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mnist_runner.py
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mnist_runner.py
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from typing import Dict, Union, Tuple
import torch
from torch import nn
import torchvision
from easytorch import Runner
from easytorch.device import to_device
from conv_net import ConvNet
class MNISTRunner(Runner):
"""MNISTRunner
"""
def init_training(self, cfg: Dict):
"""Initialize training.
Including loss, training meters, etc.
Args:
cfg (Dict): config
"""
super().init_training(cfg)
self.loss = nn.NLLLoss()
self.loss = to_device(self.loss)
self.register_epoch_meter('train_loss', 'train', '{:.2f}')
def init_validation(self, cfg: Dict):
"""Initialize validation.
Including validation meters, etc.
Args:
cfg (Dict): config
"""
super().init_validation(cfg)
self.register_epoch_meter('val_acc', 'val', '{:.2f}%')
@staticmethod
def define_model(cfg: Dict) -> nn.Module:
"""Define model.
If you have multiple models, insert the name and class into the dict below,
and select it through ```config```.
Args:
cfg (Dict): config
Returns:
model (nn.Module)
"""
return {
'conv_net': ConvNet
}[cfg['MODEL']['NAME']](**cfg['MODEL'].get('PARAM', {}))
@staticmethod
def build_train_dataset(cfg: Dict):
"""Build MNIST train dataset
Args:
cfg (Dict): config
Returns:
train dataset (Dataset)
"""
return torchvision.datasets.MNIST(
cfg['TRAIN']['DATA']['DIR'], train=True, download=True,
transform=torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(
(0.1307,), (0.3081,))
])
)
@staticmethod
def build_val_dataset(cfg: Dict):
"""Build MNIST val dataset
Args:
cfg (Dict): config
Returns:
train dataset (Dataset)
"""
return torchvision.datasets.MNIST(
cfg['VAL']['DATA']['DIR'], train=False, download=True,
transform=torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(
(0.1307,), (0.3081,))
])
)
def train_iters(self, epoch: int, iter_index: int, data: Union[torch.Tensor, Tuple]) -> torch.Tensor:
"""Training details.
Args:
epoch (int): current epoch.
iter_index (int): current iter.
data (torch.Tensor or tuple): Data provided by DataLoader
Returns:
loss (torch.Tensor)
"""
input_, target_ = data
input_ = to_device(input_)
target_ = to_device(target_)
output = self.model(input_)
loss = self.loss(output, target_)
self.update_epoch_meter('train_loss', loss.item())
return loss
def val_iters(self, iter_index: int, data: Union[torch.Tensor, Tuple]):
"""Validation details.
Args:
iter_index (int): current iter.
data (torch.Tensor or tuple): Data provided by DataLoader
"""
input_, target_ = data
input_ = to_device(input_)
target_ = to_device(target_)
output = self.model(input_)
pred = output.data.max(1, keepdim=True)[1]
self.update_epoch_meter('val_acc', 100 * pred.eq(target_.data.view_as(pred)).sum())