726 lines
28 KiB
Python
726 lines
28 KiB
Python
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# coding=utf-8
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# Copyright 2022 Microsoft Research and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" PyTorch CvT model."""
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import collections.abc
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from dataclasses import dataclass
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from typing import Optional, Tuple, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
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from ...modeling_outputs import ImageClassifierOutputWithNoAttention, ModelOutput
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from ...modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer
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from ...utils import logging
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from .configuration_cvt import CvtConfig
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logger = logging.get_logger(__name__)
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# General docstring
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_CONFIG_FOR_DOC = "CvtConfig"
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# Base docstring
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_CHECKPOINT_FOR_DOC = "microsoft/cvt-13"
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_EXPECTED_OUTPUT_SHAPE = [1, 384, 14, 14]
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# Image classification docstring
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_IMAGE_CLASS_CHECKPOINT = "microsoft/cvt-13"
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_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
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from ..deprecated._archive_maps import CVT_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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@dataclass
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class BaseModelOutputWithCLSToken(ModelOutput):
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"""
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Base class for model's outputs, with potential hidden states and attentions.
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Args:
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
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Sequence of hidden-states at the output of the last layer of the model.
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cls_token_value (`torch.FloatTensor` of shape `(batch_size, 1, hidden_size)`):
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Classification token at the output of the last layer of the model.
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `torch.FloatTensor` (one for the output of the embeddings + one for the output of each layer) of
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shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer
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plus the initial embedding outputs.
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"""
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last_hidden_state: torch.FloatTensor = None
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cls_token_value: torch.FloatTensor = None
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hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None
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# Copied from transformers.models.beit.modeling_beit.drop_path
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def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
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"""
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Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
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Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
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however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
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See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
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layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
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argument.
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"""
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if drop_prob == 0.0 or not training:
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return input
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keep_prob = 1 - drop_prob
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shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
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random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
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random_tensor.floor_() # binarize
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output = input.div(keep_prob) * random_tensor
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return output
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# Copied from transformers.models.beit.modeling_beit.BeitDropPath
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class CvtDropPath(nn.Module):
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"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
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def __init__(self, drop_prob: Optional[float] = None) -> None:
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super().__init__()
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self.drop_prob = drop_prob
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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return drop_path(hidden_states, self.drop_prob, self.training)
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def extra_repr(self) -> str:
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return "p={}".format(self.drop_prob)
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class CvtEmbeddings(nn.Module):
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"""
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Construct the CvT embeddings.
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"""
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def __init__(self, patch_size, num_channels, embed_dim, stride, padding, dropout_rate):
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super().__init__()
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self.convolution_embeddings = CvtConvEmbeddings(
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patch_size=patch_size, num_channels=num_channels, embed_dim=embed_dim, stride=stride, padding=padding
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)
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self.dropout = nn.Dropout(dropout_rate)
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def forward(self, pixel_values):
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hidden_state = self.convolution_embeddings(pixel_values)
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hidden_state = self.dropout(hidden_state)
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return hidden_state
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class CvtConvEmbeddings(nn.Module):
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"""
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Image to Conv Embedding.
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"""
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def __init__(self, patch_size, num_channels, embed_dim, stride, padding):
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super().__init__()
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patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
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self.patch_size = patch_size
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self.projection = nn.Conv2d(num_channels, embed_dim, kernel_size=patch_size, stride=stride, padding=padding)
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self.normalization = nn.LayerNorm(embed_dim)
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def forward(self, pixel_values):
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pixel_values = self.projection(pixel_values)
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batch_size, num_channels, height, width = pixel_values.shape
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hidden_size = height * width
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# rearrange "b c h w -> b (h w) c"
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pixel_values = pixel_values.view(batch_size, num_channels, hidden_size).permute(0, 2, 1)
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if self.normalization:
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pixel_values = self.normalization(pixel_values)
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# rearrange "b (h w) c" -> b c h w"
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pixel_values = pixel_values.permute(0, 2, 1).view(batch_size, num_channels, height, width)
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return pixel_values
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class CvtSelfAttentionConvProjection(nn.Module):
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def __init__(self, embed_dim, kernel_size, padding, stride):
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super().__init__()
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self.convolution = nn.Conv2d(
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embed_dim,
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embed_dim,
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kernel_size=kernel_size,
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padding=padding,
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stride=stride,
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bias=False,
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groups=embed_dim,
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)
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self.normalization = nn.BatchNorm2d(embed_dim)
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def forward(self, hidden_state):
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hidden_state = self.convolution(hidden_state)
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hidden_state = self.normalization(hidden_state)
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return hidden_state
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class CvtSelfAttentionLinearProjection(nn.Module):
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def forward(self, hidden_state):
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batch_size, num_channels, height, width = hidden_state.shape
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hidden_size = height * width
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# rearrange " b c h w -> b (h w) c"
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hidden_state = hidden_state.view(batch_size, num_channels, hidden_size).permute(0, 2, 1)
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return hidden_state
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class CvtSelfAttentionProjection(nn.Module):
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def __init__(self, embed_dim, kernel_size, padding, stride, projection_method="dw_bn"):
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super().__init__()
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if projection_method == "dw_bn":
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self.convolution_projection = CvtSelfAttentionConvProjection(embed_dim, kernel_size, padding, stride)
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self.linear_projection = CvtSelfAttentionLinearProjection()
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def forward(self, hidden_state):
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hidden_state = self.convolution_projection(hidden_state)
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hidden_state = self.linear_projection(hidden_state)
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return hidden_state
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class CvtSelfAttention(nn.Module):
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def __init__(
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self,
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num_heads,
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embed_dim,
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kernel_size,
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padding_q,
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padding_kv,
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stride_q,
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stride_kv,
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qkv_projection_method,
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qkv_bias,
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attention_drop_rate,
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with_cls_token=True,
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**kwargs,
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):
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super().__init__()
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self.scale = embed_dim**-0.5
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self.with_cls_token = with_cls_token
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self.embed_dim = embed_dim
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self.num_heads = num_heads
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self.convolution_projection_query = CvtSelfAttentionProjection(
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embed_dim,
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kernel_size,
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padding_q,
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stride_q,
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projection_method="linear" if qkv_projection_method == "avg" else qkv_projection_method,
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)
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self.convolution_projection_key = CvtSelfAttentionProjection(
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embed_dim, kernel_size, padding_kv, stride_kv, projection_method=qkv_projection_method
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)
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self.convolution_projection_value = CvtSelfAttentionProjection(
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embed_dim, kernel_size, padding_kv, stride_kv, projection_method=qkv_projection_method
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)
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self.projection_query = nn.Linear(embed_dim, embed_dim, bias=qkv_bias)
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self.projection_key = nn.Linear(embed_dim, embed_dim, bias=qkv_bias)
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self.projection_value = nn.Linear(embed_dim, embed_dim, bias=qkv_bias)
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self.dropout = nn.Dropout(attention_drop_rate)
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def rearrange_for_multi_head_attention(self, hidden_state):
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batch_size, hidden_size, _ = hidden_state.shape
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head_dim = self.embed_dim // self.num_heads
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# rearrange 'b t (h d) -> b h t d'
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return hidden_state.view(batch_size, hidden_size, self.num_heads, head_dim).permute(0, 2, 1, 3)
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def forward(self, hidden_state, height, width):
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if self.with_cls_token:
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cls_token, hidden_state = torch.split(hidden_state, [1, height * width], 1)
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batch_size, hidden_size, num_channels = hidden_state.shape
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# rearrange "b (h w) c -> b c h w"
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hidden_state = hidden_state.permute(0, 2, 1).view(batch_size, num_channels, height, width)
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key = self.convolution_projection_key(hidden_state)
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query = self.convolution_projection_query(hidden_state)
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value = self.convolution_projection_value(hidden_state)
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if self.with_cls_token:
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query = torch.cat((cls_token, query), dim=1)
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key = torch.cat((cls_token, key), dim=1)
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value = torch.cat((cls_token, value), dim=1)
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head_dim = self.embed_dim // self.num_heads
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query = self.rearrange_for_multi_head_attention(self.projection_query(query))
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key = self.rearrange_for_multi_head_attention(self.projection_key(key))
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value = self.rearrange_for_multi_head_attention(self.projection_value(value))
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attention_score = torch.einsum("bhlk,bhtk->bhlt", [query, key]) * self.scale
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attention_probs = torch.nn.functional.softmax(attention_score, dim=-1)
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attention_probs = self.dropout(attention_probs)
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context = torch.einsum("bhlt,bhtv->bhlv", [attention_probs, value])
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# rearrange"b h t d -> b t (h d)"
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_, _, hidden_size, _ = context.shape
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context = context.permute(0, 2, 1, 3).contiguous().view(batch_size, hidden_size, self.num_heads * head_dim)
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return context
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class CvtSelfOutput(nn.Module):
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"""
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The residual connection is defined in CvtLayer instead of here (as is the case with other models), due to the
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layernorm applied before each block.
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"""
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def __init__(self, embed_dim, drop_rate):
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super().__init__()
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self.dense = nn.Linear(embed_dim, embed_dim)
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self.dropout = nn.Dropout(drop_rate)
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def forward(self, hidden_state, input_tensor):
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hidden_state = self.dense(hidden_state)
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hidden_state = self.dropout(hidden_state)
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return hidden_state
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class CvtAttention(nn.Module):
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def __init__(
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self,
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num_heads,
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embed_dim,
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kernel_size,
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padding_q,
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padding_kv,
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stride_q,
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stride_kv,
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qkv_projection_method,
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qkv_bias,
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attention_drop_rate,
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drop_rate,
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with_cls_token=True,
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):
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super().__init__()
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self.attention = CvtSelfAttention(
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num_heads,
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embed_dim,
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kernel_size,
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padding_q,
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padding_kv,
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stride_q,
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stride_kv,
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qkv_projection_method,
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qkv_bias,
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attention_drop_rate,
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with_cls_token,
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)
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self.output = CvtSelfOutput(embed_dim, drop_rate)
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self.pruned_heads = set()
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def prune_heads(self, heads):
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if len(heads) == 0:
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return
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heads, index = find_pruneable_heads_and_indices(
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heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
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)
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# Prune linear layers
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self.attention.query = prune_linear_layer(self.attention.query, index)
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self.attention.key = prune_linear_layer(self.attention.key, index)
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self.attention.value = prune_linear_layer(self.attention.value, index)
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self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
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# Update hyper params and store pruned heads
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self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
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self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
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self.pruned_heads = self.pruned_heads.union(heads)
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def forward(self, hidden_state, height, width):
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self_output = self.attention(hidden_state, height, width)
|
||
|
|
attention_output = self.output(self_output, hidden_state)
|
||
|
|
return attention_output
|
||
|
|
|
||
|
|
|
||
|
|
class CvtIntermediate(nn.Module):
|
||
|
|
def __init__(self, embed_dim, mlp_ratio):
|
||
|
|
super().__init__()
|
||
|
|
self.dense = nn.Linear(embed_dim, int(embed_dim * mlp_ratio))
|
||
|
|
self.activation = nn.GELU()
|
||
|
|
|
||
|
|
def forward(self, hidden_state):
|
||
|
|
hidden_state = self.dense(hidden_state)
|
||
|
|
hidden_state = self.activation(hidden_state)
|
||
|
|
return hidden_state
|
||
|
|
|
||
|
|
|
||
|
|
class CvtOutput(nn.Module):
|
||
|
|
def __init__(self, embed_dim, mlp_ratio, drop_rate):
|
||
|
|
super().__init__()
|
||
|
|
self.dense = nn.Linear(int(embed_dim * mlp_ratio), embed_dim)
|
||
|
|
self.dropout = nn.Dropout(drop_rate)
|
||
|
|
|
||
|
|
def forward(self, hidden_state, input_tensor):
|
||
|
|
hidden_state = self.dense(hidden_state)
|
||
|
|
hidden_state = self.dropout(hidden_state)
|
||
|
|
hidden_state = hidden_state + input_tensor
|
||
|
|
return hidden_state
|
||
|
|
|
||
|
|
|
||
|
|
class CvtLayer(nn.Module):
|
||
|
|
"""
|
||
|
|
CvtLayer composed by attention layers, normalization and multi-layer perceptrons (mlps).
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
num_heads,
|
||
|
|
embed_dim,
|
||
|
|
kernel_size,
|
||
|
|
padding_q,
|
||
|
|
padding_kv,
|
||
|
|
stride_q,
|
||
|
|
stride_kv,
|
||
|
|
qkv_projection_method,
|
||
|
|
qkv_bias,
|
||
|
|
attention_drop_rate,
|
||
|
|
drop_rate,
|
||
|
|
mlp_ratio,
|
||
|
|
drop_path_rate,
|
||
|
|
with_cls_token=True,
|
||
|
|
):
|
||
|
|
super().__init__()
|
||
|
|
self.attention = CvtAttention(
|
||
|
|
num_heads,
|
||
|
|
embed_dim,
|
||
|
|
kernel_size,
|
||
|
|
padding_q,
|
||
|
|
padding_kv,
|
||
|
|
stride_q,
|
||
|
|
stride_kv,
|
||
|
|
qkv_projection_method,
|
||
|
|
qkv_bias,
|
||
|
|
attention_drop_rate,
|
||
|
|
drop_rate,
|
||
|
|
with_cls_token,
|
||
|
|
)
|
||
|
|
|
||
|
|
self.intermediate = CvtIntermediate(embed_dim, mlp_ratio)
|
||
|
|
self.output = CvtOutput(embed_dim, mlp_ratio, drop_rate)
|
||
|
|
self.drop_path = CvtDropPath(drop_prob=drop_path_rate) if drop_path_rate > 0.0 else nn.Identity()
|
||
|
|
self.layernorm_before = nn.LayerNorm(embed_dim)
|
||
|
|
self.layernorm_after = nn.LayerNorm(embed_dim)
|
||
|
|
|
||
|
|
def forward(self, hidden_state, height, width):
|
||
|
|
self_attention_output = self.attention(
|
||
|
|
self.layernorm_before(hidden_state), # in Cvt, layernorm is applied before self-attention
|
||
|
|
height,
|
||
|
|
width,
|
||
|
|
)
|
||
|
|
attention_output = self_attention_output
|
||
|
|
attention_output = self.drop_path(attention_output)
|
||
|
|
|
||
|
|
# first residual connection
|
||
|
|
hidden_state = attention_output + hidden_state
|
||
|
|
|
||
|
|
# in Cvt, layernorm is also applied after self-attention
|
||
|
|
layer_output = self.layernorm_after(hidden_state)
|
||
|
|
layer_output = self.intermediate(layer_output)
|
||
|
|
|
||
|
|
# second residual connection is done here
|
||
|
|
layer_output = self.output(layer_output, hidden_state)
|
||
|
|
layer_output = self.drop_path(layer_output)
|
||
|
|
return layer_output
|
||
|
|
|
||
|
|
|
||
|
|
class CvtStage(nn.Module):
|
||
|
|
def __init__(self, config, stage):
|
||
|
|
super().__init__()
|
||
|
|
self.config = config
|
||
|
|
self.stage = stage
|
||
|
|
if self.config.cls_token[self.stage]:
|
||
|
|
self.cls_token = nn.Parameter(torch.randn(1, 1, self.config.embed_dim[-1]))
|
||
|
|
|
||
|
|
self.embedding = CvtEmbeddings(
|
||
|
|
patch_size=config.patch_sizes[self.stage],
|
||
|
|
stride=config.patch_stride[self.stage],
|
||
|
|
num_channels=config.num_channels if self.stage == 0 else config.embed_dim[self.stage - 1],
|
||
|
|
embed_dim=config.embed_dim[self.stage],
|
||
|
|
padding=config.patch_padding[self.stage],
|
||
|
|
dropout_rate=config.drop_rate[self.stage],
|
||
|
|
)
|
||
|
|
|
||
|
|
drop_path_rates = [x.item() for x in torch.linspace(0, config.drop_path_rate[self.stage], config.depth[stage])]
|
||
|
|
|
||
|
|
self.layers = nn.Sequential(
|
||
|
|
*[
|
||
|
|
CvtLayer(
|
||
|
|
num_heads=config.num_heads[self.stage],
|
||
|
|
embed_dim=config.embed_dim[self.stage],
|
||
|
|
kernel_size=config.kernel_qkv[self.stage],
|
||
|
|
padding_q=config.padding_q[self.stage],
|
||
|
|
padding_kv=config.padding_kv[self.stage],
|
||
|
|
stride_kv=config.stride_kv[self.stage],
|
||
|
|
stride_q=config.stride_q[self.stage],
|
||
|
|
qkv_projection_method=config.qkv_projection_method[self.stage],
|
||
|
|
qkv_bias=config.qkv_bias[self.stage],
|
||
|
|
attention_drop_rate=config.attention_drop_rate[self.stage],
|
||
|
|
drop_rate=config.drop_rate[self.stage],
|
||
|
|
drop_path_rate=drop_path_rates[self.stage],
|
||
|
|
mlp_ratio=config.mlp_ratio[self.stage],
|
||
|
|
with_cls_token=config.cls_token[self.stage],
|
||
|
|
)
|
||
|
|
for _ in range(config.depth[self.stage])
|
||
|
|
]
|
||
|
|
)
|
||
|
|
|
||
|
|
def forward(self, hidden_state):
|
||
|
|
cls_token = None
|
||
|
|
hidden_state = self.embedding(hidden_state)
|
||
|
|
batch_size, num_channels, height, width = hidden_state.shape
|
||
|
|
# rearrange b c h w -> b (h w) c"
|
||
|
|
hidden_state = hidden_state.view(batch_size, num_channels, height * width).permute(0, 2, 1)
|
||
|
|
if self.config.cls_token[self.stage]:
|
||
|
|
cls_token = self.cls_token.expand(batch_size, -1, -1)
|
||
|
|
hidden_state = torch.cat((cls_token, hidden_state), dim=1)
|
||
|
|
|
||
|
|
for layer in self.layers:
|
||
|
|
layer_outputs = layer(hidden_state, height, width)
|
||
|
|
hidden_state = layer_outputs
|
||
|
|
|
||
|
|
if self.config.cls_token[self.stage]:
|
||
|
|
cls_token, hidden_state = torch.split(hidden_state, [1, height * width], 1)
|
||
|
|
hidden_state = hidden_state.permute(0, 2, 1).view(batch_size, num_channels, height, width)
|
||
|
|
return hidden_state, cls_token
|
||
|
|
|
||
|
|
|
||
|
|
class CvtEncoder(nn.Module):
|
||
|
|
def __init__(self, config):
|
||
|
|
super().__init__()
|
||
|
|
self.config = config
|
||
|
|
self.stages = nn.ModuleList([])
|
||
|
|
for stage_idx in range(len(config.depth)):
|
||
|
|
self.stages.append(CvtStage(config, stage_idx))
|
||
|
|
|
||
|
|
def forward(self, pixel_values, output_hidden_states=False, return_dict=True):
|
||
|
|
all_hidden_states = () if output_hidden_states else None
|
||
|
|
hidden_state = pixel_values
|
||
|
|
|
||
|
|
cls_token = None
|
||
|
|
for _, (stage_module) in enumerate(self.stages):
|
||
|
|
hidden_state, cls_token = stage_module(hidden_state)
|
||
|
|
if output_hidden_states:
|
||
|
|
all_hidden_states = all_hidden_states + (hidden_state,)
|
||
|
|
|
||
|
|
if not return_dict:
|
||
|
|
return tuple(v for v in [hidden_state, cls_token, all_hidden_states] if v is not None)
|
||
|
|
|
||
|
|
return BaseModelOutputWithCLSToken(
|
||
|
|
last_hidden_state=hidden_state,
|
||
|
|
cls_token_value=cls_token,
|
||
|
|
hidden_states=all_hidden_states,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
class CvtPreTrainedModel(PreTrainedModel):
|
||
|
|
"""
|
||
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
||
|
|
models.
|
||
|
|
"""
|
||
|
|
|
||
|
|
config_class = CvtConfig
|
||
|
|
base_model_prefix = "cvt"
|
||
|
|
main_input_name = "pixel_values"
|
||
|
|
|
||
|
|
def _init_weights(self, module):
|
||
|
|
"""Initialize the weights"""
|
||
|
|
if isinstance(module, (nn.Linear, nn.Conv2d)):
|
||
|
|
module.weight.data = nn.init.trunc_normal_(module.weight.data, mean=0.0, std=self.config.initializer_range)
|
||
|
|
if module.bias is not None:
|
||
|
|
module.bias.data.zero_()
|
||
|
|
elif isinstance(module, nn.LayerNorm):
|
||
|
|
module.bias.data.zero_()
|
||
|
|
module.weight.data.fill_(1.0)
|
||
|
|
elif isinstance(module, CvtStage):
|
||
|
|
if self.config.cls_token[module.stage]:
|
||
|
|
module.cls_token.data = nn.init.trunc_normal_(
|
||
|
|
torch.zeros(1, 1, self.config.embed_dim[-1]), mean=0.0, std=self.config.initializer_range
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
CVT_START_DOCSTRING = r"""
|
||
|
|
This model is 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 ([`CvtConfig`]): 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.
|
||
|
|
"""
|
||
|
|
|
||
|
|
CVT_INPUTS_DOCSTRING = r"""
|
||
|
|
Args:
|
||
|
|
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
|
||
|
|
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`CvtImageProcessor.__call__`]
|
||
|
|
for details.
|
||
|
|
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 [`~file_utils.ModelOutput`] instead of a plain tuple.
|
||
|
|
"""
|
||
|
|
|
||
|
|
|
||
|
|
@add_start_docstrings(
|
||
|
|
"The bare Cvt Model transformer outputting raw hidden-states without any specific head on top.",
|
||
|
|
CVT_START_DOCSTRING,
|
||
|
|
)
|
||
|
|
class CvtModel(CvtPreTrainedModel):
|
||
|
|
def __init__(self, config, add_pooling_layer=True):
|
||
|
|
super().__init__(config)
|
||
|
|
self.config = config
|
||
|
|
self.encoder = CvtEncoder(config)
|
||
|
|
self.post_init()
|
||
|
|
|
||
|
|
def _prune_heads(self, heads_to_prune):
|
||
|
|
"""
|
||
|
|
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
|
||
|
|
class PreTrainedModel
|
||
|
|
"""
|
||
|
|
for layer, heads in heads_to_prune.items():
|
||
|
|
self.encoder.layer[layer].attention.prune_heads(heads)
|
||
|
|
|
||
|
|
@add_start_docstrings_to_model_forward(CVT_INPUTS_DOCSTRING)
|
||
|
|
@add_code_sample_docstrings(
|
||
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
||
|
|
output_type=BaseModelOutputWithCLSToken,
|
||
|
|
config_class=_CONFIG_FOR_DOC,
|
||
|
|
modality="vision",
|
||
|
|
expected_output=_EXPECTED_OUTPUT_SHAPE,
|
||
|
|
)
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
pixel_values: Optional[torch.Tensor] = None,
|
||
|
|
output_hidden_states: Optional[bool] = None,
|
||
|
|
return_dict: Optional[bool] = None,
|
||
|
|
) -> Union[Tuple, BaseModelOutputWithCLSToken]:
|
||
|
|
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 pixel_values is None:
|
||
|
|
raise ValueError("You have to specify pixel_values")
|
||
|
|
|
||
|
|
encoder_outputs = self.encoder(
|
||
|
|
pixel_values,
|
||
|
|
output_hidden_states=output_hidden_states,
|
||
|
|
return_dict=return_dict,
|
||
|
|
)
|
||
|
|
sequence_output = encoder_outputs[0]
|
||
|
|
|
||
|
|
if not return_dict:
|
||
|
|
return (sequence_output,) + encoder_outputs[1:]
|
||
|
|
|
||
|
|
return BaseModelOutputWithCLSToken(
|
||
|
|
last_hidden_state=sequence_output,
|
||
|
|
cls_token_value=encoder_outputs.cls_token_value,
|
||
|
|
hidden_states=encoder_outputs.hidden_states,
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@add_start_docstrings(
|
||
|
|
"""
|
||
|
|
Cvt Model transformer with an image classification head on top (a linear layer on top of the final hidden state of
|
||
|
|
the [CLS] token) e.g. for ImageNet.
|
||
|
|
""",
|
||
|
|
CVT_START_DOCSTRING,
|
||
|
|
)
|
||
|
|
class CvtForImageClassification(CvtPreTrainedModel):
|
||
|
|
def __init__(self, config):
|
||
|
|
super().__init__(config)
|
||
|
|
|
||
|
|
self.num_labels = config.num_labels
|
||
|
|
self.cvt = CvtModel(config, add_pooling_layer=False)
|
||
|
|
self.layernorm = nn.LayerNorm(config.embed_dim[-1])
|
||
|
|
# Classifier head
|
||
|
|
self.classifier = (
|
||
|
|
nn.Linear(config.embed_dim[-1], config.num_labels) if config.num_labels > 0 else nn.Identity()
|
||
|
|
)
|
||
|
|
|
||
|
|
# Initialize weights and apply final processing
|
||
|
|
self.post_init()
|
||
|
|
|
||
|
|
@add_start_docstrings_to_model_forward(CVT_INPUTS_DOCSTRING)
|
||
|
|
@add_code_sample_docstrings(
|
||
|
|
checkpoint=_IMAGE_CLASS_CHECKPOINT,
|
||
|
|
output_type=ImageClassifierOutputWithNoAttention,
|
||
|
|
config_class=_CONFIG_FOR_DOC,
|
||
|
|
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
|
||
|
|
)
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
pixel_values: Optional[torch.Tensor] = None,
|
||
|
|
labels: Optional[torch.Tensor] = None,
|
||
|
|
output_hidden_states: Optional[bool] = None,
|
||
|
|
return_dict: Optional[bool] = None,
|
||
|
|
) -> Union[Tuple, ImageClassifierOutputWithNoAttention]:
|
||
|
|
r"""
|
||
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
||
|
|
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
|
||
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
||
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
||
|
|
"""
|
||
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||
|
|
outputs = self.cvt(
|
||
|
|
pixel_values,
|
||
|
|
output_hidden_states=output_hidden_states,
|
||
|
|
return_dict=return_dict,
|
||
|
|
)
|
||
|
|
|
||
|
|
sequence_output = outputs[0]
|
||
|
|
cls_token = outputs[1]
|
||
|
|
if self.config.cls_token[-1]:
|
||
|
|
sequence_output = self.layernorm(cls_token)
|
||
|
|
else:
|
||
|
|
batch_size, num_channels, height, width = sequence_output.shape
|
||
|
|
# rearrange "b c h w -> b (h w) c"
|
||
|
|
sequence_output = sequence_output.view(batch_size, num_channels, height * width).permute(0, 2, 1)
|
||
|
|
sequence_output = self.layernorm(sequence_output)
|
||
|
|
|
||
|
|
sequence_output_mean = sequence_output.mean(dim=1)
|
||
|
|
logits = self.classifier(sequence_output_mean)
|
||
|
|
|
||
|
|
loss = None
|
||
|
|
if labels is not None:
|
||
|
|
if self.config.problem_type is None:
|
||
|
|
if self.config.num_labels == 1:
|
||
|
|
self.config.problem_type = "regression"
|
||
|
|
elif self.config.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
||
|
|
self.config.problem_type = "single_label_classification"
|
||
|
|
else:
|
||
|
|
self.config.problem_type = "multi_label_classification"
|
||
|
|
|
||
|
|
if self.config.problem_type == "regression":
|
||
|
|
loss_fct = MSELoss()
|
||
|
|
if self.config.num_labels == 1:
|
||
|
|
loss = loss_fct(logits.squeeze(), labels.squeeze())
|
||
|
|
else:
|
||
|
|
loss = loss_fct(logits, labels)
|
||
|
|
elif self.config.problem_type == "single_label_classification":
|
||
|
|
loss_fct = CrossEntropyLoss()
|
||
|
|
loss = loss_fct(logits.view(-1, self.config.num_labels), labels.view(-1))
|
||
|
|
elif self.config.problem_type == "multi_label_classification":
|
||
|
|
loss_fct = BCEWithLogitsLoss()
|
||
|
|
loss = loss_fct(logits, labels)
|
||
|
|
|
||
|
|
if not return_dict:
|
||
|
|
output = (logits,) + outputs[2:]
|
||
|
|
return ((loss,) + output) if loss is not None else output
|
||
|
|
|
||
|
|
return ImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
|