28     def get_shapes(params):  argument
32         params["stride_x"] = 1 if "stride_x" not in params else params["stride_x"]
33         params["stride_y"] = 1 if "stride_y" not in params else params["stride_y"]
34         params["dilation_x"] = 1 if "dilation_x" not in params else params["dilation_x"]
35         params["dilation_y"] = 1 if "dilation_y" not in params else params["dilation_y"]
36         params["batch_size"] = 1 if "batch_size" not in params else params["batch_size"]
37         params["generate_bias"] = True if "generate_bias" not in params else params["generate_bias"]
38         if "out_activation_min" not in params:
39             params["out_activation_min"] = Lib.op_utils.get_dtype_min(params["input_data_type"])
40         if "out_activation_max" not in params:
41             params["out_activation_max"] = Lib.op_utils.get_dtype_max(params["input_data_type"])
42         if "bias_min" not in params:
43             params["bias_min"] = Lib.op_utils.get_dtype_min("int32_t")
44         if "bias_max" not in params:
45             params["bias_max"] = Lib.op_utils.get_dtype_max("int32_t")
46         if "weights_min" not in params:
47             params["weights_min"] = Lib.op_utils.get_dtype_min("int32_t")
48         if "weights_max" not in params:
49             params["weights_max"] = Lib.op_utils.get_dtype_max("int32_t")
51         in_ch = params["in_ch"]
52         out_ch = params["out_ch"]
53         groups = params["groups"]
61         shapes["input"] = (params["batch_size"], params["input_h"], params["input_w"], in_ch)
62         shapes["weight_shape"] = [params["filter_y"], params["filter_x"], filter_ch, out_ch]
64         if params["generate_bias"]:
69 …shapes["representational_dataset"] = (params["batch_size"], params["input_h"], params["input_w"], …
72     def generate_keras_model(shapes, params):  argument
75         input_shape = (params["batch_size"], params["input_h"], params["input_w"], params["in_ch"])
76 …   model.add(keras.layers.InputLayer(input_shape=input_shape[1:], batch_size=params["batch_size"]))
78         conv_layer = keras.layers.Conv2D(params["out_ch"],
79                                          kernel_size=(params["filter_y"], params["filter_x"]),
80                                          strides=(params["stride_y"], params["stride_x"]),
81                                          padding=params["padding"],
83                                          dilation_rate=(params["dilation_y"], params["dilation_x"]),
84                                          groups=params["groups"],
85                                          use_bias=params["generate_bias"])
89             shapes["weight_shape"], params["weights_min"], params["weights_max"], decimals=8)
91         if params["generate_bias"]:
93                 shapes["bias_shape"], params["bias_min"], params["bias_max"])
100     def generate_data_tflite(tflite_fname, params):  argument
117         if params["generate_bias"]:
126         if params["generate_bias"]:
140         def calculate_padding(x_output, y_output, params):  argument
141             x_input = params["input_w"]
142             y_input = params["input_h"]
144             if params["padding"] == "SAME":
146                 filter_x = (params["filter_x"] - 1) * params["dilation_x"] + 1
147                 filter_y = (params["filter_y"] - 1) * params["dilation_y"] + 1
149                 pad_along_width = max((x_output - 1) * params["stride_x"] + filter_x - x_input, 0)
150                 pad_along_height = max((y_output - 1) * params["stride_y"] + filter_y - y_input, 0)
169 … pad_y_with_offset, pad_x_with_offset, pad_y, pad_x = calculate_padding(x_output, y_output, params)
173         if params["generate_bias"]:
180         def generate_quantize_per_channel_multiplier(params, scales):  argument
186             num_channels = params["out_ch"]
202         generated_params["input_batches"] = params["batch_size"]
207         generated_params["dst_size"] = x_output * y_output * params["out_ch"] * params["batch_size"]
211 …per_channel_multiplier, per_channel_shift = generate_quantize_per_channel_multiplier(params, scale…