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Create pytorch objects inference fix #422

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merged 12 commits into from
May 4, 2022
Merged

Create pytorch objects inference fix #422

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03fbe39
added different ways to initialize headers
sarthakpati May 3, 2022
f927133
single call to populate headers
sarthakpati May 3, 2022
1c37793
Fixes additional areas where training header information is needed
psfoley May 3, 2022
1bd5af3
inference fixed
sarthakpati May 3, 2022
0cc2fc5
no need for this
sarthakpati May 3, 2022
5f2f2aa
Merge pull request #1 from sarthakpati/base_object_creator_flexibility
psfoley May 3, 2022
111bb7b
Merge branch 'master' into create_pytorch_objects_inference_fix
sarthakpati May 3, 2022
437c1f7
Headers need to be populated before loader is initialized
psfoley May 3, 2022
3e23663
Remove unnecessary header configuration
psfoley May 3, 2022
1a8246c
Black lint fix
psfoley May 3, 2022
efdf0d4
Black lint fix
psfoley May 3, 2022
c471f64
Black lint fix
psfoley May 3, 2022
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2 changes: 1 addition & 1 deletion GANDLF/compute/forward_pass.py
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Original file line number Diff line number Diff line change
Expand Up @@ -256,7 +256,7 @@ def validate_network(
label = None
if params["problem_type"] != "segmentation":
label = label_ground_truth
else:
elif "label" in patches_batch:
label = patches_batch["label"][torchio.DATA]

if label is not None:
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30 changes: 22 additions & 8 deletions GANDLF/compute/generic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,13 +34,13 @@ def create_pytorch_objects(parameters, train_csv=None, val_csv=None, device="cpu
"""
# initialize train and val loaders
train_loader, val_loader = None, None
headers_to_populate_train, headers_to_populate_val = None, None

if train_csv is not None:
# populate the data frames
parameters["training_data"], headers_train = parseTrainingCSV(
parameters["training_data"], headers_to_populate_train = parseTrainingCSV(
train_csv, train=True
)
parameters = populate_header_in_parameters(parameters, headers_train)
# get the train loader
train_loader = get_train_loader(parameters)
parameters["training_samples_size"] = len(train_loader)
Expand All @@ -52,10 +52,20 @@ def create_pytorch_objects(parameters, train_csv=None, val_csv=None, device="cpu
) = get_class_imbalance_weights(parameters["training_data"], parameters)

if val_csv is not None:
parameters["validation_data"], _ = parseTrainingCSV(val_csv, train=False)
parameters["validation_data"], headers_to_populate_val = parseTrainingCSV(
val_csv, train=False
)
# get the validation loader
val_loader = get_validation_loader(parameters)

# populate required headers
headers_to_populate = headers_to_populate_train
if headers_to_populate is None:
if headers_to_populate_val is not None:
headers_to_populate = headers_to_populate_val
if headers_to_populate is not None:
parameters = populate_header_in_parameters(parameters, headers_to_populate)

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# get the model
model = get_model(parameters)
parameters["model_parameters"] = model.parameters()
Expand All @@ -69,12 +79,16 @@ def create_pytorch_objects(parameters, train_csv=None, val_csv=None, device="cpu
model, amp=parameters["model"]["amp"], device=device, optimizer=optimizer
)

if not ("step_size" in parameters["scheduler"]):
parameters["scheduler"]["step_size"] = (
parameters["training_samples_size"] / parameters["learning_rate"]
)
# only need to create scheduler if training
if train_csv is not None:
if not ("step_size" in parameters["scheduler"]):
parameters["scheduler"]["step_size"] = (
parameters["training_samples_size"] / parameters["learning_rate"]
)

scheduler = get_scheduler(parameters)
scheduler = get_scheduler(parameters)
else:
scheduler = None

# these keys contain generators, and are not needed beyond this point in params
generator_keys_to_remove = ["optimizer_object", "model_parameters"]
Expand Down