1426 lines
54 KiB
Python
1426 lines
54 KiB
Python
# coding=utf-8
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# Copyright 2022 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 Blip 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 numpy as np
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import requests
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from transformers import BlipConfig, BlipTextConfig, BlipVisionConfig
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from transformers.testing_utils import (
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require_torch,
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require_torch_accelerator,
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require_torch_fp16,
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require_vision,
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slow,
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torch_device,
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)
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from transformers.utils import is_torch_available, is_vision_available
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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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_config_zero_init,
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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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from ...test_pipeline_mixin import PipelineTesterMixin
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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 (
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BlipForConditionalGeneration,
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BlipForImageTextRetrieval,
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BlipForQuestionAnswering,
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BlipModel,
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BlipTextModel,
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BlipVisionModel,
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)
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if is_vision_available():
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from PIL import Image
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from transformers import BlipProcessor
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class BlipVisionModelTester:
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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=2,
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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=1e-10,
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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 BlipVisionConfig(
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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 = BlipVisionModel(config=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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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 BlipVisionModelTest(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 Blip 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 = (BlipVisionModel,) if is_torch_available() else ()
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fx_compatible = False
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test_pruning = 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 = BlipVisionModelTester(self)
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self.config_tester = ConfigTester(self, config_class=BlipVisionConfig, 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="Blip 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.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_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(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant_false(self):
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pass
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@unittest.skip(reason="BlipVisionModel 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="BlipVisionModel 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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model_name = "Salesforce/blip-vqa-base"
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model = BlipVisionModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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class BlipTextModelTester:
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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=2,
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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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bos_token_id=0,
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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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self.bos_token_id = bos_token_id
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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 BlipTextConfig(
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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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bos_token_id=self.bos_token_id,
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)
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def create_and_check_model(self, config, input_ids, input_mask):
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model = BlipTextModel(config=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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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 BlipTextModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (BlipTextModel,) if is_torch_available() else ()
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fx_compatible = False
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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 = BlipTextModelTester(self)
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self.config_tester = ConfigTester(self, config_class=BlipTextConfig, 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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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant_false(self):
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pass
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@unittest.skip(reason="Blip 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="BlipTextModel 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="BlipTextModel 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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model_name = "Salesforce/blip-vqa-base"
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model = BlipTextModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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def test_pt_tf_model_equivalence(self):
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super().test_pt_tf_model_equivalence(allow_missing_keys=True)
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class BlipModelTester:
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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 = BlipTextModelTester(parent, **text_kwargs)
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self.vision_model_tester = BlipVisionModelTester(parent, **vision_kwargs)
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self.batch_size = self.text_model_tester.batch_size # need bs for batching_equivalence test
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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 BlipConfig.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 = BlipModel(config).to(torch_device).eval()
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with torch.no_grad():
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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 BlipModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (BlipModel,) if is_torch_available() else ()
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pipeline_model_mapping = (
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{
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"feature-extraction": BlipModel,
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"image-to-text": BlipForConditionalGeneration,
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"visual-question-answering": BlipForQuestionAnswering,
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}
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if is_torch_available()
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else {}
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)
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fx_compatible = False
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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 = BlipModelTester(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="BlipModel 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 initilization is different for Blip
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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 param.requires_grad:
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# check if `logit_scale` is initilized as per the original implementation
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if name == "logit_scale":
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self.assertAlmostEqual(
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param.data.item(),
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np.log(1 / 0.07),
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delta=1e-3,
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msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
|
)
|
|
else:
|
|
self.assertIn(
|
|
((param.data.mean() * 1e9).round() / 1e9).item(),
|
|
[0.0, 1.0],
|
|
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
|
)
|
|
|
|
def _create_and_check_torchscript(self, config, inputs_dict):
|
|
if not self.test_torchscript:
|
|
return
|
|
|
|
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
|
|
configs_no_init.torchscript = True
|
|
configs_no_init.return_dict = False
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config=configs_no_init)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
try:
|
|
input_ids = inputs_dict["input_ids"]
|
|
pixel_values = inputs_dict["pixel_values"] # Blip 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()
|
|
|
|
non_persistent_buffers = {}
|
|
for key in loaded_model_state_dict.keys():
|
|
if key not in model_state_dict.keys():
|
|
non_persistent_buffers[key] = loaded_model_state_dict[key]
|
|
|
|
loaded_model_state_dict = {
|
|
key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
|
|
}
|
|
|
|
self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
|
|
|
|
model_buffers = list(model.buffers())
|
|
for non_persistent_buffer in non_persistent_buffers.values():
|
|
found_buffer = False
|
|
for i, model_buffer in enumerate(model_buffers):
|
|
if torch.equal(non_persistent_buffer, model_buffer):
|
|
found_buffer = True
|
|
break
|
|
|
|
self.assertTrue(found_buffer)
|
|
model_buffers.pop(i)
|
|
|
|
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)
|
|
|
|
def test_load_vision_text_config(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
# Save BlipConfig and check if we can load BlipVisionConfig from it
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
config.save_pretrained(tmp_dir_name)
|
|
vision_config = BlipVisionConfig.from_pretrained(tmp_dir_name)
|
|
self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
|
|
|
|
# Save BlipConfig and check if we can load BlipTextConfig from it
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
config.save_pretrained(tmp_dir_name)
|
|
text_config = BlipTextConfig.from_pretrained(tmp_dir_name)
|
|
self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "Salesforce/blip-vqa-base"
|
|
model = BlipModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
def test_get_image_features(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
keys_to_pop = ["input_ids", "attention_mask", "return_loss"]
|
|
|
|
for key in keys_to_pop:
|
|
inputs_dict.pop(key)
|
|
|
|
model = BlipModel(config).to(torch_device)
|
|
model.eval()
|
|
image_features = model.get_image_features(**inputs_dict)
|
|
self.assertEqual(
|
|
image_features.shape,
|
|
(
|
|
self.model_tester.batch_size,
|
|
model.projection_dim,
|
|
),
|
|
)
|
|
|
|
def test_get_text_features(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
keys_to_pop = ["pixel_values", "return_loss"]
|
|
|
|
for key in keys_to_pop:
|
|
inputs_dict.pop(key)
|
|
|
|
model = BlipModel(config).to(torch_device)
|
|
model.eval()
|
|
text_features = model.get_text_features(**inputs_dict)
|
|
self.assertEqual(
|
|
text_features.shape,
|
|
(
|
|
self.model_tester.batch_size,
|
|
model.projection_dim,
|
|
),
|
|
)
|
|
|
|
def test_get_multimodal_features(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
keys_to_pop = ["return_loss"]
|
|
|
|
for key in keys_to_pop:
|
|
inputs_dict.pop(key)
|
|
|
|
model = BlipModel(config).to(torch_device)
|
|
model.eval()
|
|
multimodal_features = model.get_multimodal_features(**inputs_dict)
|
|
self.assertEqual(
|
|
multimodal_features.shape,
|
|
(
|
|
self.model_tester.batch_size,
|
|
model.projection_dim,
|
|
),
|
|
)
|
|
|
|
def test_pt_tf_model_equivalence(self):
|
|
super().test_pt_tf_model_equivalence(allow_missing_keys=True)
|
|
|
|
|
|
class BlipTextRetrievalModelTester:
|
|
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 = BlipTextModelTester(parent, **text_kwargs)
|
|
self.vision_model_tester = BlipVisionModelTester(parent, **vision_kwargs)
|
|
self.batch_size = self.text_model_tester.batch_size # need bs for batching_equivalence test
|
|
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 BlipConfig.from_text_vision_configs(
|
|
self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64
|
|
)
|
|
|
|
def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
|
|
model = BlipModel(config).to(torch_device).eval()
|
|
with torch.no_grad():
|
|
result = model(input_ids, pixel_values, attention_mask)
|
|
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 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 BlipTextImageModelsModelTester:
|
|
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 = BlipTextModelTester(parent, **text_kwargs)
|
|
self.vision_model_tester = BlipVisionModelTester(parent, **vision_kwargs)
|
|
self.batch_size = self.text_model_tester.batch_size # need bs for batching_equivalence test
|
|
self.seq_length = self.text_model_tester.seq_length # need seq_length for pt-tf equivalence test
|
|
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 BlipConfig.from_text_vision_configs(
|
|
self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64
|
|
)
|
|
|
|
def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
|
|
model = BlipModel(config).to(torch_device).eval()
|
|
with torch.no_grad():
|
|
result = model(input_ids, pixel_values, attention_mask)
|
|
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 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,
|
|
"labels": input_ids,
|
|
"attention_mask": attention_mask,
|
|
"pixel_values": pixel_values,
|
|
}
|
|
return config, inputs_dict
|
|
|
|
|
|
class BlipVQAModelTester:
|
|
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 = BlipTextModelTester(parent, **text_kwargs)
|
|
self.vision_model_tester = BlipVisionModelTester(parent, **vision_kwargs)
|
|
self.batch_size = self.text_model_tester.batch_size # need bs for batching_equivalence test
|
|
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 BlipConfig.from_text_vision_configs(
|
|
self.text_model_tester.get_config(), self.vision_model_tester.get_config(), projection_dim=64
|
|
)
|
|
|
|
def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
|
|
model = BlipModel(config).to(torch_device).eval()
|
|
with torch.no_grad():
|
|
result = model(input_ids, pixel_values, attention_mask)
|
|
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 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,
|
|
"labels": input_ids,
|
|
"decoder_input_ids": input_ids,
|
|
"attention_mask": attention_mask,
|
|
"pixel_values": pixel_values,
|
|
}
|
|
return config, inputs_dict
|
|
|
|
|
|
@require_torch
|
|
@require_vision
|
|
class BlipVQAModelTest(ModelTesterMixin, unittest.TestCase):
|
|
all_model_classes = (BlipForQuestionAnswering,) if is_torch_available() else ()
|
|
fx_compatible = False
|
|
test_head_masking = False
|
|
test_pruning = False
|
|
test_resize_embeddings = False
|
|
test_attention_outputs = False
|
|
test_torchscript = False
|
|
|
|
def setUp(self):
|
|
self.model_tester = BlipVQAModelTester(self)
|
|
|
|
def _prepare_inputs_for_vqa(self):
|
|
_, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
inputs_dict["labels"] = inputs_dict["input_ids"]
|
|
inputs_dict["decoder_input_ids"] = inputs_dict["input_ids"]
|
|
inputs_dict.pop("return_loss")
|
|
return inputs_dict
|
|
|
|
def test_class_name_consistency(self):
|
|
"""
|
|
Tests that all VQA models have a class name that ends with "ForQuestionAnswering"
|
|
"""
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(self.model_tester.get_config())
|
|
self.assertTrue(
|
|
model.__class__.__name__.endswith("ForQuestionAnswering"),
|
|
f"Class name should end with 'ForVisualQuestionAnswering' got {model.__class__.__name__}",
|
|
)
|
|
|
|
def test_training(self):
|
|
"""
|
|
Tests that all VQA models can be trained on a single batch
|
|
"""
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(self.model_tester.get_config()).to(torch_device)
|
|
model.train()
|
|
loss = model(**self.model_tester.prepare_config_and_inputs_for_common()[1]).loss
|
|
loss.backward()
|
|
|
|
# verify the gradients are not None
|
|
for name, param in model.named_parameters():
|
|
self.assertIsNotNone(param.grad, f"Gradients should not be None - got {param.grad} for {name}")
|
|
|
|
def test_forward_signature(self):
|
|
"""
|
|
Test if the forward function has the expected arguments.
|
|
"""
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(self.model_tester.get_config())
|
|
signature = inspect.signature(model.forward)
|
|
# signature.parameters is an OrderedDict => so args are the first n entries
|
|
args = list(signature.parameters.keys())
|
|
expected_args = [
|
|
"input_ids",
|
|
"attention_mask",
|
|
"labels",
|
|
"decoder_input_ids",
|
|
"decoder_attention_mask",
|
|
]
|
|
for arg in expected_args:
|
|
self.assertTrue(
|
|
arg in args,
|
|
f"Argument {arg} of forward function signature should include {arg}. Found {args}.",
|
|
)
|
|
|
|
@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="BlipModel does not have input/output embeddings")
|
|
def test_model_common_attributes(self):
|
|
pass
|
|
|
|
|
|
@require_torch
|
|
class BlipTextRetrievalModelTest(ModelTesterMixin, unittest.TestCase):
|
|
all_model_classes = (BlipForImageTextRetrieval,) if is_torch_available() else ()
|
|
fx_compatible = False
|
|
test_head_masking = False
|
|
test_pruning = False
|
|
test_resize_embeddings = False
|
|
test_attention_outputs = False
|
|
test_torchscript = False
|
|
|
|
def setUp(self):
|
|
self.model_tester = BlipTextRetrievalModelTester(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)
|
|
|
|
@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="BlipModel does not have input/output embeddings")
|
|
def test_model_common_attributes(self):
|
|
pass
|
|
|
|
def test_forward_signature(self):
|
|
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
signature = inspect.signature(model.forward)
|
|
# signature.parameters is an OrderedDict => so arg_names order is deterministic
|
|
arg_names = [*signature.parameters.keys()]
|
|
|
|
if model.config.is_encoder_decoder:
|
|
expected_arg_names = [
|
|
"input_ids",
|
|
"attention_mask",
|
|
"decoder_input_ids",
|
|
"decoder_attention_mask",
|
|
]
|
|
expected_arg_names.extend(
|
|
["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"]
|
|
if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names
|
|
else ["encoder_outputs"]
|
|
)
|
|
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
|
|
else:
|
|
expected_arg_names = ["input_ids"] if model_class != BlipForConditionalGeneration else ["pixel_values"]
|
|
self.assertListEqual(arg_names[:1], expected_arg_names)
|
|
|
|
def test_training(self):
|
|
if not self.model_tester.is_training:
|
|
return
|
|
|
|
for model_class in self.all_model_classes[:-1]:
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.return_dict = True
|
|
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.train()
|
|
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
|
|
# hardcode labels to be the same as input_ids
|
|
inputs["labels"] = inputs["input_ids"]
|
|
|
|
loss = model(**inputs).loss
|
|
loss.backward()
|
|
|
|
def test_training_gradient_checkpointing(self):
|
|
if not self.model_tester.is_training:
|
|
return
|
|
|
|
for model_class in self.all_model_classes[:-1]:
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.use_cache = False
|
|
config.return_dict = True
|
|
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.gradient_checkpointing_enable()
|
|
model.train()
|
|
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
|
|
# hardcode labels to be the same as input_ids
|
|
inputs["labels"] = inputs["input_ids"]
|
|
|
|
loss = model(**inputs).loss
|
|
loss.backward()
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing_use_reentrant(self):
|
|
pass
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing_use_reentrant_false(self):
|
|
pass
|
|
|
|
# override as the `logit_scale` parameter initilization is different for Blip
|
|
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.requires_grad:
|
|
# check if `logit_scale` is initilized as per the original implementation
|
|
if name == "logit_scale":
|
|
self.assertAlmostEqual(
|
|
param.data.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.data.mean() * 1e9).round() / 1e9).item(),
|
|
[0.0, 1.0],
|
|
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
|
)
|
|
|
|
def _create_and_check_torchscript(self, config, inputs_dict):
|
|
if not self.test_torchscript:
|
|
return
|
|
|
|
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
|
|
configs_no_init.torchscript = True
|
|
configs_no_init.return_dict = False
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config=configs_no_init)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
try:
|
|
input_ids = inputs_dict["input_ids"]
|
|
pixel_values = inputs_dict["pixel_values"] # Blip 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()
|
|
|
|
non_persistent_buffers = {}
|
|
for key in loaded_model_state_dict.keys():
|
|
if key not in model_state_dict.keys():
|
|
non_persistent_buffers[key] = loaded_model_state_dict[key]
|
|
|
|
loaded_model_state_dict = {
|
|
key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
|
|
}
|
|
|
|
self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
|
|
|
|
model_buffers = list(model.buffers())
|
|
for non_persistent_buffer in non_persistent_buffers.values():
|
|
found_buffer = False
|
|
for i, model_buffer in enumerate(model_buffers):
|
|
if torch.equal(non_persistent_buffer, model_buffer):
|
|
found_buffer = True
|
|
break
|
|
|
|
self.assertTrue(found_buffer)
|
|
model_buffers.pop(i)
|
|
|
|
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)
|
|
|
|
def test_load_vision_text_config(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
# Save BlipConfig and check if we can load BlipVisionConfig from it
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
config.save_pretrained(tmp_dir_name)
|
|
vision_config = BlipVisionConfig.from_pretrained(tmp_dir_name)
|
|
self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
|
|
|
|
# Save BlipConfig and check if we can load BlipTextConfig from it
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
config.save_pretrained(tmp_dir_name)
|
|
text_config = BlipTextConfig.from_pretrained(tmp_dir_name)
|
|
self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "Salesforce/blip-vqa-base"
|
|
model = BlipModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
@require_torch
|
|
class BlipTextImageModelTest(ModelTesterMixin, unittest.TestCase):
|
|
all_model_classes = (BlipForConditionalGeneration,) if is_torch_available() else ()
|
|
fx_compatible = False
|
|
test_head_masking = False
|
|
test_pruning = False
|
|
test_resize_embeddings = False
|
|
test_attention_outputs = False
|
|
test_torchscript = False
|
|
|
|
def setUp(self):
|
|
self.model_tester = BlipTextImageModelsModelTester(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)
|
|
|
|
@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="BlipModel does not have input/output embeddings")
|
|
def test_model_common_attributes(self):
|
|
pass
|
|
|
|
def test_forward_signature(self):
|
|
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
signature = inspect.signature(model.forward)
|
|
# signature.parameters is an OrderedDict => so arg_names order is deterministic
|
|
arg_names = [*signature.parameters.keys()]
|
|
|
|
if model.config.is_encoder_decoder:
|
|
expected_arg_names = [
|
|
"input_ids",
|
|
"attention_mask",
|
|
"decoder_input_ids",
|
|
"decoder_attention_mask",
|
|
]
|
|
expected_arg_names.extend(
|
|
["head_mask", "decoder_head_mask", "cross_attn_head_mask", "encoder_outputs"]
|
|
if "head_mask" and "decoder_head_mask" and "cross_attn_head_mask" in arg_names
|
|
else ["encoder_outputs"]
|
|
)
|
|
self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names)
|
|
else:
|
|
expected_arg_names = ["input_ids"] if model_class != BlipForConditionalGeneration else ["pixel_values"]
|
|
self.assertListEqual(arg_names[:1], expected_arg_names)
|
|
|
|
def test_training(self):
|
|
if not self.model_tester.is_training:
|
|
return
|
|
|
|
for model_class in self.all_model_classes[:-1]:
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.return_dict = True
|
|
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.train()
|
|
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
|
|
# hardcode labels to be the same as input_ids
|
|
inputs["labels"] = inputs["input_ids"]
|
|
|
|
loss = model(**inputs).loss
|
|
loss.backward()
|
|
|
|
def test_training_gradient_checkpointing(self):
|
|
if not self.model_tester.is_training:
|
|
return
|
|
|
|
for model_class in self.all_model_classes[:-1]:
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.use_cache = False
|
|
config.return_dict = True
|
|
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.gradient_checkpointing_enable()
|
|
model.train()
|
|
inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
|
|
|
|
# hardcode labels to be the same as input_ids
|
|
inputs["labels"] = inputs["input_ids"]
|
|
|
|
loss = model(**inputs).loss
|
|
loss.backward()
|
|
|
|
# override as the `logit_scale` parameter initilization is different for Blip
|
|
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.requires_grad:
|
|
# check if `logit_scale` is initilized as per the original implementation
|
|
if name == "logit_scale":
|
|
self.assertAlmostEqual(
|
|
param.data.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.data.mean() * 1e9).round() / 1e9).item(),
|
|
[0.0, 1.0],
|
|
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
|
)
|
|
|
|
def _create_and_check_torchscript(self, config, inputs_dict):
|
|
if not self.test_torchscript:
|
|
return
|
|
|
|
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
|
|
configs_no_init.torchscript = True
|
|
configs_no_init.return_dict = False
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config=configs_no_init)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
try:
|
|
input_ids = inputs_dict["input_ids"]
|
|
pixel_values = inputs_dict["pixel_values"] # Blip 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()
|
|
|
|
non_persistent_buffers = {}
|
|
for key in loaded_model_state_dict.keys():
|
|
if key not in model_state_dict.keys():
|
|
non_persistent_buffers[key] = loaded_model_state_dict[key]
|
|
|
|
loaded_model_state_dict = {
|
|
key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
|
|
}
|
|
|
|
self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
|
|
|
|
model_buffers = list(model.buffers())
|
|
for non_persistent_buffer in non_persistent_buffers.values():
|
|
found_buffer = False
|
|
for i, model_buffer in enumerate(model_buffers):
|
|
if torch.equal(non_persistent_buffer, model_buffer):
|
|
found_buffer = True
|
|
break
|
|
|
|
self.assertTrue(found_buffer)
|
|
model_buffers.pop(i)
|
|
|
|
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)
|
|
|
|
def test_load_vision_text_config(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
# Save BlipConfig and check if we can load BlipVisionConfig from it
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
config.save_pretrained(tmp_dir_name)
|
|
vision_config = BlipVisionConfig.from_pretrained(tmp_dir_name)
|
|
self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
|
|
|
|
# Save BlipConfig and check if we can load BlipTextConfig from it
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
config.save_pretrained(tmp_dir_name)
|
|
text_config = BlipTextConfig.from_pretrained(tmp_dir_name)
|
|
self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "Salesforce/blip-vqa-base"
|
|
model = BlipModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
# We will verify our results on an image of cute cats
|
|
def prepare_img():
|
|
url = "https://huggingface.co/hf-internal-testing/blip-test-image/resolve/main/demo.jpg"
|
|
im = Image.open(requests.get(url, stream=True).raw)
|
|
return im
|
|
|
|
|
|
@require_vision
|
|
@require_torch
|
|
@slow
|
|
class BlipModelIntegrationTest(unittest.TestCase):
|
|
def test_inference_image_captioning(self):
|
|
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(torch_device)
|
|
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
|
|
image = prepare_img()
|
|
|
|
# image only
|
|
inputs = processor(images=image, return_tensors="pt").to(torch_device)
|
|
|
|
predictions = model.generate(**inputs)
|
|
|
|
# Test output
|
|
self.assertEqual(predictions[0].tolist(), [30522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102])
|
|
|
|
# image and context
|
|
context = ["a picture of"]
|
|
inputs = processor(images=image, text=context, return_tensors="pt").to(torch_device)
|
|
|
|
predictions = model.generate(**inputs)
|
|
|
|
# Test output
|
|
self.assertEqual(
|
|
predictions[0].tolist(),
|
|
[30522, 1037, 3861, 1997, 1037, 2450, 1998, 2014, 3899, 2006, 1996, 3509, 102],
|
|
)
|
|
|
|
@require_torch_accelerator
|
|
@require_torch_fp16
|
|
def test_inference_image_captioning_fp16(self):
|
|
model = BlipForConditionalGeneration.from_pretrained(
|
|
"Salesforce/blip-image-captioning-base", torch_dtype=torch.float16
|
|
).to(torch_device)
|
|
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
|
|
image = prepare_img()
|
|
|
|
# image only
|
|
inputs = processor(images=image, return_tensors="pt").to(torch_device, torch.float16)
|
|
|
|
predictions = model.generate(**inputs)
|
|
|
|
# Test output
|
|
self.assertEqual(predictions[0].tolist(), [30522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102])
|
|
|
|
# image and context
|
|
context = ["a picture of"]
|
|
inputs = processor(images=image, text=context, return_tensors="pt").to(torch_device, torch.float16)
|
|
|
|
predictions = model.generate(**inputs)
|
|
|
|
# Test output
|
|
self.assertEqual(
|
|
predictions[0].tolist(),
|
|
[30522, 1037, 3861, 1997, 1037, 2450, 1998, 2014, 3899, 2006, 1996, 3509, 102],
|
|
)
|
|
|
|
def test_inference_interpolate_pos_encoding(self):
|
|
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base").to(torch_device)
|
|
processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
|
|
processor.image_processor.size = {"height": 500, "width": 500}
|
|
|
|
image = prepare_img()
|
|
inputs = processor(images=image, return_tensors="pt").to(torch_device)
|
|
|
|
predictions = model.generate(**inputs, interpolate_pos_encoding=True)
|
|
generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
|
|
|
|
self.assertEqual(predictions[0].tolist(), [30522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 1037, 3899, 102])
|
|
self.assertEqual(generated_text, "a woman sitting on the beach with a dog")
|
|
|
|
def test_inference_vqa(self):
|
|
model = BlipForQuestionAnswering.from_pretrained("Salesforce/blip-vqa-base").to(torch_device)
|
|
processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
|
|
|
|
image = prepare_img()
|
|
text = "how many dogs are in the picture?"
|
|
|
|
inputs = processor(image, text=text, return_tensors="pt").to(torch_device)
|
|
out = model.generate(**inputs)
|
|
|
|
# Test output
|
|
self.assertEqual(out[0].tolist(), [30522, 1015, 102])
|
|
|
|
def test_inference_itm(self):
|
|
model = BlipForImageTextRetrieval.from_pretrained("Salesforce/blip-itm-base-coco").to(torch_device)
|
|
processor = BlipProcessor.from_pretrained("Salesforce/blip-itm-base-coco")
|
|
|
|
image = prepare_img()
|
|
text = "A woman and her dog sitting in a beach"
|
|
|
|
inputs = processor(image, text, return_tensors="pt").to(torch_device)
|
|
|
|
out_itm = model(**inputs)
|
|
out = model(**inputs, use_itm_head=False)
|
|
|
|
expected_scores = torch.Tensor([[0.0029, 0.9971]])
|
|
|
|
self.assertTrue(torch.allclose(torch.nn.Softmax()(out_itm[0].cpu()), expected_scores, rtol=1e-3, atol=1e-3))
|
|
self.assertTrue(torch.allclose(out[0].cpu(), torch.Tensor([[0.5162]]), rtol=1e-3, atol=1e-3))
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