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Create and Expose SamVisionModel as public for better accessibility (#36493)

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* move encoder below

* auto modeling

* write SamVisionTester

* fix vision attention shape

* fix SamVisionTest

* minor changes to SamVisionTest

* Revert "fix vision attention shape"

This reverts commit d2a4083ae5704716e33351aed03af8f3cc45f3ae.

* fix attention output shape in new tests

* remove encoder examples

* run modular on got_ocr2

* code formatting

* fix got_ocr2

* ruff fixes

* code quality

* add sam_vision in auto modeling and auto configuration

* remove composite test

* updated index.md

* add TFSamVisionEncoder to __init__

* fix public TFSamVisionEncoder

* remove outdated todo comment

* set test_torch_exportable

Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>

* rename: VisionEncoder -> VisionModel

* bring back original SamVisionEncoder

* rename back: VisionEncoderOutput -> VisionModelOutput

* undo changes in SamModelTester

* reuse SamVisionEncoder in SamVisionModel

---------

Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
This commit is contained in:
Armaghan Shakir
2025-03-31 14:45:07 +05:00
committed by GitHub
parent f99c279d20
commit 0710e9b1e8
12 changed files with 612 additions and 7 deletions
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193
tests/models/sam/test_modeling_tf_sam.py
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@@ -34,13 +34,204 @@ from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import SamProcessor, TFSamModel
from transformers import SamProcessor, TFSamModel, TFSamVisionModel
from transformers.modeling_tf_utils import keras
if is_vision_available():
from PIL import Image
class TFSamVisionModelTester:
def __init__(
self,
parent,
hidden_size=36,
intermediate_size=72,
projection_dim=62,
output_channels=32,
num_hidden_layers=2,
num_attention_heads=4,
num_channels=3,
image_size=24,
patch_size=2,
hidden_act="gelu",
layer_norm_eps=1e-06,
dropout=0.0,
attention_dropout=0.0,
initializer_range=0.02,
initializer_factor=1.0,
qkv_bias=True,
mlp_ratio=4.0,
use_abs_pos=True,
use_rel_pos=True,
rel_pos_zero_init=False,
window_size=14,
global_attn_indexes=[2, 5, 8, 11],
num_pos_feats=16,
mlp_dim=None,
batch_size=2,
):
self.parent = parent
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.projection_dim = projection_dim
self.output_channels = output_channels
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_channels = num_channels
self.image_size = image_size
self.patch_size = patch_size
self.hidden_act = hidden_act
self.layer_norm_eps = layer_norm_eps
self.dropout = dropout
self.attention_dropout = attention_dropout
self.initializer_range = initializer_range
self.initializer_factor = initializer_factor
self.qkv_bias = qkv_bias
self.mlp_ratio = mlp_ratio
self.use_abs_pos = use_abs_pos
self.use_rel_pos = use_rel_pos
self.rel_pos_zero_init = rel_pos_zero_init
self.window_size = window_size
self.global_attn_indexes = global_attn_indexes
self.num_pos_feats = num_pos_feats
self.mlp_dim = mlp_dim
self.batch_size = batch_size
def get_config(self):
return SamVisionConfig(
image_size=self.image_size,
patch_size=self.patch_size,
num_channels=self.num_channels,
hidden_size=self.hidden_size,
projection_dim=self.projection_dim,
num_hidden_layers=self.num_hidden_layers,
num_attention_heads=self.num_attention_heads,
intermediate_size=self.intermediate_size,
dropout=self.dropout,
attention_dropout=self.attention_dropout,
initializer_range=self.initializer_range,
initializer_factor=self.initializer_factor,
output_channels=self.output_channels,
qkv_bias=self.qkv_bias,
mlp_ratio=self.mlp_ratio,
use_abs_pos=self.use_abs_pos,
use_rel_pos=self.use_rel_pos,
rel_pos_zero_init=self.rel_pos_zero_init,
window_size=self.window_size,
global_attn_indexes=self.global_attn_indexes,
num_pos_feats=self.num_pos_feats,
mlp_dim=self.mlp_dim,
)
def prepare_config_and_inputs(self):
pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
config = self.get_config()
return config, pixel_values
def create_and_check_model(self, config, pixel_values):
model = TFSamVisionModel(config=config)
result = model(pixel_values)
output_size = self.image_size // self.patch_size
self.parent.assertEqual(
result.last_hidden_state.shape, (self.batch_size, self.output_channels, output_size, output_size)
)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
config, pixel_values = config_and_inputs
inputs_dict = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class TFSamVisionModelTest(TFModelTesterMixin, unittest.TestCase):
"""
Here we also overwrite some of the tests of test_modeling_common.py, as SAM's vision encoder does not use input_ids, inputs_embeds,
attention_mask and seq_length.
"""
all_model_classes = (TFSamVisionModel,) if is_tf_available() else ()
test_pruning = False
test_resize_embeddings = False
test_head_masking = False
test_onnx = False
def setUp(self):
self.model_tester = TFSamVisionModelTester(self)
self.config_tester = ConfigTester(self, config_class=SamVisionConfig, has_text_modality=False)
def test_config(self):
self.config_tester.run_common_tests()
@unittest.skip(reason="SAM's vision encoder does not use inputs_embeds")
def test_inputs_embeds(self):
pass
def test_model_common_attributes(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
self.assertIsInstance(model.get_input_embeddings(), (keras.layers.Layer))
x = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(x, keras.layers.Dense))
def test_forward_signature(self):
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
model = model_class(config)
signature = inspect.signature(model.call)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["pixel_values"]
self.assertListEqual(arg_names[:1], expected_arg_names)
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*config_and_inputs)
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.return_dict = True
expected_attention_shape = (
self.model_tester.batch_size * self.model_tester.num_attention_heads,
196,
196,
)
for model_class in self.all_model_classes:
inputs_dict["output_attentions"] = True
inputs_dict["output_hidden_states"] = False
config.return_dict = True
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
config.output_attentions = True
model = model_class(config)
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
attentions = outputs.attentions
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-4:]),
list(expected_attention_shape),
)
@unittest.skip(reason="Hidden_states is tested in create_and_check_model tests")
def test_hidden_states_output(self):
pass
class TFSamPromptEncoderTester:
def __init__(
self,