web_tool/ModelSessionPyTorchExample.py [193:220]:
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        counts = np.zeros((height, width), dtype=np.float32) + 0.000000001
        kernel = np.ones((self.input_size, self.input_size), dtype=np.float32) * 0.1
        kernel[10:-10, 10:-10] = 1
        kernel[self.down_weight_padding:self.down_weight_padding+self.stride_y,
               self.down_weight_padding:self.down_weight_padding+self.stride_x] = 5

        batch = []
        batch_indices = []
        batch_count = 0

        for y_index in (list(range(0, height - self.input_size, self.stride_y)) + [height - self.input_size,]):
            for x_index in (list(range(0, width - self.input_size, self.stride_x)) + [width - self.input_size,]):
                naip_im = tile[y_index:y_index+self.input_size, x_index:x_index+self.input_size, :]

                batch.append(naip_im)
                batch_indices.append((y_index, x_index))
                batch_count+=1
        batch = np.array(batch)

        model_output = []
        model_feature_output = []
        for i in range(0, len(batch), batch_size):

            t_batch = batch[i:i+batch_size]
            t_batch = np.rollaxis(t_batch, 3, 1)
            t_batch = torch.from_numpy(t_batch).to(self.device)

            with torch.no_grad():
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web_tool/ModelSessionPyTorchSegmentationModel.py [201:228]:
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        counts = np.zeros((height, width), dtype=np.float32) + 0.000000001
        kernel = np.ones((self.input_size, self.input_size), dtype=np.float32) * 0.1
        kernel[10:-10, 10:-10] = 1
        kernel[self.down_weight_padding:self.down_weight_padding+self.stride_y,
               self.down_weight_padding:self.down_weight_padding+self.stride_x] = 5

        batch = []
        batch_indices = []
        batch_count = 0

        for y_index in (list(range(0, height - self.input_size, self.stride_y)) + [height - self.input_size,]):
            for x_index in (list(range(0, width - self.input_size, self.stride_x)) + [width - self.input_size,]):
                naip_im = tile[y_index:y_index+self.input_size, x_index:x_index+self.input_size, :]

                batch.append(naip_im)
                batch_indices.append((y_index, x_index))
                batch_count+=1
        batch = np.array(batch)

        model_output = []
        model_feature_output = []
        for i in range(0, len(batch), batch_size):

            t_batch = batch[i:i+batch_size]
            t_batch = np.rollaxis(t_batch, 3, 1)
            t_batch = torch.from_numpy(t_batch).to(self.device)
            
            with torch.no_grad():
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