# 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 Donut Swin model. """ import collections import inspect import unittest from transformers import DonutSwinConfig from transformers.testing_utils import require_torch, slow, torch_device from transformers.utils import is_torch_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import DonutSwinModel from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST class DonutSwinModelTester: 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, ): 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 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 DonutSwinConfig( 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, ) def create_and_check_model(self, config, pixel_values, labels): model = DonutSwinModel(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 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 DonutSwinModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (DonutSwinModel,) if is_torch_available() else () pipeline_model_mapping = {"feature-extraction": DonutSwinModel} if is_torch_available() else {} fx_compatible = True test_pruning = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = DonutSwinModelTester(self) self.config_tester = ConfigTester(self, config_class=DonutSwinConfig, embed_dim=37) 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_inputs_embeds(self): # DonutSwin does not use inputs_embeds 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) 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) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions expected_num_attentions = len(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 window_size_squared = config.window_size**2 model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.attentions self.assertEqual(len(attentions), expected_num_attentions) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_heads[0], window_size_squared, window_size_squared], ) 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) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) if hasattr(self.model_tester, "num_hidden_states_types"): added_hidden_states = self.model_tester.num_hidden_states_types else: # also another +1 for reshaped_hidden_states added_hidden_states = 2 self.assertEqual(out_len + added_hidden_states, len(outputs)) self_attentions = outputs.attentions self.assertEqual(len(self_attentions), expected_num_attentions) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_heads[0], window_size_squared, window_size_squared], ) 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) # DonutSwin 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], ) reshaped_hidden_states = outputs.reshaped_hidden_states self.assertEqual(len(reshaped_hidden_states), expected_num_layers) batch_size, num_channels, height, width = reshaped_hidden_states[0].shape reshaped_hidden_states = ( reshaped_hidden_states[0].view(batch_size, num_channels, height * width).permute(0, 2, 1) ) self.assertListEqual( list(reshaped_hidden_states.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)) @slow def test_model_from_pretrained(self): for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = DonutSwinModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): if "embeddings" not in name and param.requires_grad: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", )