# 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 YOLOS model.""" import unittest from functools import cached_property from transformers import YolosConfig 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 from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import YolosForObjectDetection, YolosModel if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class YolosModelTester: def __init__( self, parent, batch_size=13, image_size=[30, 30], patch_size=2, num_channels=3, is_training=True, use_labels=True, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, type_sequence_label_size=10, initializer_range=0.02, num_labels=3, scope=None, n_targets=8, num_detection_tokens=10, attn_implementation="eager", ): 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.use_labels = use_labels 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.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.scope = scope self.n_targets = n_targets self.num_detection_tokens = num_detection_tokens self.attn_implementation = attn_implementation # we set the expected sequence length (which is used in several tests) # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens num_patches = (image_size[1] // patch_size) * (image_size[0] // patch_size) self.expected_seq_len = num_patches + 1 + self.num_detection_tokens def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]) labels = None if self.use_labels: # labels is a list of Dict (each Dict being the labels for a given example in the batch) labels = [] for i in range(self.batch_size): target = {} target["class_labels"] = torch.randint( high=self.num_labels, size=(self.n_targets,), device=torch_device ) target["boxes"] = torch.rand(self.n_targets, 4, device=torch_device) labels.append(target) config = self.get_config() return config, pixel_values, labels def get_config(self): return YolosConfig( 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, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, is_decoder=False, initializer_range=self.initializer_range, num_detection_tokens=self.num_detection_tokens, num_labels=self.num_labels, attn_implementation=self.attn_implementation, ) def create_and_check_model(self, config, pixel_values, labels): model = YolosModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.expected_seq_len, self.hidden_size) ) def create_and_check_for_object_detection(self, config, pixel_values, labels): model = YolosForObjectDetection(config) model.to(torch_device) model.eval() result = model(pixel_values=pixel_values) result = model(pixel_values) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_detection_tokens, self.num_labels + 1)) self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_detection_tokens, 4)) result = model(pixel_values=pixel_values, labels=labels) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_detection_tokens, self.num_labels + 1)) self.parent.assertEqual(result.pred_boxes.shape, (self.batch_size, self.num_detection_tokens, 4)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, labels = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class YolosModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as YOLOS does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (YolosModel, YolosForObjectDetection) if is_torch_available() else () pipeline_model_mapping = ( {"image-feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {} ) test_resize_embeddings = False test_torch_exportable = True # special case for head model def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) if return_labels: if model_class.__name__ == "YolosForObjectDetection": labels = [] for i in range(self.model_tester.batch_size): target = {} target["class_labels"] = torch.ones( size=(self.model_tester.n_targets,), device=torch_device, dtype=torch.long ) target["boxes"] = torch.ones( self.model_tester.n_targets, 4, device=torch_device, dtype=torch.float ) labels.append(target) inputs_dict["labels"] = labels return inputs_dict def setUp(self): self.model_tester = YolosModelTester(self) self.config_tester = ConfigTester(self, config_class=YolosConfig, has_text_modality=False, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="YOLOS 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 # in YOLOS, the seq_len is different seq_len = self.model_tester.expected_seq_len 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[-3:]), [self.model_tester.num_attention_heads, seq_len, seq_len], ) out_len = len(outputs) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) added_hidden_states = 1 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, seq_len, seq_len], ) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) # YOLOS has a different seq_length seq_length = self.model_tester.expected_seq_len self.assertListEqual( list(hidden_states[0].shape[-2:]), [seq_length, self.model_tester.hidden_size], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) def test_for_object_detection(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_object_detection(*config_and_inputs) @slow def test_model_from_pretrained(self): model_name = "hustvl/yolos-small" model = YolosModel.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_torch @require_vision class YolosModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return AutoImageProcessor.from_pretrained("hustvl/yolos-small") if is_vision_available() else None @slow def test_inference_object_detection_head(self): model = YolosForObjectDetection.from_pretrained("hustvl/yolos-small").to(torch_device) image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(images=image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(inputs.pixel_values) # verify outputs expected_shape = torch.Size((1, 100, 92)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice_logits = torch.tensor( [[-23.7219, -10.3165, -14.9083], [-41.5429, -15.2403, -24.1478], [-29.3909, -12.7173, -19.4650]], device=torch_device, ) expected_slice_boxes = torch.tensor( [[0.2536, 0.5449, 0.4643], [0.2037, 0.7735, 0.3672], [0.7692, 0.4056, 0.4549]], device=torch_device ) torch.testing.assert_close(outputs.logits[0, :3, :3], expected_slice_logits, rtol=1e-4, atol=1e-4) torch.testing.assert_close(outputs.pred_boxes[0, :3, :3], expected_slice_boxes, rtol=1e-4, atol=1e-4) # verify postprocessing results = image_processor.post_process_object_detection( outputs, threshold=0.3, target_sizes=[image.size[::-1]] )[0] expected_scores = torch.tensor([0.9991, 0.9801, 0.9978, 0.9875, 0.9848]).to(torch_device) expected_labels = [75, 75, 17, 63, 17] expected_slice_boxes = torch.tensor([331.8438, 80.5440, 369.9546, 188.0579]).to(torch_device) self.assertEqual(len(results["scores"]), 5) torch.testing.assert_close(results["scores"], expected_scores, rtol=1e-4, atol=1e-4) self.assertSequenceEqual(results["labels"].tolist(), expected_labels) torch.testing.assert_close(results["boxes"][0, :], expected_slice_boxes)