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| import numpy as np | |
| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| from openrec.modeling.common import Activation | |
| class ConvBNLayer(nn.Module): | |
| def __init__(self, | |
| in_channels, | |
| out_channels, | |
| kernel_size, | |
| stride=1, | |
| groups=1, | |
| act=None): | |
| super(ConvBNLayer, self).__init__() | |
| self.conv = nn.Conv2d( | |
| in_channels=in_channels, | |
| out_channels=out_channels, | |
| kernel_size=kernel_size, | |
| stride=stride, | |
| padding=(kernel_size - 1) // 2, | |
| groups=groups, | |
| bias=False, | |
| ) | |
| self.bn = nn.BatchNorm2d(out_channels) | |
| self.act = Activation(act) if act else None | |
| def forward(self, x): | |
| x = self.conv(x) | |
| x = self.bn(x) | |
| if self.act is not None: | |
| x = self.act(x) | |
| return x | |
| class LocalizationNetwork(nn.Module): | |
| def __init__(self, in_channels, num_fiducial, loc_lr, model_name): | |
| super(LocalizationNetwork, self).__init__() | |
| self.F = num_fiducial | |
| F = num_fiducial | |
| if model_name == 'large': | |
| num_filters_list = [64, 128, 256, 512] | |
| fc_dim = 256 | |
| else: | |
| num_filters_list = [16, 32, 64, 128] | |
| fc_dim = 64 | |
| self.block_list = nn.ModuleList() | |
| for fno in range(0, len(num_filters_list)): | |
| num_filters = num_filters_list[fno] | |
| conv = ConvBNLayer( | |
| in_channels=in_channels, | |
| out_channels=num_filters, | |
| kernel_size=3, | |
| act='relu', | |
| ) | |
| self.block_list.append(conv) | |
| if fno == len(num_filters_list) - 1: | |
| pool = nn.AdaptiveAvgPool2d(1) | |
| else: | |
| pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0) | |
| in_channels = num_filters | |
| self.block_list.append(pool) | |
| self.fc1 = nn.Linear(in_channels, fc_dim) | |
| # Init fc2 in LocalizationNetwork | |
| self.fc2 = nn.Linear(fc_dim, F * 2) | |
| initial_bias = self.get_initial_fiducials() | |
| initial_bias = initial_bias.reshape(-1) | |
| self.fc2.bias.data = torch.tensor(initial_bias, dtype=torch.float32) | |
| nn.init.zeros_(self.fc2.weight.data) | |
| self.out_channels = F * 2 | |
| def forward(self, x): | |
| """ | |
| Estimating parameters of geometric transformation | |
| Args: | |
| image: input | |
| Return: | |
| batch_C_prime: the matrix of the geometric transformation | |
| """ | |
| for block in self.block_list: | |
| x = block(x) | |
| x = x.squeeze(dim=2).squeeze(dim=2) | |
| x = self.fc1(x) | |
| x = F.relu(x) | |
| x = self.fc2(x) | |
| x = x.reshape(shape=[-1, self.F, 2]) | |
| return x | |
| def get_initial_fiducials(self): | |
| """see RARE paper Fig. | |
| 6 (a) | |
| """ | |
| F = self.F | |
| ctrl_pts_x = np.linspace(-1.0, 1.0, int(F / 2)) | |
| ctrl_pts_y_top = np.linspace(0.0, -1.0, num=int(F / 2)) | |
| ctrl_pts_y_bottom = np.linspace(1.0, 0.0, num=int(F / 2)) | |
| ctrl_pts_top = np.stack([ctrl_pts_x, ctrl_pts_y_top], axis=1) | |
| ctrl_pts_bottom = np.stack([ctrl_pts_x, ctrl_pts_y_bottom], axis=1) | |
| initial_bias = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], axis=0) | |
| return initial_bias | |
| class GridGenerator(nn.Module): | |
| def __init__(self, in_channels, num_fiducial): | |
| super(GridGenerator, self).__init__() | |
| self.eps = 1e-6 | |
| self.F = num_fiducial | |
| self.fc = nn.Linear(in_channels, 6) | |
| nn.init.constant_(self.fc.weight, 0) | |
| nn.init.constant_(self.fc.bias, 0) | |
| self.fc.weight.requires_grad = False | |
| self.fc.bias.requires_grad = False | |
| def forward(self, batch_C_prime, I_r_size): | |
| """Generate the grid for the grid_sampler. | |
| Args: | |
| batch_C_prime: the matrix of the geometric transformation | |
| I_r_size: the shape of the input image | |
| Return: | |
| batch_P_prime: the grid for the grid_sampler | |
| """ | |
| C = self.build_C_paddle() | |
| P = self.build_P_paddle(I_r_size) | |
| inv_delta_C_tensor = self.build_inv_delta_C_paddle(C).float() | |
| P_hat_tensor = self.build_P_hat_paddle(C, torch.tensor(P)).float() | |
| batch_C_ex_part_tensor = self.get_expand_tensor(batch_C_prime) | |
| batch_C_prime_with_zeros = torch.cat( | |
| [batch_C_prime, batch_C_ex_part_tensor], dim=1) | |
| batch_T = torch.matmul( | |
| inv_delta_C_tensor.to(batch_C_prime_with_zeros.device), | |
| batch_C_prime_with_zeros, | |
| ) | |
| batch_P_prime = torch.matmul(P_hat_tensor.to(batch_T.device), batch_T) | |
| return batch_P_prime | |
| def build_C_paddle(self): | |
| """Return coordinates of fiducial points in I_r; C.""" | |
| F = self.F | |
| ctrl_pts_x = torch.linspace(-1.0, 1.0, int(F / 2), dtype=torch.float64) | |
| ctrl_pts_y_top = -1 * torch.ones([int(F / 2)], dtype=torch.float64) | |
| ctrl_pts_y_bottom = torch.ones([int(F / 2)], dtype=torch.float64) | |
| ctrl_pts_top = torch.stack([ctrl_pts_x, ctrl_pts_y_top], dim=1) | |
| ctrl_pts_bottom = torch.stack([ctrl_pts_x, ctrl_pts_y_bottom], dim=1) | |
| C = torch.cat([ctrl_pts_top, ctrl_pts_bottom], dim=0) | |
| return C # F x 2 | |
| def build_P_paddle(self, I_r_size): | |
| I_r_height, I_r_width = I_r_size | |
| I_r_grid_x = (torch.arange(-I_r_width, I_r_width, 2) + | |
| 1.0) / torch.tensor(np.array([I_r_width])) | |
| I_r_grid_y = (torch.arange(-I_r_height, I_r_height, 2) + | |
| 1.0) / torch.tensor(np.array([I_r_height])) | |
| # P: self.I_r_width x self.I_r_height x 2 | |
| P = torch.stack(torch.meshgrid(I_r_grid_x, I_r_grid_y), dim=2) | |
| P = torch.permute(P, [1, 0, 2]) | |
| # n (= self.I_r_width x self.I_r_height) x 2 | |
| return P.reshape([-1, 2]) | |
| def build_inv_delta_C_paddle(self, C): | |
| """Return inv_delta_C which is needed to calculate T.""" | |
| F = self.F | |
| hat_eye = torch.eye(F) # F x F | |
| hat_C = torch.norm(C.reshape([1, F, 2]) - C.reshape([F, 1, 2]), | |
| dim=2) + hat_eye | |
| hat_C = (hat_C**2) * torch.log(hat_C) | |
| delta_C = torch.cat( # F+3 x F+3 | |
| [ | |
| torch.cat([torch.ones((F, 1)), C, hat_C], dim=1), # F x F+3 | |
| torch.concat([torch.zeros( | |
| (2, 3)), C.transpose(0, 1)], dim=1), # 2 x F+3 | |
| torch.concat([torch.zeros( | |
| (1, 3)), torch.ones((1, F))], dim=1), # 1 x F+3 | |
| ], | |
| axis=0, | |
| ) | |
| inv_delta_C = torch.inverse(delta_C) | |
| return inv_delta_C # F+3 x F+3 | |
| def build_P_hat_paddle(self, C, P): | |
| F = self.F | |
| eps = self.eps | |
| n = P.shape[0] # n (= self.I_r_width x self.I_r_height) | |
| # P_tile: n x 2 -> n x 1 x 2 -> n x F x 2 | |
| P_tile = torch.tile(torch.unsqueeze(P, dim=1), (1, F, 1)) | |
| C_tile = torch.unsqueeze(C, dim=0) # 1 x F x 2 | |
| P_diff = P_tile - C_tile # n x F x 2 | |
| # rbf_norm: n x F | |
| rbf_norm = torch.norm(P_diff, p=2, dim=2, keepdim=False) | |
| # rbf: n x F | |
| rbf = torch.multiply(torch.square(rbf_norm), torch.log(rbf_norm + eps)) | |
| P_hat = torch.cat([torch.ones((n, 1)), P, rbf], dim=1) | |
| return P_hat # n x F+3 | |
| def get_expand_tensor(self, batch_C_prime): | |
| B, H, C = batch_C_prime.shape | |
| batch_C_prime = batch_C_prime.reshape([B, H * C]) | |
| batch_C_ex_part_tensor = self.fc(batch_C_prime) | |
| batch_C_ex_part_tensor = batch_C_ex_part_tensor.reshape([-1, 3, 2]) | |
| return batch_C_ex_part_tensor | |
| class TPS(nn.Module): | |
| def __init__(self, in_channels, num_fiducial, loc_lr, model_name): | |
| super(TPS, self).__init__() | |
| self.loc_net = LocalizationNetwork(in_channels, num_fiducial, loc_lr, | |
| model_name) | |
| self.grid_generator = GridGenerator(self.loc_net.out_channels, | |
| num_fiducial) | |
| self.out_channels = in_channels | |
| def forward(self, image): | |
| image.stop_gradient = False | |
| batch_C_prime = self.loc_net(image) | |
| batch_P_prime = self.grid_generator(batch_C_prime, image.shape[2:]) | |
| batch_P_prime = batch_P_prime.reshape( | |
| [-1, image.shape[2], image.shape[3], 2]) | |
| is_fp16 = False | |
| if batch_P_prime.dtype != torch.float32: | |
| data_type = batch_P_prime.dtype | |
| image = image.float() | |
| batch_P_prime = batch_P_prime.float() | |
| is_fp16 = True | |
| batch_I_r = F.grid_sample(image, grid=batch_P_prime) | |
| if is_fp16: | |
| batch_I_r = batch_I_r.astype(data_type) | |
| return batch_I_r | |