self.log("CLEANING MODEL DIR")
shutil.rmtree(model_dir, ignore_errors=True)
def add_version(name, dataset, flat, used_labels):
v.add_version(name,
dataloader=DataLoaderCallableWrapper(
BaseTorchDataLoader,
datasets=Datasets("../data/generated/rico.json",
train_data_load_function=load_data,
test_size=0.1,
validation_size=0,
used_labels=used_labels),
pytorch_dataset_factory=DatasetFactory(
dataset, used_labels=used_labels),
batch_size=4),
epocs=15,
num_classes=len(used_labels),
generate_images=False,
pretrained_model=None,
used_labels=used_labels)
v = Versions()
add_version("r4",
ObjectDetectionModelDataSet,
flat=False,
used_labels=load_semantic_classes(
"../data/generated/semnantic_colors.json"))
EXPERIMENT = Experiment(v, allow_delete_experiment_dir=False)
def _get_master_bar_write_fn(self):
def write_fn(line, end=None):
if end is not None:
print(line, end=end)
else:
print(end="\r")
self.log(line)
return write_fn
def clean_experiment_dir(self, model_dir):
self.log("CLEANING MODEL DIR")
shutil.rmtree(model_dir, ignore_errors=True)
v = Versions(None, 1, 1)
# Combined data
v.add_version("complete_combined_data_model",
dataloader=lambda: DataLoader(Datasets(
train_dataset_file_path=TRN_DATA_FILE,
test_dataset_file_path=TST_DATA_FILE,
train_data_load_function=LoadDatasetUtterance(LoadDatasetUtterance.COMBINED_FUNCTIONS),
validation_size=0)),
custom_paramters={
"oov_df": lambda: DataLoader(Datasets(
test_dataset_file_path=TST_OOV_DATA_FILE,
test_data_load_function=LoadDatasetUtterance(LoadDatasetUtterance.COMBINED_FUNCTIONS))),
})
if idx % 3 == 0:
metric_container.reset()
metric_container.log_metrics(['a', '2'])
metric_container.reset_epoch()
metric_container.log_metrics()
self.log("trained: {}".format(self.model.train()))
self.copy_related_files("experiments/exports")
def evaluate_loop(self, input_fn):
self.log("calling input fn")
input_fn()
metrics = MetricContainer(['a', 'b'])
metrics.a.update(10, 1)
metrics.b.update(2, 1)
return metrics
def export_model(self):
self.log("YAY! Exported!")
dl = DataLoaderCallableWrapper(TestingDataLoader)
v = Versions(dl, 1, 10, learning_rate=0.01)
v.add_version("version1", hyperparameter="a hyperparameter")
v.add_version("version2", custom_paramters={"hyperparameter": None})
v.add_version("version3", custom_paramters={"hyperparameter": None})
v.add_version("version4", custom_paramters={"hyperparameter": None})
v.filter_versions(blacklist_versions=["version3"])
v.filter_versions(whitelist_versions=["version1", "version2"])
v.add_version("version5", custom_paramters={"hyperparameter": None})
EXPERIMENT = TestingExperiment(versions=v)
next_func = self.dm.get_next_system_concern()
except Exception:
# print(next_func, dialogue, function_map, asked, outed, completed_functions, self.dm.context.ac, sep="\n")
raise
# If there are any functions in the function_map not in the completed_functions
# the dialogue failed
if len([f for f in function_map.keys() if f not in completed_functions]) != 0:
# print(2, *self.dm.context.contexts, sep="\n")
# print("bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb")
# print(next_func, dialogue, function_map, asked, outed, completed_functions, self.dm.context.ac, sep="\n")
# raise
return False
return True
v = Versions(None, 1, 1)
templates.print_function_output = False
v.add_version('complete_combined',
order=1,
dataloader=DataLoaderCallableWrapper(DataLoader,
test_file_path=TST_DATA_FILE,
test_oov_file_path=TST_OOV_DATA_FILE,
function_filter=None),
custom_paramters={
'root_model_path': "outputs/experiment_ckpts/ulmfit-complete_combined_data_model",
'model': ModelCombined,
# 'root_model_path': "outputs/experiment_ckpts/ulmfit-generated_data_model",
'functions': [templates.order_taxi, templates.book_room,
templates.book_ticket, templates.book_table]})
from mlpipeline import Versions, MetricContainer
from mlpipeline.base import ExperimentABC, DataLoaderABC
class Experiment(ExperimentABC):
def setup_model(self):
pass
def pre_execution_hook(self, **kwargs):
pass
def train_loop(self, input_fn, **kwargs):
pass
def evaluate_loop(self, input_fn, **kwargs):
return MetricContainer()
def export_model(self, **kwargs):
pass
def _export_model(self, export_dir):
pass
def post_execution_hook(self, **kwargs):
pass
v = Versions()
v.add_version("Run-1")
EXPERIMENT = Experiment(v)