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# coding=utf-8 | |
# Copyright 2022 Microsoft Research 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 CvT model.""" | |
import collections.abc | |
from dataclasses import dataclass | |
from typing import Optional, Tuple, Union | |
import torch | |
import torch.utils.checkpoint | |
from torch import nn | |
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss | |
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward | |
from ...modeling_outputs import ImageClassifierOutputWithNoAttention, ModelOutput | |
from ...modeling_utils import PreTrainedModel, find_pruneable_heads_and_indices, prune_linear_layer | |
from ...utils import logging | |
from .configuration_cvt import CvtConfig | |
logger = logging.get_logger(__name__) | |
# General docstring | |
_CONFIG_FOR_DOC = "CvtConfig" | |
# Base docstring | |
_CHECKPOINT_FOR_DOC = "microsoft/cvt-13" | |
_EXPECTED_OUTPUT_SHAPE = [1, 384, 14, 14] | |
# Image classification docstring | |
_IMAGE_CLASS_CHECKPOINT = "microsoft/cvt-13" | |
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat" | |
CVT_PRETRAINED_MODEL_ARCHIVE_LIST = [ | |
"microsoft/cvt-13", | |
"microsoft/cvt-13-384", | |
"microsoft/cvt-13-384-22k", | |
"microsoft/cvt-21", | |
"microsoft/cvt-21-384", | |
"microsoft/cvt-21-384-22k", | |
# See all Cvt models at https://huggingface.co/models?filter=cvt | |
] | |
class BaseModelOutputWithCLSToken(ModelOutput): | |
""" | |
Base class for model's outputs, with potential hidden states and attentions. | |
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. | |
cls_token_value (`torch.FloatTensor` of shape `(batch_size, 1, hidden_size)`): | |
Classification token 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. | |
""" | |
last_hidden_state: torch.FloatTensor = None | |
cls_token_value: torch.FloatTensor = None | |
hidden_states: Optional[Tuple[torch.FloatTensor]] = None | |
# Copied from transformers.models.beit.modeling_beit.drop_path | |
def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor: | |
""" | |
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). | |
Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks, | |
however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... | |
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the | |
layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the | |
argument. | |
""" | |
if drop_prob == 0.0 or not training: | |
return input | |
keep_prob = 1 - drop_prob | |
shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets | |
random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device) | |
random_tensor.floor_() # binarize | |
output = input.div(keep_prob) * random_tensor | |
return output | |
# Copied from transformers.models.beit.modeling_beit.BeitDropPath | |
class CvtDropPath(nn.Module): | |
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" | |
def __init__(self, drop_prob: Optional[float] = None) -> None: | |
super().__init__() | |
self.drop_prob = drop_prob | |
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: | |
return drop_path(hidden_states, self.drop_prob, self.training) | |
def extra_repr(self) -> str: | |
return "p={}".format(self.drop_prob) | |
class CvtEmbeddings(nn.Module): | |
""" | |
Construct the CvT embeddings. | |
""" | |
def __init__(self, patch_size, num_channels, embed_dim, stride, padding, dropout_rate): | |
super().__init__() | |
self.convolution_embeddings = CvtConvEmbeddings( | |
patch_size=patch_size, num_channels=num_channels, embed_dim=embed_dim, stride=stride, padding=padding | |
) | |
self.dropout = nn.Dropout(dropout_rate) | |
def forward(self, pixel_values): | |
hidden_state = self.convolution_embeddings(pixel_values) | |
hidden_state = self.dropout(hidden_state) | |
return hidden_state | |
class CvtConvEmbeddings(nn.Module): | |
""" | |
Image to Conv Embedding. | |
""" | |
def __init__(self, patch_size, num_channels, embed_dim, stride, padding): | |
super().__init__() | |
patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size) | |
self.patch_size = patch_size | |
self.projection = nn.Conv2d(num_channels, embed_dim, kernel_size=patch_size, stride=stride, padding=padding) | |
self.normalization = nn.LayerNorm(embed_dim) | |
def forward(self, pixel_values): | |
pixel_values = self.projection(pixel_values) | |
batch_size, num_channels, height, width = pixel_values.shape | |
hidden_size = height * width | |
# rearrange "b c h w -> b (h w) c" | |
pixel_values = pixel_values.view(batch_size, num_channels, hidden_size).permute(0, 2, 1) | |
if self.normalization: | |
pixel_values = self.normalization(pixel_values) | |
# rearrange "b (h w) c" -> b c h w" | |
pixel_values = pixel_values.permute(0, 2, 1).view(batch_size, num_channels, height, width) | |
return pixel_values | |
class CvtSelfAttentionConvProjection(nn.Module): | |
def __init__(self, embed_dim, kernel_size, padding, stride): | |
super().__init__() | |
self.convolution = nn.Conv2d( | |
embed_dim, | |
embed_dim, | |
kernel_size=kernel_size, | |
padding=padding, | |
stride=stride, | |
bias=False, | |
groups=embed_dim, | |
) | |
self.normalization = nn.BatchNorm2d(embed_dim) | |
def forward(self, hidden_state): | |
hidden_state = self.convolution(hidden_state) | |
hidden_state = self.normalization(hidden_state) | |
return hidden_state | |
class CvtSelfAttentionLinearProjection(nn.Module): | |
def forward(self, hidden_state): | |
batch_size, num_channels, height, width = hidden_state.shape | |
hidden_size = height * width | |
# rearrange " b c h w -> b (h w) c" | |
hidden_state = hidden_state.view(batch_size, num_channels, hidden_size).permute(0, 2, 1) | |
return hidden_state | |
class CvtSelfAttentionProjection(nn.Module): | |
def __init__(self, embed_dim, kernel_size, padding, stride, projection_method="dw_bn"): | |
super().__init__() | |
if projection_method == "dw_bn": | |
self.convolution_projection = CvtSelfAttentionConvProjection(embed_dim, kernel_size, padding, stride) | |
self.linear_projection = CvtSelfAttentionLinearProjection() | |
def forward(self, hidden_state): | |
hidden_state = self.convolution_projection(hidden_state) | |
hidden_state = self.linear_projection(hidden_state) | |
return hidden_state | |
class CvtSelfAttention(nn.Module): | |
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, | |
with_cls_token=True, | |
**kwargs, | |
): | |
super().__init__() | |
self.scale = embed_dim**-0.5 | |
self.with_cls_token = with_cls_token | |
self.embed_dim = embed_dim | |
self.num_heads = num_heads | |
self.convolution_projection_query = CvtSelfAttentionProjection( | |
embed_dim, | |
kernel_size, | |
padding_q, | |
stride_q, | |
projection_method="linear" if qkv_projection_method == "avg" else qkv_projection_method, | |
) | |
self.convolution_projection_key = CvtSelfAttentionProjection( | |
embed_dim, kernel_size, padding_kv, stride_kv, projection_method=qkv_projection_method | |
) | |
self.convolution_projection_value = CvtSelfAttentionProjection( | |
embed_dim, kernel_size, padding_kv, stride_kv, projection_method=qkv_projection_method | |
) | |
self.projection_query = nn.Linear(embed_dim, embed_dim, bias=qkv_bias) | |
self.projection_key = nn.Linear(embed_dim, embed_dim, bias=qkv_bias) | |
self.projection_value = nn.Linear(embed_dim, embed_dim, bias=qkv_bias) | |
self.dropout = nn.Dropout(attention_drop_rate) | |
def rearrange_for_multi_head_attention(self, hidden_state): | |
batch_size, hidden_size, _ = hidden_state.shape | |
head_dim = self.embed_dim // self.num_heads | |
# rearrange 'b t (h d) -> b h t d' | |
return hidden_state.view(batch_size, hidden_size, self.num_heads, head_dim).permute(0, 2, 1, 3) | |
def forward(self, hidden_state, height, width): | |
if self.with_cls_token: | |
cls_token, hidden_state = torch.split(hidden_state, [1, height * width], 1) | |
batch_size, hidden_size, num_channels = hidden_state.shape | |
# rearrange "b (h w) c -> b c h w" | |
hidden_state = hidden_state.permute(0, 2, 1).view(batch_size, num_channels, height, width) | |
key = self.convolution_projection_key(hidden_state) | |
query = self.convolution_projection_query(hidden_state) | |
value = self.convolution_projection_value(hidden_state) | |
if self.with_cls_token: | |
query = torch.cat((cls_token, query), dim=1) | |
key = torch.cat((cls_token, key), dim=1) | |
value = torch.cat((cls_token, value), dim=1) | |
head_dim = self.embed_dim // self.num_heads | |
query = self.rearrange_for_multi_head_attention(self.projection_query(query)) | |
key = self.rearrange_for_multi_head_attention(self.projection_key(key)) | |
value = self.rearrange_for_multi_head_attention(self.projection_value(value)) | |
attention_score = torch.einsum("bhlk,bhtk->bhlt", [query, key]) * self.scale | |
attention_probs = torch.nn.functional.softmax(attention_score, dim=-1) | |
attention_probs = self.dropout(attention_probs) | |
context = torch.einsum("bhlt,bhtv->bhlv", [attention_probs, value]) | |
# rearrange"b h t d -> b t (h d)" | |
_, _, hidden_size, _ = context.shape | |
context = context.permute(0, 2, 1, 3).contiguous().view(batch_size, hidden_size, self.num_heads * head_dim) | |
return context | |
class CvtSelfOutput(nn.Module): | |
""" | |
The residual connection is defined in CvtLayer instead of here (as is the case with other models), due to the | |
layernorm applied before each block. | |
""" | |
def __init__(self, embed_dim, drop_rate): | |
super().__init__() | |
self.dense = nn.Linear(embed_dim, 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) | |
return hidden_state | |
class CvtAttention(nn.Module): | |
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, | |
with_cls_token=True, | |
): | |
super().__init__() | |
self.attention = CvtSelfAttention( | |
num_heads, | |
embed_dim, | |
kernel_size, | |
padding_q, | |
padding_kv, | |
stride_q, | |
stride_kv, | |
qkv_projection_method, | |
qkv_bias, | |
attention_drop_rate, | |
with_cls_token, | |
) | |
self.output = CvtSelfOutput(embed_dim, drop_rate) | |
self.pruned_heads = set() | |
def prune_heads(self, heads): | |
if len(heads) == 0: | |
return | |
heads, index = find_pruneable_heads_and_indices( | |
heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads | |
) | |
# Prune linear layers | |
self.attention.query = prune_linear_layer(self.attention.query, index) | |
self.attention.key = prune_linear_layer(self.attention.key, index) | |
self.attention.value = prune_linear_layer(self.attention.value, index) | |
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1) | |
# Update hyper params and store pruned heads | |
self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads) | |
self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads | |
self.pruned_heads = self.pruned_heads.union(heads) | |
def forward(self, hidden_state, height, width): | |
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. | |
""" | |
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) | |
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, | |
) | |
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() | |
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) | |