# Copyright 2023 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 MGP-STR model.""" import unittest import requests from transformers import MgpstrConfig from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import MgpstrForSceneTextRecognition, MgpstrModel if is_vision_available(): from PIL import Image from transformers import MgpstrProcessor class MgpstrModelTester: def __init__( self, parent, is_training=False, batch_size=13, image_size=(32, 128), patch_size=4, num_channels=3, max_token_length=27, num_character_labels=38, num_bpe_labels=99, num_wordpiece_labels=99, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, mlp_ratio=4.0, patch_embeds_hidden_size=257, output_hidden_states=None, ): self.parent = parent self.is_training = is_training self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.max_token_length = max_token_length self.num_character_labels = num_character_labels self.num_bpe_labels = num_bpe_labels self.num_wordpiece_labels = num_wordpiece_labels self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.mlp_ratio = mlp_ratio self.patch_embeds_hidden_size = patch_embeds_hidden_size self.output_hidden_states = output_hidden_states def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]) config = self.get_config() return config, pixel_values def get_config(self): return MgpstrConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, max_token_length=self.max_token_length, num_character_labels=self.num_character_labels, num_bpe_labels=self.num_bpe_labels, num_wordpiece_labels=self.num_wordpiece_labels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, mlp_ratio=self.mlp_ratio, output_hidden_states=self.output_hidden_states, ) def create_and_check_model(self, config, pixel_values): model = MgpstrForSceneTextRecognition(config) model.to(torch_device) model.eval() with torch.no_grad(): generated_ids = model(pixel_values) self.parent.assertEqual( generated_ids[0][0].shape, (self.batch_size, self.max_token_length, self.num_character_labels) ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class MgpstrModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (MgpstrForSceneTextRecognition,) if is_torch_available() else () pipeline_model_mapping = ( {"feature-extraction": MgpstrForSceneTextRecognition, "image-feature-extraction": MgpstrModel} if is_torch_available() else {} ) test_resize_embeddings = False test_attention_outputs = False def setUp(self): self.model_tester = MgpstrModelTester(self) self.config_tester = ConfigTester(self, config_class=MgpstrConfig, has_text_modality=False) 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_batching_equivalence(self, atol=1e-4, rtol=1e-4): super().test_batching_equivalence(atol=atol, rtol=rtol) @unittest.skip(reason="MgpstrModel does not use inputs_embeds") def test_inputs_embeds(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="MgpstrModel does not support feedforward chunking") def test_feed_forward_chunking(self): pass def test_gradient_checkpointing_backward_compatibility(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: if not model_class.supports_gradient_checkpointing: continue config.gradient_checkpointing = True model = model_class(config) self.assertTrue(model.is_gradient_checkpointing) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): 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", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) self.assertListEqual( list(hidden_states[0].shape[-2:]), [self.model_tester.patch_embeds_hidden_size, self.model_tester.hidden_size], ) 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) @unittest.skip(reason="Retain_grad is tested in individual model tests") def test_retain_grad_hidden_states_attentions(self): pass # We will verify our results on an image from the IIIT-5k dataset def prepare_img(): url = "https://i.postimg.cc/ZKwLg2Gw/367-14.png" im = Image.open(requests.get(url, stream=True).raw).convert("RGB") return im @require_vision @require_torch class MgpstrModelIntegrationTest(unittest.TestCase): @slow def test_inference(self): model_name = "alibaba-damo/mgp-str-base" model = MgpstrForSceneTextRecognition.from_pretrained(model_name).to(torch_device) processor = MgpstrProcessor.from_pretrained(model_name) image = prepare_img() inputs = processor(images=image, return_tensors="pt").pixel_values.to(torch_device) # forward pass with torch.no_grad(): outputs = model(inputs) # verify the logits self.assertEqual(outputs.logits[0].shape, torch.Size((1, 27, 38))) out_strs = processor.batch_decode(outputs.logits) expected_text = "ticket" self.assertEqual(out_strs["generated_text"][0], expected_text) expected_slice = torch.tensor( [[[-39.5397, -44.4024, -36.1844], [-61.4709, -63.8639, -58.3454], [-74.0225, -68.5494, -71.2164]]], device=torch_device, ) torch.testing.assert_close(outputs.logits[0][:, 1:4, 1:4], expected_slice, rtol=1e-4, atol=1e-4)