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mmedit.models.losses.clip_loss 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional

import torch
import torch.nn as nn

from mmedit.registry import MODELS
from mmedit.utils import try_import

[文档]clip = try_import('clip')
[文档]class CLIPLossModel(torch.nn.Module): """Wrapped clip model to calculate clip loss. Ref: https://github.com/orpatashnik/StyleCLIP/blob/main/criteria/clip_loss.py # noqa Args: in_size (int, optional): Input image size. Defaults to 1024. scale_factor (int, optional): Unsampling factor. Defaults to 7. pool_size (int, optional): Pooling output size. Defaults to 224. clip_type (str, optional): A model name listed by `clip.available_models()`, or the path to a model checkpoint containing the state_dict. For more details, you can refer to https://github.com/openai/CLIP/blob/573315e83f07b53a61ff5098757e8fc885f1703e/clip/clip.py#L91 # noqa Defaults to 'ViT-B/32'. """ def __init__(self, in_size: int = 1024, scale_factor: int = 7, pool_size: int = 224, clip_type: str = 'ViT-B/32') -> None: super(CLIPLossModel, self).__init__() try: import clip except ImportError: raise 'To use clip loss, openai clip need to be installed first' assert clip is not None, ( "Cannot import 'clip'. Please install 'clip' via " "\"pip install git+https://github.com/openai/CLIP.git\".") self.model, self.preprocess = clip.load(clip_type, device='cpu') self.upsample = torch.nn.Upsample(scale_factor=scale_factor) self.avg_pool = torch.nn.AvgPool2d( kernel_size=(scale_factor * in_size // pool_size))
[文档] def forward(self, image: torch.Tensor, text: torch.Tensor) -> torch.Tensor: """Forward function.""" assert image is not None assert text is not None image = self.avg_pool(self.upsample(image)) loss = 1 - self.model(image, text)[0] / 100 return loss
@MODELS.register_module()
[文档]class CLIPLoss(nn.Module): """Clip loss. In styleclip, this loss is used to optimize the latent code to generate image that match the text. In this loss, we may need to provide ``image``, ``text``. Thus, an example of the ``data_info`` is: .. code-block:: python :linenos: data_info = dict( image='fake_imgs', text='descriptions') Then, the module will automatically construct this mapping from the input data dictionary. Args: loss_weight (float, optional): Weight of this loss item. Defaults to ``1.``. data_info (dict, optional): Dictionary contains the mapping between loss input args and data dictionary. If ``None``, this module will directly pass the input data to the loss function. Defaults to None. clip_model (dict, optional): Kwargs for clip loss model. Defaults to dict(). loss_name (str, optional): Name of the loss item. If you want this loss item to be included into the backward graph, `loss_` must be the prefix of the name. Defaults to 'loss_clip'. """ def __init__(self, loss_weight: float = 1.0, data_info: Optional[dict] = None, clip_model: dict = dict(), loss_name: str = 'loss_clip') -> None: super(CLIPLoss, self).__init__() self.loss_weight = loss_weight self.data_info = data_info self.net = CLIPLossModel(**clip_model) self._loss_name = loss_name
[文档] def forward(self, image: torch.Tensor, text: torch.Tensor) -> torch.Tensor: """Forward function. If ``self.data_info`` is not ``None``, a dictionary containing all of the data and necessary modules should be passed into this function. If this dictionary is given as a non-keyword argument, it should be offered as the first argument. If you are using keyword argument, please name it as `outputs_dict`. If ``self.data_info`` is ``None``, the input argument or key-word argument will be directly passed to loss function, ``third_party_net_loss``. """ return self.net(image, text) * self.loss_weight
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