# Copyright 2022 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 CLIPSeg model.""" import inspect import tempfile import unittest import numpy as np import requests from transformers import CLIPSegConfig, CLIPSegProcessor, CLIPSegTextConfig, CLIPSegVisionConfig from transformers.testing_utils import ( require_torch, require_vision, 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, ids_tensor, random_attention_mask, ) from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import CLIPSegForImageSegmentation, CLIPSegModel, CLIPSegTextModel, CLIPSegVisionModel from transformers.models.auto.modeling_auto import MODEL_MAPPING_NAMES if is_vision_available(): from PIL import Image class CLIPSegVisionModelTester: def __init__( self, parent, batch_size=12, image_size=30, patch_size=2, num_channels=3, is_training=True, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, initializer_range=0.02, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.is_training = is_training self.hidden_size = hidden_size 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.scope = scope # 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 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): return CLIPSegVisionConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, 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, ) def create_and_check_model(self, config, pixel_values): model = CLIPSegVisionModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(pixel_values) # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) image_size = (self.image_size, self.image_size) patch_size = (self.patch_size, self.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_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 CLIPSegVisionModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as CLIPSeg does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (CLIPSegVisionModel,) if is_torch_available() else () test_resize_embeddings = False def setUp(self): self.model_tester = CLIPSegVisionModelTester(self) self.config_tester = ConfigTester( self, config_class=CLIPSegVisionConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="CLIPSeg 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_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) @unittest.skip def test_training(self): pass @unittest.skip 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 @slow def test_model_from_pretrained(self): model_name = "CIDAS/clipseg-rd64-refined" model = CLIPSegVisionModel.from_pretrained(model_name) self.assertIsNotNone(model) class CLIPSegTextModelTester: def __init__( self, parent, batch_size=12, seq_length=7, is_training=True, use_input_mask=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, max_position_embeddings=512, initializer_range=0.02, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size 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.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) if input_mask is not None: batch_size, seq_length = input_mask.shape rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,)) for batch_idx, start_index in enumerate(rnd_start_indices): input_mask[batch_idx, :start_index] = 1 input_mask[batch_idx, start_index:] = 0 config = self.get_config() return config, input_ids, input_mask def get_config(self): return CLIPSegTextConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, 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, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, input_ids, input_mask): model = CLIPSegTextModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(input_ids, attention_mask=input_mask) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, input_mask = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict @require_torch class CLIPSegTextModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (CLIPSegTextModel,) if is_torch_available() else () model_split_percents = [0.5, 0.8, 0.9] def setUp(self): self.model_tester = CLIPSegTextModelTester(self) self.config_tester = ConfigTester(self, config_class=CLIPSegTextConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) @unittest.skip def test_training(self): pass @unittest.skip 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="CLIPSeg does not use inputs_embeds") def test_inputs_embeds(self): pass @slow def test_model_from_pretrained(self): model_name = "CIDAS/clipseg-rd64-refined" model = CLIPSegTextModel.from_pretrained(model_name) self.assertIsNotNone(model) class CLIPSegModelTester: def __init__( self, parent, text_kwargs=None, vision_kwargs=None, is_training=True, # This should respect the `num_hidden_layers` in `CLIPSegVisionModelTester` extract_layers=(1,), ): if text_kwargs is None: text_kwargs = {} if vision_kwargs is None: vision_kwargs = {} self.parent = parent self.text_model_tester = CLIPSegTextModelTester(parent, **text_kwargs) self.vision_model_tester = CLIPSegVisionModelTester(parent, **vision_kwargs) self.batch_size = self.text_model_tester.batch_size # need bs for batching_equivalence test self.is_training = is_training self.extract_layers = extract_layers def prepare_config_and_inputs(self): text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs() vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs() config = self.get_config() return config, input_ids, attention_mask, pixel_values def get_config(self): return CLIPSegConfig( text_config=self.text_model_tester.get_config().to_dict(), vision_config=self.vision_model_tester.get_config().to_dict(), projection_dim=64, reduce_dim=32, extract_layers=self.extract_layers, ) def create_and_check_model(self, config, input_ids, attention_mask, pixel_values): model = CLIPSegModel(config).to(torch_device).eval() with torch.no_grad(): result = model(input_ids, pixel_values, attention_mask) self.parent.assertEqual( result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size) ) self.parent.assertEqual( result.logits_per_text.shape, (self.text_model_tester.batch_size, self.vision_model_tester.batch_size) ) def create_and_check_model_for_image_segmentation(self, config, input_ids, attention_mask, pixel_values): model = CLIPSegForImageSegmentation(config).to(torch_device).eval() with torch.no_grad(): result = model(input_ids, pixel_values) self.parent.assertEqual( result.logits.shape, ( self.vision_model_tester.batch_size, self.vision_model_tester.image_size, self.vision_model_tester.image_size, ), ) self.parent.assertEqual( result.conditional_embeddings.shape, (self.text_model_tester.batch_size, config.projection_dim) ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, attention_mask, pixel_values = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values, } return config, inputs_dict @require_torch class CLIPSegModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (CLIPSegModel, CLIPSegForImageSegmentation) if is_torch_available() else () pipeline_model_mapping = {"feature-extraction": CLIPSegModel} if is_torch_available() else {} test_resize_embeddings = False test_attention_outputs = False def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): # CLIPSegForImageSegmentation requires special treatment if return_labels: if model_class.__name__ == "CLIPSegForImageSegmentation": batch_size, _, height, width = inputs_dict["pixel_values"].shape inputs_dict["labels"] = torch.zeros( [batch_size, height, width], device=torch_device, dtype=torch.float ) return inputs_dict def setUp(self): self.model_tester = CLIPSegModelTester(self) common_properties = ["projection_dim", "logit_scale_init_value"] self.config_tester = ConfigTester( self, config_class=CLIPSegConfig, has_text_modality=False, common_properties=common_properties ) 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_config(self): self.config_tester.run_common_tests() def test_model_for_image_segmentation(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_for_image_segmentation(*config_and_inputs) @unittest.skip(reason="Hidden_states is tested in individual model tests") def test_hidden_states_output(self): pass @unittest.skip(reason="Inputs_embeds is tested in individual model tests") def test_inputs_embeds(self): pass @unittest.skip(reason="Retain_grad is tested in individual model tests") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="CLIPSegModel does not have input/output embeddings") def test_model_get_set_embeddings(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(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 def test_load_vision_text_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Save CLIPSegConfig and check if we can load CLIPSegVisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = CLIPSegVisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save CLIPSegConfig and check if we can load CLIPSegTextConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = CLIPSegTextConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict()) def test_training(self): if not self.model_tester.is_training: self.skipTest(reason="Training test is skipped as the model was not trained") for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True if model_class.__name__ in MODEL_MAPPING_NAMES.values(): continue print("Model class:", model_class) model = model_class(config) model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) for k, v in inputs.items(): print(k, v.shape) loss = model(**inputs).loss loss.backward() @slow def test_model_from_pretrained(self): model_name = "CIDAS/clipseg-rd64-refined" model = CLIPSegModel.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) return image @require_vision @require_torch class CLIPSegModelIntegrationTest(unittest.TestCase): @slow def test_inference_image_segmentation(self): model_name = "CIDAS/clipseg-rd64-refined" processor = CLIPSegProcessor.from_pretrained(model_name) model = CLIPSegForImageSegmentation.from_pretrained(model_name).to(torch_device) image = prepare_img() texts = ["a cat", "a remote", "a blanket"] inputs = processor(text=texts, images=[image] * len(texts), padding=True, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the predicted masks self.assertEqual( outputs.logits.shape, torch.Size((3, 352, 352)), ) expected_masks_slice = torch.tensor( [[-7.4613, -7.4785, -7.3628], [-7.3268, -7.0899, -7.1333], [-6.9838, -6.7900, -6.8913]] ).to(torch_device) torch.testing.assert_close(outputs.logits[0, :3, :3], expected_masks_slice, rtol=1e-3, atol=1e-3) # verify conditional and pooled output expected_conditional = torch.tensor([0.5601, -0.0314, 0.1980]).to(torch_device) expected_pooled_output = torch.tensor([0.5036, -0.2681, -0.2644]).to(torch_device) torch.testing.assert_close(outputs.conditional_embeddings[0, :3], expected_conditional, rtol=1e-3, atol=1e-3) torch.testing.assert_close(outputs.pooled_output[0, :3], expected_pooled_output, rtol=1e-3, atol=1e-3) @slow def test_inference_interpolate_pos_encoding(self): # ViT models have an `interpolate_pos_encoding` argument in their forward method, # allowing to interpolate the pre-trained position embeddings in order to use # the model on higher resolutions. The DINO model by Facebook AI leverages this # to visualize self-attention on higher resolution images. model = CLIPSegModel.from_pretrained("openai/clip-vit-base-patch32").to(torch_device) processor = CLIPSegProcessor.from_pretrained( "openai/clip-vit-base-patch32", size={"height": 180, "width": 180}, crop_size={"height": 180, "width": 180} ) image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") inputs = processor(text="what's in the image", images=image, return_tensors="pt").to(torch_device) # interpolate_pos_encodiung false should return value error with self.assertRaises(ValueError, msg="doesn't match model"): with torch.no_grad(): model(**inputs, interpolate_pos_encoding=False) # forward pass with torch.no_grad(): outputs = model(**inputs, interpolate_pos_encoding=True) # verify the logits expected_shape = torch.Size((1, 26, 768)) self.assertEqual(outputs.vision_model_output.last_hidden_state.shape, expected_shape) expected_slice = torch.tensor( [[-0.1538, 0.0322, -0.3235], [0.2893, 0.1135, -0.5708], [0.0461, 0.1540, -0.6018]] ).to(torch_device) torch.testing.assert_close( outputs.vision_model_output.last_hidden_state[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4 )