# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # Copyright 2020 The HuggingFace 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. import inspect import os import shutil import tempfile import unittest import paddle import paddle.nn as nn from PIL import Image from paddlenlp.transformers import ( BitBackbone, BitConfig, BitForImageClassification, BitImageProcessor, BitModel, ) from paddlenlp.utils.env import CONFIG_NAME, LEGACY_CONFIG_NAME from ...testing_utils import get_tests_dir, slow from ..test_configuration_common import ConfigTester from ..test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor BIT_PRETRAINED_MODEL_ARCHIVE_LIST = [ "google/bit-50", # See all BiT models at https://huggingface.co/models?filter=bit ] class BitModelTester: def __init__( self, parent, batch_size=3, image_size=32, num_channels=3, embeddings_size=10, hidden_sizes=[8, 16, 32, 64], depths=[1, 1, 2, 1], is_training=True, use_labels=True, hidden_act="relu", num_labels=3, scope=None, out_features=["stage2", "stage3", "stage4"], num_groups=1, return_dict=True, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.num_channels = num_channels self.embeddings_size = embeddings_size self.hidden_sizes = hidden_sizes self.depths = depths self.is_training = is_training self.use_labels = use_labels self.hidden_act = hidden_act self.num_labels = num_labels self.scope = scope self.num_stages = len(hidden_sizes) self.out_features = out_features self.num_groups = num_groups self.return_dict = return_dict 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.num_labels) config = self.get_config() return config, pixel_values, labels def get_config(self): return BitConfig( num_channels=self.num_channels, embeddings_size=self.embeddings_size, hidden_sizes=self.hidden_sizes, depths=self.depths, hidden_act=self.hidden_act, num_labels=self.num_labels, out_features=self.out_features, num_groups=self.num_groups, return_dict=self.return_dict, ) def create_and_check_model(self, config, pixel_values, labels): model = BitModel(config=config) model.eval() result = model(pixel_values) self.parent.assertEqual( result.last_hidden_state.shape, [self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32], ) def create_and_check_for_image_classification(self, config, pixel_values, labels): config.num_labels = self.num_labels model = BitForImageClassification(config) model.eval() result = model(pixel_values, labels=labels) self.parent.assertEqual(result.logits.shape, [self.batch_size, self.num_labels]) def create_and_check_backbone(self, config, pixel_values, labels): model = BitBackbone(config=config) 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), [self.batch_size, self.hidden_sizes[1], 4, 4]) # 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 = BitBackbone(config=config) model.eval() result = model(pixel_values) # verify feature maps self.parent.assertEqual(len(result.feature_maps), 1) self.parent.assertListEqual(list(result.feature_maps[0].shape), [self.batch_size, self.hidden_sizes[-1], 1, 1]) # 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 class BitModelTest(ModelTesterMixin, unittest.TestCase): base_model_class = BitModel all_model_classes = (BitModel, BitForImageClassification, BitBackbone) test_resize_embeddings = False has_attentions = False def setUp(self): super().setUp() self.model_tester = BitModelTester(self) self.config_tester = ConfigTester(self, config_class=BitConfig, has_text_modality=False) 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_classes() 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_pretrained_config_save_load(self): if self.base_model_class is None or not self.base_model_class.constructed_from_pretrained_config(): return config_class = self.base_model_class.config_class with tempfile.TemporaryDirectory() as tempdir: config = config_class() config.save_pretrained(tempdir) # check the file exist self.assertFalse(os.path.exists(os.path.join(tempdir, LEGACY_CONFIG_NAME))) self.assertTrue(os.path.exists(os.path.join(tempdir, CONFIG_NAME))) # rename the CONFIG_NAME shutil.move(os.path.join(tempdir, CONFIG_NAME), os.path.join(tempdir, LEGACY_CONFIG_NAME)) loaded_config = config.__class__.from_pretrained(tempdir) self.assertEqual(config.hidden_sizes, loaded_config.hidden_sizes) @unittest.skip(reason="Bit does not use model_name_list") def test_model_name_list(self): pass @unittest.skip(reason="Bit does not output attentions") def test_attention_outputs(self): pass @unittest.skip(reason="Bit does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Bit does not support input and output embeddings") def test_model_common_attributes(self): pass 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_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_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config=config) for name, module in model.named_sublayers(): if isinstance(module, (nn.BatchNorm2D, nn.GroupNorm)): self.assertTrue( paddle.all(module.weight == 1), msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) self.assertTrue( paddle.all(module.bias == 0), msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.eval() with paddle.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.hidden_states expected_num_stages = self.model_tester.num_stages self.assertEqual(len(hidden_states), expected_num_stages + 1) # Bit's feature maps are of shape (batch_size, num_channels, height, width) self.assertListEqual( list(hidden_states[0].shape[-2:]), [self.model_tester.image_size // 4, self.model_tester.image_size // 4], ) 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="Bit 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): for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = BitModel.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): CUTE_CATS = get_tests_dir("fixtures/tests_samples/COCO/000000039769.png") image = Image.open(CUTE_CATS) return image class BitModelIntegrationTest(unittest.TestCase): def default_image_processor(self): return BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]) @slow def test_inference_image_classification_head(self): model = BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]) model.eval() image_processor = self.default_image_processor() image = prepare_img() inputs = image_processor(images=image, return_tensors="pd") # forward pass with paddle.no_grad(): outputs = model(**inputs) # verify the logits expected_shape = [1, 1000] self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = paddle.to_tensor([-0.65258133, -0.52634168, -1.43975902]) self.assertTrue(paddle.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4)) if __name__ == "__main__": unittest.main()