vit_2d.py

#!/usr/bin/env python
# -*- encoding: utf-8 -*-

import os
from pathlib import Path

BATCH_SIZE = 512
IMG_SIZE = 32
PATCH_SIZE = 4
DIM = 512
NUM_ATTENTION_HEADS = 8
SUMMA_DIM = 2
NUM_CLASSES = 10
DEPTH = 6

train_data = dict(
    dataset=dict(
        type='CIFAR10Dataset',
        root=Path(os.environ['DATA']),
        transform_pipeline=[
            dict(type='Resize', size=IMG_SIZE),
            dict(type='RandomCrop', size=IMG_SIZE, padding=4),
            dict(type='RandomHorizontalFlip'),
            dict(type='ToTensor'),
            dict(type='Normalize',
                 mean=[0.4914, 0.4822, 0.4465],
                 std=[0.2023, 0.1994, 0.2010]),
        ]
    ),
    dataloader=dict(
        batch_size=BATCH_SIZE,
        pin_memory=True,
        shuffle=True,
    )
)

test_data = dict(
    dataset=dict(
        type='CIFAR10Dataset',
        root=Path(os.environ['DATA']),
        train=False,
        transform_pipeline=[
            dict(type='Resize', size=IMG_SIZE),
            dict(type='ToTensor'),
            dict(type='Normalize',
                 mean=[0.4914, 0.4822, 0.4465],
                 std=[0.2023, 0.1994, 0.2010]
                 ),
        ]
    ),
    dataloader=dict(
        batch_size=BATCH_SIZE,
        pin_memory=True,
    )
)

optimizer = dict(
    type='Adam',
    lr=0.001,
    weight_decay=0
)

loss = dict(
    type='CrossEntropyLoss2D',
)

model = dict(
    type='VisionTransformerFromConfig',
    tensor_splitting_cfg=dict(
        type='ViTInputSplitter2D',
    ),
    embedding_cfg=dict(
        type='ViTPatchEmbedding2D',
        img_size=IMG_SIZE,
        patch_size=PATCH_SIZE,
        embed_dim=DIM,
    ),
    token_fusion_cfg=dict(
        type='ViTTokenFuser2D',
        img_size=IMG_SIZE,
        patch_size=PATCH_SIZE,
        embed_dim=DIM,
        drop_rate=0.1
    ),
    norm_cfg=dict(
        type='LayerNorm2D',
        normalized_shape=DIM,
        eps=1e-6,
    ),
    block_cfg=dict(
        type='ViTBlock',
        attention_cfg=dict(
            type='ViTSelfAttention2D',
            hidden_size=DIM,
            num_attention_heads=NUM_ATTENTION_HEADS,
            attention_dropout_prob=0.,
            hidden_dropout_prob=0.1,
            checkpoint=True
        ),
        droppath_cfg=dict(
            type='VanillaViTDropPath',
        ),
        mlp_cfg=dict(
            type='ViTMLP2D',
            in_features=DIM,
            dropout_prob=0.1,
            mlp_ratio=4,
            checkpoint=True
        ),
        norm_cfg=dict(
            type='LayerNorm2D',
            normalized_shape=DIM,
            eps=1e-6,
        ),
    ),
    head_cfg=dict(
        type='ViTHead2D',
        hidden_size=DIM,
        num_classes=NUM_CLASSES,
    ),
    embed_dim=DIM,
    depth=DEPTH,
    drop_path_rate=0.,
)

hooks = [
    dict(type='LogMetricByEpochHook'),
    dict(type='Accuracy2DHook'),
    dict(type='LossHook'),
    dict(type='TensorboardHook', log_dir='./tfb_logs'),
    # dict(type='SaveCheckpointHook', interval=5, checkpoint_dir='./ckpt'),
    # dict(type='LoadCheckpointHook', epoch=20, checkpoint_dir='./ckpt')
]

parallel = dict(
    pipeline=dict(size=1),
    tensor=dict(size=4, mode='2d'),
)

# for fp16 training
# from colossalai.engine import AMP_TYPE
# fp16 = dict(
#     mode=AMP_TYPE.PARALLEL,
#     initial_scale=2 ** 8
# )

lr_scheduler = dict(
    type='LinearWarmupLR',
    warmup_epochs=5
)

# only needed when pipeline parallel is used
# schedule = dict(
#     num_microbatches=8
# )

num_epochs = 60

logging = dict(
    root_path='./logs'
)