Add BLIP-2 (#21441)
* First draft * More improvements * More improvements * Improve conversion script * Convert all weights * Make forward pass work * Make logits match * More improvements * More improvements * More improvements * Use get_input_embeddings * Improve some more * Improve model tests * Improve model tests * More improvements * Fix processor * Update files * Update prepare_inputs_for_generation * More improvements * Fix copies * More fixes * Make fixup * More improvements * Add support for seq2seq language model * More improvements * Fix test * More improvements * Improve conversion script * Remove some todo's * Fix README's * Improve conversion script * Fix generation * Fix style and remove Blip2Model * Fix model outputs * More improvements * Set eos_token_id in config * Fix quality * Small improvements * Add processor tests * More improvements * Apply suggestions * Apply suggestions * Add integration test * Update image URL * Add integration test * Fix model_type * Update style * Improve docs * Add doc tests * Fix copies * Remove tests which are passing * Improve some more * Add tests for seq2seq language models * Minor fix * Convert more checkpoints * finalize CI * Fix blip and blip2 processors * add `accelerate` support for `blip2` * clean up * make style * Update conversion script * Update conversion script some more * Update organization * revert toc file * add blip-2 to toc file * Some more improvements * Fix docstring * Improve docs --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com> Co-authored-by: younesbelkada <younesbelkada@gmail.com>
This commit is contained in:
NielsRogge
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tests/models/blip_2/__init__.py
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tests/models/blip_2/__init__.py
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tests/models/blip_2/test_modeling_blip_2.py
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tests/models/blip_2/test_modeling_blip_2.py
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# coding=utf-8
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# Copyright 2023 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-2 model. """
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import inspect
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import tempfile
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import unittest
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import numpy as np
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import requests
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from transformers import CONFIG_MAPPING, Blip2Config, Blip2QFormerConfig, Blip2VisionConfig
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from transformers.testing_utils import require_torch, require_vision, slow, torch_device
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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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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 Blip2ForConditionalGeneration, Blip2VisionModel
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from transformers.models.blip_2.modeling_blip_2 import BLIP_2_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 Blip2Processor
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class Blip2VisionModelTester:
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def __init__(
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self,
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parent,
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batch_size=12,
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image_size=30,
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patch_size=2,
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num_channels=3,
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is_training=True,
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hidden_size=32,
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projection_dim=32,
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num_hidden_layers=5,
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num_attention_heads=4,
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intermediate_size=37,
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dropout=0.1,
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attention_dropout=0.1,
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initializer_range=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 Blip2VisionConfig(
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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 = Blip2VisionModel(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 Blip2VisionModelTest(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-2's vision encoder 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 = (Blip2VisionModel,) 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 = Blip2VisionModelTester(self)
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self.config_tester = ConfigTester(
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self, config_class=Blip2VisionConfig, has_text_modality=False, hidden_size=37
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)
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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-2's vision encoder 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(reason="Blip2VisionModel 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="Blip2VisionModel has no base class and is not available in MODEL_MAPPING")
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def test_save_load_fast_init_to_base(self):
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pass
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@slow
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def test_model_from_pretrained(self):
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for model_name in BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = Blip2VisionModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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class Blip2QFormerModelTester:
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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=6,
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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 Blip2QFormerConfig(
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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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# this class is based on `OPTModelTester` found in tests/models/opt/test_modeling_opt.py
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class Blip2TextModelDecoderOnlyTester:
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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_labels=False,
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vocab_size=99,
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hidden_size=16,
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num_hidden_layers=5,
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num_attention_heads=4,
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intermediate_size=4,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=20,
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eos_token_id=2,
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pad_token_id=1,
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bos_token_id=0,
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embed_dim=16,
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num_labels=3,
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word_embed_proj_dim=16,
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type_sequence_label_size=2,
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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_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.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.eos_token_id = eos_token_id
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self.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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self.embed_dim = embed_dim
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self.num_labels = num_labels
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self.type_sequence_label_size = type_sequence_label_size
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self.word_embed_proj_dim = word_embed_proj_dim
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self.is_encoder_decoder = False
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def prepare_config_and_inputs(self):
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config = self.get_config()
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(
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3,
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)
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input_ids[:, -1] = self.eos_token_id # Eos Token
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attention_mask = input_ids.ne(self.pad_token_id)
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return config, input_ids, attention_mask
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def get_config(self):
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return CONFIG_MAPPING["opt"](
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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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ffn_dim=self.intermediate_size,
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dropout=self.hidden_dropout_prob,
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attention_dropout=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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eos_token_id=self.eos_token_id,
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bos_token_id=self.bos_token_id,
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pad_token_id=self.pad_token_id,
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embed_dim=self.embed_dim,
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is_encoder_decoder=False,
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word_embed_proj_dim=self.word_embed_proj_dim,
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)
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# this model tester uses a decoder-only language model (OPT)
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class Blip2ForConditionalGenerationDecoderOnlyModelTester:
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def __init__(
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self, parent, vision_kwargs=None, qformer_kwargs=None, text_kwargs=None, is_training=True, num_query_tokens=10
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):
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if vision_kwargs is None:
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vision_kwargs = {}
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if qformer_kwargs is None:
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qformer_kwargs = {}
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if text_kwargs is None:
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text_kwargs = {}
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self.parent = parent
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self.vision_model_tester = Blip2VisionModelTester(parent, **vision_kwargs)
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self.qformer_model_tester = Blip2QFormerModelTester(parent, **qformer_kwargs)
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self.text_model_tester = Blip2TextModelDecoderOnlyTester(parent, **text_kwargs)
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self.is_training = is_training
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self.num_query_tokens = num_query_tokens
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def prepare_config_and_inputs(self):
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_, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
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_, input_ids, attention_mask = self.text_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 Blip2Config.from_vision_qformer_text_configs(
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vision_config=self.vision_model_tester.get_config(),
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qformer_config=self.qformer_model_tester.get_config(),
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text_config=self.text_model_tester.get_config(),
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num_query_tokens=self.num_query_tokens,
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)
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def create_and_check_for_conditional_generation(self, config, input_ids, attention_mask, pixel_values):
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model = Blip2ForConditionalGeneration(config).to(torch_device).eval()
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with torch.no_grad():
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result = model(pixel_values, input_ids, attention_mask)
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expected_seq_length = self.num_query_tokens + self.text_model_tester.seq_length
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self.parent.assertEqual(
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result.logits.shape,
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(self.vision_model_tester.batch_size, expected_seq_length, self.text_model_tester.vocab_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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"pixel_values": pixel_values,
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"labels": input_ids,
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}
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return config, inputs_dict
|
||||
|
||||
|
||||
@require_torch
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class Blip2ForConditionalGenerationDecoderOnlyTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (Blip2ForConditionalGeneration,) 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 = Blip2ForConditionalGenerationDecoderOnlyModelTester(self)
|
||||
|
||||
def test_for_conditional_generation(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_for_conditional_generation(*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="Blip2Model does not have input/output embeddings")
|
||||
def test_model_common_attributes(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="There's no base Blip2Model")
|
||||
def test_save_load_fast_init_from_base(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="There's no base Blip2Model")
|
||||
def test_save_load_fast_init_to_base(self):
|
||||
pass
|
||||
|
||||
def test_forward_signature(self):
|
||||
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
model = model_class(config)
|
||||
signature = inspect.signature(model.forward)
|
||||
# signature.parameters is an OrderedDict => so arg_names order is deterministic
|
||||
arg_names = [*signature.parameters.keys()]
|
||||
|
||||
expected_arg_names = ["pixel_values"]
|
||||
self.assertListEqual(arg_names[:1], expected_arg_names)
|
||||
|
||||
def test_load_vision_qformer_text_config(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
# Save Blip2Config and check if we can load Blip2VisionConfig from it
|
||||
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
||||
config.save_pretrained(tmp_dir_name)
|
||||
vision_config = Blip2VisionConfig.from_pretrained(tmp_dir_name)
|
||||
self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
|
||||
|
||||
# Save Blip2Config and check if we can load Blip2QFormerConfig from it
|
||||
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
||||
config.save_pretrained(tmp_dir_name)
|
||||
qformer_config = Blip2QFormerConfig.from_pretrained(tmp_dir_name)
|
||||
self.assertDictEqual(config.qformer_config.to_dict(), qformer_config.to_dict())
|
||||
|
||||
@slow
|
||||
def test_model_from_pretrained(self):
|
||||
for model_name in BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST:
|
||||
model = Blip2ForConditionalGeneration.from_pretrained(model_name)
|
||||
self.assertIsNotNone(model)
|
||||
|
||||
|
||||
# this class is based on `T5ModelTester` found in tests/models/t5/test_modeling_t5.py
|
||||
class Blip2TextModelTester:
|
||||
def __init__(
|
||||
self,
|
||||
parent,
|
||||
vocab_size=99,
|
||||
batch_size=12,
|
||||
encoder_seq_length=7,
|
||||
decoder_seq_length=9,
|
||||
# For common tests
|
||||
is_training=True,
|
||||
use_attention_mask=True,
|
||||
use_labels=True,
|
||||
hidden_size=32,
|
||||
num_hidden_layers=5,
|
||||
num_attention_heads=4,
|
||||
d_ff=37,
|
||||
relative_attention_num_buckets=8,
|
||||
dropout_rate=0.1,
|
||||
initializer_factor=0.002,
|
||||
eos_token_id=1,
|
||||
pad_token_id=0,
|
||||
decoder_start_token_id=0,
|
||||
scope=None,
|
||||
decoder_layers=None,
|
||||
):
|
||||
self.parent = parent
|
||||
self.batch_size = batch_size
|
||||
self.encoder_seq_length = encoder_seq_length
|
||||
self.decoder_seq_length = decoder_seq_length
|
||||
# For common tests
|
||||
self.seq_length = self.decoder_seq_length
|
||||
self.is_training = is_training
|
||||
self.use_attention_mask = use_attention_mask
|
||||
self.use_labels = use_labels
|
||||
self.vocab_size = vocab_size
|
||||
self.hidden_size = hidden_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.d_ff = d_ff
|
||||
self.relative_attention_num_buckets = relative_attention_num_buckets
|
||||
self.dropout_rate = dropout_rate
|
||||
self.initializer_factor = initializer_factor
|
||||
self.eos_token_id = eos_token_id
|
||||
self.pad_token_id = pad_token_id
|
||||
self.decoder_start_token_id = decoder_start_token_id
|
||||
self.scope = None
|
||||
self.decoder_layers = decoder_layers
|
||||
|
||||
def prepare_config_and_inputs(self):
|
||||
input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size)
|
||||
decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
|
||||
|
||||
attention_mask = None
|
||||
decoder_attention_mask = None
|
||||
if self.use_attention_mask:
|
||||
attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2)
|
||||
decoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2)
|
||||
|
||||
lm_labels = None
|
||||
if self.use_labels:
|
||||
lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size)
|
||||
|
||||
config = self.get_config()
|
||||
|
||||
return (
|
||||
config,
|
||||
input_ids,
|
||||
decoder_input_ids,
|
||||
attention_mask,
|
||||
decoder_attention_mask,
|
||||
lm_labels,
|
||||
)
|
||||
|
||||
def get_config(self):
|
||||
return CONFIG_MAPPING["t5"](
|
||||
vocab_size=self.vocab_size,
|
||||
d_model=self.hidden_size,
|
||||
d_ff=self.d_ff,
|
||||
d_kv=self.hidden_size // self.num_attention_heads,
|
||||
num_layers=self.num_hidden_layers,
|
||||
num_decoder_layers=self.decoder_layers,
|
||||
num_heads=self.num_attention_heads,
|
||||
relative_attention_num_buckets=self.relative_attention_num_buckets,
|
||||
dropout_rate=self.dropout_rate,
|
||||
initializer_factor=self.initializer_factor,
|
||||
eos_token_id=self.eos_token_id,
|
||||
bos_token_id=self.pad_token_id,
|
||||
pad_token_id=self.pad_token_id,
|
||||
decoder_start_token_id=self.decoder_start_token_id,
|
||||
)
|
||||
|
||||
|
||||
# this model tester uses an encoder-decoder language model (T5)
|
||||
class Blip2ForConditionalGenerationModelTester:
|
||||
def __init__(
|
||||
self, parent, vision_kwargs=None, qformer_kwargs=None, text_kwargs=None, is_training=True, num_query_tokens=10
|
||||
):
|
||||
if vision_kwargs is None:
|
||||
vision_kwargs = {}
|
||||
if qformer_kwargs is None:
|
||||
qformer_kwargs = {}
|
||||
if text_kwargs is None:
|
||||
text_kwargs = {}
|
||||
|
||||
self.parent = parent
|
||||
self.vision_model_tester = Blip2VisionModelTester(parent, **vision_kwargs)
|
||||
self.qformer_model_tester = Blip2QFormerModelTester(parent, **qformer_kwargs)
|
||||
self.text_model_tester = Blip2TextModelTester(parent, **text_kwargs)
|
||||
self.is_training = is_training
|
||||
self.num_query_tokens = num_query_tokens
|
||||
|
||||
def prepare_config_and_inputs(self):
|
||||
_, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
|
||||
(
|
||||
_,
|
||||
input_ids,
|
||||
decoder_input_ids,
|
||||
attention_mask,
|
||||
decoder_attention_mask,
|
||||
lm_labels,
|
||||
) = self.text_model_tester.prepare_config_and_inputs()
|
||||
|
||||
config = self.get_config()
|
||||
|
||||
return config, input_ids, attention_mask, pixel_values, decoder_input_ids, decoder_attention_mask, lm_labels
|
||||
|
||||
def get_config(self):
|
||||
return Blip2Config.from_vision_qformer_text_configs(
|
||||
vision_config=self.vision_model_tester.get_config(),
|
||||
qformer_config=self.qformer_model_tester.get_config(),
|
||||
text_config=self.text_model_tester.get_config(),
|
||||
num_query_tokens=self.num_query_tokens,
|
||||
)
|
||||
|
||||
def create_and_check_for_conditional_generation(
|
||||
self, config, input_ids, attention_mask, pixel_values, decoder_input_ids, decoder_attention_mask, labels
|
||||
):
|
||||
model = Blip2ForConditionalGeneration(config).to(torch_device).eval()
|
||||
with torch.no_grad():
|
||||
result = model(pixel_values, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask)
|
||||
|
||||
self.parent.assertEqual(
|
||||
result.logits.shape,
|
||||
(
|
||||
self.vision_model_tester.batch_size,
|
||||
self.text_model_tester.seq_length,
|
||||
self.text_model_tester.vocab_size,
|
||||
),
|
||||
)
|
||||
|
||||
def prepare_config_and_inputs_for_common(self):
|
||||
config_and_inputs = self.prepare_config_and_inputs()
|
||||
(
|
||||
config,
|
||||
input_ids,
|
||||
attention_mask,
|
||||
pixel_values,
|
||||
decoder_input_ids,
|
||||
decoder_attention_mask,
|
||||
labels,
|
||||
) = config_and_inputs
|
||||
inputs_dict = {
|
||||
"pixel_values": pixel_values,
|
||||
"input_ids": input_ids,
|
||||
"attention_mask": attention_mask,
|
||||
"decoder_input_ids": decoder_input_ids,
|
||||
"decoder_attention_mask": decoder_attention_mask,
|
||||
"labels": labels,
|
||||
}
|
||||
return config, inputs_dict
|
||||
|
||||
|
||||
@require_torch
|
||||
class Blip2ForConditionalGenerationTest(ModelTesterMixin, unittest.TestCase):
|
||||
all_model_classes = (Blip2ForConditionalGeneration,) 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 = Blip2ForConditionalGenerationModelTester(self)
|
||||
|
||||
def test_for_conditional_generation(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_for_conditional_generation(*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="Blip2Model does not have input/output embeddings")
|
||||
def test_model_common_attributes(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="There's no base Blip2Model")
|
||||
def test_save_load_fast_init_from_base(self):
|
||||
pass
|
||||
|
||||
@unittest.skip(reason="There's no base Blip2Model")
|
||||
def test_save_load_fast_init_to_base(self):
|
||||
pass
|
||||
|
||||
def test_forward_signature(self):
|
||||
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
for model_class in self.all_model_classes:
|
||||
model = model_class(config)
|
||||
signature = inspect.signature(model.forward)
|
||||
# signature.parameters is an OrderedDict => so arg_names order is deterministic
|
||||
arg_names = [*signature.parameters.keys()]
|
||||
|
||||
expected_arg_names = ["pixel_values"]
|
||||
self.assertListEqual(arg_names[:1], expected_arg_names)
|
||||
|
||||
def test_load_vision_qformer_text_config(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
# Save Blip2Config and check if we can load Blip2VisionConfig from it
|
||||
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
||||
config.save_pretrained(tmp_dir_name)
|
||||
vision_config = Blip2VisionConfig.from_pretrained(tmp_dir_name)
|
||||
self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
|
||||
|
||||
# Save Blip2Config and check if we can load Blip2QFormerConfig from it
|
||||
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
||||
config.save_pretrained(tmp_dir_name)
|
||||
qformer_config = Blip2QFormerConfig.from_pretrained(tmp_dir_name)
|
||||
self.assertDictEqual(config.qformer_config.to_dict(), qformer_config.to_dict())
|
||||
|
||||
@slow
|
||||
def test_model_from_pretrained(self):
|
||||
for model_name in BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST:
|
||||
model = Blip2ForConditionalGeneration.from_pretrained(model_name)
|
||||
self.assertIsNotNone(model)
|
||||
|
||||
# override from common to deal with nested configurations (`vision_config`, `text_config` and `qformer_config`)
|
||||
def test_initialization(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
configs_no_init = _config_zero_init(config)
|
||||
for key in ["vision_config", "qformer_config", "text_config"]:
|
||||
setattr(configs_no_init, key, _config_zero_init(getattr(configs_no_init, key)))
|
||||
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:
|
||||
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",
|
||||
)
|
||||
|
||||
|
||||
# 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"
|
||||
image = Image.open(requests.get(url, stream=True).raw)
|
||||
return image
|
||||
|
||||
|
||||
@require_vision
|
||||
@require_torch
|
||||
@slow
|
||||
class Blip2ModelIntegrationTest(unittest.TestCase):
|
||||
def test_inference_opt(self):
|
||||
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
|
||||
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b").to(torch_device)
|
||||
|
||||
# prepare image
|
||||
image = prepare_img()
|
||||
inputs = processor(images=image, return_tensors="pt").to(torch_device)
|
||||
|
||||
predictions = model.generate(**inputs)
|
||||
generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
|
||||
|
||||
# Test output
|
||||
self.assertEqual(predictions[0].tolist(), [2, 102, 693, 2828, 15, 5, 4105, 19, 10, 2335, 50118])
|
||||
self.assertEqual("a woman sitting on the beach with a dog", generated_text)
|
||||
|
||||
# image and context
|
||||
prompt = "Question: which city is this? Answer:"
|
||||
inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device)
|
||||
|
||||
predictions = model.generate(**inputs)
|
||||
generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
|
||||
|
||||
# Test output
|
||||
self.assertEqual(
|
||||
predictions[0].tolist(),
|
||||
[2, 24, 18, 45, 10, 343, 6, 24, 18, 10, 4105, 50118],
|
||||
)
|
||||
self.assertEqual(generated_text, "it's not a city, it's a beach")
|
||||
|
||||
def test_inference_t5(self):
|
||||
processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl")
|
||||
model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-flan-t5-xl").to(torch_device)
|
||||
|
||||
# prepare image
|
||||
image = prepare_img()
|
||||
inputs = processor(images=image, return_tensors="pt").to(torch_device)
|
||||
|
||||
predictions = model.generate(**inputs)
|
||||
generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
|
||||
|
||||
# Test output
|
||||
self.assertEqual(predictions[0].tolist(), [0, 2335, 1556, 28, 1782, 30, 8, 2608, 1])
|
||||
self.assertEqual("woman playing with dog on the beach", generated_text)
|
||||
|
||||
# image and context
|
||||
prompt = "Question: which city is this? Answer:"
|
||||
inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device)
|
||||
|
||||
predictions = model.generate(**inputs)
|
||||
generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
|
||||
|
||||
# Test output
|
||||
self.assertEqual(
|
||||
predictions[0].tolist(),
|
||||
[0, 3, 7, 152, 67, 839, 1],
|
||||
)
|
||||
self.assertEqual(generated_text, "san diego")
|
||||
151
tests/models/blip_2/test_processor_blip_2.py
Normal file
151
tests/models/blip_2/test_processor_blip_2.py
Normal file
@@ -0,0 +1,151 @@
|
||||
# Copyright 2023 The HuggingFace Team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
import shutil
|
||||
import tempfile
|
||||
import unittest
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from transformers.testing_utils import require_vision
|
||||
from transformers.utils import is_vision_available
|
||||
|
||||
|
||||
if is_vision_available():
|
||||
from PIL import Image
|
||||
|
||||
from transformers import AutoProcessor, Blip2Processor, BlipImageProcessor, GPT2Tokenizer, PreTrainedTokenizerFast
|
||||
|
||||
|
||||
@require_vision
|
||||
class Blip2ProcessorTest(unittest.TestCase):
|
||||
def setUp(self):
|
||||
self.tmpdirname = tempfile.mkdtemp()
|
||||
|
||||
image_processor = BlipImageProcessor()
|
||||
tokenizer = GPT2Tokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model")
|
||||
|
||||
processor = Blip2Processor(image_processor, tokenizer)
|
||||
|
||||
processor.save_pretrained(self.tmpdirname)
|
||||
|
||||
def get_tokenizer(self, **kwargs):
|
||||
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).tokenizer
|
||||
|
||||
def get_image_processor(self, **kwargs):
|
||||
return AutoProcessor.from_pretrained(self.tmpdirname, **kwargs).image_processor
|
||||
|
||||
def tearDown(self):
|
||||
shutil.rmtree(self.tmpdirname)
|
||||
|
||||
def prepare_image_inputs(self):
|
||||
"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
|
||||
or a list of PyTorch tensors if one specifies torchify=True.
|
||||
"""
|
||||
|
||||
image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
|
||||
|
||||
image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
|
||||
|
||||
return image_inputs
|
||||
|
||||
def test_save_load_pretrained_additional_features(self):
|
||||
processor = Blip2Processor(tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor())
|
||||
processor.save_pretrained(self.tmpdirname)
|
||||
|
||||
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
|
||||
image_processor_add_kwargs = self.get_image_processor(do_normalize=False, padding_value=1.0)
|
||||
|
||||
processor = Blip2Processor.from_pretrained(
|
||||
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
|
||||
)
|
||||
|
||||
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
|
||||
self.assertIsInstance(processor.tokenizer, PreTrainedTokenizerFast)
|
||||
|
||||
self.assertEqual(processor.image_processor.to_json_string(), image_processor_add_kwargs.to_json_string())
|
||||
self.assertIsInstance(processor.image_processor, BlipImageProcessor)
|
||||
|
||||
def test_image_processor(self):
|
||||
image_processor = self.get_image_processor()
|
||||
tokenizer = self.get_tokenizer()
|
||||
|
||||
processor = Blip2Processor(tokenizer=tokenizer, image_processor=image_processor)
|
||||
|
||||
image_input = self.prepare_image_inputs()
|
||||
|
||||
input_feat_extract = image_processor(image_input, return_tensors="np")
|
||||
input_processor = processor(images=image_input, return_tensors="np")
|
||||
|
||||
for key in input_feat_extract.keys():
|
||||
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
|
||||
|
||||
def test_tokenizer(self):
|
||||
image_processor = self.get_image_processor()
|
||||
tokenizer = self.get_tokenizer()
|
||||
|
||||
processor = Blip2Processor(tokenizer=tokenizer, image_processor=image_processor)
|
||||
|
||||
input_str = "lower newer"
|
||||
|
||||
encoded_processor = processor(text=input_str)
|
||||
|
||||
encoded_tok = tokenizer(input_str, return_token_type_ids=False)
|
||||
|
||||
for key in encoded_tok.keys():
|
||||
self.assertListEqual(encoded_tok[key], encoded_processor[key])
|
||||
|
||||
def test_processor(self):
|
||||
image_processor = self.get_image_processor()
|
||||
tokenizer = self.get_tokenizer()
|
||||
|
||||
processor = Blip2Processor(tokenizer=tokenizer, image_processor=image_processor)
|
||||
|
||||
input_str = "lower newer"
|
||||
image_input = self.prepare_image_inputs()
|
||||
|
||||
inputs = processor(text=input_str, images=image_input)
|
||||
|
||||
self.assertListEqual(list(inputs.keys()), ["pixel_values", "input_ids", "attention_mask"])
|
||||
|
||||
# test if it raises when no input is passed
|
||||
with pytest.raises(ValueError):
|
||||
processor()
|
||||
|
||||
def test_tokenizer_decode(self):
|
||||
image_processor = self.get_image_processor()
|
||||
tokenizer = self.get_tokenizer()
|
||||
|
||||
processor = Blip2Processor(tokenizer=tokenizer, image_processor=image_processor)
|
||||
|
||||
predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
|
||||
|
||||
decoded_processor = processor.batch_decode(predicted_ids)
|
||||
decoded_tok = tokenizer.batch_decode(predicted_ids)
|
||||
|
||||
self.assertListEqual(decoded_tok, decoded_processor)
|
||||
|
||||
def test_model_input_names(self):
|
||||
image_processor = self.get_image_processor()
|
||||
tokenizer = self.get_tokenizer()
|
||||
|
||||
processor = Blip2Processor(tokenizer=tokenizer, image_processor=image_processor)
|
||||
|
||||
input_str = "lower newer"
|
||||
image_input = self.prepare_image_inputs()
|
||||
|
||||
inputs = processor(text=input_str, images=image_input)
|
||||
|
||||
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
|
||||
self.assertListEqual(list(inputs.keys()), ["pixel_values", "input_ids", "attention_mask"])
|
||||
Reference in New Issue
Block a user