Lines Matching refs:data
78 data = op_type.generate_data_tflite(fpaths["tflite"], params)
81 data = op_type.generate_data_json(shapes, params)
84 replacements = {**params, **data.params, **data.scales}
85 …convert_json_to_tflite(json_template_fpath, json_output_fpath, data.tensors, replacements, args.sc…
90 params.update(data.params)
93 for name, scale in data.effective_scales.items():
104 data.tensors["input"] = input_tensor.numpy()
107 data.tensors["output"] = invoke_tflite(fpaths["tflite"], input_tensor)
109 data.tensors["output"] = invoke_tflite_runtime(fpaths["tflite"], input_tensor)
111 data.tensors["output"] = invoke_tflite_micro(fpaths["tflite"], input_tensor)
129 for name, tensor in data.tensors.items():
136 if name in data.aliases:
137 … append_alias_to_c_array_file(fpaths[name], dtype, params["name"], name, data.aliases[name])
163 data = np.random.rand(*shape)
164 yield [data.astype(np.float32)]
194 data = interpreter.get_tensor(output_index)
196 return data.flatten()
207 data = interpreter.get_tensor(output_index)
209 return data.flatten()
216 data = interpreter.get_output(0)
218 return data.flatten()
239 def write_c_array(data, fname, dtype, prefix, tensor_name, test_data_fpath, header): argument
242 values, counts = np.unique(data, return_counts=True)
244 size = 0 if data is None else data.size
248 if size and len(data) > 500:
256 data_shape = data.shape
257 format_width = len(str(data.max())) + 1
258 data = data.flatten()
261 for i in range(len(data) - 1):
266 if len(data) - 1 % data_shape[-1] == 0: