# 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 GroupViT model. """ from __future__ import annotations import inspect import os import random import tempfile import unittest from importlib import import_module import numpy as np import requests from transformers import GroupViTConfig, GroupViTTextConfig, GroupViTVisionConfig from transformers.testing_utils import ( is_pt_tf_cross_test, require_tensorflow_probability, require_tf, require_vision, slow, ) from transformers.utils import is_tf_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import TFGroupViTModel, TFGroupViTTextModel, TFGroupViTVisionModel, TFSharedEmbeddings from transformers.models.groupvit.modeling_tf_groupvit import TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import CLIPProcessor class TFGroupViTVisionModelTester: 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 = TFGroupViTVisionModel(config=config) result = model(pixel_values, training=False) 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_tf class TFGroupViTVisionModelTest(TFModelTesterMixin, 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 = (TFGroupViTVisionModel,) if is_tf_available() else () test_pruning = False test_resize_embeddings = False test_head_masking = False test_onnx = False def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=1e-4, name="outputs", attributes=None): # We override with a slightly higher tol value, as this model tends to diverge a bit more super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol, name, attributes) def setUp(self): self.model_tester = TFGroupViTVisionModelTester(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 """ During saving, TensorFlow will also run with `training=True` which trigger `gumbel_softmax` that requires `tensorflow-probability`. """ @require_tensorflow_probability @slow def test_saved_model_creation(self): super().test_saved_model_creation() @unittest.skip(reason="GroupViT does not use inputs_embeds") def test_graph_mode_with_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_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) outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False) 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) outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False) 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) outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False) 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_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False) hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) seq_length = getattr(self.model_tester, "seq_length", None) self.assertListEqual( list(hidden_states[0].shape[-2:]), [seq_length, self.model_tester.hidden_size], ) 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) @is_pt_tf_cross_test def test_pt_tf_model_equivalence(self): # `GroupViT` computes some indices using argmax, uses them as # one-hot encoding for further computation. The problem is # while PT/TF have very small difference in `y_soft` (~ 1e-9), # the argmax could be totally different, if there are at least # 2 indices with almost identical values. This leads to very # large difference in the outputs. We need specific seeds to # avoid almost identical values happening in `y_soft`. import torch 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() @slow def test_model_from_pretrained(self): for model_name in TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFGroupViTVisionModel.from_pretrained(model_name) self.assertIsNotNone(model) @unittest.skip( "TFGroupViTVisionModel does not convert `hidden_states` and `attentions` to tensors as they are all of" " different dimensions, and we get `Got a non-Tensor value` error when saving the model." ) @slow def test_saved_model_creation_extended(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = True if hasattr(config, "use_cache"): config.use_cache = True seq_len = getattr(self.model_tester, "seq_length", None) for model_class in self.all_model_classes: class_inputs_dict = self._prepare_for_class(inputs_dict, model_class) model = model_class(config) num_out = len(model(class_inputs_dict)) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname, saved_model=True) saved_model_dir = os.path.join(tmpdirname, "saved_model", "1") model = tf.keras.models.load_model(saved_model_dir) outputs = model(class_inputs_dict) output_hidden_states = outputs["hidden_states"] output_attentions = outputs["attentions"] # Check num outputs self.assertEqual(len(outputs), num_out) # Check num layers expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(output_hidden_states), expected_num_layers) self.assertEqual(len(output_attentions), self.model_tester.num_hidden_layers) # Check attention outputs image_size = (self.model_tester.image_size, self.model_tester.image_size) patch_size = (self.model_tester.patch_size, self.model_tester.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) seq_len = num_patches + 1 self.assertListEqual( list(output_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, seq_len, seq_len], ) # Check hidden states self.assertListEqual( list(output_hidden_states[0].shape[-2:]), [seq_len, self.model_tester.hidden_size], ) class TFGroupViTTextModelTester: 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]) # make sure the first token has attention mask `1` to ensure that, after combining the causal mask, there # is still at least one token being attended to for each batch. # TODO: Change `random_attention_mask` in PT/TF/Flax common test file, after a discussion with the team. input_mask = tf.concat( [tf.ones_like(input_mask[:, :1], dtype=input_mask.dtype), input_mask[:, 1:]], axis=-1 ) 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 = TFGroupViTTextModel(config=config) result = model(input_ids, attention_mask=input_mask, training=False) result = model(input_ids, training=False) 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_tf class TFGroupViTTextModelTest(TFModelTesterMixin, unittest.TestCase): all_model_classes = (TFGroupViTTextModel,) if is_tf_available() else () test_pruning = False test_head_masking = False test_onnx = False def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=1e-4, name="outputs", attributes=None): # We override with a slightly higher tol value, as this model tends to diverge a bit more super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol, name, attributes) def setUp(self): self.model_tester = TFGroupViTTextModelTester(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) @unittest.skip(reason="GroupViTTextModel does not use inputs_embeds") def test_inputs_embeds(self): pass @slow def test_model_from_pretrained(self): for model_name in TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFGroupViTTextModel.from_pretrained(model_name) self.assertIsNotNone(model) @slow def test_saved_model_creation_extended(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = True if hasattr(config, "use_cache"): config.use_cache = True for model_class in self.all_model_classes: class_inputs_dict = self._prepare_for_class(inputs_dict, model_class) model = model_class(config) num_out = len(model(class_inputs_dict)) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname, saved_model=True) saved_model_dir = os.path.join(tmpdirname, "saved_model", "1") model = tf.keras.models.load_model(saved_model_dir) outputs = model(class_inputs_dict) output_hidden_states = outputs["hidden_states"] output_attentions = outputs["attentions"] # Check number of outputs self.assertEqual(len(outputs), num_out) # Check number of layers expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) # Check hidden states self.assertEqual(len(output_hidden_states), expected_num_layers) self.assertListEqual( list(output_hidden_states[0].shape[-2:]), [self.model_tester.seq_length, self.model_tester.hidden_size], ) # Check attention outputs self.assertEqual(len(output_attentions), self.model_tester.num_hidden_layers) seq_length = self.model_tester.seq_length key_length = getattr(self.model_tester, "key_length", seq_length) self.assertListEqual( list(output_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, seq_length, key_length], ) class TFGroupViTModelTester: def __init__(self, parent, is_training=True): self.parent = parent self.text_model_tester = TFGroupViTTextModelTester(parent) self.vision_model_tester = TFGroupViTVisionModelTester(parent) 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 = TFGroupViTModel(config) result = model(input_ids, pixel_values, attention_mask, training=False) 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_tf class TFGroupViTModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (TFGroupViTModel,) if is_tf_available() else () pipeline_model_mapping = {"feature-extraction": TFGroupViTModel} if is_tf_available() else {} test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False test_onnx = False def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=1e-4, name="outputs", attributes=None): # We override with a slightly higher tol value, as this model tends to diverge a bit more super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol, name, attributes) def setUp(self): self.model_tester = TFGroupViTModelTester(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="CLIPModel does not have input/output embeddings") def test_model_common_attributes(self): pass @require_tensorflow_probability @slow def test_keras_fit(self): super().test_keras_fit() @is_pt_tf_cross_test def test_pt_tf_model_equivalence(self): # `GroupViT` computes some indices using argmax, uses them as # one-hot encoding for further computation. The problem is # while PT/TF have very small difference in `y_soft` (~ 1e-9), # the argmax could be totally different, if there are at least # 2 indices with almost identical values. This leads to very # large difference in the outputs. We need specific seeds to # avoid almost identical values happening in `y_soft`. import torch seed = 158 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() # overwrite from common since `TFGroupViTModelTester` set `return_loss` to `True` and causes the preparation of # `symbolic_inputs` failed. def test_keras_save_load(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # remove `return_loss` to make code work if self.__class__.__name__ == "TFGroupViTModelTest": inputs_dict.pop("return_loss", None) 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) } for main_layer_class in tf_main_layer_classes: # T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter if "T5" in main_layer_class.__name__: # Take the same values than in TFT5ModelTester for this shared layer shared = TFSharedEmbeddings(99, 32, name="shared") config.use_cache = inputs_dict.pop("use_cache", None) main_layer = main_layer_class(config, embed_tokens=shared) else: 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) if "T5" in main_layer_class.__name__: model = tf.keras.models.load_model( filepath, custom_objects={ main_layer_class.__name__: main_layer_class, "TFSharedEmbeddings": TFSharedEmbeddings, }, ) else: 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) @slow def test_model_from_pretrained(self): for model_name in TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFGroupViTModel.from_pretrained(model_name) self.assertIsNotNone(model) @unittest.skip(reason="Currently `saved_model` doesn't work with nested outputs.") @slow def test_saved_model_creation(self): pass @unittest.skip(reason="`saved_model` doesn't work with nested outputs so no preparation happens.") @slow def test_prepare_serving_output(self): pass # 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_tf class TFGroupViTModelIntegrationTest(unittest.TestCase): @slow def test_inference(self): model_name = "nvidia/groupvit-gcc-yfcc" model = TFGroupViTModel.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="tf" ) outputs = model(**inputs, training=False) # verify the logits self.assertEqual( outputs.logits_per_image.shape, tf.TensorShape((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])), ) self.assertEqual( outputs.logits_per_text.shape, tf.TensorShape((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])), ) expected_logits = tf.constant([[13.3523, 6.3629]]) tf.debugging.assert_near(outputs.logits_per_image, expected_logits, atol=1e-3)