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ModelOutputType is now a Flag instead of regular enum. Combinations of the base flags are provided for all of the previous output types for convenience. All checks in the code now use the basic flags and not the complex types.

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Signed-off-by: abigailt <abigailt@il.ibm.com>
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abigailt 2024-07-03 13:29:37 +03:00
parent 2895b40f05
commit 367cae679b
10 changed files with 126 additions and 100 deletions
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16
tests/test_pytorch.py
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@ -6,7 +6,9 @@ from scipy.special import expit
from art.utils import check_and_transform_label_format
from apt.utils.datasets.datasets import PytorchData
from apt.utils.models import ModelOutputType
from apt.utils.models import CLASSIFIER_SINGLE_OUTPUT_CLASS_LOGITS, CLASSIFIER_SINGLE_OUTPUT_BINARY_LOGITS, \
CLASSIFIER_SINGLE_OUTPUT_BINARY_PROBABILITIES, CLASSIFIER_MULTI_OUTPUT_CLASS_LOGITS, \
CLASSIFIER_MULTI_OUTPUT_BINARY_LOGITS
from apt.utils.models.pytorch_model import PyTorchClassifier
from art.utils import load_nursery
from apt.utils import dataset_utils
@ -128,7 +130,7 @@ def test_pytorch_nursery_state_dict():
optimizer = optim.Adam(inner_model.parameters(), lr=0.01)
model = PyTorchClassifier(model=inner_model,
output_type=ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_LOGITS,
output_type=CLASSIFIER_SINGLE_OUTPUT_CLASS_LOGITS,
loss=criterion,
optimizer=optimizer,
input_shape=(24,),
@ -161,7 +163,7 @@ def test_pytorch_nursery_save_entire_model():
optimizer = optim.Adam(inner_model.parameters(), lr=0.01)
model = PyTorchClassifier(model=inner_model,
output_type=ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_CLASS_LOGITS,
output_type=CLASSIFIER_SINGLE_OUTPUT_CLASS_LOGITS,
loss=criterion,
optimizer=optimizer,
input_shape=(24,),
@ -201,7 +203,7 @@ def test_pytorch_predictions_single_label_binary():
criterion = nn.BCEWithLogitsLoss()
optimizer = optim.Adam(inner_model.parameters(), lr=0.01)
model = PyTorchClassifier(model=inner_model, output_type=ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_BINARY_LOGITS,
model = PyTorchClassifier(model=inner_model, output_type=CLASSIFIER_SINGLE_OUTPUT_BINARY_LOGITS,
loss=criterion,
optimizer=optimizer, input_shape=(4,),
nb_classes=2)
@ -238,7 +240,7 @@ def test_pytorch_predictions_single_label_binary_prob():
optimizer = optim.Adam(inner_model.parameters(), lr=0.01)
model = PyTorchClassifier(model=inner_model,
output_type=ModelOutputType.CLASSIFIER_SINGLE_OUTPUT_BINARY_PROBABILITIES,
output_type=CLASSIFIER_SINGLE_OUTPUT_BINARY_PROBABILITIES,
loss=criterion,
optimizer=optimizer, input_shape=(4,),
nb_classes=2)
@ -307,7 +309,7 @@ def test_pytorch_predictions_multi_label_cat():
optimizer.step()
model = PyTorchClassifier(model=inner_model,
output_type=ModelOutputType.CLASSIFIER_MULTI_OUTPUT_CLASS_LOGITS,
output_type=CLASSIFIER_MULTI_OUTPUT_CLASS_LOGITS,
loss=criterion,
optimizer=optimizer,
input_shape=(24,),
@ -348,7 +350,7 @@ def test_pytorch_predictions_multi_label_binary():
optimizer = optim.RMSprop(inner_model.parameters(), lr=0.01)
model = PyTorchClassifier(model=inner_model,
output_type=ModelOutputType.CLASSIFIER_MULTI_OUTPUT_BINARY_LOGITS,
output_type=CLASSIFIER_MULTI_OUTPUT_BINARY_LOGITS,
loss=criterion,
optimizer=optimizer,
input_shape=(24,),