: + semantic_segmentation = segmentation.argmax(dim=1) + semantic_segmentation = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])] + + return semantic_segmentation + + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.post_process_instance_segmentation with Detr->ConditionalDetr + def post_process_instance_segmentation( + self, + outputs, + threshold: float = 0.5, + mask_threshold: float = 0.5, + overlap_mask_area_threshold: float = 0.8, + target_sizes: Optional[List[Tuple[int, int]]] = None, + return_coco_annotation: Optional[bool] = False, + ) -> List[Dict]: + """ + Converts the output of [`ConditionalDetrForSegmentation`] into instance segmentation predictions. Only supports PyTorch. + + Args: + outputs ([`ConditionalDetrForSegmentation`]): + Raw outputs of the model. + threshold (`float`, *optional*, defaults to 0.5): + The probability score threshold to keep predicted instance masks. + mask_threshold (`float`, *optional*, defaults to 0.5): + Threshold to use when turning the predicted masks into binary values. + overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8): + The overlap mask area threshold to merge or discard small disconnected parts within each binary + instance mask. + target_sizes (`List[Tuple]`, *optional*): + List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested + final size (height, width) of each prediction. If unset, predictions will not be resized. + return_coco_annotation (`bool`, *optional*): + Defaults to `False`. If set to `True`, segmentation maps are returned in COCO run-length encoding (RLE) + format. + Returns: + `List[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys: + - **segmentation** -- A tensor of shape `(height, width)` where each pixel represents a `segment_id` or + `List[List]` run-length encoding (RLE) of the segmentation map if return_coco_annotation is set to + `True`. Set to `None` if no mask if found above `threshold`. + - **segments_info** -- A dictionary that contains additional information on each segment. + - **id** -- An integer representing the `segment_id`. + - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`. + - **score** -- Prediction score of segment with `segment_id`. + """ + class_queries_logits = outputs.logits # [batch_size, num_queries, num_classes+1] + masks_queries_logits = outputs.pred_masks # [batch_size, num_queries, height, width] + + batch_size = class_queries_logits.shape[0] + num_labels = class_queries_logits.shape[-1] - 1 + + mask_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width] + + # Predicted label and score of each query (batch_size, num_queries) + pred_scores, pred_labels = nn.functional.softmax(class_queries_logits, dim=-1).max(-1) + + # Loop over items in batch size + results: List[Dict[str, TensorType]] = [] + + for i in range(batch_size): + mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects( + mask_probs[i], pred_scores[i], pred_labels[i], threshold, num_labels + ) + + # No mask found + if mask_probs_item.shape[0] <= 0: + height, width = target_sizes[i] if target_sizes is not None else mask_probs_item.shape[1:] + segmentation = torch.zeros((height, width)) - 1 + results.append({"segmentation": segmentation, "segments_info": []}) + continue + + # Get segmentation map and segment information of batch item + target_size = target_sizes[i] if target_sizes is not None else None + segmentation, segments = compute_segments( + mask_probs=mask_probs_item, + pred_scores=pred_scores_item, + pred_labels=pred_labels_item, + mask_threshold=mask_threshold, + overlap_mask_area_threshold=overlap_mask_area_threshold, + label_ids_to_fuse=[], + target_size=target_size, + ) + + # Return segmentation map in run-length encoding (RLE) format + if return_coco_annotation: + segmentation = convert_segmentation_to_rle(segmentation) + + results.append({"segmentation": segmentation, "segments_info": segments}) + return results + + # Copied from transformers.models.detr.image_processing_detr.DetrImageProcessor.post_process_panoptic_segmentation with Detr->ConditionalDetr + def post_process_panoptic_segmentation( + self, + outputs, + threshold: float = 0.5, + mask_threshold: float = 0.5, + overlap_mask_area_threshold: float = 0.8, + label_ids_to_fuse: Optional[Set[int]] = None, + target_sizes: Optional[List[Tuple[int, int]]] = None, + ) -> List[Dict]: + """ + Converts the output of [`ConditionalDetrForSegmentation`] into image panoptic segmentation predictions. Only supports + PyTorch. + + Args: + outputs ([`ConditionalDetrForSegmentation`]): + The outputs from [`ConditionalDetrForSegmentation`]. + threshold (`float`, *optional*, defaults to 0.5): + The probability score threshold to keep predicted instance masks. + mask_threshold (`float`, *optional*, defaults to 0.5): + Threshold to use when turning the predicted masks into binary values. + overlap_mask_area_threshold (`float`, *optional*, defaults to 0.8): + The overlap mask area threshold to merge or discard small disconnected parts within each binary + instance mask. + label_ids_to_fuse (`Set[int]`, *optional*): + The labels in this state will have all their instances be fused together. For instance we could say + there can only be one sky in an image, but several persons, so the label ID for sky would be in that + set, but not the one for person. + target_sizes (`List[Tuple]`, *optional*): + List of length (batch_size), where each list item (`Tuple[int, int]]`) corresponds to the requested + final size (height, width) of each prediction in batch. If unset, predictions will not be resized. + Returns: + `List[Dict]`: A list of dictionaries, one per image, each dictionary containing two keys: + - **segmentation** -- a tensor of shape `(height, width)` where each pixel represents a `segment_id` or + `None` if no mask if found above `threshold`. If `target_sizes` is specified, segmentation is resized to + the corresponding `target_sizes` entry. + - **segments_info** -- A dictionary that contains additional information on each segment. + - **id** -- an integer representing the `segment_id`. + - **label_id** -- An integer representing the label / semantic class id corresponding to `segment_id`. + - **was_fused** -- a boolean, `True` if `label_id` was in `label_ids_to_fuse`, `False` otherwise. + Multiple instances of the same class / label were fused and assigned a single `segment_id`. + - **score** -- Prediction score of segment with `segment_id`. + """ + + if label_ids_to_fuse is None: + logger.warning_once("`label_ids_to_fuse` unset. No instance will be fused.") + label_ids_to_fuse = set() + + class_queries_logits = outputs.logits # [batch_size, num_queries, num_classes+1] + masks_queries_logits = outputs.pred_masks # [batch_size, num_queries, height, width] + + batch_size = class_queries_logits.shape[0] + num_labels = class_queries_logits.shape[-1] - 1 + + mask_probs = masks_queries_logits.sigmoid() # [batch_size, num_queries, height, width] + + # Predicted label and score of each query (batch_size, num_queries) + pred_scores, pred_labels = nn.functional.softmax(class_queries_logits, dim=-1).max(-1) + + # Loop over items in batch size + results: List[Dict[str, TensorType]] = [] + + for i in range(batch_size): + mask_probs_item, pred_scores_item, pred_labels_item = remove_low_and_no_objects( + mask_probs[i], pred_scores[i], pred_labels[i], threshold, num_labels + ) + + # No mask found + if mask_probs_item.shape[0] <= 0: + height, width = target_sizes[i] if target_sizes is not None else mask_probs_item.shape[1:] + segmentation = torch.zeros((height, width)) - 1 + results.append({"segmentation": segmentation, "segments_info": []}) + continue + + # Get segmentation map and segment information of batch item + target_size = target_sizes[i] if target_sizes is not None else None + segmentation, segments = compute_segments( + mask_probs=mask_probs_item, + pred_scores=pred_scores_item, + pred_labels=pred_labels_item, + mask_threshold=mask_threshold, + overlap_mask_area_threshold=overlap_mask_area_threshold, + label_ids_to_fuse=label_ids_to_fuse, + target_size=target_size, + ) + + results.append({"segmentation": segmentation, "segments_info": segments}) + return results diff --git a/llmeval-env/lib/python3.10/site-packages/transformers/models/conditional_detr/modeling_conditional_detr.py b/llmeval-env/lib/python3.10/site-packages/transformers/models/conditional_detr/modeling_conditional_detr.py new file mode 100644 index 0000000000000000000000000000000000000000..d8ff371fad77d1d148179054a0eea2d811fea56c --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/transformers/models/conditional_detr/modeling_conditional_detr.py @@ -0,0 +1,2759 @@ +# coding=utf-8 +# Copyright 2022 Microsoft Research Asia and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch Conditional DETR model.""" + + +import math +from dataclasses import dataclass +from typing import Dict, List, Optional, Tuple, Union + +import torch +from torch import Tensor, nn + +from ...activations import ACT2FN +from ...modeling_attn_mask_utils import _prepare_4d_attention_mask +from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithCrossAttentions, Seq2SeqModelOutput +from ...modeling_utils import PreTrainedModel +from ...utils import ( + ModelOutput, + add_start_docstrings, + add_start_docstrings_to_model_forward, + is_accelerate_available, + is_scipy_available, + is_timm_available, + is_vision_available, + logging, + replace_return_docstrings, + requires_backends, +) +from ...utils.backbone_utils import load_backbone +from .configuration_conditional_detr import ConditionalDetrConfig + + +if is_accelerate_available(): + from accelerate import PartialState + from accelerate.utils import reduce + +if is_scipy_available(): + from scipy.optimize import linear_sum_assignment + +if is_timm_available(): + from timm import create_model + +if is_vision_available(): + from ...image_transforms import center_to_corners_format + +logger = logging.get_logger(__name__) + +_CONFIG_FOR_DOC = "ConditionalDetrConfig" +_CHECKPOINT_FOR_DOC = "microsoft/conditional-detr-resnet-50" + + +from ..deprecated._archive_maps import CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +@dataclass +class ConditionalDetrDecoderOutput(BaseModelOutputWithCrossAttentions): + """ + Base class for outputs of the Conditional DETR decoder. This class adds one attribute to + BaseModelOutputWithCrossAttentions, namely an optional stack of intermediate decoder activations, i.e. the output + of each decoder layer, each of them gone through a layernorm. This is useful when training the model with auxiliary + decoding losses. + + Args: + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the model. + hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer + plus the initial embedding outputs. + attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in + the self-attention heads. + cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and `config.add_cross_attention=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, + used to compute the weighted average in the cross-attention heads. + intermediate_hidden_states (`torch.FloatTensor` of shape `(config.decoder_layers, batch_size, num_queries, hidden_size)`, *optional*, returned when `config.auxiliary_loss=True`): + Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a + layernorm. + """ + + intermediate_hidden_states: Optional[torch.FloatTensor] = None + reference_points: Optional[Tuple[torch.FloatTensor]] = None + + +@dataclass +class ConditionalDetrModelOutput(Seq2SeqModelOutput): + """ + Base class for outputs of the Conditional DETR encoder-decoder model. This class adds one attribute to + Seq2SeqModelOutput, namely an optional stack of intermediate decoder activations, i.e. the output of each decoder + layer, each of them gone through a layernorm. This is useful when training the model with auxiliary decoding + losses. + + Args: + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Sequence of hidden-states at the output of the last layer of the decoder of the model. + decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each + layer plus the initial embedding outputs. + decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the + weighted average in the self-attention heads. + cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, + used to compute the weighted average in the cross-attention heads. + encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder of the model. + encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each + layer plus the initial embedding outputs. + encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the + weighted average in the self-attention heads. + intermediate_hidden_states (`torch.FloatTensor` of shape `(config.decoder_layers, batch_size, sequence_length, hidden_size)`, *optional*, returned when `config.auxiliary_loss=True`): + Intermediate decoder activations, i.e. the output of each decoder layer, each of them gone through a + layernorm. + """ + + intermediate_hidden_states: Optional[torch.FloatTensor] = None + reference_points: Optional[Tuple[torch.FloatTensor]] = None + + +@dataclass +# Copied from transformers.models.detr.modeling_detr.DetrObjectDetectionOutput with Detr->ConditionalDetr +class ConditionalDetrObjectDetectionOutput(ModelOutput): + """ + Output type of [`ConditionalDetrForObjectDetection`]. + + Args: + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)): + Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a + bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized + scale-invariant IoU loss. + loss_dict (`Dict`, *optional*): + A dictionary containing the individual losses. Useful for logging. + logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`): + Classification logits (including no-object) for all queries. + pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These + values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding + possible padding). You can use [`~ConditionalDetrImageProcessor.post_process_object_detection`] to retrieve the + unnormalized bounding boxes. + auxiliary_outputs (`list[Dict]`, *optional*): + Optional, only returned when auxilary losses are activated (i.e. `config.auxiliary_loss` is set to `True`) + and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and + `pred_boxes`) for each decoder layer. + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the decoder of the model. + decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each + layer plus the initial embedding outputs. + decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the + weighted average in the self-attention heads. + cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, + used to compute the weighted average in the cross-attention heads. + encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder of the model. + encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each + layer plus the initial embedding outputs. + encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the + weighted average in the self-attention heads. + """ + + loss: Optional[torch.FloatTensor] = None + loss_dict: Optional[Dict] = None + logits: torch.FloatTensor = None + pred_boxes: torch.FloatTensor = None + auxiliary_outputs: Optional[List[Dict]] = None + last_hidden_state: Optional[torch.FloatTensor] = None + decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None + decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None + cross_attentions: Optional[Tuple[torch.FloatTensor]] = None + encoder_last_hidden_state: Optional[torch.FloatTensor] = None + encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None + encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None + + +@dataclass +# Copied from transformers.models.detr.modeling_detr.DetrSegmentationOutput with Detr->ConditionalDetr +class ConditionalDetrSegmentationOutput(ModelOutput): + """ + Output type of [`ConditionalDetrForSegmentation`]. + + Args: + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` are provided)): + Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a + bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized + scale-invariant IoU loss. + loss_dict (`Dict`, *optional*): + A dictionary containing the individual losses. Useful for logging. + logits (`torch.FloatTensor` of shape `(batch_size, num_queries, num_classes + 1)`): + Classification logits (including no-object) for all queries. + pred_boxes (`torch.FloatTensor` of shape `(batch_size, num_queries, 4)`): + Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These + values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding + possible padding). You can use [`~ConditionalDetrImageProcessor.post_process_object_detection`] to retrieve the + unnormalized bounding boxes. + pred_masks (`torch.FloatTensor` of shape `(batch_size, num_queries, height/4, width/4)`): + Segmentation masks logits for all queries. See also + [`~ConditionalDetrImageProcessor.post_process_semantic_segmentation`] or + [`~ConditionalDetrImageProcessor.post_process_instance_segmentation`] + [`~ConditionalDetrImageProcessor.post_process_panoptic_segmentation`] to evaluate semantic, instance and panoptic + segmentation masks respectively. + auxiliary_outputs (`list[Dict]`, *optional*): + Optional, only returned when auxiliary losses are activated (i.e. `config.auxiliary_loss` is set to `True`) + and labels are provided. It is a list of dictionaries containing the two above keys (`logits` and + `pred_boxes`) for each decoder layer. + last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the decoder of the model. + decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the decoder at the output of each + layer plus the initial embedding outputs. + decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights of the decoder, after the attention softmax, used to compute the + weighted average in the self-attention heads. + cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights of the decoder's cross-attention layer, after the attention softmax, + used to compute the weighted average in the cross-attention heads. + encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder of the model. + encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of + shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the encoder at the output of each + layer plus the initial embedding outputs. + encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. Attentions weights of the encoder, after the attention softmax, used to compute the + weighted average in the self-attention heads. + """ + + loss: Optional[torch.FloatTensor] = None + loss_dict: Optional[Dict] = None + logits: torch.FloatTensor = None + pred_boxes: torch.FloatTensor = None + pred_masks: torch.FloatTensor = None + auxiliary_outputs: Optional[List[Dict]] = None + last_hidden_state: Optional[torch.FloatTensor] = None + decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None + decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None + cross_attentions: Optional[Tuple[torch.FloatTensor]] = None + encoder_last_hidden_state: Optional[torch.FloatTensor] = None + encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None + encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None + + +# Copied from transformers.models.detr.modeling_detr.DetrFrozenBatchNorm2d with Detr->ConditionalDetr +class ConditionalDetrFrozenBatchNorm2d(nn.Module): + """ + BatchNorm2d where the batch statistics and the affine parameters are fixed. + + Copy-paste from torchvision.misc.ops with added eps before rqsrt, without which any other models than + torchvision.models.resnet[18,34,50,101] produce nans. + """ + + def __init__(self, n): + super().__init__() + self.register_buffer("weight", torch.ones(n)) + self.register_buffer("bias", torch.zeros(n)) + self.register_buffer("running_mean", torch.zeros(n)) + self.register_buffer("running_var", torch.ones(n)) + + def _load_from_state_dict( + self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ): + num_batches_tracked_key = prefix + "num_batches_tracked" + if num_batches_tracked_key in state_dict: + del state_dict[num_batches_tracked_key] + + super()._load_from_state_dict( + state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ) + + def forward(self, x): + # move reshapes to the beginning + # to make it user-friendly + weight = self.weight.reshape(1, -1, 1, 1) + bias = self.bias.reshape(1, -1, 1, 1) + running_var = self.running_var.reshape(1, -1, 1, 1) + running_mean = self.running_mean.reshape(1, -1, 1, 1) + epsilon = 1e-5 + scale = weight * (running_var + epsilon).rsqrt() + bias = bias - running_mean * scale + return x * scale + bias + + +# Copied from transformers.models.detr.modeling_detr.replace_batch_norm with Detr->ConditionalDetr +def replace_batch_norm(model): + r""" + Recursively replace all `torch.nn.BatchNorm2d` with `ConditionalDetrFrozenBatchNorm2d`. + + Args: + model (torch.nn.Module): + input model + """ + for name, module in model.named_children(): + if isinstance(module, nn.BatchNorm2d): + new_module = ConditionalDetrFrozenBatchNorm2d(module.num_features) + + if not module.weight.device == torch.device("meta"): + new_module.weight.data.copy_(module.weight) + new_module.bias.data.copy_(module.bias) + new_module.running_mean.data.copy_(module.running_mean) + new_module.running_var.data.copy_(module.running_var) + + model._modules[name] = new_module + + if len(list(module.children())) > 0: + replace_batch_norm(module) + + +# Copied from transformers.models.detr.modeling_detr.DetrConvEncoder +class ConditionalDetrConvEncoder(nn.Module): + """ + Convolutional backbone, using either the AutoBackbone API or one from the timm library. + + nn.BatchNorm2d layers are replaced by DetrFrozenBatchNorm2d as defined above. + + """ + + def __init__(self, config): + super().__init__() + + self.config = config + + if config.use_timm_backbone: + requires_backends(self, ["timm"]) + kwargs = {} + if config.dilation: + kwargs["output_stride"] = 16 + backbone = create_model( + config.backbone, + pretrained=config.use_pretrained_backbone, + features_only=True, + out_indices=(1, 2, 3, 4), + in_chans=config.num_channels, + **kwargs, + ) + else: + backbone = load_backbone(config) + + # replace batch norm by frozen batch norm + with torch.no_grad(): + replace_batch_norm(backbone) + self.model = backbone + self.intermediate_channel_sizes = ( + self.model.feature_info.channels() if config.use_timm_backbone else self.model.channels + ) + + backbone_model_type = config.backbone if config.use_timm_backbone else config.backbone_config.model_type + if "resnet" in backbone_model_type: + for name, parameter in self.model.named_parameters(): + if config.use_timm_backbone: + if "layer2" not in name and "layer3" not in name and "layer4" not in name: + parameter.requires_grad_(False) + else: + if "stage.1" not in name and "stage.2" not in name and "stage.3" not in name: + parameter.requires_grad_(False) + + def forward(self, pixel_values: torch.Tensor, pixel_mask: torch.Tensor): + # send pixel_values through the model to get list of feature maps + features = self.model(pixel_values) if self.config.use_timm_backbone else self.model(pixel_values).feature_maps + + out = [] + for feature_map in features: + # downsample pixel_mask to match shape of corresponding feature_map + mask = nn.functional.interpolate(pixel_mask[None].float(), size=feature_map.shape[-2:]).to(torch.bool)[0] + out.append((feature_map, mask)) + return out + + +# Copied from transformers.models.detr.modeling_detr.DetrConvModel with Detr->ConditionalDetr +class ConditionalDetrConvModel(nn.Module): + """ + This module adds 2D position embeddings to all intermediate feature maps of the convolutional encoder. + """ + + def __init__(self, conv_encoder, position_embedding): + super().__init__() + self.conv_encoder = conv_encoder + self.position_embedding = position_embedding + + def forward(self, pixel_values, pixel_mask): + # send pixel_values and pixel_mask through backbone to get list of (feature_map, pixel_mask) tuples + out = self.conv_encoder(pixel_values, pixel_mask) + pos = [] + for feature_map, mask in out: + # position encoding + pos.append(self.position_embedding(feature_map, mask).to(feature_map.dtype)) + + return out, pos + + +class ConditionalDetrSinePositionEmbedding(nn.Module): + """ + This is a more standard version of the position embedding, very similar to the one used by the Attention is all you + need paper, generalized to work on images. + """ + + def __init__(self, embedding_dim=64, temperature=10000, normalize=False, scale=None): + super().__init__() + self.embedding_dim = embedding_dim + self.temperature = temperature + self.normalize = normalize + if scale is not None and normalize is False: + raise ValueError("normalize should be True if scale is passed") + if scale is None: + scale = 2 * math.pi + self.scale = scale + + def forward(self, pixel_values, pixel_mask): + if pixel_mask is None: + raise ValueError("No pixel mask provided") + y_embed = pixel_mask.cumsum(1, dtype=torch.float32) + x_embed = pixel_mask.cumsum(2, dtype=torch.float32) + if self.normalize: + y_embed = y_embed / (y_embed[:, -1:, :] + 1e-6) * self.scale + x_embed = x_embed / (x_embed[:, :, -1:] + 1e-6) * self.scale + + dim_t = torch.arange(self.embedding_dim, dtype=torch.int64, device=pixel_values.device).float() + dim_t = self.temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / self.embedding_dim) + + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + + +# Copied from transformers.models.detr.modeling_detr.DetrLearnedPositionEmbedding with Detr->ConditionalDetr +class ConditionalDetrLearnedPositionEmbedding(nn.Module): + """ + This module learns positional embeddings up to a fixed maximum size. + """ + + def __init__(self, embedding_dim=256): + super().__init__() + self.row_embeddings = nn.Embedding(50, embedding_dim) + self.column_embeddings = nn.Embedding(50, embedding_dim) + + def forward(self, pixel_values, pixel_mask=None): + height, width = pixel_values.shape[-2:] + width_values = torch.arange(width, device=pixel_values.device) + height_values = torch.arange(height, device=pixel_values.device) + x_emb = self.column_embeddings(width_values) + y_emb = self.row_embeddings(height_values) + pos = torch.cat([x_emb.unsqueeze(0).repeat(height, 1, 1), y_emb.unsqueeze(1).repeat(1, width, 1)], dim=-1) + pos = pos.permute(2, 0, 1) + pos = pos.unsqueeze(0) + pos = pos.repeat(pixel_values.shape[0], 1, 1, 1) + return pos + + +# Copied from transformers.models.detr.modeling_detr.build_position_encoding with Detr->ConditionalDetr +def build_position_encoding(config): + n_steps = config.d_model // 2 + if config.position_embedding_type == "sine": + # TODO find a better way of exposing other arguments + position_embedding = ConditionalDetrSinePositionEmbedding(n_steps, normalize=True) + elif config.position_embedding_type == "learned": + position_embedding = ConditionalDetrLearnedPositionEmbedding(n_steps) + else: + raise ValueError(f"Not supported {config.position_embedding_type}") + + return position_embedding + + +# function to generate sine positional embedding for 2d coordinates +def gen_sine_position_embeddings(pos_tensor, d_model): + scale = 2 * math.pi + dim = d_model // 2 + dim_t = torch.arange(dim, dtype=torch.float32, device=pos_tensor.device) + dim_t = 10000 ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / dim) + x_embed = pos_tensor[:, :, 0] * scale + y_embed = pos_tensor[:, :, 1] * scale + pos_x = x_embed[:, :, None] / dim_t + pos_y = y_embed[:, :, None] / dim_t + pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2) + pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2) + pos = torch.cat((pos_y, pos_x), dim=2) + return pos + + +def inverse_sigmoid(x, eps=1e-5): + x = x.clamp(min=0, max=1) + x1 = x.clamp(min=eps) + x2 = (1 - x).clamp(min=eps) + return torch.log(x1 / x2) + + +# Copied from transformers.models.detr.modeling_detr.DetrAttention +class DetrAttention(nn.Module): + """ + Multi-headed attention from 'Attention Is All You Need' paper. + + Here, we add position embeddings to the queries and keys (as explained in the DETR paper). + """ + + def __init__( + self, + embed_dim: int, + num_heads: int, + dropout: float = 0.0, + bias: bool = True, + ): + super().__init__() + self.embed_dim = embed_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + if self.head_dim * num_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" + f" {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + + self.k_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.v_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.q_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + self.out_proj = nn.Linear(embed_dim, embed_dim, bias=bias) + + def _shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int): + return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def with_pos_embed(self, tensor: torch.Tensor, object_queries: Optional[Tensor], **kwargs): + position_embeddings = kwargs.pop("position_embeddings", None) + + if kwargs: + raise ValueError(f"Unexpected arguments {kwargs.keys()}") + + if position_embeddings is not None and object_queries is not None: + raise ValueError( + "Cannot specify both position_embeddings and object_queries. Please use just object_queries" + ) + + if position_embeddings is not None: + logger.warning_once( + "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead" + ) + object_queries = position_embeddings + + return tensor if object_queries is None else tensor + object_queries + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + object_queries: Optional[torch.Tensor] = None, + key_value_states: Optional[torch.Tensor] = None, + spatial_position_embeddings: Optional[torch.Tensor] = None, + output_attentions: bool = False, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + """Input shape: Batch x Time x Channel""" + + position_embeddings = kwargs.pop("position_ebmeddings", None) + key_value_position_embeddings = kwargs.pop("key_value_position_embeddings", None) + + if kwargs: + raise ValueError(f"Unexpected arguments {kwargs.keys()}") + + if position_embeddings is not None and object_queries is not None: + raise ValueError( + "Cannot specify both position_embeddings and object_queries. Please use just object_queries" + ) + + if key_value_position_embeddings is not None and spatial_position_embeddings is not None: + raise ValueError( + "Cannot specify both key_value_position_embeddings and spatial_position_embeddings. Please use just spatial_position_embeddings" + ) + + if position_embeddings is not None: + logger.warning_once( + "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead" + ) + object_queries = position_embeddings + + if key_value_position_embeddings is not None: + logger.warning_once( + "key_value_position_embeddings has been deprecated and will be removed in v4.34. Please use spatial_position_embeddings instead" + ) + spatial_position_embeddings = key_value_position_embeddings + + # if key_value_states are provided this layer is used as a cross-attention layer + # for the decoder + is_cross_attention = key_value_states is not None + batch_size, target_len, embed_dim = hidden_states.size() + + # add position embeddings to the hidden states before projecting to queries and keys + if object_queries is not None: + hidden_states_original = hidden_states + hidden_states = self.with_pos_embed(hidden_states, object_queries) + + # add key-value position embeddings to the key value states + if spatial_position_embeddings is not None: + key_value_states_original = key_value_states + key_value_states = self.with_pos_embed(key_value_states, spatial_position_embeddings) + + # get query proj + query_states = self.q_proj(hidden_states) * self.scaling + # get key, value proj + if is_cross_attention: + # cross_attentions + key_states = self._shape(self.k_proj(key_value_states), -1, batch_size) + value_states = self._shape(self.v_proj(key_value_states_original), -1, batch_size) + else: + # self_attention + key_states = self._shape(self.k_proj(hidden_states), -1, batch_size) + value_states = self._shape(self.v_proj(hidden_states_original), -1, batch_size) + + proj_shape = (batch_size * self.num_heads, -1, self.head_dim) + query_states = self._shape(query_states, target_len, batch_size).view(*proj_shape) + key_states = key_states.view(*proj_shape) + value_states = value_states.view(*proj_shape) + + source_len = key_states.size(1) + + attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) + + if attn_weights.size() != (batch_size * self.num_heads, target_len, source_len): + raise ValueError( + f"Attention weights should be of size {(batch_size * self.num_heads, target_len, source_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (batch_size, 1, target_len, source_len): + raise ValueError( + f"Attention mask should be of size {(batch_size, 1, target_len, source_len)}, but is" + f" {attention_mask.size()}" + ) + attn_weights = attn_weights.view(batch_size, self.num_heads, target_len, source_len) + attention_mask + attn_weights = attn_weights.view(batch_size * self.num_heads, target_len, source_len) + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + if output_attentions: + # this operation is a bit awkward, but it's required to + # make sure that attn_weights keeps its gradient. + # In order to do so, attn_weights have to reshaped + # twice and have to be reused in the following + attn_weights_reshaped = attn_weights.view(batch_size, self.num_heads, target_len, source_len) + attn_weights = attn_weights_reshaped.view(batch_size * self.num_heads, target_len, source_len) + else: + attn_weights_reshaped = None + + attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + + attn_output = torch.bmm(attn_probs, value_states) + + if attn_output.size() != (batch_size * self.num_heads, target_len, self.head_dim): + raise ValueError( + f"`attn_output` should be of size {(batch_size, self.num_heads, target_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.view(batch_size, self.num_heads, target_len, self.head_dim) + attn_output = attn_output.transpose(1, 2) + attn_output = attn_output.reshape(batch_size, target_len, embed_dim) + + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights_reshaped + + +class ConditionalDetrAttention(nn.Module): + """ + Cross-Attention used in Conditional DETR 'Conditional DETR for Fast Training Convergence' paper. + + The key q_proj, k_proj, v_proj are defined outside the attention. This attention allows the dim of q, k to be + different to v. + """ + + def __init__( + self, + embed_dim: int, + out_dim: int, + num_heads: int, + dropout: float = 0.0, + bias: bool = True, + ): + super().__init__() + self.embed_dim = embed_dim + self.out_dim = out_dim + self.num_heads = num_heads + self.dropout = dropout + self.head_dim = embed_dim // num_heads + if self.head_dim * num_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" + f" {num_heads})." + ) + # head dimension of values + self.v_head_dim = out_dim // num_heads + if self.v_head_dim * num_heads != self.out_dim: + raise ValueError( + f"out_dim must be divisible by num_heads (got `out_dim`: {self.out_dim} and `num_heads`: {num_heads})." + ) + self.scaling = self.head_dim**-0.5 + + self.out_proj = nn.Linear(out_dim, out_dim, bias=bias) + + def _qk_shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int): + return tensor.view(batch_size, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def _v_shape(self, tensor: torch.Tensor, seq_len: int, batch_size: int): + return tensor.view(batch_size, seq_len, self.num_heads, self.v_head_dim).transpose(1, 2).contiguous() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + key_states: Optional[torch.Tensor] = None, + value_states: Optional[torch.Tensor] = None, + output_attentions: bool = False, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + """Input shape: Batch x Time x Channel""" + + batch_size, target_len, _ = hidden_states.size() + + # get query proj + query_states = hidden_states * self.scaling + # get key, value proj + key_states = self._qk_shape(key_states, -1, batch_size) + value_states = self._v_shape(value_states, -1, batch_size) + + proj_shape = (batch_size * self.num_heads, -1, self.head_dim) + v_proj_shape = (batch_size * self.num_heads, -1, self.v_head_dim) + query_states = self._qk_shape(query_states, target_len, batch_size).view(*proj_shape) + key_states = key_states.view(*proj_shape) + value_states = value_states.view(*v_proj_shape) + + source_len = key_states.size(1) + + attn_weights = torch.bmm(query_states, key_states.transpose(1, 2)) + + if attn_weights.size() != (batch_size * self.num_heads, target_len, source_len): + raise ValueError( + f"Attention weights should be of size {(batch_size * self.num_heads, target_len, source_len)}, but is" + f" {attn_weights.size()}" + ) + + if attention_mask is not None: + if attention_mask.size() != (batch_size, 1, target_len, source_len): + raise ValueError( + f"Attention mask should be of size {(batch_size, 1, target_len, source_len)}, but is" + f" {attention_mask.size()}" + ) + attn_weights = attn_weights.view(batch_size, self.num_heads, target_len, source_len) + attention_mask + attn_weights = attn_weights.view(batch_size * self.num_heads, target_len, source_len) + + attn_weights = nn.functional.softmax(attn_weights, dim=-1) + + if output_attentions: + # this operation is a bit awkward, but it's required to + # make sure that attn_weights keeps its gradient. + # In order to do so, attn_weights have to reshaped + # twice and have to be reused in the following + attn_weights_reshaped = attn_weights.view(batch_size, self.num_heads, target_len, source_len) + attn_weights = attn_weights_reshaped.view(batch_size * self.num_heads, target_len, source_len) + else: + attn_weights_reshaped = None + + attn_probs = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) + + attn_output = torch.bmm(attn_probs, value_states) + + if attn_output.size() != (batch_size * self.num_heads, target_len, self.v_head_dim): + raise ValueError( + f"`attn_output` should be of size {(batch_size, self.num_heads, target_len, self.v_head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.view(batch_size, self.num_heads, target_len, self.v_head_dim) + attn_output = attn_output.transpose(1, 2) + attn_output = attn_output.reshape(batch_size, target_len, self.out_dim) + + attn_output = self.out_proj(attn_output) + + return attn_output, attn_weights_reshaped + + +# Copied from transformers.models.detr.modeling_detr.DetrEncoderLayer with DetrEncoderLayer->ConditionalDetrEncoderLayer,DetrConfig->ConditionalDetrConfig +class ConditionalDetrEncoderLayer(nn.Module): + def __init__(self, config: ConditionalDetrConfig): + super().__init__() + self.embed_dim = config.d_model + self.self_attn = DetrAttention( + embed_dim=self.embed_dim, + num_heads=config.encoder_attention_heads, + dropout=config.attention_dropout, + ) + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.activation_dropout = config.activation_dropout + self.fc1 = nn.Linear(self.embed_dim, config.encoder_ffn_dim) + self.fc2 = nn.Linear(config.encoder_ffn_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: torch.Tensor, + object_queries: torch.Tensor = None, + output_attentions: bool = False, + **kwargs, + ): + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative + values. + object_queries (`torch.FloatTensor`, *optional*): + Object queries (also called content embeddings), to be added to the hidden states. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + """ + position_embeddings = kwargs.pop("position_embeddings", None) + + if kwargs: + raise ValueError(f"Unexpected arguments {kwargs.keys()}") + + if position_embeddings is not None and object_queries is not None: + raise ValueError( + "Cannot specify both position_embeddings and object_queries. Please use just object_queries" + ) + + if position_embeddings is not None: + logger.warning_once( + "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead" + ) + object_queries = position_embeddings + + residual = hidden_states + hidden_states, attn_weights = self.self_attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + object_queries=object_queries, + output_attentions=output_attentions, + ) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + residual = hidden_states + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + hidden_states = residual + hidden_states + hidden_states = self.final_layer_norm(hidden_states) + + if self.training: + if torch.isinf(hidden_states).any() or torch.isnan(hidden_states).any(): + clamp_value = torch.finfo(hidden_states.dtype).max - 1000 + hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attn_weights,) + + return outputs + + +class ConditionalDetrDecoderLayer(nn.Module): + def __init__(self, config: ConditionalDetrConfig): + super().__init__() + self.embed_dim = config.d_model + + d_model = config.d_model + # Decoder Self-Attention projections + self.sa_qcontent_proj = nn.Linear(d_model, d_model) + self.sa_qpos_proj = nn.Linear(d_model, d_model) + self.sa_kcontent_proj = nn.Linear(d_model, d_model) + self.sa_kpos_proj = nn.Linear(d_model, d_model) + self.sa_v_proj = nn.Linear(d_model, d_model) + + self.self_attn = ConditionalDetrAttention( + embed_dim=self.embed_dim, + out_dim=self.embed_dim, + num_heads=config.decoder_attention_heads, + dropout=config.attention_dropout, + ) + self.dropout = config.dropout + self.activation_fn = ACT2FN[config.activation_function] + self.activation_dropout = config.activation_dropout + + self.self_attn_layer_norm = nn.LayerNorm(self.embed_dim) + + # Decoder Cross-Attention projections + self.ca_qcontent_proj = nn.Linear(d_model, d_model) + self.ca_qpos_proj = nn.Linear(d_model, d_model) + self.ca_kcontent_proj = nn.Linear(d_model, d_model) + self.ca_kpos_proj = nn.Linear(d_model, d_model) + self.ca_v_proj = nn.Linear(d_model, d_model) + self.ca_qpos_sine_proj = nn.Linear(d_model, d_model) + + self.encoder_attn = ConditionalDetrAttention( + self.embed_dim * 2, self.embed_dim, config.decoder_attention_heads, dropout=config.attention_dropout + ) + self.encoder_attn_layer_norm = nn.LayerNorm(self.embed_dim) + self.fc1 = nn.Linear(self.embed_dim, config.decoder_ffn_dim) + self.fc2 = nn.Linear(config.decoder_ffn_dim, self.embed_dim) + self.final_layer_norm = nn.LayerNorm(self.embed_dim) + self.nhead = config.decoder_attention_heads + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + object_queries: Optional[torch.Tensor] = None, + query_position_embeddings: Optional[torch.Tensor] = None, + query_sine_embed: Optional[torch.Tensor] = None, + encoder_hidden_states: Optional[torch.Tensor] = None, + encoder_attention_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[bool] = False, + is_first: Optional[bool] = False, + **kwargs, + ): + """ + Args: + hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)` + attention_mask (`torch.FloatTensor`): attention mask of size + `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative + values. + object_queries (`torch.FloatTensor`, *optional*): + object_queries that are added to the queries and keys + in the cross-attention layer. + query_position_embeddings (`torch.FloatTensor`, *optional*): + object_queries that are added to the queries and keys + in the self-attention layer. + encoder_hidden_states (`torch.FloatTensor`): + cross attention input to the layer of shape `(seq_len, batch, embed_dim)` + encoder_attention_mask (`torch.FloatTensor`): encoder attention mask of size + `(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative + values. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + """ + position_embeddings = kwargs.pop("position_embeddings", None) + + if kwargs: + raise ValueError(f"Unexpected arguments {kwargs.keys()}") + + if position_embeddings is not None and object_queries is not None: + raise ValueError( + "Cannot specify both position_embeddings and object_queries. Please use just object_queries" + ) + + if position_embeddings is not None: + logger.warning_once( + "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead" + ) + object_queries = position_embeddings + + residual = hidden_states + + # ========== Begin of Self-Attention ============= + # Apply projections here + # shape: num_queries x batch_size x 256 + q_content = self.sa_qcontent_proj( + hidden_states + ) # target is the input of the first decoder layer. zero by default. + q_pos = self.sa_qpos_proj(query_position_embeddings) + k_content = self.sa_kcontent_proj(hidden_states) + k_pos = self.sa_kpos_proj(query_position_embeddings) + v = self.sa_v_proj(hidden_states) + + _, num_queries, n_model = q_content.shape + + q = q_content + q_pos + k = k_content + k_pos + hidden_states, self_attn_weights = self.self_attn( + hidden_states=q, + attention_mask=attention_mask, + key_states=k, + value_states=v, + output_attentions=output_attentions, + ) + # ============ End of Self-Attention ============= + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.self_attn_layer_norm(hidden_states) + + # ========== Begin of Cross-Attention ============= + # Apply projections here + # shape: num_queries x batch_size x 256 + q_content = self.ca_qcontent_proj(hidden_states) + k_content = self.ca_kcontent_proj(encoder_hidden_states) + v = self.ca_v_proj(encoder_hidden_states) + + batch_size, num_queries, n_model = q_content.shape + _, source_len, _ = k_content.shape + + k_pos = self.ca_kpos_proj(object_queries) + + # For the first decoder layer, we concatenate the positional embedding predicted from + # the object query (the positional embedding) into the original query (key) in DETR. + if is_first: + q_pos = self.ca_qpos_proj(query_position_embeddings) + q = q_content + q_pos + k = k_content + k_pos + else: + q = q_content + k = k_content + + q = q.view(batch_size, num_queries, self.nhead, n_model // self.nhead) + query_sine_embed = self.ca_qpos_sine_proj(query_sine_embed) + query_sine_embed = query_sine_embed.view(batch_size, num_queries, self.nhead, n_model // self.nhead) + q = torch.cat([q, query_sine_embed], dim=3).view(batch_size, num_queries, n_model * 2) + k = k.view(batch_size, source_len, self.nhead, n_model // self.nhead) + k_pos = k_pos.view(batch_size, source_len, self.nhead, n_model // self.nhead) + k = torch.cat([k, k_pos], dim=3).view(batch_size, source_len, n_model * 2) + + # Cross-Attention Block + cross_attn_weights = None + if encoder_hidden_states is not None: + residual = hidden_states + + hidden_states, cross_attn_weights = self.encoder_attn( + hidden_states=q, + attention_mask=encoder_attention_mask, + key_states=k, + value_states=v, + output_attentions=output_attentions, + ) + + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.encoder_attn_layer_norm(hidden_states) + + # ============ End of Cross-Attention ============= + + # Fully Connected + residual = hidden_states + hidden_states = self.activation_fn(self.fc1(hidden_states)) + hidden_states = nn.functional.dropout(hidden_states, p=self.activation_dropout, training=self.training) + hidden_states = self.fc2(hidden_states) + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + hidden_states = residual + hidden_states + hidden_states = self.final_layer_norm(hidden_states) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights, cross_attn_weights) + + return outputs + + +# Copied from transformers.models.detr.modeling_detr.DetrClassificationHead with Detr->ConditionalDetr +class ConditionalDetrClassificationHead(nn.Module): + """Head for sentence-level classification tasks.""" + + def __init__(self, input_dim: int, inner_dim: int, num_classes: int, pooler_dropout: float): + super().__init__() + self.dense = nn.Linear(input_dim, inner_dim) + self.dropout = nn.Dropout(p=pooler_dropout) + self.out_proj = nn.Linear(inner_dim, num_classes) + + def forward(self, hidden_states: torch.Tensor): + hidden_states = self.dropout(hidden_states) + hidden_states = self.dense(hidden_states) + hidden_states = torch.tanh(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.out_proj(hidden_states) + return hidden_states + + +# Copied from transformers.models.detr.modeling_detr.DetrMLPPredictionHead with DetrMLPPredictionHead->MLP +class MLP(nn.Module): + """ + Very simple multi-layer perceptron (MLP, also called FFN), used to predict the normalized center coordinates, + height and width of a bounding box w.r.t. an image. + + Copied from https://github.com/facebookresearch/detr/blob/master/models/detr.py + + """ + + def __init__(self, input_dim, hidden_dim, output_dim, num_layers): + super().__init__() + self.num_layers = num_layers + h = [hidden_dim] * (num_layers - 1) + self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) + + def forward(self, x): + for i, layer in enumerate(self.layers): + x = nn.functional.relu(layer(x)) if i < self.num_layers - 1 else layer(x) + return x + + +# Copied from transformers.models.detr.modeling_detr.DetrPreTrainedModel with Detr->ConditionalDetr +class ConditionalDetrPreTrainedModel(PreTrainedModel): + config_class = ConditionalDetrConfig + base_model_prefix = "model" + main_input_name = "pixel_values" + _no_split_modules = [r"ConditionalDetrConvEncoder", r"ConditionalDetrEncoderLayer", r"ConditionalDetrDecoderLayer"] + + def _init_weights(self, module): + std = self.config.init_std + xavier_std = self.config.init_xavier_std + + if isinstance(module, ConditionalDetrMHAttentionMap): + nn.init.zeros_(module.k_linear.bias) + nn.init.zeros_(module.q_linear.bias) + nn.init.xavier_uniform_(module.k_linear.weight, gain=xavier_std) + nn.init.xavier_uniform_(module.q_linear.weight, gain=xavier_std) + elif isinstance(module, ConditionalDetrLearnedPositionEmbedding): + nn.init.uniform_(module.row_embeddings.weight) + nn.init.uniform_(module.column_embeddings.weight) + if isinstance(module, (nn.Linear, nn.Conv2d, nn.BatchNorm2d)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=std) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=std) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + + +CONDITIONAL_DETR_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`ConditionalDetrConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +CONDITIONAL_DETR_INPUTS_DOCSTRING = r""" + Args: + pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): + Pixel values. Padding will be ignored by default should you provide it. + + Pixel values can be obtained using [`AutoImageProcessor`]. See [`ConditionalDetrImageProcessor.__call__`] + for details. + + pixel_mask (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*): + Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`: + + - 1 for pixels that are real (i.e. **not masked**), + - 0 for pixels that are padding (i.e. **masked**). + + [What are attention masks?](../glossary#attention-mask) + + decoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, num_queries)`, *optional*): + Not used by default. Can be used to mask object queries. + encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*): + Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`) + `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of + hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you + can choose to directly pass a flattened representation of an image. + decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`, *optional*): + Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an + embedded representation. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. +""" + + +# Copied from transformers.models.detr.modeling_detr.DetrEncoder with Detr->ConditionalDetr,DETR->ConditionalDETR +class ConditionalDetrEncoder(ConditionalDetrPreTrainedModel): + """ + Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a + [`ConditionalDetrEncoderLayer`]. + + The encoder updates the flattened feature map through multiple self-attention layers. + + Small tweak for ConditionalDETR: + + - object_queries are added to the forward pass. + + Args: + config: ConditionalDetrConfig + """ + + def __init__(self, config: ConditionalDetrConfig): + super().__init__(config) + + self.dropout = config.dropout + self.layerdrop = config.encoder_layerdrop + + self.layers = nn.ModuleList([ConditionalDetrEncoderLayer(config) for _ in range(config.encoder_layers)]) + + # in the original ConditionalDETR, no layernorm is used at the end of the encoder, as "normalize_before" is set to False by default + + # Initialize weights and apply final processing + self.post_init() + + def forward( + self, + inputs_embeds=None, + attention_mask=None, + object_queries=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + **kwargs, + ): + r""" + Args: + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Flattened feature map (output of the backbone + projection layer) that is passed to the encoder. + + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding pixel features. Mask values selected in `[0, 1]`: + + - 1 for pixel features that are real (i.e. **not masked**), + - 0 for pixel features that are padding (i.e. **masked**). + + [What are attention masks?](../glossary#attention-mask) + + object_queries (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + Object queries that are added to the queries in each self-attention layer. + + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + """ + position_embeddings = kwargs.pop("position_embeddings", None) + + if kwargs: + raise ValueError(f"Unexpected arguments {kwargs.keys()}") + + if position_embeddings is not None and object_queries is not None: + raise ValueError( + "Cannot specify both position_embeddings and object_queries. Please use just object_queries" + ) + + if position_embeddings is not None: + logger.warning_once( + "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead" + ) + object_queries = position_embeddings + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + hidden_states = inputs_embeds + hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, training=self.training) + + # expand attention_mask + if attention_mask is not None: + # [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len] + attention_mask = _prepare_4d_attention_mask(attention_mask, inputs_embeds.dtype) + + encoder_states = () if output_hidden_states else None + all_attentions = () if output_attentions else None + for i, encoder_layer in enumerate(self.layers): + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + to_drop = False + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: # skip the layer + to_drop = True + + if to_drop: + layer_outputs = (None, None) + else: + # we add object_queries as extra input to the encoder_layer + layer_outputs = encoder_layer( + hidden_states, + attention_mask, + object_queries=object_queries, + output_attentions=output_attentions, + ) + + hidden_states = layer_outputs[0] + + if output_attentions: + all_attentions = all_attentions + (layer_outputs[1],) + + if output_hidden_states: + encoder_states = encoder_states + (hidden_states,) + + if not return_dict: + return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) + return BaseModelOutput( + last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions + ) + + +class ConditionalDetrDecoder(ConditionalDetrPreTrainedModel): + """ + Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`ConditionalDetrDecoderLayer`]. + + The decoder updates the query embeddings through multiple self-attention and cross-attention layers. + + Some small tweaks for Conditional DETR: + + - object_queries and query_position_embeddings are added to the forward pass. + - if self.config.auxiliary_loss is set to True, also returns a stack of activations from all decoding layers. + + Args: + config: ConditionalDetrConfig + """ + + def __init__(self, config: ConditionalDetrConfig): + super().__init__(config) + self.dropout = config.dropout + self.layerdrop = config.decoder_layerdrop + + self.layers = nn.ModuleList([ConditionalDetrDecoderLayer(config) for _ in range(config.decoder_layers)]) + # in Conditional DETR, the decoder uses layernorm after the last decoder layer output + self.layernorm = nn.LayerNorm(config.d_model) + d_model = config.d_model + self.gradient_checkpointing = False + + # query_scale is the FFN applied on f to generate transformation T + self.query_scale = MLP(d_model, d_model, d_model, 2) + self.ref_point_head = MLP(d_model, d_model, 2, 2) + for layer_id in range(config.decoder_layers - 1): + self.layers[layer_id + 1].ca_qpos_proj = None + + # Initialize weights and apply final processing + self.post_init() + + def forward( + self, + inputs_embeds=None, + attention_mask=None, + encoder_hidden_states=None, + encoder_attention_mask=None, + object_queries=None, + query_position_embeddings=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + **kwargs, + ): + r""" + Args: + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): + The query embeddings that are passed into the decoder. + + attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on certain queries. Mask values selected in `[0, 1]`: + + - 1 for queries that are **not masked**, + - 0 for queries that are **masked**. + + [What are attention masks?](../glossary#attention-mask) + encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention + of the decoder. + encoder_attention_mask (`torch.LongTensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): + Mask to avoid performing cross-attention on padding pixel_values of the encoder. Mask values selected + in `[0, 1]`: + + - 1 for pixels that are real (i.e. **not masked**), + - 0 for pixels that are padding (i.e. **masked**). + + object_queries (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Position embeddings that are added to the queries and keys in each cross-attention layer. + query_position_embeddings (`torch.FloatTensor` of shape `(batch_size, num_queries, hidden_size)`): + , *optional*): Position embeddings that are added to the queries and keys in each self-attention layer. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors + for more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + """ + position_embeddings = kwargs.pop("position_embeddings", None) + + if kwargs: + raise ValueError(f"Unexpected arguments {kwargs.keys()}") + + if position_embeddings is not None and object_queries is not None: + raise ValueError( + "Cannot specify both position_embeddings and object_queries. Please use just object_queries" + ) + + if position_embeddings is not None: + logger.warning_once( + "position_embeddings has been deprecated and will be removed in v4.34. Please use object_queries instead" + ) + object_queries = position_embeddings + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if inputs_embeds is not None: + hidden_states = inputs_embeds + input_shape = inputs_embeds.size()[:-1] + + # expand encoder attention mask + if encoder_hidden_states is not None and encoder_attention_mask is not None: + # [batch_size, seq_len] -> [batch_size, 1, target_seq_len, source_seq_len] + encoder_attention_mask = _prepare_4d_attention_mask( + encoder_attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1] + ) + + # optional intermediate hidden states + intermediate = () if self.config.auxiliary_loss else None + + # decoder layers + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None + + reference_points_before_sigmoid = self.ref_point_head( + query_position_embeddings + ) # [num_queries, batch_size, 2] + reference_points = reference_points_before_sigmoid.sigmoid().transpose(0, 1) + obj_center = reference_points[..., :2].transpose(0, 1) + # get sine embedding for the query vector + query_sine_embed_before_transformation = gen_sine_position_embeddings(obj_center, self.config.d_model) + + for idx, decoder_layer in enumerate(self.layers): + # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) + if output_hidden_states: + all_hidden_states += (hidden_states,) + if self.training: + dropout_probability = torch.rand([]) + if dropout_probability < self.layerdrop: + continue + if idx == 0: + pos_transformation = 1 + else: + pos_transformation = self.query_scale(hidden_states) + # apply transformation + query_sine_embed = query_sine_embed_before_transformation * pos_transformation + if self.gradient_checkpointing and self.training: + layer_outputs = self._gradient_checkpointing_func( + decoder_layer.__call__, + hidden_states, + None, + object_queries, + query_position_embeddings, + query_sine_embed, + encoder_hidden_states, + encoder_attention_mask, + None, + None, + ) + else: + layer_outputs = decoder_layer( + hidden_states, + attention_mask=None, + object_queries=object_queries, + query_position_embeddings=query_position_embeddings, + query_sine_embed=query_sine_embed, + encoder_hidden_states=encoder_hidden_states, + encoder_attention_mask=encoder_attention_mask, + output_attentions=output_attentions, + is_first=(idx == 0), + ) + + hidden_states = layer_outputs[0] + + if self.config.auxiliary_loss: + hidden_states = self.layernorm(hidden_states) + intermediate += (hidden_states,) + + if output_attentions: + all_self_attns += (layer_outputs[1],) + + if encoder_hidden_states is not None: + all_cross_attentions += (layer_outputs[2],) + + # finally, apply layernorm + hidden_states = self.layernorm(hidden_states) + + # add hidden states from the last decoder layer + if output_hidden_states: + all_hidden_states += (hidden_states,) + + # stack intermediate decoder activations + if self.config.auxiliary_loss: + intermediate = torch.stack(intermediate) + + if not return_dict: + return tuple( + v + for v in [ + hidden_states, + all_hidden_states, + all_self_attns, + all_cross_attentions, + intermediate, + reference_points, + ] + if v is not None + ) + return ConditionalDetrDecoderOutput( + last_hidden_state=hidden_states, + hidden_states=all_hidden_states, + attentions=all_self_attns, + cross_attentions=all_cross_attentions, + intermediate_hidden_states=intermediate, + reference_points=reference_points, + ) + + +@add_start_docstrings( + """ + The bare Conditional DETR Model (consisting of a backbone and encoder-decoder Transformer) outputting raw + hidden-states without any specific head on top. + """, + CONDITIONAL_DETR_START_DOCSTRING, +) +class ConditionalDetrModel(ConditionalDetrPreTrainedModel): + def __init__(self, config: ConditionalDetrConfig): + super().__init__(config) + + # Create backbone + positional encoding + backbone = ConditionalDetrConvEncoder(config) + object_queries = build_position_encoding(config) + self.backbone = ConditionalDetrConvModel(backbone, object_queries) + + # Create projection layer + self.input_projection = nn.Conv2d(backbone.intermediate_channel_sizes[-1], config.d_model, kernel_size=1) + + self.query_position_embeddings = nn.Embedding(config.num_queries, config.d_model) + + self.encoder = ConditionalDetrEncoder(config) + self.decoder = ConditionalDetrDecoder(config) + + # Initialize weights and apply final processing + self.post_init() + + def get_encoder(self): + return self.encoder + + def get_decoder(self): + return self.decoder + + def freeze_backbone(self): + for name, param in self.backbone.conv_encoder.model.named_parameters(): + param.requires_grad_(False) + + def unfreeze_backbone(self): + for name, param in self.backbone.conv_encoder.model.named_parameters(): + param.requires_grad_(True) + + @add_start_docstrings_to_model_forward(CONDITIONAL_DETR_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=ConditionalDetrModelOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + pixel_values: torch.FloatTensor, + pixel_mask: Optional[torch.LongTensor] = None, + decoder_attention_mask: Optional[torch.LongTensor] = None, + encoder_outputs: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + decoder_inputs_embeds: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.FloatTensor], ConditionalDetrModelOutput]: + r""" + Returns: + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, AutoModel + >>> from PIL import Image + >>> import requests + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50") + >>> model = AutoModel.from_pretrained("microsoft/conditional-detr-resnet-50") + + >>> # prepare image for the model + >>> inputs = image_processor(images=image, return_tensors="pt") + + >>> # forward pass + >>> outputs = model(**inputs) + + >>> # the last hidden states are the final query embeddings of the Transformer decoder + >>> # these are of shape (batch_size, num_queries, hidden_size) + >>> last_hidden_states = outputs.last_hidden_state + >>> list(last_hidden_states.shape) + [1, 300, 256] + ```""" + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + batch_size, num_channels, height, width = pixel_values.shape + device = pixel_values.device + + if pixel_mask is None: + pixel_mask = torch.ones(((batch_size, height, width)), device=device) + + # First, sent pixel_values + pixel_mask through Backbone to obtain the features + # pixel_values should be of shape (batch_size, num_channels, height, width) + # pixel_mask should be of shape (batch_size, height, width) + features, object_queries_list = self.backbone(pixel_values, pixel_mask) + + # get final feature map and downsampled mask + feature_map, mask = features[-1] + + if mask is None: + raise ValueError("Backbone does not return downsampled pixel mask") + + # Second, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default) + projected_feature_map = self.input_projection(feature_map) + + # Third, flatten the feature map + object_queries of shape NxCxHxW to NxCxHW, and permute it to NxHWxC + # In other words, turn their shape into (batch_size, sequence_length, hidden_size) + flattened_features = projected_feature_map.flatten(2).permute(0, 2, 1) + object_queries = object_queries_list[-1].flatten(2).permute(0, 2, 1) + + flattened_mask = mask.flatten(1) + + # Fourth, sent flattened_features + flattened_mask + object_queries through encoder + # flattened_features is a Tensor of shape (batch_size, heigth*width, hidden_size) + # flattened_mask is a Tensor of shape (batch_size, heigth*width) + if encoder_outputs is None: + encoder_outputs = self.encoder( + inputs_embeds=flattened_features, + attention_mask=flattened_mask, + object_queries=object_queries, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True + elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + # Fifth, sent query embeddings + object_queries through the decoder (which is conditioned on the encoder output) + query_position_embeddings = self.query_position_embeddings.weight.unsqueeze(0).repeat(batch_size, 1, 1) + queries = torch.zeros_like(query_position_embeddings) + + # decoder outputs consists of (dec_features, dec_hidden, dec_attn) + decoder_outputs = self.decoder( + inputs_embeds=queries, + attention_mask=None, + object_queries=object_queries, + query_position_embeddings=query_position_embeddings, + encoder_hidden_states=encoder_outputs[0], + encoder_attention_mask=flattened_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + if not return_dict: + return decoder_outputs + encoder_outputs + + return ConditionalDetrModelOutput( + last_hidden_state=decoder_outputs.last_hidden_state, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + intermediate_hidden_states=decoder_outputs.intermediate_hidden_states, + reference_points=decoder_outputs.reference_points, + ) + + +@add_start_docstrings( + """ + CONDITIONAL_DETR Model (consisting of a backbone and encoder-decoder Transformer) with object detection heads on + top, for tasks such as COCO detection. + """, + CONDITIONAL_DETR_START_DOCSTRING, +) +class ConditionalDetrForObjectDetection(ConditionalDetrPreTrainedModel): + def __init__(self, config: ConditionalDetrConfig): + super().__init__(config) + + # CONDITIONAL DETR encoder-decoder model + self.model = ConditionalDetrModel(config) + + # Object detection heads + self.class_labels_classifier = nn.Linear( + config.d_model, config.num_labels + ) # We add one for the "no object" class + self.bbox_predictor = ConditionalDetrMLPPredictionHead( + input_dim=config.d_model, hidden_dim=config.d_model, output_dim=4, num_layers=3 + ) + + # Initialize weights and apply final processing + self.post_init() + + # taken from https://github.com/Atten4Vis/conditionalDETR/blob/master/models/conditional_detr.py + @torch.jit.unused + def _set_aux_loss(self, outputs_class, outputs_coord): + # this is a workaround to make torchscript happy, as torchscript + # doesn't support dictionary with non-homogeneous values, such + # as a dict having both a Tensor and a list. + return [{"logits": a, "pred_boxes": b} for a, b in zip(outputs_class[:-1], outputs_coord[:-1])] + + @add_start_docstrings_to_model_forward(CONDITIONAL_DETR_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=ConditionalDetrObjectDetectionOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + pixel_values: torch.FloatTensor, + pixel_mask: Optional[torch.LongTensor] = None, + decoder_attention_mask: Optional[torch.LongTensor] = None, + encoder_outputs: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + decoder_inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[List[dict]] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.FloatTensor], ConditionalDetrObjectDetectionOutput]: + r""" + labels (`List[Dict]` of len `(batch_size,)`, *optional*): + Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the + following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch + respectively). The class labels themselves should be a `torch.LongTensor` of len `(number of bounding boxes + in the image,)` and the boxes a `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)`. + + Returns: + + Examples: + + ```python + >>> from transformers import AutoImageProcessor, AutoModelForObjectDetection + >>> from PIL import Image + >>> import requests + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50") + >>> model = AutoModelForObjectDetection.from_pretrained("microsoft/conditional-detr-resnet-50") + + >>> inputs = image_processor(images=image, return_tensors="pt") + + >>> outputs = model(**inputs) + + >>> # convert outputs (bounding boxes and class logits) to Pascal VOC format (xmin, ymin, xmax, ymax) + >>> target_sizes = torch.tensor([image.size[::-1]]) + >>> results = image_processor.post_process_object_detection(outputs, threshold=0.5, target_sizes=target_sizes)[ + ... 0 + ... ] + >>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): + ... box = [round(i, 2) for i in box.tolist()] + ... print( + ... f"Detected {model.config.id2label[label.item()]} with confidence " + ... f"{round(score.item(), 3)} at location {box}" + ... ) + Detected remote with confidence 0.833 at location [38.31, 72.1, 177.63, 118.45] + Detected cat with confidence 0.831 at location [9.2, 51.38, 321.13, 469.0] + Detected cat with confidence 0.804 at location [340.3, 16.85, 642.93, 370.95] + Detected remote with confidence 0.683 at location [334.48, 73.49, 366.37, 190.01] + Detected couch with confidence 0.535 at location [0.52, 1.19, 640.35, 475.1] + ```""" + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + # First, sent images through CONDITIONAL_DETR base model to obtain encoder + decoder outputs + outputs = self.model( + pixel_values, + pixel_mask=pixel_mask, + decoder_attention_mask=decoder_attention_mask, + encoder_outputs=encoder_outputs, + inputs_embeds=inputs_embeds, + decoder_inputs_embeds=decoder_inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + # class logits + predicted bounding boxes + logits = self.class_labels_classifier(sequence_output) + + reference = outputs.reference_points if return_dict else outputs[-1] + reference_before_sigmoid = inverse_sigmoid(reference).transpose(0, 1) + outputs_coords = [] + hs = sequence_output + tmp = self.bbox_predictor(hs) + tmp[..., :2] += reference_before_sigmoid + pred_boxes = tmp.sigmoid() + # pred_boxes = self.bbox_predictor(sequence_output).sigmoid() + + loss, loss_dict, auxiliary_outputs = None, None, None + if labels is not None: + # First: create the matcher + matcher = ConditionalDetrHungarianMatcher( + class_cost=self.config.class_cost, bbox_cost=self.config.bbox_cost, giou_cost=self.config.giou_cost + ) + # Second: create the criterion + losses = ["labels", "boxes", "cardinality"] + criterion = ConditionalDetrLoss( + matcher=matcher, + num_classes=self.config.num_labels, + focal_alpha=self.config.focal_alpha, + losses=losses, + ) + criterion.to(self.device) + # Third: compute the losses, based on outputs and labels + outputs_loss = {} + outputs_loss["logits"] = logits + outputs_loss["pred_boxes"] = pred_boxes + if self.config.auxiliary_loss: + intermediate = outputs.intermediate_hidden_states if return_dict else outputs[4] + outputs_class = self.class_labels_classifier(intermediate) + + for lvl in range(intermediate.shape[0]): + tmp = self.bbox_predictor(intermediate[lvl]) + tmp[..., :2] += reference_before_sigmoid + outputs_coord = tmp.sigmoid() + outputs_coords.append(outputs_coord) + outputs_coord = torch.stack(outputs_coords) + + auxiliary_outputs = self._set_aux_loss(outputs_class, outputs_coord) + outputs_loss["auxiliary_outputs"] = auxiliary_outputs + + loss_dict = criterion(outputs_loss, labels) + # Fourth: compute total loss, as a weighted sum of the various losses + weight_dict = {"loss_ce": self.config.cls_loss_coefficient, "loss_bbox": self.config.bbox_loss_coefficient} + weight_dict["loss_giou"] = self.config.giou_loss_coefficient + if self.config.auxiliary_loss: + aux_weight_dict = {} + for i in range(self.config.decoder_layers - 1): + aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()}) + weight_dict.update(aux_weight_dict) + loss = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict) + + if not return_dict: + if auxiliary_outputs is not None: + output = (logits, pred_boxes) + auxiliary_outputs + outputs + else: + output = (logits, pred_boxes) + outputs + return ((loss, loss_dict) + output) if loss is not None else output + + return ConditionalDetrObjectDetectionOutput( + loss=loss, + loss_dict=loss_dict, + logits=logits, + pred_boxes=pred_boxes, + auxiliary_outputs=auxiliary_outputs, + last_hidden_state=outputs.last_hidden_state, + decoder_hidden_states=outputs.decoder_hidden_states, + decoder_attentions=outputs.decoder_attentions, + cross_attentions=outputs.cross_attentions, + encoder_last_hidden_state=outputs.encoder_last_hidden_state, + encoder_hidden_states=outputs.encoder_hidden_states, + encoder_attentions=outputs.encoder_attentions, + ) + + +@add_start_docstrings( + """ + CONDITIONAL_DETR Model (consisting of a backbone and encoder-decoder Transformer) with a segmentation head on top, + for tasks such as COCO panoptic. + + """, + CONDITIONAL_DETR_START_DOCSTRING, +) +class ConditionalDetrForSegmentation(ConditionalDetrPreTrainedModel): + def __init__(self, config: ConditionalDetrConfig): + super().__init__(config) + + # object detection model + self.conditional_detr = ConditionalDetrForObjectDetection(config) + + # segmentation head + hidden_size, number_of_heads = config.d_model, config.encoder_attention_heads + intermediate_channel_sizes = self.conditional_detr.model.backbone.conv_encoder.intermediate_channel_sizes + + self.mask_head = ConditionalDetrMaskHeadSmallConv( + hidden_size + number_of_heads, intermediate_channel_sizes[::-1][-3:], hidden_size + ) + + self.bbox_attention = ConditionalDetrMHAttentionMap( + hidden_size, hidden_size, number_of_heads, dropout=0.0, std=config.init_xavier_std + ) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(CONDITIONAL_DETR_INPUTS_DOCSTRING) + @replace_return_docstrings(output_type=ConditionalDetrSegmentationOutput, config_class=_CONFIG_FOR_DOC) + def forward( + self, + pixel_values: torch.FloatTensor, + pixel_mask: Optional[torch.LongTensor] = None, + decoder_attention_mask: Optional[torch.FloatTensor] = None, + encoder_outputs: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + decoder_inputs_embeds: Optional[torch.FloatTensor] = None, + labels: Optional[List[dict]] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.FloatTensor], ConditionalDetrSegmentationOutput]: + r""" + labels (`List[Dict]` of len `(batch_size,)`, *optional*): + Labels for computing the bipartite matching loss, DICE/F-1 loss and Focal loss. List of dicts, each + dictionary containing at least the following 3 keys: 'class_labels', 'boxes' and 'masks' (the class labels, + bounding boxes and segmentation masks of an image in the batch respectively). The class labels themselves + should be a `torch.LongTensor` of len `(number of bounding boxes in the image,)`, the boxes a + `torch.FloatTensor` of shape `(number of bounding boxes in the image, 4)` and the masks a + `torch.FloatTensor` of shape `(number of bounding boxes in the image, height, width)`. + + Returns: + + Examples: + + ```python + >>> import io + >>> import requests + >>> from PIL import Image + >>> import torch + >>> import numpy + + >>> from transformers import ( + ... AutoImageProcessor, + ... ConditionalDetrConfig, + ... ConditionalDetrForSegmentation, + ... ) + >>> from transformers.image_transforms import rgb_to_id + + >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" + >>> image = Image.open(requests.get(url, stream=True).raw) + + >>> image_processor = AutoImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50") + + >>> # randomly initialize all weights of the model + >>> config = ConditionalDetrConfig() + >>> model = ConditionalDetrForSegmentation(config) + + >>> # prepare image for the model + >>> inputs = image_processor(images=image, return_tensors="pt") + + >>> # forward pass + >>> outputs = model(**inputs) + + >>> # Use the `post_process_panoptic_segmentation` method of the `image_processor` to retrieve post-processed panoptic segmentation maps + >>> # Segmentation results are returned as a list of dictionaries + >>> result = image_processor.post_process_panoptic_segmentation(outputs, target_sizes=[(300, 500)]) + >>> # A tensor of shape (height, width) where each value denotes a segment id, filled with -1 if no segment is found + >>> panoptic_seg = result[0]["segmentation"] + >>> # Get prediction score and segment_id to class_id mapping of each segment + >>> panoptic_segments_info = result[0]["segments_info"] + ```""" + + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + batch_size, num_channels, height, width = pixel_values.shape + device = pixel_values.device + + if pixel_mask is None: + pixel_mask = torch.ones((batch_size, height, width), device=device) + + # First, get list of feature maps and object_queries + features, object_queries_list = self.conditional_detr.model.backbone(pixel_values, pixel_mask=pixel_mask) + + # Second, apply 1x1 convolution to reduce the channel dimension to d_model (256 by default) + feature_map, mask = features[-1] + batch_size, num_channels, height, width = feature_map.shape + projected_feature_map = self.conditional_detr.model.input_projection(feature_map) + + # Third, flatten the feature map + object_queries of shape NxCxHxW to NxCxHW, and permute it to NxHWxC + # In other words, turn their shape into (batch_size, sequence_length, hidden_size) + flattened_features = projected_feature_map.flatten(2).permute(0, 2, 1) + object_queries = object_queries_list[-1].flatten(2).permute(0, 2, 1) + + flattened_mask = mask.flatten(1) + + # Fourth, sent flattened_features + flattened_mask + object_queries through encoder + # flattened_features is a Tensor of shape (batch_size, heigth*width, hidden_size) + # flattened_mask is a Tensor of shape (batch_size, heigth*width) + if encoder_outputs is None: + encoder_outputs = self.conditional_detr.model.encoder( + inputs_embeds=flattened_features, + attention_mask=flattened_mask, + object_queries=object_queries, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + # If the user passed a tuple for encoder_outputs, we wrap it in a BaseModelOutput when return_dict=True + elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): + encoder_outputs = BaseModelOutput( + last_hidden_state=encoder_outputs[0], + hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, + attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, + ) + + # Fifth, sent query embeddings + object_queries through the decoder (which is conditioned on the encoder output) + query_position_embeddings = self.conditional_detr.model.query_position_embeddings.weight.unsqueeze(0).repeat( + batch_size, 1, 1 + ) + queries = torch.zeros_like(query_position_embeddings) + + # decoder outputs consists of (dec_features, dec_hidden, dec_attn) + decoder_outputs = self.conditional_detr.model.decoder( + inputs_embeds=queries, + attention_mask=None, + object_queries=object_queries, + query_position_embeddings=query_position_embeddings, + encoder_hidden_states=encoder_outputs[0], + encoder_attention_mask=flattened_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = decoder_outputs[0] + + # Sixth, compute logits, pred_boxes and pred_masks + logits = self.conditional_detr.class_labels_classifier(sequence_output) + pred_boxes = self.conditional_detr.bbox_predictor(sequence_output).sigmoid() + + memory = encoder_outputs[0].permute(0, 2, 1).view(batch_size, self.config.d_model, height, width) + mask = flattened_mask.view(batch_size, height, width) + + # FIXME h_boxes takes the last one computed, keep this in mind + # important: we need to reverse the mask, since in the original implementation the mask works reversed + # bbox_mask is of shape (batch_size, num_queries, number_of_attention_heads in bbox_attention, height/32, width/32) + bbox_mask = self.bbox_attention(sequence_output, memory, mask=~mask) + + seg_masks = self.mask_head(projected_feature_map, bbox_mask, [features[2][0], features[1][0], features[0][0]]) + + pred_masks = seg_masks.view( + batch_size, self.conditional_detr.config.num_queries, seg_masks.shape[-2], seg_masks.shape[-1] + ) + + loss, loss_dict, auxiliary_outputs = None, None, None + if labels is not None: + # First: create the matcher + matcher = ConditionalDetrHungarianMatcher( + class_cost=self.config.class_cost, bbox_cost=self.config.bbox_cost, giou_cost=self.config.giou_cost + ) + # Second: create the criterion + losses = ["labels", "boxes", "cardinality", "masks"] + criterion = ConditionalDetrLoss( + matcher=matcher, + num_classes=self.config.num_labels, + focal_alpha=self.config.focal_alpha, + losses=losses, + ) + criterion.to(self.device) + # Third: compute the losses, based on outputs and labels + outputs_loss = {} + outputs_loss["logits"] = logits + outputs_loss["pred_boxes"] = pred_boxes + outputs_loss["pred_masks"] = pred_masks + if self.config.auxiliary_loss: + intermediate = decoder_outputs.intermediate_hidden_states if return_dict else decoder_outputs[-1] + outputs_class = self.conditional_detr.class_labels_classifier(intermediate) + outputs_coord = self.conditional_detr.bbox_predictor(intermediate).sigmoid() + auxiliary_outputs = self.conditional_detr._set_aux_loss(outputs_class, outputs_coord) + outputs_loss["auxiliary_outputs"] = auxiliary_outputs + + loss_dict = criterion(outputs_loss, labels) + # Fourth: compute total loss, as a weighted sum of the various losses + weight_dict = {"loss_ce": 1, "loss_bbox": self.config.bbox_loss_coefficient} + weight_dict["loss_giou"] = self.config.giou_loss_coefficient + weight_dict["loss_mask"] = self.config.mask_loss_coefficient + weight_dict["loss_dice"] = self.config.dice_loss_coefficient + if self.config.auxiliary_loss: + aux_weight_dict = {} + for i in range(self.config.decoder_layers - 1): + aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()}) + weight_dict.update(aux_weight_dict) + loss = sum(loss_dict[k] * weight_dict[k] for k in loss_dict.keys() if k in weight_dict) + + if not return_dict: + if auxiliary_outputs is not None: + output = (logits, pred_boxes, pred_masks) + auxiliary_outputs + decoder_outputs + encoder_outputs + else: + output = (logits, pred_boxes, pred_masks) + decoder_outputs + encoder_outputs + return ((loss, loss_dict) + output) if loss is not None else output + + return ConditionalDetrSegmentationOutput( + loss=loss, + loss_dict=loss_dict, + logits=logits, + pred_boxes=pred_boxes, + pred_masks=pred_masks, + auxiliary_outputs=auxiliary_outputs, + last_hidden_state=decoder_outputs.last_hidden_state, + decoder_hidden_states=decoder_outputs.hidden_states, + decoder_attentions=decoder_outputs.attentions, + cross_attentions=decoder_outputs.cross_attentions, + encoder_last_hidden_state=encoder_outputs.last_hidden_state, + encoder_hidden_states=encoder_outputs.hidden_states, + encoder_attentions=encoder_outputs.attentions, + ) + + +def _expand(tensor, length: int): + return tensor.unsqueeze(1).repeat(1, int(length), 1, 1, 1).flatten(0, 1) + + +# Copied from transformers.models.detr.modeling_detr.DetrMaskHeadSmallConv with Detr->ConditionalDetr +class ConditionalDetrMaskHeadSmallConv(nn.Module): + """ + Simple convolutional head, using group norm. Upsampling is done using a FPN approach + """ + + def __init__(self, dim, fpn_dims, context_dim): + super().__init__() + + if dim % 8 != 0: + raise ValueError( + "The hidden_size + number of attention heads must be divisible by 8 as the number of groups in" + " GroupNorm is set to 8" + ) + + inter_dims = [dim, context_dim // 2, context_dim // 4, context_dim // 8, context_dim // 16, context_dim // 64] + + self.lay1 = nn.Conv2d(dim, dim, 3, padding=1) + self.gn1 = nn.GroupNorm(8, dim) + self.lay2 = nn.Conv2d(dim, inter_dims[1], 3, padding=1) + self.gn2 = nn.GroupNorm(min(8, inter_dims[1]), inter_dims[1]) + self.lay3 = nn.Conv2d(inter_dims[1], inter_dims[2], 3, padding=1) + self.gn3 = nn.GroupNorm(min(8, inter_dims[2]), inter_dims[2]) + self.lay4 = nn.Conv2d(inter_dims[2], inter_dims[3], 3, padding=1) + self.gn4 = nn.GroupNorm(min(8, inter_dims[3]), inter_dims[3]) + self.lay5 = nn.Conv2d(inter_dims[3], inter_dims[4], 3, padding=1) + self.gn5 = nn.GroupNorm(min(8, inter_dims[4]), inter_dims[4]) + self.out_lay = nn.Conv2d(inter_dims[4], 1, 3, padding=1) + + self.dim = dim + + self.adapter1 = nn.Conv2d(fpn_dims[0], inter_dims[1], 1) + self.adapter2 = nn.Conv2d(fpn_dims[1], inter_dims[2], 1) + self.adapter3 = nn.Conv2d(fpn_dims[2], inter_dims[3], 1) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_uniform_(m.weight, a=1) + nn.init.constant_(m.bias, 0) + + def forward(self, x: Tensor, bbox_mask: Tensor, fpns: List[Tensor]): + # here we concatenate x, the projected feature map, of shape (batch_size, d_model, heigth/32, width/32) with + # the bbox_mask = the attention maps of shape (batch_size, n_queries, n_heads, height/32, width/32). + # We expand the projected feature map to match the number of heads. + x = torch.cat([_expand(x, bbox_mask.shape[1]), bbox_mask.flatten(0, 1)], 1) + + x = self.lay1(x) + x = self.gn1(x) + x = nn.functional.relu(x) + x = self.lay2(x) + x = self.gn2(x) + x = nn.functional.relu(x) + + cur_fpn = self.adapter1(fpns[0]) + if cur_fpn.size(0) != x.size(0): + cur_fpn = _expand(cur_fpn, x.size(0) // cur_fpn.size(0)) + x = cur_fpn + nn.functional.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest") + x = self.lay3(x) + x = self.gn3(x) + x = nn.functional.relu(x) + + cur_fpn = self.adapter2(fpns[1]) + if cur_fpn.size(0) != x.size(0): + cur_fpn = _expand(cur_fpn, x.size(0) // cur_fpn.size(0)) + x = cur_fpn + nn.functional.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest") + x = self.lay4(x) + x = self.gn4(x) + x = nn.functional.relu(x) + + cur_fpn = self.adapter3(fpns[2]) + if cur_fpn.size(0) != x.size(0): + cur_fpn = _expand(cur_fpn, x.size(0) // cur_fpn.size(0)) + x = cur_fpn + nn.functional.interpolate(x, size=cur_fpn.shape[-2:], mode="nearest") + x = self.lay5(x) + x = self.gn5(x) + x = nn.functional.relu(x) + + x = self.out_lay(x) + return x + + +# Copied from transformers.models.detr.modeling_detr.DetrMHAttentionMap with Detr->ConditionalDetr +class ConditionalDetrMHAttentionMap(nn.Module): + """This is a 2D attention module, which only returns the attention softmax (no multiplication by value)""" + + def __init__(self, query_dim, hidden_dim, num_heads, dropout=0.0, bias=True, std=None): + super().__init__() + self.num_heads = num_heads + self.hidden_dim = hidden_dim + self.dropout = nn.Dropout(dropout) + + self.q_linear = nn.Linear(query_dim, hidden_dim, bias=bias) + self.k_linear = nn.Linear(query_dim, hidden_dim, bias=bias) + + self.normalize_fact = float(hidden_dim / self.num_heads) ** -0.5 + + def forward(self, q, k, mask: Optional[Tensor] = None): + q = self.q_linear(q) + k = nn.functional.conv2d(k, self.k_linear.weight.unsqueeze(-1).unsqueeze(-1), self.k_linear.bias) + queries_per_head = q.view(q.shape[0], q.shape[1], self.num_heads, self.hidden_dim // self.num_heads) + keys_per_head = k.view(k.shape[0], self.num_heads, self.hidden_dim // self.num_heads, k.shape[-2], k.shape[-1]) + weights = torch.einsum("bqnc,bnchw->bqnhw", queries_per_head * self.normalize_fact, keys_per_head) + + if mask is not None: + weights.masked_fill_(mask.unsqueeze(1).unsqueeze(1), torch.finfo(weights.dtype).min) + weights = nn.functional.softmax(weights.flatten(2), dim=-1).view(weights.size()) + weights = self.dropout(weights) + return weights + + +# Copied from transformers.models.detr.modeling_detr.dice_loss +def dice_loss(inputs, targets, num_boxes): + """ + Compute the DICE loss, similar to generalized IOU for masks + + Args: + inputs: A float tensor of arbitrary shape. + The predictions for each example. + targets: A float tensor with the same shape as inputs. Stores the binary + classification label for each element in inputs (0 for the negative class and 1 for the positive + class). + """ + inputs = inputs.sigmoid() + inputs = inputs.flatten(1) + numerator = 2 * (inputs * targets).sum(1) + denominator = inputs.sum(-1) + targets.sum(-1) + loss = 1 - (numerator + 1) / (denominator + 1) + return loss.sum() / num_boxes + + +# Copied from transformers.models.detr.modeling_detr.sigmoid_focal_loss +def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2): + """ + Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002. + + Args: + inputs (`torch.FloatTensor` of arbitrary shape): + The predictions for each example. + targets (`torch.FloatTensor` with the same shape as `inputs`) + A tensor storing the binary classification label for each element in the `inputs` (0 for the negative class + and 1 for the positive class). + alpha (`float`, *optional*, defaults to `0.25`): + Optional weighting factor in the range (0,1) to balance positive vs. negative examples. + gamma (`int`, *optional*, defaults to `2`): + Exponent of the modulating factor (1 - p_t) to balance easy vs hard examples. + + Returns: + Loss tensor + """ + prob = inputs.sigmoid() + ce_loss = nn.functional.binary_cross_entropy_with_logits(inputs, targets, reduction="none") + # add modulating factor + p_t = prob * targets + (1 - prob) * (1 - targets) + loss = ce_loss * ((1 - p_t) ** gamma) + + if alpha >= 0: + alpha_t = alpha * targets + (1 - alpha) * (1 - targets) + loss = alpha_t * loss + + return loss.mean(1).sum() / num_boxes + + +class ConditionalDetrLoss(nn.Module): + """ + This class computes the losses for ConditionalDetrForObjectDetection/ConditionalDetrForSegmentation. The process + happens in two steps: 1) we compute hungarian assignment between ground truth boxes and the outputs of the model 2) + we supervise each pair of matched ground-truth / prediction (supervise class and box). + + Args: + matcher (`ConditionalDetrHungarianMatcher`): + Module able to compute a matching between targets and proposals. + num_classes (`int`): + Number of object categories, omitting the special no-object category. + focal_alpha (`float`): + Alpha parameter in focal loss. + losses (`List[str]`): + List of all the losses to be applied. See `get_loss` for a list of all available losses. + """ + + # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrLoss.__init__ + def __init__(self, matcher, num_classes, focal_alpha, losses): + super().__init__() + self.matcher = matcher + self.num_classes = num_classes + self.focal_alpha = focal_alpha + self.losses = losses + + # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrLoss.loss_labels + def loss_labels(self, outputs, targets, indices, num_boxes): + """ + Classification loss (Binary focal loss) targets dicts must contain the key "class_labels" containing a tensor + of dim [nb_target_boxes] + """ + if "logits" not in outputs: + raise KeyError("No logits were found in the outputs") + source_logits = outputs["logits"] + + idx = self._get_source_permutation_idx(indices) + target_classes_o = torch.cat([t["class_labels"][J] for t, (_, J) in zip(targets, indices)]) + target_classes = torch.full( + source_logits.shape[:2], self.num_classes, dtype=torch.int64, device=source_logits.device + ) + target_classes[idx] = target_classes_o + + target_classes_onehot = torch.zeros( + [source_logits.shape[0], source_logits.shape[1], source_logits.shape[2] + 1], + dtype=source_logits.dtype, + layout=source_logits.layout, + device=source_logits.device, + ) + target_classes_onehot.scatter_(2, target_classes.unsqueeze(-1), 1) + + target_classes_onehot = target_classes_onehot[:, :, :-1] + loss_ce = ( + sigmoid_focal_loss(source_logits, target_classes_onehot, num_boxes, alpha=self.focal_alpha, gamma=2) + * source_logits.shape[1] + ) + losses = {"loss_ce": loss_ce} + + return losses + + @torch.no_grad() + # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrLoss.loss_cardinality + def loss_cardinality(self, outputs, targets, indices, num_boxes): + """ + Compute the cardinality error, i.e. the absolute error in the number of predicted non-empty boxes. + + This is not really a loss, it is intended for logging purposes only. It doesn't propagate gradients. + """ + logits = outputs["logits"] + device = logits.device + target_lengths = torch.as_tensor([len(v["class_labels"]) for v in targets], device=device) + # Count the number of predictions that are NOT "no-object" (which is the last class) + card_pred = (logits.argmax(-1) != logits.shape[-1] - 1).sum(1) + card_err = nn.functional.l1_loss(card_pred.float(), target_lengths.float()) + losses = {"cardinality_error": card_err} + return losses + + # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrLoss.loss_boxes + def loss_boxes(self, outputs, targets, indices, num_boxes): + """ + Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss. + + Targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4]. The target boxes + are expected in format (center_x, center_y, w, h), normalized by the image size. + """ + if "pred_boxes" not in outputs: + raise KeyError("No predicted boxes found in outputs") + idx = self._get_source_permutation_idx(indices) + source_boxes = outputs["pred_boxes"][idx] + target_boxes = torch.cat([t["boxes"][i] for t, (_, i) in zip(targets, indices)], dim=0) + + loss_bbox = nn.functional.l1_loss(source_boxes, target_boxes, reduction="none") + + losses = {} + losses["loss_bbox"] = loss_bbox.sum() / num_boxes + + loss_giou = 1 - torch.diag( + generalized_box_iou(center_to_corners_format(source_boxes), center_to_corners_format(target_boxes)) + ) + losses["loss_giou"] = loss_giou.sum() / num_boxes + return losses + + # Copied from transformers.models.detr.modeling_detr.DetrLoss.loss_masks + def loss_masks(self, outputs, targets, indices, num_boxes): + """ + Compute the losses related to the masks: the focal loss and the dice loss. + + Targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w]. + """ + if "pred_masks" not in outputs: + raise KeyError("No predicted masks found in outputs") + + source_idx = self._get_source_permutation_idx(indices) + target_idx = self._get_target_permutation_idx(indices) + source_masks = outputs["pred_masks"] + source_masks = source_masks[source_idx] + masks = [t["masks"] for t in targets] + # TODO use valid to mask invalid areas due to padding in loss + target_masks, valid = nested_tensor_from_tensor_list(masks).decompose() + target_masks = target_masks.to(source_masks) + target_masks = target_masks[target_idx] + + # upsample predictions to the target size + source_masks = nn.functional.interpolate( + source_masks[:, None], size=target_masks.shape[-2:], mode="bilinear", align_corners=False + ) + source_masks = source_masks[:, 0].flatten(1) + + target_masks = target_masks.flatten(1) + target_masks = target_masks.view(source_masks.shape) + losses = { + "loss_mask": sigmoid_focal_loss(source_masks, target_masks, num_boxes), + "loss_dice": dice_loss(source_masks, target_masks, num_boxes), + } + return losses + + # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrLoss._get_source_permutation_idx + def _get_source_permutation_idx(self, indices): + # permute predictions following indices + batch_idx = torch.cat([torch.full_like(source, i) for i, (source, _) in enumerate(indices)]) + source_idx = torch.cat([source for (source, _) in indices]) + return batch_idx, source_idx + + # Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrLoss._get_target_permutation_idx + def _get_target_permutation_idx(self, indices): + # permute targets following indices + batch_idx = torch.cat([torch.full_like(target, i) for i, (_, target) in enumerate(indices)]) + target_idx = torch.cat([target for (_, target) in indices]) + return batch_idx, target_idx + + # Copied from transformers.models.detr.modeling_detr.DetrLoss.get_loss + def get_loss(self, loss, outputs, targets, indices, num_boxes): + loss_map = { + "labels": self.loss_labels, + "cardinality": self.loss_cardinality, + "boxes": self.loss_boxes, + "masks": self.loss_masks, + } + if loss not in loss_map: + raise ValueError(f"Loss {loss} not supported") + return loss_map[loss](outputs, targets, indices, num_boxes) + + # Copied from transformers.models.detr.modeling_detr.DetrLoss.forward + def forward(self, outputs, targets): + """ + This performs the loss computation. + + Args: + outputs (`dict`, *optional*): + Dictionary of tensors, see the output specification of the model for the format. + targets (`List[dict]`, *optional*): + List of dicts, such that `len(targets) == batch_size`. The expected keys in each dict depends on the + losses applied, see each loss' doc. + """ + outputs_without_aux = {k: v for k, v in outputs.items() if k != "auxiliary_outputs"} + + # Retrieve the matching between the outputs of the last layer and the targets + indices = self.matcher(outputs_without_aux, targets) + + # Compute the average number of target boxes across all nodes, for normalization purposes + num_boxes = sum(len(t["class_labels"]) for t in targets) + num_boxes = torch.as_tensor([num_boxes], dtype=torch.float, device=next(iter(outputs.values())).device) + + world_size = 1 + if is_accelerate_available(): + if PartialState._shared_state != {}: + num_boxes = reduce(num_boxes) + world_size = PartialState().num_processes + num_boxes = torch.clamp(num_boxes / world_size, min=1).item() + + # Compute all the requested losses + losses = {} + for loss in self.losses: + losses.update(self.get_loss(loss, outputs, targets, indices, num_boxes)) + + # In case of auxiliary losses, we repeat this process with the output of each intermediate layer. + if "auxiliary_outputs" in outputs: + for i, auxiliary_outputs in enumerate(outputs["auxiliary_outputs"]): + indices = self.matcher(auxiliary_outputs, targets) + for loss in self.losses: + if loss == "masks": + # Intermediate masks losses are too costly to compute, we ignore them. + continue + l_dict = self.get_loss(loss, auxiliary_outputs, targets, indices, num_boxes) + l_dict = {k + f"_{i}": v for k, v in l_dict.items()} + losses.update(l_dict) + + return losses + + +# Copied from transformers.models.detr.modeling_detr.DetrMLPPredictionHead with Detr->ConditionalDetr +class ConditionalDetrMLPPredictionHead(nn.Module): + """ + Very simple multi-layer perceptron (MLP, also called FFN), used to predict the normalized center coordinates, + height and width of a bounding box w.r.t. an image. + + Copied from https://github.com/facebookresearch/detr/blob/master/models/detr.py + + """ + + def __init__(self, input_dim, hidden_dim, output_dim, num_layers): + super().__init__() + self.num_layers = num_layers + h = [hidden_dim] * (num_layers - 1) + self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])) + + def forward(self, x): + for i, layer in enumerate(self.layers): + x = nn.functional.relu(layer(x)) if i < self.num_layers - 1 else layer(x) + return x + + +# Copied from transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrHungarianMatcher with DeformableDetr->ConditionalDetr +class ConditionalDetrHungarianMatcher(nn.Module): + """ + This class computes an assignment between the targets and the predictions of the network. + + For efficiency reasons, the targets don't include the no_object. Because of this, in general, there are more + predictions than targets. In this case, we do a 1-to-1 matching of the best predictions, while the others are + un-matched (and thus treated as non-objects). + + Args: + class_cost: + The relative weight of the classification error in the matching cost. + bbox_cost: + The relative weight of the L1 error of the bounding box coordinates in the matching cost. + giou_cost: + The relative weight of the giou loss of the bounding box in the matching cost. + """ + + def __init__(self, class_cost: float = 1, bbox_cost: float = 1, giou_cost: float = 1): + super().__init__() + requires_backends(self, ["scipy"]) + + self.class_cost = class_cost + self.bbox_cost = bbox_cost + self.giou_cost = giou_cost + if class_cost == 0 and bbox_cost == 0 and giou_cost == 0: + raise ValueError("All costs of the Matcher can't be 0") + + @torch.no_grad() + def forward(self, outputs, targets): + """ + Args: + outputs (`dict`): + A dictionary that contains at least these entries: + * "logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits + * "pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted box coordinates. + targets (`List[dict]`): + A list of targets (len(targets) = batch_size), where each target is a dict containing: + * "class_labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of + ground-truth + objects in the target) containing the class labels + * "boxes": Tensor of dim [num_target_boxes, 4] containing the target box coordinates. + + Returns: + `List[Tuple]`: A list of size `batch_size`, containing tuples of (index_i, index_j) where: + - index_i is the indices of the selected predictions (in order) + - index_j is the indices of the corresponding selected targets (in order) + For each batch element, it holds: len(index_i) = len(index_j) = min(num_queries, num_target_boxes) + """ + batch_size, num_queries = outputs["logits"].shape[:2] + + # We flatten to compute the cost matrices in a batch + out_prob = outputs["logits"].flatten(0, 1).sigmoid() # [batch_size * num_queries, num_classes] + out_bbox = outputs["pred_boxes"].flatten(0, 1) # [batch_size * num_queries, 4] + + # Also concat the target labels and boxes + target_ids = torch.cat([v["class_labels"] for v in targets]) + target_bbox = torch.cat([v["boxes"] for v in targets]) + + # Compute the classification cost. + alpha = 0.25 + gamma = 2.0 + neg_cost_class = (1 - alpha) * (out_prob**gamma) * (-(1 - out_prob + 1e-8).log()) + pos_cost_class = alpha * ((1 - out_prob) ** gamma) * (-(out_prob + 1e-8).log()) + class_cost = pos_cost_class[:, target_ids] - neg_cost_class[:, target_ids] + + # Compute the L1 cost between boxes + bbox_cost = torch.cdist(out_bbox, target_bbox, p=1) + + # Compute the giou cost between boxes + giou_cost = -generalized_box_iou(center_to_corners_format(out_bbox), center_to_corners_format(target_bbox)) + + # Final cost matrix + cost_matrix = self.bbox_cost * bbox_cost + self.class_cost * class_cost + self.giou_cost * giou_cost + cost_matrix = cost_matrix.view(batch_size, num_queries, -1).cpu() + + sizes = [len(v["boxes"]) for v in targets] + indices = [linear_sum_assignment(c[i]) for i, c in enumerate(cost_matrix.split(sizes, -1))] + return [(torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64)) for i, j in indices] + + +# Copied from transformers.models.detr.modeling_detr._upcast +def _upcast(t: Tensor) -> Tensor: + # Protects from numerical overflows in multiplications by upcasting to the equivalent higher type + if t.is_floating_point(): + return t if t.dtype in (torch.float32, torch.float64) else t.float() + else: + return t if t.dtype in (torch.int32, torch.int64) else t.int() + + +# Copied from transformers.models.detr.modeling_detr.box_area +def box_area(boxes: Tensor) -> Tensor: + """ + Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates. + + Args: + boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`): + Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format with `0 <= x1 + < x2` and `0 <= y1 < y2`. + + Returns: + `torch.FloatTensor`: a tensor containing the area for each box. + """ + boxes = _upcast(boxes) + return (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) + + +# Copied from transformers.models.detr.modeling_detr.box_iou +def box_iou(boxes1, boxes2): + area1 = box_area(boxes1) + area2 = box_area(boxes2) + + left_top = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2] + right_bottom = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2] + + width_height = (right_bottom - left_top).clamp(min=0) # [N,M,2] + inter = width_height[:, :, 0] * width_height[:, :, 1] # [N,M] + + union = area1[:, None] + area2 - inter + + iou = inter / union + return iou, union + + +# Copied from transformers.models.detr.modeling_detr.generalized_box_iou +def generalized_box_iou(boxes1, boxes2): + """ + Generalized IoU from https://giou.stanford.edu/. The boxes should be in [x0, y0, x1, y1] (corner) format. + + Returns: + `torch.FloatTensor`: a [N, M] pairwise matrix, where N = len(boxes1) and M = len(boxes2) + """ + # degenerate boxes gives inf / nan results + # so do an early check + if not (boxes1[:, 2:] >= boxes1[:, :2]).all(): + raise ValueError(f"boxes1 must be in [x0, y0, x1, y1] (corner) format, but got {boxes1}") + if not (boxes2[:, 2:] >= boxes2[:, :2]).all(): + raise ValueError(f"boxes2 must be in [x0, y0, x1, y1] (corner) format, but got {boxes2}") + iou, union = box_iou(boxes1, boxes2) + + top_left = torch.min(boxes1[:, None, :2], boxes2[:, :2]) + bottom_right = torch.max(boxes1[:, None, 2:], boxes2[:, 2:]) + + width_height = (bottom_right - top_left).clamp(min=0) # [N,M,2] + area = width_height[:, :, 0] * width_height[:, :, 1] + + return iou - (area - union) / area + + +# Copied from transformers.models.detr.modeling_detr._max_by_axis +def _max_by_axis(the_list): + # type: (List[List[int]]) -> List[int] + maxes = the_list[0] + for sublist in the_list[1:]: + for index, item in enumerate(sublist): + maxes[index] = max(maxes[index], item) + return maxes + + +# Copied from transformers.models.detr.modeling_detr.NestedTensor +class NestedTensor(object): + def __init__(self, tensors, mask: Optional[Tensor]): + self.tensors = tensors + self.mask = mask + + def to(self, device): + cast_tensor = self.tensors.to(device) + mask = self.mask + if mask is not None: + cast_mask = mask.to(device) + else: + cast_mask = None + return NestedTensor(cast_tensor, cast_mask) + + def decompose(self): + return self.tensors, self.mask + + def __repr__(self): + return str(self.tensors) + + +# Copied from transformers.models.detr.modeling_detr.nested_tensor_from_tensor_list +def nested_tensor_from_tensor_list(tensor_list: List[Tensor]): + if tensor_list[0].ndim == 3: + max_size = _max_by_axis([list(img.shape) for img in tensor_list]) + batch_shape = [len(tensor_list)] + max_size + batch_size, num_channels, height, width = batch_shape + dtype = tensor_list[0].dtype + device = tensor_list[0].device + tensor = torch.zeros(batch_shape, dtype=dtype, device=device) + mask = torch.ones((batch_size, height, width), dtype=torch.bool, device=device) + for img, pad_img, m in zip(tensor_list, tensor, mask): + pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img) + m[: img.shape[1], : img.shape[2]] = False + else: + raise ValueError("Only 3-dimensional tensors are supported") + return NestedTensor(tensor, mask) diff --git a/llmeval-env/lib/python3.10/site-packages/transformers/models/fastspeech2_conformer/__init__.py b/llmeval-env/lib/python3.10/site-packages/transformers/models/fastspeech2_conformer/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1fd5cbf1dc272ec4d9747e2dd1616ed37d5acdbf --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/transformers/models/fastspeech2_conformer/__init__.py @@ -0,0 +1,77 @@ +# Copyright 2023 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +from typing import TYPE_CHECKING + +from ...utils import ( + OptionalDependencyNotAvailable, + _LazyModule, + is_torch_available, +) + + +_import_structure = { + "configuration_fastspeech2_conformer": [ + "FASTSPEECH2_CONFORMER_HIFIGAN_PRETRAINED_CONFIG_ARCHIVE_MAP", + "FASTSPEECH2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", + "FASTSPEECH2_CONFORMER_WITH_HIFIGAN_PRETRAINED_CONFIG_ARCHIVE_MAP", + "FastSpeech2ConformerConfig", + "FastSpeech2ConformerHifiGanConfig", + "FastSpeech2ConformerWithHifiGanConfig", + ], + "tokenization_fastspeech2_conformer": ["FastSpeech2ConformerTokenizer"], +} + +try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() +except OptionalDependencyNotAvailable: + pass +else: + _import_structure["modeling_fastspeech2_conformer"] = [ + "FASTSPEECH2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST", + "FastSpeech2ConformerWithHifiGan", + "FastSpeech2ConformerHifiGan", + "FastSpeech2ConformerModel", + "FastSpeech2ConformerPreTrainedModel", + ] + +if TYPE_CHECKING: + from .configuration_fastspeech2_conformer import ( + FASTSPEECH2_CONFORMER_HIFIGAN_PRETRAINED_CONFIG_ARCHIVE_MAP, + FASTSPEECH2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, + FASTSPEECH2_CONFORMER_WITH_HIFIGAN_PRETRAINED_CONFIG_ARCHIVE_MAP, + FastSpeech2ConformerConfig, + FastSpeech2ConformerHifiGanConfig, + FastSpeech2ConformerWithHifiGanConfig, + ) + from .tokenization_fastspeech2_conformer import FastSpeech2ConformerTokenizer + + try: + if not is_torch_available(): + raise OptionalDependencyNotAvailable() + except OptionalDependencyNotAvailable: + pass + else: + from .modeling_fastspeech2_conformer import ( + FASTSPEECH2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, + FastSpeech2ConformerHifiGan, + FastSpeech2ConformerModel, + FastSpeech2ConformerPreTrainedModel, + FastSpeech2ConformerWithHifiGan, + ) + +else: + import sys + + sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) diff --git a/llmeval-env/lib/python3.10/site-packages/transformers/models/fastspeech2_conformer/__pycache__/__init__.cpython-310.pyc b/llmeval-env/lib/python3.10/site-packages/transformers/models/fastspeech2_conformer/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..10b56010af4eb071e2ded6416b53617d272aeaa7 Binary files /dev/null and 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b/llmeval-env/lib/python3.10/site-packages/transformers/models/fastspeech2_conformer/__pycache__/tokenization_fastspeech2_conformer.cpython-310.pyc differ diff --git a/llmeval-env/lib/python3.10/site-packages/transformers/models/fastspeech2_conformer/configuration_fastspeech2_conformer.py b/llmeval-env/lib/python3.10/site-packages/transformers/models/fastspeech2_conformer/configuration_fastspeech2_conformer.py new file mode 100644 index 0000000000000000000000000000000000000000..adb038ad1b2a0b2b7072f76f2a9fc714b5937a0d --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/transformers/models/fastspeech2_conformer/configuration_fastspeech2_conformer.py @@ -0,0 +1,482 @@ +# coding=utf-8 +# Copyright 2023 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" FastSpeech2Conformer model configuration""" + +from typing import Dict + +from ...configuration_utils import PretrainedConfig +from ...utils import logging + + +logger = logging.get_logger(__name__) + + +from ..deprecated._archive_maps import ( # noqa: F401, E402 + FASTSPEECH2_CONFORMER_HIFIGAN_PRETRAINED_CONFIG_ARCHIVE_MAP, # noqa: F401, E402 + FASTSPEECH2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, # noqa: F401, E402 + FASTSPEECH2_CONFORMER_WITH_HIFIGAN_PRETRAINED_CONFIG_ARCHIVE_MAP, # noqa: F401, E402 +) + + +class FastSpeech2ConformerConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`FastSpeech2ConformerModel`]. It is used to + instantiate a FastSpeech2Conformer model according to the specified arguments, defining the model architecture. + Instantiating a configuration with the defaults will yield a similar configuration to that of the + FastSpeech2Conformer [espnet/fastspeech2_conformer](https://huggingface.co/espnet/fastspeech2_conformer) + architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + hidden_size (`int`, *optional*, defaults to 384): + The dimensionality of the hidden layers. + vocab_size (`int`, *optional*, defaults to 78): + The size of the vocabulary. + num_mel_bins (`int`, *optional*, defaults to 80): + The number of mel filters used in the filter bank. + encoder_num_attention_heads (`int`, *optional*, defaults to 2): + The number of attention heads in the encoder. + encoder_layers (`int`, *optional*, defaults to 4): + The number of layers in the encoder. + encoder_linear_units (`int`, *optional*, defaults to 1536): + The number of units in the linear layer of the encoder. + decoder_layers (`int`, *optional*, defaults to 4): + The number of layers in the decoder. + decoder_num_attention_heads (`int`, *optional*, defaults to 2): + The number of attention heads in the decoder. + decoder_linear_units (`int`, *optional*, defaults to 1536): + The number of units in the linear layer of the decoder. + speech_decoder_postnet_layers (`int`, *optional*, defaults to 5): + The number of layers in the post-net of the speech decoder. + speech_decoder_postnet_units (`int`, *optional*, defaults to 256): + The number of units in the post-net layers of the speech decoder. + speech_decoder_postnet_kernel (`int`, *optional*, defaults to 5): + The kernel size in the post-net of the speech decoder. + positionwise_conv_kernel_size (`int`, *optional*, defaults to 3): + The size of the convolution kernel used in the position-wise layer. + encoder_normalize_before (`bool`, *optional*, defaults to `False`): + Specifies whether to normalize before encoder layers. + decoder_normalize_before (`bool`, *optional*, defaults to `False`): + Specifies whether to normalize before decoder layers. + encoder_concat_after (`bool`, *optional*, defaults to `False`): + Specifies whether to concatenate after encoder layers. + decoder_concat_after (`bool`, *optional*, defaults to `False`): + Specifies whether to concatenate after decoder layers. + reduction_factor (`int`, *optional*, defaults to 1): + The factor by which the speech frame rate is reduced. + speaking_speed (`float`, *optional*, defaults to 1.0): + The speed of the speech produced. + use_macaron_style_in_conformer (`bool`, *optional*, defaults to `True`): + Specifies whether to use macaron style in the conformer. + use_cnn_in_conformer (`bool`, *optional*, defaults to `True`): + Specifies whether to use convolutional neural networks in the conformer. + encoder_kernel_size (`int`, *optional*, defaults to 7): + The kernel size used in the encoder. + decoder_kernel_size (`int`, *optional*, defaults to 31): + The kernel size used in the decoder. + duration_predictor_layers (`int`, *optional*, defaults to 2): + The number of layers in the duration predictor. + duration_predictor_channels (`int`, *optional*, defaults to 256): + The number of channels in the duration predictor. + duration_predictor_kernel_size (`int`, *optional*, defaults to 3): + The kernel size used in the duration predictor. + energy_predictor_layers (`int`, *optional*, defaults to 2): + The number of layers in the energy predictor. + energy_predictor_channels (`int`, *optional*, defaults to 256): + The number of channels in the energy predictor. + energy_predictor_kernel_size (`int`, *optional*, defaults to 3): + The kernel size used in the energy predictor. + energy_predictor_dropout (`float`, *optional*, defaults to 0.5): + The dropout rate in the energy predictor. + energy_embed_kernel_size (`int`, *optional*, defaults to 1): + The kernel size used in the energy embed layer. + energy_embed_dropout (`float`, *optional*, defaults to 0.0): + The dropout rate in the energy embed layer. + stop_gradient_from_energy_predictor (`bool`, *optional*, defaults to `False`): + Specifies whether to stop gradients from the energy predictor. + pitch_predictor_layers (`int`, *optional*, defaults to 5): + The number of layers in the pitch predictor. + pitch_predictor_channels (`int`, *optional*, defaults to 256): + The number of channels in the pitch predictor. + pitch_predictor_kernel_size (`int`, *optional*, defaults to 5): + The kernel size used in the pitch predictor. + pitch_predictor_dropout (`float`, *optional*, defaults to 0.5): + The dropout rate in the pitch predictor. + pitch_embed_kernel_size (`int`, *optional*, defaults to 1): + The kernel size used in the pitch embed layer. + pitch_embed_dropout (`float`, *optional*, defaults to 0.0): + The dropout rate in the pitch embed layer. + stop_gradient_from_pitch_predictor (`bool`, *optional*, defaults to `True`): + Specifies whether to stop gradients from the pitch predictor. + encoder_dropout_rate (`float`, *optional*, defaults to 0.2): + The dropout rate in the encoder. + encoder_positional_dropout_rate (`float`, *optional*, defaults to 0.2): + The positional dropout rate in the encoder. + encoder_attention_dropout_rate (`float`, *optional*, defaults to 0.2): + The attention dropout rate in the encoder. + decoder_dropout_rate (`float`, *optional*, defaults to 0.2): + The dropout rate in the decoder. + decoder_positional_dropout_rate (`float`, *optional*, defaults to 0.2): + The positional dropout rate in the decoder. + decoder_attention_dropout_rate (`float`, *optional*, defaults to 0.2): + The attention dropout rate in the decoder. + duration_predictor_dropout_rate (`float`, *optional*, defaults to 0.2): + The dropout rate in the duration predictor. + speech_decoder_postnet_dropout (`float`, *optional*, defaults to 0.5): + The dropout rate in the speech decoder postnet. + max_source_positions (`int`, *optional*, defaults to 5000): + if `"relative"` position embeddings are used, defines the maximum source input positions. + use_masking (`bool`, *optional*, defaults to `True`): + Specifies whether to use masking in the model. + use_weighted_masking (`bool`, *optional*, defaults to `False`): + Specifies whether to use weighted masking in the model. + num_speakers (`int`, *optional*): + Number of speakers. If set to > 1, assume that the speaker ids will be provided as the input and use + speaker id embedding layer. + num_languages (`int`, *optional*): + Number of languages. If set to > 1, assume that the language ids will be provided as the input and use the + languge id embedding layer. + speaker_embed_dim (`int`, *optional*): + Speaker embedding dimension. If set to > 0, assume that speaker_embedding will be provided as the input. + is_encoder_decoder (`bool`, *optional*, defaults to `True`): + Specifies whether the model is an encoder-decoder. + + Example: + + ```python + >>> from transformers import FastSpeech2ConformerModel, FastSpeech2ConformerConfig + + >>> # Initializing a FastSpeech2Conformer style configuration + >>> configuration = FastSpeech2ConformerConfig() + + >>> # Initializing a model from the FastSpeech2Conformer style configuration + >>> model = FastSpeech2ConformerModel(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "fastspeech2_conformer" + attribute_map = {"num_hidden_layers": "encoder_layers", "num_attention_heads": "encoder_num_attention_heads"} + + def __init__( + self, + hidden_size=384, + vocab_size=78, + num_mel_bins=80, + encoder_num_attention_heads=2, + encoder_layers=4, + encoder_linear_units=1536, + decoder_layers=4, + decoder_num_attention_heads=2, + decoder_linear_units=1536, + speech_decoder_postnet_layers=5, + speech_decoder_postnet_units=256, + speech_decoder_postnet_kernel=5, + positionwise_conv_kernel_size=3, + encoder_normalize_before=False, + decoder_normalize_before=False, + encoder_concat_after=False, + decoder_concat_after=False, + reduction_factor=1, + speaking_speed=1.0, + use_macaron_style_in_conformer=True, + use_cnn_in_conformer=True, + encoder_kernel_size=7, + decoder_kernel_size=31, + duration_predictor_layers=2, + duration_predictor_channels=256, + duration_predictor_kernel_size=3, + energy_predictor_layers=2, + energy_predictor_channels=256, + energy_predictor_kernel_size=3, + energy_predictor_dropout=0.5, + energy_embed_kernel_size=1, + energy_embed_dropout=0.0, + stop_gradient_from_energy_predictor=False, + pitch_predictor_layers=5, + pitch_predictor_channels=256, + pitch_predictor_kernel_size=5, + pitch_predictor_dropout=0.5, + pitch_embed_kernel_size=1, + pitch_embed_dropout=0.0, + stop_gradient_from_pitch_predictor=True, + encoder_dropout_rate=0.2, + encoder_positional_dropout_rate=0.2, + encoder_attention_dropout_rate=0.2, + decoder_dropout_rate=0.2, + decoder_positional_dropout_rate=0.2, + decoder_attention_dropout_rate=0.2, + duration_predictor_dropout_rate=0.2, + speech_decoder_postnet_dropout=0.5, + max_source_positions=5000, + use_masking=True, + use_weighted_masking=False, + num_speakers=None, + num_languages=None, + speaker_embed_dim=None, + is_encoder_decoder=True, + **kwargs, + ): + if positionwise_conv_kernel_size % 2 == 0: + raise ValueError( + f"positionwise_conv_kernel_size must be odd, but got {positionwise_conv_kernel_size} instead." + ) + if encoder_kernel_size % 2 == 0: + raise ValueError(f"encoder_kernel_size must be odd, but got {encoder_kernel_size} instead.") + if decoder_kernel_size % 2 == 0: + raise ValueError(f"decoder_kernel_size must be odd, but got {decoder_kernel_size} instead.") + if duration_predictor_kernel_size % 2 == 0: + raise ValueError( + f"duration_predictor_kernel_size must be odd, but got {duration_predictor_kernel_size} instead." + ) + if energy_predictor_kernel_size % 2 == 0: + raise ValueError( + f"energy_predictor_kernel_size must be odd, but got {energy_predictor_kernel_size} instead." + ) + if energy_embed_kernel_size % 2 == 0: + raise ValueError(f"energy_embed_kernel_size must be odd, but got {energy_embed_kernel_size} instead.") + if pitch_predictor_kernel_size % 2 == 0: + raise ValueError( + f"pitch_predictor_kernel_size must be odd, but got {pitch_predictor_kernel_size} instead." + ) + if pitch_embed_kernel_size % 2 == 0: + raise ValueError(f"pitch_embed_kernel_size must be odd, but got {pitch_embed_kernel_size} instead.") + if hidden_size % encoder_num_attention_heads != 0: + raise ValueError("The hidden_size must be evenly divisible by encoder_num_attention_heads.") + if hidden_size % decoder_num_attention_heads != 0: + raise ValueError("The hidden_size must be evenly divisible by decoder_num_attention_heads.") + if use_masking and use_weighted_masking: + raise ValueError("Either use_masking or use_weighted_masking can be True, but not both.") + + self.hidden_size = hidden_size + self.vocab_size = vocab_size + self.num_mel_bins = num_mel_bins + self.encoder_config = { + "num_attention_heads": encoder_num_attention_heads, + "layers": encoder_layers, + "kernel_size": encoder_kernel_size, + "attention_dropout_rate": encoder_attention_dropout_rate, + "dropout_rate": encoder_dropout_rate, + "positional_dropout_rate": encoder_positional_dropout_rate, + "linear_units": encoder_linear_units, + "normalize_before": encoder_normalize_before, + "concat_after": encoder_concat_after, + } + self.decoder_config = { + "num_attention_heads": decoder_num_attention_heads, + "layers": decoder_layers, + "kernel_size": decoder_kernel_size, + "attention_dropout_rate": decoder_attention_dropout_rate, + "dropout_rate": decoder_dropout_rate, + "positional_dropout_rate": decoder_positional_dropout_rate, + "linear_units": decoder_linear_units, + "normalize_before": decoder_normalize_before, + "concat_after": decoder_concat_after, + } + self.encoder_num_attention_heads = encoder_num_attention_heads + self.encoder_layers = encoder_layers + self.duration_predictor_channels = duration_predictor_channels + self.duration_predictor_kernel_size = duration_predictor_kernel_size + self.duration_predictor_layers = duration_predictor_layers + self.energy_embed_dropout = energy_embed_dropout + self.energy_embed_kernel_size = energy_embed_kernel_size + self.energy_predictor_channels = energy_predictor_channels + self.energy_predictor_dropout = energy_predictor_dropout + self.energy_predictor_kernel_size = energy_predictor_kernel_size + self.energy_predictor_layers = energy_predictor_layers + self.pitch_embed_dropout = pitch_embed_dropout + self.pitch_embed_kernel_size = pitch_embed_kernel_size + self.pitch_predictor_channels = pitch_predictor_channels + self.pitch_predictor_dropout = pitch_predictor_dropout + self.pitch_predictor_kernel_size = pitch_predictor_kernel_size + self.pitch_predictor_layers = pitch_predictor_layers + self.positionwise_conv_kernel_size = positionwise_conv_kernel_size + self.speech_decoder_postnet_units = speech_decoder_postnet_units + self.speech_decoder_postnet_dropout = speech_decoder_postnet_dropout + self.speech_decoder_postnet_kernel = speech_decoder_postnet_kernel + self.speech_decoder_postnet_layers = speech_decoder_postnet_layers + self.reduction_factor = reduction_factor + self.speaking_speed = speaking_speed + self.stop_gradient_from_energy_predictor = stop_gradient_from_energy_predictor + self.stop_gradient_from_pitch_predictor = stop_gradient_from_pitch_predictor + self.max_source_positions = max_source_positions + self.use_cnn_in_conformer = use_cnn_in_conformer + self.use_macaron_style_in_conformer = use_macaron_style_in_conformer + self.use_masking = use_masking + self.use_weighted_masking = use_weighted_masking + self.num_speakers = num_speakers + self.num_languages = num_languages + self.speaker_embed_dim = speaker_embed_dim + self.duration_predictor_dropout_rate = duration_predictor_dropout_rate + self.is_encoder_decoder = is_encoder_decoder + + super().__init__( + is_encoder_decoder=is_encoder_decoder, + **kwargs, + ) + + +class FastSpeech2ConformerHifiGanConfig(PretrainedConfig): + r""" + This is the configuration class to store the configuration of a [`FastSpeech2ConformerHifiGanModel`]. It is used to + instantiate a FastSpeech2Conformer HiFi-GAN vocoder model according to the specified arguments, defining the model + architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the + FastSpeech2Conformer + [espnet/fastspeech2_conformer_hifigan](https://huggingface.co/espnet/fastspeech2_conformer_hifigan) architecture. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + model_in_dim (`int`, *optional*, defaults to 80): + The number of frequency bins in the input log-mel spectrogram. + upsample_initial_channel (`int`, *optional*, defaults to 512): + The number of input channels into the upsampling network. + upsample_rates (`Tuple[int]` or `List[int]`, *optional*, defaults to `[8, 8, 2, 2]`): + A tuple of integers defining the stride of each 1D convolutional layer in the upsampling network. The + length of *upsample_rates* defines the number of convolutional layers and has to match the length of + *upsample_kernel_sizes*. + upsample_kernel_sizes (`Tuple[int]` or `List[int]`, *optional*, defaults to `[16, 16, 4, 4]`): + A tuple of integers defining the kernel size of each 1D convolutional layer in the upsampling network. The + length of *upsample_kernel_sizes* defines the number of convolutional layers and has to match the length of + *upsample_rates*. + resblock_kernel_sizes (`Tuple[int]` or `List[int]`, *optional*, defaults to `[3, 7, 11]`): + A tuple of integers defining the kernel sizes of the 1D convolutional layers in the multi-receptive field + fusion (MRF) module. + resblock_dilation_sizes (`Tuple[Tuple[int]]` or `List[List[int]]`, *optional*, defaults to `[[1, 3, 5], [1, 3, 5], [1, 3, 5]]`): + A nested tuple of integers defining the dilation rates of the dilated 1D convolutional layers in the + multi-receptive field fusion (MRF) module. + initializer_range (`float`, *optional*, defaults to 0.01): + The standard deviation of the truncated_normal_initializer for initializing all weight matrices. + leaky_relu_slope (`float`, *optional*, defaults to 0.1): + The angle of the negative slope used by the leaky ReLU activation. + normalize_before (`bool`, *optional*, defaults to `True`): + Whether or not to normalize the spectrogram before vocoding using the vocoder's learned mean and variance. + + Example: + + ```python + >>> from transformers import FastSpeech2ConformerHifiGan, FastSpeech2ConformerHifiGanConfig + + >>> # Initializing a FastSpeech2ConformerHifiGan configuration + >>> configuration = FastSpeech2ConformerHifiGanConfig() + + >>> # Initializing a model (with random weights) from the configuration + >>> model = FastSpeech2ConformerHifiGan(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ```""" + + model_type = "hifigan" + + def __init__( + self, + model_in_dim=80, + upsample_initial_channel=512, + upsample_rates=[8, 8, 2, 2], + upsample_kernel_sizes=[16, 16, 4, 4], + resblock_kernel_sizes=[3, 7, 11], + resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]], + initializer_range=0.01, + leaky_relu_slope=0.1, + normalize_before=True, + **kwargs, + ): + self.model_in_dim = model_in_dim + self.upsample_initial_channel = upsample_initial_channel + self.upsample_rates = upsample_rates + self.upsample_kernel_sizes = upsample_kernel_sizes + self.resblock_kernel_sizes = resblock_kernel_sizes + self.resblock_dilation_sizes = resblock_dilation_sizes + self.initializer_range = initializer_range + self.leaky_relu_slope = leaky_relu_slope + self.normalize_before = normalize_before + super().__init__(**kwargs) + + +class FastSpeech2ConformerWithHifiGanConfig(PretrainedConfig): + """ + This is the configuration class to store the configuration of a [`FastSpeech2ConformerWithHifiGan`]. It is used to + instantiate a `FastSpeech2ConformerWithHifiGanModel` model according to the specified sub-models configurations, + defining the model architecture. + + Instantiating a configuration with the defaults will yield a similar configuration to that of the + FastSpeech2ConformerModel [espnet/fastspeech2_conformer](https://huggingface.co/espnet/fastspeech2_conformer) and + FastSpeech2ConformerHifiGan + [espnet/fastspeech2_conformer_hifigan](https://huggingface.co/espnet/fastspeech2_conformer_hifigan) architectures. + + Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the + documentation from [`PretrainedConfig`] for more information. + + Args: + model_config (`typing.Dict`, *optional*): + Configuration of the text-to-speech model. + vocoder_config (`typing.Dict`, *optional*): + Configuration of the vocoder model. + model_config ([`FastSpeech2ConformerConfig`], *optional*): + Configuration of the text-to-speech model. + vocoder_config ([`FastSpeech2ConformerHiFiGanConfig`], *optional*): + Configuration of the vocoder model. + + Example: + + ```python + >>> from transformers import ( + ... FastSpeech2ConformerConfig, + ... FastSpeech2ConformerHifiGanConfig, + ... FastSpeech2ConformerWithHifiGanConfig, + ... FastSpeech2ConformerWithHifiGan, + ... ) + + >>> # Initializing FastSpeech2ConformerWithHifiGan sub-modules configurations. + >>> model_config = FastSpeech2ConformerConfig() + >>> vocoder_config = FastSpeech2ConformerHifiGanConfig() + + >>> # Initializing a FastSpeech2ConformerWithHifiGan module style configuration + >>> configuration = FastSpeech2ConformerWithHifiGanConfig(model_config.to_dict(), vocoder_config.to_dict()) + + >>> # Initializing a model (with random weights) + >>> model = FastSpeech2ConformerWithHifiGan(configuration) + + >>> # Accessing the model configuration + >>> configuration = model.config + ``` + """ + + model_type = "fastspeech2_conformer_with_hifigan" + is_composition = True + + def __init__( + self, + model_config: Dict = None, + vocoder_config: Dict = None, + **kwargs, + ): + if model_config is None: + model_config = {} + logger.info("model_config is None. initializing the model with default values.") + + if vocoder_config is None: + vocoder_config = {} + logger.info("vocoder_config is None. initializing the coarse model with default values.") + + self.model_config = FastSpeech2ConformerConfig(**model_config) + self.vocoder_config = FastSpeech2ConformerHifiGanConfig(**vocoder_config) + + super().__init__(**kwargs) diff --git a/llmeval-env/lib/python3.10/site-packages/transformers/models/fastspeech2_conformer/convert_fastspeech2_conformer_original_pytorch_checkpoint_to_pytorch.py b/llmeval-env/lib/python3.10/site-packages/transformers/models/fastspeech2_conformer/convert_fastspeech2_conformer_original_pytorch_checkpoint_to_pytorch.py new file mode 100644 index 0000000000000000000000000000000000000000..bb9c432f82292f0a22c276821130f65e30f45e6a --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/transformers/models/fastspeech2_conformer/convert_fastspeech2_conformer_original_pytorch_checkpoint_to_pytorch.py @@ -0,0 +1,210 @@ +# coding=utf-8 +# Copyright 2023 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert FastSpeech2Conformer checkpoint.""" + +import argparse +import json +import re +from pathlib import Path +from tempfile import TemporaryDirectory + +import torch +import yaml + +from transformers import ( + FastSpeech2ConformerConfig, + FastSpeech2ConformerModel, + FastSpeech2ConformerTokenizer, + logging, +) + + +logging.set_verbosity_info() +logger = logging.get_logger("transformers.models.FastSpeech2Conformer") + +CONFIG_MAPPING = { + "adim": "hidden_size", + "aheads": "num_attention_heads", + "conformer_dec_kernel_size": "decoder_kernel_size", + "conformer_enc_kernel_size": "encoder_kernel_size", + "decoder_normalize_before": "decoder_normalize_before", + "dlayers": "decoder_layers", + "dunits": "decoder_linear_units", + "duration_predictor_chans": "duration_predictor_channels", + "duration_predictor_kernel_size": "duration_predictor_kernel_size", + "duration_predictor_layers": "duration_predictor_layers", + "elayers": "encoder_layers", + "encoder_normalize_before": "encoder_normalize_before", + "energy_embed_dropout": "energy_embed_dropout", + "energy_embed_kernel_size": "energy_embed_kernel_size", + "energy_predictor_chans": "energy_predictor_channels", + "energy_predictor_dropout": "energy_predictor_dropout", + "energy_predictor_kernel_size": "energy_predictor_kernel_size", + "energy_predictor_layers": "energy_predictor_layers", + "eunits": "encoder_linear_units", + "pitch_embed_dropout": "pitch_embed_dropout", + "pitch_embed_kernel_size": "pitch_embed_kernel_size", + "pitch_predictor_chans": "pitch_predictor_channels", + "pitch_predictor_dropout": "pitch_predictor_dropout", + "pitch_predictor_kernel_size": "pitch_predictor_kernel_size", + "pitch_predictor_layers": "pitch_predictor_layers", + "positionwise_conv_kernel_size": "positionwise_conv_kernel_size", + "postnet_chans": "speech_decoder_postnet_units", + "postnet_filts": "speech_decoder_postnet_kernel", + "postnet_layers": "speech_decoder_postnet_layers", + "reduction_factor": "reduction_factor", + "stop_gradient_from_energy_predictor": "stop_gradient_from_energy_predictor", + "stop_gradient_from_pitch_predictor": "stop_gradient_from_pitch_predictor", + "transformer_dec_attn_dropout_rate": "decoder_attention_dropout_rate", + "transformer_dec_dropout_rate": "decoder_dropout_rate", + "transformer_dec_positional_dropout_rate": "decoder_positional_dropout_rate", + "transformer_enc_attn_dropout_rate": "encoder_attention_dropout_rate", + "transformer_enc_dropout_rate": "encoder_dropout_rate", + "transformer_enc_positional_dropout_rate": "encoder_positional_dropout_rate", + "use_cnn_in_conformer": "use_cnn_in_conformer", + "use_macaron_style_in_conformer": "use_macaron_style_in_conformer", + "use_masking": "use_masking", + "use_weighted_masking": "use_weighted_masking", + "idim": "input_dim", + "odim": "num_mel_bins", + "spk_embed_dim": "speaker_embed_dim", + "langs": "num_languages", + "spks": "num_speakers", +} + + +def remap_model_yaml_config(yaml_config_path): + with Path(yaml_config_path).open("r", encoding="utf-8") as f: + args = yaml.safe_load(f) + args = argparse.Namespace(**args) + + remapped_config = {} + + model_params = args.tts_conf["text2mel_params"] + # espnet_config_key -> hf_config_key, any keys not included are ignored + for espnet_config_key, hf_config_key in CONFIG_MAPPING.items(): + if espnet_config_key in model_params: + remapped_config[hf_config_key] = model_params[espnet_config_key] + + return remapped_config, args.g2p, args.token_list + + +def convert_espnet_state_dict_to_hf(state_dict): + new_state_dict = {} + for key in state_dict: + if "tts.generator.text2mel." in key: + new_key = key.replace("tts.generator.text2mel.", "") + if "postnet" in key: + new_key = new_key.replace("postnet.postnet", "speech_decoder_postnet.layers") + new_key = new_key.replace(".0.weight", ".conv.weight") + new_key = new_key.replace(".1.weight", ".batch_norm.weight") + new_key = new_key.replace(".1.bias", ".batch_norm.bias") + new_key = new_key.replace(".1.running_mean", ".batch_norm.running_mean") + new_key = new_key.replace(".1.running_var", ".batch_norm.running_var") + new_key = new_key.replace(".1.num_batches_tracked", ".batch_norm.num_batches_tracked") + if "feat_out" in key: + if "weight" in key: + new_key = "speech_decoder_postnet.feat_out.weight" + if "bias" in key: + new_key = "speech_decoder_postnet.feat_out.bias" + if "encoder.embed.0.weight" in key: + new_key = new_key.replace("0.", "") + if "w_1" in key: + new_key = new_key.replace("w_1", "conv1") + if "w_2" in key: + new_key = new_key.replace("w_2", "conv2") + if "predictor.conv" in key: + new_key = new_key.replace(".conv", ".conv_layers") + pattern = r"(\d)\.(\d)" + replacement = ( + r"\1.conv" if ("2.weight" not in new_key) and ("2.bias" not in new_key) else r"\1.layer_norm" + ) + new_key = re.sub(pattern, replacement, new_key) + if "pitch_embed" in key or "energy_embed" in key: + new_key = new_key.replace("0", "conv") + if "encoders" in key: + new_key = new_key.replace("encoders", "conformer_layers") + new_key = new_key.replace("norm_final", "final_layer_norm") + new_key = new_key.replace("norm_mha", "self_attn_layer_norm") + new_key = new_key.replace("norm_ff_macaron", "ff_macaron_layer_norm") + new_key = new_key.replace("norm_ff", "ff_layer_norm") + new_key = new_key.replace("norm_conv", "conv_layer_norm") + if "lid_emb" in key: + new_key = new_key.replace("lid_emb", "language_id_embedding") + if "sid_emb" in key: + new_key = new_key.replace("sid_emb", "speaker_id_embedding") + + new_state_dict[new_key] = state_dict[key] + + return new_state_dict + + +@torch.no_grad() +def convert_FastSpeech2ConformerModel_checkpoint( + checkpoint_path, + yaml_config_path, + pytorch_dump_folder_path, + repo_id=None, +): + model_params, tokenizer_name, vocab = remap_model_yaml_config(yaml_config_path) + config = FastSpeech2ConformerConfig(**model_params) + + # Prepare the model + model = FastSpeech2ConformerModel(config) + + espnet_checkpoint = torch.load(checkpoint_path) + hf_compatible_state_dict = convert_espnet_state_dict_to_hf(espnet_checkpoint) + + model.load_state_dict(hf_compatible_state_dict) + + model.save_pretrained(pytorch_dump_folder_path) + + # Prepare the tokenizer + with TemporaryDirectory() as tempdir: + vocab = {token: id for id, token in enumerate(vocab)} + vocab_file = Path(tempdir) / "vocab.json" + with open(vocab_file, "w") as f: + json.dump(vocab, f) + should_strip_spaces = "no_space" in tokenizer_name + tokenizer = FastSpeech2ConformerTokenizer(str(vocab_file), should_strip_spaces=should_strip_spaces) + + tokenizer.save_pretrained(pytorch_dump_folder_path) + + if repo_id: + print("Pushing to the hub...") + model.push_to_hub(repo_id) + tokenizer.push_to_hub(repo_id) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint") + parser.add_argument( + "--yaml_config_path", required=True, default=None, type=str, help="Path to config.yaml of model to convert" + ) + parser.add_argument( + "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model." + ) + parser.add_argument( + "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub." + ) + + args = parser.parse_args() + convert_FastSpeech2ConformerModel_checkpoint( + args.checkpoint_path, + args.yaml_config_path, + args.pytorch_dump_folder_path, + args.push_to_hub, + ) diff --git a/llmeval-env/lib/python3.10/site-packages/transformers/models/fastspeech2_conformer/convert_hifigan.py b/llmeval-env/lib/python3.10/site-packages/transformers/models/fastspeech2_conformer/convert_hifigan.py new file mode 100644 index 0000000000000000000000000000000000000000..ec9f57ce7142d619259555fb89f4f1366947fe71 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/transformers/models/fastspeech2_conformer/convert_hifigan.py @@ -0,0 +1,134 @@ +# coding=utf-8 +# Copyright 2023 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert FastSpeech2Conformer HiFi-GAN checkpoint.""" + +import argparse +from pathlib import Path + +import torch +import yaml + +from transformers import FastSpeech2ConformerHifiGan, FastSpeech2ConformerHifiGanConfig, logging + + +logging.set_verbosity_info() +logger = logging.get_logger("transformers.models.FastSpeech2Conformer") + + +def load_weights(checkpoint, hf_model, config): + vocoder_key_prefix = "tts.generator.vocoder." + checkpoint = {k.replace(vocoder_key_prefix, ""): v for k, v in checkpoint.items() if vocoder_key_prefix in k} + + hf_model.apply_weight_norm() + + hf_model.conv_pre.weight_g.data = checkpoint["input_conv.weight_g"] + hf_model.conv_pre.weight_v.data = checkpoint["input_conv.weight_v"] + hf_model.conv_pre.bias.data = checkpoint["input_conv.bias"] + + for i in range(len(config.upsample_rates)): + hf_model.upsampler[i].weight_g.data = checkpoint[f"upsamples.{i}.1.weight_g"] + hf_model.upsampler[i].weight_v.data = checkpoint[f"upsamples.{i}.1.weight_v"] + hf_model.upsampler[i].bias.data = checkpoint[f"upsamples.{i}.1.bias"] + + for i in range(len(config.upsample_rates) * len(config.resblock_kernel_sizes)): + for j in range(len(config.resblock_dilation_sizes)): + hf_model.resblocks[i].convs1[j].weight_g.data = checkpoint[f"blocks.{i}.convs1.{j}.1.weight_g"] + hf_model.resblocks[i].convs1[j].weight_v.data = checkpoint[f"blocks.{i}.convs1.{j}.1.weight_v"] + hf_model.resblocks[i].convs1[j].bias.data = checkpoint[f"blocks.{i}.convs1.{j}.1.bias"] + + hf_model.resblocks[i].convs2[j].weight_g.data = checkpoint[f"blocks.{i}.convs2.{j}.1.weight_g"] + hf_model.resblocks[i].convs2[j].weight_v.data = checkpoint[f"blocks.{i}.convs2.{j}.1.weight_v"] + hf_model.resblocks[i].convs2[j].bias.data = checkpoint[f"blocks.{i}.convs2.{j}.1.bias"] + + hf_model.conv_post.weight_g.data = checkpoint["output_conv.1.weight_g"] + hf_model.conv_post.weight_v.data = checkpoint["output_conv.1.weight_v"] + hf_model.conv_post.bias.data = checkpoint["output_conv.1.bias"] + + hf_model.remove_weight_norm() + + +def remap_hifigan_yaml_config(yaml_config_path): + with Path(yaml_config_path).open("r", encoding="utf-8") as f: + args = yaml.safe_load(f) + args = argparse.Namespace(**args) + + vocoder_type = args.tts_conf["vocoder_type"] + if vocoder_type != "hifigan_generator": + raise TypeError(f"Vocoder config must be for `hifigan_generator`, but got {vocoder_type}") + + remapped_dict = {} + vocoder_params = args.tts_conf["vocoder_params"] + + # espnet_config_key -> hf_config_key + key_mappings = { + "channels": "upsample_initial_channel", + "in_channels": "model_in_dim", + "resblock_dilations": "resblock_dilation_sizes", + "resblock_kernel_sizes": "resblock_kernel_sizes", + "upsample_kernel_sizes": "upsample_kernel_sizes", + "upsample_scales": "upsample_rates", + } + for espnet_config_key, hf_config_key in key_mappings.items(): + remapped_dict[hf_config_key] = vocoder_params[espnet_config_key] + remapped_dict["sampling_rate"] = args.tts_conf["sampling_rate"] + remapped_dict["normalize_before"] = False + remapped_dict["leaky_relu_slope"] = vocoder_params["nonlinear_activation_params"]["negative_slope"] + + return remapped_dict + + +@torch.no_grad() +def convert_hifigan_checkpoint( + checkpoint_path, + pytorch_dump_folder_path, + yaml_config_path=None, + repo_id=None, +): + if yaml_config_path is not None: + config_kwargs = remap_hifigan_yaml_config(yaml_config_path) + config = FastSpeech2ConformerHifiGanConfig(**config_kwargs) + else: + config = FastSpeech2ConformerHifiGanConfig() + + model = FastSpeech2ConformerHifiGan(config) + + orig_checkpoint = torch.load(checkpoint_path) + load_weights(orig_checkpoint, model, config) + + model.save_pretrained(pytorch_dump_folder_path) + + if repo_id: + print("Pushing to the hub...") + model.push_to_hub(repo_id) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint") + parser.add_argument("--yaml_config_path", default=None, type=str, help="Path to config.yaml of model to convert") + parser.add_argument( + "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model." + ) + parser.add_argument( + "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub." + ) + + args = parser.parse_args() + convert_hifigan_checkpoint( + args.checkpoint_path, + args.pytorch_dump_folder_path, + args.yaml_config_path, + args.push_to_hub, + ) diff --git a/llmeval-env/lib/python3.10/site-packages/transformers/models/fastspeech2_conformer/convert_model_with_hifigan.py b/llmeval-env/lib/python3.10/site-packages/transformers/models/fastspeech2_conformer/convert_model_with_hifigan.py new file mode 100644 index 0000000000000000000000000000000000000000..2a780d5cf0b8ea8a69a0bfc7f02796fbaa2b8c5b --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/transformers/models/fastspeech2_conformer/convert_model_with_hifigan.py @@ -0,0 +1,102 @@ +# coding=utf-8 +# Copyright 2023 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Convert FastSpeech2Conformer checkpoint.""" + +import argparse + +import torch + +from transformers import ( + FastSpeech2ConformerConfig, + FastSpeech2ConformerHifiGan, + FastSpeech2ConformerHifiGanConfig, + FastSpeech2ConformerModel, + FastSpeech2ConformerWithHifiGan, + FastSpeech2ConformerWithHifiGanConfig, + logging, +) + +from .convert_fastspeech2_conformer_original_pytorch_checkpoint_to_pytorch import ( + convert_espnet_state_dict_to_hf, + remap_model_yaml_config, +) +from .convert_hifigan import load_weights, remap_hifigan_yaml_config + + +logging.set_verbosity_info() +logger = logging.get_logger("transformers.models.FastSpeech2Conformer") + + +def convert_FastSpeech2ConformerWithHifiGan_checkpoint( + checkpoint_path, + yaml_config_path, + pytorch_dump_folder_path, + repo_id=None, +): + # Prepare the model + model_params, *_ = remap_model_yaml_config(yaml_config_path) + model_config = FastSpeech2ConformerConfig(**model_params) + + model = FastSpeech2ConformerModel(model_config) + + espnet_checkpoint = torch.load(checkpoint_path) + hf_compatible_state_dict = convert_espnet_state_dict_to_hf(espnet_checkpoint) + model.load_state_dict(hf_compatible_state_dict) + + # Prepare the vocoder + config_kwargs = remap_hifigan_yaml_config(yaml_config_path) + vocoder_config = FastSpeech2ConformerHifiGanConfig(**config_kwargs) + + vocoder = FastSpeech2ConformerHifiGan(vocoder_config) + load_weights(espnet_checkpoint, vocoder, vocoder_config) + + # Prepare the model + vocoder + config = FastSpeech2ConformerWithHifiGanConfig.from_sub_model_configs(model_config, vocoder_config) + with_hifigan_model = FastSpeech2ConformerWithHifiGan(config) + with_hifigan_model.model = model + with_hifigan_model.vocoder = vocoder + + with_hifigan_model.save_pretrained(pytorch_dump_folder_path) + + if repo_id: + print("Pushing to the hub...") + with_hifigan_model.push_to_hub(repo_id) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint") + parser.add_argument( + "--yaml_config_path", required=True, default=None, type=str, help="Path to config.yaml of model to convert" + ) + parser.add_argument( + "--pytorch_dump_folder_path", + required=True, + default=None, + type=str, + help="Path to the output `FastSpeech2ConformerModel` PyTorch model.", + ) + parser.add_argument( + "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub." + ) + + args = parser.parse_args() + + convert_FastSpeech2ConformerWithHifiGan_checkpoint( + args.checkpoint_path, + args.yaml_config_path, + args.pytorch_dump_folder_path, + args.push_to_hub, + ) diff --git a/llmeval-env/lib/python3.10/site-packages/transformers/models/fastspeech2_conformer/modeling_fastspeech2_conformer.py b/llmeval-env/lib/python3.10/site-packages/transformers/models/fastspeech2_conformer/modeling_fastspeech2_conformer.py new file mode 100644 index 0000000000000000000000000000000000000000..c46ef2a8365f0cb8c632cf50ba2b9f8b44d48c69 --- /dev/null +++ b/llmeval-env/lib/python3.10/site-packages/transformers/models/fastspeech2_conformer/modeling_fastspeech2_conformer.py @@ -0,0 +1,1684 @@ +# coding=utf-8 +# Copyright 2023 The Espnet authors, IMS Toucan authors, and the HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" PyTorch FastSpeech2Conformer model.""" + +import math +from dataclasses import dataclass +from typing import Optional, Tuple, Union + +import torch +from torch import nn + +from ...modeling_outputs import BaseModelOutput +from ...modeling_utils import PreTrainedModel +from ...utils import ModelOutput, add_start_docstrings, logging, replace_return_docstrings +from .configuration_fastspeech2_conformer import ( + FastSpeech2ConformerConfig, + FastSpeech2ConformerHifiGanConfig, + FastSpeech2ConformerWithHifiGanConfig, +) + + +logger = logging.get_logger(__name__) + + +from ..deprecated._archive_maps import FASTSPEECH2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402 + + +@dataclass +class FastSpeech2ConformerModelOutput(ModelOutput): + """ + Output type of [`FastSpeech2ConformerModel`]. + + Args: + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Spectrogram generation loss. + spectrogram (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_bins)`): + The predicted spectrogram. + encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder of the model. + encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. + encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the + self-attention heads. + decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. + decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the + self-attention heads. + duration_outputs (`torch.LongTensor` of shape `(batch_size, max_text_length + 1)`, *optional*): + Outputs of the duration predictor. + pitch_outputs (`torch.FloatTensor` of shape `(batch_size, max_text_length + 1, 1)`, *optional*): + Outputs of the pitch predictor. + energy_outputs (`torch.FloatTensor` of shape `(batch_size, max_text_length + 1, 1)`, *optional*): + Outputs of the energy predictor. + + """ + + loss: Optional[torch.FloatTensor] = None + spectrogram: torch.FloatTensor = None + encoder_last_hidden_state: Optional[torch.FloatTensor] = None + encoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None + encoder_attentions: Optional[Tuple[torch.FloatTensor]] = None + decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None + decoder_attentions: Optional[Tuple[torch.FloatTensor]] = None + duration_outputs: torch.LongTensor = None + pitch_outputs: torch.FloatTensor = None + energy_outputs: torch.FloatTensor = None + + +@dataclass +class FastSpeech2ConformerWithHifiGanOutput(FastSpeech2ConformerModelOutput): + """ + Output type of [`FastSpeech2ConformerWithHifiGan`]. + + Args: + waveform (`torch.FloatTensor` of shape `(batch_size, audio_length)`): + Speech output as a result of passing the predicted mel spectrogram through the vocoder. + loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): + Spectrogram generation loss. + spectrogram (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_bins)`): + The predicted spectrogram. + encoder_last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Sequence of hidden-states at the output of the last layer of the encoder of the model. + encoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the encoder at the output of each layer plus the initial embedding outputs. + encoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the + self-attention heads. + decoder_hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): + Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. + + Hidden-states of the decoder at the output of each layer plus the initial embedding outputs. + decoder_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): + Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, + sequence_length)`. + + Attentions weights of the decoder, after the attention softmax, used to compute the weighted average in the + self-attention heads. + duration_outputs (`torch.LongTensor` of shape `(batch_size, max_text_length + 1)`, *optional*): + Outputs of the duration predictor. + pitch_outputs (`torch.FloatTensor` of shape `(batch_size, max_text_length + 1, 1)`, *optional*): + Outputs of the pitch predictor. + energy_outputs (`torch.FloatTensor` of shape `(batch_size, max_text_length + 1, 1)`, *optional*): + Outputs of the energy predictor. + """ + + waveform: torch.FloatTensor = None + + +_CONFIG_FOR_DOC = "FastSpeech2ConformerConfig" + +FASTSPEECH2_CONFORMER_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`FastSpeech2ConformerConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +HIFIGAN_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`FastSpeech2ConformerConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + +FASTSPEECH2_CONFORMER_WITH_HIFIGAN_START_DOCSTRING = r""" + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`FastSpeech2ConformerWithHifiGanConfig`]): + Model configuration class with all the parameters of the model. Initializing with a config file does not + load the weights associated with the model, only the configuration. Check out the + [`~PreTrainedModel.from_pretrained`] method to load the model weights. +""" + + +def length_regulator(encoded_embeddings, duration_labels, speaking_speed=1.0): + """ + Length regulator for feed-forward Transformer. + + This is the length regulator module described in `FastSpeech: Fast, Robust and Controllable Text to Speech` + https://arxiv.org/pdf/1905.09263.pdf. The length regulator expands char or phoneme-level embedding features to + frame-level by repeating each feature based on the corresponding predicted durations. + + Args: + encoded_embeddings (`torch.Tensor` of shape `(batch_size, max_text_length, embedding_dim)`): + Batch of sequences of char or phoneme embeddings. + duration_labels (`torch.LongTensor` of shape `(batch_size, time)`): + Batch of durations of each frame. + speaking_speed (`float`, *optional*, defaults to 1.0): + Value to control speed of speech. + + Returns: + `torch.Tensor`: + Replicated input tensor based on durations (batch_size, time*, embedding_dim). + """ + + if speaking_speed <= 0: + raise ValueError("`speaking_speed` must be greater than 0.") + elif speaking_speed != 1.0: + duration_labels = torch.round(duration_labels.float() * speaking_speed).long() + + if duration_labels.sum() == 0: + duration_labels[duration_labels.sum(dim=1).eq(0)] = 1 + + # Calculate the maximum length needed + max_len = torch.sum(duration_labels, dim=1).max() + + # Create a padded tensor to hold the results + hidden_states = torch.zeros( + (encoded_embeddings.size(0), max_len, encoded_embeddings.size(2)), + dtype=torch.float, + device=encoded_embeddings.device, + ) + + # Loop through the batch and fill in the data + for i, (encoded_embedding, target_duration) in enumerate(zip(encoded_embeddings, duration_labels)): + repeated = torch.repeat_interleave(encoded_embedding, target_duration, dim=0) + hidden_states[i, : repeated.size(0)] = repeated + + return hidden_states + + +class FastSpeech2ConformerDurationPredictor(nn.Module): + """ + Duration predictor module. + + This is a module of duration predictor described in the paper 'FastSpeech: Fast, Robust and Controllable Text to + Speech' https://arxiv.org/pdf/1905.09263.pdf The duration predictor predicts a duration of each frame in log domain + from the hidden embeddings of encoder. + + Note: + The calculation domain of outputs is different between in `forward` and in `inference`. In `forward`, the + outputs are calculated in log domain but in `inference`, those are calculated in linear domain. + + """ + + def __init__(self, config: FastSpeech2ConformerConfig): + super().__init__() + + self.conv_layers = nn.ModuleList() + self.log_domain_offset = 1.0 + + for layer_idx in range(config.duration_predictor_layers): + num_chans = config.duration_predictor_channels + input_channels = config.hidden_size if layer_idx == 0 else num_chans + layer = FastSpeech2ConformerPredictorLayer( + input_channels, + num_chans, + config.duration_predictor_kernel_size, + config.duration_predictor_dropout_rate, + ) + self.conv_layers.append(layer) + self.linear = nn.Linear(config.duration_predictor_channels, 1) + + def forward(self, encoder_hidden_states): + """ + Args: + hidden_states (`torch.Tensor` of shape `(batch_size, max_text_length, input_dim)`): + Batch of input sequences. + padding_masks (`torch.ByteTensor` of shape `(batch_size, max_text_length)`, *optional*): + Batch of masks indicating padded part. + + Returns: + `torch.Tensor`: Batch of predicted durations in log domain `(batch_size, max_text_length)`. + + """ + # (batch_size, input_dim, max_text_length) + hidden_states = encoder_hidden_states.transpose(1, -1) + for layer in self.conv_layers: + hidden_states = layer(hidden_states) + + # NOTE: calculate in log domain, (batch_size, max_text_length) + hidden_states = self.linear(hidden_states.transpose(1, -1)).squeeze(-1) + + if not self.training: + # NOTE: calculate in linear domain + hidden_states = torch.clamp(torch.round(hidden_states.exp() - self.log_domain_offset), min=0).long() + + return hidden_states + + +# Copied from transformers.models.speecht5.modeling_speecht5.SpeechT5BatchNormConvLayer +class FastSpeech2ConformerBatchNormConvLayer(nn.Module): + def __init__(self, config, layer_id=0): + super().__init__() + + if layer_id == 0: + in_conv_dim = config.num_mel_bins + else: + in_conv_dim = config.speech_decoder_postnet_units + + if layer_id == config.speech_decoder_postnet_layers - 1: + out_conv_dim = config.num_mel_bins + else: + out_conv_dim = config.speech_decoder_postnet_units + + self.conv = nn.Conv1d( + in_conv_dim, + out_conv_dim, + kernel_size=config.speech_decoder_postnet_kernel, + stride=1, + padding=(config.speech_decoder_postnet_kernel - 1) // 2, + bias=False, + ) + self.batch_norm = nn.BatchNorm1d(out_conv_dim) + + if layer_id < config.speech_decoder_postnet_layers - 1: + self.activation = nn.Tanh() + else: + self.activation = None + + self.dropout = nn.Dropout(config.speech_decoder_postnet_dropout) + + def forward(self, hidden_states): + hidden_states = self.conv(hidden_states) + hidden_states = self.batch_norm(hidden_states) + if self.activation is not None: + hidden_states = self.activation(hidden_states) + hidden_states = self.dropout(hidden_states) + return hidden_states + + +class FastSpeech2ConformerSpeechDecoderPostnet(nn.Module): + def __init__(self, config): + super().__init__() + self.config = config + self.feat_out = nn.Linear(config.hidden_size, config.num_mel_bins * config.reduction_factor) + self.layers = nn.ModuleList( + [FastSpeech2ConformerBatchNormConvLayer(config, i) for i in range(config.speech_decoder_postnet_layers)] + ) + + def forward(self, hidden_states: torch.Tensor): + outputs_before_postnet = self.feat_out(hidden_states).view(hidden_states.size(0), -1, self.config.num_mel_bins) + layer_output = outputs_before_postnet.transpose(1, 2) + for layer in self.layers: + layer_output = layer(layer_output) + outputs_after_postnet = outputs_before_postnet + layer_output.transpose(1, 2) + return outputs_before_postnet, outputs_after_postnet + + +class FastSpeech2ConformerPredictorLayer(nn.Module): + def __init__(self, input_channels, num_chans, kernel_size, dropout_rate): + super().__init__() + self.conv = nn.Conv1d( + input_channels, + num_chans, + kernel_size, + stride=1, + padding=(kernel_size - 1) // 2, + ) + self.activation = nn.ReLU() + self.layer_norm = nn.LayerNorm(num_chans) + self.dropout = nn.Dropout(dropout_rate) + + def forward(self, hidden_states): + hidden_states = self.conv(hidden_states) + hidden_states = self.activation(hidden_states) + + # Perform layer norm on dimension 1 + hidden_states = hidden_states.transpose(1, -1) + hidden_states = self.layer_norm(hidden_states) + hidden_states = hidden_states.transpose(1, -1) + + hidden_states = self.dropout(hidden_states) + + return hidden_states + + +class FastSpeech2ConformerVariancePredictor(nn.Module): + def __init__( + self, + config: FastSpeech2ConformerConfig, + num_layers=2, + num_chans=384, + kernel_size=3, + dropout_rate=0.5, + ): + """ + Initilize variance predictor module. + + Args: + input_dim (`int`): Input dimension. + num_layers (`int`, *optional*, defaults to 2): Number of convolutional layers. + num_chans (`int`, *optional*, defaults to 384): Number of channels of convolutional layers. + kernel_size (`int`, *optional*, defaults to 3): Kernel size of convolutional layers. + dropout_rate (`float`, *optional*, defaults to 0.5): Dropout rate. + """ + super().__init__() + self.conv_layers = nn.ModuleList() + for idx in range(num_layers): + input_channels = config.hidden_size if idx == 0 else num_chans + layer = FastSpeech2ConformerPredictorLayer(input_channels, num_chans, kernel_size, dropout_rate) + self.conv_layers.append(layer) + self.linear = nn.Linear(num_chans, 1) + + def forward(self, encoder_hidden_states, padding_masks=None): + """ + Calculate forward propagation. + + Args: + encoder_hidden_states (`torch.Tensor` of shape `(batch_size, max_text_length, input_dim)`): + Batch of input sequences. + padding_masks (`torch.ByteTensor` of shape `(batch_size, max_text_length)`, *optional*): + Batch of masks indicating padded part. + + Returns: + Tensor: Batch of predicted sequences `(batch_size, max_text_length, 1)`. + """ + # (batch_size, input_dim, max_text_length) + hidden_states = encoder_hidden_states.transpose(1, -1) + for layer in self.conv_layers: + hidden_states = layer(hidden_states) + + hidden_states = self.linear(hidden_states.transpose(1, 2)) + + if padding_masks is not None: + hidden_states = hidden_states.masked_fill(padding_masks, 0.0) + + return hidden_states + + +class FastSpeech2ConformerVarianceEmbedding(nn.Module): + def __init__( + self, + in_channels=1, + out_channels=384, + kernel_size=1, + padding=0, + dropout_rate=0.0, + ): + super().__init__() + self.conv = nn.Conv1d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + padding=padding, + ) + self.dropout = nn.Dropout(dropout_rate) + + def forward(self, hidden_states): + hidden_states = hidden_states.transpose(1, 2) + hidden_states = self.conv(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = hidden_states.transpose(1, 2) + return hidden_states + + +class FastSpeech2ConformerAttention(nn.Module): + """ + Multi-Head attention layer with relative position encoding. Details can be found in + https://github.com/espnet/espnet/pull/2816. Paper: https://arxiv.org/abs/1901.02860. + """ + + def __init__(self, config: FastSpeech2ConformerConfig, module_config): + """Construct an FastSpeech2ConformerAttention object.""" + super().__init__() + # We assume d_v always equals dim_key + self.num_heads = module_config["num_attention_heads"] + self.hidden_size = config.hidden_size + self.dim_key = self.hidden_size // self.num_heads + self.head_dim = self.hidden_size // self.num_heads + self.linear_q = nn.Linear(self.hidden_size, self.hidden_size) + self.linear_k = nn.Linear(self.hidden_size, self.hidden_size) + self.linear_v = nn.Linear(self.hidden_size, self.hidden_size) + self.linear_out = nn.Linear(self.hidden_size, self.hidden_size) + self.dropout = nn.Dropout(p=module_config["attention_dropout_rate"]) + + # linear transformation for positional encoding + self.linear_pos = nn.Linear(self.hidden_size, self.hidden_size, bias=False) + # these two learnable bias are used in matrix c and matrix d + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + self.pos_bias_u = nn.Parameter(torch.Tensor(self.num_heads, self.head_dim)) + self.pos_bias_v = nn.Parameter(torch.Tensor(self.num_heads, self.head_dim)) + + def shift_relative_position_tensor(self, pos_tensor): + """ + Args: + pos_tensor (torch.Tensor of shape (batch_size, head, time1, 2*time1-1)): Input tensor. + """ + zero_pad = torch.zeros((*pos_tensor.size()[:3], 1), device=pos_tensor.device, dtype=pos_tensor.dtype) + pos_tensor_padded = torch.cat([zero_pad, pos_tensor], dim=-1) + + pos_tensor_padded = pos_tensor_padded.view(*pos_tensor.size()[:2], pos_tensor.size(3) + 1, pos_tensor.size(2)) + # only keep the positions from 0 to time2 + pos_tensor = pos_tensor_padded[:, :, 1:].view_as(pos_tensor)[:, :, :, : pos_tensor.size(-1) // 2 + 1] + + return pos_tensor + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + pos_emb: Optional[torch.Tensor] = None, + output_attentions: Optional[torch.Tensor] = False, + ) -> Tuple[torch.Tensor, torch.Tensor]: + """ + Compute 'Scaled Dot Product Attention' with rel. positional encoding. + + Args: + hidden_states (`torch.Tensor` of shape `(batch, time2, size)`): Values of the hidden states + attention_mask (`torch.Tensor` of shape `(batch, time1, time2)`): Mask tensor. + pos_emb (`torch.Tensor` of shape `(batch, 2*time1-1, size)`): Positional embedding tensor. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + Returns: + `torch.Tensor`: Output tensor of shape `(batch, time1, d_model)`. + """ + bsz, q_len, _ = hidden_states.size() + query_states = self.linear_q(hidden_states).view(bsz, -1, self.num_heads, self.head_dim) + key_states = self.linear_k(hidden_states).view(bsz, -1, self.num_heads, self.head_dim) + value_states = self.linear_v(hidden_states).view(bsz, -1, self.num_heads, self.head_dim) + + bsz_pos = pos_emb.size(0) + pos_encoding = self.linear_pos(pos_emb).view(bsz_pos, -1, self.num_heads, self.head_dim) + + # (batch_size, head, time1, dim_key) + query_with_bias_u = (query_states + self.pos_bias_u).transpose(1, 2) + # (batch_size, head, time1, dim_key) + query_with_bias_v = (query_states + self.pos_bias_v).transpose(1, 2) + + # compute attention score + # first compute matrix a and matrix c + # as described in https://arxiv.org/abs/1901.02860 Section 3.3 + # (batch_size, head, time1, time2) + matrix_ac = torch.matmul(query_with_bias_u, key_states.permute(0, 2, 3, 1)) + + # compute matrix b and matrix d + # (batch_size, head, time1, 2*time1-1) + matrix_bd = torch.matmul(query_with_bias_v, pos_encoding.permute(0, 2, 3, 1)) + matrix_bd = self.shift_relative_position_tensor(matrix_bd) + + # (batch_size, head, time1, time2) + scores = (matrix_ac + matrix_bd) / math.sqrt(self.dim_key) + + # Forward attention + if attention_mask is not None: + expected_size = (bsz, 1, q_len) + if attention_mask.size() != expected_size: + raise ValueError(f"Attention mask should be of size {expected_size}, but is {attention_mask.size()}") + attention_mask = attention_mask.unsqueeze(1).eq(0) + min_value = float(torch.finfo(scores.dtype).min) + scores = scores.masked_fill(attention_mask, min_value) + attn_weights = torch.softmax(scores, dim=-1).masked_fill(attention_mask, 0.0) + else: + attn_weights = torch.softmax(scores, dim=-1) + + attn_weights = self.dropout(attn_weights) + attn_output = torch.matmul(attn_weights, value_states.transpose(1, 2)) + attn_output = attn_output.transpose(1, 2).contiguous().view(bsz, q_len, -1) + + attn_output = self.linear_out(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights + + +class FastSpeech2ConformerConvolutionModule(nn.Module): + def __init__(self, config: FastSpeech2ConformerConfig, module_config): + super().__init__() + # kernel_size should be an odd number for 'SAME' padding + channels = config.hidden_size + kernel_size = module_config["kernel_size"] + self.pointwise_conv1 = nn.Conv1d(channels, 2 * channels, kernel_size=1, stride=1, padding=0, bias=True) + self.depthwise_conv = nn.Conv1d( + channels, channels, kernel_size, stride=1, padding=(kernel_size - 1) // 2, groups=channels, bias=True + ) + self.norm = nn.BatchNorm1d(channels) + self.pointwise_conv2 = nn.Conv1d(channels, channels, kernel_size=1, stride=1, padding=0, bias=True) + + def forward(self, hidden_states): + """ + Compute convolution module. + + Args: + hidden_states (`torch.Tensor` of shape `(batch, time, channels)`): Input tensor. + + Returns: + `torch.Tensor`: Output tensor of shape `(batch, time, channels)`. + + """ + # exchange the temporal dimension and the feature dimension + hidden_states = hidden_states.transpose(1, 2) + + # GLU mechanism, (batch_size, 2*channel, dim) + hidden_states = self.pointwise_conv1(hidden_states) + # (batch_size, channel, dim) + hidden_states = nn.functional.glu(hidden_states, dim=1) + + # 1D Depthwise Conv + hidden_states = self.depthwise_conv(hidden_states) + hidden_states = self.norm(hidden_states) + + hidden_states = hidden_states * torch.sigmoid(hidden_states) + + hidden_states = self.pointwise_conv2(hidden_states) + + return hidden_states.transpose(1, 2) + + +class FastSpeech2ConformerEncoderLayer(nn.Module): + def __init__(self, config: FastSpeech2ConformerConfig, module_config): + super().__init__() + + # self-attention module definition + self.self_attn = FastSpeech2ConformerAttention(config, module_config) + + # feed-forward module definition + self.feed_forward = FastSpeech2ConformerMultiLayeredConv1d(config, module_config) + + self.macaron_style = config.use_macaron_style_in_conformer + if self.macaron_style: + self.feed_forward_macaron = FastSpeech2ConformerMultiLayeredConv1d(config, module_config) + self.ff_macaron_layer_norm = nn.LayerNorm(config.hidden_size) + self.ff_scale = 0.5 + else: + self.ff_scale = 1.0 + + # convolution module definition + self.use_cnn_module = config.use_cnn_in_conformer + if self.use_cnn_module: + self.conv_module = FastSpeech2ConformerConvolutionModule(config, module_config) + self.conv_layer_norm = nn.LayerNorm(config.hidden_size) + self.final_layer_norm = nn.LayerNorm(config.hidden_size) + + self.ff_layer_norm = nn.LayerNorm(config.hidden_size) + + self.self_attn_layer_norm = nn.LayerNorm(config.hidden_size) + + self.dropout = nn.Dropout(module_config["dropout_rate"]) + self.size = config.hidden_size + self.normalize_before = module_config["normalize_before"] + self.concat_after = module_config["concat_after"] + if self.concat_after: + self.concat_linear = nn.Linear(config.hidden_size + config.hidden_size, config.hidden_size) + + def forward( + self, + hidden_states: torch.Tensor, + pos_emb: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + output_attentions: Optional[torch.Tensor] = False, + ): + """ + Compute encoded features. + + Args: + hidden_states (`torch.Tensor` of shape `(batch, time, size)`): Input tensor. + pos_emb (`torch.Tensor` of shape `(1, time, size)`): Positional embeddings tensor. + attention_mask (`torch.Tensor` of shape `(batch, time)`): Attention mask tensor for the input. + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See `attentions` under + returned tensors for more detail. + Returns: + `torch.Tensor`: Output tensor of shape `(batch, time, size)`. + + """ + # whether to use macaron style + if self.macaron_style: + residual = hidden_states + if self.normalize_before: + hidden_states = self.ff_macaron_layer_norm(hidden_states) + hidden_states = residual + self.ff_scale * self.dropout(self.feed_forward_macaron(hidden_states)) + if not self.normalize_before: + hidden_states = self.ff_macaron_layer_norm(hidden_states) + + # multi-headed self-attention module + residual = hidden_states + if self.normalize_before: + hidden_states = self.self_attn_layer_norm(hidden_states) + + attention_output, attention_scores = self.self_attn( + hidden_states, attention_mask=attention_mask, pos_emb=pos_emb, output_attentions=output_attentions + ) + + if self.concat_after: + x_concat = torch.cat((hidden_states, attention_output), dim=-1) + hidden_states = self.concat_linear(x_concat) + hidden_states = residual + hidden_states + else: + hidden_states = self.dropout(attention_output) + hidden_states = residual + hidden_states + if not self.normalize_before: + hidden_states = self.self_attn_layer_norm(hidden_states) + + # convolution module + if self.use_cnn_module: + residual = hidden_states + if self.normalize_before: + hidden_states = self.conv_layer_norm(hidden_states) + hidden_states = self.conv_module(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = residual + hidden_states + if not self.normalize_before: + hidden_states = self.conv_layer_norm(hidden_states) + + # feed forward module + residual = hidden_states + if self.normalize_before: + hidden_states = self.ff_layer_norm(hidden_states) + hidden_states = self.feed_forward(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = residual + self.ff_scale * hidden_states + if not self.normalize_before: + hidden_states = self.ff_layer_norm(hidden_states) + + if self.conv_module is not None: + hidden_states = self.final_layer_norm(hidden_states) + + outputs = (hidden_states,) + + if output_attentions: + outputs += (attention_scores,) + + return outputs + + +class FastSpeech2ConformerMultiLayeredConv1d(nn.Module): + """ + Multi-layered conv1d for Transformer block. + + This is a module of multi-layered conv1d designed to replace positionwise feed-forward network in Transformer + block, which is introduced in 'FastSpeech: Fast, Robust and Controllable Text to Speech' + https://arxiv.org/pdf/1905.09263.pdf + """ + + def __init__(self, config: FastSpeech2ConformerConfig, module_config): + """ + Initialize FastSpeech2ConformerMultiLayeredConv1d module. + + Args: + input_channels (`int`): Number of input channels. + hidden_channels (`int`): Number of hidden channels. + kernel_size (`int`): Kernel size of conv1d. + dropout_rate (`float`): Dropout rate. + """ + super().__init__() + input_channels = config.hidden_size + hidden_channels = module_config["linear_units"] + kernel_size = config.positionwise_conv_kernel_size + self.conv1 = nn.Conv1d(input_channels, hidden_channels, kernel_size, stride=1, padding=(kernel_size - 1) // 2) + self.conv2 = nn.Conv1d(hidden_channels, input_channels, kernel_size, stride=1, padding=(kernel_size - 1) // 2) + self.dropout = nn.Dropout(module_config["dropout_rate"]) + + def forward(self, hidden_states): + """ + Calculate forward propagation. + + Args: + hidden_states (torch.Tensor): Batch of input tensors (batch_size, time, input_channels). + + Returns: + torch.Tensor: Batch of output tensors (batch_size, time, hidden_channels). + """ + hidden_states = hidden_states.transpose(-1, 1) + hidden_states = self.conv1(hidden_states) + hidden_states = torch.relu(hidden_states) + hidden_states = self.dropout(hidden_states) + hidden_states = self.conv2(hidden_states) + hidden_states = hidden_states.transpose(-1, 1) + return hidden_states + + +class FastSpeech2ConformerRelPositionalEncoding(nn.Module): + """ + Args: + Relative positional encoding module (new implementation). Details can be found in + https://github.com/espnet/espnet/pull/2816. See : Appendix Batch in https://arxiv.org/abs/1901.02860 + config (`FastSpeech2ConformerConfig`): + FastSpeech2ConformerConfig instance. + module_config (`dict`): + Dictionary containing the encoder or decoder module configuration from the `FastSpeech2ConformerConfig`. + """ + + def __init__(self, config: FastSpeech2ConformerConfig, module_config): + """ + Construct an PositionalEncoding object. + """ + super().__init__() + self.embed_dim = config.hidden_size + self.input_scale = math.sqrt(self.embed_dim) + self.dropout = nn.Dropout(p=module_config["positional_dropout_rate"]) + self.pos_enc = None + self.max_len = 5000 + self.extend_pos_enc(torch.tensor(0.0).expand(1, self.max_len)) + + def extend_pos_enc(self, x): + """Reset the positional encodings.""" + if self.pos_enc is not None: + # self.pos_enc contains both positive and negative parts + # the length of self.pos_enc is 2 * input_len - 1 + if self.pos_enc.size(1) >= x.size(1) * 2 - 1: + if self.pos_enc.dtype != x.dtype or self.pos_enc.device != x.device: + self.pos_enc = self.pos_enc.to(dtype=x.dtype, device=x.device) + return + # Suppose `i` means to the position of query vector and `j` means the + # position of key vector. We use position relative positions when keys + # are to the left (i>j) and negative relative positions otherwise (i