def test_should_throw_on_invalid_model_trainer_config(self):
config_dict = YamlConfigurationParser.parse_section(
self.INVALID_CONFIG_FILE_PATH, self.MODELS_CONFIG_YML_TAG)
assert_that(
calling(ModelConfiguration.from_dict).with_args(
self.SIMPLE_NET_NAME, config_dict[self.SIMPLE_NET_NAME]),
raises(InvalidConfigurationError))
def setUp(self) -> None:
config_dict = YamlConfigurationParser.parse_section(
self.VALID_CONFIG_FILE_PATH, self.MODELS_CONFIG_YML_TAG)
self._model_trainer_configs = [
ModelConfiguration.from_dict(self.SIMPLE_NET_NAME,
config_dict[self.SIMPLE_NET_NAME]),
ModelConfiguration.from_dict(self.SIMPLE_NET_NAME_2,
config_dict[self.SIMPLE_NET_NAME_2])
]
self._run_config = RunConfiguration()
self._model = nn.Conv2d(1, 32, (3, 3))
def from_yml(cls, yml_file, yml_tag="visdom"):
config = YamlConfigurationParser.parse_section(yml_file, yml_tag)
return VisdomConfiguration.from_dict(config)
if __name__ == '__main__':
# Basic settings
logging.basicConfig(level=logging.INFO)
torch.set_num_threads(multiprocessing.cpu_count())
torch.set_num_interop_threads(multiprocessing.cpu_count())
args = ArgsParserFactory.create_parser(
ArgsParserType.MODEL_TRAINING).parse_args()
# Create configurations.
run_config = RunConfiguration(use_amp=args.use_amp,
local_rank=args.local_rank,
amp_opt_level=args.amp_opt_level)
model_trainer_configs, training_config = YamlConfigurationParser.parse(
args.config_file)
dataset_configs = YamlConfigurationParser.parse_section(
args.config_file, "dataset")
dataset_configs = {
k: DatasetConfiguration(v)
for k, v, in dataset_configs.items()
}
data_augmentation_config = YamlConfigurationParser.parse_section(
args.config_file, "data_augmentation")
config_html = [
training_config.to_html(),
list(map(lambda config: config.to_html(), dataset_configs.values())),
list(map(lambda config: config.to_html(), model_trainer_configs))
]
# Prepare the data.
train_datasets = list()
valid_datasets = list()
cudnn.enabled = True
np.random.seed(42)
random.seed(42)
if __name__ == '__main__':
# Basic settings
logging.basicConfig(level=logging.INFO)
torch.set_num_threads(multiprocessing.cpu_count())
torch.set_num_interop_threads(multiprocessing.cpu_count())
args = ArgsParserFactory.create_parser(ArgsParserType.MODEL_TRAINING).parse_args()
# Create configurations.
run_config = RunConfiguration(use_amp=args.use_amp, local_rank=args.local_rank, amp_opt_level=args.amp_opt_level)
model_trainer_configs, training_config = YamlConfigurationParser.parse(args.config_file)
dataset_configs = YamlConfigurationParser.parse_section(args.config_file, "dataset")
dataset_configs = {k: DatasetConfiguration(v) for k, v, in dataset_configs.items()}
config_html = [training_config.to_html(), list(map(lambda config: config.to_html(), dataset_configs.values())),
list(map(lambda config: config.to_html(), model_trainer_configs))]
# Prepare the data.
train_datasets = list()
valid_datasets = list()
test_datasets = list()
reconstruction_datasets = list()
augmented_reconstruction_datasets = list()
normalized_reconstructors = list()
segmentation_reconstructors = list()
input_reconstructors = list()
gt_reconstructors = list()
augmented_input_reconstructors = list()
def test_should_parse_valid_model_trainer_config(self):
expected_config_dict = {
self.SIMPLE_NET_NAME: {
'type': self.SIMPLE_NET_TYPE,
'optimizer': {
'type': self.SIMPLE_NET_OPTIMIZER_TYPE,
'params': self.SIMPLE_NET_OPTIMIZER_PARAMS
},
'scheduler': {
'type': self.SIMPLE_NET_SCHEDULER_TYPE,
'params': self.SIMPLE_NET_SCHEDULER_PARAMS
},
'criterion': {
"cycle": {
'type': self.SIMPLE_NET_CRITERION_TYPE
},
"gan": {
'type': self.SIMPLE_NET_CRITERION_TYPE_2
}
},
'metrics': {
'Dice': {
'type': self.SIMPLE_NET_METRIC_TYPE_1,
'params': self.SIMPLE_NET_METRIC_PARAMS_1
},
'Accuracy': {
'type': self.SIMPLE_NET_METRIC_TYPE_2
}
},
'gradients': self.SIMPLE_NET_GRADIENT_CLIPPING
},
self.SIMPLE_NET_NAME_2: {
'type': self.SIMPLE_NET_TYPE,
'optimizer': {
'type': self.SIMPLE_NET_OPTIMIZER_TYPE,
'params': self.SIMPLE_NET_OPTIMIZER_PARAMS
},
'scheduler': {
'type': self.SIMPLE_NET_SCHEDULER_TYPE,
'params': self.SIMPLE_NET_SCHEDULER_PARAMS
},
'criterion': {
"cycle": {
'type': self.SIMPLE_NET_CRITERION_TYPE
},
"gan": {
'type': self.SIMPLE_NET_CRITERION_TYPE_2
}
},
'metrics': {
'Dice': {
'type': self.SIMPLE_NET_METRIC_TYPE_1,
'params': self.SIMPLE_NET_METRIC_PARAMS_1
},
'Accuracy': {
'type': self.SIMPLE_NET_METRIC_TYPE_2
}
},
'gradients': self.SIMPLE_NET_GRADIENT_CLIPPING
}
}
config_dict = YamlConfigurationParser.parse_section(
self.VALID_CONFIG_FILE_PATH, self.MODELS_CONFIG_YML_TAG)
model_trainer_config = ModelConfiguration.from_dict(
self.SIMPLE_NET_NAME, config_dict[self.SIMPLE_NET_NAME])
assert_that(config_dict, equal_to(expected_config_dict))
assert_that(model_trainer_config.optimizer_config.type,
equal_to(self.SIMPLE_NET_OPTIMIZER_TYPE))
assert_that(model_trainer_config.optimizer_config.params,
equal_to(self.SIMPLE_NET_OPTIMIZER_PARAMS))
assert_that(model_trainer_config.scheduler_config.type,
equal_to(self.SIMPLE_NET_SCHEDULER_TYPE))
assert_that(model_trainer_config.scheduler_config.params,
equal_to(self.SIMPLE_NET_SCHEDULER_PARAMS))
assert_that(model_trainer_config.criterions_configs[0].type,
equal_to(self.SIMPLE_NET_CRITERION_TYPE))
assert_that(model_trainer_config.criterions_configs[1].type,
equal_to(self.SIMPLE_NET_CRITERION_TYPE_2))
assert_that(model_trainer_config.metrics_configs[0].type,
equal_to(self.SIMPLE_NET_METRIC_TYPE_1))
assert_that(model_trainer_config.metrics_configs[0].params,
equal_to(self.SIMPLE_NET_METRIC_PARAMS_1))