# 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 SegFormer model. """ from __future__ import annotations import inspect import unittest from typing import List, Tuple from transformers import SegformerConfig from transformers.file_utils import is_tf_available, is_vision_available from transformers.testing_utils import require_tf, 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 numpy as np import tensorflow as tf from transformers import TFSegformerForImageClassification, TFSegformerForSemanticSegmentation, TFSegformerModel from transformers.models.segformer.modeling_tf_segformer import TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import SegformerImageProcessor class TFSegformerConfigTester(ConfigTester): def create_and_test_config_common_properties(self): config = self.config_class(**self.inputs_dict) self.parent.assertTrue(hasattr(config, "hidden_sizes")) self.parent.assertTrue(hasattr(config, "num_attention_heads")) self.parent.assertTrue(hasattr(config, "num_encoder_blocks")) class TFSegformerModelTester: def __init__( self, parent, batch_size=13, image_size=64, num_channels=3, num_encoder_blocks=4, depths=[1, 1, 1, 1], sr_ratios=[8, 4, 2, 1], hidden_sizes=[8, 8, 16, 16], downsampling_rates=[1, 4, 8, 16], num_attention_heads=[1, 1, 2, 2], is_training=True, use_labels=True, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, 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_encoder_blocks = num_encoder_blocks self.sr_ratios = sr_ratios self.depths = depths self.hidden_sizes = hidden_sizes self.downsampling_rates = downsampling_rates self.num_attention_heads = num_attention_heads self.is_training = is_training self.use_labels = use_labels self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.initializer_range = initializer_range self.num_labels = num_labels 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.image_size, self.image_size], self.num_labels) config = self.get_config() return config, pixel_values, labels def get_config(self): return SegformerConfig( image_size=self.image_size, num_channels=self.num_channels, num_encoder_blocks=self.num_encoder_blocks, depths=self.depths, hidden_sizes=self.hidden_sizes, num_attention_heads=self.num_attention_heads, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, initializer_range=self.initializer_range, num_labels=self.num_labels, ) def create_and_check_model(self, config, pixel_values, labels): model = TFSegformerModel(config=config) result = model(pixel_values, training=False) expected_height = expected_width = self.image_size // (self.downsampling_rates[-1] * 2) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width) ) def create_and_check_for_image_segmentation(self, config, pixel_values, labels): config.num_labels = self.num_labels model = TFSegformerForSemanticSegmentation(config) result = model(pixel_values, training=False) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) ) result = model(pixel_values, labels=labels, training=False) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4) ) 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 def prepare_config_and_inputs_for_keras_fit(self, for_segmentation: bool = False): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, seg_labels = config_and_inputs if for_segmentation: inputs_dict = {"pixel_values": pixel_values, "labels": seg_labels} else: inputs_dict = {"pixel_values": pixel_values, "labels": tf.zeros((self.batch_size))} return config, inputs_dict @require_tf class TFSegformerModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( (TFSegformerModel, TFSegformerForImageClassification, TFSegformerForSemanticSegmentation) if is_tf_available() else () ) pipeline_model_mapping = ( {"feature-extraction": TFSegformerModel, "image-classification": TFSegformerForImageClassification} if is_tf_available() else {} ) test_head_masking = False test_onnx = False test_pruning = False test_resize_embeddings = False def setUp(self): self.model_tester = TFSegformerModelTester(self) self.config_tester = TFSegformerConfigTester(self, config_class=SegformerConfig, has_text_modality=False) 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("SegFormer does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip("SegFormer does not have get_input_embeddings method and get_output_embeddings methods") 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_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True 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)) attentions = outputs.attentions expected_num_attentions = sum(self.model_tester.depths) self.assertEqual(len(attentions), expected_num_attentions) # 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)) attentions = outputs.attentions self.assertEqual(len(attentions), expected_num_attentions) # verify the first attentions (first block, first layer) expected_seq_len = (self.model_tester.image_size // 4) ** 2 expected_reduced_seq_len = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2 self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len], ) # verify the last attentions (last block, last layer) expected_seq_len = (self.model_tester.image_size // 32) ** 2 expected_reduced_seq_len = (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2 self.assertListEqual( list(attentions[-1].shape[-3:]), [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len], ) 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)) self.assertEqual(out_len + 1, len(outputs)) self_attentions = outputs.attentions self.assertEqual(len(self_attentions), expected_num_attentions) # verify the first attentions (first block, first layer) expected_seq_len = (self.model_tester.image_size // 4) ** 2 expected_reduced_seq_len = (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2 self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_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)) hidden_states = outputs.hidden_states expected_num_layers = self.model_tester.num_encoder_blocks self.assertEqual(len(hidden_states), expected_num_layers) # verify the first hidden states (first block) self.assertListEqual( list(hidden_states[0].shape[-3:]), [ self.model_tester.hidden_sizes[0], 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) 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) dict_inputs = self._prepare_for_class(inputs_dict, model_class) check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True}) if self.has_attentions: 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_attentions": True}) # todo: incorporate label support for semantic segmentation in `test_modeling_tf_common.py`. @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 check_keras_fit_results(self, val_loss1, val_loss2, atol=2e-1, rtol=2e-1): self.assertTrue(np.allclose(val_loss1, val_loss2, atol=atol, rtol=rtol)) @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): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: # Since `TFSegformerModel` cannot operate with the default `fit()` method. if model_class.__name__ != "TFSegformerModel": model = model_class(config) if getattr(model, "hf_compute_loss", None): super().test_keras_fit() def test_loss_computation(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def apply(model): for_segmentation = True if model_class.__name__ == "TFSegformerForSemanticSegmentation" else False # The number of elements in the loss should be the same as the number of elements in the label _, prepared_for_class = self.model_tester.prepare_config_and_inputs_for_keras_fit( for_segmentation=for_segmentation ) added_label = prepared_for_class[sorted(prepared_for_class.keys() - inputs_dict.keys(), reverse=True)[0]] loss_size = tf.size(added_label) # Test that model correctly compute the loss with kwargs possible_input_names = {"input_ids", "pixel_values", "input_features"} input_name = possible_input_names.intersection(set(prepared_for_class)).pop() model_input = prepared_for_class.pop(input_name) loss = model(model_input, **prepared_for_class)[0] if model_class.__name__ == "TFSegformerForSemanticSegmentation": # Semantic segmentation loss is computed similarly as # https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_tf_utils.py#L210. self.assertEqual(loss.shape, (1,)) else: self.assertEqual(loss.shape, [loss_size]) # Test that model correctly compute the loss with a dict _, prepared_for_class = self.model_tester.prepare_config_and_inputs_for_keras_fit( for_segmentation=for_segmentation ) loss = model(**prepared_for_class)[0] if model_class.__name__ == "TFSegformerForSemanticSegmentation": self.assertEqual(loss.shape, (1,)) else: self.assertEqual(loss.shape, [loss_size]) # Test that model correctly compute the loss with a tuple label_keys = prepared_for_class.keys() - inputs_dict.keys() signature = inspect.signature(model.call).parameters signature_names = list(signature.keys()) # Create a dictionary holding the location of the tensors in the tuple tuple_index_mapping = {0: input_name} for label_key in label_keys: label_key_index = signature_names.index(label_key) tuple_index_mapping[label_key_index] = label_key sorted_tuple_index_mapping = sorted(tuple_index_mapping.items()) # Initialize a list with their default values, update the values and convert to a tuple list_input = [] for name in signature_names: if name != "kwargs": list_input.append(signature[name].default) for index, value in sorted_tuple_index_mapping: list_input[index] = prepared_for_class[value] tuple_input = tuple(list_input) # Send to model loss = model(tuple_input[:-1])[0] if model_class.__name__ == "TFSegformerForSemanticSegmentation": self.assertEqual(loss.shape, (1,)) else: self.assertEqual(loss.shape, [loss_size]) for model_class in self.all_model_classes: # Since `TFSegformerModel` won't have labels against which we # could compute loss. if model_class.__name__ != "TFSegformerModel": model = model_class(config) apply(model) def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=2e-4, name="outputs", attributes=None): # We override with a slightly higher tol value, as semseg models tend to diverge a bit more super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol, name, attributes) @slow def test_model_from_pretrained(self): for model_name in TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFSegformerModel.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_tf class TFSegformerModelIntegrationTest(unittest.TestCase): @slow def test_inference_image_segmentation_ade(self): # only resize + normalize image_processor = SegformerImageProcessor( image_scale=(512, 512), keep_ratio=False, align=False, do_random_crop=False ) model = TFSegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512") image = prepare_img() encoded_inputs = image_processor(images=image, return_tensors="tf") pixel_values = encoded_inputs.pixel_values outputs = model(pixel_values, training=False) expected_shape = tf.TensorShape((1, model.config.num_labels, 128, 128)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = tf.constant( [ [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]], [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]], [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]], ] ) tf.debugging.assert_near(outputs.logits[0, :3, :3, :3], expected_slice, atol=1e-4) @slow def test_inference_image_segmentation_city(self): # only resize + normalize image_processor = SegformerImageProcessor( image_scale=(512, 512), keep_ratio=False, align=False, do_random_crop=False ) model = TFSegformerForSemanticSegmentation.from_pretrained( "nvidia/segformer-b1-finetuned-cityscapes-1024-1024" ) image = prepare_img() encoded_inputs = image_processor(images=image, return_tensors="tf") pixel_values = encoded_inputs.pixel_values outputs = model(pixel_values, training=False) expected_shape = tf.TensorShape((1, model.config.num_labels, 128, 128)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = tf.constant( [ [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]], [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]], [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]], ] ) tf.debugging.assert_near(outputs.logits[0, :3, :3, :3], expected_slice, atol=1e-1)