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HEAT / models /resnet.py
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import torch
import torch.nn as nn
from torchvision import models
def convrelu(in_channels, out_channels, kernel, padding):
return nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel, padding=padding),
nn.ReLU(inplace=True),
)
class ResNetBackbone(nn.Module):
def __init__(self):
super().__init__()
self.base_model = models.resnet50(pretrained=False)
self.base_layers = list(self.base_model.children())
self.conv_original_size0 = convrelu(3, 64, 3, 1)
self.conv_original_size1 = convrelu(64, 64, 3, 1)
self.layer0 = nn.Sequential(*self.base_layers[:3]) # size=(N, 64, x.H/2, x.W/2)
self.layer1 = nn.Sequential(*self.base_layers[3:5]) # size=(N, 64, x.H/4, x.W/4)
self.layer2 = self.base_layers[5] # size=(N, 128, x.H/8, x.W/8)
self.layer3 = self.base_layers[6] # size=(N, 256, x.H/16, x.W/16)
self.layer4 = self.base_layers[7] # size=(N, 512, x.H/32, x.W/32)
self.strides = [8, 16, 32]
self.num_channels = [512, 1024, 2048]
def forward(self, inputs):
x_original = self.conv_original_size0(inputs)
x_original = self.conv_original_size1(x_original)
layer0 = self.layer0(inputs)
layer1 = self.layer1(layer0)
layer2 = self.layer2(layer1)
layer3 = self.layer3(layer2)
layer4 = self.layer4(layer3)
xs = {"0": layer2, "1": layer3, "2": layer4}
all_feats = {'layer0': layer0, 'layer1': layer1, 'layer2': layer2,
'layer3': layer3, 'layer4': layer4, 'x_original': x_original}
mask = torch.zeros(inputs.shape)[:, 0, :, :].to(layer4.device)
return xs, mask, all_feats
def train(self, mode=True):
# Override train so that the training mode is set as we want
nn.Module.train(self, mode)
if mode:
# fix all bn layers
def set_bn_eval(m):
classname = m.__class__.__name__
if classname.find('BatchNorm') != -1:
m.eval()
self.apply(set_bn_eval)
class ResNetUNet(nn.Module):
def __init__(self, n_class, out_dim=None, ms_feat=False):
super().__init__()
self.return_ms_feat = ms_feat
self.out_dim = out_dim
self.base_model = models.resnet50(pretrained=True)
self.base_layers = list(self.base_model.children())
self.layer0 = nn.Sequential(*self.base_layers[:3]) # size=(N, 64, x.H/2, x.W/2)
# self.layer0_1x1 = convrelu(64, 64, 1, 0)
self.layer1 = nn.Sequential(*self.base_layers[3:5]) # size=(N, 64, x.H/4, x.W/4)
# self.layer1_1x1 = convrelu(256, 256, 1, 0)
self.layer2 = self.base_layers[5] # size=(N, 128, x.H/8, x.W/8)
# self.layer2_1x1 = convrelu(512, 512, 1, 0)
self.layer3 = self.base_layers[6] # size=(N, 256, x.H/16, x.W/16)
# self.layer3_1x1 = convrelu(1024, 1024, 1, 0)
self.layer4 = self.base_layers[7] # size=(N, 512, x.H/32, x.W/32)
# self.layer4_1x1 = convrelu(2048, 2048, 1, 0)
self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.conv_up3 = convrelu(1024 + 2048, 1024, 3, 1)
self.conv_up2 = convrelu(512 + 1024, 512, 3, 1)
self.conv_up1 = convrelu(256 + 512, 256, 3, 1)
self.conv_up0 = convrelu(64 + 256, 128, 3, 1)
# self.conv_up1 = convrelu(512, 256, 3, 1)
# self.conv_up0 = convrelu(256, 128, 3, 1)
self.conv_original_size0 = convrelu(3, 64, 3, 1)
self.conv_original_size1 = convrelu(64, 64, 3, 1)
self.conv_original_size2 = convrelu(64 + 128, 64, 3, 1)
# self.conv_last = nn.Conv2d(128, n_class, 1)
self.conv_last = nn.Conv2d(64, n_class, 1)
if out_dim:
self.conv_out = nn.Conv2d(64, out_dim, 1)
# self.conv_out = nn.Conv2d(128, out_dim, 1)
# return_layers = {"layer2": "0", "layer3": "1", "layer4": "2"}
self.strides = [8, 16, 32]
self.num_channels = [512, 1024, 2048]
def forward(self, inputs):
x_original = self.conv_original_size0(inputs)
x_original = self.conv_original_size1(x_original)
layer0 = self.layer0(inputs)
layer1 = self.layer1(layer0)
layer2 = self.layer2(layer1)
layer3 = self.layer3(layer2)
layer4 = self.layer4(layer3)
# layer4 = self.layer4_1x1(layer4)
x = self.upsample(layer4)
# layer3 = self.layer3_1x1(layer3)
x = torch.cat([x, layer3], dim=1)
x = self.conv_up3(x)
layer3_up = x
x = self.upsample(x)
# layer2 = self.layer2_1x1(layer2)
x = torch.cat([x, layer2], dim=1)
x = self.conv_up2(x)
layer2_up = x
x = self.upsample(x)
# layer1 = self.layer1_1x1(layer1)
x = torch.cat([x, layer1], dim=1)
x = self.conv_up1(x)
x = self.upsample(x)
# layer0 = self.layer0_1x1(layer0)
x = torch.cat([x, layer0], dim=1)
x = self.conv_up0(x)
x = self.upsample(x)
x = torch.cat([x, x_original], dim=1)
x = self.conv_original_size2(x)
out = self.conv_last(x)
out = out.sigmoid().squeeze(1)
# xs = {"0": layer2, "1": layer3, "2": layer4}
xs = {"0": layer2_up, "1": layer3_up, "2": layer4}
mask = torch.zeros(inputs.shape)[:, 0, :, :].to(layer4.device)
# ms_feats = self.ms_feat(xs, mask)
if self.return_ms_feat:
if self.out_dim:
out_feat = self.conv_out(x)
out_feat = out_feat.permute(0, 2, 3, 1)
return xs, mask, out, out_feat
else:
return xs, mask, out
else:
return out
def train(self, mode=True):
# Override train so that the training mode is set as we want
nn.Module.train(self, mode)
if mode:
# fix all bn layers
def set_bn_eval(m):
classname = m.__class__.__name__
if classname.find('BatchNorm') != -1:
m.eval()
self.apply(set_bn_eval)