mirror of https://github.com/hpcaitech/ColossalAI
112 lines
2.5 KiB
Python
112 lines
2.5 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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import os
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from pathlib import Path
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from colossalai.context import ParallelMode
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from colossalai.engine import AMP_TYPE
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try:
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import model_zoo
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except:
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print('You need to set model_zoo to your PYTHONPATH to use the models in the collection')
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BATCH_SIZE = 512
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IMG_SIZE = 32
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NUM_EPOCHS = 60
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train_data = dict(
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dataset=dict(
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type='CIFAR10Dataset',
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root=Path(os.environ['DATA']),
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transform_pipeline=[
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dict(type='RandomCrop', size=IMG_SIZE, padding=4),
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dict(type='RandomHorizontalFlip'),
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dict(type='ToTensor'),
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dict(type='Normalize',
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mean=[0.4914, 0.4822, 0.4465],
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std=[0.2023, 0.1994, 0.2010]),
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]
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),
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dataloader=dict(
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batch_size=BATCH_SIZE,
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pin_memory=True,
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num_workers=2,
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shuffle=True,
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)
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)
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test_data = dict(
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dataset=dict(
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type='CIFAR10Dataset',
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root=Path(os.environ['DATA']),
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train=False,
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transform_pipeline=[
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dict(type='ToTensor'),
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dict(type='Normalize',
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mean=[0.4914, 0.4822, 0.4465],
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std=[0.2023, 0.1994, 0.2010]
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),
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]
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),
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dataloader=dict(
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batch_size=BATCH_SIZE,
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pin_memory=True,
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num_workers=2,
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)
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)
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optimizer = dict(
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type='Adam',
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lr=0.001
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)
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loss = dict(
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type='CrossEntropyLoss3D',
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input_parallel_mode=ParallelMode.PARALLEL_3D_OUTPUT,
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weight_parallel_mode=ParallelMode.PARALLEL_3D_WEIGHT,
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)
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model = dict(
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type='vit_tiny_3d_patch4_32',
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drop_rate=0.1,
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)
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hooks = [
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dict(type='LogMetricByEpochHook'),
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dict(type='LogTimingByEpochHook'),
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dict(type='LogMemoryByEpochHook'),
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dict(
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type='Accuracy3DHook',
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input_parallel_mode=ParallelMode.PARALLEL_3D_OUTPUT,
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weight_parallel_mode=ParallelMode.PARALLEL_3D_WEIGHT,
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),
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dict(type='LossHook'),
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dict(type='TensorboardHook', log_dir='./tfb_logs'),
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dict(
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type='LRSchedulerHook',
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by_epoch=True,
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lr_scheduler_cfg=dict(
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type='LinearWarmupLR',
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warmup_steps=5
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)
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),
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# dict(type='SaveCheckpointHook', interval=5, checkpoint_dir='./ckpt'),
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# dict(type='LoadCheckpointHook', epoch=20, checkpoint_dir='./ckpt')
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]
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parallel = dict(
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pipeline=dict(size=1),
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tensor=dict(size=8, mode='3d'),
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)
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fp16 = dict(
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mode=AMP_TYPE.PARALLEL,
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initial_scale=2 ** 8
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)
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logging = dict(
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root_path='./logs'
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)
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