forked from lancedb/lancedb
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathopen_clip.py
More file actions
180 lines (161 loc) · 6.48 KB
/
open_clip.py
File metadata and controls
180 lines (161 loc) · 6.48 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
# Copyright (c) 2023. LanceDB Developers
#
# 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 concurrent.futures
import io
import os
import urllib.parse as urlparse
from typing import TYPE_CHECKING, List, Union
import numpy as np
import pyarrow as pa
from pydantic import PrivateAttr
from tqdm import tqdm
from ..util import attempt_import_or_raise
from .base import EmbeddingFunction
from .registry import register
from .utils import IMAGES, url_retrieve
if TYPE_CHECKING:
import PIL
import torch
@register("open-clip")
class OpenClipEmbeddings(EmbeddingFunction):
"""
An embedding function that uses the OpenClip API
For multi-modal text-to-image search
https://github.com/mlfoundations/open_clip
"""
name: str = "ViT-B-32"
pretrained: str = "laion2b_s34b_b79k"
device: str = "cpu"
batch_size: int = 64
normalize: bool = True
_model = PrivateAttr()
_preprocess = PrivateAttr()
_tokenizer = PrivateAttr()
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
open_clip = attempt_import_or_raise("open_clip", "open-clip")
model, _, preprocess = open_clip.create_model_and_transforms(
self.name, pretrained=self.pretrained
)
model.to(self.device)
self._model, self._preprocess = model, preprocess
self._tokenizer = open_clip.get_tokenizer(self.name)
self._ndims = None
def ndims(self):
if self._ndims is None:
self._ndims = self.generate_text_embeddings("foo").shape[0]
return self._ndims
def compute_query_embeddings(
self, query: Union[str, "PIL.Image.Image"], *args, **kwargs
) -> List[np.ndarray]:
"""
Compute the embeddings for a given user query
Parameters
----------
query : Union[str, PIL.Image.Image]
The query to embed. A query can be either text or an image.
"""
if isinstance(query, str):
return [self.generate_text_embeddings(query)]
else:
PIL = attempt_import_or_raise("PIL", "pillow")
if isinstance(query, PIL.Image.Image):
return [self.generate_image_embedding(query)]
else:
raise TypeError("OpenClip supports str or PIL Image as query")
def generate_text_embeddings(self, text: str) -> np.ndarray:
torch = attempt_import_or_raise("torch")
text = self.sanitize_input(text)
text = self._tokenizer(text)
text.to(self.device)
with torch.no_grad():
text_features = self._model.encode_text(text.to(self.device))
if self.normalize:
text_features /= text_features.norm(dim=-1, keepdim=True)
return text_features.cpu().numpy().squeeze()
def sanitize_input(self, images: IMAGES) -> Union[List[bytes], np.ndarray]:
"""
Sanitize the input to the embedding function.
"""
if isinstance(images, (str, bytes)):
images = [images]
elif isinstance(images, pa.Array):
images = images.to_pylist()
elif isinstance(images, pa.ChunkedArray):
images = images.combine_chunks().to_pylist()
return images
def compute_source_embeddings(
self, images: IMAGES, *args, **kwargs
) -> List[np.array]:
"""
Get the embeddings for the given images
"""
images = self.sanitize_input(images)
embeddings = []
for i in range(0, len(images), self.batch_size):
j = min(i + self.batch_size, len(images))
batch = images[i:j]
embeddings.extend(self._parallel_get(batch))
return embeddings
def _parallel_get(self, images: Union[List[str], List[bytes]]) -> List[np.ndarray]:
"""
Issue concurrent requests to retrieve the image data
"""
with concurrent.futures.ThreadPoolExecutor() as executor:
futures = [
executor.submit(self.generate_image_embedding, image)
for image in images
]
return [future.result() for future in tqdm(futures)]
def generate_image_embedding(
self, image: Union[str, bytes, "PIL.Image.Image"]
) -> np.ndarray:
"""
Generate the embedding for a single image
Parameters
----------
image : Union[str, bytes, PIL.Image.Image]
The image to embed. If the image is a str, it is treated as a uri.
If the image is bytes, it is treated as the raw image bytes.
"""
torch = attempt_import_or_raise("torch")
# TODO handle retry and errors for https
image = self._to_pil(image)
image = self._preprocess(image).unsqueeze(0)
with torch.no_grad():
return self._encode_and_normalize_image(image)
def _to_pil(self, image: Union[str, bytes]):
PIL = attempt_import_or_raise("PIL", "pillow")
if isinstance(image, bytes):
return PIL.Image.open(io.BytesIO(image))
if isinstance(image, PIL.Image.Image):
return image
elif isinstance(image, str):
parsed = urlparse.urlparse(image)
# TODO handle drive letter on windows.
if parsed.scheme == "file":
return PIL.Image.open(parsed.path)
elif parsed.scheme == "":
return PIL.Image.open(image if os.name == "nt" else parsed.path)
elif parsed.scheme.startswith("http"):
return PIL.Image.open(io.BytesIO(url_retrieve(image)))
else:
raise NotImplementedError("Only local and http(s) urls are supported")
def _encode_and_normalize_image(self, image_tensor: "torch.Tensor"):
"""
encode a single image tensor and optionally normalize the output
"""
image_features = self._model.encode_image(image_tensor.to(self.device))
if self.normalize:
image_features /= image_features.norm(dim=-1, keepdim=True)
return image_features.cpu().numpy().squeeze()