from torch import nn import torch.nn.functional as F import torch import cv2 import numpy as np from models.resnet import resnet34 from models.layers.residual import Res2dBlock,Res1dBlock,DownRes2dBlock from sync_batchnorm import SynchronizedBatchNorm2d as BatchNorm2d def myres2Dblock(indim,outdim,k_size = 3,padding = 1, normalize = "batch",nonlinearity = "relu",order = "NACNAC"): return Res2dBlock(indim,outdim,k_size,padding,activation_norm_type=normalize,nonlinearity=nonlinearity,inplace_nonlinearity=True,order = order) def myres1Dblock(indim,outdim,k_size = 3,padding = 1, normalize = "batch",nonlinearity = "relu",order = "NACNAC"): return Res1dBlock(indim,outdim,k_size,padding,activation_norm_type=normalize,nonlinearity=nonlinearity,inplace_nonlinearity=True,order = order) def mydownres2Dblock(indim,outdim,k_size = 3,padding = 1, normalize = "batch",nonlinearity = "leakyrelu",order = "NACNAC"): return DownRes2dBlock(indim,outdim,k_size,padding=padding,activation_norm_type=normalize,nonlinearity=nonlinearity,inplace_nonlinearity=True,order = order) def gaussian2kp(heatmap): """ Extract the mean and from a heatmap """ shape = heatmap.shape heatmap = heatmap.unsqueeze(-1) grid = make_coordinate_grid(shape[2:], heatmap.type()).unsqueeze_(0).unsqueeze_(0) value = (heatmap * grid).sum(dim=(2, 3)) kp = {'value': value} return kp def kp2gaussian(kp, spatial_size, kp_variance): """ Transform a keypoint into gaussian like representation """ mean = kp['value'] #bs*numkp*2 coordinate_grid = make_coordinate_grid(spatial_size, mean.type()) #h*w*2 number_of_leading_dimensions = len(mean.shape) - 1 shape = (1,) * number_of_leading_dimensions + coordinate_grid.shape #1*1*h*w*2 coordinate_grid = coordinate_grid.view(*shape) repeats = mean.shape[:number_of_leading_dimensions] + (1, 1, 1) coordinate_grid = coordinate_grid.repeat(*repeats) #bs*numkp*h*w*2 # Preprocess kp shape shape = mean.shape[:number_of_leading_dimensions] + (1, 1, 2) mean = mean.view(*shape) mean_sub = (coordinate_grid - mean) out = torch.exp(-0.5 * (mean_sub ** 2).sum(-1) / kp_variance) return out def make_coordinate_grid(spatial_size, type): """ Create a meshgrid [-1,1] x [-1,1] of given spatial_size. """ h, w = spatial_size x = torch.arange(w).type(type) y = torch.arange(h).type(type) x = (2 * (x / (w - 1)) - 1) y = (2 * (y / (h - 1)) - 1) yy = y.view(-1, 1).repeat(1, w) xx = x.view(1, -1).repeat(h, 1) meshed = torch.cat([xx.unsqueeze_(2), yy.unsqueeze_(2)], 2) return meshed class ResBlock2d(nn.Module): """ Res block, preserve spatial resolution. """ def __init__(self, in_features, kernel_size, padding): super(ResBlock2d, self).__init__() self.conv1 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size, padding=padding) self.conv2 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size, padding=padding) self.norm1 = BatchNorm2d(in_features, affine=True) self.norm2 = BatchNorm2d(in_features, affine=True) def forward(self, x): out = self.norm1(x) out = F.relu(out,inplace=True) out = self.conv1(out) out = self.norm2(out) out = F.relu(out,inplace=True) out = self.conv2(out) out += x return out class UpBlock2d(nn.Module): """ Upsampling block for use in decoder. """ def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1): super(UpBlock2d, self).__init__() self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, padding=padding, groups=groups) self.norm = BatchNorm2d(out_features, affine=True) def forward(self, x): out = F.interpolate(x, scale_factor=2) del x out = self.conv(out) out = self.norm(out) out = F.relu(out,inplace=True) return out class DownBlock2d(nn.Module): """ Downsampling block for use in encoder. """ def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1): super(DownBlock2d, self).__init__() self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, padding=padding, groups=groups) self.norm = BatchNorm2d(out_features, affine=True) self.pool = nn.AvgPool2d(kernel_size=(2, 2)) def forward(self, x): out = self.conv(x) del x out = self.norm(out) out = F.relu(out,inplace=True) out = self.pool(out) return out class SameBlock2d(nn.Module): """ Simple block, preserve spatial resolution. """ def __init__(self, in_features, out_features, groups=1, kernel_size=3, padding=1): super(SameBlock2d, self).__init__() self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size, padding=padding, groups=groups) self.norm = BatchNorm2d(out_features, affine=True) def forward(self, x): out = self.conv(x) out = self.norm(out) out = F.relu(out,inplace=True) return out class Encoder(nn.Module): """ Hourglass Encoder """ def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256): super(Encoder, self).__init__() down_blocks = [] for i in range(num_blocks): down_blocks.append(DownBlock2d(in_features if i == 0 else min(max_features, block_expansion * (2 ** i)), min(max_features, block_expansion * (2 ** (i + 1))), kernel_size=3, padding=1)) self.down_blocks = nn.ModuleList(down_blocks) def forward(self, x): outs = [x] for down_block in self.down_blocks: outs.append(down_block(outs[-1])) return outs class Decoder(nn.Module): """ Hourglass Decoder """ def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256): super(Decoder, self).__init__() up_blocks = [] for i in range(num_blocks)[::-1]: in_filters = (1 if i == num_blocks - 1 else 2) * min(max_features, block_expansion * (2 ** (i + 1))) out_filters = min(max_features, block_expansion * (2 ** i)) up_blocks.append(UpBlock2d(in_filters, out_filters, kernel_size=3, padding=1)) self.up_blocks = nn.ModuleList(up_blocks) self.out_filters = block_expansion + in_features def forward(self, x): out = x.pop() for up_block in self.up_blocks: out = up_block(out) skip = x.pop() out = torch.cat([out, skip], dim=1) return out class Hourglass(nn.Module): """ Hourglass architecture. """ def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256): super(Hourglass, self).__init__() self.encoder = Encoder(block_expansion, in_features, num_blocks, max_features) self.decoder = Decoder(block_expansion, in_features, num_blocks, max_features) self.out_filters = self.decoder.out_filters def forward(self, x): return self.decoder(self.encoder(x)) class AntiAliasInterpolation2d(nn.Module): """ Band-limited downsampling, for better preservation of the input signal. """ def __init__(self, channels, scale): super(AntiAliasInterpolation2d, self).__init__() sigma = (1 / scale - 1) / 2 kernel_size = 2 * round(sigma * 4) + 1 self.ka = kernel_size // 2 self.kb = self.ka - 1 if kernel_size % 2 == 0 else self.ka kernel_size = [kernel_size, kernel_size] sigma = [sigma, sigma] # The gaussian kernel is the product of the # gaussian function of each dimension. kernel = 1 meshgrids = torch.meshgrid( [ torch.arange(size, dtype=torch.float32) for size in kernel_size ] ) for size, std, mgrid in zip(kernel_size, sigma, meshgrids): mean = (size - 1) / 2 kernel *= torch.exp(-(mgrid - mean) ** 2 / (2 * std ** 2)) # Make sure sum of values in gaussian kernel equals 1. kernel = kernel / torch.sum(kernel) # Reshape to depthwise convolutional weight kernel = kernel.view(1, 1, *kernel.size()) kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1)) self.register_buffer('weight', kernel) self.groups = channels self.scale = scale def forward(self, input): if self.scale == 1.0: return input out = F.pad(input, (self.ka, self.kb, self.ka, self.kb)) out = F.conv2d(out, weight=self.weight, groups=self.groups) out = F.interpolate(out, scale_factor=(self.scale, self.scale)) return out def draw_annotation_box( image, rotation_vector, translation_vector, color=(255, 255, 255), line_width=2): """Draw a 3D box as annotation of pose""" camera_matrix = np.array( [[233.333, 0, 128], [0, 233.333, 128], [0, 0, 1]], dtype="double") dist_coeefs = np.zeros((4, 1)) point_3d = [] rear_size = 75 rear_depth = 0 point_3d.append((-rear_size, -rear_size, rear_depth)) point_3d.append((-rear_size, rear_size, rear_depth)) point_3d.append((rear_size, rear_size, rear_depth)) point_3d.append((rear_size, -rear_size, rear_depth)) point_3d.append((-rear_size, -rear_size, rear_depth)) front_size = 100 front_depth = 100 point_3d.append((-front_size, -front_size, front_depth)) point_3d.append((-front_size, front_size, front_depth)) point_3d.append((front_size, front_size, front_depth)) point_3d.append((front_size, -front_size, front_depth)) point_3d.append((-front_size, -front_size, front_depth)) point_3d = np.array(point_3d, dtype=np.float).reshape(-1, 3) # Map to 2d image points (point_2d, _) = cv2.projectPoints(point_3d, rotation_vector, translation_vector, camera_matrix, dist_coeefs) point_2d = np.int32(point_2d.reshape(-1, 2)) # Draw all the lines cv2.polylines(image, [point_2d], True, color, line_width, cv2.LINE_AA) cv2.line(image, tuple(point_2d[1]), tuple( point_2d[6]), color, line_width, cv2.LINE_AA) cv2.line(image, tuple(point_2d[2]), tuple( point_2d[7]), color, line_width, cv2.LINE_AA) cv2.line(image, tuple(point_2d[3]), tuple( point_2d[8]), color, line_width, cv2.LINE_AA) class up_sample(nn.Module): def __init__(self, scale_factor): super(up_sample, self).__init__() self.interp = nn.functional.interpolate self.scale_factor = scale_factor def forward(self, x): x = self.interp(x, scale_factor=self.scale_factor,mode = 'linear',align_corners = True) return x class MyResNet34(nn.Module): def __init__(self,embedding_dim,input_channel = 3): super(MyResNet34, self).__init__() self.resnet = resnet34(norm_layer = BatchNorm2d,num_classes=embedding_dim,input_channel = input_channel) def forward(self, x): return self.resnet(x) class ImagePyramide(torch.nn.Module): """ Create image pyramide for computing pyramide perceptual loss. See Sec 3.3 """ def __init__(self, scales, num_channels): super(ImagePyramide, self).__init__() downs = {} for scale in scales: downs[str(scale).replace('.', '-')] = AntiAliasInterpolation2d(num_channels, scale) self.downs = nn.ModuleDict(downs) def forward(self, x): out_dict = {} for scale, down_module in self.downs.items(): out_dict['prediction_' + str(scale).replace('-', '.')] = down_module(x) return out_dict