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576 lines
21 KiB
Python
576 lines
21 KiB
Python
# coding=utf-8
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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 PyTorch CLIP model. """
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import inspect
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import os
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import tempfile
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import unittest
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import requests
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from transformers import CLIPConfig, CLIPTextConfig, CLIPVisionConfig
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from transformers.file_utils import is_torch_available, is_vision_available
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from transformers.testing_utils import require_torch, require_vision, slow, torch_device
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from .test_configuration_common import ConfigTester
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from .test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, random_attention_mask
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if is_torch_available():
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import torch
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from torch import nn
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from transformers import CLIPModel, CLIPTextModel, CLIPVisionModel
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from transformers.models.clip.modeling_clip import CLIP_PRETRAINED_MODEL_ARCHIVE_LIST
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if is_vision_available():
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from PIL import Image
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from transformers import CLIPProcessor
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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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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.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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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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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.to(torch_device)
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model.eval()
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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 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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@require_torch
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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,) if is_torch_available() else ()
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test_pruning = False
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test_torchscript = False
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test_resize_embeddings = False
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test_head_masking = 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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def test_inputs_embeds(self):
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# CLIP does not use inputs_embeds
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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.Module))
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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_attention_outputs(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.return_dict = True
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# in CLIP, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
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image_size = (self.model_tester.image_size, self.model_tester.image_size)
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patch_size = (self.model_tester.patch_size, self.model_tester.patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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seq_len = num_patches + 1
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for model_class in self.all_model_classes:
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = False
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config.return_dict = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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# check that output_attentions also work using config
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del inputs_dict["output_attentions"]
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config.output_attentions = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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out_len = len(outputs)
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# Check attention is always last and order is fine
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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added_hidden_states = 1
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self.assertEqual(out_len + added_hidden_states, len(outputs))
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self_attentions = outputs.attentions
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self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(self_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, seq_len, seq_len],
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)
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def test_hidden_states_output(self):
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def check_hidden_states_output(inputs_dict, config, model_class):
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
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expected_num_layers = getattr(
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self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
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)
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self.assertEqual(len(hidden_states), expected_num_layers)
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# CLIP has a different seq_length
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image_size = (self.model_tester.image_size, self.model_tester.image_size)
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patch_size = (self.model_tester.patch_size, self.model_tester.patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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seq_length = num_patches + 1
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self.assertListEqual(
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list(hidden_states[0].shape[-2:]),
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[seq_length, self.model_tester.hidden_size],
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)
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config, inputs_dict = 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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inputs_dict["output_hidden_states"] = True
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check_hidden_states_output(inputs_dict, config, model_class)
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# check that output_hidden_states also work using config
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del inputs_dict["output_hidden_states"]
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config.output_hidden_states = True
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check_hidden_states_output(inputs_dict, config, model_class)
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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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# skip this test as CLIPVisionModel has no base class and is
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# 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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# skip this test as CLIPVisionModel has no base class and is
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# 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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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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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.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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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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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.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask)
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result = model(input_ids)
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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 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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@require_torch
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class CLIPTextModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (CLIPTextModel,) if is_torch_available() else ()
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test_pruning = False
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test_head_masking = 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_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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def test_inputs_embeds(self):
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# CLIP does not use inputs_embeds
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pass
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# skip this test as CLIPTextModel has no base class and is
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# 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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# skip this test as CLIPTextModel has no base class and is
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# 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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class CLIPModelTester:
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def __init__(self, parent, is_training=True):
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self.parent = parent
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self.text_model_tester = CLIPTextModelTester(parent)
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self.vision_model_tester = CLIPVisionModelTester(parent)
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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).to(torch_device).eval()
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result = model(input_ids, pixel_values, 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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@require_torch
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class CLIPModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (CLIPModel,) if is_torch_available() else ()
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test_head_masking = False
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test_pruning = False
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test_resize_embeddings = False
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test_attention_outputs = 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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# hidden_states are tested in individual model tests
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def test_hidden_states_output(self):
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pass
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# input_embeds are tested in individual model tests
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def test_inputs_embeds(self):
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pass
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# 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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# 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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def _create_and_check_torchscript(self, config, inputs_dict):
|
|
if not self.test_torchscript:
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|
return
|
|
|
|
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
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|
configs_no_init.torchscript = True
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|
configs_no_init.return_dict = False
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|
for model_class in self.all_model_classes:
|
|
model = model_class(config=configs_no_init)
|
|
model.to(torch_device)
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|
model.eval()
|
|
|
|
try:
|
|
input_ids = inputs_dict["input_ids"]
|
|
pixel_values = inputs_dict["pixel_values"] # CLIP needs pixel_values
|
|
traced_model = torch.jit.trace(model, (input_ids, pixel_values))
|
|
except RuntimeError:
|
|
self.fail("Couldn't trace module.")
|
|
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
|
|
|
|
try:
|
|
torch.jit.save(traced_model, pt_file_name)
|
|
except Exception:
|
|
self.fail("Couldn't save module.")
|
|
|
|
try:
|
|
loaded_model = torch.jit.load(pt_file_name)
|
|
except Exception:
|
|
self.fail("Couldn't load module.")
|
|
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
loaded_model.to(torch_device)
|
|
loaded_model.eval()
|
|
|
|
model_state_dict = model.state_dict()
|
|
loaded_model_state_dict = loaded_model.state_dict()
|
|
|
|
self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
|
|
|
|
models_equal = True
|
|
for layer_name, p1 in model_state_dict.items():
|
|
p2 = loaded_model_state_dict[layer_name]
|
|
if p1.data.ne(p2.data).sum() > 0:
|
|
models_equal = False
|
|
|
|
self.assertTrue(models_equal)
|
|
|
|
@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():
|
|
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
im = Image.open(requests.get(url, stream=True).raw)
|
|
return im
|
|
|
|
|
|
@require_vision
|
|
class CLIPModelIntegrationTest(unittest.TestCase):
|
|
@slow
|
|
def test_inference(self):
|
|
model_name = "openai/clip-vit-base-patch32"
|
|
model = CLIPModel.from_pretrained(model_name).to(torch_device)
|
|
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="pt"
|
|
).to(torch_device)
|
|
|
|
# forward pass
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
|
|
# verify the logits
|
|
self.assertEqual(
|
|
outputs.logits_per_image.shape,
|
|
torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])),
|
|
)
|
|
self.assertEqual(
|
|
outputs.logits_per_text.shape,
|
|
torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
|
|
)
|
|
|
|
expected_logits = torch.tensor([[24.5701, 19.3049]], device=torch_device)
|
|
|
|
self.assertTrue(torch.allclose(outputs.logits_per_image, expected_logits, atol=1e-3))
|