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Florent Altché
68 lines
2.1 KiB
Python
68 lines
2.1 KiB
Python
# Copyright 2020 DeepMind Technologies Limited.
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#
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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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# https://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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"""Config file for evaluation experiment."""
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from typing import Text
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from byol.utils import dataset
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def get_config(checkpoint_to_evaluate: Text, batch_size: int):
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"""Return config object for training."""
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train_images_per_epoch = dataset.Split.TRAIN_AND_VALID.num_examples
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config = dict(
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random_seed=0,
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enable_double_transpose=True,
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max_steps=80 * train_images_per_epoch // batch_size,
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num_classes=1000,
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batch_size=batch_size,
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checkpoint_to_evaluate=checkpoint_to_evaluate,
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# If True, allows training without loading a checkpoint.
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allow_train_from_scratch=False,
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# Whether the backbone should be frozen (linear evaluation) or
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# trainable (fine-tuning).
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freeze_backbone=True,
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optimizer_config=dict(
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momentum=0.9,
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nesterov=True,
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),
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lr_schedule_config=dict(
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base_learning_rate=0.2,
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warmup_steps=0,
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),
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network_config=dict( # Should match the evaluated checkpoint
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encoder_class='ResNet50', # Should match a class in utils/networks.
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encoder_config=dict(
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resnet_v2=False,
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width_multiplier=1),
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bn_decay_rate=0.9,
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),
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evaluation_config=dict(
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subset='test',
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batch_size=100,
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),
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checkpointing_config=dict(
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use_checkpointing=True,
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checkpoint_dir='/tmp/byol',
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save_checkpoint_interval=300,
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filename='linear-eval.pkl'
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),
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)
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return config
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