forked from google-deepmind/deepmind-research
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy patheval.py
67 lines (59 loc) · 2.07 KB
/
eval.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
# Copyright 2020 DeepMind Technologies Limited.
#
#
# 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
#
# https://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.
"""Config file for evaluation experiment."""
from typing import Text
from byol.utils import dataset
def get_config(checkpoint_to_evaluate: Text, batch_size: int):
"""Return config object for training."""
train_images_per_epoch = dataset.Split.TRAIN_AND_VALID.num_examples
config = dict(
random_seed=0,
enable_double_transpose=True,
max_steps=80 * train_images_per_epoch // batch_size,
num_classes=1000,
batch_size=batch_size,
checkpoint_to_evaluate=checkpoint_to_evaluate,
# If True, allows training without loading a checkpoint.
allow_train_from_scratch=False,
# Whether the backbone should be frozen (linear evaluation) or
# trainable (fine-tuning).
freeze_backbone=True,
optimizer_config=dict(
momentum=0.9,
nesterov=True,
),
lr_schedule_config=dict(
base_learning_rate=0.2,
warmup_steps=0,
),
network_config=dict( # Should match the evaluated checkpoint
encoder_class='ResNet50', # Should match a class in utils/networks.
encoder_config=dict(
resnet_v2=False,
width_multiplier=1),
bn_decay_rate=0.9,
),
evaluation_config=dict(
subset='test',
batch_size=100,
),
checkpointing_config=dict(
use_checkpointing=True,
checkpoint_dir='/tmp/byol',
save_checkpoint_interval=300,
filename='linear-eval.pkl'
),
)
return config