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| import cv2 | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| def calculate_points(heatmaps): | |
| # change heatmaps to landmarks | |
| B, N, H, W = heatmaps.shape | |
| HW = H * W | |
| BN_range = np.arange(B * N) | |
| heatline = heatmaps.reshape(B, N, HW) | |
| indexes = np.argmax(heatline, axis=2) | |
| preds = np.stack((indexes % W, indexes // W), axis=2) | |
| preds = preds.astype(np.float, copy=False) | |
| inr = indexes.ravel() | |
| heatline = heatline.reshape(B * N, HW) | |
| x_up = heatline[BN_range, inr + 1] | |
| x_down = heatline[BN_range, inr - 1] | |
| # y_up = heatline[BN_range, inr + W] | |
| if any((inr + W) >= 4096): | |
| y_up = heatline[BN_range, 4095] | |
| else: | |
| y_up = heatline[BN_range, inr + W] | |
| if any((inr - W) <= 0): | |
| y_down = heatline[BN_range, 0] | |
| else: | |
| y_down = heatline[BN_range, inr - W] | |
| think_diff = np.sign(np.stack((x_up - x_down, y_up - y_down), axis=1)) | |
| think_diff *= .25 | |
| preds += think_diff.reshape(B, N, 2) | |
| preds += .5 | |
| return preds | |
| class AddCoordsTh(nn.Module): | |
| def __init__(self, x_dim=64, y_dim=64, with_r=False, with_boundary=False): | |
| super(AddCoordsTh, self).__init__() | |
| self.x_dim = x_dim | |
| self.y_dim = y_dim | |
| self.with_r = with_r | |
| self.with_boundary = with_boundary | |
| def forward(self, input_tensor, heatmap=None): | |
| """ | |
| input_tensor: (batch, c, x_dim, y_dim) | |
| """ | |
| batch_size_tensor = input_tensor.shape[0] | |
| xx_ones = torch.ones([1, self.y_dim], dtype=torch.int32, device=input_tensor.device) | |
| xx_ones = xx_ones.unsqueeze(-1) | |
| xx_range = torch.arange(self.x_dim, dtype=torch.int32, device=input_tensor.device).unsqueeze(0) | |
| xx_range = xx_range.unsqueeze(1) | |
| xx_channel = torch.matmul(xx_ones.float(), xx_range.float()) | |
| xx_channel = xx_channel.unsqueeze(-1) | |
| yy_ones = torch.ones([1, self.x_dim], dtype=torch.int32, device=input_tensor.device) | |
| yy_ones = yy_ones.unsqueeze(1) | |
| yy_range = torch.arange(self.y_dim, dtype=torch.int32, device=input_tensor.device).unsqueeze(0) | |
| yy_range = yy_range.unsqueeze(-1) | |
| yy_channel = torch.matmul(yy_range.float(), yy_ones.float()) | |
| yy_channel = yy_channel.unsqueeze(-1) | |
| xx_channel = xx_channel.permute(0, 3, 2, 1) | |
| yy_channel = yy_channel.permute(0, 3, 2, 1) | |
| xx_channel = xx_channel / (self.x_dim - 1) | |
| yy_channel = yy_channel / (self.y_dim - 1) | |
| xx_channel = xx_channel * 2 - 1 | |
| yy_channel = yy_channel * 2 - 1 | |
| xx_channel = xx_channel.repeat(batch_size_tensor, 1, 1, 1) | |
| yy_channel = yy_channel.repeat(batch_size_tensor, 1, 1, 1) | |
| if self.with_boundary and heatmap is not None: | |
| boundary_channel = torch.clamp(heatmap[:, -1:, :, :], 0.0, 1.0) | |
| zero_tensor = torch.zeros_like(xx_channel) | |
| xx_boundary_channel = torch.where(boundary_channel > 0.05, xx_channel, zero_tensor) | |
| yy_boundary_channel = torch.where(boundary_channel > 0.05, yy_channel, zero_tensor) | |
| if self.with_boundary and heatmap is not None: | |
| xx_boundary_channel = xx_boundary_channel.to(input_tensor.device) | |
| yy_boundary_channel = yy_boundary_channel.to(input_tensor.device) | |
| ret = torch.cat([input_tensor, xx_channel, yy_channel], dim=1) | |
| if self.with_r: | |
| rr = torch.sqrt(torch.pow(xx_channel, 2) + torch.pow(yy_channel, 2)) | |
| rr = rr / torch.max(rr) | |
| ret = torch.cat([ret, rr], dim=1) | |
| if self.with_boundary and heatmap is not None: | |
| ret = torch.cat([ret, xx_boundary_channel, yy_boundary_channel], dim=1) | |
| return ret | |
| class CoordConvTh(nn.Module): | |
| """CoordConv layer as in the paper.""" | |
| def __init__(self, x_dim, y_dim, with_r, with_boundary, in_channels, first_one=False, *args, **kwargs): | |
| super(CoordConvTh, self).__init__() | |
| self.addcoords = AddCoordsTh(x_dim=x_dim, y_dim=y_dim, with_r=with_r, with_boundary=with_boundary) | |
| in_channels += 2 | |
| if with_r: | |
| in_channels += 1 | |
| if with_boundary and not first_one: | |
| in_channels += 2 | |
| self.conv = nn.Conv2d(in_channels=in_channels, *args, **kwargs) | |
| def forward(self, input_tensor, heatmap=None): | |
| ret = self.addcoords(input_tensor, heatmap) | |
| last_channel = ret[:, -2:, :, :] | |
| ret = self.conv(ret) | |
| return ret, last_channel | |
| def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False, dilation=1): | |
| '3x3 convolution with padding' | |
| return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=strd, padding=padding, bias=bias, dilation=dilation) | |
| class BasicBlock(nn.Module): | |
| expansion = 1 | |
| def __init__(self, inplanes, planes, stride=1, downsample=None): | |
| super(BasicBlock, self).__init__() | |
| self.conv1 = conv3x3(inplanes, planes, stride) | |
| # self.bn1 = nn.BatchNorm2d(planes) | |
| self.relu = nn.ReLU(inplace=True) | |
| self.conv2 = conv3x3(planes, planes) | |
| # self.bn2 = nn.BatchNorm2d(planes) | |
| self.downsample = downsample | |
| self.stride = stride | |
| def forward(self, x): | |
| residual = x | |
| out = self.conv1(x) | |
| out = self.relu(out) | |
| out = self.conv2(out) | |
| if self.downsample is not None: | |
| residual = self.downsample(x) | |
| out += residual | |
| out = self.relu(out) | |
| return out | |
| class ConvBlock(nn.Module): | |
| def __init__(self, in_planes, out_planes): | |
| super(ConvBlock, self).__init__() | |
| self.bn1 = nn.BatchNorm2d(in_planes) | |
| self.conv1 = conv3x3(in_planes, int(out_planes / 2)) | |
| self.bn2 = nn.BatchNorm2d(int(out_planes / 2)) | |
| self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4), padding=1, dilation=1) | |
| self.bn3 = nn.BatchNorm2d(int(out_planes / 4)) | |
| self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4), padding=1, dilation=1) | |
| if in_planes != out_planes: | |
| self.downsample = nn.Sequential( | |
| nn.BatchNorm2d(in_planes), | |
| nn.ReLU(True), | |
| nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, bias=False), | |
| ) | |
| else: | |
| self.downsample = None | |
| def forward(self, x): | |
| residual = x | |
| out1 = self.bn1(x) | |
| out1 = F.relu(out1, True) | |
| out1 = self.conv1(out1) | |
| out2 = self.bn2(out1) | |
| out2 = F.relu(out2, True) | |
| out2 = self.conv2(out2) | |
| out3 = self.bn3(out2) | |
| out3 = F.relu(out3, True) | |
| out3 = self.conv3(out3) | |
| out3 = torch.cat((out1, out2, out3), 1) | |
| if self.downsample is not None: | |
| residual = self.downsample(residual) | |
| out3 += residual | |
| return out3 | |
| class HourGlass(nn.Module): | |
| def __init__(self, num_modules, depth, num_features, first_one=False): | |
| super(HourGlass, self).__init__() | |
| self.num_modules = num_modules | |
| self.depth = depth | |
| self.features = num_features | |
| self.coordconv = CoordConvTh( | |
| x_dim=64, | |
| y_dim=64, | |
| with_r=True, | |
| with_boundary=True, | |
| in_channels=256, | |
| first_one=first_one, | |
| out_channels=256, | |
| kernel_size=1, | |
| stride=1, | |
| padding=0) | |
| self._generate_network(self.depth) | |
| def _generate_network(self, level): | |
| self.add_module('b1_' + str(level), ConvBlock(256, 256)) | |
| self.add_module('b2_' + str(level), ConvBlock(256, 256)) | |
| if level > 1: | |
| self._generate_network(level - 1) | |
| else: | |
| self.add_module('b2_plus_' + str(level), ConvBlock(256, 256)) | |
| self.add_module('b3_' + str(level), ConvBlock(256, 256)) | |
| def _forward(self, level, inp): | |
| # Upper branch | |
| up1 = inp | |
| up1 = self._modules['b1_' + str(level)](up1) | |
| # Lower branch | |
| low1 = F.avg_pool2d(inp, 2, stride=2) | |
| low1 = self._modules['b2_' + str(level)](low1) | |
| if level > 1: | |
| low2 = self._forward(level - 1, low1) | |
| else: | |
| low2 = low1 | |
| low2 = self._modules['b2_plus_' + str(level)](low2) | |
| low3 = low2 | |
| low3 = self._modules['b3_' + str(level)](low3) | |
| up2 = F.interpolate(low3, scale_factor=2, mode='nearest') | |
| return up1 + up2 | |
| def forward(self, x, heatmap): | |
| x, last_channel = self.coordconv(x, heatmap) | |
| return self._forward(self.depth, x), last_channel | |
| class FAN(nn.Module): | |
| def __init__(self, num_modules=1, end_relu=False, gray_scale=False, num_landmarks=68, device='cuda'): | |
| super(FAN, self).__init__() | |
| self.device = device | |
| self.num_modules = num_modules | |
| self.gray_scale = gray_scale | |
| self.end_relu = end_relu | |
| self.num_landmarks = num_landmarks | |
| # Base part | |
| if self.gray_scale: | |
| self.conv1 = CoordConvTh( | |
| x_dim=256, | |
| y_dim=256, | |
| with_r=True, | |
| with_boundary=False, | |
| in_channels=3, | |
| out_channels=64, | |
| kernel_size=7, | |
| stride=2, | |
| padding=3) | |
| else: | |
| self.conv1 = CoordConvTh( | |
| x_dim=256, | |
| y_dim=256, | |
| with_r=True, | |
| with_boundary=False, | |
| in_channels=3, | |
| out_channels=64, | |
| kernel_size=7, | |
| stride=2, | |
| padding=3) | |
| self.bn1 = nn.BatchNorm2d(64) | |
| self.conv2 = ConvBlock(64, 128) | |
| self.conv3 = ConvBlock(128, 128) | |
| self.conv4 = ConvBlock(128, 256) | |
| # Stacking part | |
| for hg_module in range(self.num_modules): | |
| if hg_module == 0: | |
| first_one = True | |
| else: | |
| first_one = False | |
| self.add_module('m' + str(hg_module), HourGlass(1, 4, 256, first_one)) | |
| self.add_module('top_m_' + str(hg_module), ConvBlock(256, 256)) | |
| self.add_module('conv_last' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) | |
| self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256)) | |
| self.add_module('l' + str(hg_module), nn.Conv2d(256, num_landmarks + 1, kernel_size=1, stride=1, padding=0)) | |
| if hg_module < self.num_modules - 1: | |
| self.add_module('bl' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) | |
| self.add_module('al' + str(hg_module), | |
| nn.Conv2d(num_landmarks + 1, 256, kernel_size=1, stride=1, padding=0)) | |
| def forward(self, x): | |
| x, _ = self.conv1(x) | |
| x = F.relu(self.bn1(x), True) | |
| # x = F.relu(self.bn1(self.conv1(x)), True) | |
| x = F.avg_pool2d(self.conv2(x), 2, stride=2) | |
| x = self.conv3(x) | |
| x = self.conv4(x) | |
| previous = x | |
| outputs = [] | |
| boundary_channels = [] | |
| tmp_out = None | |
| for i in range(self.num_modules): | |
| hg, boundary_channel = self._modules['m' + str(i)](previous, tmp_out) | |
| ll = hg | |
| ll = self._modules['top_m_' + str(i)](ll) | |
| ll = F.relu(self._modules['bn_end' + str(i)](self._modules['conv_last' + str(i)](ll)), True) | |
| # Predict heatmaps | |
| tmp_out = self._modules['l' + str(i)](ll) | |
| if self.end_relu: | |
| tmp_out = F.relu(tmp_out) # HACK: Added relu | |
| outputs.append(tmp_out) | |
| boundary_channels.append(boundary_channel) | |
| if i < self.num_modules - 1: | |
| ll = self._modules['bl' + str(i)](ll) | |
| tmp_out_ = self._modules['al' + str(i)](tmp_out) | |
| previous = previous + ll + tmp_out_ | |
| return outputs, boundary_channels | |
| def get_landmarks(self, img): | |
| H, W, _ = img.shape | |
| offset = W / 64, H / 64, 0, 0 | |
| img = cv2.resize(img, (256, 256)) | |
| inp = img[..., ::-1] | |
| inp = torch.from_numpy(np.ascontiguousarray(inp.transpose((2, 0, 1)))).float() | |
| inp = inp.to(self.device) | |
| inp.div_(255.0).unsqueeze_(0) | |
| outputs, _ = self.forward(inp) | |
| out = outputs[-1][:, :-1, :, :] | |
| heatmaps = out.detach().cpu().numpy() | |
| pred = calculate_points(heatmaps).reshape(-1, 2) | |
| pred *= offset[:2] | |
| pred += offset[-2:] | |
| return pred | |