432 lines
16 KiB
Python
432 lines
16 KiB
Python
# coding=utf-8
|
|
# Copyright 2024 the Fast authors 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 TextNet model."""
|
|
|
|
from typing import Any, Optional, Union
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
from torch import Tensor
|
|
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
|
|
|
from transformers import PreTrainedModel
|
|
from transformers.activations import ACT2CLS
|
|
from transformers.modeling_outputs import (
|
|
BackboneOutput,
|
|
BaseModelOutputWithNoAttention,
|
|
BaseModelOutputWithPoolingAndNoAttention,
|
|
ImageClassifierOutputWithNoAttention,
|
|
)
|
|
from transformers.models.textnet.configuration_textnet import TextNetConfig
|
|
from transformers.utils import logging
|
|
from transformers.utils.backbone_utils import BackboneMixin
|
|
|
|
from ...utils import auto_docstring
|
|
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
|
|
class TextNetConvLayer(nn.Module):
|
|
def __init__(self, config: TextNetConfig):
|
|
super().__init__()
|
|
|
|
self.kernel_size = config.stem_kernel_size
|
|
self.stride = config.stem_stride
|
|
self.activation_function = config.stem_act_func
|
|
|
|
padding = (
|
|
(config.kernel_size[0] // 2, config.kernel_size[1] // 2)
|
|
if isinstance(config.stem_kernel_size, tuple)
|
|
else config.stem_kernel_size // 2
|
|
)
|
|
|
|
self.conv = nn.Conv2d(
|
|
config.stem_num_channels,
|
|
config.stem_out_channels,
|
|
kernel_size=config.stem_kernel_size,
|
|
stride=config.stem_stride,
|
|
padding=padding,
|
|
bias=False,
|
|
)
|
|
self.batch_norm = nn.BatchNorm2d(config.stem_out_channels, config.batch_norm_eps)
|
|
|
|
self.activation = nn.Identity()
|
|
if self.activation_function is not None:
|
|
self.activation = ACT2CLS[self.activation_function]()
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.conv(hidden_states)
|
|
hidden_states = self.batch_norm(hidden_states)
|
|
return self.activation(hidden_states)
|
|
|
|
|
|
class TextNetRepConvLayer(nn.Module):
|
|
r"""
|
|
This layer supports re-parameterization by combining multiple convolutional branches
|
|
(e.g., main convolution, vertical, horizontal, and identity branches) during training.
|
|
At inference time, these branches can be collapsed into a single convolution for
|
|
efficiency, as per the re-parameterization paradigm.
|
|
|
|
The "Rep" in the name stands for "re-parameterization" (introduced by RepVGG).
|
|
"""
|
|
|
|
def __init__(self, config: TextNetConfig, in_channels: int, out_channels: int, kernel_size: int, stride: int):
|
|
super().__init__()
|
|
|
|
self.num_channels = in_channels
|
|
self.out_channels = out_channels
|
|
self.kernel_size = kernel_size
|
|
self.stride = stride
|
|
|
|
padding = ((kernel_size[0] - 1) // 2, (kernel_size[1] - 1) // 2)
|
|
|
|
self.activation_function = nn.ReLU()
|
|
|
|
self.main_conv = nn.Conv2d(
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=kernel_size,
|
|
stride=stride,
|
|
padding=padding,
|
|
bias=False,
|
|
)
|
|
self.main_batch_norm = nn.BatchNorm2d(num_features=out_channels, eps=config.batch_norm_eps)
|
|
|
|
vertical_padding = ((kernel_size[0] - 1) // 2, 0)
|
|
horizontal_padding = (0, (kernel_size[1] - 1) // 2)
|
|
|
|
if kernel_size[1] != 1:
|
|
self.vertical_conv = nn.Conv2d(
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=(kernel_size[0], 1),
|
|
stride=stride,
|
|
padding=vertical_padding,
|
|
bias=False,
|
|
)
|
|
self.vertical_batch_norm = nn.BatchNorm2d(num_features=out_channels, eps=config.batch_norm_eps)
|
|
else:
|
|
self.vertical_conv, self.vertical_batch_norm = None, None
|
|
|
|
if kernel_size[0] != 1:
|
|
self.horizontal_conv = nn.Conv2d(
|
|
in_channels=in_channels,
|
|
out_channels=out_channels,
|
|
kernel_size=(1, kernel_size[1]),
|
|
stride=stride,
|
|
padding=horizontal_padding,
|
|
bias=False,
|
|
)
|
|
self.horizontal_batch_norm = nn.BatchNorm2d(num_features=out_channels, eps=config.batch_norm_eps)
|
|
else:
|
|
self.horizontal_conv, self.horizontal_batch_norm = None, None
|
|
|
|
self.rbr_identity = (
|
|
nn.BatchNorm2d(num_features=in_channels, eps=config.batch_norm_eps)
|
|
if out_channels == in_channels and stride == 1
|
|
else None
|
|
)
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
main_outputs = self.main_conv(hidden_states)
|
|
main_outputs = self.main_batch_norm(main_outputs)
|
|
|
|
# applies a convolution with a vertical kernel
|
|
if self.vertical_conv is not None:
|
|
vertical_outputs = self.vertical_conv(hidden_states)
|
|
vertical_outputs = self.vertical_batch_norm(vertical_outputs)
|
|
main_outputs = main_outputs + vertical_outputs
|
|
|
|
# applies a convolution with a horizontal kernel
|
|
if self.horizontal_conv is not None:
|
|
horizontal_outputs = self.horizontal_conv(hidden_states)
|
|
horizontal_outputs = self.horizontal_batch_norm(horizontal_outputs)
|
|
main_outputs = main_outputs + horizontal_outputs
|
|
|
|
if self.rbr_identity is not None:
|
|
id_out = self.rbr_identity(hidden_states)
|
|
main_outputs = main_outputs + id_out
|
|
|
|
return self.activation_function(main_outputs)
|
|
|
|
|
|
class TextNetStage(nn.Module):
|
|
def __init__(self, config: TextNetConfig, depth: int):
|
|
super().__init__()
|
|
kernel_size = config.conv_layer_kernel_sizes[depth]
|
|
stride = config.conv_layer_strides[depth]
|
|
|
|
num_layers = len(kernel_size)
|
|
stage_in_channel_size = config.hidden_sizes[depth]
|
|
stage_out_channel_size = config.hidden_sizes[depth + 1]
|
|
|
|
in_channels = [stage_in_channel_size] + [stage_out_channel_size] * (num_layers - 1)
|
|
out_channels = [stage_out_channel_size] * num_layers
|
|
|
|
stage = []
|
|
for stage_config in zip(in_channels, out_channels, kernel_size, stride):
|
|
stage.append(TextNetRepConvLayer(config, *stage_config))
|
|
self.stage = nn.ModuleList(stage)
|
|
|
|
def forward(self, hidden_state):
|
|
for block in self.stage:
|
|
hidden_state = block(hidden_state)
|
|
return hidden_state
|
|
|
|
|
|
class TextNetEncoder(nn.Module):
|
|
def __init__(self, config: TextNetConfig):
|
|
super().__init__()
|
|
|
|
stages = []
|
|
num_stages = len(config.conv_layer_kernel_sizes)
|
|
for stage_ix in range(num_stages):
|
|
stages.append(TextNetStage(config, stage_ix))
|
|
|
|
self.stages = nn.ModuleList(stages)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_state: torch.Tensor,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> BaseModelOutputWithNoAttention:
|
|
hidden_states = [hidden_state]
|
|
for stage in self.stages:
|
|
hidden_state = stage(hidden_state)
|
|
hidden_states.append(hidden_state)
|
|
|
|
if not return_dict:
|
|
output = (hidden_state,)
|
|
return output + (hidden_states,) if output_hidden_states else output
|
|
|
|
return BaseModelOutputWithNoAttention(last_hidden_state=hidden_state, hidden_states=hidden_states)
|
|
|
|
|
|
@auto_docstring
|
|
class TextNetPreTrainedModel(PreTrainedModel):
|
|
config: TextNetConfig
|
|
base_model_prefix = "textnet"
|
|
main_input_name = "pixel_values"
|
|
|
|
def _init_weights(self, module):
|
|
if isinstance(module, (nn.Linear, nn.Conv2d)):
|
|
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.BatchNorm2d):
|
|
module.weight.data.fill_(1.0)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
|
|
|
|
@auto_docstring
|
|
class TextNetModel(TextNetPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.stem = TextNetConvLayer(config)
|
|
self.encoder = TextNetEncoder(config)
|
|
self.pooler = nn.AdaptiveAvgPool2d((2, 2))
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self, pixel_values: Tensor, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None
|
|
) -> Union[tuple[Any, list[Any]], tuple[Any], BaseModelOutputWithPoolingAndNoAttention]:
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
|
|
hidden_state = self.stem(pixel_values)
|
|
|
|
encoder_outputs = self.encoder(
|
|
hidden_state, output_hidden_states=output_hidden_states, return_dict=return_dict
|
|
)
|
|
|
|
last_hidden_state = encoder_outputs[0]
|
|
pooled_output = self.pooler(last_hidden_state)
|
|
|
|
if not return_dict:
|
|
output = (last_hidden_state, pooled_output)
|
|
return output + (encoder_outputs[1],) if output_hidden_states else output
|
|
|
|
return BaseModelOutputWithPoolingAndNoAttention(
|
|
last_hidden_state=last_hidden_state,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs[1] if output_hidden_states else None,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
TextNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
|
|
ImageNet.
|
|
"""
|
|
)
|
|
class TextNetForImageClassification(TextNetPreTrainedModel):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.textnet = TextNetModel(config)
|
|
self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
|
|
self.flatten = nn.Flatten()
|
|
self.fc = nn.Linear(config.hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity()
|
|
|
|
# classification head
|
|
self.classifier = nn.ModuleList([self.avg_pool, self.flatten])
|
|
|
|
# initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.FloatTensor] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> 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).
|
|
|
|
Examples:
|
|
```python
|
|
>>> import torch
|
|
>>> import requests
|
|
>>> from transformers import TextNetForImageClassification, TextNetImageProcessor
|
|
>>> from PIL import Image
|
|
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
|
|
>>> processor = TextNetImageProcessor.from_pretrained("czczup/textnet-base")
|
|
>>> model = TextNetForImageClassification.from_pretrained("czczup/textnet-base")
|
|
|
|
>>> inputs = processor(images=image, return_tensors="pt")
|
|
>>> with torch.no_grad():
|
|
... outputs = model(**inputs)
|
|
>>> outputs.logits.shape
|
|
torch.Size([1, 2])
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.textnet(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
|
|
last_hidden_state = outputs[0]
|
|
for layer in self.classifier:
|
|
last_hidden_state = layer(last_hidden_state)
|
|
logits = self.fc(last_hidden_state)
|
|
loss = None
|
|
|
|
if labels is not None:
|
|
if self.config.problem_type is None:
|
|
if self.num_labels == 1:
|
|
self.config.problem_type = "regression"
|
|
elif self.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.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.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)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
TextNet backbone, to be used with frameworks like DETR and MaskFormer.
|
|
"""
|
|
)
|
|
class TextNetBackbone(TextNetPreTrainedModel, BackboneMixin):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
super()._init_backbone(config)
|
|
|
|
self.textnet = TextNetModel(config)
|
|
self.num_features = config.hidden_sizes
|
|
|
|
# initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self, pixel_values: Tensor, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None
|
|
) -> Union[tuple[tuple], BackboneOutput]:
|
|
r"""
|
|
Examples:
|
|
|
|
```python
|
|
>>> import torch
|
|
>>> import requests
|
|
>>> from PIL import Image
|
|
>>> from transformers import AutoImageProcessor, AutoBackbone
|
|
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
|
|
>>> processor = AutoImageProcessor.from_pretrained("czczup/textnet-base")
|
|
>>> model = AutoBackbone.from_pretrained("czczup/textnet-base")
|
|
|
|
>>> inputs = processor(image, return_tensors="pt")
|
|
>>> with torch.no_grad():
|
|
>>> outputs = model(**inputs)
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
|
|
outputs = self.textnet(pixel_values, output_hidden_states=True, return_dict=return_dict)
|
|
|
|
hidden_states = outputs.hidden_states if return_dict else outputs[2]
|
|
|
|
feature_maps = ()
|
|
for idx, stage in enumerate(self.stage_names):
|
|
if stage in self.out_features:
|
|
feature_maps += (hidden_states[idx],)
|
|
|
|
if not return_dict:
|
|
output = (feature_maps,)
|
|
if output_hidden_states:
|
|
hidden_states = outputs.hidden_states if return_dict else outputs[2]
|
|
output += (hidden_states,)
|
|
return output
|
|
|
|
return BackboneOutput(
|
|
feature_maps=feature_maps,
|
|
hidden_states=outputs.hidden_states if output_hidden_states else None,
|
|
attentions=None,
|
|
)
|
|
|
|
|
|
__all__ = ["TextNetBackbone", "TextNetModel", "TextNetPreTrainedModel", "TextNetForImageClassification"]
|