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