# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. # Copyright 2022 The OpenAI Team Authors and 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 copy import inspect import tempfile import unittest import numpy as np import paddle import requests from paddle import nn from PIL import Image from paddlenlp.transformers import ( CLIPSegConfig, CLIPSegForImageSegmentation, CLIPSegModel, CLIPSegProcessor, CLIPSegTextConfig, CLIPSegTextModel, CLIPSegVisionConfig, CLIPSegVisionModel, ) from paddlenlp.transformers.clipseg.modeling import ( CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST, ) from ...testing_utils import slow from ..test_configuration_common import ConfigTester from ..test_modeling_common import ( ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask, ) def _config_zero_init(config): configs_no_init = copy.deepcopy(config) for key in configs_no_init.__dict__.keys(): if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key: setattr(configs_no_init, key, 1e-10) return configs_no_init class CLIPSegVisionModelTester: def __init__( self, parent, batch_size=12, image_size=30, patch_size=2, num_channels=3, is_training=True, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, initializer_range=0.02, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.is_training = is_training self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.attention_dropout = attention_dropout self.initializer_range = initializer_range self.scope = scope # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token) num_patches = (image_size // patch_size) ** 2 self.seq_length = num_patches + 1 def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) config = self.get_config() return config, pixel_values def get_config(self): return CLIPSegVisionConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, dropout=self.dropout, attention_dropout=self.attention_dropout, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, pixel_values): model = CLIPSegVisionModel(config=config) model.eval() with paddle.no_grad(): result = model(pixel_values, return_dict=True) # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) image_size = (self.image_size, self.image_size) patch_size = (self.patch_size, self.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.parent.assertEqual(result.last_hidden_state.shape, [self.batch_size, num_patches + 1, self.hidden_size]) self.parent.assertEqual(result.pooler_output.shape, [self.batch_size, self.hidden_size]) 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 class CLIPSegVisionModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as CLIPSeg does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (CLIPSegVisionModel,) fx_compatible = False test_pruning = False test_resize_embeddings = False test_head_masking = False use_test_model_name_list = False def setUp(self): self.model_tester = CLIPSegVisionModelTester(self) self.config_tester = ConfigTester( self, config_class=CLIPSegVisionConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="CLIPSeg does not use inputs_embeds") def test_inputs_embeds(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.Layer)) 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_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_training(self): pass def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="CLIPSegVisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="CLIPSegVisionModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): for model_name in CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = CLIPSegVisionModel.from_pretrained(model_name) self.assertIsNotNone(model) class CLIPSegTextModelTester: def __init__( self, parent, batch_size=12, seq_length=7, is_training=True, use_input_mask=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, max_position_embeddings=512, initializer_range=0.02, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.attention_dropout = attention_dropout self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) if input_mask is not None: batch_size, seq_length = input_mask.shape rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,)) for batch_idx, start_index in enumerate(rnd_start_indices): input_mask[batch_idx, :start_index] = 1 input_mask[batch_idx, start_index:] = 0 config = self.get_config() return config, input_ids, input_mask def get_config(self): return CLIPSegTextConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, dropout=self.dropout, attention_dropout=self.attention_dropout, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, input_ids, input_mask): model = CLIPSegTextModel(config=config) model.eval() with paddle.no_grad(): result = model(input_ids, attention_mask=input_mask, return_dict=True) result = model(input_ids, return_dict=True) self.parent.assertEqual(result.last_hidden_state.shape, [self.batch_size, self.seq_length, self.hidden_size]) self.parent.assertEqual(result.pooler_output.shape, [self.batch_size, self.hidden_size]) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, input_mask = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict class CLIPSegTextModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (CLIPSegTextModel,) fx_compatible = False test_pruning = False test_head_masking = False use_test_model_name_list = False def setUp(self): self.model_tester = CLIPSegTextModelTester(self) self.config_tester = ConfigTester(self, config_class=CLIPSegTextConfig, hidden_size=37) 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_training(self): pass def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="CLIPSeg does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="CLIPSegTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_from_base(self): pass @unittest.skip(reason="CLIPSegTextModel has no base class and is not available in MODEL_MAPPING") def test_save_load_fast_init_to_base(self): pass @slow def test_model_from_pretrained(self): for model_name in CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = CLIPSegTextModel.from_pretrained(model_name) self.assertIsNotNone(model) class CLIPSegModelTester: def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True): if text_kwargs is None: text_kwargs = {} if vision_kwargs is None: vision_kwargs = {} self.parent = parent self.text_model_tester = CLIPSegTextModelTester(parent, **text_kwargs) self.vision_model_tester = CLIPSegVisionModelTester(parent, **vision_kwargs) self.is_training = is_training def prepare_config_and_inputs(self): text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs() vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs() config = self.get_config() return config, input_ids, attention_mask, pixel_values def get_config(self): return CLIPSegConfig.from_text_vision_configs( self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64, reduce_dim=32, extract_layers=[1, 2, 3], ) def create_and_check_model(self, config, input_ids, attention_mask, pixel_values): model = CLIPSegModel(config) model.eval() with paddle.no_grad(): result = model(input_ids, pixel_values, attention_mask, return_dict=True) self.parent.assertEqual( result.logits_per_image.shape, [self.vision_model_tester.batch_size, self.text_model_tester.batch_size] ) self.parent.assertEqual( result.logits_per_text.shape, [self.text_model_tester.batch_size, self.vision_model_tester.batch_size] ) def create_and_check_model_for_image_segmentation(self, config, input_ids, attention_maks, pixel_values): model = CLIPSegForImageSegmentation(config) model.eval() with paddle.no_grad(): result = model(input_ids, pixel_values, return_dict=True) self.parent.assertEqual( result.logits.shape, [ self.vision_model_tester.batch_size, self.vision_model_tester.image_size, self.vision_model_tester.image_size, ], ) self.parent.assertEqual( result.conditional_embeddings.shape, [self.text_model_tester.batch_size, config.projection_dim] ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, attention_mask, pixel_values = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values, } return config, inputs_dict class CLIPSegModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (CLIPSegModel, CLIPSegForImageSegmentation) pipeline_model_mapping = {"feature-extraction": CLIPSegModel} fx_compatible = False test_head_masking = False test_pruning = False test_resize_embeddings = False test_attention_outputs = False use_test_model_name_list = False def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): # CLIPSegForImageSegmentation requires special treatment if return_labels: if model_class.__name__ == "CLIPSegForImageSegmentation": batch_size, _, height, width = inputs_dict["pixel_values"].shape inputs_dict["labels"] = paddle.zeros([batch_size, height, width], dtype="float32") return inputs_dict def setUp(self): self.model_tester = CLIPSegModelTester(self) 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_model_for_image_segmentation(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model_for_image_segmentation(*config_and_inputs) @unittest.skip(reason="Hidden_states is tested in individual model tests") def test_hidden_states_output(self): pass @unittest.skip(reason="Inputs_embeds is tested in individual model tests") def test_inputs_embeds(self): pass @unittest.skip(reason="Retain_grad is tested in individual model tests") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="CLIPSegModel does not have input/output embeddings") def test_model_common_attributes(self): pass # override as the some parameters require custom initialization 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 param.stop_gradient is False: # check if `logit_scale` is initialized as per the original implementation] if "logit_scale" in name: self.assertAlmostEqual( param.item(), np.log(1 / 0.07), delta=1e-3, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) elif "film" in name or "transposed_conv" in name or "reduce" in name: # those parameters use PyTorch' default nn.Linear initialization scheme pass else: self.assertIn( ((param.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) def test_load_vision_text_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Save CLIPSegConfig and check if we can load CLIPSegVisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = CLIPSegVisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save CLIPSegConfig and check if we can load CLIPSegTextConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) text_config = CLIPSegTextConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict()) @slow def test_model_from_pretrained(self): for model_name in CLIPSEG_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = CLIPSegModel.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) return image class CLIPSegModelIntegrationTest(unittest.TestCase): @slow def test_inference_image_segmentation(self): model_name = "CIDAS/clipseg-rd64-refined" processor = CLIPSegProcessor.from_pretrained(model_name) model = CLIPSegForImageSegmentation.from_pretrained(model_name) image = prepare_img() texts = ["a cat", "a remote", "a blanket"] inputs = processor(text=texts, images=[image] * len(texts), padding=True, return_tensors="pd") # forward pass with paddle.no_grad(): outputs = model(**inputs, return_dict=True) # verify the predicted masks self.assertEqual( outputs.logits.shape, [3, 352, 352], ) expected_masks_slice = paddle.to_tensor( [[-7.4613, -7.4785, -7.3628], [-7.3268, -7.0899, -7.1333], [-6.9838, -6.7900, -6.8913]] ) # Modified atol for tf32 self.assertTrue(paddle.allclose(outputs.logits[0, :3, :3], expected_masks_slice, atol=1e-2)) # verify conditional and pooled output expected_conditional = paddle.to_tensor([0.5601, -0.0314, 0.1980]) expected_pooled_output = paddle.to_tensor([0.5036, -0.2681, -0.2644]) self.assertTrue(paddle.allclose(outputs.conditional_embeddings[0, :3], expected_conditional, atol=1e-2)) self.assertTrue(paddle.allclose(outputs.pooled_output[0, :3], expected_pooled_output, atol=1e-2))