forked from mrq/DL-Art-School
587a4f4050
I'm being really lazy here - these nets are not really different from each other except at which layer they terminate. This one terminates at 2x downsampling, which is simply indicative of a direction I want to go for testing these pixpro networks.
87 lines
3.5 KiB
Python
87 lines
3.5 KiB
Python
import torch
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import torch.nn as nn
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from torchvision.models.resnet import BasicBlock, Bottleneck, conv1x1, conv3x3
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from torchvision.models.utils import load_state_dict_from_url
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import torchvision
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from models.arch_util import ConvBnRelu
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from models.pixel_level_contrastive_learning.resnet_unet import ReverseBottleneck
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from trainer.networks import register_model
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from utils.util import checkpoint, opt_get
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class UResNet50_3(torchvision.models.resnet.ResNet):
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def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
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groups=1, width_per_group=64, replace_stride_with_dilation=None,
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norm_layer=None, out_dim=128):
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super().__init__(block, layers, num_classes, zero_init_residual, groups, width_per_group,
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replace_stride_with_dilation, norm_layer)
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if norm_layer is None:
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norm_layer = nn.BatchNorm2d
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'''
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# For reference:
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self.layer1 = self._make_layer(block, 64, layers[0])
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self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
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dilate=replace_stride_with_dilation[0])
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self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
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dilate=replace_stride_with_dilation[1])
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self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
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dilate=replace_stride_with_dilation[2])
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'''
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uplayers = []
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inplanes = 2048
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first = True
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for i in range(3):
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uplayers.append(ReverseBottleneck(inplanes, inplanes // 2, norm_layer=norm_layer, passthrough=not first))
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inplanes = inplanes // 2
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first = False
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self.uplayers = nn.ModuleList(uplayers)
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# These two variables are separated out and renamed so that I can re-use parameters from a pretrained resnet_unet2.
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self.last_uplayer = ReverseBottleneck(256, 128, norm_layer=norm_layer, passthrough=True)
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self.tail3 = nn.Sequential(conv1x1(192, 128),
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norm_layer(128),
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nn.ReLU(),
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conv1x1(128, out_dim))
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del self.fc # Not used in this implementation and just consumes a ton of GPU memory.
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def _forward_impl(self, x):
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x0 = self.relu(self.bn1(self.conv1(x)))
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x = self.maxpool(x0)
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x1 = checkpoint(self.layer1, x)
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x2 = checkpoint(self.layer2, x1)
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x3 = checkpoint(self.layer3, x2)
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x4 = checkpoint(self.layer4, x3)
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unused = self.avgpool(x4) # This is performed for instance-level pixpro learning, even though it is unused.
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x = checkpoint(self.uplayers[0], x4)
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x = checkpoint(self.uplayers[1], x, x3)
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x = checkpoint(self.uplayers[2], x, x2)
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x = checkpoint(self.last_uplayer, x, x1)
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return checkpoint(self.tail3, torch.cat([x, x0], dim=1))
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def forward(self, x):
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return self._forward_impl(x)
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@register_model
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def register_u_resnet50_3(opt_net, opt):
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model = UResNet50_3(Bottleneck, [3, 4, 6, 3], out_dim=opt_net['odim'])
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if opt_get(opt_net, ['use_pretrained_base'], False):
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state_dict = load_state_dict_from_url('https://download.pytorch.org/models/resnet50-19c8e357.pth', progress=True)
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model.load_state_dict(state_dict, strict=False)
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return model
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if __name__ == '__main__':
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model = UResNet50_3(Bottleneck, [3,4,6,3])
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samp = torch.rand(1,3,224,224)
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y = model(samp)
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print(y.shape)
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# For pixpro: attach to "tail.3"
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