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| import itertools | |
| import math | |
| import numpy as np | |
| import torch | |
| from torch import nn | |
| from torch.nn import functional as F | |
| def conv3x3_block(in_planes, out_planes, stride=1): | |
| """3x3 convolution with padding.""" | |
| conv_layer = nn.Conv2d(in_planes, | |
| out_planes, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1) | |
| block = nn.Sequential( | |
| conv_layer, | |
| nn.BatchNorm2d(out_planes), | |
| nn.ReLU(inplace=True), | |
| ) | |
| return block | |
| class STNHead(nn.Module): | |
| def __init__(self, in_planes, num_ctrlpoints, activation='none'): | |
| super(STNHead, self).__init__() | |
| self.in_planes = in_planes | |
| self.num_ctrlpoints = num_ctrlpoints | |
| self.activation = activation | |
| self.stn_convnet = nn.Sequential( | |
| conv3x3_block(in_planes, 32), # 32*64 | |
| nn.MaxPool2d(kernel_size=2, stride=2), | |
| conv3x3_block(32, 64), # 16*32 | |
| nn.MaxPool2d(kernel_size=2, stride=2), | |
| conv3x3_block(64, 128), # 8*16 | |
| nn.MaxPool2d(kernel_size=2, stride=2), | |
| conv3x3_block(128, 256), # 4*8 | |
| nn.MaxPool2d(kernel_size=2, stride=2), | |
| conv3x3_block(256, 256), # 2*4, | |
| nn.MaxPool2d(kernel_size=2, stride=2), | |
| conv3x3_block(256, 256)) # 1*2 | |
| self.stn_fc1 = nn.Sequential(nn.Linear(2 * 256, 512), | |
| nn.BatchNorm1d(512), | |
| nn.ReLU(inplace=True)) | |
| self.stn_fc2 = nn.Linear(512, num_ctrlpoints * 2) | |
| self.init_weights(self.stn_convnet) | |
| self.init_weights(self.stn_fc1) | |
| self.init_stn(self.stn_fc2) | |
| def init_weights(self, module): | |
| for m in module.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels | |
| m.weight.data.normal_(0, math.sqrt(2. / n)) | |
| if m.bias is not None: | |
| m.bias.data.zero_() | |
| elif isinstance(m, nn.BatchNorm2d): | |
| m.weight.data.fill_(1) | |
| m.bias.data.zero_() | |
| elif isinstance(m, nn.Linear): | |
| m.weight.data.normal_(0, 0.001) | |
| m.bias.data.zero_() | |
| def init_stn(self, stn_fc2): | |
| margin = 0.01 | |
| sampling_num_per_side = int(self.num_ctrlpoints / 2) | |
| ctrl_pts_x = np.linspace(margin, 1. - margin, sampling_num_per_side) | |
| ctrl_pts_y_top = np.ones(sampling_num_per_side) * margin | |
| ctrl_pts_y_bottom = np.ones(sampling_num_per_side) * (1 - margin) | |
| 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) | |
| ctrl_points = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], | |
| axis=0).astype(np.float32) | |
| if self.activation == 'none': | |
| pass | |
| elif self.activation == 'sigmoid': | |
| ctrl_points = -np.log(1. / ctrl_points - 1.) | |
| stn_fc2.weight.data.zero_() | |
| stn_fc2.bias.data = torch.Tensor(ctrl_points).view(-1) | |
| def forward(self, x): | |
| x = self.stn_convnet(x) | |
| batch_size, _, h, w = x.size() | |
| x = x.view(batch_size, -1) | |
| img_feat = self.stn_fc1(x) | |
| x = self.stn_fc2(0.1 * img_feat) | |
| if self.activation == 'sigmoid': | |
| x = F.sigmoid(x) | |
| x = x.view(-1, self.num_ctrlpoints, 2) | |
| return x | |
| def grid_sample(input, grid, canvas=None): | |
| output = F.grid_sample(input, grid) | |
| if canvas is None: | |
| return output | |
| else: | |
| input_mask = input.data.new(input.size()).fill_(1) | |
| output_mask = F.grid_sample(input_mask, grid) | |
| padded_output = output * output_mask + canvas * (1 - output_mask) | |
| return padded_output | |
| # phi(x1, x2) = r^2 * log(r), where r = ||x1 - x2||_2 | |
| def compute_partial_repr(input_points, control_points): | |
| N = input_points.size(0) | |
| M = control_points.size(0) | |
| pairwise_diff = input_points.view(N, 1, 2) - control_points.view(1, M, 2) | |
| # original implementation, very slow | |
| # pairwise_dist = torch.sum(pairwise_diff ** 2, dim = 2) # square of distance | |
| pairwise_diff_square = pairwise_diff * pairwise_diff | |
| pairwise_dist = pairwise_diff_square[:, :, 0] + pairwise_diff_square[:, :, | |
| 1] | |
| repr_matrix = 0.5 * pairwise_dist * torch.log(pairwise_dist) | |
| # fix numerical error for 0 * log(0), substitute all nan with 0 | |
| mask = repr_matrix != repr_matrix | |
| repr_matrix.masked_fill_(mask, 0) | |
| return repr_matrix | |
| # output_ctrl_pts are specified, according to our task. | |
| def build_output_control_points(num_control_points, margins): | |
| margin_x, margin_y = margins | |
| num_ctrl_pts_per_side = num_control_points // 2 | |
| ctrl_pts_x = np.linspace(margin_x, 1.0 - margin_x, num_ctrl_pts_per_side) | |
| ctrl_pts_y_top = np.ones(num_ctrl_pts_per_side) * margin_y | |
| ctrl_pts_y_bottom = np.ones(num_ctrl_pts_per_side) * (1.0 - margin_y) | |
| 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) | |
| # ctrl_pts_top = ctrl_pts_top[1:-1,:] | |
| # ctrl_pts_bottom = ctrl_pts_bottom[1:-1,:] | |
| output_ctrl_pts_arr = np.concatenate([ctrl_pts_top, ctrl_pts_bottom], | |
| axis=0) | |
| output_ctrl_pts = torch.Tensor(output_ctrl_pts_arr) | |
| return output_ctrl_pts | |
| class TPSSpatialTransformer(nn.Module): | |
| def __init__( | |
| self, | |
| output_image_size, | |
| num_control_points, | |
| margins, | |
| ): | |
| super(TPSSpatialTransformer, self).__init__() | |
| self.output_image_size = output_image_size | |
| self.num_control_points = num_control_points | |
| self.margins = margins | |
| self.target_height, self.target_width = output_image_size | |
| target_control_points = build_output_control_points( | |
| num_control_points, margins) | |
| N = num_control_points | |
| # N = N - 4 | |
| # create padded kernel matrix | |
| forward_kernel = torch.zeros(N + 3, N + 3) | |
| target_control_partial_repr = compute_partial_repr( | |
| target_control_points, target_control_points) | |
| forward_kernel[:N, :N].copy_(target_control_partial_repr) | |
| forward_kernel[:N, -3].fill_(1) | |
| forward_kernel[-3, :N].fill_(1) | |
| forward_kernel[:N, -2:].copy_(target_control_points) | |
| forward_kernel[-2:, :N].copy_(target_control_points.transpose(0, 1)) | |
| # compute inverse matrix | |
| inverse_kernel = torch.inverse(forward_kernel) | |
| # create target cordinate matrix | |
| HW = self.target_height * self.target_width | |
| target_coordinate = list( | |
| itertools.product(range(self.target_height), | |
| range(self.target_width))) | |
| target_coordinate = torch.Tensor(target_coordinate) # HW x 2 | |
| Y, X = target_coordinate.split(1, dim=1) | |
| Y = Y / (self.target_height - 1) | |
| X = X / (self.target_width - 1) | |
| target_coordinate = torch.cat([X, Y], | |
| dim=1) # convert from (y, x) to (x, y) | |
| target_coordinate_partial_repr = compute_partial_repr( | |
| target_coordinate, target_control_points) | |
| target_coordinate_repr = torch.cat([ | |
| target_coordinate_partial_repr, | |
| torch.ones(HW, 1), target_coordinate | |
| ], | |
| dim=1) | |
| # register precomputed matrices | |
| self.register_buffer('inverse_kernel', inverse_kernel) | |
| self.register_buffer('padding_matrix', torch.zeros(3, 2)) | |
| self.register_buffer('target_coordinate_repr', target_coordinate_repr) | |
| self.register_buffer('target_control_points', target_control_points) | |
| def forward(self, input, source_control_points): | |
| assert source_control_points.ndimension() == 3 | |
| assert source_control_points.size(1) == self.num_control_points | |
| assert source_control_points.size(2) == 2 | |
| batch_size = source_control_points.size(0) | |
| Y = torch.cat([ | |
| source_control_points, | |
| self.padding_matrix.expand(batch_size, 3, 2) | |
| ], 1) | |
| mapping_matrix = torch.matmul(self.inverse_kernel, Y) | |
| source_coordinate = torch.matmul(self.target_coordinate_repr, | |
| mapping_matrix) | |
| grid = source_coordinate.view(-1, self.target_height, | |
| self.target_width, 2) | |
| grid = torch.clamp( | |
| grid, 0, 1) # the source_control_points may be out of [0, 1]. | |
| # the input to grid_sample is normalized [-1, 1], but what we get is [0, 1] | |
| grid = 2.0 * grid - 1.0 | |
| output_maps = grid_sample(input, grid, canvas=None) | |
| return output_maps | |
| class Aster_TPS(nn.Module): | |
| def __init__( | |
| self, | |
| in_channels, | |
| tps_inputsize=[32, 64], | |
| tps_outputsize=[32, 100], | |
| num_control_points=20, | |
| tps_margins=[0.05, 0.05], | |
| ) -> None: | |
| super().__init__() | |
| self.in_channels = in_channels | |
| #TODO | |
| self.out_channels = in_channels | |
| self.tps_inputsize = tps_inputsize | |
| self.num_control_points = num_control_points | |
| self.stn_head = STNHead( | |
| in_planes=3, | |
| num_ctrlpoints=num_control_points, | |
| ) | |
| self.tps = TPSSpatialTransformer( | |
| output_image_size=tps_outputsize, | |
| num_control_points=num_control_points, | |
| margins=tps_margins, | |
| ) | |
| def forward(self, img): | |
| stn_input = F.interpolate(img, | |
| self.tps_inputsize, | |
| mode='bilinear', | |
| align_corners=True) | |
| ctrl_points = self.stn_head(stn_input) | |
| img = self.tps(img, ctrl_points) | |
| return img | |