512 lines
19 KiB
Python
512 lines
19 KiB
Python
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# coding=utf-8
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# Copyright 2022 Microsoft Research, 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 ResNet 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 ...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 ...modeling_utils import PreTrainedModel
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from ...utils import (
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add_code_sample_docstrings,
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add_start_docstrings,
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add_start_docstrings_to_model_forward,
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logging,
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replace_return_docstrings,
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)
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from ...utils.backbone_utils import BackboneMixin
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from .configuration_resnet import ResNetConfig
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logger = logging.get_logger(__name__)
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# General docstring
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_CONFIG_FOR_DOC = "ResNetConfig"
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# Base docstring
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_CHECKPOINT_FOR_DOC = "microsoft/resnet-50"
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_EXPECTED_OUTPUT_SHAPE = [1, 2048, 7, 7]
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# Image classification docstring
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_IMAGE_CLASS_CHECKPOINT = "microsoft/resnet-50"
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_IMAGE_CLASS_EXPECTED_OUTPUT = "tiger cat"
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from ..deprecated._archive_maps import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
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class ResNetConvLayer(nn.Module):
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def __init__(
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self, in_channels: int, out_channels: int, kernel_size: int = 3, stride: int = 1, activation: 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, out_channels, kernel_size=kernel_size, stride=stride, padding=kernel_size // 2, 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, 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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hidden_state = self.activation(hidden_state)
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return hidden_state
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class ResNetEmbeddings(nn.Module):
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"""
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ResNet Embeddings (stem) composed of a single aggressive convolution.
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"""
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def __init__(self, config: ResNetConfig):
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super().__init__()
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self.embedder = ResNetConvLayer(
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config.num_channels, config.embedding_size, kernel_size=7, stride=2, activation=config.hidden_act
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)
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self.pooler = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.num_channels = config.num_channels
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def forward(self, pixel_values: Tensor) -> Tensor:
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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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embedding = self.embedder(pixel_values)
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embedding = self.pooler(embedding)
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return embedding
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class ResNetShortCut(nn.Module):
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"""
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ResNet 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 ResNetBasicLayer(nn.Module):
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"""
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A classic ResNet's residual layer composed by two `3x3` convolutions.
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"""
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def __init__(self, in_channels: int, out_channels: int, stride: int = 1, activation: str = "relu"):
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super().__init__()
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should_apply_shortcut = in_channels != out_channels or stride != 1
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self.shortcut = (
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ResNetShortCut(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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ResNetConvLayer(in_channels, out_channels, stride=stride),
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ResNetConvLayer(out_channels, out_channels, activation=None),
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)
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self.activation = ACT2FN[activation]
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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 ResNetBottleNeckLayer(nn.Module):
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"""
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A classic ResNet's bottleneck layer composed by three `3x3` convolutions.
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The first `1x1` convolution reduces the input by a factor of `reduction` in order to make the second `3x3`
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convolution faster. The last `1x1` convolution remaps the reduced features to `out_channels`. If
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`downsample_in_bottleneck` is true, downsample will be in the first layer instead of the second layer.
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"""
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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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stride: int = 1,
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activation: str = "relu",
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reduction: int = 4,
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downsample_in_bottleneck: bool = False,
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):
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super().__init__()
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should_apply_shortcut = in_channels != out_channels or stride != 1
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reduces_channels = out_channels // reduction
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self.shortcut = (
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ResNetShortCut(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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ResNetConvLayer(
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in_channels, reduces_channels, kernel_size=1, stride=stride if downsample_in_bottleneck else 1
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),
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ResNetConvLayer(reduces_channels, reduces_channels, stride=stride if not downsample_in_bottleneck else 1),
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ResNetConvLayer(reduces_channels, out_channels, kernel_size=1, activation=None),
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)
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self.activation = ACT2FN[activation]
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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 ResNetStage(nn.Module):
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"""
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A ResNet 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: ResNetConfig,
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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 = ResNetBottleNeckLayer if config.layer_type == "bottleneck" else ResNetBasicLayer
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if config.layer_type == "bottleneck":
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first_layer = layer(
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in_channels,
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out_channels,
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stride=stride,
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activation=config.hidden_act,
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downsample_in_bottleneck=config.downsample_in_bottleneck,
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)
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else:
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first_layer = layer(in_channels, out_channels, stride=stride, activation=config.hidden_act)
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self.layers = nn.Sequential(
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first_layer, *[layer(out_channels, out_channels, activation=config.hidden_act) for _ in range(depth - 1)]
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)
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def forward(self, input: Tensor) -> Tensor:
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hidden_state = input
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for layer in self.layers:
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hidden_state = layer(hidden_state)
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return hidden_state
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class ResNetEncoder(nn.Module):
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def __init__(self, config: ResNetConfig):
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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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ResNetStage(
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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(ResNetStage(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(
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last_hidden_state=hidden_state,
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hidden_states=hidden_states,
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)
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class ResNetPreTrainedModel(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 = ResNetConfig
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base_model_prefix = "resnet"
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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.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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RESNET_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 matter related to general usage and
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behavior.
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Parameters:
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config ([`ResNetConfig`]): 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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RESNET_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 [`~utils.ModelOutput`] instead of a plain tuple.
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"""
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@add_start_docstrings(
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"The bare ResNet model outputting raw features without any specific head on top.",
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RESNET_START_DOCSTRING,
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)
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class ResNetModel(ResNetPreTrainedModel):
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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 = ResNetEmbeddings(config)
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self.encoder = ResNetEncoder(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(RESNET_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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ResNet 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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""",
|
||
|
|
RESNET_START_DOCSTRING,
|
||
|
|
)
|
||
|
|
class ResNetForImageClassification(ResNetPreTrainedModel):
|
||
|
|
def __init__(self, config):
|
||
|
|
super().__init__(config)
|
||
|
|
self.num_labels = config.num_labels
|
||
|
|
self.resnet = ResNetModel(config)
|
||
|
|
# classification head
|
||
|
|
self.classifier = nn.Sequential(
|
||
|
|
nn.Flatten(),
|
||
|
|
nn.Linear(config.hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity(),
|
||
|
|
)
|
||
|
|
# initialize weights and apply final processing
|
||
|
|
self.post_init()
|
||
|
|
|
||
|
|
@add_start_docstrings_to_model_forward(RESNET_INPUTS_DOCSTRING)
|
||
|
|
@add_code_sample_docstrings(
|
||
|
|
checkpoint=_IMAGE_CLASS_CHECKPOINT,
|
||
|
|
output_type=ImageClassifierOutputWithNoAttention,
|
||
|
|
config_class=_CONFIG_FOR_DOC,
|
||
|
|
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
|
||
|
|
)
|
||
|
|
def forward(
|
||
|
|
self,
|
||
|
|
pixel_values: Optional[torch.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 classification loss is computed (Cross-Entropy).
|
||
|
|
"""
|
||
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||
|
|
|
||
|
|
outputs = self.resnet(pixel_values, output_hidden_states=output_hidden_states, return_dict=return_dict)
|
||
|
|
|
||
|
|
pooled_output = outputs.pooler_output if return_dict else outputs[1]
|
||
|
|
|
||
|
|
logits = self.classifier(pooled_output)
|
||
|
|
|
||
|
|
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)
|
||
|
|
|
||
|
|
|
||
|
|
@add_start_docstrings(
|
||
|
|
"""
|
||
|
|
ResNet backbone, to be used with frameworks like DETR and MaskFormer.
|
||
|
|
""",
|
||
|
|
RESNET_START_DOCSTRING,
|
||
|
|
)
|
||
|
|
class ResNetBackbone(ResNetPreTrainedModel, BackboneMixin):
|
||
|
|
def __init__(self, config):
|
||
|
|
super().__init__(config)
|
||
|
|
super()._init_backbone(config)
|
||
|
|
|
||
|
|
self.num_features = [config.embedding_size] + config.hidden_sizes
|
||
|
|
self.embedder = ResNetEmbeddings(config)
|
||
|
|
self.encoder = ResNetEncoder(config)
|
||
|
|
|
||
|
|
# initialize weights and apply final processing
|
||
|
|
self.post_init()
|
||
|
|
|
||
|
|
@add_start_docstrings_to_model_forward(RESNET_INPUTS_DOCSTRING)
|
||
|
|
@replace_return_docstrings(output_type=BackboneOutput, config_class=_CONFIG_FOR_DOC)
|
||
|
|
def forward(
|
||
|
|
self, pixel_values: Tensor, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None
|
||
|
|
) -> BackboneOutput:
|
||
|
|
"""
|
||
|
|
Returns:
|
||
|
|
|
||
|
|
Examples:
|
||
|
|
|
||
|
|
```python
|
||
|
|
>>> from transformers import AutoImageProcessor, AutoBackbone
|
||
|
|
>>> import torch
|
||
|
|
>>> from PIL import Image
|
||
|
|
>>> import requests
|
||
|
|
|
||
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
||
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
||
|
|
|
||
|
|
>>> processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50")
|
||
|
|
>>> model = AutoBackbone.from_pretrained(
|
||
|
|
... "microsoft/resnet-50", out_features=["stage1", "stage2", "stage3", "stage4"]
|
||
|
|
... )
|
||
|
|
|
||
|
|
>>> inputs = processor(image, return_tensors="pt")
|
||
|
|
|
||
|
|
>>> outputs = model(**inputs)
|
||
|
|
>>> feature_maps = outputs.feature_maps
|
||
|
|
>>> list(feature_maps[-1].shape)
|
||
|
|
[1, 2048, 7, 7]
|
||
|
|
```"""
|
||
|
|
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
|
||
|
|
)
|
||
|
|
|
||
|
|
embedding_output = self.embedder(pixel_values)
|
||
|
|
|
||
|
|
outputs = self.encoder(embedding_output, output_hidden_states=True, return_dict=True)
|
||
|
|
|
||
|
|
hidden_states = outputs.hidden_states
|
||
|
|
|
||
|
|
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:
|
||
|
|
output += (outputs.hidden_states,)
|
||
|
|
return output
|
||
|
|
|
||
|
|
return BackboneOutput(
|
||
|
|
feature_maps=feature_maps,
|
||
|
|
hidden_states=outputs.hidden_states if output_hidden_states else None,
|
||
|
|
attentions=None,
|
||
|
|
)
|