# 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 TensorFlow ViTMAE model. """ from __future__ import annotations import copy import inspect import json import math import os import tempfile import unittest from importlib import import_module import numpy as np from transformers import ViTMAEConfig from transformers.file_utils import cached_property, is_tf_available, is_vision_available from transformers.testing_utils import require_tf, require_vision, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import TFViTMAEForPreTraining, TFViTMAEModel if is_vision_available(): from PIL import Image from transformers import ViTImageProcessor class TFViTMAEModelTester: 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, decoder_hidden_size=self.hidden_size, decoder_num_hidden_layers=self.num_hidden_layers, decoder_num_attention_heads=self.num_attention_heads, decoder_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, ) def create_and_check_model(self, config, pixel_values, labels): model = TFViTMAEModel(config=config) result = model(pixel_values, training=False) 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 = TFViTMAEForPreTraining(config) result = model(pixel_values, training=False) # expected sequence length = num_patches 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 = TFViTMAEForPreTraining(config) pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]) result = model(pixel_values, training=False) 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_tf class TFViTMAEModelTest(TFModelTesterMixin, 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 = (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else () pipeline_model_mapping = {"feature-extraction": TFViTMAEModel} if is_tf_available() else {} test_pruning = False test_onnx = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = TFViTMAEModelTester(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(), (tf.keras.layers.Layer)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, tf.keras.layers.Layer)) 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.call) # 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 TFViTMAEForPretraining has random masking, we need to fix the noise # to generate masks during test def test_keyword_and_dict_args(self): # make the mask reproducible np.random.seed(2) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() num_patches = int((config.image_size // config.patch_size) ** 2) noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches)) for model_class in self.all_model_classes: model = model_class(config) inputs = self._prepare_for_class(inputs_dict, model_class) outputs_dict = model(inputs, noise=noise) inputs_keywords = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class)) outputs_keywords = model(**inputs_keywords, noise=noise) output_dict = outputs_dict[0].numpy() output_keywords = outputs_keywords[0].numpy() self.assertLess(np.sum(np.abs(output_dict - output_keywords)), 1e-6) # overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise # to generate masks during test def test_numpy_arrays_inputs(self): # make the mask reproducible np.random.seed(2) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() num_patches = int((config.image_size // config.patch_size) ** 2) noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches)) def prepare_numpy_arrays(inputs_dict): inputs_np_dict = {} for k, v in inputs_dict.items(): if tf.is_tensor(v): inputs_np_dict[k] = v.numpy() else: inputs_np_dict[k] = np.array(k) return inputs_np_dict for model_class in self.all_model_classes: model = model_class(config) inputs = self._prepare_for_class(inputs_dict, model_class) inputs_np = prepare_numpy_arrays(inputs) output_for_dict_input = model(inputs_np, noise=noise) output_for_kw_input = model(**inputs_np, noise=noise) self.assert_outputs_same(output_for_dict_input, output_for_kw_input) # overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise # to generate masks during test def check_pt_tf_models(self, tf_model, pt_model, tf_inputs_dict): # make masks reproducible np.random.seed(2) num_patches = int((tf_model.config.image_size // tf_model.config.patch_size) ** 2) noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches)) tf_noise = tf.constant(noise) # Add `noise` argument. # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument tf_inputs_dict["noise"] = tf_noise super().check_pt_tf_models(tf_model, pt_model, tf_inputs_dict) # overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise # to generate masks during test def test_keras_save_load(self): # make mask reproducible np.random.seed(2) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() tf_main_layer_classes = { module_member for model_class in self.all_model_classes for module in (import_module(model_class.__module__),) for module_member_name in dir(module) if module_member_name.endswith("MainLayer") # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`. and module_member_name[: -len("MainLayer")] == model_class.__name__[: -len("Model")] for module_member in (getattr(module, module_member_name),) if isinstance(module_member, type) and tf.keras.layers.Layer in module_member.__bases__ and getattr(module_member, "_keras_serializable", False) } num_patches = int((config.image_size // config.patch_size) ** 2) noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches)) noise = tf.convert_to_tensor(noise) inputs_dict.update({"noise": noise}) for main_layer_class in tf_main_layer_classes: main_layer = main_layer_class(config) symbolic_inputs = { name: tf.keras.Input(tensor.shape[1:], dtype=tensor.dtype) for name, tensor in inputs_dict.items() } model = tf.keras.Model(symbolic_inputs, outputs=main_layer(symbolic_inputs)) outputs = model(inputs_dict) with tempfile.TemporaryDirectory() as tmpdirname: filepath = os.path.join(tmpdirname, "keras_model.h5") model.save(filepath) model = tf.keras.models.load_model( filepath, custom_objects={main_layer_class.__name__: main_layer_class} ) assert isinstance(model, tf.keras.Model) after_outputs = model(inputs_dict) self.assert_outputs_same(after_outputs, outputs) # overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise # to generate masks during test @slow def test_save_load(self): # make mask reproducible np.random.seed(2) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() num_patches = int((config.image_size // config.patch_size) ** 2) noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches)) for model_class in self.all_model_classes: model = model_class(config) model_input = self._prepare_for_class(inputs_dict, model_class) outputs = model(model_input, noise=noise) if model_class.__name__ == "TFViTMAEModel": out_2 = outputs.last_hidden_state.numpy() out_2[np.isnan(out_2)] = 0 else: out_2 = outputs.logits.numpy() out_2[np.isnan(out_2)] = 0 with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname, saved_model=False) model = model_class.from_pretrained(tmpdirname) after_outputs = model(model_input, noise=noise) if model_class.__name__ == "TFViTMAEModel": out_1 = after_outputs["last_hidden_state"].numpy() out_1[np.isnan(out_1)] = 0 else: out_1 = after_outputs["logits"].numpy() out_1[np.isnan(out_1)] = 0 max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) # overwrite from common since TFViTMAEForPretraining has random masking, we need to fix the noise # to generate masks during test def test_save_load_config(self): # make mask reproducible np.random.seed(2) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() num_patches = int((config.image_size // config.patch_size) ** 2) noise = np.random.uniform(size=(self.model_tester.batch_size, num_patches)) for model_class in self.all_model_classes: model = model_class(config) model_inputs = self._prepare_for_class(inputs_dict, model_class) outputs = model(model_inputs, noise=noise) model_config = model.get_config() # make sure that returned config is jsonifiable, which is required by keras json.dumps(model_config) new_model = model_class.from_config(model.get_config()) # make sure it also accepts a normal config _ = model_class.from_config(model.config) _ = new_model(model_inputs) # Build model new_model.set_weights(model.get_weights()) after_outputs = new_model(model_inputs, noise=noise) self.assert_outputs_same(after_outputs, outputs) @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""") def test_model_outputs_equivalence(self): pass @slow def test_model_from_pretrained(self): model = TFViTMAEModel.from_pretrained("google/vit-base-patch16-224") 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_tf @require_vision class TFViTMAEModelIntegrationTest(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 = TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base") image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(images=image, return_tensors="tf") # 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 outputs = model(**inputs, noise=noise) # verify the logits expected_shape = tf.convert_to_tensor([1, 196, 768]) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = tf.convert_to_tensor( [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]] ) tf.debugging.assert_near(outputs.logits[0, :3, :3], expected_slice, atol=1e-4)