Upload configuration_diffusiondet.py

configuration_diffusiondet.py

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+from transformers import PretrainedConfig
+
+from transformers.models.auto import CONFIG_MAPPING
+from transformers.utils.backbone_utils import verify_backbone_config_arguments
+
+from transformers.utils import logging, PushToHubMixin
+
+logger = logging.get_logger(__name__)
+
+class DiffusionDetConfig(PretrainedConfig):
+
+    model_type = "diffusiondet"
+
+    def __init__(
+            self,
+            use_timm_backbone=True,
+            backbone_config=None,
+            num_channels=3,
+            pixel_mean=(123.675, 116.280, 103.530),
+            pixel_std=(58.395, 57.120, 57.375),
+            resnet_out_features=("res2", "res3", "res4", "res5"),
+            resnet_in_features=("res2", "res3", "res4", "res5"),
+            roi_head_in_features=("p2", "p3", "p4", "p5"),
+            fpn_out_channels=256,
+            pooler_resolution=7,
+            sampling_ratio=2,
+            num_proposals=300,
+            num_attn_heads=8,
+            dropout=0.0,
+            dim_feedforward=2048,
+            activation="relu",
+            hidden_dim=256,
+            num_cls=1,
+            num_reg=3,
+            num_heads=6,
+            num_dynamic=2,
+            dim_dynamic=64,
+            class_weight=2.0,
+            giou_weight=2.0,
+            l1_weight=5.0,
+            deep_supervision=True,
+            no_object_weight=0.1,
+            use_focal=True,
+            use_fed_loss=False,
+            alpha=0.25,
+            gamma=2.0,
+            prior_prob=0.01,
+            ota_k=5,
+            snr_scale=2.0,
+            sample_step=1,
+            use_nms=True,
+            swin_size="B",
+            use_swin_checkpoint=False,
+            swin_out_features=(0, 1, 2, 3),
+            optimizer="ADAMW",
+            backbone_multiplier=1.0,
+            backbone='resnet50',
+            use_pretrained_backbone=True,
+            backbone_kwargs=None,
+            dilation=False,
+            **kwargs
+    ):
+        # We default to values which were previously hard-coded in the model. This enables configurability of the config
+        # while keeping the default behavior the same.
+        if use_timm_backbone and backbone_kwargs is None:
+            backbone_kwargs = {}
+            if dilation:
+                backbone_kwargs["output_stride"] = 16
+            backbone_kwargs["out_indices"] = [1, 2, 3, 4]
+            backbone_kwargs["in_chans"] = num_channels
+        # Backwards compatibility
+        elif not use_timm_backbone and backbone in (None, "resnet50"):
+            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)
+            backbone = None
+            # set timm attributes to None
+            dilation = None
+
+        verify_backbone_config_arguments(
+            use_timm_backbone=use_timm_backbone,
+            use_pretrained_backbone=use_pretrained_backbone,
+            backbone=backbone,
+            backbone_config=backbone_config,
+            backbone_kwargs=backbone_kwargs,
+        )
+
+        # Auto mapping
+        self.auto_map = {
+            "AutoConfig": "diffusiondet.configuration_diffusiondet.DiffusionDetConfig",
+            "AutoModelForObjectDetection": "diffusiondet.modeling_diffusiondet.DiffusionDet"
+        }
+
+        # Backbone.
+        self.use_timm_backbone = use_timm_backbone
+        self.backbone_config = backbone_config
+        self.num_channels = num_channels
+        self.backbone = backbone
+        self.use_pretrained_backbone = use_pretrained_backbone
+        self.backbone_kwargs = backbone_kwargs
+        self.dilation = dilation
+        self.fpn_out_channels = fpn_out_channels
+
+        # Model.
+        self.pixel_mean = pixel_mean
+        self.pixel_std = pixel_std
+        self.resnet_out_features = resnet_out_features
+        self.resnet_in_features = resnet_in_features
+        self.roi_head_in_features = roi_head_in_features
+        self.pooler_resolution = pooler_resolution
+        self.sampling_ratio = sampling_ratio
+        self.num_proposals = num_proposals
+
+        # RCNN Head.
+        self.num_attn_heads = num_attn_heads
+        self.dropout = dropout
+        self.dim_feedforward = dim_feedforward
+        self.activation = activation
+        self.hidden_dim = hidden_dim
+        self.num_cls = num_cls
+        self.num_reg = num_reg
+        self.num_heads = num_heads
+
+        # Dynamic Conv.
+        self.num_dynamic = num_dynamic
+        self.dim_dynamic = dim_dynamic
+
+        # Loss.
+        self.class_weight = class_weight
+        self.giou_weight = giou_weight
+        self.l1_weight = l1_weight
+        self.deep_supervision = deep_supervision
+        self.no_object_weight = no_object_weight
+
+        # Focal Loss.
+        self.use_focal = use_focal
+        self.use_fed_loss = use_fed_loss
+        self.alpha = alpha
+        self.gamma = gamma
+        self.prior_prob = prior_prob
+
+        # Dynamic K
+        self.ota_k = ota_k
+
+        # Diffusion
+        self.snr_scale = snr_scale
+        self.sample_step = sample_step
+
+        # Inference
+        self.use_nms = use_nms
+
+        # Swin Backbones
+        self.swin_size = swin_size
+        self.use_swin_checkpoint = use_swin_checkpoint
+        self.swin_out_features = swin_out_features
+
+        # Optimizer.
+        self.optimizer = optimizer
+        self.backbone_multiplier = backbone_multiplier
+
+        self.num_labels = 80
+
+        super().__init__()