web_tool/ModelSessionKerasExample.py [136:159]:
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        return {
            "message": "Model reset successfully",
            "success": True
        }

    def run_model_on_tile(self, tile, batch_size=32):
        height = tile.shape[0]
        width = tile.shape[1]
        
        output = np.zeros((height, width, self.output_channels), dtype=np.float32)
        output_features = np.zeros((height, width, self.output_features), dtype=np.float32)

        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,]):
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web_tool/ModelSessionPyTorchExample.py [180:204]:
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        return {
            "message": "Model reset successfully",
            "success": True
        }


    def run_model_on_tile(self, tile, batch_size=32):
        height = tile.shape[0]
        width = tile.shape[1]
        
        output = np.zeros((height, width, self.output_channels), dtype=np.float32)
        output_features = np.zeros((height, width, self.output_features), dtype=np.float32)

        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,]):
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