wehub-resource-sync
762 lines
29 KiB
Python
762 lines
29 KiB
Python
# coding=utf-8
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Testing suite for the Paddle CLIP model. """
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import copy
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import inspect
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import tempfile
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import unittest
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import numpy as np
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import paddle
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from paddle import nn
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from PIL import Image
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from paddlenlp.transformers import (
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CLIPConfig,
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CLIPModel,
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CLIPProcessor,
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CLIPTextConfig,
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CLIPTextModel,
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CLIPTextModelWithProjection,
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CLIPVisionConfig,
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CLIPVisionModel,
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CLIPVisionModelWithProjection,
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CLIPVisionTransformer,
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)
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from paddlenlp.transformers.clip.modeling import CLIP_PRETRAINED_MODEL_ARCHIVE_LIST
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from ...testing_utils import get_tests_dir, require_package, slow
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from ..test_configuration_common import ConfigTester
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from ..test_modeling_common import (
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ModelTesterMixin,
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floats_tensor,
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ids_tensor,
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random_attention_mask,
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)
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def _config_zero_init(config):
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configs_no_init = copy.deepcopy(config)
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for key in configs_no_init.__dict__.keys():
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if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
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setattr(configs_no_init, key, 1e-10)
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return configs_no_init
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class CLIPVisionModelTester:
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def __init__(
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self,
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parent,
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batch_size=12,
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image_size=30,
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patch_size=2,
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num_channels=3,
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is_training=True,
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hidden_size=32,
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projection_dim=32,
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num_hidden_layers=5,
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num_attention_heads=4,
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intermediate_size=37,
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dropout=0.1,
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attention_dropout=0.1,
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initializer_range=0.02,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.is_training = is_training
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self.hidden_size = hidden_size
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self.projection_dim = projection_dim
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.dropout = dropout
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self.attention_dropout = attention_dropout
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self.initializer_range = initializer_range
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self.scope = scope
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# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
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num_patches = (image_size // patch_size) ** 2
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self.seq_length = num_patches + 1
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def prepare_config_and_inputs(self):
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pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
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config = self.get_config()
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return config, pixel_values
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def get_config(self):
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return CLIPVisionConfig(
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image_size=self.image_size,
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patch_size=self.patch_size,
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num_channels=self.num_channels,
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hidden_size=self.hidden_size,
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projection_dim=self.projection_dim,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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dropout=self.dropout,
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attention_dropout=self.attention_dropout,
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initializer_range=self.initializer_range,
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)
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def create_and_check_model(self, config, pixel_values):
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model = CLIPVisionModel(config=config)
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model.eval()
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with paddle.no_grad():
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result = model(pixel_values)
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# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
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image_size = (self.image_size, self.image_size)
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patch_size = (self.patch_size, self.patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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self.parent.assertEqual(result.last_hidden_state.shape, [self.batch_size, num_patches + 1, self.hidden_size])
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self.parent.assertEqual(result.pooler_output.shape, [self.batch_size, self.hidden_size])
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def create_and_check_model_with_projection(self, config, pixel_values):
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model = CLIPVisionModelWithProjection(config=config)
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model.eval()
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with paddle.no_grad():
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result = model(pixel_values)
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# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
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image_size = (self.image_size, self.image_size)
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patch_size = (self.patch_size, self.patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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self.parent.assertEqual(result.last_hidden_state.shape, [self.batch_size, num_patches + 1, self.hidden_size])
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self.parent.assertEqual(result.image_embeds.shape, [self.batch_size, self.projection_dim])
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, pixel_values = config_and_inputs
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inputs_dict = {"pixel_values": pixel_values}
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return config, inputs_dict
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class CLIPVisionModelTest(ModelTesterMixin, unittest.TestCase):
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"""
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Here we also overwrite some of the tests of test_modeling_common.py, as CLIP does not use input_ids, inputs_embeds,
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attention_mask and seq_length.
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"""
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all_model_classes = (CLIPVisionModel, CLIPVisionModelWithProjection)
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test_resize_embeddings = False
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use_test_model_name_list = False
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def setUp(self):
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self.model_tester = CLIPVisionModelTester(self)
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self.config_tester = ConfigTester(self, config_class=CLIPVisionConfig, has_text_modality=False, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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@unittest.skip(reason="CLIP does not use inputs_embeds")
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def test_inputs_embeds(self):
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pass
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def test_model_common_attributes(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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self.assertIsInstance(model.get_input_embeddings(), (nn.Layer))
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x = model.get_output_embeddings()
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self.assertTrue(x is None or isinstance(x, nn.Linear))
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def test_forward_signature(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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signature = inspect.signature(model.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = ["pixel_values"]
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self.assertListEqual(arg_names[:1], expected_arg_names)
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_model_with_projection(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model_with_projection(*config_and_inputs)
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def test_training(self):
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pass
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def test_training_gradient_checkpointing(self):
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pass
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@unittest.skip(reason="CLIPVisionModel has no base class and is not available in MODEL_MAPPING")
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def test_save_load_fast_init_from_base(self):
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pass
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@unittest.skip(reason="CLIPVisionModel has no base class and is not available in MODEL_MAPPING")
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def test_save_load_fast_init_to_base(self):
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pass
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@slow
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def test_model_from_pretrained(self):
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for model_name in CLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = CLIPVisionModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@slow
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def test_model_with_projection_from_pretrained(self):
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for model_name in CLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = CLIPVisionModelWithProjection.from_pretrained(model_name)
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self.assertIsNotNone(model)
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if isinstance(model.vision_model, CLIPVisionTransformer):
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self.assertTrue(hasattr(model, "vision_projection"))
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class CLIPTextModelTester:
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def __init__(
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self,
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parent,
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batch_size=12,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_labels=True,
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vocab_size=99,
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hidden_size=32,
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projection_dim=32,
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num_hidden_layers=5,
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num_attention_heads=4,
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intermediate_size=37,
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dropout=0.1,
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attention_dropout=0.1,
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max_position_embeddings=512,
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initializer_range=0.02,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.projection_dim = projection_dim
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.dropout = dropout
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self.attention_dropout = attention_dropout
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self.max_position_embeddings = max_position_embeddings
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self.initializer_range = initializer_range
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self.scope = scope
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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if input_mask is not None:
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batch_size, seq_length = input_mask.shape
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rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
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for batch_idx, start_index in enumerate(rnd_start_indices):
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input_mask[batch_idx, :start_index] = 1
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input_mask[batch_idx, start_index:] = 0
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config = self.get_config()
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return config, input_ids, input_mask
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def get_config(self):
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return CLIPTextConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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projection_dim=self.projection_dim,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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dropout=self.dropout,
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attention_dropout=self.attention_dropout,
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max_position_embeddings=self.max_position_embeddings,
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initializer_range=self.initializer_range,
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)
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def create_and_check_model(self, config, input_ids, input_mask):
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model = CLIPTextModel(config=config)
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model.eval()
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with paddle.no_grad():
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result = model(input_ids, attention_mask=input_mask)
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self.parent.assertEqual(result.last_hidden_state.shape, [self.batch_size, self.seq_length, self.hidden_size])
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self.parent.assertEqual(result.pooler_output.shape, [self.batch_size, self.hidden_size])
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def create_and_check_model_with_projection(self, config, input_ids, input_mask):
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model = CLIPTextModelWithProjection(config=config)
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model.eval()
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with paddle.no_grad():
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result = model(input_ids, attention_mask=input_mask)
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self.parent.assertEqual(result.last_hidden_state.shape, [self.batch_size, self.seq_length, self.hidden_size])
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self.parent.assertEqual(result.text_embeds.shape, [self.batch_size, self.projection_dim])
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, input_ids, input_mask = config_and_inputs
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inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
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return config, inputs_dict
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class CLIPTextModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (CLIPTextModel, CLIPTextModelWithProjection)
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use_test_model_name_list = False
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def setUp(self):
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self.model_tester = CLIPTextModelTester(self)
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self.config_tester = ConfigTester(self, config_class=CLIPTextConfig, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_model_with_projection(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model_with_projection(*config_and_inputs)
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def test_training(self):
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pass
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def test_training_gradient_checkpointing(self):
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pass
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@unittest.skip(reason="CLIP does not use inputs_embeds")
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def test_inputs_embeds(self):
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pass
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@unittest.skip(reason="CLIPTextModel has no base class and is not available in MODEL_MAPPING")
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def test_save_load_fast_init_from_base(self):
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pass
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@unittest.skip(reason="CLIPTextModel has no base class and is not available in MODEL_MAPPING")
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def test_save_load_fast_init_to_base(self):
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pass
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@slow
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def test_model_from_pretrained(self):
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for model_name in CLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = CLIPTextModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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@slow
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def test_model_with_projection_from_pretrained(self):
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for model_name in CLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = CLIPTextModelWithProjection.from_pretrained(model_name)
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self.assertIsNotNone(model)
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self.assertTrue(hasattr(model, "text_projection"))
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class CLIPModelTester:
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def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True):
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if text_kwargs is None:
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text_kwargs = {}
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if vision_kwargs is None:
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vision_kwargs = {}
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self.parent = parent
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self.text_model_tester = CLIPTextModelTester(parent, **text_kwargs)
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self.vision_model_tester = CLIPVisionModelTester(parent, **vision_kwargs)
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self.is_training = is_training
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def prepare_config_and_inputs(self):
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text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
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vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
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config = self.get_config()
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return config, input_ids, attention_mask, pixel_values
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def get_config(self):
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return CLIPConfig.from_text_vision_configs(
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self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64
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)
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def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
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model = CLIPModel(config)
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model.eval()
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with paddle.no_grad():
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result = model(input_ids, pixel_values, attention_mask=attention_mask)
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self.parent.assertEqual(
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result.logits_per_image.shape, [self.vision_model_tester.batch_size, self.text_model_tester.batch_size]
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)
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self.parent.assertEqual(
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result.logits_per_text.shape, [self.text_model_tester.batch_size, self.vision_model_tester.batch_size]
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)
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, input_ids, attention_mask, pixel_values = config_and_inputs
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inputs_dict = {
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"pixel_values": pixel_values,
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"return_loss": True,
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}
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return config, inputs_dict
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class CLIPModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (CLIPModel,)
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test_resize_embeddings = False
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test_attention_outputs = False
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use_test_model_name_list = False
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def setUp(self):
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self.model_tester = CLIPModelTester(self)
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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@unittest.skip(reason="Hidden_states is tested in individual model tests")
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def test_hidden_states_output(self):
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pass
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@unittest.skip(reason="Inputs_embeds is tested in individual model tests")
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def test_inputs_embeds(self):
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pass
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@unittest.skip(reason="Retain_grad is tested in individual model tests")
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def test_retain_grad_hidden_states_attentions(self):
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pass
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@unittest.skip(reason="CLIPModel does not have input/output embeddings")
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def test_model_common_attributes(self):
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pass
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# override as the `logit_scale` parameter initialization is different for CLIP
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def test_initialization(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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configs_no_init = _config_zero_init(config)
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for model_class in self.all_model_classes:
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model = model_class(config=configs_no_init)
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for name, param in model.named_parameters():
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if not param.stop_gradient:
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# check if `logit_scale` is initialized as per the original implementation
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if name == "logit_scale":
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|
self.assertAlmostEqual(
|
|
param.item(),
|
|
np.log(1 / 0.07),
|
|
delta=1e-3,
|
|
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
|
)
|
|
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 CLIPConfig and check if we can load CLIPVisionConfig from it
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
config.save_pretrained(tmp_dir_name)
|
|
vision_config = CLIPVisionConfig.from_pretrained(tmp_dir_name)
|
|
self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
|
|
|
|
# Save CLIPConfig and check if we can load CLIPTextConfig from it
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
config.save_pretrained(tmp_dir_name)
|
|
text_config = CLIPTextConfig.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 CLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
|
|
model = CLIPModel.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 CLIPModelCompatibilityTest(unittest.TestCase):
|
|
model_id = "hf-internal-testing/tiny-random-CLIPModel"
|
|
|
|
def setUp(self):
|
|
# 1. create input
|
|
processor = CLIPProcessor.from_pretrained(self.model_id, from_hf_hub=True)
|
|
image = prepare_img()
|
|
self.inputs = processor(
|
|
text=["a photo of a cat", "a photo of a dog"], images=image, padding=True, return_tensors="np"
|
|
)
|
|
|
|
@unittest.skip("model diff exists, need to be fixed")
|
|
@require_package("transformers", "torch")
|
|
def test_clip_converter(self):
|
|
with tempfile.TemporaryDirectory() as tempdir:
|
|
# 1. create input
|
|
inputs = self.inputs
|
|
|
|
# 2. forward the paddle model
|
|
from paddlenlp.transformers import CLIPModel
|
|
|
|
paddle_model = CLIPModel.from_pretrained(self.model_id, from_hf_hub=True, cache_dir=tempdir)
|
|
paddle_model.eval()
|
|
paddle_logit = paddle_model(
|
|
input_ids=paddle.to_tensor(inputs["input_ids"]), pixel_values=paddle.to_tensor(inputs["pixel_values"])
|
|
)
|
|
|
|
# 3. forward the torch model
|
|
import torch
|
|
from transformers import CLIPModel
|
|
|
|
torch_model = CLIPModel.from_pretrained(self.model_id, cache_dir=tempdir)
|
|
torch_model.eval()
|
|
torch_logit = torch_model(
|
|
input_ids=torch.tensor(inputs["input_ids"]), pixel_values=torch.tensor(inputs["pixel_values"])
|
|
)
|
|
|
|
# 4. compare results
|
|
self.assertTrue(
|
|
np.allclose(
|
|
paddle_logit.logits_per_image.detach().cpu().numpy(),
|
|
torch_logit.logits_per_image.detach().cpu().numpy(),
|
|
rtol=1e-4,
|
|
)
|
|
)
|
|
|
|
self.assertTrue(
|
|
np.allclose(
|
|
paddle_logit.logits_per_text.detach().cpu().numpy(),
|
|
torch_logit.logits_per_text.detach().cpu().numpy(),
|
|
rtol=1e-4,
|
|
)
|
|
)
|
|
|
|
@unittest.skip("model diff exists, need to be fixed")
|
|
@require_package("transformers", "torch")
|
|
def test_clip_converter_from_local_dir(self):
|
|
with tempfile.TemporaryDirectory() as tempdir:
|
|
# 1. create input
|
|
inputs = self.inputs
|
|
|
|
# 2. forward the torch model
|
|
import torch
|
|
from transformers import CLIPModel
|
|
|
|
torch_model = CLIPModel.from_pretrained(self.model_id)
|
|
torch_model.eval()
|
|
torch_model.save_pretrained(tempdir)
|
|
torch_logit = torch_model(
|
|
input_ids=torch.tensor(inputs["input_ids"]), pixel_values=torch.tensor(inputs["pixel_values"])
|
|
)
|
|
|
|
# 3. forward the paddle model
|
|
from paddlenlp.transformers import CLIPModel
|
|
|
|
paddle_model = CLIPModel.from_pretrained(tempdir, convert_from_torch=True)
|
|
paddle_model.eval()
|
|
paddle_logit = paddle_model(
|
|
input_ids=paddle.to_tensor(inputs["input_ids"]), pixel_values=paddle.to_tensor(inputs["pixel_values"])
|
|
)
|
|
|
|
# 4. compare results
|
|
self.assertTrue(
|
|
np.allclose(
|
|
paddle_logit.logits_per_image.detach().cpu().numpy(),
|
|
torch_logit.logits_per_image.detach().cpu().numpy(),
|
|
rtol=1e-4,
|
|
)
|
|
)
|
|
|
|
self.assertTrue(
|
|
np.allclose(
|
|
paddle_logit.logits_per_text.detach().cpu().numpy(),
|
|
torch_logit.logits_per_text.detach().cpu().numpy(),
|
|
rtol=1e-4,
|
|
)
|
|
)
|
|
|
|
@require_package("transformers", "torch")
|
|
def test_clip_text_converter(self):
|
|
with tempfile.TemporaryDirectory() as tempdir:
|
|
# 1. create input
|
|
inputs = self.inputs
|
|
|
|
# 2. forward the paddle model
|
|
from paddlenlp.transformers import CLIPTextModel
|
|
|
|
paddle_model = CLIPTextModel.from_pretrained(self.model_id, from_hf_hub=True, cache_dir=tempdir)
|
|
paddle_model.eval()
|
|
paddle_logit = paddle_model(input_ids=paddle.to_tensor(inputs["input_ids"]))
|
|
|
|
# 3. forward the torch model
|
|
import torch
|
|
from transformers import CLIPTextModel
|
|
|
|
torch_model = CLIPTextModel.from_pretrained(self.model_id, cache_dir=tempdir)
|
|
torch_model.eval()
|
|
torch_logit = torch_model(input_ids=torch.tensor(inputs["input_ids"]))
|
|
|
|
# 4. compare results
|
|
np.testing.assert_equal(
|
|
paddle_logit.last_hidden_state.shape,
|
|
torch_logit.last_hidden_state.shape,
|
|
)
|
|
np.testing.assert_equal(
|
|
paddle_logit.pooler_output.shape,
|
|
torch_logit.pooler_output.shape,
|
|
)
|
|
|
|
@require_package("transformers", "torch")
|
|
def test_clip_vision_converter(self):
|
|
with tempfile.TemporaryDirectory() as tempdir:
|
|
# 1. create input
|
|
inputs = self.inputs
|
|
|
|
# 2. forward the paddle model
|
|
from paddlenlp.transformers import CLIPVisionModel
|
|
|
|
paddle_model = CLIPVisionModel.from_pretrained(self.model_id, from_hf_hub=True, cache_dir=tempdir)
|
|
paddle_model.eval()
|
|
paddle_logit = paddle_model(pixel_values=paddle.to_tensor(inputs["pixel_values"]))
|
|
|
|
# 3. forward the torch model
|
|
import torch
|
|
from transformers import CLIPVisionModel
|
|
|
|
torch_model = CLIPVisionModel.from_pretrained(self.model_id, cache_dir=tempdir)
|
|
torch_model.eval()
|
|
torch_logit = torch_model(pixel_values=torch.tensor(inputs["pixel_values"]))
|
|
|
|
# 4. compare results
|
|
np.testing.assert_equal(
|
|
paddle_logit.last_hidden_state.shape,
|
|
torch_logit.last_hidden_state.shape,
|
|
)
|
|
np.testing.assert_equal(
|
|
paddle_logit.pooler_output.shape,
|
|
torch_logit.pooler_output.shape,
|
|
)
|
|
|
|
@require_package("transformers", "torch")
|
|
def test_clip_text_with_projection_converter(self):
|
|
with tempfile.TemporaryDirectory() as tempdir:
|
|
# 1. create input
|
|
inputs = self.inputs
|
|
|
|
# 2. forward the paddle model
|
|
from paddlenlp.transformers import CLIPTextModelWithProjection
|
|
|
|
paddle_model = CLIPTextModelWithProjection.from_pretrained(
|
|
self.model_id, from_hf_hub=True, cache_dir=tempdir
|
|
)
|
|
paddle_model.eval()
|
|
paddle_logit = paddle_model(input_ids=paddle.to_tensor(inputs["input_ids"]))
|
|
|
|
# 3. forward the torch model
|
|
import torch
|
|
from transformers import CLIPTextModelWithProjection
|
|
|
|
torch_model = CLIPTextModelWithProjection.from_pretrained(
|
|
self.model_id, cache_dir=tempdir, ignore_mismatched_sizes=True
|
|
)
|
|
torch_model.eval()
|
|
torch_logit = torch_model(input_ids=torch.tensor(inputs["input_ids"]))
|
|
|
|
# 4. compare results
|
|
np.testing.assert_equal(
|
|
paddle_logit.last_hidden_state.shape,
|
|
torch_logit.last_hidden_state.shape,
|
|
)
|
|
|
|
@require_package("transformers", "torch")
|
|
def test_clip_vision_with_projection_converter(self):
|
|
with tempfile.TemporaryDirectory() as tempdir:
|
|
# 1. create input
|
|
inputs = self.inputs
|
|
|
|
# 2. forward the paddle model
|
|
from paddlenlp.transformers import CLIPVisionModelWithProjection
|
|
|
|
paddle_model = CLIPVisionModelWithProjection.from_pretrained(
|
|
self.model_id, from_hf_hub=True, cache_dir=tempdir
|
|
)
|
|
paddle_model.eval()
|
|
paddle_logit = paddle_model(pixel_values=paddle.to_tensor(inputs["pixel_values"]))
|
|
|
|
# 3. forward the torch model
|
|
import torch
|
|
from transformers import CLIPVisionModelWithProjection
|
|
|
|
torch_model = CLIPVisionModelWithProjection.from_pretrained(
|
|
self.model_id, cache_dir=tempdir, ignore_mismatched_sizes=True
|
|
)
|
|
torch_model.eval()
|
|
torch_logit = torch_model(pixel_values=torch.tensor(inputs["pixel_values"]))
|
|
|
|
# 4. compare results
|
|
np.testing.assert_equal(
|
|
paddle_logit.last_hidden_state.shape,
|
|
torch_logit.last_hidden_state.shape,
|
|
)
|
|
|
|
|
|
class CLIPModelIntegrationTest(unittest.TestCase):
|
|
@slow
|
|
def test_inference(self):
|
|
model_name = "openai/clip-vit-base-patch32"
|
|
model = CLIPModel.from_pretrained(model_name)
|
|
model.eval()
|
|
processor = CLIPProcessor.from_pretrained(model_name)
|
|
|
|
image = prepare_img()
|
|
inputs = processor(
|
|
text=["a photo of a cat", "a photo of a dog"], images=image, padding=True, return_tensors="pd"
|
|
)
|
|
|
|
# forward pass
|
|
with paddle.no_grad():
|
|
outputs = model(**inputs)
|
|
|
|
# verify the logits
|
|
self.assertEqual(
|
|
outputs.logits_per_image.shape,
|
|
[inputs.pixel_values.shape[0], inputs.input_ids.shape[0]],
|
|
)
|
|
self.assertEqual(
|
|
outputs.logits_per_text.shape,
|
|
[inputs.input_ids.shape[0], inputs.pixel_values.shape[0]],
|
|
)
|
|
|
|
expected_logits = paddle.to_tensor([[24.5701, 19.3049]])
|
|
|
|
self.assertTrue(paddle.allclose(outputs.logits_per_image, expected_logits, atol=1e-3))
|