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train.py
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import torch.nn as nn
import time
from torch.autograd import Variable
from torch.utils.data import DataLoader, random_split
from utils import *
from datasets.dataset import *
def get_train_loader(video_path, annotation_path, dataset_name):
kwargs = {'num_workers': 2, 'pin_memory': True} if torch.cuda.is_available() else {
'num_workers': 2}
train_dataset = get_dataset(video_path, annotation_path, dataset_name,train= True)
val_split = 0.05
total_train_samples = len(train_dataset)
total_val_samples = round(total_train_samples * val_split)
train, val = random_split(train_dataset, [total_train_samples - total_val_samples,
total_val_samples])
train_loader = DataLoader(train, batch_size=32, shuffle=True, **kwargs)
val_loader = DataLoader(val, batch_size=16, shuffle=True, **kwargs)
return train_loader, val_loader
def train_epoch(epoch, data_loader, model, optimizer):
print('train at epoch {}'.format(epoch))
model.train()
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
end_time = time.time()
for i, data in enumerate(data_loader):
inputs, targets = data[0], data[-1]
data_time.update(time.time() - end_time)
if torch.cuda.is_available():
targets = targets.cuda()
inputs = inputs.cuda()
inputs = Variable(inputs)
targets = Variable(targets)
outputs = model(inputs)
criterion = nn.CrossEntropyLoss()
loss = criterion(outputs, targets)
losses.update(loss.data, inputs.size(0))
prec1, prec5 = calculate_accuracy(outputs.data, targets.data, topk=(1, 5))
top1.update(prec1, inputs.size(0))
top5.update(prec5, inputs.size(0))
optimizer.zero_grad()
loss.backward()
optimizer.step()
batch_time.update(time.time() - end_time)
end_time = time.time()
if i % 50 == 0:
print('Epoch: [{0}][{1}/{2}]\t lr: {lr:.5f}\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.5f} ({top1.avg:.5f})\t'
'Prec@5 {top5.val:.5f} ({top5.avg:.5f})'.format(
epoch,
i,
len(data_loader),
batch_time=batch_time,
data_time=data_time,
loss=losses,
top1=top1,
top5=top5,
lr=optimizer.param_groups[0]['lr']))
def val_epoch(epoch, data_loader, model):
print('validation at epoch {}'.format(epoch))
model.eval()
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
end_time = time.time()
with torch.no_grad():
for i, data in enumerate(data_loader):
inputs, targets = data[0], data[-1]
data_time.update(time.time() - end_time)
if torch.cuda.is_available():
targets = targets.cuda()
inputs = inputs.cuda()
inputs = Variable(inputs)
targets = Variable(targets)
outputs = model(inputs)
criterion = nn.CrossEntropyLoss()
loss = criterion(outputs, targets)
prec1, prec5 = calculate_accuracy(outputs.data, targets.data, topk=(1, 5))
top1.update(prec1, inputs.size(0))
top5.update(prec5, inputs.size(0))
losses.update(loss.data, inputs.size(0))
batch_time.update(time.time() - end_time)
end_time = time.time()
print('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.5f} ({batch_time.avg:.5f})\t'
'Data {data_time.val:.5f} ({data_time.avg:.5f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.5f} ({top1.avg:.5f})\t'
'Prec@5 {top5.val:.5f} ({top5.avg:.5f})'.format(
epoch,
i + 1,
len(data_loader),
batch_time=batch_time,
data_time=data_time,
loss=losses,
top1=top1,
top5=top5))
return losses.avg.item(), top1.avg.item()