def test_collect_run_params(self): run_info = {} run_parameters = { "batch_size": 32, "synthetic_data": True, "train_epochs": 100.00, "dtype": "fp16", "resnet_size": 50, "random_tensor": tf.constant(2.0) } logger._collect_run_params(run_info, run_parameters) self.assertEqual(len(run_info["run_parameters"]), 6) self.assertEqual(run_info["run_parameters"][0], {"name": "batch_size", "long_value": 32}) self.assertEqual(run_info["run_parameters"][1], {"name": "dtype", "string_value": "fp16"}) v1_tensor = {"name": "random_tensor", "string_value": "Tensor(\"Const:0\", shape=(), dtype=float32)"} v2_tensor = {"name": "random_tensor", "string_value": "tf.Tensor(2.0, shape=(), dtype=float32)"} self.assertIn(run_info["run_parameters"][2], [v1_tensor, v2_tensor]) self.assertEqual(run_info["run_parameters"][3], {"name": "resnet_size", "long_value": 50}) self.assertEqual(run_info["run_parameters"][4], {"name": "synthetic_data", "bool_value": "True"}) self.assertEqual(run_info["run_parameters"][5], {"name": "train_epochs", "float_value": 100.00})
def test_collect_run_params(self): run_info = {} run_parameters = { "batch_size": 32, "synthetic_data": True, "train_epochs": 100.00, "dtype": "fp16", "resnet_size": 50, "random_tensor": tf.constant(2.0) } logger._collect_run_params(run_info, run_parameters) self.assertEqual(len(run_info["run_parameters"]), 6) self.assertEqual(run_info["run_parameters"][0], {"name": "batch_size", "long_value": 32}) self.assertEqual(run_info["run_parameters"][1], {"name": "dtype", "string_value": "fp16"}) self.assertEqual(run_info["run_parameters"][2], {"name": "random_tensor", "string_value": "Tensor(\"Const:0\", shape=(), dtype=float32)"}) self.assertEqual(run_info["run_parameters"][3], {"name": "resnet_size", "long_value": 50}) self.assertEqual(run_info["run_parameters"][4], {"name": "synthetic_data", "bool_value": "True"}) self.assertEqual(run_info["run_parameters"][5], {"name": "train_epochs", "float_value": 100.00})