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# By Yuxiang Sun, Aug. 2, 2019
# Email: sun.yuxiang@outlook.com
import torch
import torch.nn as nn
import torchvision.models as models
import cv2
import matplotlib.pyplot as plt
import numpy as np
from scipy.ndimage import gaussian_filter
class RTFNet(nn.Module):
def __init__(self, n_class):
super(RTFNet, self).__init__()
self.num_resnet_layers = 152
if self.num_resnet_layers == 18:
resnet_raw_model1 = models.resnet18(pretrained=True)
resnet_raw_model2 = models.resnet18(pretrained=True)
self.inplanes = 512
elif self.num_resnet_layers == 34:
resnet_raw_model1 = models.resnet34(pretrained=True)
resnet_raw_model2 = models.resnet34(pretrained=True)
self.inplanes = 512
elif self.num_resnet_layers == 50:
resnet_raw_model1 = models.resnet50(pretrained=True)
resnet_raw_model2 = models.resnet50(pretrained=True)
self.inplanes = 2048
elif self.num_resnet_layers == 101:
resnet_raw_model1 = models.resnet101(pretrained=True)
resnet_raw_model2 = models.resnet101(pretrained=True)
self.inplanes = 2048
elif self.num_resnet_layers == 152:
resnet_raw_model1 = models.resnet152(pretrained=True)
resnet_raw_model2 = models.resnet152(pretrained=True)
self.inplanes = 2048
######## Thermal ENCODER ########
self.encoder_thermal_conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.encoder_thermal_conv1.weight.data = torch.unsqueeze(torch.mean(resnet_raw_model1.conv1.weight.data, dim=1), dim=1)
self.encoder_thermal_bn1 = resnet_raw_model1.bn1
self.encoder_thermal_relu = resnet_raw_model1.relu
self.encoder_thermal_maxpool = resnet_raw_model1.maxpool
self.encoder_thermal_layer1 = resnet_raw_model1.layer1
self.encoder_thermal_layer2 = resnet_raw_model1.layer2
self.encoder_thermal_layer3 = resnet_raw_model1.layer3
self.encoder_thermal_layer4 = resnet_raw_model1.layer4
######## RGB ENCODER ########
self.encoder_rgb_conv1 = resnet_raw_model2.conv1
self.encoder_rgb_bn1 = resnet_raw_model2.bn1
self.encoder_rgb_relu = resnet_raw_model2.relu
self.encoder_rgb_maxpool = resnet_raw_model2.maxpool
self.encoder_rgb_layer1 = resnet_raw_model2.layer1
self.encoder_rgb_layer2 = resnet_raw_model2.layer2
self.encoder_rgb_layer3 = resnet_raw_model2.layer3
self.encoder_rgb_layer4 = resnet_raw_model2.layer4
######## DECODER ########
self.deconv1 = self._make_transpose_layer(TransBottleneck, self.inplanes//2, 2, stride=2) # using // for python 3.6
self.deconv2 = self._make_transpose_layer(TransBottleneck, self.inplanes//2, 2, stride=2) # using // for python 3.6
self.deconv3 = self._make_transpose_layer(TransBottleneck, self.inplanes//2, 2, stride=2) # using // for python 3.6
self.deconv4 = self._make_transpose_layer(TransBottleneck, self.inplanes//2, 2, stride=2) # using // for python 3.6
self.deconv5 = self._make_transpose_layer(TransBottleneck, n_class, 2, stride=2)
def _make_transpose_layer(self, block, planes, blocks, stride=1):
upsample = None
if stride != 1:
upsample = nn.Sequential(
nn.ConvTranspose2d(self.inplanes, planes, kernel_size=2, stride=stride, padding=0, bias=False),
nn.BatchNorm2d(planes),
)
elif self.inplanes != planes:
upsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes, kernel_size=1, stride=stride, padding=0, bias=False),
nn.BatchNorm2d(planes),
)
for m in upsample.modules():
if isinstance(m, nn.ConvTranspose2d):
nn.init.xavier_uniform_(m.weight.data)
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
layers = []
for i in range(1, blocks):
layers.append(block(self.inplanes, self.inplanes))
layers.append(block(self.inplanes, planes, stride, upsample))
self.inplanes = planes
return nn.Sequential(*layers)
def forward(self, input):
rgb = input[:,:3]
thermal = input[:,3:]
verbose = False
# encoder
######################################################################
if verbose: print("rgb.size() original: ", rgb.size()) # (480, 640)
if verbose: print("thermal.size() original: ", thermal.size()) # (480, 640)
######################################################################
rgb = self.encoder_rgb_conv1(rgb)
if verbose: print("rgb.size() after conv1: ", rgb.size()) # (240, 320)
rgb = self.encoder_rgb_bn1(rgb)
if verbose: print("rgb.size() after bn1: ", rgb.size()) # (240, 320)
rgb = self.encoder_rgb_relu(rgb)
if verbose: print("rgb.size() after relu: ", rgb.size()) # (240, 320)
thermal = self.encoder_thermal_conv1(thermal)
if verbose: print("thermal.size() after conv1: ", thermal.size()) # (240, 320)
thermal = self.encoder_thermal_bn1(thermal)
if verbose: print("thermal.size() after bn1: ", thermal.size()) # (240, 320)
thermal = self.encoder_thermal_relu(thermal)
if verbose: print("thermal.size() after relu: ", thermal.size()) # (240, 320)
rgb = rgb + thermal
rgb = self.encoder_rgb_maxpool(rgb)
if verbose: print("rgb.size() after maxpool: ", rgb.size()) # (120, 160)
thermal = self.encoder_thermal_maxpool(thermal)
if verbose: print("thermal.size() after maxpool: ", thermal.size()) # (120, 160)
######################################################################
rgb = self.encoder_rgb_layer1(rgb)
if verbose: print("rgb.size() after layer1: ", rgb.size()) # (120, 160)
thermal = self.encoder_thermal_layer1(thermal)
if verbose: print("thermal.size() after layer1: ", thermal.size()) # (120, 160)
rgb = rgb + thermal
######################################################################
rgb = self.encoder_rgb_layer2(rgb)
if verbose: print("rgb.size() after layer2: ", rgb.size()) # (60, 80)
thermal = self.encoder_thermal_layer2(thermal)
if verbose: print("thermal.size() after layer2: ", thermal.size()) # (60, 80)
rgb = rgb + thermal
######################################################################
rgb = self.encoder_rgb_layer3(rgb)
if verbose: print("rgb.size() after layer3: ", rgb.size()) # (30, 40)
thermal = self.encoder_thermal_layer3(thermal)
if verbose: print("thermal.size() after layer3: ", thermal.size()) # (30, 40)
rgb = rgb + thermal
######################################################################
rgb = self.encoder_rgb_layer4(rgb)
if verbose: print("rgb.size() after layer4: ", rgb.size()) # (15, 20)
thermal = self.encoder_thermal_layer4(thermal)
if verbose: print("thermal.size() after layer4: ", thermal.size()) # (15, 20)
chenzeyang = thermal[0]
chenzeyang = torch.squeeze(chenzeyang)
att = chenzeyang.sum(axis=0).detach().cpu().numpy()
att = gaussian_filter(att, sigma=2)
att -= att.min()
att /= att.max()
# att1 = att.detach().cpu().numpy()
attmap = cv2.resize(att, (640, 480), interpolation=cv2.INTER_CUBIC)
attmap_uint8 = np.uint8(255 * attmap)
heatmap = cv2.applyColorMap(attmap_uint8, cv2.COLORMAP_JET)
plt.imshow(cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB))
# attmap = 255 - attmap.astype("uint8")
# plt.imshow(attmap)
plt.savefig("/opt/data/private/RTFNet/RTF_heatrgb_039.png")
plt.show()
#cv2.imwrite("/opt/data/private/czy/heatmap_00001D.png", heatmap)
chenbo = 1
fuse = rgb + thermal
######################################################################
# decoder
fuse = self.deconv1(fuse)
if verbose: print("fuse after deconv1: ", fuse.size()) # (30, 40)
fuse = self.deconv2(fuse)
if verbose: print("fuse after deconv2: ", fuse.size()) # (60, 80)
fuse = self.deconv3(fuse)
if verbose: print("fuse after deconv3: ", fuse.size()) # (120, 160)
fuse = self.deconv4(fuse)
if verbose: print("fuse after deconv4: ", fuse.size()) # (240, 320)
fuse = self.deconv5(fuse)
if verbose: print("fuse after deconv5: ", fuse.size()) # (480, 640)
return fuse
class TransBottleneck(nn.Module):
def __init__(self, inplanes, planes, stride=1, upsample=None):
super(TransBottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
if upsample is not None and stride != 1:
self.conv3 = nn.ConvTranspose2d(planes, planes, kernel_size=2, stride=stride, padding=0, bias=False)
else:
self.conv3 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.upsample = upsample
self.stride = stride
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.xavier_uniform_(m.weight.data)
elif isinstance(m, nn.ConvTranspose2d):
nn.init.xavier_uniform_(m.weight.data)
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.upsample is not None:
residual = self.upsample(x)
out += residual
out = self.relu(out)
return out
def unit_test():
num_minibatch = 2
rgb = torch.randn(num_minibatch, 3, 480, 640).cuda(0)
thermal = torch.randn(num_minibatch, 1, 480, 640).cuda(0)
rtf_net = RTFNet(20).cuda(0)
input = torch.cat((rgb, thermal), dim=1)
rtf_net(input)
#print('The model: ', rtf_net.modules)
if __name__ == '__main__':
unit_test()
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