PyTorch Lightning 1.1: research : CIFAR100 (ResNeXt)
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 : 02/25/2021 (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 (ResNeXt)
仕様
- Total params: 9,221,028 (9.2M)
- Trainable params: 9,221,028
- Non-trainable params: 0
結果
- ResNeXt29_2x64d
- {‘test_acc’: 0.7299000024795532, ‘test_loss’: 1.0691789388656616}
- 100 エポック ; Wall time: 3h 51min 18s
- 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
class Block(nn.Module):
'''Grouped convolution block.'''
expansion = 2
def __init__(self, in_planes, cardinality=32, bottleneck_width=4, stride=1):
super(Block, self).__init__()
group_width = cardinality * bottleneck_width
self.conv1 = nn.Conv2d(in_planes, group_width, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(group_width)
self.conv2 = nn.Conv2d(group_width, group_width, kernel_size=3, stride=stride, padding=1, groups=cardinality, bias=False)
self.bn2 = nn.BatchNorm2d(group_width)
self.conv3 = nn.Conv2d(group_width, self.expansion*group_width, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(self.expansion*group_width)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion*group_width:
self.shortcut = nn.Sequential(
nn.Conv2d(in_planes, self.expansion*group_width, kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(self.expansion*group_width)
)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = F.relu(self.bn2(self.conv2(out)))
out = self.bn3(self.conv3(out))
out += self.shortcut(x)
out = F.relu(out)
return out
class ResNeXt(nn.Module):
def __init__(self, num_blocks, cardinality, bottleneck_width, num_classes=100):
super(ResNeXt, self).__init__()
self.cardinality = cardinality
self.bottleneck_width = bottleneck_width
self.in_planes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self._make_layer(num_blocks[0], 1)
self.layer2 = self._make_layer(num_blocks[1], 2)
self.layer3 = self._make_layer(num_blocks[2], 2)
# self.layer4 = self._make_layer(num_blocks[3], 2)
self.linear = nn.Linear(cardinality*bottleneck_width*8, num_classes)
def _make_layer(self, num_blocks, stride):
strides = [stride] + [1]*(num_blocks-1)
layers = []
for stride in strides:
layers.append(Block(self.in_planes, self.cardinality, self.bottleneck_width, stride))
self.in_planes = Block.expansion * self.cardinality * self.bottleneck_width
# Increase bottleneck_width by 2 after each stage.
self.bottleneck_width *= 2
return nn.Sequential(*layers)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
# out = self.layer4(out)
out = F.avg_pool2d(out, 8)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
def ResNeXt29_2x64d():
return ResNeXt(num_blocks=[3,3,3], cardinality=2, bottleneck_width=64)
def ResNeXt29_4x64d():
return ResNeXt(num_blocks=[3,3,3], cardinality=4, bottleneck_width=64)
def ResNeXt29_8x64d():
return ResNeXt(num_blocks=[3,3,3], cardinality=8, bottleneck_width=64)
def ResNeXt29_32x4d():
return ResNeXt(num_blocks=[3,3,3], cardinality=32, bottleneck_width=4)
net = ResNeXt29_2x64d() print(net) y = net(torch.randn(1, 3, 32, 32)) print(y.size())
ResNeXt(
(conv1): Conv2d(3, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(layer1): Sequential(
(0): Block(
(conv1): Conv2d(64, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2, bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential(
(0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Block(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2, bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential()
)
(2): Block(
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2, bias=False)
(bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential()
)
)
(layer2): Sequential(
(0): Block(
(conv1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=2, bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential(
(0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Block(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2, bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential()
)
(2): Block(
(conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2, bias=False)
(bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential()
)
)
(layer3): Sequential(
(0): Block(
(conv1): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=2, bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential(
(0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
(1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)
)
(1): Block(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2, bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential()
)
(2): Block(
(conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=2, bias=False)
(bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(conv3): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
(shortcut): Sequential()
)
)
(linear): Linear(in_features=1024, out_features=100, bias=True)
)
torch.Size([1, 100])
from torchsummary import summary
summary(ResNeXt29_2x64d().to('cuda'), (3, 32, 32))
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 32, 32] 192
BatchNorm2d-2 [-1, 64, 32, 32] 128
Conv2d-3 [-1, 128, 32, 32] 8,192
BatchNorm2d-4 [-1, 128, 32, 32] 256
Conv2d-5 [-1, 128, 32, 32] 73,728
BatchNorm2d-6 [-1, 128, 32, 32] 256
Conv2d-7 [-1, 256, 32, 32] 32,768
BatchNorm2d-8 [-1, 256, 32, 32] 512
Conv2d-9 [-1, 256, 32, 32] 16,384
BatchNorm2d-10 [-1, 256, 32, 32] 512
Block-11 [-1, 256, 32, 32] 0
Conv2d-12 [-1, 128, 32, 32] 32,768
BatchNorm2d-13 [-1, 128, 32, 32] 256
Conv2d-14 [-1, 128, 32, 32] 73,728
BatchNorm2d-15 [-1, 128, 32, 32] 256
Conv2d-16 [-1, 256, 32, 32] 32,768
BatchNorm2d-17 [-1, 256, 32, 32] 512
Block-18 [-1, 256, 32, 32] 0
Conv2d-19 [-1, 128, 32, 32] 32,768
BatchNorm2d-20 [-1, 128, 32, 32] 256
Conv2d-21 [-1, 128, 32, 32] 73,728
BatchNorm2d-22 [-1, 128, 32, 32] 256
Conv2d-23 [-1, 256, 32, 32] 32,768
BatchNorm2d-24 [-1, 256, 32, 32] 512
Block-25 [-1, 256, 32, 32] 0
Conv2d-26 [-1, 256, 32, 32] 65,536
BatchNorm2d-27 [-1, 256, 32, 32] 512
Conv2d-28 [-1, 256, 16, 16] 294,912
BatchNorm2d-29 [-1, 256, 16, 16] 512
Conv2d-30 [-1, 512, 16, 16] 131,072
BatchNorm2d-31 [-1, 512, 16, 16] 1,024
Conv2d-32 [-1, 512, 16, 16] 131,072
BatchNorm2d-33 [-1, 512, 16, 16] 1,024
Block-34 [-1, 512, 16, 16] 0
Conv2d-35 [-1, 256, 16, 16] 131,072
BatchNorm2d-36 [-1, 256, 16, 16] 512
Conv2d-37 [-1, 256, 16, 16] 294,912
BatchNorm2d-38 [-1, 256, 16, 16] 512
Conv2d-39 [-1, 512, 16, 16] 131,072
BatchNorm2d-40 [-1, 512, 16, 16] 1,024
Block-41 [-1, 512, 16, 16] 0
Conv2d-42 [-1, 256, 16, 16] 131,072
BatchNorm2d-43 [-1, 256, 16, 16] 512
Conv2d-44 [-1, 256, 16, 16] 294,912
BatchNorm2d-45 [-1, 256, 16, 16] 512
Conv2d-46 [-1, 512, 16, 16] 131,072
BatchNorm2d-47 [-1, 512, 16, 16] 1,024
Block-48 [-1, 512, 16, 16] 0
Conv2d-49 [-1, 512, 16, 16] 262,144
BatchNorm2d-50 [-1, 512, 16, 16] 1,024
Conv2d-51 [-1, 512, 8, 8] 1,179,648
BatchNorm2d-52 [-1, 512, 8, 8] 1,024
Conv2d-53 [-1, 1024, 8, 8] 524,288
BatchNorm2d-54 [-1, 1024, 8, 8] 2,048
Conv2d-55 [-1, 1024, 8, 8] 524,288
BatchNorm2d-56 [-1, 1024, 8, 8] 2,048
Block-57 [-1, 1024, 8, 8] 0
Conv2d-58 [-1, 512, 8, 8] 524,288
BatchNorm2d-59 [-1, 512, 8, 8] 1,024
Conv2d-60 [-1, 512, 8, 8] 1,179,648
BatchNorm2d-61 [-1, 512, 8, 8] 1,024
Conv2d-62 [-1, 1024, 8, 8] 524,288
BatchNorm2d-63 [-1, 1024, 8, 8] 2,048
Block-64 [-1, 1024, 8, 8] 0
Conv2d-65 [-1, 512, 8, 8] 524,288
BatchNorm2d-66 [-1, 512, 8, 8] 1,024
Conv2d-67 [-1, 512, 8, 8] 1,179,648
BatchNorm2d-68 [-1, 512, 8, 8] 1,024
Conv2d-69 [-1, 1024, 8, 8] 524,288
BatchNorm2d-70 [-1, 1024, 8, 8] 2,048
Block-71 [-1, 1024, 8, 8] 0
Linear-72 [-1, 100] 102,500
================================================================
Total params: 9,221,028
Trainable params: 9,221,028
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.01
Forward/backward pass size (MB): 65.00
Params size (MB): 35.18
Estimated Total Size (MB): 100.19
----------------------------------------------------------------
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 = ResNeXt29_2x64d()
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='resnext29_2x64d'),
callbacks=[LearningRateMonitor(logging_interval='step')],
)
trainer.fit(model, cifar100_dm)
trainer.test(model, datamodule=cifar100_dm);
| Name | Type | Params
----------------------------------
0 | model | ResNeXt | 9.2 M
----------------------------------
9.2 M Trainable params
0 Non-trainable params
9.2 M Total params
36.884 Total estimated model params size (MB)
(...)
Epoch 34: reducing learning rate of group 0 to 2.5000e-02.
Epoch 41: reducing learning rate of group 0 to 1.2500e-02.
Epoch 49: reducing learning rate of group 0 to 6.2500e-03.
Epoch 63: reducing learning rate of group 0 to 3.1250e-03.
Epoch 77: reducing learning rate of group 0 to 1.5625e-03.
Epoch 84: reducing learning rate of group 0 to 7.8125e-04.
Epoch 91: reducing learning rate of group 0 to 3.9063e-04.
Epoch 98: reducing learning rate of group 0 to 1.9531e-04.
(...)
--------------------------------------------------------------------------------
DATALOADER:0 TEST RESULTS
{'test_acc': 0.7299000024795532, 'test_loss': 1.0691789388656616}
--------------------------------------------------------------------------------
CPU times: user 2h 18min 57s, sys: 1h 29min 52s, total: 3h 48min 50s
Wall time: 3h 51min 18s
以上