mirror of
https://github.com/huggingface/pytorch-image-models.git
synced 2025-06-03 15:01:08 +08:00
388 lines
13 KiB
Python
388 lines
13 KiB
Python
""" Pytorch Inception-V4 implementation
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Sourced from https://github.com/Cadene/tensorflow-model-zoo.torch (MIT License) which is
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based upon Google's Tensorflow implementation and pretrained weights (Apache 2.0 License)
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"""
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from functools import partial
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from typing import List, Optional, Tuple, Union
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import torch
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import torch.nn as nn
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from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
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from timm.layers import create_classifier, ConvNormAct
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from ._builder import build_model_with_cfg
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from ._features import feature_take_indices
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from ._registry import register_model, generate_default_cfgs
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__all__ = ['InceptionV4']
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class Mixed3a(nn.Module):
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def __init__(self, conv_block=ConvNormAct):
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super(Mixed3a, self).__init__()
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self.maxpool = nn.MaxPool2d(3, stride=2)
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self.conv = conv_block(64, 96, kernel_size=3, stride=2)
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def forward(self, x):
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x0 = self.maxpool(x)
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x1 = self.conv(x)
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out = torch.cat((x0, x1), 1)
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return out
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class Mixed4a(nn.Module):
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def __init__(self, conv_block=ConvNormAct):
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super(Mixed4a, self).__init__()
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self.branch0 = nn.Sequential(
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conv_block(160, 64, kernel_size=1, stride=1),
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conv_block(64, 96, kernel_size=3, stride=1)
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)
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self.branch1 = nn.Sequential(
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conv_block(160, 64, kernel_size=1, stride=1),
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conv_block(64, 64, kernel_size=(1, 7), stride=1, padding=(0, 3)),
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conv_block(64, 64, kernel_size=(7, 1), stride=1, padding=(3, 0)),
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conv_block(64, 96, kernel_size=(3, 3), stride=1)
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)
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def forward(self, x):
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x0 = self.branch0(x)
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x1 = self.branch1(x)
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out = torch.cat((x0, x1), 1)
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return out
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class Mixed5a(nn.Module):
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def __init__(self, conv_block=ConvNormAct):
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super(Mixed5a, self).__init__()
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self.conv = conv_block(192, 192, kernel_size=3, stride=2)
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self.maxpool = nn.MaxPool2d(3, stride=2)
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def forward(self, x):
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x0 = self.conv(x)
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x1 = self.maxpool(x)
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out = torch.cat((x0, x1), 1)
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return out
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class InceptionA(nn.Module):
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def __init__(self, conv_block=ConvNormAct):
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super(InceptionA, self).__init__()
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self.branch0 = conv_block(384, 96, kernel_size=1, stride=1)
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self.branch1 = nn.Sequential(
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conv_block(384, 64, kernel_size=1, stride=1),
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conv_block(64, 96, kernel_size=3, stride=1, padding=1)
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)
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self.branch2 = nn.Sequential(
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conv_block(384, 64, kernel_size=1, stride=1),
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conv_block(64, 96, kernel_size=3, stride=1, padding=1),
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conv_block(96, 96, kernel_size=3, stride=1, padding=1)
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)
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self.branch3 = nn.Sequential(
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nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
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conv_block(384, 96, kernel_size=1, stride=1)
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)
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def forward(self, x):
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x0 = self.branch0(x)
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x1 = self.branch1(x)
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x2 = self.branch2(x)
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x3 = self.branch3(x)
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out = torch.cat((x0, x1, x2, x3), 1)
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return out
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class ReductionA(nn.Module):
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def __init__(self, conv_block=ConvNormAct):
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super(ReductionA, self).__init__()
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self.branch0 = conv_block(384, 384, kernel_size=3, stride=2)
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self.branch1 = nn.Sequential(
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conv_block(384, 192, kernel_size=1, stride=1),
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conv_block(192, 224, kernel_size=3, stride=1, padding=1),
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conv_block(224, 256, kernel_size=3, stride=2)
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)
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self.branch2 = nn.MaxPool2d(3, stride=2)
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def forward(self, x):
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x0 = self.branch0(x)
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x1 = self.branch1(x)
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x2 = self.branch2(x)
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out = torch.cat((x0, x1, x2), 1)
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return out
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class InceptionB(nn.Module):
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def __init__(self, conv_block=ConvNormAct):
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super(InceptionB, self).__init__()
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self.branch0 = conv_block(1024, 384, kernel_size=1, stride=1)
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self.branch1 = nn.Sequential(
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conv_block(1024, 192, kernel_size=1, stride=1),
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conv_block(192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)),
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conv_block(224, 256, kernel_size=(7, 1), stride=1, padding=(3, 0))
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)
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self.branch2 = nn.Sequential(
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conv_block(1024, 192, kernel_size=1, stride=1),
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conv_block(192, 192, kernel_size=(7, 1), stride=1, padding=(3, 0)),
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conv_block(192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)),
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conv_block(224, 224, kernel_size=(7, 1), stride=1, padding=(3, 0)),
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conv_block(224, 256, kernel_size=(1, 7), stride=1, padding=(0, 3))
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)
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self.branch3 = nn.Sequential(
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nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
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conv_block(1024, 128, kernel_size=1, stride=1)
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)
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def forward(self, x):
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x0 = self.branch0(x)
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x1 = self.branch1(x)
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x2 = self.branch2(x)
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x3 = self.branch3(x)
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out = torch.cat((x0, x1, x2, x3), 1)
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return out
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class ReductionB(nn.Module):
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def __init__(self, conv_block=ConvNormAct):
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super(ReductionB, self).__init__()
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self.branch0 = nn.Sequential(
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conv_block(1024, 192, kernel_size=1, stride=1),
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conv_block(192, 192, kernel_size=3, stride=2)
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)
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self.branch1 = nn.Sequential(
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conv_block(1024, 256, kernel_size=1, stride=1),
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conv_block(256, 256, kernel_size=(1, 7), stride=1, padding=(0, 3)),
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conv_block(256, 320, kernel_size=(7, 1), stride=1, padding=(3, 0)),
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conv_block(320, 320, kernel_size=3, stride=2)
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)
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self.branch2 = nn.MaxPool2d(3, stride=2)
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def forward(self, x):
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x0 = self.branch0(x)
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x1 = self.branch1(x)
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x2 = self.branch2(x)
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out = torch.cat((x0, x1, x2), 1)
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return out
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class InceptionC(nn.Module):
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def __init__(self, conv_block=ConvNormAct):
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super(InceptionC, self).__init__()
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self.branch0 = conv_block(1536, 256, kernel_size=1, stride=1)
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self.branch1_0 = conv_block(1536, 384, kernel_size=1, stride=1)
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self.branch1_1a = conv_block(384, 256, kernel_size=(1, 3), stride=1, padding=(0, 1))
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self.branch1_1b = conv_block(384, 256, kernel_size=(3, 1), stride=1, padding=(1, 0))
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self.branch2_0 = conv_block(1536, 384, kernel_size=1, stride=1)
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self.branch2_1 = conv_block(384, 448, kernel_size=(3, 1), stride=1, padding=(1, 0))
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self.branch2_2 = conv_block(448, 512, kernel_size=(1, 3), stride=1, padding=(0, 1))
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self.branch2_3a = conv_block(512, 256, kernel_size=(1, 3), stride=1, padding=(0, 1))
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self.branch2_3b = conv_block(512, 256, kernel_size=(3, 1), stride=1, padding=(1, 0))
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self.branch3 = nn.Sequential(
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nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
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conv_block(1536, 256, kernel_size=1, stride=1)
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)
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def forward(self, x):
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x0 = self.branch0(x)
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x1_0 = self.branch1_0(x)
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x1_1a = self.branch1_1a(x1_0)
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x1_1b = self.branch1_1b(x1_0)
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x1 = torch.cat((x1_1a, x1_1b), 1)
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x2_0 = self.branch2_0(x)
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x2_1 = self.branch2_1(x2_0)
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x2_2 = self.branch2_2(x2_1)
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x2_3a = self.branch2_3a(x2_2)
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x2_3b = self.branch2_3b(x2_2)
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x2 = torch.cat((x2_3a, x2_3b), 1)
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x3 = self.branch3(x)
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out = torch.cat((x0, x1, x2, x3), 1)
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return out
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class InceptionV4(nn.Module):
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def __init__(
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self,
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num_classes=1000,
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in_chans=3,
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output_stride=32,
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drop_rate=0.,
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global_pool='avg',
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norm_layer='batchnorm2d',
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norm_eps=1e-3,
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act_layer='relu',
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):
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super(InceptionV4, self).__init__()
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assert output_stride == 32
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self.num_classes = num_classes
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self.num_features = self.head_hidden_size = 1536
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conv_block = partial(
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ConvNormAct,
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padding=0,
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norm_layer=norm_layer,
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act_layer=act_layer,
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norm_kwargs=dict(eps=norm_eps),
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act_kwargs=dict(inplace=True),
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)
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features = [
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conv_block(in_chans, 32, kernel_size=3, stride=2),
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conv_block(32, 32, kernel_size=3, stride=1),
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conv_block(32, 64, kernel_size=3, stride=1, padding=1),
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Mixed3a(conv_block),
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Mixed4a(conv_block),
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Mixed5a(conv_block),
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]
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features += [InceptionA(conv_block) for _ in range(4)]
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features += [ReductionA(conv_block)] # Mixed6a
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features += [InceptionB(conv_block) for _ in range(7)]
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features += [ReductionB(conv_block)] # Mixed7a
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features += [InceptionC(conv_block) for _ in range(3)]
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self.features = nn.Sequential(*features)
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self.feature_info = [
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dict(num_chs=64, reduction=2, module='features.2'),
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dict(num_chs=160, reduction=4, module='features.3'),
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dict(num_chs=384, reduction=8, module='features.9'),
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dict(num_chs=1024, reduction=16, module='features.17'),
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dict(num_chs=1536, reduction=32, module='features.21'),
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]
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self.global_pool, self.head_drop, self.last_linear = create_classifier(
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self.num_features, self.num_classes, pool_type=global_pool, drop_rate=drop_rate)
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@torch.jit.ignore
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def group_matcher(self, coarse=False):
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return dict(
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stem=r'^features\.[012]\.',
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blocks=r'^features\.(\d+)'
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)
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@torch.jit.ignore
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def set_grad_checkpointing(self, enable=True):
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assert not enable, 'gradient checkpointing not supported'
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@torch.jit.ignore
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def get_classifier(self) -> nn.Module:
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return self.last_linear
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def reset_classifier(self, num_classes: int, global_pool: str = 'avg'):
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self.num_classes = num_classes
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self.global_pool, self.last_linear = create_classifier(
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self.num_features, self.num_classes, pool_type=global_pool)
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def forward_intermediates(
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self,
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x: torch.Tensor,
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indices: Optional[Union[int, List[int]]] = None,
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norm: bool = False,
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stop_early: bool = False,
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output_fmt: str = 'NCHW',
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intermediates_only: bool = False,
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) -> Union[List[torch.Tensor], Tuple[torch.Tensor, List[torch.Tensor]]]:
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""" Forward features that returns intermediates.
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Args:
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x: Input image tensor
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indices: Take last n blocks if int, all if None, select matching indices if sequence
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norm: Apply norm layer to compatible intermediates
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stop_early: Stop iterating over blocks when last desired intermediate hit
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output_fmt: Shape of intermediate feature outputs
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intermediates_only: Only return intermediate features
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Returns:
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"""
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assert output_fmt in ('NCHW',), 'Output shape must be NCHW.'
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intermediates = []
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stage_ends = [int(info['module'].split('.')[-1]) for info in self.feature_info]
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take_indices, max_index = feature_take_indices(len(stage_ends), indices)
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take_indices = [stage_ends[i] for i in take_indices]
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max_index = stage_ends[max_index]
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# forward pass
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if torch.jit.is_scripting() or not stop_early: # can't slice blocks in torchscript
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stages = self.features
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else:
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stages = self.features[:max_index + 1]
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for feat_idx, stage in enumerate(stages):
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x = stage(x)
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if feat_idx in take_indices:
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intermediates.append(x)
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if intermediates_only:
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return intermediates
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return x, intermediates
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def prune_intermediate_layers(
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self,
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indices: Union[int, List[int]] = 1,
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prune_norm: bool = False,
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prune_head: bool = True,
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):
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""" Prune layers not required for specified intermediates.
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"""
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stage_ends = [int(info['module'].split('.')[-1]) for info in self.feature_info]
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take_indices, max_index = feature_take_indices(len(stage_ends), indices)
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max_index = stage_ends[max_index]
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self.features = self.features[:max_index + 1] # truncate blocks w/ stem as idx 0
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if prune_head:
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self.reset_classifier(0, '')
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return take_indices
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def forward_features(self, x):
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return self.features(x)
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def forward_head(self, x, pre_logits: bool = False):
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x = self.global_pool(x)
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x = self.head_drop(x)
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return x if pre_logits else self.last_linear(x)
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def forward(self, x):
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x = self.forward_features(x)
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x = self.forward_head(x)
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return x
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def _create_inception_v4(variant, pretrained=False, **kwargs) -> InceptionV4:
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return build_model_with_cfg(
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InceptionV4,
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variant,
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pretrained,
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feature_cfg=dict(flatten_sequential=True),
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**kwargs,
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)
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default_cfgs = generate_default_cfgs({
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'inception_v4.tf_in1k': {
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'hf_hub_id': 'timm/',
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'num_classes': 1000, 'input_size': (3, 299, 299), 'pool_size': (8, 8),
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'crop_pct': 0.875, 'interpolation': 'bicubic',
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'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
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'first_conv': 'features.0.conv', 'classifier': 'last_linear',
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}
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})
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@register_model
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def inception_v4(pretrained=False, **kwargs):
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return _create_inception_v4('inception_v4', pretrained, **kwargs)
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