# 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 ViTMAE model. """ import inspect import math import tempfile import unittest import numpy as np from transformers import ViTMAEConfig from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import cached_property, is_torch_available, is_vision_available 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 ViTMAEForPreTraining, ViTMAEModel from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import ViTImageProcessor class ViTMAEModelTester: 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, num_labels=3, mask_ratio=0.6, 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.mask_ratio = mask_ratio self.scope = scope # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above # (we add 1 for the [CLS] token) num_patches = (image_size // patch_size) ** 2 self.seq_length = int(math.ceil((1 - mask_ratio) * (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]) 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 ViTMAEConfig( 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, mask_ratio=self.mask_ratio, decoder_hidden_size=self.hidden_size, decoder_intermediate_size=self.intermediate_size, decoder_num_attention_heads=self.num_attention_heads, decoder_num_hidden_layers=self.num_hidden_layers, ) def create_and_check_model(self, config, pixel_values, labels): model = ViTMAEModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_for_pretraining(self, config, pixel_values, labels): model = ViTMAEForPreTraining(config) model.to(torch_device) model.eval() result = model(pixel_values) num_patches = (self.image_size // self.patch_size) ** 2 expected_num_channels = self.patch_size**2 * self.num_channels self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels)) # test greyscale images config.num_channels = 1 model = ViTMAEForPreTraining(config) model.to(torch_device) model.eval() pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]) result = model(pixel_values) expected_num_channels = self.patch_size**2 self.parent.assertEqual(result.logits.shape, (self.batch_size, num_patches, expected_num_channels)) 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 ViTMAEModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as ViTMAE does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else () pipeline_model_mapping = {"feature-extraction": ViTMAEModel} 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 = ViTMAEModelTester(self) self.config_tester = ConfigTester(self, config_class=ViTMAEConfig, has_text_modality=False, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="ViTMAE does not use inputs_embeds") def test_inputs_embeds(self): pass 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_for_pretraining(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_pretraining(*config_and_inputs) # overwrite from common since ViTMAEForPretraining has random masking, we need to fix the noise # to generate masks during test def check_pt_tf_models(self, tf_model, pt_model, pt_inputs_dict): # make masks reproducible np.random.seed(2) num_patches = int((pt_model.config.image_size // pt_model.config.patch_size) ** 2) noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches)) pt_noise = torch.from_numpy(noise) # Add `noise` argument. # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument pt_inputs_dict["noise"] = pt_noise super().check_pt_tf_models(tf_model, pt_model, pt_inputs_dict) def test_save_load(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() # make random mask reproducible torch.manual_seed(2) with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) out_2 = outputs[0].cpu().numpy() out_2[np.isnan(out_2)] = 0 with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model = model_class.from_pretrained(tmpdirname) model.to(torch_device) # make random mask reproducible torch.manual_seed(2) with torch.no_grad(): after_outputs = model(**self._prepare_for_class(inputs_dict, model_class)) # Make sure we don't have nans out_1 = after_outputs[0].cpu().numpy() out_1[np.isnan(out_1)] = 0 max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) @unittest.skip( reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load to get deterministic results.""" ) def test_determinism(self): pass @unittest.skip( reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load to get deterministic results.""" ) def test_save_load_fast_init_from_base(self): pass @unittest.skip( reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load to get deterministic results.""" ) def test_save_load_fast_init_to_base(self): pass @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load""") def test_model_outputs_equivalence(self): pass @slow def test_model_from_pretrained(self): for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = ViTMAEModel.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 ViTMAEModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return ViTImageProcessor.from_pretrained("facebook/vit-mae-base") if is_vision_available() else None @slow def test_inference_for_pretraining(self): # make random mask reproducible across the PT and TF model np.random.seed(2) model = ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base").to(torch_device) image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(images=image, return_tensors="pt").to(torch_device) # prepare a noise vector that will be also used for testing the TF model # (this way we can ensure that the PT and TF models operate on the same inputs) vit_mae_config = ViTMAEConfig() num_patches = int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2) noise = np.random.uniform(size=(1, num_patches)) # forward pass with torch.no_grad(): outputs = model(**inputs, noise=torch.from_numpy(noise).to(device=torch_device)) # verify the logits expected_shape = torch.Size((1, 196, 768)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = torch.tensor( [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] ) self.assertTrue(torch.allclose(outputs.logits[0, :3, :3], expected_slice.to(torch_device), atol=1e-4))