(x, y, z, xi, yi, interp='nn') #create a matplotlib figure and adjust the width and heights to output contours to a resolution very close to the original sitemap fig = plt.figure(figsize=(image['width']/72, image['height']/72)) #create a single subplot, just takes over the ...
第一步:使用roi pool,使用x1 / width, x2/width, y1/height, y2/height获得比例用于获得部分卷积层,使用tf.image.resize_and_crop()每个边框调整后的卷积层,输入为256, 14, 14, 512, 然后使用池化层,输出的维度为256, 7, 7, 512 第二步:将输出的结果进行维度变化,适合进行全连接操作,接上3层全连接...
img_height//downsample_disp[0],dtype=torch.float32,device='cuda')# heightvs=torch.linspace(crop_x1+0.5*downsample_disp[1],crop_x2-0.5*downsample_disp[1],img_width//downsample_disp[1],dtype=torch.float32,device='cuda')# widthifdownsample_disp[2]==4:ds=self.downsampled_depth.cuda()eli...
linspace(start=0, stop=image_height, num=num_y_anchors + 2)[1:-1] center_xs = np.linspace(start=0, stop=image_width, num=num_x_anchors + 2)[1:-1] ratios = np.array(self._anchor_ratios) ratios = ratios[:, 0] / ratios[:, 1] sizes = np.array(self._anchor_sizes) # ...
center_h = (torch.arange(in_height, device=device) + offset_h) * steps_h center_w = (torch.arange(in_width, device=device) + offset_w) * steps_w shift_y, shift_x = torch.meshgrid(center_h, center_w) shift_y, shift_x = torch.meshgrid(center_h, center_w, indexing='ij') sh...
Return coordinate matrices from coordinate vectors.Make N-D coordinate arrays for vectorized evaluations of N-D scalar/vector fields over N-D grids, given one-dimensional coordinate arrays x1, x2,…, xn.Changed in version 1.9: 1-D and 0-D cases are allowed.