446 lines
17 KiB
Python
446 lines
17 KiB
Python
# coding=utf-8
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# Copyright 2022 Meta Platforms, Inc. 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 RegNet model."""
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from typing import Optional
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import torch
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import torch.utils.checkpoint
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from torch import Tensor, nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from ...activations import ACT2FN
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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 (
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BaseModelOutputWithNoAttention,
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BaseModelOutputWithPoolingAndNoAttention,
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ImageClassifierOutputWithNoAttention,
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)
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from ...modeling_utils import PreTrainedModel
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from ...utils import logging
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from .configuration_regnet import RegNetConfig
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logger = logging.get_logger(__name__)
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# General docstring
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_CONFIG_FOR_DOC = "RegNetConfig"
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# Base docstring
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_CHECKPOINT_FOR_DOC = "facebook/regnet-y-040"
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_EXPECTED_OUTPUT_SHAPE = [1, 1088, 7, 7]
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# Image classification docstring
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_IMAGE_CLASS_CHECKPOINT = "facebook/regnet-y-040"
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_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
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from ..deprecated._archive_maps import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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class RegNetConvLayer(nn.Module):
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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kernel_size: int = 3,
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stride: int = 1,
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groups: int = 1,
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activation: Optional[str] = "relu",
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):
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super().__init__()
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self.convolution = nn.Conv2d(
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in_channels,
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out_channels,
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kernel_size=kernel_size,
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stride=stride,
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padding=kernel_size // 2,
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groups=groups,
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bias=False,
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)
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self.normalization = nn.BatchNorm2d(out_channels)
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self.activation = ACT2FN[activation] if activation is not None else nn.Identity()
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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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hidden_state = self.activation(hidden_state)
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return hidden_state
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class RegNetEmbeddings(nn.Module):
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"""
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RegNet Embedddings (stem) composed of a single aggressive convolution.
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"""
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def __init__(self, config: RegNetConfig):
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super().__init__()
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self.embedder = RegNetConvLayer(
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config.num_channels, config.embedding_size, kernel_size=3, stride=2, activation=config.hidden_act
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)
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self.num_channels = config.num_channels
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def forward(self, pixel_values):
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num_channels = pixel_values.shape[1]
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if num_channels != self.num_channels:
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raise ValueError(
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"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
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)
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hidden_state = self.embedder(pixel_values)
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return hidden_state
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# Copied from transformers.models.resnet.modeling_resnet.ResNetShortCut with ResNet->RegNet
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class RegNetShortCut(nn.Module):
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"""
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RegNet shortcut, used to project the residual features to the correct size. If needed, it is also used to
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downsample the input using `stride=2`.
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"""
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def __init__(self, in_channels: int, out_channels: int, stride: int = 2):
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super().__init__()
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self.convolution = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False)
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self.normalization = nn.BatchNorm2d(out_channels)
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def forward(self, input: Tensor) -> Tensor:
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hidden_state = self.convolution(input)
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hidden_state = self.normalization(hidden_state)
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return hidden_state
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class RegNetSELayer(nn.Module):
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"""
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Squeeze and Excitation layer (SE) proposed in [Squeeze-and-Excitation Networks](https://arxiv.org/abs/1709.01507).
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"""
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def __init__(self, in_channels: int, reduced_channels: int):
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super().__init__()
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self.pooler = nn.AdaptiveAvgPool2d((1, 1))
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self.attention = nn.Sequential(
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nn.Conv2d(in_channels, reduced_channels, kernel_size=1),
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nn.ReLU(),
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nn.Conv2d(reduced_channels, in_channels, kernel_size=1),
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nn.Sigmoid(),
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)
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def forward(self, hidden_state):
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# b c h w -> b c 1 1
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pooled = self.pooler(hidden_state)
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attention = self.attention(pooled)
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hidden_state = hidden_state * attention
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return hidden_state
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class RegNetXLayer(nn.Module):
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"""
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RegNet's layer composed by three `3x3` convolutions, same as a ResNet bottleneck layer with reduction = 1.
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"""
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def __init__(self, config: RegNetConfig, in_channels: int, out_channels: int, stride: int = 1):
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super().__init__()
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should_apply_shortcut = in_channels != out_channels or stride != 1
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groups = max(1, out_channels // config.groups_width)
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self.shortcut = (
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RegNetShortCut(in_channels, out_channels, stride=stride) if should_apply_shortcut else nn.Identity()
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)
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self.layer = nn.Sequential(
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RegNetConvLayer(in_channels, out_channels, kernel_size=1, activation=config.hidden_act),
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RegNetConvLayer(out_channels, out_channels, stride=stride, groups=groups, activation=config.hidden_act),
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RegNetConvLayer(out_channels, out_channels, kernel_size=1, activation=None),
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)
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self.activation = ACT2FN[config.hidden_act]
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def forward(self, hidden_state):
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residual = hidden_state
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hidden_state = self.layer(hidden_state)
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residual = self.shortcut(residual)
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hidden_state += residual
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hidden_state = self.activation(hidden_state)
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return hidden_state
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class RegNetYLayer(nn.Module):
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"""
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RegNet's Y layer: an X layer with Squeeze and Excitation.
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"""
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def __init__(self, config: RegNetConfig, in_channels: int, out_channels: int, stride: int = 1):
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super().__init__()
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should_apply_shortcut = in_channels != out_channels or stride != 1
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groups = max(1, out_channels // config.groups_width)
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self.shortcut = (
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RegNetShortCut(in_channels, out_channels, stride=stride) if should_apply_shortcut else nn.Identity()
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)
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self.layer = nn.Sequential(
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RegNetConvLayer(in_channels, out_channels, kernel_size=1, activation=config.hidden_act),
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RegNetConvLayer(out_channels, out_channels, stride=stride, groups=groups, activation=config.hidden_act),
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RegNetSELayer(out_channels, reduced_channels=int(round(in_channels / 4))),
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RegNetConvLayer(out_channels, out_channels, kernel_size=1, activation=None),
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)
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self.activation = ACT2FN[config.hidden_act]
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def forward(self, hidden_state):
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residual = hidden_state
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hidden_state = self.layer(hidden_state)
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residual = self.shortcut(residual)
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hidden_state += residual
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hidden_state = self.activation(hidden_state)
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return hidden_state
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class RegNetStage(nn.Module):
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"""
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A RegNet stage composed by stacked layers.
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"""
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def __init__(
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self,
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config: RegNetConfig,
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in_channels: int,
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out_channels: int,
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stride: int = 2,
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depth: int = 2,
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):
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super().__init__()
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layer = RegNetXLayer if config.layer_type == "x" else RegNetYLayer
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self.layers = nn.Sequential(
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# downsampling is done in the first layer with stride of 2
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layer(
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config,
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in_channels,
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out_channels,
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stride=stride,
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),
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*[layer(config, out_channels, out_channels) for _ in range(depth - 1)],
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)
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def forward(self, hidden_state):
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hidden_state = self.layers(hidden_state)
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return hidden_state
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class RegNetEncoder(nn.Module):
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def __init__(self, config: RegNetConfig):
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super().__init__()
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self.stages = nn.ModuleList([])
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# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
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self.stages.append(
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RegNetStage(
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config,
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config.embedding_size,
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config.hidden_sizes[0],
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stride=2 if config.downsample_in_first_stage else 1,
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depth=config.depths[0],
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)
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)
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in_out_channels = zip(config.hidden_sizes, config.hidden_sizes[1:])
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for (in_channels, out_channels), depth in zip(in_out_channels, config.depths[1:]):
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self.stages.append(RegNetStage(config, in_channels, out_channels, depth=depth))
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def forward(
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self, hidden_state: Tensor, output_hidden_states: bool = False, return_dict: bool = True
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) -> BaseModelOutputWithNoAttention:
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hidden_states = () if output_hidden_states else None
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for stage_module in self.stages:
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if output_hidden_states:
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hidden_states = hidden_states + (hidden_state,)
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hidden_state = stage_module(hidden_state)
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if output_hidden_states:
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hidden_states = hidden_states + (hidden_state,)
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if not return_dict:
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return tuple(v for v in [hidden_state, hidden_states] if v is not None)
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return BaseModelOutputWithNoAttention(last_hidden_state=hidden_state, hidden_states=hidden_states)
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class RegNetPreTrainedModel(PreTrainedModel):
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"""
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An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
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models.
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"""
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config_class = RegNetConfig
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base_model_prefix = "regnet"
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main_input_name = "pixel_values"
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# Copied from transformers.models.resnet.modeling_resnet.ResNetPreTrainedModel._init_weights
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def _init_weights(self, module):
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if isinstance(module, nn.Conv2d):
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nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu")
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elif isinstance(module, (nn.BatchNorm2d, nn.GroupNorm)):
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nn.init.constant_(module.weight, 1)
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nn.init.constant_(module.bias, 0)
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REGNET_START_DOCSTRING = r"""
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This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
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as a regular PyTorch Module and refer to the PyTorch documentation for all matters related to general usage and
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behavior.
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Parameters:
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config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
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Initializing with a config file does not load the weights associated with the model, only the
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configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
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"""
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REGNET_INPUTS_DOCSTRING = r"""
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Args:
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pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
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Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
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[`ConvNextImageProcessor.__call__`] for details.
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output_hidden_states (`bool`, *optional*):
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Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
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more detail.
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return_dict (`bool`, *optional*):
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Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
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"""
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@add_start_docstrings(
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"The bare RegNet model outputting raw features without any specific head on top.",
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REGNET_START_DOCSTRING,
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)
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# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
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class RegNetModel(RegNetPreTrainedModel):
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def __init__(self, config):
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super().__init__(config)
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self.config = config
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self.embedder = RegNetEmbeddings(config)
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self.encoder = RegNetEncoder(config)
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self.pooler = nn.AdaptiveAvgPool2d((1, 1))
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# Initialize weights and apply final processing
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self.post_init()
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@add_start_docstrings_to_model_forward(REGNET_INPUTS_DOCSTRING)
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@add_code_sample_docstrings(
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checkpoint=_CHECKPOINT_FOR_DOC,
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output_type=BaseModelOutputWithPoolingAndNoAttention,
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config_class=_CONFIG_FOR_DOC,
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modality="vision",
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expected_output=_EXPECTED_OUTPUT_SHAPE,
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)
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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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) -> BaseModelOutputWithPoolingAndNoAttention:
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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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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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embedding_output = self.embedder(pixel_values)
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encoder_outputs = self.encoder(
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embedding_output, 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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return (last_hidden_state, pooled_output) + encoder_outputs[1:]
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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.hidden_states,
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)
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@add_start_docstrings(
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"""
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RegNet 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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REGNET_START_DOCSTRING,
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)
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# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
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class RegNetForImageClassification(RegNetPreTrainedModel):
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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.regnet = RegNetModel(config)
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# classification head
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self.classifier = nn.Sequential(
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nn.Flatten(),
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nn.Linear(config.hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity(),
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)
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# initialize weights and apply final processing
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self.post_init()
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@add_start_docstrings_to_model_forward(REGNET_INPUTS_DOCSTRING)
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@add_code_sample_docstrings(
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checkpoint=_IMAGE_CLASS_CHECKPOINT,
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output_type=ImageClassifierOutputWithNoAttention,
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config_class=_CONFIG_FOR_DOC,
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expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
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)
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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 classification loss is computed (Cross-Entropy).
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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.regnet(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
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pooled_output = outputs.pooler_output if return_dict else outputs[1]
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logits = self.classifier(pooled_output)
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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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