# Copyright 2024 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 VitPose backbone model.""" import inspect import unittest import pytest from transformers import VitPoseBackboneConfig 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 if is_torch_available(): import torch from transformers import VitPoseBackbone class VitPoseBackboneModelTester: def __init__( self, parent, batch_size=13, image_size=[16 * 8, 12 * 8], patch_size=[8, 8], 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=2, 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.num_labels = num_labels self.scope = scope # in VitPoseBackbone, the seq length equals the number of patches num_patches = (image_size[0] // patch_size[0]) * (image_size[1] // patch_size[1]) self.seq_length = num_patches 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 = ids_tensor([self.batch_size], self.type_sequence_label_size) config = self.get_config() return config, pixel_values, labels def get_config(self): return VitPoseBackboneConfig( 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, initializer_range=self.initializer_range, num_labels=self.num_labels, ) 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 VitPoseBackboneModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as VitPoseBackbone does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (VitPoseBackbone,) if is_torch_available() else () test_resize_embeddings = False test_torch_exportable = True def setUp(self): self.model_tester = VitPoseBackboneModelTester(self) self.config_tester = ConfigTester( self, config_class=VitPoseBackboneConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() def test_batching_equivalence(self, atol=3e-4, rtol=3e-4): super().test_batching_equivalence(atol=atol, rtol=rtol) @unittest.skip(reason="VitPoseBackbone does not support input and output embeddings") def test_model_common_attributes(self): pass @unittest.skip(reason="VitPoseBackbone does not support input and output embeddings") def test_inputs_embeds(self): pass @unittest.skip(reason="VitPoseBackbone does not support input and output embeddings") def test_model_get_set_embeddings(self): pass @unittest.skip(reason="VitPoseBackbone does not support feedforward chunking") def test_feed_forward_chunking(self): pass @unittest.skip(reason="VitPoseBackbone does not output a loss") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="VitPoseBackbone does not support training yet") def test_training(self): pass @unittest.skip(reason="VitPoseBackbone does not support training yet") def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="VitPoseBackbone does not support training yet") def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip(reason="VitPoseBackbone does not support training yet") def test_training_gradient_checkpointing_use_reentrant_false(self): pass 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) @pytest.mark.torch_export_test def test_torch_export(self): # Dense architecture super().test_torch_export() # MOE architecture config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.num_experts = 2 config.part_features = config.hidden_size // config.num_experts inputs_dict["dataset_index"] = torch.tensor([0] * self.model_tester.batch_size, device=torch_device) super().test_torch_export(config=config, inputs_dict=inputs_dict) @require_torch class VitPoseBackboneTest(unittest.TestCase, BackboneTesterMixin): all_model_classes = (VitPoseBackbone,) if is_torch_available() else () config_class = VitPoseBackboneConfig has_attentions = False def setUp(self): self.model_tester = VitPoseBackboneModelTester(self)