-
Notifications
You must be signed in to change notification settings - Fork 387
/
clip_score.py
168 lines (135 loc) · 6.46 KB
/
clip_score.py
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
# Copyright The Lightning team.
#
# 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 os
from typing import Any, List, Optional, Sequence, Union
import torch
from torch import Tensor
from typing_extensions import Literal
from torchmetrics import Metric
from torchmetrics.functional.multimodal.clip_score import _clip_score_update, _get_model_and_processor
from torchmetrics.utilities.checks import _SKIP_SLOW_DOCTEST, _try_proceed_with_timeout
from torchmetrics.utilities.imports import _MATPLOTLIB_AVAILABLE, _PESQ_AVAILABLE, _TRANSFORMERS_AVAILABLE
from torchmetrics.utilities.plot import _AX_TYPE, _PLOT_OUT_TYPE
if not _MATPLOTLIB_AVAILABLE:
__doctest_skip__ = ["CLIPScore.plot"]
_DEFAULT_MODEL: str = "openai/clip-vit-large-patch14"
if _TRANSFORMERS_AVAILABLE:
from transformers import CLIPModel as _CLIPModel
from transformers import CLIPProcessor as _CLIPProcessor
def _download_clip() -> None:
_CLIPModel.from_pretrained(_DEFAULT_MODEL)
_CLIPProcessor.from_pretrained(_DEFAULT_MODEL)
if _SKIP_SLOW_DOCTEST and not _try_proceed_with_timeout(_download_clip):
__doctest_skip__ = ["CLIPScore", "CLIPScore.plot"]
else:
__doctest_skip__ = ["CLIPScore", "CLIPScore.plot"]
class CLIPScore(Metric):
r"""Calculates `CLIP Score`_ which is a text-to-image similarity metric.
CLIP is a reference free metric that can be used to evaluate the correlation between a generated caption for an
image and the actual content of the image. It has been found to be highly correlated with human judgement. The
metric is defined as:
.. math::
\text{CLIPScore(I, C)} = max(100 * cos(E_I, E_C), 0)
which corresponds to the cosine similarity between visual CLIP embedding :math:`E_i` for an image :math:`i` and
textual CLIP embedding :math:`E_C` for an caption :math:`C`. The score is bound between 0 and 100 and the closer
to 100 the better.
.. note:: Metric is not scriptable
Args:
model_name_or_path: string indicating the version of the CLIP model to use. Available models are:
- `"openai/clip-vit-base-patch16"`
- `"openai/clip-vit-base-patch32"`
- `"openai/clip-vit-large-patch14-336"`
- `"openai/clip-vit-large-patch14"`
kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info.
Raises:
ModuleNotFoundError:
If transformers package is not installed or version is lower than 4.10.0
Example:
>>> import torch
>>> _ = torch.manual_seed(42)
>>> from torchmetrics.multimodal import CLIPScore
>>> metric = CLIPScore(model_name_or_path="openai/clip-vit-base-patch16")
>>> score = metric(torch.randint(255, (3, 224, 224)), "a photo of a cat")
>>> print(score.detach())
tensor(24.7691)
"""
is_differentiable: bool = False
higher_is_better: bool = True
full_state_update: bool = True
plot_lower_bound: float = 0.0
score: Tensor
n_samples: Tensor
plot_upper_bound = 100.0
def __init__(
self,
model_name_or_path: Literal[
"openai/clip-vit-base-patch16",
"openai/clip-vit-base-patch32",
"openai/clip-vit-large-patch14-336",
"openai/clip-vit-large-patch14",
] = _DEFAULT_MODEL, # type: ignore[assignment]
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
self.model, self.processor = _get_model_and_processor(model_name_or_path)
self.add_state("score", torch.tensor(0.0), dist_reduce_fx="sum")
self.add_state("n_samples", torch.tensor(0, dtype=torch.long), dist_reduce_fx="sum")
def update(self, images: Union[Tensor, List[Tensor]], text: Union[str, List[str]]) -> None:
"""Update CLIP score on a batch of images and text.
Args:
images: Either a single [N, C, H, W] tensor or a list of [C, H, W] tensors
text: Either a single caption or a list of captions
Raises:
ValueError:
If not all images have format [C, H, W]
ValueError:
If the number of images and captions do not match
"""
score, n_samples = _clip_score_update(images, text, self.model, self.processor)
self.score += score.sum(0)
self.n_samples += n_samples
def compute(self) -> Tensor:
"""Compute accumulated clip score."""
return torch.max(self.score / self.n_samples, torch.zeros_like(self.score))
def plot(self, val: Union[Tensor, Sequence[Tensor], None] = None, ax: Optional[_AX_TYPE] = None) -> _PLOT_OUT_TYPE:
"""Plot a single or multiple values from the metric.
Args:
val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results.
If no value is provided, will automatically call `metric.compute` and plot that result.
ax: An matplotlib axis object. If provided will add plot to that axis
Returns:
Figure and Axes object
Raises:
ModuleNotFoundError:
If `matplotlib` is not installed
.. plot::
:scale: 75
>>> # Example plotting a single value
>>> import torch
>>> from torchmetrics.multimodal import CLIPScore
>>> metric = CLIPScore(model_name_or_path="openai/clip-vit-base-patch16")
>>> metric.update(torch.randint(255, (3, 224, 224)), "a photo of a cat")
>>> fig_, ax_ = metric.plot()
.. plot::
:scale: 75
>>> # Example plotting multiple values
>>> import torch
>>> from torchmetrics.multimodal import CLIPScore
>>> metric = CLIPScore(model_name_or_path="openai/clip-vit-base-patch16")
>>> values = [ ]
>>> for _ in range(10):
... values.append(metric(torch.randint(255, (3, 224, 224)), "a photo of a cat"))
>>> fig_, ax_ = metric.plot(values)
"""
return self._plot(val, ax)