# 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 MaskFormer Swin model.""" import collections import unittest from transformers import MaskFormerSwinConfig from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device from transformers.utils import is_torch_available from ...test_backbone_common import BackboneTesterMixin 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 MaskFormerSwinBackbone from transformers.models.maskformer import MaskFormerSwinModel class MaskFormerSwinModelTester: def __init__( self, parent, batch_size=13, image_size=32, patch_size=2, num_channels=3, embed_dim=16, depths=[1, 2, 1], num_heads=[2, 2, 4], window_size=2, mlp_ratio=2.0, qkv_bias=True, hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, drop_path_rate=0.1, hidden_act="gelu", use_absolute_embeddings=False, patch_norm=True, initializer_range=0.02, layer_norm_eps=1e-5, is_training=True, scope=None, use_labels=True, type_sequence_label_size=10, encoder_stride=8, out_features=["stage1", "stage2", "stage3"], out_indices=[1, 2, 3], ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.embed_dim = embed_dim self.depths = depths self.num_heads = num_heads self.window_size = window_size self.mlp_ratio = mlp_ratio self.qkv_bias = qkv_bias self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.drop_path_rate = drop_path_rate self.hidden_act = hidden_act self.use_absolute_embeddings = use_absolute_embeddings self.patch_norm = patch_norm self.layer_norm_eps = layer_norm_eps self.initializer_range = initializer_range self.is_training = is_training self.scope = scope self.use_labels = use_labels self.type_sequence_label_size = type_sequence_label_size self.encoder_stride = encoder_stride self.out_features = out_features self.out_indices = out_indices 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 MaskFormerSwinConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, embed_dim=self.embed_dim, depths=self.depths, num_heads=self.num_heads, window_size=self.window_size, mlp_ratio=self.mlp_ratio, qkv_bias=self.qkv_bias, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, drop_path_rate=self.drop_path_rate, hidden_act=self.hidden_act, use_absolute_embeddings=self.use_absolute_embeddings, path_norm=self.patch_norm, layer_norm_eps=self.layer_norm_eps, initializer_range=self.initializer_range, encoder_stride=self.encoder_stride, out_features=self.out_features, out_indices=self.out_indices, ) def create_and_check_model(self, config, pixel_values, labels): model = MaskFormerSwinModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) expected_seq_len = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1)) expected_dim = int(config.embed_dim * 2 ** (len(config.depths) - 1)) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, expected_seq_len, expected_dim)) def create_and_check_backbone(self, config, pixel_values, labels): model = MaskFormerSwinBackbone(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # verify feature maps self.parent.assertEqual(len(result.feature_maps), len(config.out_features)) self.parent.assertListEqual(list(result.feature_maps[0].shape), [13, 16, 16, 16]) # verify channels self.parent.assertEqual(len(model.channels), len(config.out_features)) self.parent.assertListEqual(model.channels, [16, 32, 64]) # verify ValueError with self.parent.assertRaises(ValueError): config.out_features = ["stem"] model = MaskFormerSwinBackbone(config=config) 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 MaskFormerSwinModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( MaskFormerSwinModel, MaskFormerSwinBackbone, ) if is_torch_available() else () ) pipeline_model_mapping = {"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {} test_resize_embeddings = False test_torch_exportable = True def setUp(self): self.model_tester = MaskFormerSwinModelTester(self) self.config_tester = ConfigTester( self, config_class=MaskFormerSwinConfig, has_text_modality=False, embed_dim=37, common_properties=["image_size", "patch_size", "num_channels"], ) @require_torch_multi_gpu @unittest.skip( reason=( "`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with" " `nn.DataParallel`" ) ) def test_multi_gpu_data_parallel_forward(self): pass 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) def test_backbone(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_backbone(*config_and_inputs) @unittest.skip(reason="Swin does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Swin does not support feedforward chunking") def test_feed_forward_chunking(self): pass def test_model_get_set_embeddings(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)) @unittest.skip(reason="MaskFormerSwin is only used as backbone and doesn't support output_attentions") def test_attention_outputs(self): pass def check_hidden_states_output(self, inputs_dict, config, model_class, image_size): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", len(self.model_tester.depths) + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) # Swin has a different seq_length patch_size = ( config.patch_size if isinstance(config.patch_size, collections.abc.Iterable) else (config.patch_size, config.patch_size) ) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.assertListEqual( list(hidden_states[0].shape[-2:]), [num_patches, self.model_tester.embed_dim], ) def test_hidden_states_output(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() image_size = ( self.model_tester.image_size if isinstance(self.model_tester.image_size, collections.abc.Iterable) else (self.model_tester.image_size, self.model_tester.image_size) ) for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True self.check_hidden_states_output(inputs_dict, config, model_class, image_size) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True self.check_hidden_states_output(inputs_dict, config, model_class, image_size) def test_hidden_states_output_with_padding(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.patch_size = 3 image_size = ( self.model_tester.image_size if isinstance(self.model_tester.image_size, collections.abc.Iterable) else (self.model_tester.image_size, self.model_tester.image_size) ) patch_size = ( config.patch_size if isinstance(config.patch_size, collections.abc.Iterable) else (config.patch_size, config.patch_size) ) padded_height = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0]) padded_width = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1]) for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True self.check_hidden_states_output(inputs_dict, config, model_class, (padded_height, padded_width)) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True self.check_hidden_states_output(inputs_dict, config, model_class, (padded_height, padded_width)) @unittest.skip(reason="MaskFormerSwin doesn't have pretrained checkpoints") def test_model_from_pretrained(self): pass @unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin") def test_gradient_checkpointing_backward_compatibility(self): pass def test_model_outputs_equivalence(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() def set_nan_tensor_to_zero(t): t[t != t] = 0 return t def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}): with torch.no_grad(): 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 isinstance(tuple_object, dict): for tuple_iterable_value, dict_iterable_value in zip( tuple_object.values(), dict_object.values() ): recursive_check(tuple_iterable_value, dict_iterable_value) elif tuple_object is None: return else: self.assertTrue( torch.allclose( set_nan_tensor_to_zero(tuple_object), set_nan_tensor_to_zero(dict_object), atol=1e-5 ), msg=( "Tuple and dict output are not equal. Difference:" f" {torch.max(torch.abs(tuple_object - dict_object))}. Tuple has `nan`:" f" {torch.isnan(tuple_object).any()} and `inf`: {torch.isinf(tuple_object)}. Dict has" f" `nan`: {torch.isnan(dict_object).any()} and `inf`: {torch.isinf(dict_object)}." ), ) recursive_check(tuple_output, dict_output) for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() 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}) @require_torch class MaskFormerSwinBackboneTest(unittest.TestCase, BackboneTesterMixin): all_model_classes = (MaskFormerSwinBackbone,) if is_torch_available() else () config_class = MaskFormerSwinConfig def setUp(self): self.model_tester = MaskFormerSwinModelTester(self) # Overriding as returned hidden states are tuples of tensors instead of a single tensor def test_backbone_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() batch_size = inputs_dict["pixel_values"].shape[0] for backbone_class in self.all_model_classes: backbone = backbone_class(config) backbone.to(torch_device) backbone.eval() outputs = backbone(**inputs_dict) # Test default outputs and verify feature maps self.assertIsInstance(outputs.feature_maps, tuple) self.assertTrue(len(outputs.feature_maps) == len(backbone.channels)) for feature_map, n_channels in zip(outputs.feature_maps, backbone.channels): self.assertTrue(feature_map.shape[:2], (batch_size, n_channels)) self.assertIsNone(outputs.hidden_states) self.assertIsNone(outputs.attentions) # Test output_hidden_states=True outputs = backbone(**inputs_dict, output_hidden_states=True) self.assertIsNotNone(outputs.hidden_states) self.assertTrue(len(outputs.hidden_states), len(backbone.stage_names)) # We skip the stem layer for hidden_states, n_channels in zip(outputs.hidden_states[1:], backbone.channels): for hidden_state in hidden_states: # Hidden states are in the format (batch_size, (height * width), n_channels) h_batch_size, _, h_n_channels = hidden_state.shape self.assertTrue((h_batch_size, h_n_channels), (batch_size, n_channels)) # Test output_attentions=True if self.has_attentions: outputs = backbone(**inputs_dict, output_attentions=True) self.assertIsNotNone(outputs.attentions)