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tests/models/sam2/test_modeling_sam2.py
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Cyril Vallez
[core] Faster and thread-safe `check_model_inputs` implementation (#43765)
06 фев 2026, 19:08
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06 фев 2026, 19:08
f8f2834
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# Copyright 2025 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Testing suite for the PyTorch SAM2 model.""" import gc import tempfile import unittest import requests from transformers import ( Sam2Config, Sam2HieraDetConfig, Sam2MaskDecoderConfig, Sam2Processor, Sam2PromptEncoderConfig, Sam2VisionConfig, pipeline, ) from transformers.testing_utils import ( backend_empty_cache, require_torch, slow, torch_device, ) from transformers.utils import is_torch_available, is_vision_available from transformers.video_utils import load_video from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import Sam2Model, Sam2Processor, Sam2VisionModel if is_vision_available(): from PIL import Image class Sam2VisionModelTester: def __init__( self, parent, hidden_size=12, embed_dim_per_stage=[12, 24, 48, 96], num_attention_heads_per_stage=[1, 2, 4, 8], num_channels=3, image_size=128, patch_kernel_size=7, patch_stride=4, patch_padding=3, batch_size=2, blocks_per_stage=[1, 2, 7, 2], backbone_channel_list=[96, 48, 24, 12], backbone_feature_sizes=[[32, 32], [16, 16], [8, 8]], fpn_hidden_size=32, is_training=True, ): self.parent = parent self.hidden_size = hidden_size self.image_size = image_size self.num_channels = num_channels self.patch_kernel_size = patch_kernel_size self.patch_stride = patch_stride self.patch_padding = patch_padding self.batch_size = batch_size self.is_training = is_training self.blocks_per_stage = blocks_per_stage self.embed_dim_per_stage = embed_dim_per_stage self.num_attention_heads_per_stage = num_attention_heads_per_stage self.backbone_channel_list = backbone_channel_list self.backbone_feature_sizes = backbone_feature_sizes self.fpn_hidden_size = fpn_hidden_size def get_config(self): backbone_config = Sam2HieraDetConfig( hidden_size=self.hidden_size, num_channels=self.num_channels, image_size=self.image_size, patch_stride=self.patch_stride, patch_kernel_size=self.patch_kernel_size, patch_padding=self.patch_padding, blocks_per_stage=self.blocks_per_stage, embed_dim_per_stage=self.embed_dim_per_stage, num_attention_heads_per_stage=self.num_attention_heads_per_stage, ) return Sam2VisionConfig( backbone_config=backbone_config, backbone_channel_list=self.backbone_channel_list, backbone_feature_sizes=self.backbone_feature_sizes, fpn_hidden_size=self.fpn_hidden_size, ) 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 = Sam2VisionModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(pixel_values) output_size = self.image_size // self.patch_stride // (2 * len(self.blocks_per_stage)) output_channels = self.hidden_size * 2 * len(self.blocks_per_stage) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, output_size, output_size, output_channels) ) 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_torch class Sam2VisionModelTest(ModelTesterMixin, 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 = (Sam2VisionModel,) if is_torch_available() else () test_resize_embeddings = False def setUp(self): self.model_tester = Sam2VisionModelTester(self) self.config_tester = ConfigTester(self, config_class=Sam2VisionConfig, has_text_modality=False) def test_config(self): self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.create_and_test_config_with_num_labels() self.config_tester.check_config_can_be_init_without_params() self.config_tester.check_config_arguments_init() @unittest.skip(reason="SAM's vision encoder does not use inputs_embeds") def test_inputs_embeds(self): pass def test_model_get_set_embeddings(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(), (nn.Module)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) # Overriding as attention shape depends on window_size def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True 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._from_config(config, attn_implementation="eager") config = model.config model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions expected_num_attentions = sum(self.model_tester.blocks_per_stage) self.assertEqual(len(attentions), expected_num_attentions) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True config.backbone_config.output_attentions = True window_size = config.backbone_config.window_size_per_stage[0] out_dim = config.backbone_config.hidden_size patch_stride = config.backbone_config.patch_stride num_windows = ( self.model_tester.batch_size * (config.backbone_config.image_size // (window_size * patch_stride)) ** 2 ) model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertEqual(len(attentions), expected_num_attentions) self.assertListEqual( list(attentions[0].shape[-4:]), [num_windows, window_size, window_size, out_dim], ) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertEqual(len(attentions), expected_num_attentions) self.assertListEqual( list(attentions[0].shape[-4:]), [num_windows, window_size, window_size, out_dim], ) # Overriding as attention shape depends on window_size def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class, image_size): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.hidden_states expected_num_layers = sum(self.model_tester.blocks_per_stage) + 1 self.assertEqual(len(hidden_states), expected_num_layers) self.assertListEqual( list(hidden_states[0].shape[-4:]), [ self.model_tester.batch_size, self.model_tester.image_size // self.model_tester.patch_stride, self.model_tester.image_size // self.model_tester.patch_stride, self.model_tester.hidden_size, ], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() image_size = self.model_tester.image_size for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class, image_size) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True config.backbone_config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class, image_size) # Override as diffence slightly higher than the threshold def test_batching_equivalence(self, atol=5e-4, rtol=5e-4): super().test_batching_equivalence(atol=atol, rtol=rtol) def test_sdpa_can_compile_dynamic(self): self.skipTest(reason="SAM model can't be compiled dynamic yet") class Sam2PromptEncoderTester: def __init__( self, hidden_size=32, input_image_size=128, patch_size=16, mask_input_channels=8, num_point_embeddings=4, hidden_act="gelu", ): self.hidden_size = hidden_size self.input_image_size = input_image_size self.patch_size = patch_size self.mask_input_channels = mask_input_channels self.num_point_embeddings = num_point_embeddings self.hidden_act = hidden_act def get_config(self): return Sam2PromptEncoderConfig( image_size=self.input_image_size, patch_size=self.patch_size, mask_input_channels=self.mask_input_channels, hidden_size=self.hidden_size, num_point_embeddings=self.num_point_embeddings, hidden_act=self.hidden_act, ) def prepare_config_and_inputs(self): dummy_points = floats_tensor([self.batch_size, 3, 2]) config = self.get_config() return config, dummy_points class Sam2MaskDecoderTester: def __init__( self, hidden_size=32, hidden_act="relu", mlp_dim=64, num_hidden_layers=2, num_attention_heads=4, attention_downsample_rate=2, num_multimask_outputs=3, iou_head_depth=3, iou_head_hidden_dim=32, ): self.hidden_size = hidden_size self.hidden_act = hidden_act self.mlp_dim = mlp_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.attention_downsample_rate = attention_downsample_rate self.num_multimask_outputs = num_multimask_outputs self.iou_head_depth = iou_head_depth self.iou_head_hidden_dim = iou_head_hidden_dim def get_config(self): return Sam2MaskDecoderConfig( hidden_size=self.hidden_size, hidden_act=self.hidden_act, mlp_dim=self.mlp_dim, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, attention_downsample_rate=self.attention_downsample_rate, num_multimask_outputs=self.num_multimask_outputs, iou_head_depth=self.iou_head_depth, iou_head_hidden_dim=self.iou_head_hidden_dim, ) def prepare_config_and_inputs(self): config = self.get_config() dummy_inputs = { "image_embedding": floats_tensor([self.batch_size, self.hidden_size]), } return config, dummy_inputs class Sam2ModelTester: def __init__( self, parent, num_channels=3, image_size=128, hidden_size=12, patch_kernel_size=7, patch_stride=4, patch_padding=3, blocks_per_stage=[1, 2, 7, 2], embed_dim_per_stage=[12, 24, 48, 96], backbone_channel_list=[96, 48, 24, 12], backbone_feature_sizes=[[32, 32], [16, 16], [8, 8]], fpn_hidden_size=32, memory_encoder_hidden_size=32, batch_size=2, is_training=True, ): self.parent = parent self.image_size = image_size self.hidden_size = hidden_size self.patch_kernel_size = patch_kernel_size self.patch_stride = patch_stride self.patch_padding = patch_padding self.blocks_per_stage = blocks_per_stage self.embed_dim_per_stage = embed_dim_per_stage self.backbone_channel_list = backbone_channel_list self.backbone_feature_sizes = backbone_feature_sizes self.fpn_hidden_size = fpn_hidden_size self.batch_size = batch_size self.num_channels = num_channels self.is_training = is_training self.memory_encoder_hidden_size = memory_encoder_hidden_size self.prompt_encoder_tester = Sam2PromptEncoderTester() self.mask_decoder_tester = Sam2MaskDecoderTester() 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 get_config(self): backbone_config = Sam2HieraDetConfig( hidden_size=self.hidden_size, num_channels=self.num_channels, image_size=self.image_size, patch_stride=self.patch_stride, patch_kernel_size=self.patch_kernel_size, patch_padding=self.patch_padding, blocks_per_stage=self.blocks_per_stage, embed_dim_per_stage=self.embed_dim_per_stage, ) vision_config = Sam2VisionConfig( backbone_config=backbone_config, backbone_channel_list=self.backbone_channel_list, backbone_feature_sizes=self.backbone_feature_sizes, fpn_hidden_size=self.fpn_hidden_size, ) prompt_encoder_config = self.prompt_encoder_tester.get_config() mask_decoder_config = self.mask_decoder_tester.get_config() return Sam2Config( vision_config=vision_config, prompt_encoder_config=prompt_encoder_config, mask_decoder_config=mask_decoder_config, memory_attention_hidden_size=self.hidden_size, memory_encoder_hidden_size=self.memory_encoder_hidden_size, image_size=self.image_size, mask_downsampler_embed_dim=32, memory_fuser_embed_dim=32, memory_attention_num_layers=1, memory_attention_feed_forward_hidden_size=32, ) def create_and_check_model(self, config, pixel_values): model = Sam2Model(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(pixel_values) self.parent.assertEqual(result.iou_scores.shape, (self.batch_size, 1, 3)) self.parent.assertEqual(result.pred_masks.shape[:3], (self.batch_size, 1, 3)) 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_torch class Sam2ModelTest(ModelTesterMixin, PipelineTesterMixin, 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 = (Sam2Model,) if is_torch_available() else () pipeline_model_mapping = ( {"feature-extraction": Sam2Model, "mask-generation": Sam2Model} if is_torch_available() else {} ) test_resize_embeddings = False _is_composite = True def setUp(self): self.model_tester = Sam2ModelTester(self) common_properties = ["initializer_range"] self.config_tester = ConfigTester( self, config_class=Sam2Config, has_text_modality=False, common_properties=common_properties ) 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_get_set_embeddings(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(), (nn.Module)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) # Overriding as attention shape depends on window_size def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True 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._from_config(config, attn_implementation="eager") config = model.config model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.vision_attentions expected_num_attentions = sum(self.model_tester.blocks_per_stage) self.assertEqual(len(attentions), expected_num_attentions) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.mask_decoder_config.output_attentions = True config.vision_config.output_attentions = True config.vision_config.backbone_config.output_attentions = True config.output_attentions = True model = model_class._from_config(config, attn_implementation="eager") window_size = config.vision_config.backbone_config.window_size_per_stage[0] out_dim = self.model_tester.hidden_size patch_stride = self.model_tester.patch_stride num_windows = ( self.model_tester.batch_size * (self.model_tester.image_size // (window_size * patch_stride)) ** 2 ) model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.vision_attentions self.assertEqual(len(attentions), expected_num_attentions) self.assertListEqual( list(attentions[0].shape[-4:]), [num_windows, window_size, window_size, out_dim], ) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.vision_attentions self.assertEqual(len(attentions), expected_num_attentions) self.assertListEqual( list(attentions[0].shape[-4:]), [num_windows, window_size, window_size, out_dim], ) # Override as Sam2Model has different sub-modules def test_sdpa_can_dispatch_composite_models(self): """ Tests if composite models dispatch correctly on SDPA/eager when requested so when loading the model. This tests only by looking at layer names, as usually SDPA layers are called "SDPAAttention". In contrast to the above test, this one checks if the "config._attn_implementation" is a dict after the model is loaded, because we manually replicate requested attn implementation on each sub-config when loading. See https://github.com/huggingface/transformers/pull/32238 for more info The test tries to cover most general cases of composite models, VLMs with vision and text configs. Any model that has a different set of sub-configs has to overwrite this test. """ if not self.has_attentions: self.skipTest(reason="Model architecture does not support attentions") if not self._is_composite: self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA") for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model_sdpa = model_class.from_pretrained(tmpdirname, attn_implementation="sdpa") model_sdpa = model_sdpa.eval().to(torch_device) vision_encoder_sdpa = getattr(model_sdpa, "vision_encoder") mask_decoder_sdpa = getattr(model_sdpa, "mask_decoder") # `None` as it is the requested one which will be assigned to each sub-config # Sub-model will dispatch to SDPA if it can (checked below that `SDPA` layers are present) self.assertTrue(mask_decoder_sdpa.config._attn_implementation == "sdpa") self.assertTrue(vision_encoder_sdpa.config._attn_implementation == "sdpa") model_eager = model_class.from_pretrained(tmpdirname, attn_implementation="eager") model_eager = model_eager.eval().to(torch_device) self.assertTrue(getattr(model_eager, "mask_decoder").config._attn_implementation == "eager") self.assertTrue(getattr(model_eager, "vision_encoder").config._attn_implementation == "eager") for name, submodule in model_eager.named_modules(): class_name = submodule.__class__.__name__ if ( class_name.endswith("Attention") and getattr(submodule, "config", None) and submodule.config._attn_implementation == "sdpa" ): raise ValueError("The eager model should not have SDPA attention layers") # Override as Sam2Model doesn't have hidden states def flash_attn_inference_equivalence( self, attn_implementation: str, padding_side: str, atol: float = 4e-2, rtol: float = 4e-2 ): r""" Tests the equivalence between the eager and flash attention implementations. This test is only for inference and runs with `dtype=torch.bfloat16`. """ if not self.has_attentions: self.skipTest(reason="Model architecture does not support attentions") # TODO take a look at this # head size needs to be a multiple of 8 but needs more adjustments than our current `_prepare_config_headdim` if attn_implementation != "flash_attention_2": self.skipTest( reason="Model fails for every other FA implementation than FA2 due to dim incompatibilities." ) for model_class in self.all_model_classes: if not getattr(model_class, "_supports_flash_attn"): self.skipTest(f"{model_class.__name__} does not support Flash Attention") config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model_fa = model_class.from_pretrained( tmpdirname, dtype=torch.bfloat16, attn_implementation=attn_implementation ) model_fa.to(torch_device) model = model_class.from_pretrained(tmpdirname, dtype=torch.bfloat16) model.to(torch_device) dummy_input = inputs_dict[model.main_input_name][:1] if dummy_input.dtype in [torch.float32, torch.float16]: dummy_input = dummy_input.to(torch.bfloat16) dummy_attention_mask = inputs_dict.get("attention_mask", None) if dummy_attention_mask is not None: dummy_attention_mask = dummy_attention_mask[:1] if padding_side == "left": dummy_attention_mask[:, 1:] = 1 dummy_attention_mask[:, :1] = 0 else: dummy_attention_mask[:, :-1] = 1 dummy_attention_mask[:, -1:] = 0 if model.config.is_encoder_decoder: decoder_input_ids = inputs_dict.get("decoder_input_ids", dummy_input)[:1] outputs = model(dummy_input, decoder_input_ids=decoder_input_ids, output_hidden_states=True) outputs_fa = model_fa(dummy_input, decoder_input_ids=decoder_input_ids, output_hidden_states=True) else: outputs = model(dummy_input, output_hidden_states=True) outputs_fa = model_fa(dummy_input, output_hidden_states=True) logits = outputs.vision_hidden_states[-1] logits_fa = outputs_fa.vision_hidden_states[-1] assert torch.allclose(logits_fa, logits, atol=atol, rtol=rtol) if model.config.is_encoder_decoder: other_inputs = { "decoder_input_ids": decoder_input_ids, "decoder_attention_mask": dummy_attention_mask, "output_hidden_states": True, } if dummy_attention_mask is not None: other_inputs["attention_mask"] = dummy_attention_mask outputs = model(dummy_input, **other_inputs) outputs_fa = model_fa(dummy_input, **other_inputs) else: other_inputs = { "output_hidden_states": True, } if dummy_attention_mask is not None: other_inputs["attention_mask"] = dummy_attention_mask outputs = model(dummy_input, **other_inputs) outputs_fa = model_fa(dummy_input, **other_inputs) logits = outputs.vision_hidden_states[-1] logits_fa = outputs_fa.vision_hidden_states[-1] if padding_side == "left": assert torch.allclose(logits_fa[1:], logits[1:], atol=atol, rtol=rtol) # check with inference + dropout model.train() _ = model_fa(dummy_input, **other_inputs) else: assert torch.allclose(logits_fa[:-1], logits[:-1], atol=atol, rtol=rtol) # Override as difference slightly higher than the threshold def test_batching_equivalence(self, atol=5e-4, rtol=5e-4): super().test_batching_equivalence(atol=atol, rtol=rtol) @unittest.skip(reason="Sam2Model does not support training") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="Hidden_states is tested in sub modules tests") def test_hidden_states_output(self): pass @slow def test_model_from_pretrained(self): model_name = "facebook/sam2.1-hiera-tiny" model = Sam2Model.from_pretrained(model_name) self.assertIsNotNone(model) def test_sdpa_can_compile_dynamic(self): self.skipTest(reason="SAM2 model can't be compiled dynamic yet") def _image_features_get_expected_num_attentions(self, model_tester=None): if model_tester is None: model_tester = self.model_tester return sum(model_tester.blocks_per_stage) def _image_features_get_expected_num_hidden_states(self, model_tester=None): if model_tester is None: model_tester = self.model_tester return sum(model_tester.blocks_per_stage) + 1 def prepare_image(): img_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/truck.jpg" raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB") return raw_image def prepare_groceries_image(): img_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/groceries.jpg" raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB") return raw_image def prepare_dog_img(): img_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/dog-sam.png" raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB") return raw_image def prepare_video(): video_url = "https://huggingface.co/datasets/hf-internal-testing/sam2-fixtures/resolve/main/bedroom.mp4" raw_video, _ = load_video(video_url) return raw_video @slow class Sam2ModelIntegrationTest(unittest.TestCase): def setUp(self): super().setUp() self.model = Sam2Model.from_pretrained("facebook/sam2.1-hiera-tiny").to(torch.float32) self.processor = Sam2Processor.from_pretrained("facebook/sam2.1-hiera-tiny") self.model.to(torch_device) self.model.eval() def tearDown(self): super().tearDown() # clean-up as much as possible GPU memory occupied by PyTorch gc.collect() backend_empty_cache(torch_device) def test_inference_mask_generation_one_point_multimask(self): raw_image = prepare_image() input_points = [[[[500, 375]]]] input_labels = [[[1]]] inputs = self.processor( images=raw_image, input_points=input_points, input_labels=input_labels, return_tensors="pt" ).to(torch_device) with torch.no_grad(): outputs = self.model(**inputs) self.assertEqual(outputs.iou_scores.shape, (1, 1, 3)) self.assertEqual(outputs.pred_masks.shape, (1, 1, 3, 256, 256)) sorted_indices = torch.argsort(outputs.iou_scores.squeeze(), descending=True) scores = outputs.iou_scores.squeeze()[sorted_indices] masks_logits = outputs.pred_masks.squeeze()[sorted_indices][0, :3, :3] torch.testing.assert_close( scores, torch.tensor([0.9547, 0.4932, 0.0427]).to(torch_device), atol=1e-4, rtol=1e-4 ) torch.testing.assert_close( masks_logits, torch.tensor( [[-24.9288, -41.7466, -31.0128], [-34.5113, -31.1054, -36.5913], [-25.2597, -37.5912, -33.4030]] ).to(torch_device), atol=1e-4, rtol=1e-4, ) def test_inference_mask_generation_one_point_no_multimask(self): raw_image = prepare_image() input_points = [[[[500, 375]]]] input_labels = [[[1]]] inputs = self.processor( images=raw_image, input_points=input_points, input_labels=input_labels, return_tensors="pt" ).to(torch_device) with torch.no_grad(): outputs = self.model(**inputs, multimask_output=False) self.assertEqual(outputs.iou_scores.shape, (1, 1, 1)) self.assertEqual(outputs.pred_masks.shape, (1, 1, 1, 256, 256)) scores = outputs.iou_scores.squeeze((0, 1)) masks_logits = outputs.pred_masks.squeeze((0, 1))[0, :3, :3] torch.testing.assert_close(scores, torch.tensor([0.9364]).to(torch_device), atol=1e-4, rtol=1e-4) torch.testing.assert_close( masks_logits, torch.tensor( [[-7.0462, -13.3857, -9.6419], [-10.4565, -9.7174, -12.3528], [-7.3704, -12.4391, -10.5539]] ).to(torch_device), atol=1e-4, rtol=1e-4, ) def test_inference_mask_generation_batched_images_multi_points(self): raw_image1 = prepare_image() raw_image2 = prepare_dog_img() input_points = [[[[500, 375]]], [[[770, 200], [730, 120]]]] input_labels = [[[1]], [[1, 0]]] inputs = self.processor( images=[raw_image1, raw_image2], input_points=input_points, input_labels=input_labels, return_tensors="pt" ).to(torch_device) with torch.no_grad(): outputs = self.model(**inputs) self.assertEqual(outputs.iou_scores.shape, (2, 1, 3)) self.assertEqual(outputs.pred_masks.shape, (2, 1, 3, 256, 256)) sorted_indices = torch.argsort(outputs.iou_scores[0].squeeze(), descending=True) scores1 = outputs.iou_scores[0].squeeze()[sorted_indices] masks_logits1 = outputs.pred_masks[0].squeeze()[sorted_indices][0, :3, :3] sorted_indices = torch.argsort(outputs.iou_scores[1].squeeze(), descending=True) scores2 = outputs.iou_scores[1].squeeze()[sorted_indices] masks_logits2 = outputs.pred_masks[1].squeeze()[sorted_indices][0, :3, :3] torch.testing.assert_close( scores1, torch.tensor([0.9586, 0.4913, 0.0448]).to(torch_device), atol=1e-4, rtol=1e-4 ) torch.testing.assert_close( masks_logits1, torch.tensor( [[-22.2555, -37.9250, -27.8928], [-30.8681, -27.9519, -32.8032], [-22.4133, -33.9966, -29.7111]] ).to(torch_device), atol=1e-4, rtol=1e-4, ) torch.testing.assert_close( scores2, torch.tensor([0.9504, 0.8117, 0.7426]).to(torch_device), atol=1e-4, rtol=1e-4 ) torch.testing.assert_close( masks_logits2, torch.tensor( [[-13.1182, -17.3217, -14.9651], [-16.2372, -12.7739, -17.6346], [-13.5013, -17.1549, -15.6614]] ).to(torch_device), atol=1e-4, rtol=1e-4, ) def test_inference_mask_generation_batched_images_batched_points_multi_points(self): raw_image1 = prepare_image() raw_image2 = prepare_groceries_image() input_points = [[[[500, 375]], [[650, 750]]], [[[400, 300]], [[630, 300], [550, 300]]]] input_labels = [[[1], [1]], [[1], [1, 1]]] inputs = self.processor( images=[raw_image1, raw_image2], input_points=input_points, input_labels=input_labels, return_tensors="pt" ).to(torch_device) with torch.no_grad(): outputs = self.model(**inputs, multimask_output=False) self.assertEqual(outputs.iou_scores.shape, (2, 2, 1)) self.assertEqual(outputs.pred_masks.shape, (2, 2, 1, 256, 256)) torch.testing.assert_close( outputs.iou_scores, torch.tensor([[[0.9500], [0.9718]], [[0.9568], [0.9114]]]).to(torch_device), atol=1e-4, rtol=1e-4, ) torch.testing.assert_close( outputs.pred_masks[:, :, :, :2, :2], torch.tensor( [ [[[[-5.8131, -11.3020], [-8.6487, -8.0690]]], [[[-4.7731, -8.7606], [-6.2399, -7.0738]]]], [[[[-13.8661, -19.1254], [-20.2477, -14.1636]]], [[[-8.8229, -10.2760], [-11.3797, -8.7189]]]], ] ).to(torch_device), atol=1e-4, rtol=1e-4, ) def test_inference_batched_images_batched_boxes(self): raw_image1 = prepare_image() raw_image2 = prepare_groceries_image() input_boxes = [ [[75, 275, 1725, 850], [425, 600, 700, 875], [1375, 550, 1650, 800], [1240, 675, 1400, 750]], [[450, 170, 520, 350], [350, 190, 450, 350], [500, 170, 580, 350], [580, 170, 640, 350]], ] inputs = self.processor(images=[raw_image1, raw_image2], input_boxes=input_boxes, return_tensors="pt").to( torch_device ) with torch.no_grad(): outputs = self.model(**inputs, multimask_output=False) self.assertEqual(outputs.iou_scores.shape, (2, 4, 1)) self.assertEqual(outputs.pred_masks.shape, (2, 4, 1, 256, 256)) torch.testing.assert_close( outputs.iou_scores, torch.tensor([[[0.9904], [0.9689], [0.9770], [0.9079]], [[0.9739], [0.9816], [0.9838], [0.9781]]]).to( torch_device ), atol=1e-4, rtol=1e-4, ) torch.testing.assert_close( outputs.pred_masks[:, :, :, :2, :2], torch.tensor( [ [ [[[-11.1540, -18.3994], [-12.4230, -17.4403]]], [[[-19.3144, -29.3947], [-24.6341, -24.1144]]], [[[-24.2983, -37.6470], [-31.6659, -31.0893]]], [[[-25.4313, -44.0231], [-34.0903, -34.7447]]], ], [ [[[-22.5539, -30.4633], [-32.8940, -21.6813]]], [[[-23.6637, -31.3489], [-32.5095, -22.4442]]], [[[-25.2987, -30.9999], [-34.6243, -24.1717]]], [[[-26.3150, -30.5313], [-35.0152, -24.0271]]], ], ] ).to(torch_device), atol=1e-4, rtol=1e-4, ) def test_inference_mask_generation_from_existing_points_and_mask(self): raw_image = prepare_image() input_points = [[[[500, 375]]]] input_labels = [[[1]]] original_inputs = self.processor( images=raw_image, input_points=input_points, input_labels=input_labels, return_tensors="pt" ).to(torch_device) with torch.no_grad(): outputs = self.model(**original_inputs) # best mask to use as input for new points mask_input = outputs.pred_masks[:, :, torch.argmax(outputs.iou_scores)] new_input_points = [[[[500, 375], [1125, 625]]]] new_input_labels = [[[1, 1]]] inputs = self.processor( input_points=new_input_points, input_labels=new_input_labels, original_sizes=original_inputs["original_sizes"], return_tensors="pt", ).to(torch_device) with torch.no_grad(): outputs = self.model( **inputs, input_masks=mask_input, image_embeddings=outputs.image_embeddings, multimask_output=False, ) self.assertEqual(outputs.iou_scores.shape, (1, 1, 1)) self.assertEqual(outputs.pred_masks.shape, (1, 1, 1, 256, 256)) scores = outputs.iou_scores.squeeze((0, 1)) masks_logits = outputs.pred_masks.squeeze((0, 1))[0, :3, :3] torch.testing.assert_close(scores, torch.tensor([0.9738]).to(torch_device), atol=1e-4, rtol=1e-4) torch.testing.assert_close( masks_logits, torch.tensor([[-5.3899, -9.7908, -8.4931], [-5.5144, -8.8731, -8.3000], [-5.5976, -9.9249, -9.0761]]).to( torch_device ), atol=1e-4, rtol=1e-4, ) # with negative point new_input_points = [[[[500, 375], [1125, 625]]]] new_input_labels = [[[1, 0]]] inputs = self.processor( input_points=new_input_points, input_labels=new_input_labels, original_sizes=original_inputs["original_sizes"], return_tensors="pt", ).to(torch_device) with torch.no_grad(): outputs = self.model( **inputs, input_masks=mask_input, image_embeddings=outputs.image_embeddings, multimask_output=False, ) self.assertEqual(outputs.iou_scores.shape, (1, 1, 1)) self.assertEqual(outputs.pred_masks.shape, (1, 1, 1, 256, 256)) scores = outputs.iou_scores.squeeze((0, 1)) masks_logits = outputs.pred_masks.squeeze((0, 1))[0, :3, :3] torch.testing.assert_close(scores, torch.tensor([0.9719]).to(torch_device), atol=1e-4, rtol=1e-4) torch.testing.assert_close( masks_logits, torch.tensor( [[-15.5081, -21.8641, -18.0479], [-17.4401, -17.4754, -23.6469], [-14.3975, -19.4346, -18.5884]] ).to(torch_device), atol=1e-4, rtol=1e-4, ) def test_dummy_pipeline_generation(self): generator = pipeline("mask-generation", model="facebook/sam2.1-hiera-tiny", device=torch_device) raw_image = prepare_image() _ = generator(raw_image, points_per_batch=64)