丁度一年前にpytorchの記事を書いた。
割と簡単に動かせたので、今回も簡単だろうと高をくくっていたので、ちょっと慌てた。
導入のページが、ちょっとな気がする。
でも、わかってしまうとむしろ参考①のアニメーションが秀逸なことに気が付いた。
ということで、これの解説しますかね。
※結局、やったことしか書けていない
【参考】
①PyTorchLightning/pytorch-lightning
②STEP-BY-STEP WALK-THROUGH
今回の環境は以下のとおりです。
>python -m pip install pytorch-lightning
...
Successfully installed fsspec-0.8.5 pytorch-lightning-1.1.2 tensorboard-2.4.0 tensorboard-plugin-wit-1.7.0 tqdm-4.55.0
>python -m pip install tensorboard
Requirement already satisfied:...
>>> import tensorboard
>>> tensorboard.__version__
'2.4.0'
>python
Python 3.7.5 (default, Oct 31 2019, 15:18:51) [MSC v.1916 64 bit (AMD64)] :: Anaconda, Inc. on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import pytorch_lightning
>>> pytorch_lightning.__version__
'1.1.2'
>>> import torch
>>> torch.__version__
'1.7.1'
###やったこと
・pytorch-lightningの肝
・Pytorch振り返り
・pytorch-lightning
###・pytorch-lightningの肝
Lightning Philosophy
Lightning structures your deep learning code in 4 parts:
・Research code
・Engineering code
・Non-essential code
・Data code
これらをpytorchのコードから、再配置してClassに集約したんですね。
それが、上のアニメーションです。
※この動画貼れそうなので、解説する必要ないような気がしてきました
ということで、Pytorchで書かれたコードを上のような分析をして、以下のフレームに載せるだけで動きます。
pytorch-lightningテンプレート
※実際に動くMNISTのカテゴライズのコードにしました
import torch
from torch.nn import functional as F
from torch import nn
from pytorch_lightning.core.lightning import LightningModule
from pytorch_lightning import Trainer
import pytorch_lightning as pl
from torch.utils.data import DataLoader, random_split
from torchvision.datasets import MNIST
import os
from torchvision import datasets, transforms
from torch.optim import Adam
class LitMNIST(LightningModule): #LightningModuleの継承が必須
def __init__(self, data_dir='./'): #初期化が必要なものはここに記載
super().__init__()
self.data_dir=data_dir
self.transform=transforms.Compose([transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))])
# mnist images are (1, 28, 28) (channels, width, height)
#ネットワーク定義するための関数；forwardで使う
self.layer_1 = torch.nn.Linear(28 * 28, 128)
self.layer_2 = torch.nn.Linear(128, 256)
self.layer_3 = torch.nn.Linear(256, 10)
self.train_acc = pl.metrics.Accuracy()
self.val_acc = pl.metrics.Accuracy()
self.test_acc = pl.metrics.Accuracy()
def forward(self, x): #推論をする
batch_size, channels, width, height = x.size()
# (b, 1, 28, 28) -> (b, 1*28*28)
x = x.view(batch_size, -1)
x = self.layer_1(x)
x = F.relu(x)
x = self.layer_2(x)
x = F.relu(x)
x = self.layer_3(x)
x = F.log_softmax(x, dim=1)
return x
def training_step(self, batch, batch_idx): #trainingする
x, y = batch
logits = self(x)
loss = F.nll_loss(logits, y)
self.log('my_loss', loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
return loss
def validation_step(self, batch, batch_idx): #汎化性能確認のため実施
x, t = batch
y = self(x)
loss = F.nll_loss(y, t)
preds = torch.argmax(y, dim=1)
# Calling self.log will surface up scalars for you in TensorBoard
#log記録；上で定義したself.val_accなどを記録
self.log('val_loss', loss, prog_bar=True)
self.log('val_acc', self.val_acc(y,t), prog_bar=True)
return loss
def test_step(self, batch, batch_idx): #testの実行；最終的なlossとaccの確認
# Here we just reuse the validation_step for testing
return self.validation_step(batch, batch_idx)
def configure_optimizers(self): #optimizerの定義
return Adam(self.parameters(), lr=1e-3)
def prepare_data(self): #データダウンロード
# download
MNIST(self.data_dir, train=True, download=True)
MNIST(self.data_dir, train=False, download=True)
def setup(self, stage=None): #train, val, testデータ分割
# Assign train/val datasets for use in dataloaders
mnist_full =MNIST(self.data_dir, train=True, transform=self.transform)
n_train = int(len(mnist_full)*0.8)
n_val = len(mnist_full)-n_train
self.mnist_train, self.mnist_val = torch.utils.data.random_split(mnist_full, [n_train, n_val])
self.mnist_test = MNIST(self.data_dir, train=False, transform=self.transform)
def train_dataloader(self): #trainデータ作成
# prepare transforms standard to MNIST
mnist_train = MNIST(self.data_dir, train=True, transform=self.transform)
return DataLoader(mnist_train, batch_size=64)
def val_dataloader(self): #valデータ作成
mnist_val = MNIST(self.data_dir, train=False, transform=self.transform)
return DataLoader(mnist_val, batch_size=64)
def test_dataloader(self): #testデータ作成
mnist_test = MNIST(self.data_dir, train=False, transform=self.transform)
return DataLoader(mnist_test, batch_size=64)
#Gpu利用のためにdevice定義
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#乱数初期値固定
pl.seed_everything(0)
#netを定義
net = LitMNIST()
#GPUに載せる
model = net.to(device)
#Trainnigの回数などを定義
trainer = Trainer(gpus=1, max_epochs=10)
#fitting（学習）する
trainer.fit(model)
#testデータで検証する
results = trainer.test()
#検証結果出力
print(results)
>python Lit_MNIST.py
GPU available: True, used: True
TPU available: None, using: 0 TPU cores
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
2020-12-28 19:01:18.414240: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
2020-12-28 19:01:18.414340: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
| Name | Type | Params
---------------------------------------
0 | layer_1 | Linear | 100 K
1 | layer_2 | Linear | 33.0 K
2 | layer_3 | Linear | 2.6 K
3 | train_acc | Accuracy | 0
4 | val_acc | Accuracy | 0
5 | test_acc | Accuracy | 0
---------------------------------------
136 K Trainable params
0 Non-trainable params
136 K Total params
Epoch 9: 100%|████| 1095/1095 [00:12<00:00, 87.94it/s, loss=0.02, v_num=62, val_loss=0.105, val_acc=0.977, my_loss_step=0.000118, my_loss_epoch=0.0252]
Testing: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 157/157 [00:01<00:00, 107.82it/s]
--------------------------------------------------------------------------------
DATALOADER:0 TEST RESULTS
{'val_acc': tensor(0.9758, device='cuda:0'),
'val_loss': tensor(0.0901, device='cuda:0')}
--------------------------------------------------------------------------------
[{'val_loss': 0.09011910110712051, 'val_acc': 0.9757999777793884}]
tensorboardでlogを確認するには、以下の通りのコマンドで
ブラウザで
http://localhost:6006/
を見れば見える。
>tensorboard --logdir ./lightning_logs
．．．
TensorBoard 2.4.0 at http://localhost:6006/ (Press CTRL+C to quit)
###・Pytorch振り返り
ここで終わると、なんだかいい感じですが、やはりpytorchでやれていたことが上では、出来ていません。
・上のコードだとnetworkの定義が煩雑です
・学習をコントロールしたい。まあ学習率は変更したい
・GANだと、複数のoptimizerに対応したい
ということで、一つずつ解決しました。
####・上のコードだとnetworkの定義が煩雑です
networkを別classで与えてみます。
⇒出来ました
思っていた以上にすっきりして、大きなnetworkもまんま使えて便利です。
⇒もともとのpytorch-lightningの狙いであるModelに集中（分離して集中）して考えることが容易です
def __init__(self, data_dir='./'):
super().__init__()
...
self.net = VGG16()
...
def forward(self, x):
return self.net(x)
####・学習をコントロールしたい。まあ学習率は変更したい
これは三つ目と結局同じ部分ですが、optimizerの定義に関係しました。
参考は、以下の参考③のとおりです。
参考④は直接関係ありませんが、pytorchでの学習率の与え方のまとめです。
【参考】
③OPTIMIZATION
④PyTorchのSchedulerまとめ
ということで、以下のように書き換えました。
def configure_optimizers(self):
optimizer = optim.Adam(self.parameters(),lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False)
scheduler = {'scheduler': optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.2)}
print("CF;Ir = ", optimizer.param_groups[0]['lr'])
return [optimizer], [scheduler]
ほんとに変わっているか確認のために、以下で出力してみました。
ただし、このコードは１ステップ毎に実行されるので、一度確認してコメントアウトしています。
def training_step(self, batch, batch_idx):
x, t =batch
y = self(x)
loss = F.cross_entropy(y, t)
print('Ir =',self.optimizers().param_groups[0]['lr'])
return loss
また、
class MyPrintingCallback(Callback):
で出力しようと試みましたが出来ませんでした。
####・GANだと、複数のoptimizerに対応したい
これは、実際にはまだやれていませんので、次回の宿題にします。
しかし、上記のoptimizerの所で解説されていて、以下のようにやれば出来そうです。
【参考】
⑤Use multiple optimizers (like GANs)
そして、複雑に設定したいときは結局以下の戦略を取るんですね。
⑥Manual optimization
###まとめ
・pytorch-lightningで遊んでみた
・簡単なものは容易に見えるが、奥は深いと感じた
・思うようにコードを操るのは時間がかかりそう
・次回は複数optimizerを駆使してGANを学習したいと思う
ちなみに、おまけにVGG16でのコードと実行結果を添付します。
本当は、このコード読まないとだけど、今回は、optimizerの所だけみました。
PyTorchLightning/pytorch-lightning
###おまけ
>python torch_lightning_cifar10.py
cuda:0
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 32, 32] 1,792
BatchNorm2d-2 [-1, 64, 32, 32] 128
ReLU-3 [-1, 64, 32, 32] 0
Conv2d-4 [-1, 64, 32, 32] 36,928
BatchNorm2d-5 [-1, 64, 32, 32] 128
ReLU-6 [-1, 64, 32, 32] 0
MaxPool2d-7 [-1, 64, 16, 16] 0
Conv2d-8 [-1, 128, 16, 16] 73,856
BatchNorm2d-9 [-1, 128, 16, 16] 256
ReLU-10 [-1, 128, 16, 16] 0
Conv2d-11 [-1, 128, 16, 16] 147,584
BatchNorm2d-12 [-1, 128, 16, 16] 256
ReLU-13 [-1, 128, 16, 16] 0
MaxPool2d-14 [-1, 128, 8, 8] 0
Conv2d-15 [-1, 256, 8, 8] 295,168
BatchNorm2d-16 [-1, 256, 8, 8] 512
ReLU-17 [-1, 256, 8, 8] 0
Conv2d-18 [-1, 256, 8, 8] 590,080
BatchNorm2d-19 [-1, 256, 8, 8] 512
ReLU-20 [-1, 256, 8, 8] 0
Conv2d-21 [-1, 256, 8, 8] 590,080
BatchNorm2d-22 [-1, 256, 8, 8] 512
ReLU-23 [-1, 256, 8, 8] 0
MaxPool2d-24 [-1, 256, 4, 4] 0
Conv2d-25 [-1, 512, 4, 4] 1,180,160
BatchNorm2d-26 [-1, 512, 4, 4] 1,024
ReLU-27 [-1, 512, 4, 4] 0
Conv2d-28 [-1, 512, 4, 4] 2,359,808
BatchNorm2d-29 [-1, 512, 4, 4] 1,024
ReLU-30 [-1, 512, 4, 4] 0
Conv2d-31 [-1, 512, 4, 4] 2,359,808
BatchNorm2d-32 [-1, 512, 4, 4] 1,024
ReLU-33 [-1, 512, 4, 4] 0
MaxPool2d-34 [-1, 512, 2, 2] 0
Conv2d-35 [-1, 512, 2, 2] 2,359,808
BatchNorm2d-36 [-1, 512, 2, 2] 1,024
ReLU-37 [-1, 512, 2, 2] 0
Conv2d-38 [-1, 512, 2, 2] 2,359,808
BatchNorm2d-39 [-1, 512, 2, 2] 1,024
ReLU-40 [-1, 512, 2, 2] 0
Conv2d-41 [-1, 512, 2, 2] 2,359,808
BatchNorm2d-42 [-1, 512, 2, 2] 1,024
ReLU-43 [-1, 512, 2, 2] 0
MaxPool2d-44 [-1, 512, 1, 1] 0
Linear-45 [-1, 512] 262,656
ReLU-46 [-1, 512] 0
Linear-47 [-1, 32] 16,416
ReLU-48 [-1, 32] 0
Linear-49 [-1, 10] 330
VGG16-50 [-1, 10] 0
================================================================
Total params: 15,002,538
Trainable params: 15,002,538
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.01
Forward/backward pass size (MB): 6.57
Params size (MB): 57.23
Estimated Total Size (MB): 63.82
----------------------------------------------------------------
Starting to init trainer!
GPU available: True, used: True
TPU available: None, using: 0 TPU cores
LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
Trainer is init now
Files already downloaded and verified
Files already downloaded and verified
CF;Ir = 0.001
2020-12-28 20:36:51.786876: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found
2020-12-28 20:36:51.787050: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
| Name | Type | Params
---------------------------------------
0 | net | VGG16 | 15.0 M
1 | train_acc | Accuracy | 0
2 | val_acc | Accuracy | 0
3 | test_acc | Accuracy | 0
---------------------------------------
15.0 M Trainable params
0 Non-trainable params
15.0 M Total params
dog cat deer plane
Epoch 0: 100%|██████████████████████████████████████████████████| 1563/1563 [01:08<00:00, 22.89it/s, loss=1.63, v_num=63, val_loss=1.58, val_acc=0.344]
Epoch 1: 100%|██████████████████████████████████████████████████| 1563/1563 [01:08<00:00, 22.72it/s, loss=1.41, v_num=63, val_loss=1.33, val_acc=0.481]
Epoch 2: 100%|███████████████████████████████████████████████████| 1563/1563 [01:08<00:00, 22.66it/s, loss=1.17, v_num=63, val_loss=1.07, val_acc=0.62]
Epoch 3: 100%|████████████████████████████████████████████████| 1563/1563 [01:09<00:00, 22.61it/s, loss=0.994, v_num=63, val_loss=0.922, val_acc=0.684]
Epoch 4: 100%|████████████████████████████████████████████████| 1563/1563 [01:09<00:00, 22.62it/s, loss=0.833, v_num=63, val_loss=0.839, val_acc=0.713]
Epoch 5: 100%|████████████████████████████████████████████████| 1563/1563 [01:09<00:00, 22.40it/s, loss=0.601, v_num=63, val_loss=0.649, val_acc=0.775]
Epoch 6: 100%|█████████████████████████████████████████████████| 1563/1563 [01:10<00:00, 22.14it/s, loss=0.569, v_num=63, val_loss=0.62, val_acc=0.788]
Epoch 7: 100%|██████████████████████████████████████████████████| 1563/1563 [01:09<00:00, 22.58it/s, loss=0.519, v_num=63, val_loss=0.595, val_acc=0.8]
Epoch 8: 100%|████████████████████████████████████████████████| 1563/1563 [01:09<00:00, 22.57it/s, loss=0.435, v_num=63, val_loss=0.599, val_acc=0.803]
Epoch 9: 100%|█████████████████████████████████████████████████| 1563/1563 [01:09<00:00, 22.57it/s, loss=0.43, v_num=63, val_loss=0.579, val_acc=0.809]
Epoch 10: 100%|████████████████████████████████████████████████| 1563/1563 [01:10<00:00, 22.29it/s, loss=0.289, v_num=63, val_loss=0.58, val_acc=0.814]
Epoch 11: 100%|███████████████████████████████████████████████| 1563/1563 [01:10<00:00, 22.08it/s, loss=0.344, v_num=63, val_loss=0.595, val_acc=0.817]
Epoch 12: 100%|███████████████████████████████████████████████| 1563/1563 [01:10<00:00, 22.09it/s, loss=0.337, v_num=63, val_loss=0.601, val_acc=0.816]
Epoch 13: 100%|██████████████████████████████████████████████████| 1563/1563 [01:10<00:00, 22.10it/s, loss=0.28, v_num=63, val_loss=0.6, val_acc=0.817]
Epoch 14: 100%|███████████████████████████████████████████████| 1563/1563 [01:10<00:00, 22.10it/s, loss=0.288, v_num=63, val_loss=0.603, val_acc=0.819]
Epoch 15: 100%|███████████████████████████████████████████████| 1563/1563 [01:10<00:00, 22.10it/s, loss=0.296, v_num=63, val_loss=0.605, val_acc=0.819]
Epoch 16: 100%|███████████████████████████████████████████████| 1563/1563 [01:10<00:00, 22.09it/s, loss=0.252, v_num=63, val_loss=0.604, val_acc=0.819]
do something when training ends███████████████████████████████| 1563/1563 [01:10<00:00, 22.10it/s, loss=0.253, v_num=63, val_loss=0.616, val_acc=0.817]
Epoch 18: 100%|███████████████████████████████████████████████| 1563/1563 [01:10<00:00, 22.13it/s, loss=0.221, v_num=63, val_loss=0.611, val_acc=0.819]
Files already downloaded and verified
Files already downloaded and verified
Testing: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 313/313 [00:05<00:00, 54.47it/s]
--------------------------------------------------------------------------------
DATALOADER:0 TEST RESULTS███████████████████████████████████████████████████████████████████████████████████████████ | 310/313 [00:05<00:00, 55.76it/s]
{'val_acc': tensor(0.8030, device='cuda:0'),
'val_loss': tensor(0.6054, device='cuda:0')}
--------------------------------------------------------------------------------
[{'val_loss': 0.6053957343101501, 'val_acc': 0.8029999732971191}]
elapsed time: 1357.867 [sec]
import argparse
import time
import numpy as np
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from pytorch_lightning import Trainer
import torch.nn as nn
import torch.nn.functional as F
import torch
import torchvision
import torchvision.transforms as transforms
from torchvision.datasets import CIFAR10
import torch.optim as optim
from torchsummary import summary
import matplotlib.pyplot as plt
import tensorboard
#from net_cifar10 import Net_cifar10
from net_vgg16 import VGG16
# functions to show an image
def imshow(img):
img = img / 2 + 0.5 # unnormalize
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
plt.pause(1)
plt.close()
from pytorch_lightning.callbacks import Callback
from pytorch_lightning.callbacks import EarlyStopping, ProgressBar,LearningRateMonitor
class MyPrintingCallback(Callback):
def on_init_start(self, trainer):
print('Starting to init trainer!')
def on_init_end(self, trainer):
print('Trainer is init now')
def on_epoch_end(self, trainer, pl_module):
#print("pl_module = ",pl_module)
#print("trainer = ",trainer)
print('')
def on_train_end(self, trainer, pl_module):
print('do something when training ends')
class LitProgressBar(ProgressBar):
def init_validation_tqdm(self):
bar = super().init_validation_tqdm()
bar.set_description('running validation ...')
return bar
class Net(pl.LightningModule):
def __init__(self, data_dir='./'):
super().__init__()
self.data_dir = data_dir
# Hardcode some dataset specific attributes
self.num_classes = 10
self.dims = (3, 32, 32)
channels, width, height = self.dims
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
#self.net = Net_cifar10()
self.net = VGG16()
self.train_acc = pl.metrics.Accuracy()
self.val_acc = pl.metrics.Accuracy()
self.test_acc = pl.metrics.Accuracy()
def forward(self, x):
return self.net(x)
def training_step(self, batch, batch_idx):
x, t =batch
y = self(x)
loss = F.cross_entropy(y, t)
#print('Ir =',self.optimizers().param_groups[0]['lr'])
self.log('test_loss', loss, on_step = False, on_epoch = True)
self.log('test_acc', self.val_acc(y, t), on_step = False, on_epoch = True)
return loss
def validation_step(self, batch, batch_idx):
x, t = batch
y = self(x)
loss = F.cross_entropy(y, t)
preds = torch.argmax(y, dim=1)
# Calling self.log will surface up scalars for you in TensorBoard
self.log('val_loss', loss, prog_bar=True)
self.log('val_acc', self.val_acc(y,t), prog_bar=True)
return loss
def test_step(self, batch, batch_idx):
# Here we just reuse the validation_step for testing
return self.validation_step(batch, batch_idx)
def configure_optimizers(self):
#optimizer = optim.SGD(self.parameters(), lr=0.001, momentum=0.9)
optimizer = optim.Adam(self.parameters(),lr=0.001, betas=(0.9, 0.999), eps=1e-08, weight_decay=0, amsgrad=False)
#scheduler = {'scheduler': optim.lr_scheduler.LambdaLR(optimizer, lr_lambda = lambda epoch: 0.95 ** epoch)}
scheduler = {'scheduler': optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.2)}
print("CF;Ir = ", optimizer.param_groups[0]['lr'])
return [optimizer], [scheduler]
def prepare_data(self):
# download
CIFAR10(self.data_dir, train=True, download=True)
CIFAR10(self.data_dir, train=False, download=True)
def setup(self, stage=None):
# Assign train/val datasets for use in dataloaders
if stage == 'fit' or stage is None:
cifar_full =CIFAR10(self.data_dir, train=True, transform=self.transform)
n_train = int(len(cifar_full)*0.8)
n_val = len(cifar_full)-n_train
self.cifar_train, self.cifar_val = torch.utils.data.random_split(cifar_full, [n_train, n_val])
# Assign test dataset for use in dataloader(s)
if stage == 'test' or stage is None:
self.cifar_test = CIFAR10(self.data_dir, train=False, transform=self.transform)
def train_dataloader(self):
#classes = tuple(np.linspace(0, 9, 10, dtype=np.uint8))
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
trainloader = DataLoader(self.cifar_train, batch_size=32)
# get some random training images
dataiter = iter(trainloader)
images, labels = dataiter.next()
# show images
imshow(torchvision.utils.make_grid(images))
# print labels
print(' '.join('%5s' % classes[labels[j]] for j in range(4)))
return trainloader
def val_dataloader(self):
return DataLoader(self.cifar_val, batch_size=32)
def test_dataloader(self):
return DataLoader(self.cifar_test, batch_size=32)
def main():
'''
main
'''
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") #for gpu
# Assuming that we are on a CUDA machine, this should print a CUDA device:
print(device)
pl.seed_everything(0)
# model
net = Net()
model = net.to(device) #for gpu
summary(model,(3,32,32))
early_stopping = EarlyStopping('val_acc') #('val_loss'
bar = LitProgressBar(refresh_rate = 10, process_position = 1)
#lr_monitor = LearningRateMonitor(logging_interval='step')
trainer = pl.Trainer(gpus=1, max_epochs=20,callbacks=[MyPrintingCallback(),early_stopping, bar]) # progress_bar_refresh_rate=10
trainer.fit(model)
results = trainer.test()
print(results)
if __name__ == '__main__':
start_time = time.time()
main()
print('elapsed time: {:.3f} [sec]'.format(time.time() - start_time))
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 32, 32] 1,792
BatchNorm2d-2 [-1, 64, 32, 32] 128
ReLU-3 [-1, 64, 32, 32] 0
Conv2d-4 [-1, 64, 32, 32] 36,928
BatchNorm2d-5 [-1, 64, 32, 32] 128
ReLU-6 [-1, 64, 32, 32] 0
MaxPool2d-7 [-1, 64, 16, 16] 0
Conv2d-8 [-1, 128, 16, 16] 73,856
BatchNorm2d-9 [-1, 128, 16, 16] 256
ReLU-10 [-1, 128, 16, 16] 0
Conv2d-11 [-1, 128, 16, 16] 147,584
BatchNorm2d-12 [-1, 128, 16, 16] 256
ReLU-13 [-1, 128, 16, 16] 0
MaxPool2d-14 [-1, 128, 8, 8] 0
Conv2d-15 [-1, 256, 8, 8] 295,168
BatchNorm2d-16 [-1, 256, 8, 8] 512
ReLU-17 [-1, 256, 8, 8] 0
Conv2d-18 [-1, 256, 8, 8] 590,080
BatchNorm2d-19 [-1, 256, 8, 8] 512
ReLU-20 [-1, 256, 8, 8] 0
Conv2d-21 [-1, 256, 8, 8] 590,080
BatchNorm2d-22 [-1, 256, 8, 8] 512
ReLU-23 [-1, 256, 8, 8] 0
MaxPool2d-24 [-1, 256, 4, 4] 0
Conv2d-25 [-1, 512, 4, 4] 1,180,160
BatchNorm2d-26 [-1, 512, 4, 4] 1,024
ReLU-27 [-1, 512, 4, 4] 0
Conv2d-28 [-1, 512, 4, 4] 2,359,808
BatchNorm2d-29 [-1, 512, 4, 4] 1,024
ReLU-30 [-1, 512, 4, 4] 0
Conv2d-31 [-1, 512, 4, 4] 2,359,808
BatchNorm2d-32 [-1, 512, 4, 4] 1,024
ReLU-33 [-1, 512, 4, 4] 0
MaxPool2d-34 [-1, 512, 2, 2] 0
Conv2d-35 [-1, 512, 2, 2] 2,359,808
BatchNorm2d-36 [-1, 512, 2, 2] 1,024
ReLU-37 [-1, 512, 2, 2] 0
Conv2d-38 [-1, 512, 2, 2] 2,359,808
BatchNorm2d-39 [-1, 512, 2, 2] 1,024
ReLU-40 [-1, 512, 2, 2] 0
Conv2d-41 [-1, 512, 2, 2] 2,359,808
BatchNorm2d-42 [-1, 512, 2, 2] 1,024
ReLU-43 [-1, 512, 2, 2] 0
MaxPool2d-44 [-1, 512, 1, 1] 0
Linear-45 [-1, 512] 262,656
ReLU-46 [-1, 512] 0
Linear-47 [-1, 32] 16,416
ReLU-48 [-1, 32] 0
Linear-49 [-1, 10] 330
================================================================
Total params: 15,002,538
Trainable params: 15,002,538
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.01
Forward/backward pass size (MB): 6.57
Params size (MB): 57.23
Estimated Total Size (MB): 63.82
----------------------------------------------------------------
Ir = 0.00025
[1, 200] loss: 1.946 train_acc: 31.54 % val_acc: 31.29 %
[1, 400] loss: 1.691 train_acc: 36.00 % val_acc: 35.82 %
[1, 600] loss: 1.570 train_acc: 36.58 % val_acc: 36.43 %
...
[20, 800] loss: 0.017 train_acc: 99.66 % val_acc: 83.27 %
[20, 1000] loss: 0.016 train_acc: 99.61 % val_acc: 83.11 %
[20, 1200] loss: 0.014 train_acc: 99.61 % val_acc: 83.11 %
Finished Training
Accuracy: 83.27 %
GroundTruth: cat ship ship plane
Predicted: cat ship ship plane
Accuracy of plane : 84 %
Accuracy of car : 91 %
Accuracy of bird : 75 %
Accuracy of cat : 66 %
Accuracy of deer : 81 %
Accuracy of dog : 78 %
Accuracy of frog : 85 %
Accuracy of horse : 85 %
Accuracy of ship : 94 %
Accuracy of truck : 92 %
elapsed time: 4545.419 [sec]
MaxPool2d-44 [-1, 512, 1, 1] 0
Linear-45 [-1, 512] 262,656
ReLU-46 [-1, 512] 0
Dropout-47 [-1, 512] 0
Linear-48 [-1, 32] 16,416
ReLU-49 [-1, 32] 0
Dropout-50 [-1, 32] 0
Linear-51 [-1, 10] 330
================================================================
Total params: 15,002,538
Trainable params: 15,002,538
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.01
Forward/backward pass size (MB): 6.58
Params size (MB): 57.23
Estimated Total Size (MB): 63.82
----------------------------------------------------------------
Ir = 0.001
Ir = 0.00025
[1, 200] loss: 2.173 Accuracy: 16.72 %
[1, 400] loss: 2.036 Accuracy: 19.80 %
[1, 600] loss: 1.962 Accuracy: 22.53 %
[1, 800] loss: 1.892 Accuracy: 26.34 %
...
[20, 800] loss: 0.418 Accuracy: 75.94 %
[20, 1000] loss: 0.433 Accuracy: 75.89 %
[20, 1200] loss: 0.429 Accuracy: 75.61 %
Finished Training
Accuracy: 76.10 %
GroundTruth: cat ship ship plane
Predicted: cat ship ship ship
Accuracy of plane : 76 %
Accuracy of car : 90 %
Accuracy of bird : 61 %
Accuracy of cat : 46 %
Accuracy of deer : 69 %
Accuracy of dog : 62 %
Accuracy of frog : 85 %
Accuracy of horse : 82 %
Accuracy of ship : 91 %
Accuracy of truck : 88 %
elapsed time: 1903.856 [sec]
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 6, 28, 28] 456
MaxPool2d-2 [-1, 6, 14, 14] 0
Conv2d-3 [-1, 16, 10, 10] 2,416
MaxPool2d-4 [-1, 16, 5, 5] 0
Linear-5 [-1, 120] 48,120
Linear-6 [-1, 84] 10,164
Linear-7 [-1, 10] 850
================================================================
Total params: 62,006
Trainable params: 62,006
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.01
Forward/backward pass size (MB): 0.06
Params size (MB): 0.24
Estimated Total Size (MB): 0.31
----------------------------------------------------------------
Ir = 0.00025
[1, 200] loss: 2.126 train_acc: 29.71 % val_acc: 29.66 %
[1, 400] loss: 1.889 train_acc: 33.12 % val_acc: 33.43 %
[1, 600] loss: 1.801 train_acc: 35.55 % val_acc: 35.70 %
...
[20, 800] loss: 1.156 train_acc: 59.33 % val_acc: 55.87 %
[20, 1000] loss: 1.133 train_acc: 59.28 % val_acc: 55.71 %
[20, 1200] loss: 1.167 train_acc: 59.23 % val_acc: 55.81 %
Finished Training
Accuracy: 56.42 %
GroundTruth: cat ship ship plane
Predicted: cat car car ship
Accuracy of plane : 65 %
Accuracy of car : 74 %
Accuracy of bird : 40 %
Accuracy of cat : 36 %
Accuracy of deer : 36 %
Accuracy of dog : 45 %
Accuracy of frog : 69 %
Accuracy of horse : 63 %
Accuracy of ship : 73 %
Accuracy of truck : 57 %
elapsed time: 1277.975 [sec]
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 64, 32, 32] 1,792
Conv2d-2 [-1, 64, 32, 32] 36,928
MaxPool2d-3 [-1, 64, 16, 16] 0
Conv2d-4 [-1, 128, 16, 16] 73,856
Conv2d-5 [-1, 128, 16, 16] 147,584
MaxPool2d-6 [-1, 128, 8, 8] 0
Conv2d-7 [-1, 256, 8, 8] 295,168
Conv2d-8 [-1, 256, 8, 8] 590,080
Conv2d-9 [-1, 256, 8, 8] 590,080
Conv2d-10 [-1, 256, 8, 8] 590,080
MaxPool2d-11 [-1, 256, 4, 4] 0
Linear-12 [-1, 1024] 4,195,328
Linear-13 [-1, 1024] 1,049,600
Linear-14 [-1, 10] 10,250
================================================================
Total params: 7,580,746
Trainable params: 7,580,746
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.01
Forward/backward pass size (MB): 2.23
Params size (MB): 28.92
Estimated Total Size (MB): 31.16
----------------------------------------------------------------
Ir = 0.00025
Accuracy: 79.23 %
GroundTruth: cat ship ship plane
Predicted: cat ship ship plane
Accuracy of plane : 78 %
Accuracy of car : 90 %
Accuracy of bird : 72 %
Accuracy of cat : 64 %
Accuracy of deer : 72 %
Accuracy of dog : 69 %
Accuracy of frog : 82 %
Accuracy of horse : 83 %
Accuracy of ship : 88 %
Accuracy of truck : 93 %
elapsed time: 1068.527 [sec]
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 256, 28, 28] 19,456
MaxPool2d-2 [-1, 256, 14, 14] 0
BatchNorm2d-3 [-1, 256, 14, 14] 512
Conv2d-4 [-1, 512, 10, 10] 3,277,312
MaxPool2d-5 [-1, 512, 5, 5] 0
BatchNorm2d-6 [-1, 512, 5, 5] 1,024
Conv2d-7 [-1, 1924, 4, 4] 3,942,276
MaxPool2d-8 [-1, 1924, 2, 2] 0
BatchNorm2d-9 [-1, 1924, 2, 2] 3,848
Linear-10 [-1, 160] 1,231,520
Linear-11 [-1, 10] 1,610
================================================================
Total params: 8,477,558
Trainable params: 8,477,558
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.01
Forward/backward pass size (MB): 3.24
Params size (MB): 32.34
Estimated Total Size (MB): 35.59
----------------------------------------------------------------
Ir = 0.00025
[1, 200] loss: 1.651 train_acc: 49.06 % val_acc: 47.93 %
[1, 400] loss: 1.375 train_acc: 58.22 % val_acc: 55.22 %
[1, 600] loss: 1.222 train_acc: 62.61 % val_acc: 59.38 %
...
[20, 800] loss: 0.000 train_acc: 100.00 % val_acc: 79.74 %
[20, 1000] loss: 0.000 train_acc: 100.00 % val_acc: 79.77 %
[20, 1200] loss: 0.000 train_acc: 100.00 % val_acc: 79.79 %
Finished Training
Accuracy: 80.05 %
GroundTruth: cat ship ship plane
Predicted: cat ship ship plane
Accuracy of plane : 85 %
Accuracy of car : 93 %
Accuracy of bird : 73 %
Accuracy of cat : 62 %
Accuracy of deer : 74 %
Accuracy of dog : 68 %
Accuracy of frog : 88 %
Accuracy of horse : 86 %
Accuracy of ship : 89 %
Accuracy of truck : 88 %
elapsed time: 3917.718 [sec]
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 256, 28, 28] 19,456
MaxPool2d-2 [-1, 256, 14, 14] 0
BatchNorm2d-3 [-1, 256, 14, 14] 512
Conv2d-4 [-1, 512, 10, 10] 3,277,312
MaxPool2d-5 [-1, 512, 5, 5] 0
BatchNorm2d-6 [-1, 512, 5, 5] 1,024
Linear-7 [-1, 160] 2,048,160
Linear-8 [-1, 10] 1,610
================================================================
Total params: 5,348,074
Trainable params: 5,348,074
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.01
Forward/backward pass size (MB): 2.88
Params size (MB): 20.40
Estimated Total Size (MB): 23.30
----------------------------------------------------------------
Ir = 0.00025
[1, 200] loss: 1.654 train_acc: 51.47 % val_acc: 49.53 %
[1, 400] loss: 1.380 train_acc: 59.47 % val_acc: 55.60 %
[1, 600] loss: 1.223 train_acc: 63.02 % val_acc: 58.57 %
...
[20, 800] loss: 0.001 train_acc: 100.00 % val_acc: 77.33 %
[20, 1000] loss: 0.001 train_acc: 100.00 % val_acc: 77.37 %
[20, 1200] loss: 0.001 train_acc: 100.00 % val_acc: 77.42 %
Finished Training
Accuracy: 77.37 %
GroundTruth: cat ship ship plane
Predicted: cat ship ship plane
Accuracy of plane : 84 %
Accuracy of car : 89 %
Accuracy of bird : 65 %
Accuracy of cat : 51 %
Accuracy of deer : 71 %
Accuracy of dog : 65 %
Accuracy of frog : 85 %
Accuracy of horse : 83 %
Accuracy of ship : 88 %
Accuracy of truck : 90 %
elapsed time: 3419.362 [sec]