# Copyright 2023 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 SAM model.""" import tempfile import unittest import pytest import requests from transformers import SamConfig, SamMaskDecoderConfig, SamPromptEncoderConfig, SamVisionConfig, pipeline from transformers.testing_utils import Expectations, cleanup, require_torch, slow, torch_device from transformers.utils import is_torch_available, is_vision_available 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 SamModel, SamProcessor, SamVisionModel if is_vision_available(): from PIL import Image class SamVisionModelTester: 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 # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token) num_patches = (image_size // patch_size) ** 2 self.seq_length = num_patches + 1 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 = SamVisionModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): 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_torch class SamVisionModelTest(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 = (SamVisionModel,) if is_torch_available() else () test_resize_embeddings = False test_torch_exportable = True def setUp(self): self.model_tester = SamVisionModelTester(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_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) 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._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 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) 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), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-4:]), list(expected_attention_shape), ) @unittest.skip(reason="SamVisionModel does not support training") def test_training(self): pass @unittest.skip(reason="SamVisionModel does not support training") def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="SamVisionModel does not support training") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="Hidden_states is tested in create_and_check_model tests") def test_hidden_states_output(self): pass @pytest.mark.torch_compile_test def test_sdpa_can_compile_dynamic(self): self.skipTest(reason="SAM model can't be compiled dynamic yet") class SamPromptEncoderTester: def __init__( self, hidden_size=32, input_image_size=24, patch_size=2, mask_input_channels=4, 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 SamPromptEncoderConfig( 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 SamMaskDecoderTester: 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, layer_norm_eps=1e-6, ): 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 self.layer_norm_eps = layer_norm_eps def get_config(self): return SamMaskDecoderConfig( 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, layer_norm_eps=self.layer_norm_eps, ) 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 SamModelTester: 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.image_size = image_size self.patch_size = patch_size self.output_channels = output_channels self.num_channels = num_channels self.hidden_size = hidden_size self.projection_dim = projection_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.attention_dropout = attention_dropout self.initializer_range = initializer_range self.initializer_factor = initializer_factor self.hidden_act = hidden_act self.layer_norm_eps = layer_norm_eps 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 # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token) num_patches = (image_size // patch_size) ** 2 self.seq_length = num_patches + 1 self.prompt_encoder_tester = SamPromptEncoderTester() self.mask_decoder_tester = SamMaskDecoderTester() 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): vision_config = 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, ) prompt_encoder_config = self.prompt_encoder_tester.get_config() mask_decoder_config = self.mask_decoder_tester.get_config() return SamConfig( vision_config=vision_config, prompt_encoder_config=prompt_encoder_config, mask_decoder_config=mask_decoder_config, ) def create_and_check_model(self, config, pixel_values): model = SamModel(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 create_and_check_get_image_features(self, config, pixel_values): model = SamModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model.get_image_embeddings(pixel_values) self.parent.assertEqual(result[0].shape, (self.output_channels, 12, 12)) def create_and_check_get_image_hidden_states(self, config, pixel_values): model = SamModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model.vision_encoder( pixel_values, output_hidden_states=True, return_dict=True, ) # after computing the convolutional features expected_hidden_states_shape = (self.batch_size, 12, 12, 36) self.parent.assertEqual(len(result[1]), self.num_hidden_layers + 1) self.parent.assertEqual(result[1][0].shape, expected_hidden_states_shape) with torch.no_grad(): result = model.vision_encoder( pixel_values, output_hidden_states=True, return_dict=False, ) # after computing the convolutional features expected_hidden_states_shape = (self.batch_size, 12, 12, 36) self.parent.assertEqual(len(result[1]), self.num_hidden_layers + 1) self.parent.assertEqual(result[1][0].shape, expected_hidden_states_shape) 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 SamModelTest(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 = (SamModel,) if is_torch_available() else () pipeline_model_mapping = ( {"feature-extraction": SamModel, "mask-generation": SamModel} if is_torch_available() else {} ) test_resize_embeddings = False _is_composite = True # TODO: Fix me @Arthur: `run_batch_test` in `tests/test_pipeline_mixin.py` not working def is_pipeline_test_to_skip( self, pipeline_test_case_name, config_class, model_architecture, tokenizer_name, image_processor_name, feature_extractor_name, processor_name, ): return True def setUp(self): self.model_tester = SamModelTester(self) common_properties = ["initializer_range"] self.config_tester = ConfigTester( self, config_class=SamConfig, 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) def test_get_image_features(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_get_image_features(*config_and_inputs) def test_image_hidden_states(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_get_image_hidden_states(*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_vision_attention_shape = ( self.model_tester.batch_size * self.model_tester.num_attention_heads, 196, 196, ) expected_mask_decoder_attention_shape = (self.model_tester.batch_size, 1, 144, 32) 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() print(model.__class__, model._can_record_outputs) with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) vision_attentions = outputs.vision_attentions self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers) mask_decoder_attentions = outputs.mask_decoder_attentions self.assertEqual(len(mask_decoder_attentions), self.model_tester.mask_decoder_tester.num_hidden_layers) # 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.output_attentions = True model = model_class._from_config(config, attn_implementation="eager") model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) vision_attentions = outputs.vision_attentions self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers) mask_decoder_attentions = outputs.mask_decoder_attentions self.assertEqual(len(mask_decoder_attentions), self.model_tester.mask_decoder_tester.num_hidden_layers) self.assertListEqual( list(vision_attentions[0].shape[-4:]), list(expected_vision_attention_shape), ) self.assertListEqual( list(mask_decoder_attentions[0].shape[-4:]), list(expected_mask_decoder_attention_shape), ) @unittest.skip(reason="SamModel does not support training") def test_training(self): pass @unittest.skip(reason="SamModel does not support training") def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="SamModel does not support training") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="Hidden_states is tested in create_and_check_model tests") def test_hidden_states_output(self): pass @slow def test_model_from_pretrained(self): model_name = "facebook/sam-vit-huge" model = SamModel.from_pretrained(model_name) self.assertIsNotNone(model) @pytest.mark.torch_compile_test def test_sdpa_can_compile_dynamic(self): self.skipTest(reason="SAM model can't be compiled dynamic yet") 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) model_eager = model_class.from_pretrained(tmpdirname, attn_implementation="eager") model_eager = model_eager.eval().to(torch_device) # Root model determines SDPA support attn_impl = "sdpa" if model._supports_sdpa else "eager" # Check config propagation to submodels that support it self.assertTrue(model_sdpa.config._attn_implementation == "sdpa") self.assertTrue(model_sdpa.vision_encoder.config._attn_implementation == attn_impl) self.assertTrue(model_sdpa.mask_decoder.config._attn_implementation == attn_impl) self.assertTrue(model_eager.config._attn_implementation == "eager") self.assertTrue(model_eager.vision_encoder.config._attn_implementation == "eager") self.assertTrue(model_eager.mask_decoder.config._attn_implementation == "eager") # Verify SDPA/eager layer presence has_sdpa = False for name, submodule in model_sdpa.named_modules(): class_name = submodule.__class__.__name__ if "SdpaAttention" in class_name or "SdpaSelfAttention" in class_name: has_sdpa = True break if not has_sdpa and attn_impl == "sdpa": raise ValueError("The SDPA model should have SDPA attention layers") for name, submodule in model_eager.named_modules(): class_name = submodule.__class__.__name__ if "SdpaAttention" in class_name or "SdpaSelfAttention" in class_name: raise ValueError("The eager model should not have SDPA attention layers") def prepare_image(): img_url = "https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png" 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 @slow class SamModelIntegrationTest(unittest.TestCase): def tearDown(self): super().tearDown() # clean-up as much as possible GPU memory occupied by PyTorch cleanup(torch_device, gc_collect=True) def test_inference_mask_generation_no_point(self): model = SamModel.from_pretrained("facebook/sam-vit-base") processor = SamProcessor.from_pretrained("facebook/sam-vit-base") model.to(torch_device) model.eval() raw_image = prepare_image() inputs = processor(images=raw_image, return_tensors="pt").to(torch_device) with torch.no_grad(): outputs = model(**inputs) scores = outputs.iou_scores.squeeze().cpu() masks = outputs.pred_masks[0, 0, 0, 0, :3].cpu() torch.testing.assert_close(scores[-1], torch.tensor(0.4515), rtol=2e-4, atol=2e-4) torch.testing.assert_close(masks, torch.tensor([-4.1800, -3.4948, -3.4481]), rtol=2e-4, atol=2e-4) def test_inference_mask_generation_one_point_one_bb(self): model = SamModel.from_pretrained("facebook/sam-vit-base") processor = SamProcessor.from_pretrained("facebook/sam-vit-base") model.to(torch_device) model.eval() raw_image = prepare_image() input_boxes = [[[650, 900, 1000, 1250]]] input_points = [[[820, 1080]]] inputs = processor( images=raw_image, input_boxes=input_boxes, input_points=input_points, return_tensors="pt" ).to(torch_device) with torch.no_grad(): outputs = model(**inputs) scores = outputs.iou_scores.squeeze().cpu() masks = outputs.pred_masks[0, 0, 0, 0, :3] expectations = Expectations( { (None, None): [-12.7729, -12.3665, -12.6061], ("cuda", 8): [-12.7731, -12.3667, -12.6063], } ) expected_masks = torch.tensor(expectations.get_expectation()).to(torch_device) torch.testing.assert_close(scores[-1], torch.tensor(0.9566), rtol=2e-4, atol=2e-4) torch.testing.assert_close(masks, expected_masks, rtol=2e-4, atol=2e-4) def test_inference_mask_generation_batched_points_batched_images(self): model = SamModel.from_pretrained("facebook/sam-vit-base") processor = SamProcessor.from_pretrained("facebook/sam-vit-base") model.to(torch_device) model.eval() raw_image = prepare_image() input_points = [ [[[820, 1080]], [[820, 1080]], [[820, 1080]], [[820, 1080]]], [[[510, 1080]], [[820, 1080]], [[820, 1080]], [[820, 1080]]], ] inputs = processor(images=[raw_image, raw_image], input_points=input_points, return_tensors="pt").to( torch_device ) with torch.no_grad(): outputs = model(**inputs) scores = outputs.iou_scores.squeeze().cpu() masks = outputs.pred_masks[0, 0, 0, 0, :3].cpu() EXPECTED_SCORES = torch.tensor( [ [ [0.6765, 0.9379, 0.8803], [0.6765, 0.9379, 0.8803], [0.6765, 0.9379, 0.8803], [0.6765, 0.9379, 0.8803], ], [ [0.3317, 0.7264, 0.7646], [0.6765, 0.9379, 0.8803], [0.6765, 0.9379, 0.8803], [0.6765, 0.9379, 0.8803], ], ] ) EXPECTED_MASKS = torch.tensor([-2.8550, -2.7988, -2.9625]) torch.testing.assert_close(scores, EXPECTED_SCORES, rtol=1e-3, atol=1e-3) torch.testing.assert_close(masks, EXPECTED_MASKS, rtol=1e-3, atol=1e-3) def test_inference_mask_generation_one_point_one_bb_zero(self): model = SamModel.from_pretrained("facebook/sam-vit-base") processor = SamProcessor.from_pretrained("facebook/sam-vit-base") model.to(torch_device) model.eval() raw_image = prepare_image() input_boxes = [[[620, 900, 1000, 1255]]] input_points = [[[820, 1080]]] labels = [[0]] inputs = processor( images=raw_image, input_boxes=input_boxes, input_points=input_points, input_labels=labels, return_tensors="pt", ).to(torch_device) with torch.no_grad(): outputs = model(**inputs) scores = outputs.iou_scores.squeeze().cpu() torch.testing.assert_close(scores[-1], torch.tensor(0.7894), rtol=1e-4, atol=1e-4) def test_inference_mask_generation_one_point(self): model = SamModel.from_pretrained("facebook/sam-vit-base") processor = SamProcessor.from_pretrained("facebook/sam-vit-base") model.to(torch_device) model.eval() raw_image = prepare_image() input_points = [[[400, 650]]] input_labels = [[1]] inputs = processor( images=raw_image, input_points=input_points, input_labels=input_labels, return_tensors="pt" ).to(torch_device) with torch.no_grad(): outputs = model(**inputs) scores = outputs.iou_scores.squeeze().cpu() torch.testing.assert_close(scores[-1], torch.tensor(0.9675), rtol=1e-4, atol=1e-4) # With no label input_points = [[[400, 650]]] inputs = processor(images=raw_image, input_points=input_points, return_tensors="pt").to(torch_device) with torch.no_grad(): outputs = model(**inputs) scores = outputs.iou_scores.squeeze().cpu() torch.testing.assert_close(scores[-1], torch.tensor(0.9675), rtol=1e-4, atol=1e-4) def test_inference_mask_generation_two_points(self): model = SamModel.from_pretrained("facebook/sam-vit-base") processor = SamProcessor.from_pretrained("facebook/sam-vit-base") model.to(torch_device) model.eval() raw_image = prepare_image() input_points = [[[400, 650], [800, 650]]] input_labels = [[1, 1]] inputs = processor( images=raw_image, input_points=input_points, input_labels=input_labels, return_tensors="pt" ).to(torch_device) with torch.no_grad(): outputs = model(**inputs) scores = outputs.iou_scores.squeeze().cpu() torch.testing.assert_close(scores[-1], torch.tensor(0.9762), rtol=1e-4, atol=1e-4) # no labels inputs = processor(images=raw_image, input_points=input_points, return_tensors="pt").to(torch_device) with torch.no_grad(): outputs = model(**inputs) scores = outputs.iou_scores.squeeze().cpu() torch.testing.assert_close(scores[-1], torch.tensor(0.9762), rtol=1e-4, atol=1e-4) def test_inference_mask_generation_two_points_batched(self): model = SamModel.from_pretrained("facebook/sam-vit-base") processor = SamProcessor.from_pretrained("facebook/sam-vit-base") model.to(torch_device) model.eval() raw_image = prepare_image() input_points = [[[400, 650], [800, 650]], [[400, 650]]] input_labels = [[1, 1], [1]] inputs = processor( images=[raw_image, raw_image], input_points=input_points, input_labels=input_labels, return_tensors="pt" ).to(torch_device) with torch.no_grad(): outputs = model(**inputs) scores = outputs.iou_scores.squeeze().cpu() torch.testing.assert_close(scores[0][-1], torch.tensor(0.9762), rtol=1e-4, atol=1e-4) torch.testing.assert_close(scores[1][-1], torch.tensor(0.9637), rtol=1e-4, atol=1e-4) def test_inference_mask_generation_one_box(self): model = SamModel.from_pretrained("facebook/sam-vit-base") processor = SamProcessor.from_pretrained("facebook/sam-vit-base") model.to(torch_device) model.eval() raw_image = prepare_image() input_boxes = [[[75, 275, 1725, 850]]] inputs = processor(images=raw_image, input_boxes=input_boxes, return_tensors="pt").to(torch_device) with torch.no_grad(): outputs = model(**inputs) scores = outputs.iou_scores.squeeze().cpu() torch.testing.assert_close(scores[-1], torch.tensor(0.7937), rtol=1e-4, atol=1e-4) def test_inference_mask_generation_batched_image_one_point(self): model = SamModel.from_pretrained("facebook/sam-vit-base") processor = SamProcessor.from_pretrained("facebook/sam-vit-base") model.to(torch_device) model.eval() raw_image = prepare_image() raw_dog_image = prepare_dog_img() input_points = [[[820, 1080]], [[220, 470]]] inputs = processor(images=[raw_image, raw_dog_image], input_points=input_points, return_tensors="pt").to( torch_device ) with torch.no_grad(): outputs = model(**inputs) scores_batched = outputs.iou_scores.squeeze().cpu() input_points = [[[220, 470]]] inputs = processor(images=raw_dog_image, input_points=input_points, return_tensors="pt").to(torch_device) with torch.no_grad(): outputs = model(**inputs) scores_single = outputs.iou_scores.squeeze().cpu() torch.testing.assert_close(scores_batched[1, :], scores_single, rtol=1e-4, atol=1e-4) def test_inference_mask_generation_two_points_point_batch(self): model = SamModel.from_pretrained("facebook/sam-vit-base") processor = SamProcessor.from_pretrained("facebook/sam-vit-base") model.to(torch_device) model.eval() raw_image = prepare_image() input_points = torch.Tensor([[[400, 650]], [[220, 470]]]).cpu() # fmt: skip input_points = input_points.unsqueeze(0) inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to(torch_device) with torch.no_grad(): outputs = model(**inputs) iou_scores = outputs.iou_scores.cpu() self.assertTrue(iou_scores.shape == (1, 2, 3)) torch.testing.assert_close( iou_scores, torch.tensor([[[0.9105, 0.9825, 0.9675], [0.7646, 0.7943, 0.7774]]]), atol=1e-4, rtol=1e-4 ) def test_inference_mask_generation_three_boxes_point_batch(self): model = SamModel.from_pretrained("facebook/sam-vit-base") processor = SamProcessor.from_pretrained("facebook/sam-vit-base") model.to(torch_device) model.eval() raw_image = prepare_image() # fmt: off input_boxes = torch.Tensor([[[620, 900, 1000, 1255]], [[75, 275, 1725, 850]], [[75, 275, 1725, 850]]]).cpu() EXPECTED_IOU = torch.tensor([[ [0.9773, 0.9881, 0.9522], [0.5996, 0.7661, 0.7937], [0.5996, 0.7661, 0.7937], ]]) # fmt: on input_boxes = input_boxes.unsqueeze(0) inputs = processor(raw_image, input_boxes=input_boxes, return_tensors="pt").to(torch_device) with torch.no_grad(): outputs = model(**inputs) iou_scores = outputs.iou_scores.cpu() self.assertTrue(iou_scores.shape == (1, 3, 3)) torch.testing.assert_close(iou_scores, EXPECTED_IOU, rtol=1e-4, atol=1e-4) def test_dummy_pipeline_generation(self): generator = pipeline("mask-generation", model="facebook/sam-vit-base", device=torch_device) raw_image = prepare_image() _ = generator(raw_image, points_per_batch=64)