# 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 PyTorch MaskFormer Swin model. """ import collections import inspect import unittest from typing import Dict, List, Tuple 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 {} fx_compatible = False test_torchscript = False test_pruning = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = MaskFormerSwinModelTester(self) self.config_tester = ConfigTester(self, config_class=MaskFormerSwinConfig, embed_dim=37) @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.create_and_test_config_common_properties() self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.create_and_test_config_with_num_labels() self.config_tester.check_config_can_be_init_without_params() self.config_tester.check_config_arguments_init() def create_and_test_config_common_properties(self): return 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("Swin does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip("Swin does not support feedforward chunking") def test_feed_forward_chunking(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(), (nn.Module)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) 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.forward) # 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) @unittest.skip(reason="MaskFormerSwin is only used as backbone and doesn't support output_attentions") def test_attention_outputs(self): pass @unittest.skip(reason="MaskFormerSwin is only used as an internal backbone") def test_save_load_fast_init_to_base(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_initialization(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)