# 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 TensorFlow SAM model. """ import inspect import unittest import numpy as np import requests from transformers import SamConfig, SamMaskDecoderConfig, SamPromptEncoderConfig, SamVisionConfig from transformers.testing_utils import require_tf, slow from transformers.utils import is_tf_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import SamProcessor, TFSamModel if is_vision_available(): from PIL import Image class TFSamPromptEncoderTester: 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 TFSamMaskDecoderTester: 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 TFSamModelTester: 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 = TFSamPromptEncoderTester() self.mask_decoder_tester = TFSamMaskDecoderTester() 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 = TFSamModel(config=config) 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 = TFSamModel(config=config) 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 = TFSamModel(config=config) 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) 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_tf class TFSamModelTest(TFModelTesterMixin, 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 = (TFSamModel,) if is_tf_available() else () pipeline_model_mapping = ( {"feature-extraction": TFSamModel, "mask-generation": TFSamModel} if is_tf_available() else {} ) test_pruning = False test_resize_embeddings = False test_head_masking = False test_onnx = False # TODO: Fix me @Arthur: `run_batch_test` in `tests/test_pipeline_mixin.py` not working def is_pipeline_test_to_skip( self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name ): return True def setUp(self): self.model_tester = TFSamModelTester(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(), (tf.keras.layers.Layer)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, tf.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_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) 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) 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="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 = TFSamModel.from_pretrained("facebook/sam-vit-base") # sam-vit-huge blows out our memory self.assertIsNotNone(model) def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=5e-4, name="outputs", attributes=None): super().check_pt_tf_outputs( tf_outputs=tf_outputs, pt_outputs=pt_outputs, model_class=model_class, tol=tol, name=name, attributes=attributes, ) 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 = TFSamModel.from_pretrained("facebook/sam-vit-base") processor = SamProcessor.from_pretrained("facebook/sam-vit-base") raw_image = prepare_image() inputs = processor(images=raw_image, return_tensors="tf") outputs = model(**inputs) scores = tf.squeeze(outputs.iou_scores) masks = outputs.pred_masks[0, 0, 0, 0, :3] self.assertTrue(np.allclose(scores[-1].numpy(), np.array(0.4515), atol=2e-4)) self.assertTrue(np.allclose(masks.numpy(), np.array([-4.1807, -3.4949, -3.4483]), atol=1e-2)) def test_inference_mask_generation_one_point_one_bb(self): model = TFSamModel.from_pretrained("facebook/sam-vit-base") processor = SamProcessor.from_pretrained("facebook/sam-vit-base") 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="tf") outputs = model(**inputs) scores = tf.squeeze(outputs.iou_scores) masks = outputs.pred_masks[0, 0, 0, 0, :3] self.assertTrue(np.allclose(scores[-1], np.array(0.9566), atol=2e-4)) self.assertTrue(np.allclose(masks.numpy(), np.array([-12.7657, -12.3683, -12.5985]), atol=2e-2)) def test_inference_mask_generation_batched_points_batched_images(self): model = TFSamModel.from_pretrained("facebook/sam-vit-base") processor = SamProcessor.from_pretrained("facebook/sam-vit-base") 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="tf") outputs = model(**inputs) scores = tf.squeeze(outputs.iou_scores) masks = outputs.pred_masks[0, 0, 0, 0, :3] EXPECTED_SCORES = np.array( [ [ [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 = np.array([-2.8552, -2.7990, -2.9612]) self.assertTrue(np.allclose(scores.numpy(), EXPECTED_SCORES, atol=1e-3)) self.assertTrue(np.allclose(masks.numpy(), EXPECTED_MASKS, atol=3e-2)) def test_inference_mask_generation_one_point_one_bb_zero(self): model = TFSamModel.from_pretrained("facebook/sam-vit-base") processor = SamProcessor.from_pretrained("facebook/sam-vit-base") 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="tf", ) outputs = model(**inputs) scores = tf.squeeze(outputs.iou_scores) self.assertTrue(np.allclose(scores[-1].numpy(), np.array(0.7894), atol=1e-4)) def test_inference_mask_generation_one_point(self): model = TFSamModel.from_pretrained("facebook/sam-vit-base") processor = SamProcessor.from_pretrained("facebook/sam-vit-base") 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="tf") outputs = model(**inputs) scores = tf.squeeze(outputs.iou_scores) self.assertTrue(np.allclose(scores[-1], np.array(0.9675), atol=1e-4)) # With no label input_points = [[[400, 650]]] inputs = processor(images=raw_image, input_points=input_points, return_tensors="tf") outputs = model(**inputs) scores = tf.squeeze(outputs.iou_scores) self.assertTrue(np.allclose(scores[-1].numpy(), np.array(0.9675), atol=1e-4)) def test_inference_mask_generation_two_points(self): model = TFSamModel.from_pretrained("facebook/sam-vit-base") processor = SamProcessor.from_pretrained("facebook/sam-vit-base") 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="tf") outputs = model(**inputs) scores = tf.squeeze(outputs.iou_scores) self.assertTrue(np.allclose(scores[-1].numpy(), np.array(0.9762), atol=1e-4)) # no labels inputs = processor(images=raw_image, input_points=input_points, return_tensors="tf") outputs = model(**inputs) scores = tf.squeeze(outputs.iou_scores) self.assertTrue(np.allclose(scores[-1].numpy(), np.array(0.9762), atol=1e-4)) def test_inference_mask_generation_two_points_batched(self): model = TFSamModel.from_pretrained("facebook/sam-vit-base") processor = SamProcessor.from_pretrained("facebook/sam-vit-base") 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="tf" ) outputs = model(**inputs) scores = tf.squeeze(outputs.iou_scores) self.assertTrue(np.allclose(scores[0][-1].numpy(), np.array(0.9762), atol=1e-4)) self.assertTrue(np.allclose(scores[1][-1], np.array(0.9637), atol=1e-4)) def test_inference_mask_generation_one_box(self): model = TFSamModel.from_pretrained("facebook/sam-vit-base") processor = SamProcessor.from_pretrained("facebook/sam-vit-base") raw_image = prepare_image() input_boxes = [[[75, 275, 1725, 850]]] inputs = processor(images=raw_image, input_boxes=input_boxes, return_tensors="tf") outputs = model(**inputs) scores = tf.squeeze(outputs.iou_scores) self.assertTrue(np.allclose(scores[-1].numpy(), np.array(0.7937), atol=1e-4)) def test_inference_mask_generation_batched_image_one_point(self): model = TFSamModel.from_pretrained("facebook/sam-vit-base") processor = SamProcessor.from_pretrained("facebook/sam-vit-base") 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="tf") outputs = model(**inputs) scores_batched = tf.squeeze(outputs.iou_scores) input_points = [[[220, 470]]] inputs = processor(images=raw_dog_image, input_points=input_points, return_tensors="tf") outputs = model(**inputs) scores_single = tf.squeeze(outputs.iou_scores) self.assertTrue(np.allclose(scores_batched[1, :].numpy(), scores_single.numpy(), atol=1e-4)) def test_inference_mask_generation_two_points_point_batch(self): model = TFSamModel.from_pretrained("facebook/sam-vit-base") processor = SamProcessor.from_pretrained("facebook/sam-vit-base") raw_image = prepare_image() # fmt: off input_points = tf.convert_to_tensor([[[400, 650]], [[220, 470]]]) # fmt: on input_points = tf.expand_dims(input_points, 0) inputs = processor(raw_image, input_points=input_points, return_tensors="tf") outputs = model(**inputs) iou_scores = outputs.iou_scores self.assertTrue(iou_scores.shape == (1, 2, 3)) self.assertTrue( np.allclose( iou_scores.numpy(), np.array([[[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 = TFSamModel.from_pretrained("facebook/sam-vit-base") processor = SamProcessor.from_pretrained("facebook/sam-vit-base") raw_image = prepare_image() # fmt: off input_boxes = tf.convert_to_tensor([[[620, 900, 1000, 1255]], [[75, 275, 1725, 850]], [[75, 275, 1725, 850]]]) EXPECTED_IOU = np.array([[[0.9773, 0.9881, 0.9522], [0.5996, 0.7661, 0.7937], [0.5996, 0.7661, 0.7937]]]) # fmt: on input_boxes = tf.expand_dims(input_boxes, 0) inputs = processor(raw_image, input_boxes=input_boxes, return_tensors="tf") outputs = model(**inputs) iou_scores = outputs.iou_scores self.assertTrue(iou_scores.shape == (1, 3, 3)) self.assertTrue(np.allclose(iou_scores.numpy(), EXPECTED_IOU, atol=1e-4, rtol=1e-4))