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Fix dtype support for SegmentAnythingModel #2207

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merged 4 commits into from
Dec 4, 2023
Merged

Fix dtype support for SegmentAnythingModel #2207

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f1bc06d
Fix dtype support for SAM
tirthasheshpatel Dec 1, 2023
8711e24
Update keras_cv/models/segmentation/segment_anything/sam_test.py
tirthasheshpatel Dec 1, 2023
2cf0f88
Fix Keras 2 failures
tirthasheshpatel Dec 2, 2023
910081d
Fix F401 lint error; remove unused import
tirthasheshpatel Dec 2, 2023
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14 changes: 8 additions & 6 deletions keras_cv/models/segmentation/segment_anything/sam_layers.py
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Original file line number Diff line number Diff line change
Expand Up @@ -99,7 +99,7 @@ def call(self, query, value, key):
# Attention
C_PH = ops.shape(query)[-1]
out = query @ ops.transpose(key, (0, 1, 3, 2))
out = out / ops.sqrt(ops.cast(C_PH, dtype=self.dtype))
out = out / ops.sqrt(ops.cast(C_PH, dtype=self.compute_dtype))
out = ops.softmax(out, axis=-1)

# Get output
Expand Down Expand Up @@ -278,7 +278,7 @@ def __init__(self, num_positional_features, scale, **kwargs):
self.positional_encoding_gaussian_matrix = self.add_weight(
name="positional_encoding_gaussian_matrix",
shape=(2, self.num_positional_features),
dtype=self.dtype,
dtype=self.variable_dtype,
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trainable=False,
initializer=keras.initializers.get("normal"),
)
Expand All @@ -288,7 +288,9 @@ def build(self, input_shape=None):

def __positional_encodings(self, coords):
coords = coords * 2 - 1
coords = coords @ self.positional_encoding_gaussian_matrix
coords = coords @ ops.cast(
self.positional_encoding_gaussian_matrix, dtype=self.compute_dtype
)
coords = coords * (2 * math.pi)
return ops.concatenate([ops.sin(coords), ops.cos(coords)], axis=-1)

Expand All @@ -305,11 +307,11 @@ def encode_image(self, size):
tensor: Positional encoding of the image.
"""
H, W = size
grid = ops.ones(shape=(H, W), dtype=self.dtype)
grid = ops.ones(shape=(H, W), dtype=self.compute_dtype)
y_embed = ops.cumsum(grid, axis=0) - 0.5
x_embed = ops.cumsum(grid, axis=1) - 0.5
y_embed = y_embed / ops.cast(H, self.dtype)
x_embed = x_embed / ops.cast(W, self.dtype)
y_embed = y_embed / ops.cast(H, self.compute_dtype)
x_embed = x_embed / ops.cast(W, self.compute_dtype)
return self.__positional_encodings(
ops.stack([x_embed, y_embed], axis=-1)
)
Expand Down
98 changes: 59 additions & 39 deletions keras_cv/models/segmentation/segment_anything/sam_test.py
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Expand Up @@ -220,48 +220,68 @@ def test_mask_decoder(self):
self.assertEqual(num_parameters, 4_058_340)

@pytest.mark.large
def test_end_to_end_model_predict(self):
model = SegmentAnythingModel(
backbone=self.image_encoder,
prompt_encoder=self.prompt_encoder,
mask_decoder=self.mask_decoder,
)

# We use box-only prompting for this test.
mask_prompts = self.get_prompts(1, "boxes")
inputs = {
"images": np.ones((1, 1024, 1024, 3)),
}
inputs.update(mask_prompts)

# Check the number of parameters
num_parameters = np.sum([np.prod(x.shape) for x in model.weights])
self.assertEqual(num_parameters, 89_670_912 + 6_476 + 4_058_340)
@parameterized.named_parameters(
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why is this test marked as large? (just for my own learning)

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The model initialized here is a ViT Base model with 130M parameters. Creating and evaluating it takes about 15-20 seconds which is significantly more than small unit tests in KerasCV.

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Gotcha, thanks! No need on this PR, but in general it will be good to separate small checks (like dtype stuff) into fast running tests, and keep the large test only for the things that must inherently by large parameter count and slow (like preset tests).

Did a big rewrite of KerasNLP backbones to this effect a bit ago. e.g. https://github.com/keras-team/keras-nlp/blob/a05f411a27eab437e71a1651c97e9addf26298ef/keras_nlp/models/bert/bert_backbone_test.py#L38-L80

[
("float32", "float32"),
("mixed_float16", "mixed_float16"),
("bfloat16", "bfloat16"),
]
)
def test_end_to_end_model_predict(self, dtype_policy):
import threading

with threading.Lock():
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what's with this? are we running our cv testing multi-processed ever?

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@tirthasheshpatel tirthasheshpatel Dec 1, 2023 •
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It can be multi-processed with the -n <num_threads> argumment in pytest. PyTest uses multi-processing and not multi-threading so locking should not be necessary here. I just added this as a safeguard if anyone ever tries to run these tests using Python threads.

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Long term, we could move towards Model(dtype=policy) support, so that these tests can run effectively without mutating global state.

# We are changing the global dtype policy here but don't want any
# other tests to use that policy, so compute under a lock until
# we reset the global policy.
old_policy = getattr(
keras.mixed_precision, "dtype_policy", lambda: "float32"
)()
keras.mixed_precision.set_global_policy(dtype_policy)
model = SegmentAnythingModel(
backbone=self.image_encoder,
prompt_encoder=self.prompt_encoder,
mask_decoder=self.mask_decoder,
)

# Forward pass through the model
outputs = model.predict(inputs)
masks, iou_pred = outputs["masks"], outputs["iou_pred"]
# We use box-only prompting for this test.
mask_prompts = self.get_prompts(1, "boxes")
inputs = {
"images": np.ones((1, 1024, 1024, 3)),
}
inputs.update(mask_prompts)

# Check the number of parameters
num_parameters = np.sum([np.prod(x.shape) for x in model.weights])
self.assertEqual(num_parameters, 89_670_912 + 6_476 + 4_058_340)

# Forward pass through the model
outputs = model.predict(inputs)
masks, iou_pred = outputs["masks"], outputs["iou_pred"]

# Check the output is equal to the one we expect if we
# run each component separately. This is to confirm that
# the graph is getting compiled correctly i.e. the jitted
# execution is equivalent to the eager execution.
features = self.image_encoder(inputs["images"])
outputs_ex = self.prompt_encoder(
{k: v for k, v in inputs.items() if k != "images"}
)
outputs_ex = self.mask_decoder(
{
"image_embeddings": features,
"image_pe": outputs_ex["dense_positional_embeddings"],
"sparse_prompt_embeddings": outputs_ex["sparse_embeddings"],
"dense_prompt_embeddings": outputs_ex["dense_embeddings"],
},
)
masks_ex, iou_pred_ex = outputs_ex["masks"], outputs_ex["iou_pred"]

# Check the output is equal to the one we expect if we
# run each component separately. This is to confirm that
# the graph is getting compiled correctly i.e. the jitted
# execution is equivalent to the eager execution.
features = self.image_encoder(inputs["images"])
outputs_ex = self.prompt_encoder(
{k: v for k, v in inputs.items() if k != "images"}
)
outputs_ex = self.mask_decoder(
{
"image_embeddings": features,
"image_pe": outputs_ex["dense_positional_embeddings"],
"sparse_prompt_embeddings": outputs_ex["sparse_embeddings"],
"dense_prompt_embeddings": outputs_ex["dense_embeddings"],
},
)
masks_ex, iou_pred_ex = outputs_ex["masks"], outputs_ex["iou_pred"]
self.assertAllClose(masks, masks_ex, atol=1e-4)
self.assertAllClose(iou_pred, iou_pred_ex, atol=1e-4)

self.assertAllClose(masks, masks_ex, atol=1e-4)
self.assertAllClose(iou_pred, iou_pred_ex, atol=1e-4)
# Reset the global policy
keras.mixed_precision.set_dtype_policy(old_policy)

@pytest.mark.extra_large
def test_end_to_end_model_save(self):
Expand Down
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