# coding:utf-8 # 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()