PyTorch Lightning 1.1: research : CIFAR100 (ShuffleNet)
作成 : (株)クラスキャット セールスインフォメーション
作成日時 : 02/24/2021 (1.1.x)
* 本ページは以下の CIFAR10 用リソースを参考に CIFAR100 で遂行した実験結果のレポートです：
- notebooks : PyTorch Lightning CIFAR10 ~94% Baseline Tutorial
- Train CIFAR10 with PyTorch
- Pytorch-cifar100
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research: CIFAR100 (ShuffleNet)
仕様
- Total params: 1,360,896 (1.4M)
- Trainable params: 1,360,896
- Non-trainable params: 0
結果
100 エポック
- {‘test_acc’: 0.6672999858856201, ‘test_loss’: 1.4519164562225342}
- Wall time: 1h 26min 15s
- Tesla T4
- ReduceLROnPlateau
CIFAR 100 DM
from typing import Any, Callable, Optional, Sequence, Union
from pl_bolts.datamodules.vision_datamodule import VisionDataModule
#from pl_bolts.datasets import TrialCIFAR10
#from pl_bolts.transforms.dataset_normalizations import cifar10_normalization
from pl_bolts.utils import _TORCHVISION_AVAILABLE
from pl_bolts.utils.warnings import warn_missing_pkg
if _TORCHVISION_AVAILABLE:
from torchvision import transforms
#from torchvision import transforms as transform_lib
from torchvision.datasets import CIFAR100
else: # pragma: no cover
warn_missing_pkg('torchvision')
CIFAR100 = None
def cifar100_normalization():
if not _TORCHVISION_AVAILABLE: # pragma: no cover
raise ModuleNotFoundError(
'You want to use `torchvision` which is not installed yet, install it with `pip install torchvision`.'
)
normalize = transforms.Normalize(
mean=[x / 255.0 for x in [129.3, 124.1, 112.4]],
std=[x / 255.0 for x in [68.2, 65.4, 70.4]],
# cifar10
#mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
#std=[x / 255.0 for x in [63.0, 62.1, 66.7]],
)
return normalize
class CIFAR100DataModule(VisionDataModule):
"""
.. figure:: https://3qeqpr26caki16dnhd19sv6by6v-wpengine.netdna-ssl.com/wp-content/uploads/2019/01/
Plot-of-a-Subset-of-Images-from-the-CIFAR-10-Dataset.png
:width: 400
:alt: CIFAR-10
Specs:
- 10 classes (1 per class)
- Each image is (3 x 32 x 32)
Standard CIFAR10, train, val, test splits and transforms
Transforms::
mnist_transforms = transform_lib.Compose([
transform_lib.ToTensor(),
transforms.Normalize(
mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
std=[x / 255.0 for x in [63.0, 62.1, 66.7]]
)
])
Example::
from pl_bolts.datamodules import CIFAR10DataModule
dm = CIFAR10DataModule(PATH)
model = LitModel()
Trainer().fit(model, datamodule=dm)
Or you can set your own transforms
Example::
dm.train_transforms = ...
dm.test_transforms = ...
dm.val_transforms = ...
"""
name = "cifar100"
dataset_cls = CIFAR100
dims = (3, 32, 32)
def __init__(
self,
data_dir: Optional[str] = None,
val_split: Union[int, float] = 0.2,
num_workers: int = 16,
normalize: bool = False,
batch_size: int = 32,
seed: int = 42,
shuffle: bool = False,
pin_memory: bool = False,
drop_last: bool = False,
*args: Any,
**kwargs: Any,
) -> None:
"""
Args:
data_dir: Where to save/load the data
val_split: Percent (float) or number (int) of samples to use for the validation split
num_workers: How many workers to use for loading data
normalize: If true applies image normalize
batch_size: How many samples per batch to load
seed: Random seed to be used for train/val/test splits
shuffle: If true shuffles the train data every epoch
pin_memory: If true, the data loader will copy Tensors into CUDA pinned memory before
returning them
drop_last: If true drops the last incomplete batch
"""
super().__init__( # type: ignore[misc]
data_dir=data_dir,
val_split=val_split,
num_workers=num_workers,
normalize=normalize,
batch_size=batch_size,
seed=seed,
shuffle=shuffle,
pin_memory=pin_memory,
drop_last=drop_last,
*args,
**kwargs,
)
@property
def num_samples(self) -> int:
train_len, _ = self._get_splits(len_dataset=50_000)
return train_len
@property
def num_classes(self) -> int:
"""
Return:
10
"""
return 100
def default_transforms(self) -> Callable:
if self.normalize:
cf100_transforms = transforms.Compose([transform_lib.ToTensor(), cifar100_normalization()])
else:
cf100_transforms = transforms.Compose([transform_lib.ToTensor()])
return cf100_transforms
モデル
import torch import torch.nn as nn import torch.nn.functional as F
def channel_split(x, split):
"""split a tensor into two pieces along channel dimension
Args:
x: input tensor
split:(int) channel size for each pieces
"""
assert x.size(1) == split * 2
return torch.split(x, split, dim=1)
def channel_shuffle(x, groups):
"""channel shuffle operation
Args:
x: input tensor
groups: input branch number
"""
batch_size, channels, height, width = x.size()
channels_per_group = int(channels // groups)
x = x.view(batch_size, groups, channels_per_group, height, width)
x = x.transpose(1, 2).contiguous()
x = x.view(batch_size, -1, height, width)
return x
class ShuffleUnit(nn.Module):
def __init__(self, in_channels, out_channels, stride):
super().__init__()
self.stride = stride
self.in_channels = in_channels
self.out_channels = out_channels
if stride != 1 or in_channels != out_channels:
self.residual = nn.Sequential(
nn.Conv2d(in_channels, in_channels, 1),
nn.BatchNorm2d(in_channels),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels, in_channels, 3, stride=stride, padding=1, groups=in_channels),
nn.BatchNorm2d(in_channels),
nn.Conv2d(in_channels, int(out_channels / 2), 1),
nn.BatchNorm2d(int(out_channels / 2)),
nn.ReLU(inplace=True)
)
self.shortcut = nn.Sequential(
nn.Conv2d(in_channels, in_channels, 3, stride=stride, padding=1, groups=in_channels),
nn.BatchNorm2d(in_channels),
nn.Conv2d(in_channels, int(out_channels / 2), 1),
nn.BatchNorm2d(int(out_channels / 2)),
nn.ReLU(inplace=True)
)
else:
self.shortcut = nn.Sequential()
in_channels = int(in_channels / 2)
self.residual = nn.Sequential(
nn.Conv2d(in_channels, in_channels, 1),
nn.BatchNorm2d(in_channels),
nn.ReLU(inplace=True),
nn.Conv2d(in_channels, in_channels, 3, stride=stride, padding=1, groups=in_channels),
nn.BatchNorm2d(in_channels),
nn.Conv2d(in_channels, in_channels, 1),
nn.BatchNorm2d(in_channels),
nn.ReLU(inplace=True)
)
def forward(self, x):
if self.stride == 1 and self.out_channels == self.in_channels:
shortcut, residual = channel_split(x, int(self.in_channels / 2))
else:
shortcut = x
residual = x
shortcut = self.shortcut(shortcut)
residual = self.residual(residual)
x = torch.cat([shortcut, residual], dim=1)
x = channel_shuffle(x, 2)
return x
class ShuffleNetV2(nn.Module):
def __init__(self, ratio=1, class_num=100):
super().__init__()
if ratio == 0.5:
out_channels = [48, 96, 192, 1024]
elif ratio == 1:
out_channels = [116, 232, 464, 1024]
elif ratio == 1.5:
out_channels = [176, 352, 704, 1024]
elif ratio == 2:
out_channels = [244, 488, 976, 2048]
else:
ValueError('unsupported ratio number')
self.pre = nn.Sequential(
nn.Conv2d(3, 24, 3, padding=1),
nn.BatchNorm2d(24)
)
self.stage2 = self._make_stage(24, out_channels[0], 3)
self.stage3 = self._make_stage(out_channels[0], out_channels[1], 7)
self.stage4 = self._make_stage(out_channels[1], out_channels[2], 3)
self.conv5 = nn.Sequential(
nn.Conv2d(out_channels[2], out_channels[3], 1),
nn.BatchNorm2d(out_channels[3]),
nn.ReLU(inplace=True)
)
self.fc = nn.Linear(out_channels[3], class_num)
def forward(self, x):
x = self.pre(x)
x = self.stage2(x)
x = self.stage3(x)
x = self.stage4(x)
x = self.conv5(x)
x = F.adaptive_avg_pool2d(x, 1)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def _make_stage(self, in_channels, out_channels, repeat):
layers = []
layers.append(ShuffleUnit(in_channels, out_channels, 2))
while repeat:
layers.append(ShuffleUnit(out_channels, out_channels, 1))
repeat -= 1
return nn.Sequential(*layers)
def shufflenetv2():
return ShuffleNetV2()
net = squeezenet() print(net) y = net(torch.randn(1, 3, 32, 32)) print(y.size())
SqueezeNet(
(stem): Sequential(
(0): Conv2d(3, 96, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
(3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(fire2): Fire(
(squeeze): Sequential(
(0): Conv2d(96, 16, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(expand_1x1): Sequential(
(0): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(expand_3x3): Sequential(
(0): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
)
(fire3): Fire(
(squeeze): Sequential(
(0): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(expand_1x1): Sequential(
(0): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(expand_3x3): Sequential(
(0): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
)
(fire4): Fire(
(squeeze): Sequential(
(0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(expand_1x1): Sequential(
(0): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(expand_3x3): Sequential(
(0): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
)
(fire5): Fire(
(squeeze): Sequential(
(0): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(expand_1x1): Sequential(
(0): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(expand_3x3): Sequential(
(0): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
)
(fire6): Fire(
(squeeze): Sequential(
(0): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(expand_1x1): Sequential(
(0): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(expand_3x3): Sequential(
(0): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
)
(fire7): Fire(
(squeeze): Sequential(
(0): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(48, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(expand_1x1): Sequential(
(0): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(expand_3x3): Sequential(
(0): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
)
(fire8): Fire(
(squeeze): Sequential(
(0): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(expand_1x1): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(expand_3x3): Sequential(
(0): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
)
(fire9): Fire(
(squeeze): Sequential(
(0): Conv2d(512, 64, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(expand_1x1): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1))
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
(expand_3x3): Sequential(
(0): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(2): ReLU(inplace=True)
)
)
(conv10): Conv2d(512, 100, kernel_size=(1, 1), stride=(1, 1))
(avg): AdaptiveAvgPool2d(output_size=1)
(maxpool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
torch.Size([1, 100])
from torchsummary import summary
summary(shufflenetv2().to('cuda'), (3, 32, 32))
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 24, 32, 32] 672
BatchNorm2d-2 [-1, 24, 32, 32] 48
Conv2d-3 [-1, 24, 16, 16] 240
BatchNorm2d-4 [-1, 24, 16, 16] 48
Conv2d-5 [-1, 58, 16, 16] 1,450
BatchNorm2d-6 [-1, 58, 16, 16] 116
ReLU-7 [-1, 58, 16, 16] 0
Conv2d-8 [-1, 24, 32, 32] 600
BatchNorm2d-9 [-1, 24, 32, 32] 48
ReLU-10 [-1, 24, 32, 32] 0
Conv2d-11 [-1, 24, 16, 16] 240
BatchNorm2d-12 [-1, 24, 16, 16] 48
Conv2d-13 [-1, 58, 16, 16] 1,450
BatchNorm2d-14 [-1, 58, 16, 16] 116
ReLU-15 [-1, 58, 16, 16] 0
ShuffleUnit-16 [-1, 116, 16, 16] 0
Conv2d-17 [-1, 58, 16, 16] 3,422
BatchNorm2d-18 [-1, 58, 16, 16] 116
ReLU-19 [-1, 58, 16, 16] 0
Conv2d-20 [-1, 58, 16, 16] 580
BatchNorm2d-21 [-1, 58, 16, 16] 116
Conv2d-22 [-1, 58, 16, 16] 3,422
BatchNorm2d-23 [-1, 58, 16, 16] 116
ReLU-24 [-1, 58, 16, 16] 0
ShuffleUnit-25 [-1, 116, 16, 16] 0
Conv2d-26 [-1, 58, 16, 16] 3,422
BatchNorm2d-27 [-1, 58, 16, 16] 116
ReLU-28 [-1, 58, 16, 16] 0
Conv2d-29 [-1, 58, 16, 16] 580
BatchNorm2d-30 [-1, 58, 16, 16] 116
Conv2d-31 [-1, 58, 16, 16] 3,422
BatchNorm2d-32 [-1, 58, 16, 16] 116
ReLU-33 [-1, 58, 16, 16] 0
ShuffleUnit-34 [-1, 116, 16, 16] 0
Conv2d-35 [-1, 58, 16, 16] 3,422
BatchNorm2d-36 [-1, 58, 16, 16] 116
ReLU-37 [-1, 58, 16, 16] 0
Conv2d-38 [-1, 58, 16, 16] 580
BatchNorm2d-39 [-1, 58, 16, 16] 116
Conv2d-40 [-1, 58, 16, 16] 3,422
BatchNorm2d-41 [-1, 58, 16, 16] 116
ReLU-42 [-1, 58, 16, 16] 0
ShuffleUnit-43 [-1, 116, 16, 16] 0
Conv2d-44 [-1, 116, 8, 8] 1,160
BatchNorm2d-45 [-1, 116, 8, 8] 232
Conv2d-46 [-1, 116, 8, 8] 13,572
BatchNorm2d-47 [-1, 116, 8, 8] 232
ReLU-48 [-1, 116, 8, 8] 0
Conv2d-49 [-1, 116, 16, 16] 13,572
BatchNorm2d-50 [-1, 116, 16, 16] 232
ReLU-51 [-1, 116, 16, 16] 0
Conv2d-52 [-1, 116, 8, 8] 1,160
BatchNorm2d-53 [-1, 116, 8, 8] 232
Conv2d-54 [-1, 116, 8, 8] 13,572
BatchNorm2d-55 [-1, 116, 8, 8] 232
ReLU-56 [-1, 116, 8, 8] 0
ShuffleUnit-57 [-1, 232, 8, 8] 0
Conv2d-58 [-1, 116, 8, 8] 13,572
BatchNorm2d-59 [-1, 116, 8, 8] 232
ReLU-60 [-1, 116, 8, 8] 0
Conv2d-61 [-1, 116, 8, 8] 1,160
BatchNorm2d-62 [-1, 116, 8, 8] 232
Conv2d-63 [-1, 116, 8, 8] 13,572
BatchNorm2d-64 [-1, 116, 8, 8] 232
ReLU-65 [-1, 116, 8, 8] 0
ShuffleUnit-66 [-1, 232, 8, 8] 0
Conv2d-67 [-1, 116, 8, 8] 13,572
BatchNorm2d-68 [-1, 116, 8, 8] 232
ReLU-69 [-1, 116, 8, 8] 0
Conv2d-70 [-1, 116, 8, 8] 1,160
BatchNorm2d-71 [-1, 116, 8, 8] 232
Conv2d-72 [-1, 116, 8, 8] 13,572
BatchNorm2d-73 [-1, 116, 8, 8] 232
ReLU-74 [-1, 116, 8, 8] 0
ShuffleUnit-75 [-1, 232, 8, 8] 0
Conv2d-76 [-1, 116, 8, 8] 13,572
BatchNorm2d-77 [-1, 116, 8, 8] 232
ReLU-78 [-1, 116, 8, 8] 0
Conv2d-79 [-1, 116, 8, 8] 1,160
BatchNorm2d-80 [-1, 116, 8, 8] 232
Conv2d-81 [-1, 116, 8, 8] 13,572
BatchNorm2d-82 [-1, 116, 8, 8] 232
ReLU-83 [-1, 116, 8, 8] 0
ShuffleUnit-84 [-1, 232, 8, 8] 0
Conv2d-85 [-1, 116, 8, 8] 13,572
BatchNorm2d-86 [-1, 116, 8, 8] 232
ReLU-87 [-1, 116, 8, 8] 0
Conv2d-88 [-1, 116, 8, 8] 1,160
BatchNorm2d-89 [-1, 116, 8, 8] 232
Conv2d-90 [-1, 116, 8, 8] 13,572
BatchNorm2d-91 [-1, 116, 8, 8] 232
ReLU-92 [-1, 116, 8, 8] 0
ShuffleUnit-93 [-1, 232, 8, 8] 0
Conv2d-94 [-1, 116, 8, 8] 13,572
BatchNorm2d-95 [-1, 116, 8, 8] 232
ReLU-96 [-1, 116, 8, 8] 0
Conv2d-97 [-1, 116, 8, 8] 1,160
BatchNorm2d-98 [-1, 116, 8, 8] 232
Conv2d-99 [-1, 116, 8, 8] 13,572
BatchNorm2d-100 [-1, 116, 8, 8] 232
ReLU-101 [-1, 116, 8, 8] 0
ShuffleUnit-102 [-1, 232, 8, 8] 0
Conv2d-103 [-1, 116, 8, 8] 13,572
BatchNorm2d-104 [-1, 116, 8, 8] 232
ReLU-105 [-1, 116, 8, 8] 0
Conv2d-106 [-1, 116, 8, 8] 1,160
BatchNorm2d-107 [-1, 116, 8, 8] 232
Conv2d-108 [-1, 116, 8, 8] 13,572
BatchNorm2d-109 [-1, 116, 8, 8] 232
ReLU-110 [-1, 116, 8, 8] 0
ShuffleUnit-111 [-1, 232, 8, 8] 0
Conv2d-112 [-1, 116, 8, 8] 13,572
BatchNorm2d-113 [-1, 116, 8, 8] 232
ReLU-114 [-1, 116, 8, 8] 0
Conv2d-115 [-1, 116, 8, 8] 1,160
BatchNorm2d-116 [-1, 116, 8, 8] 232
Conv2d-117 [-1, 116, 8, 8] 13,572
BatchNorm2d-118 [-1, 116, 8, 8] 232
ReLU-119 [-1, 116, 8, 8] 0
ShuffleUnit-120 [-1, 232, 8, 8] 0
Conv2d-121 [-1, 232, 4, 4] 2,320
BatchNorm2d-122 [-1, 232, 4, 4] 464
Conv2d-123 [-1, 232, 4, 4] 54,056
BatchNorm2d-124 [-1, 232, 4, 4] 464
ReLU-125 [-1, 232, 4, 4] 0
Conv2d-126 [-1, 232, 8, 8] 54,056
BatchNorm2d-127 [-1, 232, 8, 8] 464
ReLU-128 [-1, 232, 8, 8] 0
Conv2d-129 [-1, 232, 4, 4] 2,320
BatchNorm2d-130 [-1, 232, 4, 4] 464
Conv2d-131 [-1, 232, 4, 4] 54,056
BatchNorm2d-132 [-1, 232, 4, 4] 464
ReLU-133 [-1, 232, 4, 4] 0
ShuffleUnit-134 [-1, 464, 4, 4] 0
Conv2d-135 [-1, 232, 4, 4] 54,056
BatchNorm2d-136 [-1, 232, 4, 4] 464
ReLU-137 [-1, 232, 4, 4] 0
Conv2d-138 [-1, 232, 4, 4] 2,320
BatchNorm2d-139 [-1, 232, 4, 4] 464
Conv2d-140 [-1, 232, 4, 4] 54,056
BatchNorm2d-141 [-1, 232, 4, 4] 464
ReLU-142 [-1, 232, 4, 4] 0
ShuffleUnit-143 [-1, 464, 4, 4] 0
Conv2d-144 [-1, 232, 4, 4] 54,056
BatchNorm2d-145 [-1, 232, 4, 4] 464
ReLU-146 [-1, 232, 4, 4] 0
Conv2d-147 [-1, 232, 4, 4] 2,320
BatchNorm2d-148 [-1, 232, 4, 4] 464
Conv2d-149 [-1, 232, 4, 4] 54,056
BatchNorm2d-150 [-1, 232, 4, 4] 464
ReLU-151 [-1, 232, 4, 4] 0
ShuffleUnit-152 [-1, 464, 4, 4] 0
Conv2d-153 [-1, 232, 4, 4] 54,056
BatchNorm2d-154 [-1, 232, 4, 4] 464
ReLU-155 [-1, 232, 4, 4] 0
Conv2d-156 [-1, 232, 4, 4] 2,320
BatchNorm2d-157 [-1, 232, 4, 4] 464
Conv2d-158 [-1, 232, 4, 4] 54,056
BatchNorm2d-159 [-1, 232, 4, 4] 464
ReLU-160 [-1, 232, 4, 4] 0
ShuffleUnit-161 [-1, 464, 4, 4] 0
Conv2d-162 [-1, 1024, 4, 4] 476,160
BatchNorm2d-163 [-1, 1024, 4, 4] 2,048
ReLU-164 [-1, 1024, 4, 4] 0
Linear-165 [-1, 100] 102,500
================================================================
Total params: 1,360,896
Trainable params: 1,360,896
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.01
Forward/backward pass size (MB): 12.66
Params size (MB): 5.19
Estimated Total Size (MB): 17.86
----------------------------------------------------------------
Lightning モジュール
import torch import torch.nn as nn import torch.nn.functional as F from torch.optim.lr_scheduler import OneCycleLR, CyclicLR, ExponentialLR, CosineAnnealingLR, ReduceLROnPlateau from torch.optim.swa_utils import AveragedModel, update_bn import torchvision import pytorch_lightning as pl from pytorch_lightning.callbacks import LearningRateMonitor, GPUStatsMonitor, EarlyStopping from pytorch_lightning.metrics.functional import accuracy #from pl_bolts.datamodules import CIFAR10DataModule #from pl_bolts.transforms.dataset_normalizations import cifar10_normalization
pl.seed_everything(7);
batch_size = 50
train_transforms = torchvision.transforms.Compose([
torchvision.transforms.RandomCrop(32, padding=4),
torchvision.transforms.RandomHorizontalFlip(),
torchvision.transforms.ToTensor(),
cifar100_normalization(),
])
test_transforms = torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
cifar100_normalization(),
])
cifar100_dm = CIFAR100DataModule(
batch_size=batch_size,
num_workers=8,
train_transforms=train_transforms,
test_transforms=test_transforms,
val_transforms=test_transforms,
)
class LitCifar100(pl.LightningModule):
def __init__(self, lr=0.05, factor=0.8):
super().__init__()
self.save_hyperparameters()
self.model = shufflenetv2()
def forward(self, x):
out = self.model(x)
return F.log_softmax(out, dim=1)
def training_step(self, batch, batch_idx):
x, y = batch
logits = F.log_softmax(self.model(x), dim=1)
loss = F.nll_loss(logits, y)
self.log('train_loss', loss)
return loss
def evaluate(self, batch, stage=None):
x, y = batch
logits = self(x)
loss = F.nll_loss(logits, y)
preds = torch.argmax(logits, dim=1)
acc = accuracy(preds, y)
if stage:
self.log(f'{stage}_loss', loss, prog_bar=True)
self.log(f'{stage}_acc', acc, prog_bar=True)
def validation_step(self, batch, batch_idx):
self.evaluate(batch, 'val')
def test_step(self, batch, batch_idx):
self.evaluate(batch, 'test')
def configure_optimizers(self):
optimizer = torch.optim.SGD(self.parameters(), lr=self.hparams.lr, momentum=0.9, weight_decay=5e-4)
return {
'optimizer': optimizer,
'lr_scheduler': ReduceLROnPlateau(optimizer, 'max', patience=5, factor=self.hparams.factor, verbose=True, threshold=0.0001, threshold_mode='abs', cooldown=1, min_lr=1e-5),
'monitor': 'val_acc'
}
訓練 / 評価
%%time
model = LitCifar100(lr=0.05, factor=0.5)
model.datamodule = cifar100_dm
trainer = pl.Trainer(
gpus=1,
max_epochs=100,
progress_bar_refresh_rate=100,
logger=pl.loggers.TensorBoardLogger('tblogs/', name='shufflenetv2'),
callbacks=[LearningRateMonitor(logging_interval='step')],
)
trainer.fit(model, cifar100_dm)
trainer.test(model, datamodule=cifar100_dm);
| Name | Type | Params
---------------------------------------
0 | model | ShuffleNetV2 | 1.4 M
---------------------------------------
1.4 M Trainable params
0 Non-trainable params
1.4 M Total params
5.444 Total estimated model params size (MB)
(...)
Epoch 32: reducing learning rate of group 0 to 2.5000e-02.
Epoch 39: reducing learning rate of group 0 to 1.2500e-02.
Epoch 47: reducing learning rate of group 0 to 6.2500e-03.
Epoch 56: reducing learning rate of group 0 to 3.1250e-03.
Epoch 66: reducing learning rate of group 0 to 1.5625e-03.
Epoch 78: reducing learning rate of group 0 to 7.8125e-04.
Epoch 85: reducing learning rate of group 0 to 3.9063e-04.
Epoch 95: reducing learning rate of group 0 to 1.9531e-04.
(...)
--------------------------------------------------------------------------------
DATALOADER:0 TEST RESULTS
{'test_acc': 0.6672999858856201, 'test_loss': 1.4519164562225342}
--------------------------------------------------------------------------------
CPU times: user 1h 21min 24s, sys: 1min 37s, total: 1h 23min 1s
Wall time: 1h 26min 15s
以上