# coding=utf-8 # 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 GroupViT model. """ import inspect import os import random import tempfile import unittest import numpy as np import requests from transformers import GroupViTConfig, GroupViTTextConfig, GroupViTVisionConfig from transformers.testing_utils import is_pt_tf_cross_test, 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, _config_zero_init, 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 GroupViTModel, GroupViTTextModel, GroupViTVisionModel from transformers.models.groupvit.modeling_groupvit import GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import CLIPProcessor class GroupViTVisionModelTester: def __init__( self, parent, batch_size=12, image_size=30, patch_size=2, num_channels=3, is_training=True, hidden_size=32, depths=[6, 3, 3], num_group_tokens=[64, 8, 0], num_output_groups=[64, 8, 8], 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.depths = depths self.num_hidden_layers = sum(depths) self.expected_num_hidden_layers = len(depths) + 1 self.num_group_tokens = num_group_tokens self.num_output_groups = num_output_groups 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 num_patches = (image_size // patch_size) ** 2 # no [CLS] token for GroupViT self.seq_length = num_patches def prepare_config_and_inputs(self): rng = random.Random(0) pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size], rng=rng) config = self.get_config() return config, pixel_values def get_config(self): return GroupViTVisionConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, depths=self.depths, num_group_tokens=self.num_group_tokens, num_output_groups=self.num_output_groups, 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 = GroupViTVisionModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(pixel_values) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.num_output_groups[-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 GroupViTVisionModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as GROUPVIT does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (GroupViTVisionModel,) if is_torch_available() else () test_pruning = False test_torchscript = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = GroupViTVisionModelTester(self) self.config_tester = ConfigTester( self, config_class=GroupViTVisionConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="GroupViT does not use inputs_embeds") def test_inputs_embeds(self): pass @is_pt_tf_cross_test def test_pt_tf_model_equivalence(self): import tensorflow as tf seed = 338 random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) tf.random.set_seed(seed) return super().test_pt_tf_model_equivalence() 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(), (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) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True seq_len = getattr(self.model_tester, "seq_length", None) expected_num_attention_outputs = sum(g > 0 for g in self.model_tester.num_group_tokens) 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) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions # GroupViT returns attention grouping of each stage self.assertEqual(len(attentions), sum(g > 0 for g in self.model_tester.num_group_tokens)) # 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 # GroupViT returns attention grouping of each stage self.assertEqual(len(attentions), expected_num_attention_outputs) 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 # GroupViT returns attention grouping of each stage self.assertEqual(len(self_attentions), expected_num_attention_outputs) for i, self_attn in enumerate(self_attentions): if self_attn is None: continue self.assertListEqual( list(self_attentions[i].shape[-2:]), [ self.model_tester.num_output_groups[i], self.model_tester.num_output_groups[i - 1] if i > 0 else seq_len, ], ) def test_training(self): pass def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="GroupViTVisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="GroupViTVisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass # override since the attention mask from GroupViT is not used to compute loss, thus no grad 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] if config.is_encoder_decoder: # Seq2Seq models encoder_hidden_states = outputs.encoder_hidden_states[0] encoder_hidden_states.retain_grad() decoder_hidden_states = outputs.decoder_hidden_states[0] decoder_hidden_states.retain_grad() if self.has_attentions: encoder_attentions = outputs.encoder_attentions[0] encoder_attentions.retain_grad() decoder_attentions = outputs.decoder_attentions[0] decoder_attentions.retain_grad() cross_attentions = outputs.cross_attentions[0] cross_attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(encoder_hidden_states.grad) self.assertIsNotNone(decoder_hidden_states.grad) if self.has_attentions: self.assertIsNotNone(encoder_attentions.grad) self.assertIsNotNone(decoder_attentions.grad) self.assertIsNotNone(cross_attentions.grad) else: # Encoder-/Decoder-only models hidden_states = outputs.hidden_states[0] hidden_states.retain_grad() if self.has_attentions: attentions = outputs.attentions[0] attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) if self.has_attentions: self.assertIsNone(attentions.grad) @slow def test_model_from_pretrained(self): for model_name in GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = GroupViTVisionModel.from_pretrained(model_name) self.assertIsNotNone(model) class GroupViTTextModelTester: 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): rng = random.Random(0) input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size, rng=rng) 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 GroupViTTextConfig( 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 = GroupViTTextModel(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 GroupViTTextModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (GroupViTTextModel,) if is_torch_available() else () test_pruning = False test_head_masking = False def setUp(self): self.model_tester = GroupViTTextModelTester(self) self.config_tester = ConfigTester(self, config_class=GroupViTTextConfig, 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) def test_training(self): pass def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="GroupViTTextModel does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="GroupViTTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="GroupViTTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): for model_name in GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = GroupViTTextModel.from_pretrained(model_name) self.assertIsNotNone(model) class GroupViTModelTester: def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True): if text_kwargs is None: text_kwargs = {} if vision_kwargs is None: vision_kwargs = {} self.parent = parent self.text_model_tester = GroupViTTextModelTester(parent, **text_kwargs) self.vision_model_tester = GroupViTVisionModelTester(parent, **vision_kwargs) self.is_training = is_training 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 GroupViTConfig.from_text_vision_configs( self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64 ) def create_and_check_model(self, config, input_ids, attention_mask, pixel_values): model = GroupViTModel(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 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_loss": True, } return config, inputs_dict @require_torch class GroupViTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (GroupViTModel,) if is_torch_available() else () pipeline_model_mapping = {"feature-extraction": GroupViTModel} if is_torch_available() else {} test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False def setUp(self): self.model_tester = GroupViTModelTester(self) 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(reason="hidden_states are tested in individual model tests") def test_hidden_states_output(self): pass @unittest.skip(reason="input_embeds are tested in individual model tests") def test_inputs_embeds(self): pass @unittest.skip(reason="tested in individual model tests") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="GroupViTModel does not have input/output embeddings") def test_model_common_attributes(self): pass # overwritten from parent as this equivalent test needs a specific `seed` and hard to get a good one! def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=2e-5, name="outputs", attributes=None): super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol=tol, name=name, attributes=attributes) @is_pt_tf_cross_test def test_pt_tf_model_equivalence(self): import tensorflow as tf seed = 163 random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) tf.random.set_seed(seed) return super().test_pt_tf_model_equivalence() # override as the `logit_scale` parameter initilization is different for GROUPVIT def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): if param.requires_grad: # check if `logit_scale` is initilized as per the original implementation if name == "logit_scale": self.assertAlmostEqual( param.data.item(), np.log(1 / 0.07), delta=1e-3, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) else: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) def _create_and_check_torchscript(self, config, inputs_dict): if not self.test_torchscript: return configs_no_init = _config_zero_init(config) # To be sure we have no Nan configs_no_init.torchscript = True configs_no_init.return_dict = False for model_class in self.all_model_classes: model = model_class(config=configs_no_init) model.to(torch_device) model.eval() try: input_ids = inputs_dict["input_ids"] pixel_values = inputs_dict["pixel_values"] # GROUPVIT needs pixel_values traced_model = torch.jit.trace(model, (input_ids, pixel_values)) except RuntimeError: self.fail("Couldn't trace module.") with tempfile.TemporaryDirectory() as tmp_dir_name: pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt") try: torch.jit.save(traced_model, pt_file_name) except Exception: self.fail("Couldn't save module.") try: loaded_model = torch.jit.load(pt_file_name) except Exception: self.fail("Couldn't load module.") model.to(torch_device) model.eval() loaded_model.to(torch_device) loaded_model.eval() model_state_dict = model.state_dict() loaded_model_state_dict = loaded_model.state_dict() non_persistent_buffers = {} for key in loaded_model_state_dict.keys(): if key not in model_state_dict.keys(): non_persistent_buffers[key] = loaded_model_state_dict[key] loaded_model_state_dict = { key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers } self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys())) model_buffers = list(model.buffers()) for non_persistent_buffer in non_persistent_buffers.values(): found_buffer = False for i, model_buffer in enumerate(model_buffers): if torch.equal(non_persistent_buffer, model_buffer): found_buffer = True break self.assertTrue(found_buffer) model_buffers.pop(i) models_equal = True for layer_name, p1 in model_state_dict.items(): p2 = loaded_model_state_dict[layer_name] if p1.data.ne(p2.data).sum() > 0: models_equal = False self.assertTrue(models_equal) def test_load_vision_text_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Save GroupViTConfig and check if we can load GroupViTVisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = GroupViTVisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save GroupViTConfig and check if we can load GroupViTTextConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = GroupViTTextConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict()) @slow def test_model_from_pretrained(self): for model_name in GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = GroupViTModel.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" im = Image.open(requests.get(url, stream=True).raw) return im @require_vision @require_torch class GroupViTModelIntegrationTest(unittest.TestCase): @slow def test_inference(self): model_name = "nvidia/groupvit-gcc-yfcc" model = GroupViTModel.from_pretrained(model_name) processor = CLIPProcessor.from_pretrained(model_name) image = prepare_img() inputs = processor( text=["a photo of a cat", "a photo of a dog"], images=image, padding=True, return_tensors="pt" ) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the logits self.assertEqual( outputs.logits_per_image.shape, torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])), ) self.assertEqual( outputs.logits_per_text.shape, torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])), ) expected_logits = torch.tensor([[13.3523, 6.3629]]) self.assertTrue(torch.allclose(outputs.logits_per_image, expected_logits, atol=1e-3))