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Fix Segformer test for num_classes 1 with softmax #2401

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Fix Segformer test for num_classes 1 with softmax #2401

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ddf6e46
Fix Segformer test for num_classes 1 with softmax
sampathweb Mar 21, 2024
2028392
fix segformer test gpu
sampathweb Mar 22, 2024
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7 changes: 4 additions & 3 deletions keras_cv/models/segmentation/segformer/segformer_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,12 +54,13 @@ def test_segformer_preset_error(self):
def test_segformer_call(self):
backbone = MiTBackbone.from_preset("mit_b0")
mit_model = SegFormer(backbone=backbone, num_classes=1)

mit_model.compile(loss=keras.losses.BinaryCrossentropy())
images = np.random.uniform(size=(2, 224, 224, 3))
mit_output = mit_model(images)
mit_pred = mit_model.predict(images)

seg_model = SegFormer.from_preset("segformer_b0", num_classes=1)
seg_model.compile(loss=keras.losses.BinaryCrossentropy())
seg_output = seg_model(images)
seg_pred = seg_model.predict(images)

Expand Down Expand Up @@ -98,7 +99,7 @@ def test_saved_model(self):
target_size = [512, 512, 3]

backbone = MiTBackbone.from_preset("mit_b0", input_shape=[512, 512, 3])
model = SegFormer(backbone=backbone, num_classes=1)
model = SegFormer(backbone=backbone, num_classes=2)

input_batch = np.ones(shape=[2] + target_size)
model_output = model(input_batch)
Expand All @@ -121,7 +122,7 @@ def test_saved_model(self):
def test_preset_saved_model(self):
target_size = [224, 224, 3]

model = SegFormer.from_preset("segformer_b0", num_classes=1)
model = SegFormer.from_preset("segformer_b0", num_classes=2)

input_batch = np.ones(shape=[2] + target_size)
model_output = model(input_batch)
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