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| # SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. | |
| # SPDX-License-Identifier: Apache-2.0 | |
| # | |
| # 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. | |
| """The continuous image tokenizer with VAE or AE formulation for 2D data.""" | |
| from collections import OrderedDict, namedtuple | |
| import torch | |
| from loguru import logger as logging | |
| from torch import nn | |
| from cosmos_predict1.tokenizer.modules import ContinuousFormulation, DecoderType, EncoderType | |
| NetworkEval = namedtuple("NetworkEval", ["reconstructions", "posteriors", "latent"]) | |
| class ContinuousImageTokenizer(nn.Module): | |
| def __init__(self, z_channels: int, z_factor: int, latent_channels: int, **kwargs) -> None: | |
| super().__init__() | |
| self.name = kwargs.get("name", "ContinuousImageTokenizer") | |
| self.latent_channels = latent_channels | |
| encoder_name = kwargs.get("encoder", EncoderType.Default.name) | |
| self.encoder = EncoderType[encoder_name].value(z_channels=z_factor * z_channels, **kwargs) | |
| decoder_name = kwargs.get("decoder", DecoderType.Default.name) | |
| self.decoder = DecoderType[decoder_name].value(z_channels=z_channels, **kwargs) | |
| self.quant_conv = torch.nn.Conv2d(z_factor * z_channels, z_factor * latent_channels, 1) | |
| self.post_quant_conv = torch.nn.Conv2d(latent_channels, z_channels, 1) | |
| formulation_name = kwargs.get("formulation", ContinuousFormulation.AE.name) | |
| self.distribution = ContinuousFormulation[formulation_name].value() | |
| logging.info(f"{self.name} based on {formulation_name} formulation, with {kwargs}.") | |
| num_parameters = sum(param.numel() for param in self.parameters()) | |
| logging.info(f"model={self.name}, num_parameters={num_parameters:,}") | |
| logging.info(f"z_channels={z_channels}, latent_channels={self.latent_channels}.") | |
| def encoder_jit(self): | |
| return nn.Sequential( | |
| OrderedDict( | |
| [ | |
| ("encoder", self.encoder), | |
| ("quant_conv", self.quant_conv), | |
| ("distribution", self.distribution), | |
| ] | |
| ) | |
| ) | |
| def decoder_jit(self): | |
| return nn.Sequential( | |
| OrderedDict( | |
| [ | |
| ("post_quant_conv", self.post_quant_conv), | |
| ("decoder", self.decoder), | |
| ] | |
| ) | |
| ) | |
| def last_decoder_layer(self): | |
| return self.decoder.conv_out | |
| def encode(self, x): | |
| h = self.encoder(x) | |
| moments = self.quant_conv(h) | |
| return self.distribution(moments) | |
| def decode(self, z): | |
| z = self.post_quant_conv(z) | |
| dec = self.decoder(z) | |
| return dec | |
| def forward(self, input) -> dict[str, torch.Tensor] | NetworkEval: | |
| latent, posteriors = self.encode(input) | |
| dec = self.decode(latent) | |
| if self.training: | |
| return dict(reconstructions=dec, posteriors=posteriors, latent=latent) | |
| return NetworkEval(reconstructions=dec, posteriors=posteriors, latent=latent) | |