# 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 ViTDet model.""" import unittest from transformers import VitDetConfig from transformers.testing_utils import require_torch, torch_device from transformers.utils import is_torch_available from ...test_backbone_common import BackboneTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import VitDetBackbone, VitDetModel class VitDetModelTester: def __init__( self, parent, batch_size=13, image_size=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, 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.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.scope = scope self.num_patches_one_direction = self.image_size // self.patch_size self.seq_length = (self.image_size // self.patch_size) ** 2 def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) labels = None if self.use_labels: labels = ids_tensor([self.batch_size], self.type_sequence_label_size) config = self.get_config() return config, pixel_values, labels def get_config(self): return VitDetConfig( image_size=self.image_size, pretrain_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, ) def create_and_check_model(self, config, pixel_values, labels): model = VitDetModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.hidden_size, self.num_patches_one_direction, self.num_patches_one_direction), ) def create_and_check_backbone(self, config, pixel_values, labels): model = VitDetBackbone(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # verify hidden states self.parent.assertEqual(len(result.feature_maps), len(config.out_features)) self.parent.assertListEqual( list(result.feature_maps[0].shape), [self.batch_size, self.hidden_size, self.num_patches_one_direction, self.num_patches_one_direction], ) # verify channels self.parent.assertEqual(len(model.channels), len(config.out_features)) self.parent.assertListEqual(model.channels, [config.hidden_size]) # verify backbone works with out_features=None config.out_features = None model = VitDetBackbone(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # verify feature maps self.parent.assertEqual(len(result.feature_maps), 1) self.parent.assertListEqual( list(result.feature_maps[0].shape), [self.batch_size, self.hidden_size, self.num_patches_one_direction, self.num_patches_one_direction], ) # verify channels self.parent.assertEqual(len(model.channels), 1) self.parent.assertListEqual(model.channels, [config.hidden_size]) 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 VitDetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as VitDet does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (VitDetModel, VitDetBackbone) if is_torch_available() else () pipeline_model_mapping = {"feature-extraction": VitDetModel} if is_torch_available() else {} test_resize_embeddings = False test_torch_exportable = True def setUp(self): self.model_tester = VitDetModelTester(self) self.config_tester = ConfigTester(self, config_class=VitDetConfig, has_text_modality=False, hidden_size=37) # TODO: Fix me (once this model gets more usage) @unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.") def test_cpu_offload(self): pass # TODO: Fix me (once this model gets more usage) @unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.") def test_disk_offload_bin(self): pass @unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.") def test_disk_offload_safetensors(self): pass # TODO: Fix me (once this model gets more usage) @unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.") def test_model_parallelism(self): pass def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="VitDet 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_backbone(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_backbone(*config_and_inputs) 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_stages = self.model_tester.num_hidden_layers self.assertEqual(len(hidden_states), expected_num_stages + 1) # VitDet's feature maps are of shape (batch_size, num_channels, height, width) self.assertListEqual( list(hidden_states[0].shape[-2:]), [ self.model_tester.num_patches_one_direction, self.model_tester.num_patches_one_direction, ], ) 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) # overwrite since VitDet only supports retraining gradients of hidden states def test_retain_grad_hidden_states_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = self.has_attentions # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) inputs = self._prepare_for_class(inputs_dict, model_class) outputs = model(**inputs) output = outputs[0] # Encoder-/Decoder-only models hidden_states = outputs.hidden_states[0] hidden_states.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) @unittest.skip(reason="VitDet does not support feedforward chunking") def test_feed_forward_chunking(self): pass @unittest.skip(reason="VitDet does not have standalone checkpoints since it used as backbone in other models") def test_model_from_pretrained(self): pass def test_non_square_image(self): non_square_image_size = (32, 40) patch_size = (2, 2) config = self.model_tester.get_config() config.image_size = non_square_image_size config.patch_size = patch_size model = VitDetModel(config=config) model.to(torch_device) model.eval() batch_size = self.model_tester.batch_size # Create a dummy input tensor with non-square spatial dimensions. pixel_values = floats_tensor( [batch_size, config.num_channels, non_square_image_size[0], non_square_image_size[1]] ) result = model(pixel_values) expected_height = non_square_image_size[0] / patch_size[0] expected_width = non_square_image_size[1] / patch_size[1] expected_shape = (batch_size, config.hidden_size, expected_height, expected_width) self.assertEqual(result.last_hidden_state.shape, expected_shape) @require_torch class VitDetBackboneTest(unittest.TestCase, BackboneTesterMixin): all_model_classes = (VitDetBackbone,) if is_torch_available() else () config_class = VitDetConfig has_attentions = False def setUp(self): self.model_tester = VitDetModelTester(self)