# 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 ConvNext model. """ from __future__ import annotations import inspect import unittest from typing import List, Tuple from transformers import ConvNextConfig from transformers.testing_utils import require_tf, require_vision, slow from transformers.utils import cached_property, is_tf_available, is_vision_available 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 TFConvNextForImageClassification, TFConvNextModel if is_vision_available(): from PIL import Image from transformers import ConvNextImageProcessor class TFConvNextModelTester: def __init__( self, parent, batch_size=13, image_size=32, num_channels=3, num_stages=4, hidden_sizes=[10, 20, 30, 40], depths=[2, 2, 3, 2], is_training=True, use_labels=True, intermediate_size=37, hidden_act="gelu", type_sequence_label_size=10, initializer_range=0.02, num_labels=3, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.num_channels = num_channels self.num_stages = num_stages self.hidden_sizes = hidden_sizes self.depths = depths self.is_training = is_training self.use_labels = use_labels self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.scope = scope 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 ConvNextConfig( num_channels=self.num_channels, hidden_sizes=self.hidden_sizes, depths=self.depths, num_stages=self.num_stages, hidden_act=self.hidden_act, is_decoder=False, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, pixel_values, labels): model = TFConvNextModel(config=config) result = model(pixel_values, training=False) # expected last hidden states: B, C, H // 32, W // 32 self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32), ) def create_and_check_for_image_classification(self, config, pixel_values, labels): config.num_labels = self.type_sequence_label_size model = TFConvNextForImageClassification(config) result = model(pixel_values, labels=labels, training=False) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)) 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 TFConvNextModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as ConvNext does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else () pipeline_model_mapping = ( {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification} if is_tf_available() else {} ) test_pruning = False test_onnx = False test_resize_embeddings = False test_head_masking = False has_attentions = False def setUp(self): self.model_tester = TFConvNextModelTester(self) self.config_tester = ConfigTester( self, config_class=ConvNextConfig, has_text_modality=False, hidden_size=37, ) @unittest.skip(reason="ConvNext does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skipIf( not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0, reason="TF does not support backprop for grouped convolutions on CPU.", ) @slow def test_keras_fit(self): super().test_keras_fit() @unittest.skip(reason="ConvNext does not support input and output embeddings") def test_model_common_attributes(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.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) @unittest.skipIf( not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0, reason="TF does not support backprop for grouped convolutions on CPU.", ) def test_dataset_conversion(self): super().test_dataset_conversion() 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)) hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_stages = self.model_tester.num_stages self.assertEqual(len(hidden_states), expected_num_stages + 1) # ConvNext's feature maps are of shape (batch_size, num_channels, height, width) self.assertListEqual( list(hidden_states[0].shape[-2:]), [self.model_tester.image_size // 4, self.model_tester.image_size // 4], ) 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) # Since ConvNext does not have any attention we need to rewrite this test. def test_model_outputs_equivalence(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}): tuple_output = model(tuple_inputs, return_dict=False, **additional_kwargs) dict_output = model(dict_inputs, return_dict=True, **additional_kwargs).to_tuple() def recursive_check(tuple_object, dict_object): if isinstance(tuple_object, (List, Tuple)): for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object): recursive_check(tuple_iterable_value, dict_iterable_value) elif tuple_object is None: return else: self.assertTrue( all(tf.equal(tuple_object, dict_object)), msg=( "Tuple and dict output are not equal. Difference:" f" {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}" ), ) recursive_check(tuple_output, dict_output) for model_class in self.all_model_classes: model = model_class(config) tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs) tuple_inputs = self._prepare_for_class(inputs_dict, model_class) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True}) tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True}) def test_for_image_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): model = TFConvNextModel.from_pretrained("facebook/convnext-tiny-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 TFConvNextModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return ConvNextImageProcessor.from_pretrained("facebook/convnext-tiny-224") if is_vision_available() else None @slow def test_inference_image_classification_head(self): model = TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224") image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(images=image, return_tensors="tf") # forward pass outputs = model(**inputs) # verify the logits expected_shape = tf.TensorShape((1, 1000)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = tf.constant([-0.0260, -0.4739, 0.1911]) tf.debugging.assert_near(outputs.logits[0, :3], expected_slice, atol=1e-4)