341 lines
12 KiB
Python
341 lines
12 KiB
Python
""" Pytorch Inception-Resnet-V2 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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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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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 ._manipulate import flatten_modules
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from ._registry import register_model, generate_default_cfgs, register_model_deprecations
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__all__ = ['InceptionResnetV2']
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class Mixed_5b(nn.Module):
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def __init__(self, conv_block=None):
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super(Mixed_5b, self).__init__()
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conv_block = conv_block or ConvNormAct
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self.branch0 = conv_block(192, 96, kernel_size=1, stride=1)
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self.branch1 = nn.Sequential(
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conv_block(192, 48, kernel_size=1, stride=1),
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conv_block(48, 64, kernel_size=5, stride=1, padding=2)
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)
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self.branch2 = nn.Sequential(
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conv_block(192, 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(192, 64, 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 Block35(nn.Module):
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def __init__(self, scale=1.0, conv_block=None):
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super(Block35, self).__init__()
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self.scale = scale
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conv_block = conv_block or ConvNormAct
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self.branch0 = conv_block(320, 32, kernel_size=1, stride=1)
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self.branch1 = nn.Sequential(
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conv_block(320, 32, kernel_size=1, stride=1),
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conv_block(32, 32, 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(320, 32, kernel_size=1, stride=1),
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conv_block(32, 48, kernel_size=3, stride=1, padding=1),
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conv_block(48, 64, kernel_size=3, stride=1, padding=1)
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)
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self.conv2d = nn.Conv2d(128, 320, kernel_size=1, stride=1)
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self.act = nn.ReLU()
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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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out = self.conv2d(out)
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out = out * self.scale + x
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out = self.act(out)
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return out
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class Mixed_6a(nn.Module):
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def __init__(self, conv_block=None):
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super(Mixed_6a, self).__init__()
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conv_block = conv_block or ConvNormAct
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self.branch0 = conv_block(320, 384, kernel_size=3, stride=2)
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self.branch1 = nn.Sequential(
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conv_block(320, 256, kernel_size=1, stride=1),
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conv_block(256, 256, kernel_size=3, stride=1, padding=1),
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conv_block(256, 384, 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 Block17(nn.Module):
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def __init__(self, scale=1.0, conv_block=None):
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super(Block17, self).__init__()
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self.scale = scale
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conv_block = conv_block or ConvNormAct
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self.branch0 = conv_block(1088, 192, kernel_size=1, stride=1)
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self.branch1 = nn.Sequential(
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conv_block(1088, 128, kernel_size=1, stride=1),
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conv_block(128, 160, kernel_size=(1, 7), stride=1, padding=(0, 3)),
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conv_block(160, 192, kernel_size=(7, 1), stride=1, padding=(3, 0))
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)
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self.conv2d = nn.Conv2d(384, 1088, kernel_size=1, stride=1)
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self.act = nn.ReLU()
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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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out = self.conv2d(out)
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out = out * self.scale + x
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out = self.act(out)
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return out
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class Mixed_7a(nn.Module):
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def __init__(self, conv_block=None):
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super(Mixed_7a, self).__init__()
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conv_block = conv_block or ConvNormAct
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self.branch0 = nn.Sequential(
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conv_block(1088, 256, kernel_size=1, stride=1),
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conv_block(256, 384, kernel_size=3, stride=2)
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)
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self.branch1 = nn.Sequential(
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conv_block(1088, 256, kernel_size=1, stride=1),
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conv_block(256, 288, kernel_size=3, stride=2)
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)
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self.branch2 = nn.Sequential(
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conv_block(1088, 256, kernel_size=1, stride=1),
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conv_block(256, 288, kernel_size=3, stride=1, padding=1),
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conv_block(288, 320, kernel_size=3, stride=2)
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)
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self.branch3 = 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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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 Block8(nn.Module):
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def __init__(self, scale=1.0, no_relu=False, conv_block=None):
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super(Block8, self).__init__()
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self.scale = scale
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conv_block = conv_block or ConvNormAct
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self.branch0 = conv_block(2080, 192, kernel_size=1, stride=1)
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self.branch1 = nn.Sequential(
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conv_block(2080, 192, kernel_size=1, stride=1),
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conv_block(192, 224, kernel_size=(1, 3), stride=1, padding=(0, 1)),
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conv_block(224, 256, kernel_size=(3, 1), stride=1, padding=(1, 0))
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)
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self.conv2d = nn.Conv2d(448, 2080, kernel_size=1, stride=1)
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self.relu = None if no_relu else nn.ReLU()
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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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out = self.conv2d(out)
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out = out * self.scale + x
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if self.relu is not None:
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out = self.relu(out)
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return out
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class InceptionResnetV2(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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drop_rate=0.,
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output_stride=32,
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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(InceptionResnetV2, self).__init__()
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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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assert output_stride == 32
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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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self.conv2d_1a = conv_block(in_chans, 32, kernel_size=3, stride=2)
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self.conv2d_2a = conv_block(32, 32, kernel_size=3, stride=1)
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self.conv2d_2b = conv_block(32, 64, kernel_size=3, stride=1, padding=1)
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self.feature_info = [dict(num_chs=64, reduction=2, module='conv2d_2b')]
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self.maxpool_3a = nn.MaxPool2d(3, stride=2)
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self.conv2d_3b = conv_block(64, 80, kernel_size=1, stride=1)
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self.conv2d_4a = conv_block(80, 192, kernel_size=3, stride=1)
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self.feature_info += [dict(num_chs=192, reduction=4, module='conv2d_4a')]
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self.maxpool_5a = nn.MaxPool2d(3, stride=2)
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self.mixed_5b = Mixed_5b(conv_block=conv_block)
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self.repeat = nn.Sequential(*[Block35(scale=0.17, conv_block=conv_block) for _ in range(10)])
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self.feature_info += [dict(num_chs=320, reduction=8, module='repeat')]
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self.mixed_6a = Mixed_6a(conv_block=conv_block)
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self.repeat_1 = nn.Sequential(*[Block17(scale=0.10, conv_block=conv_block) for _ in range(20)])
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self.feature_info += [dict(num_chs=1088, reduction=16, module='repeat_1')]
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self.mixed_7a = Mixed_7a(conv_block=conv_block)
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self.repeat_2 = nn.Sequential(*[Block8(scale=0.20, conv_block=conv_block) for _ in range(9)])
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self.block8 = Block8(no_relu=True, conv_block=conv_block)
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self.conv2d_7b = conv_block(2080, self.num_features, kernel_size=1, stride=1)
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self.feature_info += [dict(num_chs=self.num_features, reduction=32, module='conv2d_7b')]
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self.global_pool, self.head_drop, self.classif = 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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module_map = {k: i for i, (k, _) in enumerate(flatten_modules(self.named_children(), prefix=()))}
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module_map.pop(('classif',))
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def _matcher(name):
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if any([name.startswith(n) for n in ('conv2d_1', 'conv2d_2')]):
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return 0
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elif any([name.startswith(n) for n in ('conv2d_3', 'conv2d_4')]):
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return 1
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elif any([name.startswith(n) for n in ('block8', 'conv2d_7')]):
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return len(module_map) + 1
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else:
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for k in module_map.keys():
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if k == tuple(name.split('.')[:len(k)]):
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return module_map[k]
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return float('inf')
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return _matcher
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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, "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.classif
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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.classif = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
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def forward_features(self, x):
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x = self.conv2d_1a(x)
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x = self.conv2d_2a(x)
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x = self.conv2d_2b(x)
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x = self.maxpool_3a(x)
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x = self.conv2d_3b(x)
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x = self.conv2d_4a(x)
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x = self.maxpool_5a(x)
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x = self.mixed_5b(x)
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x = self.repeat(x)
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x = self.mixed_6a(x)
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x = self.repeat_1(x)
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x = self.mixed_7a(x)
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x = self.repeat_2(x)
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x = self.block8(x)
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x = self.conv2d_7b(x)
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return 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.classif(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_resnet_v2(variant, pretrained=False, **kwargs):
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return build_model_with_cfg(InceptionResnetV2, variant, pretrained, **kwargs)
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default_cfgs = generate_default_cfgs({
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# ported from http://download.tensorflow.org/models/inception_resnet_v2_2016_08_30.tar.gz
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'inception_resnet_v2.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': 0.8975, 'interpolation': 'bicubic',
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'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
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'first_conv': 'conv2d_1a.conv', 'classifier': 'classif',
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},
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# As per https://arxiv.org/abs/1705.07204 and
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# ported from http://download.tensorflow.org/models/ens_adv_inception_resnet_v2_2017_08_18.tar.gz
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'inception_resnet_v2.tf_ens_adv_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': 0.8975, 'interpolation': 'bicubic',
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'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
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'first_conv': 'conv2d_1a.conv', 'classifier': 'classif',
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}
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})
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@register_model
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def inception_resnet_v2(pretrained=False, **kwargs) -> InceptionResnetV2:
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return _create_inception_resnet_v2('inception_resnet_v2', pretrained=pretrained, **kwargs)
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register_model_deprecations(__name__, {
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'ens_adv_inception_resnet_v2': 'inception_resnet_v2.tf_ens_adv_in1k',
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}) |