Lines Matching refs:params
25 def get_shapes(params): argument
29 params["batch_size"] = 1 if "batch_size" not in params else params["batch_size"]
30 params["generate_bias"] = True if "generate_bias" not in params else params["generate_bias"]
31 if "out_activation_min" not in params:
32 params["out_activation_min"] = Lib.op_utils.get_dtype_min(params["input_data_type"])
33 if "out_activation_max" not in params:
34 params["out_activation_max"] = Lib.op_utils.get_dtype_max(params["input_data_type"])
35 if "bias_min" not in params:
36 params["bias_min"] = Lib.op_utils.get_dtype_min("int32_t")
37 if "bias_max" not in params:
38 params["bias_max"] = Lib.op_utils.get_dtype_max("int32_t")
39 if "weights_min" not in params:
40 params["weights_min"] = Lib.op_utils.get_dtype_min("int32_t")
41 if "weights_max" not in params:
42 params["weights_max"] = Lib.op_utils.get_dtype_max("int32_t")
44 in_ch = params["in_ch"]
45 out_ch = params["out_ch"]
47 shapes["input"] = (params["batch_size"], in_ch)
50 if params["generate_bias"]:
52 params["json_template"] = "fully_connected.json"
55 params["json_template"] = "fully_connected_null_bias.json"
59 def generate_data_json(shapes, params): argument
66 generated_params["input_batches"] = params["batch_size"]
69 generated_params["dst_size"] = params["out_ch"] * params["batch_size"]
70 generated_params["accumulation_depth"] = params["in_ch"]
71 generated_params["input_offset"] = -params["input_zp"]
72 generated_params["output_offset"] = params["output_zp"]
92 if params["generate_bias"]:
110 params["input_scale"], params["w_scale"], params["output_scale"])