# coding=utf-8 # 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 inspect import unittest import requests from transformers import SamConfig, SamMaskDecoderConfig, SamPromptEncoderConfig, SamVisionConfig from transformers.testing_utils import 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 if is_torch_available(): import torch from torch import nn from transformers import SamModel, SamProcessor from transformers.models.sam.modeling_sam import SAM_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image 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, 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 () fx_compatible = False test_pruning = False test_resize_embeddings = False test_head_masking = False test_torchscript = False def setUp(self): self.model_tester = SamModelTester(self) self.vision_config_tester = ConfigTester(self, config_class=SamVisionConfig, has_text_modality=False) self.prompt_encoder_config_tester = ConfigTester( self, config_class=SamPromptEncoderConfig, has_text_modality=False, num_attention_heads=12, num_hidden_layers=2, ) self.mask_decoder_config_tester = ConfigTester( self, config_class=SamMaskDecoderConfig, has_text_modality=False ) def test_config(self): self.vision_config_tester.run_common_tests() self.prompt_encoder_config_tester.run_common_tests() self.mask_decoder_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(), (nn.Module)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) 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.forward) # 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_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(config) 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) # 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)) 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="SamModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="SamModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(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): for model_name in SAM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = SamModel.from_pretrained(model_name) self.assertIsNotNone(model) 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 test_inference_mask_generation_no_point(self): model = SamModel.from_pretrained("facebook/sam-vit-huge") processor = SamProcessor.from_pretrained("facebook/sam-vit-huge") 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() self.assertTrue(torch.allclose(scores[-1], torch.tensor(0.5798), atol=1e-4)) def test_inference_mask_generation_one_point_one_bb(self): model = SamModel.from_pretrained("facebook/sam-vit-huge") processor = SamProcessor.from_pretrained("facebook/sam-vit-huge") 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() self.assertTrue(torch.allclose(scores[-1], torch.tensor(0.9935), atol=1e-4)) def test_inference_mask_generation_batched_points_batched_images(self): model = SamModel.from_pretrained("facebook/sam-vit-huge") processor = SamProcessor.from_pretrained("facebook/sam-vit-huge") 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() EXPECTED_SCORES = torch.tensor( [ [ [0.9673, 0.9441, 0.9084], [0.9673, 0.9441, 0.9084], [0.9673, 0.9441, 0.9084], [0.9673, 0.9441, 0.9084], ], [ [0.8405, 0.6292, 0.3840], [0.9673, 0.9441, 0.9084], [0.9673, 0.9441, 0.9084], [0.9673, 0.9441, 0.9084], ], ] ) self.assertTrue(torch.allclose(scores, EXPECTED_SCORES, atol=1e-3)) def test_inference_mask_generation_one_point_one_bb_zero(self): model = SamModel.from_pretrained("facebook/sam-vit-huge") processor = SamProcessor.from_pretrained("facebook/sam-vit-huge") 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() self.assertTrue(torch.allclose(scores[-1], torch.tensor(0.9689), atol=1e-4)) def test_inference_mask_generation_one_point(self): model = SamModel.from_pretrained("facebook/sam-vit-huge") processor = SamProcessor.from_pretrained("facebook/sam-vit-huge") 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() self.assertTrue(torch.allclose(scores[-1], torch.tensor(0.9712), 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() self.assertTrue(torch.allclose(scores[-1], torch.tensor(0.9712), atol=1e-4)) def test_inference_mask_generation_two_points(self): model = SamModel.from_pretrained("facebook/sam-vit-huge") processor = SamProcessor.from_pretrained("facebook/sam-vit-huge") 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() self.assertTrue(torch.allclose(scores[-1], torch.tensor(0.9936), 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() self.assertTrue(torch.allclose(scores[-1], torch.tensor(0.9936), atol=1e-4)) def test_inference_mask_generation_two_points_batched(self): model = SamModel.from_pretrained("facebook/sam-vit-huge") processor = SamProcessor.from_pretrained("facebook/sam-vit-huge") 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() self.assertTrue(torch.allclose(scores[0][-1], torch.tensor(0.9936), atol=1e-4)) self.assertTrue(torch.allclose(scores[1][-1], torch.tensor(0.9716), atol=1e-4)) def test_inference_mask_generation_one_box(self): model = SamModel.from_pretrained("facebook/sam-vit-huge") processor = SamProcessor.from_pretrained("facebook/sam-vit-huge") 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() self.assertTrue(torch.allclose(scores[-1], torch.tensor(0.8686), atol=1e-4)) def test_inference_mask_generation_batched_image_one_point(self): model = SamModel.from_pretrained("facebook/sam-vit-huge") processor = SamProcessor.from_pretrained("facebook/sam-vit-huge") 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() 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() self.assertTrue(torch.allclose(scores_batched[1, :], scores_single, atol=1e-4)) def test_inference_mask_generation_two_points_point_batch(self): model = SamModel.from_pretrained("facebook/sam-vit-huge") processor = SamProcessor.from_pretrained("facebook/sam-vit-huge") model.to(torch_device) model.eval() raw_image = prepare_image() # fmt: off input_points = torch.Tensor([[[400, 650]], [[220, 470]]]).cpu() # fmt: on 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_allclose( iou_scores, torch.tensor([[[0.9848, 0.9788, 0.9713], [0.9211, 0.9128, 0.7427]]]), atol=1e-4, rtol=1e-4 ) def test_inference_mask_generation_three_boxes_point_batch(self): model = SamModel.from_pretrained("facebook/sam-vit-huge") processor = SamProcessor.from_pretrained("facebook/sam-vit-huge") 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([[[1.0071, 1.0032, 0.9946], [0.4962, 0.8770, 0.8686], [0.4962, 0.8770, 0.8686]]]) # 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_allclose(iou_scores, EXPECTED_IOU, atol=1e-4, rtol=1e-4)