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January 16, 2019 17:28
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import sys | |
import collections | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
import torchvision | |
import torchvision.transforms as transforms | |
import catalyst | |
from catalyst.dl.callbacks import ( | |
ClassificationLossCallback, | |
Logger, TensorboardLogger, | |
OptimizerCallback, SchedulerCallback, CheckpointCallback, | |
PrecisionCallback, OneCycleLR) | |
from catalyst.dl.runner import ClassificationRunner | |
class Net(nn.Module): | |
def __init__(self): | |
super(Net, self).__init__() | |
self.conv1 = nn.Conv2d(3, 6, 5) | |
self.pool = nn.MaxPool2d(2, 2) | |
self.conv2 = nn.Conv2d(6, 16, 5) | |
self.fc1 = nn.Linear(16 * 5 * 5, 120) | |
self.fc2 = nn.Linear(120, 84) | |
self.fc3 = nn.Linear(84, 10) | |
def forward(self, x): | |
x = self.pool(F.relu(self.conv1(x))) | |
x = self.pool(F.relu(self.conv2(x))) | |
x = x.view(-1, 16 * 5 * 5) | |
x = F.relu(self.fc1(x)) | |
x = F.relu(self.fc2(x)) | |
x = self.fc3(x) | |
return x | |
def main(): | |
print('Python version', sys.version) | |
print('Catalyst version:', catalyst.__version__) | |
bs = 32 | |
n_workers = 0 | |
data_transform = transforms.Compose([ | |
transforms.ToTensor(), | |
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) | |
loaders = collections.OrderedDict() | |
trainset = torchvision.datasets.CIFAR10( | |
root='./data', train=True, | |
download=True, transform=data_transform) | |
trainloader = torch.utils.data.DataLoader( | |
trainset, batch_size=bs, | |
shuffle=True, num_workers=n_workers) | |
testset = torchvision.datasets.CIFAR10( | |
root='./data', train=False, | |
download=True, transform=data_transform) | |
testloader = torch.utils.data.DataLoader( | |
testset, batch_size=bs, | |
shuffle=False, num_workers=n_workers) | |
loaders["train"] = trainloader | |
loaders["valid"] = testloader | |
model = Net().cuda() | |
criterion = nn.CrossEntropyLoss() | |
optimizer = torch.optim.Adam(model.parameters()) | |
# scheduler = None # for OneCycle usage | |
# scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[3, 8], gamma=0.3) | |
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.5, patience=2, verbose=True) | |
# the only tricky part | |
n_epochs = 10 | |
logdir = "./logs/cifar_simple_notebook" | |
callbacks = collections.OrderedDict() | |
callbacks["loss"] = ClassificationLossCallback() | |
callbacks["optimizer"] = OptimizerCallback() | |
callbacks["precision"] = PrecisionCallback( | |
precision_args=[1, 3, 5]) | |
# OneCylce custom scheduler callback | |
# callbacks["scheduler"] = OneCycleLR( | |
# cycle_len=n_epochs, | |
# div=3, cut_div=4, momentum_range=(0.95, 0.85)) | |
# Pytorch scheduler callback | |
callbacks["scheduler"] = SchedulerCallback( | |
reduce_metric="precision01") | |
callbacks["saver"] = CheckpointCallback() | |
callbacks["logger"] = Logger() | |
callbacks["tflogger"] = TensorboardLogger() | |
runner = ClassificationRunner( | |
model=model, | |
criterion=criterion, | |
optimizer=optimizer, | |
scheduler=scheduler) | |
runner.train( | |
loaders=loaders, | |
callbacks=callbacks, | |
logdir=logdir, | |
epochs=n_epochs, verbose=True) | |
if __name__ == '__main__': | |
main() |
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