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fun.py
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fun.py
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"""
Just a minimal training script for trying out the efficientnet implementation
on CIFAR
"""
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import numpy as np
from tqdm import tqdm
from efficientnet import EfficientNet, Config
from efficientnet.randaugment import RandAugment
parser = argparse.ArgumentParser(description="CIFAR10 Training")
parser.add_argument('--lr', default=0.1, type=float, help="learning_rate")
parser.add_argument('--epochs', default=10, type=int, help="number of epochs")
parser.add_argument('--timm', action="store_true", help="Use TIMM implementation")
args = parser.parse_args()
# Cuda stuff
device = 'cuda' if torch.cuda.is_available() else 'cpu'
# Load the model
if args.timm:
import timm
model = timm.create_model('efficientnet_b0', pretrained=False)
else:
model = EfficientNet(Config.B0, num_classes=10)
model.to(device)
# some data fun
transform = transforms.Compose(
[
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
]
)
transform_test = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))
]
)
trainset = torchvision.datasets.CIFAR10(
root='./data',
train=True,
download=True,
transform=transform
)
trainloader = torch.utils.data.DataLoader(
trainset,
batch_size=32,
shuffle=True
)
testset = torchvision.datasets.CIFAR10(
root='./data',
train=False,
download=True,
transform=transform_test
)
testloader = torch.utils.data.DataLoader(
testset,
batch_size=32,
shuffle=False
)
EPOCHS = 10
criterion = nn.CrossEntropyLoss()
opt = optim.SGD(model.parameters(), lr=0.1,
momentum=0.9, weight_decay=5e-4)
def train(epoch):
model.train()
train_loss = 0
correct = 0
total = 0
loop = tqdm(enumerate(trainloader), total=len(trainloader))
for i, (inputs, labels) in loop:
inputs, labels = inputs.to(device), labels.to(device)
opt.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
opt.step()
train_loss += loss.item()
_, predicted = outputs.max(1)
total += labels.size(0)
correct += predicted.eq(labels).sum().item()
# Update progress bar
loop.set_description("EPOCH [{}/{}]".format(epoch+1, EPOCHS))
loop.set_postfix(loss=train_loss/(i+1),
acc=100.*correct/total)
def test(epoch):
model.eval()
test_loss = 0
correct = 0
total = 0
loop = tqdm(enumerate(testloader), total=len(testloader))
for i, (inputs, labels) in loop:
inputs, labels = inputs.to(device), labels.to(device)
outputs = model(inputs)
loss = criterion(outputs, labels)
test_loss += loss.item()
_, predicted = outputs.max(1)
total += labels.size(0)
correct += predicted.eq(labels).sum().item()
# Update progress bar
loop.set_description("EPOCH [{}/{}]".format(epoch+1, EPOCHS))
loop.set_postfix(loss=test_loss/(i+1),
acc=100.*correct/total)
for i in range(EPOCHS):
train(i)
test(i)