# Copyright 2024 the Fast authors and 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 TextNet model.""" import unittest import requests from PIL import Image from transformers import TextNetConfig from transformers.models.textnet.image_processing_textnet import TextNetImageProcessor from transformers.testing_utils import ( require_torch, require_vision, slow, 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 transformers import TextNetBackbone, TextNetForImageClassification, TextNetModel class TextNetConfigTester(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 TextNetModelTester: def __init__( self, parent, stem_kernel_size=3, stem_stride=2, stem_in_channels=3, stem_out_channels=32, stem_act_func="relu", dropout_rate=0, ops_order="weight_bn_act", conv_layer_kernel_sizes=[ [[3, 3]], [[3, 3]], [[3, 3]], [[3, 3]], ], conv_layer_strides=[ [2], [2], [2], [2], ], out_features=["stage1", "stage2", "stage3", "stage4"], out_indices=[1, 2, 3, 4], batch_size=3, num_channels=3, image_size=[32, 32], is_training=True, use_labels=True, num_labels=3, hidden_sizes=[32, 32, 32, 32, 32], ): self.parent = parent self.stem_kernel_size = stem_kernel_size self.stem_stride = stem_stride self.stem_in_channels = stem_in_channels self.stem_out_channels = stem_out_channels self.act_func = stem_act_func self.dropout_rate = dropout_rate self.ops_order = ops_order self.conv_layer_kernel_sizes = conv_layer_kernel_sizes self.conv_layer_strides = conv_layer_strides self.out_features = out_features self.out_indices = out_indices self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.is_training = is_training self.use_labels = use_labels self.num_labels = num_labels self.hidden_sizes = hidden_sizes self.num_stages = 5 def get_config(self): return TextNetConfig( stem_kernel_size=self.stem_kernel_size, stem_stride=self.stem_stride, stem_num_channels=self.stem_in_channels, stem_out_channels=self.stem_out_channels, act_func=self.act_func, dropout_rate=self.dropout_rate, ops_order=self.ops_order, conv_layer_kernel_sizes=self.conv_layer_kernel_sizes, conv_layer_strides=self.conv_layer_strides, out_features=self.out_features, out_indices=self.out_indices, hidden_sizes=self.hidden_sizes, image_size=self.image_size, ) def create_and_check_model(self, config, pixel_values, labels): model = TextNetModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) scale_h = self.image_size[0] // 32 scale_w = self.image_size[1] // 32 self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], scale_h, scale_w), ) def create_and_check_for_image_classification(self, config, pixel_values, labels): config.num_labels = self.num_labels model = TextNetForImageClassification(config) model.to(torch_device) model.eval() result = model(pixel_values, labels=labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]) labels = None if self.use_labels: labels = ids_tensor([self.batch_size], self.num_labels) config = self.get_config() return config, pixel_values, labels def create_and_check_backbone(self, config, pixel_values, labels): model = TextNetBackbone(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)) scale_h = self.image_size[0] // 32 scale_w = self.image_size[1] // 32 self.parent.assertListEqual( list(result.feature_maps[0].shape), [self.batch_size, self.hidden_sizes[1], 8 * scale_h, 8 * scale_w] ) # verify channels self.parent.assertEqual(len(model.channels), len(config.out_features)) self.parent.assertListEqual(model.channels, config.hidden_sizes[1:]) # verify backbone works with out_features=None config.out_features = None model = TextNetBackbone(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # verify feature maps self.parent.assertEqual(len(result.feature_maps), 1) scale_h = self.image_size[0] // 32 scale_w = self.image_size[1] // 32 self.parent.assertListEqual( list(result.feature_maps[0].shape), [self.batch_size, self.hidden_sizes[0], scale_h, scale_w] ) # verify channels self.parent.assertEqual(len(model.channels), 1) self.parent.assertListEqual(model.channels, [config.hidden_sizes[-1]]) 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 TextNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some tests of test_modeling_common.py, as TextNet does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (TextNetModel, TextNetForImageClassification, TextNetBackbone) if is_torch_available() else () pipeline_model_mapping = ( {"feature-extraction": TextNetModel, "image-classification": TextNetForImageClassification} if is_torch_available() else {} ) test_resize_embeddings = False test_torch_exportable = True has_attentions = False def setUp(self): self.model_tester = TextNetModelTester(self) self.config_tester = TextNetConfigTester(self, config_class=TextNetConfig, has_text_modality=False) @unittest.skip(reason="TextNet does not output attentions") def test_attention_outputs(self): pass @unittest.skip(reason="TextNet does not have input/output embeddings") def test_model_get_set_embeddings(self): pass @unittest.skip(reason="TextNet does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="TextNet does not support input and output embeddings") def test_model_common_attributes(self): pass 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) 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.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states self.assertEqual(len(hidden_states), self.model_tester.num_stages) self.assertListEqual( list(hidden_states[0].shape[-2:]), [self.model_tester.image_size[0] // 2, self.model_tester.image_size[1] // 2], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() layers_type = ["preactivation", "bottleneck"] for model_class in self.all_model_classes: for layer_type in layers_type: config.layer_type = layer_type 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="TextNet does not use feedforward chunking") def test_feed_forward_chunking(self): pass def test_for_image_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): model_name = "czczup/textnet-base" model = TextNetModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_torch @require_vision class TextNetModelIntegrationTest(unittest.TestCase): @slow def test_inference_no_head(self): processor = TextNetImageProcessor.from_pretrained("czczup/textnet-base") model = TextNetModel.from_pretrained("czczup/textnet-base").to(torch_device) # prepare image url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(images=image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): output = model(**inputs) # verify output self.assertEqual(output.last_hidden_state.shape, torch.Size([1, 512, 20, 27])) expected_slice_backbone = torch.tensor( [ [0.0000, 1.7415, 1.2660], [0.0000, 1.0084, 1.9692], [0.0000, 1.7464, 1.7892], ], device=torch_device, ) torch.testing.assert_close( output.last_hidden_state[0, 12, :3, :3], expected_slice_backbone, rtol=1e-2, atol=1e-2 ) @require_torch # Copied from tests.models.bit.test_modeling_bit.BitBackboneTest with Bit->TextNet class TextNetBackboneTest(BackboneTesterMixin, unittest.TestCase): all_model_classes = (TextNetBackbone,) if is_torch_available() else () config_class = TextNetConfig has_attentions = False def setUp(self): self.model_tester = TextNetModelTester(self)