我们从Python开源项目中，提取了以下7个代码示例，用于说明如何使用torch.nn.functional.grid_sample()。
def forward(self, x): x_shape = x.size() # (b, c, h, w) offset = self.offset_filter(x) # (b, 2*c, h, w) offset_w, offset_h = torch.split(offset, self.regular_filter.in_channels, 1) # (b, c, h, w) offset_w = offset_w.contiguous().view(-1, int(x_shape[2]), int(x_shape[3])) # (b*c, h, w) offset_h = offset_h.contiguous().view(-1, int(x_shape[2]), int(x_shape[3])) # (b*c, h, w) if not self.input_shape or self.input_shape != x_shape: self.input_shape = x_shape grid_w, grid_h = np.meshgrid(np.linspace(-1, 1, x_shape[3]), np.linspace(-1, 1, x_shape[2])) # (h, w) grid_w = torch.Tensor(grid_w) grid_h = torch.Tensor(grid_h) if self.cuda: grid_w = grid_w.cuda() grid_h = grid_h.cuda() self.grid_w = nn.Parameter(grid_w) self.grid_h = nn.Parameter(grid_h) offset_w = offset_w + self.grid_w # (b*c, h, w) offset_h = offset_h + self.grid_h # (b*c, h, w) x = x.contiguous().view(-1, int(x_shape[2]), int(x_shape[3])).unsqueeze(1) # (b*c, 1, h, w) x = F.grid_sample(x, torch.stack((offset_h, offset_w), 3)) # (b*c, h, w) x = x.contiguous().view(-1, int(x_shape[1]), int(x_shape[2]), int(x_shape[3])) # (b, c, h, w) x = self.regular_filter(x) return x
def _crop_pool_layer(self, bottom, rois, max_pool=True): # implement it using stn # box to affine # input (x1,y1,x2,y2) """ [ x2-x1 x1 + x2 - W + 1 ] [ ----- 0 --------------- ] [ W - 1 W - 1 ] [ ] [ y2-y1 y1 + y2 - H + 1 ] [ 0 ----- --------------- ] [ H - 1 H - 1 ] """ rois = rois.detach() x1 = rois[:, 1::4] / 16.0 y1 = rois[:, 2::4] / 16.0 x2 = rois[:, 3::4] / 16.0 y2 = rois[:, 4::4] / 16.0 height = bottom.size(2) width = bottom.size(3) # affine theta theta = Variable(rois.data.new(rois.size(0), 2, 3).zero_()) theta[:, 0, 0] = (x2 - x1) / (width - 1) theta[:, 0 ,2] = (x1 + x2 - width + 1) / (width - 1) theta[:, 1, 1] = (y2 - y1) / (height - 1) theta[:, 1, 2] = (y1 + y2 - height + 1) / (height - 1) if max_pool: pre_pool_size = cfg.POOLING_SIZE * 2 grid = F.affine_grid(theta, torch.Size((rois.size(0), 1, pre_pool_size, pre_pool_size))) crops = F.grid_sample(bottom.expand(rois.size(0), bottom.size(1), bottom.size(2), bottom.size(3)), grid) crops = F.max_pool2d(crops, 2, 2) else: grid = F.affine_grid(theta, torch.Size((rois.size(0), 1, cfg.POOLING_SIZE, cfg.POOLING_SIZE))) crops = F.grid_sample(bottom.expand(rois.size(0), bottom.size(1), bottom.size(2), bottom.size(3)), grid) return crops
def window_to_image(z_where, window_size, image_size, windows): n = windows.size(0) assert windows.size(1) == window_size ** 2, 'Size mismatch.' theta = expand_z_where(z_where) grid = F.affine_grid(theta, torch.Size((n, 1, image_size, image_size))) out = F.grid_sample(windows.view(n, 1, window_size, window_size), grid) return out.view(n, image_size, image_size)
def image_to_window(z_where, window_size, image_size, images): n = images.size(0) assert images.size(1) == images.size(2) == image_size, 'Size mismatch.' theta_inv = expand_z_where(z_where_inv(z_where)) grid = F.affine_grid(theta_inv, torch.Size((n, 1, window_size, window_size))) out = F.grid_sample(images.view(n, 1, image_size, image_size), grid) return out.view(n, -1) # Helper to expand parameters to the size of the mini-batch. I would # like to remove this and just write `t.expand(n, -1)` inline, but the # `-1` argument of `expand` doesn't seem to work with PyTorch 0.2.0.
def compare_grid_sample(): # do gradcheck N = random.randint(1, 8) C = 2 # random.randint(1, 8) H = 5 # random.randint(1, 8) W = 4 # random.randint(1, 8) input = Variable(torch.randn(N, C, H, W).cuda(), requires_grad=True) input_p = input.clone().data.contiguous() grid = Variable(torch.randn(N, H, W, 2).cuda(), requires_grad=True) grid_clone = grid.clone().contiguous() out_offcial = F.grid_sample(input, grid) grad_outputs = Variable(torch.rand(out_offcial.size()).cuda()) grad_outputs_clone = grad_outputs.clone().contiguous() grad_inputs = torch.autograd.grad(out_offcial, (input, grid), grad_outputs.contiguous()) grad_input_off = grad_inputs[0] crf = RoICropFunction() grid_yx = torch.stack([grid_clone.data[:,:,:,1], grid_clone.data[:,:,:,0]], 3).contiguous().cuda() out_stn = crf.forward(input_p, grid_yx) grad_inputs = crf.backward(grad_outputs_clone.data) grad_input_stn = grad_inputs[0] pdb.set_trace() delta = (grad_input_off.data - grad_input_stn).sum()
def stn(self, x): xs = self.localization(x) xs = xs.view(-1, 10 * 3 * 3) theta = self.fc_loc(xs) theta = theta.view(-1, 2, 3) grid = F.affine_grid(theta, x.size()) x = F.grid_sample(x, grid) return x
def _crop_pool_layer(bottom, rois, max_pool=True): # code modified from # https://github.com/ruotianluo/pytorch-faster-rcnn # implement it using stn # box to affine # input (x1,y1,x2,y2) """ [ x2-x1 x1 + x2 - W + 1 ] [ ----- 0 --------------- ] [ W - 1 W - 1 ] [ ] [ y2-y1 y1 + y2 - H + 1 ] [ 0 ----- --------------- ] [ H - 1 H - 1 ] """ rois = rois.detach() batch_size = bottom.size(0) D = bottom.size(1) H = bottom.size(2) W = bottom.size(3) roi_per_batch = rois.size(0) / batch_size x1 = rois[:, 1::4] / 16.0 y1 = rois[:, 2::4] / 16.0 x2 = rois[:, 3::4] / 16.0 y2 = rois[:, 4::4] / 16.0 height = bottom.size(2) width = bottom.size(3) # affine theta zero = Variable(rois.data.new(rois.size(0), 1).zero_()) theta = torch.cat([\ (x2 - x1) / (width - 1), zero, (x1 + x2 - width + 1) / (width - 1), zero, (y2 - y1) / (height - 1), (y1 + y2 - height + 1) / (height - 1)], 1).view(-1, 2, 3) if max_pool: pre_pool_size = cfg.POOLING_SIZE * 2 grid = F.affine_grid(theta, torch.Size((rois.size(0), 1, pre_pool_size, pre_pool_size))) bottom = bottom.view(1, batch_size, D, H, W).contiguous().expand(roi_per_batch, batch_size, D, H, W)\ .contiguous().view(-1, D, H, W) crops = F.grid_sample(bottom, grid) crops = F.max_pool2d(crops, 2, 2) else: grid = F.affine_grid(theta, torch.Size((rois.size(0), 1, cfg.POOLING_SIZE, cfg.POOLING_SIZE))) bottom = bottom.view(1, batch_size, D, H, W).contiguous().expand(roi_per_batch, batch_size, D, H, W)\ .contiguous().view(-1, D, H, W) crops = F.grid_sample(bottom, grid) return crops, grid
