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CLIP update input #2358

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CLIP update input #2358

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84625c9
enable clip large GPU tests and fix jax broadcast_to error
Feb 26, 2024
b5d64a0
update to use ops
Feb 26, 2024
42a73e4
Merge branch 'keras-team:master' into fix_clip_jax
divyashreepathihalli Feb 27, 2024
d676c35
update model input format
Mar 11, 2024
52f3cc5
update golden values
Mar 11, 2024
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2 changes: 2 additions & 0 deletions .kokoro/github/ubuntu/gpu/build.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -70,6 +70,7 @@ then
keras_cv/models/object_detection/yolo_v8 \
keras_cv/models/object_detection_3d \
keras_cv/models/segmentation \
keras_cv/models/feature_extractor/clip \
keras_cv/models/stable_diffusion
else
pytest --cache-clear --check_gpu --run_large --durations 0 \
Expand All @@ -84,5 +85,6 @@ else
keras_cv/models/object_detection/yolo_v8 \
keras_cv/models/object_detection_3d \
keras_cv/models/segmentation \
keras_cv/models/feature_extractor/clip \
keras_cv/models/stable_diffusion
fi
14 changes: 13 additions & 1 deletion keras_cv/models/feature_extractor/clip/clip_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -95,6 +95,13 @@ def __init__(
self.transformer_layers = transformer_layers

vision_heads = self.vision_width // 64
self.image_input = keras.layers.Input(shape=(None,), name="image")
self.text_input = keras.layers.Input(
shape=(None, None, self.context_length), name="text"
)
self.attention_mask_input = keras.layers.Input(
shape=(None, None, self.context_length), name="attention_mask"
)
self.image_encoder = CLIPImageEncoder(
input_resolution=self.image_resolution,
patch_size=self.vision_patch_size,
Expand Down Expand Up @@ -133,7 +140,12 @@ def encode_images(self, image):
def encode_text(self, text, attention_mask=None):
return self.text_encoder(text, attention_mask=attention_mask)

def call(self, image, text, attention_mask=None):
def call(self, inputs):
image, text = inputs["image"], inputs["text"]
if "attention_mask" in inputs:
attention_mask = inputs["attention_mask"]
else:
attention_mask = None
self.image_embeddings = self.encode_images(image)
self.text_embeddings = self.encode_text(
text, attention_mask=attention_mask
Expand Down
61 changes: 41 additions & 20 deletions keras_cv/models/feature_extractor/clip/clip_model_test.py
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Expand Up @@ -34,27 +34,24 @@
"https://storage.googleapis.com/keras-cv/models/clip/merges.txt",
)

MODEL_PATH = keras.utils.get_file(
None,
"https://storage.googleapis.com/keras-cv/models/clip/clip-vit-base-patch32.weights.h5", # noqa: E501
)


class CLIPTest(TestCase):
@pytest.mark.large
def test_clip_model_golden_values(self):
model = CLIP()
model.load_weights(MODEL_PATH)
model = CLIP.from_preset("clip-vit-base-patch32")
processed_image = np.ones(shape=[1, 224, 224, 3])
processed_text = np.ones(shape=[3, 77])
attention_mask = np.ones(shape=[3, 77])
image_logits, text_logits = model(
processed_image, processed_text, attention_mask
{
"image": processed_image,
"text": processed_text,
"attention_mask": attention_mask,
}
)
print(image_logits)
self.assertAllClose(image_logits, [[1.896713, 1.896713, 1.896713]])
self.assertAllClose(image_logits, [[1.896712, 1.896712, 1.896712]])
self.assertAllClose(
text_logits, ops.transpose([[1.896713, 1.896713, 1.896713]])
text_logits, ops.transpose([[1.896712, 1.896712, 1.896712]])
)

def test_clip_preprocessor(self):
Expand Down Expand Up @@ -83,20 +80,29 @@ def test_presets(self):
processed_text = np.ones(shape=[3, 77])
attention_mask = np.ones(shape=[3, 77])
image_logits, text_logits = model(
processed_image, processed_text, attention_mask
{
"image": processed_image,
"text": processed_text,
"attention_mask": attention_mask,
}
)

@pytest.mark.large
def test_image_encoder_golden_values(self):
model = CLIP()
model.load_weights(MODEL_PATH)
model = CLIP.from_preset("clip-vit-base-patch32")
processed_image = np.ones(shape=[1, 224, 224, 3])
processed_text = np.ones(shape=[3, 77])
attention_mask = np.ones(shape=[3, 77])
model(processed_image, processed_text, attention_mask)
model(
{
"image": processed_image,
"text": processed_text,
"attention_mask": attention_mask,
}
)
self.assertAllClose(
model.image_embeddings[:, :5],
[[0.023215, 0.026526, 0.008914, -0.091689, 0.021791]],
[[0.023215, 0.026526, 0.008914, -0.091689, 0.021791]],
)

@pytest.mark.large
Expand All @@ -105,8 +111,13 @@ def test_text_encoder_golden_values(self):
processed_image = np.ones(shape=[1, 224, 224, 3])
processed_text = np.ones(shape=[3, 77])
attention_mask = np.ones(shape=[3, 77])
model(processed_image, processed_text, attention_mask)
print(model.text_embeddings)
model(
{
"image": processed_image,
"text": processed_text,
"attention_mask": attention_mask,
}
)
self.assertAllClose(
model.text_embeddings[0, :3],
[0.007531, -0.038361, -0.035686],
Expand All @@ -118,7 +129,13 @@ def test_saved_model(self):
processed_image = np.ones(shape=[1, 224, 224, 3])
processed_text = np.ones(shape=[3, 77])
attention_mask = np.ones(shape=[3, 77])
model_output, _ = model(processed_image, processed_text, attention_mask)
model_output, _ = model(
{
"image": processed_image,
"text": processed_text,
"attention_mask": attention_mask,
}
)
save_path = os.path.join(self.get_temp_dir(), "model.keras")
if keras_3():
model.save(save_path)
Expand All @@ -130,6 +147,10 @@ def test_saved_model(self):
self.assertIsInstance(restored_model, CLIP)
# Check that output matches.
restored_output, _ = restored_model(
processed_image, processed_text, attention_mask
{
"image": processed_image,
"text": processed_text,
"attention_mask": attention_mask,
}
)
self.assertAllClose(model_output, restored_output)
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