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main.py
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main.py
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import os
import sys
import json
import numpy as np
import torch
from torch import nn
from torch import optim
from torch.optim import lr_scheduler
from opts import parse_opts
from model import generate_model
from mean import get_mean, get_std
from spatial_transforms import *
from temporal_transforms import *
from target_transforms import ClassLabel, VideoID
from target_transforms import Compose as TargetCompose
from dataset import get_training_set, get_validation_set, get_test_set
from utils import *
from train import train_epoch
from validation import val_epoch
import test
if __name__ == '__main__':
opt = parse_opts()
if opt.root_path != '':
opt.video_path = os.path.join(opt.root_path, opt.video_path)
opt.annotation_path = os.path.join(opt.root_path, opt.annotation_path)
opt.result_path = os.path.join(opt.root_path, opt.result_path)
if not os.path.exists(opt.result_path):
os.makedirs(opt.result_path)
if opt.resume_path:
opt.resume_path = os.path.join(opt.root_path, opt.resume_path)
if opt.pretrain_path:
opt.pretrain_path = os.path.join(opt.root_path, opt.pretrain_path)
opt.scales = [opt.initial_scale]
for i in range(1, opt.n_scales):
opt.scales.append(opt.scales[-1] * opt.scale_step)
opt.arch = '{}'.format(opt.model)
opt.mean = get_mean(opt.norm_value, dataset=opt.mean_dataset)
opt.std = get_std(opt.norm_value)
opt.store_name = '_'.join([opt.dataset, opt.model, str(opt.width_mult) + 'x',
opt.modality, str(opt.sample_duration)])
print(opt)
with open(os.path.join(opt.result_path, 'opts.json'), 'w') as opt_file:
json.dump(vars(opt), opt_file)
torch.manual_seed(opt.manual_seed)
model, parameters = generate_model(opt)
print(model)
criterion = nn.CrossEntropyLoss()
if not opt.no_cuda:
criterion = criterion.cuda()
if opt.no_mean_norm and not opt.std_norm:
norm_method = Normalize([0, 0, 0], [1, 1, 1])
elif not opt.std_norm:
norm_method = Normalize(opt.mean, [1, 1, 1])
else:
norm_method = Normalize(opt.mean, opt.std)
if not opt.no_train:
assert opt.train_crop in ['random', 'corner', 'center']
if opt.train_crop == 'random':
crop_method = MultiScaleRandomCrop(opt.scales, opt.sample_size)
elif opt.train_crop == 'corner':
crop_method = MultiScaleCornerCrop(opt.scales, opt.sample_size)
elif opt.train_crop == 'center':
crop_method = MultiScaleCornerCrop(
opt.scales, opt.sample_size, crop_positions=['c'])
spatial_transform = Compose([
RandomHorizontalFlip(),
#RandomRotate(),
#RandomResize(),
crop_method,
#MultiplyValues(),
#Dropout(),
#SaltImage(),
#Gaussian_blur(),
#SpatialElasticDisplacement(),
ToTensor(opt.norm_value), norm_method
])
temporal_transform = TemporalRandomCrop(opt.sample_duration, opt.downsample)
target_transform = ClassLabel()
training_data = get_training_set(opt, spatial_transform,
temporal_transform, target_transform)
train_loader = torch.utils.data.DataLoader(
training_data,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.n_threads,
pin_memory=True)
train_logger = Logger(
os.path.join(opt.result_path, 'train.log'),
['epoch', 'loss', 'prec1', 'prec5', 'lr'])
train_batch_logger = Logger(
os.path.join(opt.result_path, 'train_batch.log'),
['epoch', 'batch', 'iter', 'loss', 'prec1', 'prec5', 'lr'])
if opt.nesterov:
dampening = 0
else:
dampening = opt.dampening
optimizer = optim.SGD(
parameters,
lr=opt.learning_rate,
momentum=opt.momentum,
dampening=dampening,
weight_decay=opt.weight_decay,
nesterov=opt.nesterov)
scheduler = lr_scheduler.ReduceLROnPlateau(
optimizer, 'min', patience=opt.lr_patience)
if not opt.no_val:
spatial_transform = Compose([
Scale(opt.sample_size),
CenterCrop(opt.sample_size),
ToTensor(opt.norm_value), norm_method
])
#temporal_transform = LoopPadding(opt.sample_duration)
temporal_transform = TemporalCenterCrop(opt.sample_duration, opt.downsample)
target_transform = ClassLabel()
validation_data = get_validation_set(
opt, spatial_transform, temporal_transform, target_transform)
val_loader = torch.utils.data.DataLoader(
validation_data,
batch_size=16,
shuffle=False,
num_workers=opt.n_threads,
pin_memory=True)
val_logger = Logger(
os.path.join(opt.result_path, 'val.log'), ['epoch', 'loss', 'prec1', 'prec5'])
best_prec1 = 0
if opt.resume_path:
print('loading checkpoint {}'.format(opt.resume_path))
checkpoint = torch.load(opt.resume_path)
assert opt.arch == checkpoint['arch']
best_prec1 = checkpoint['best_prec1']
opt.begin_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
print('run')
for i in range(opt.begin_epoch, opt.n_epochs + 1):
if not opt.no_train:
adjust_learning_rate(optimizer, i, opt)
train_epoch(i, train_loader, model, criterion, optimizer, opt,
train_logger, train_batch_logger)
state = {
'epoch': i,
'arch': opt.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_prec1': best_prec1
}
save_checkpoint(state, False, opt)
if not opt.no_val:
validation_loss, prec1 = val_epoch(i, val_loader, model, criterion, opt,
val_logger)
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
state = {
'epoch': i,
'arch': opt.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
'best_prec1': best_prec1
}
save_checkpoint(state, is_best, opt)
if opt.test:
spatial_transform = Compose([
Scale(int(opt.sample_size / opt.scale_in_test)),
CornerCrop(opt.sample_size, opt.crop_position_in_test),
ToTensor(opt.norm_value), norm_method
])
# temporal_transform = LoopPadding(opt.sample_duration, opt.downsample)
temporal_transform = TemporalRandomCrop(opt.sample_duration, opt.downsample)
target_transform = VideoID()
test_data = get_test_set(opt, spatial_transform, temporal_transform,
target_transform)
test_loader = torch.utils.data.DataLoader(
test_data,
batch_size=16,
shuffle=False,
num_workers=opt.n_threads,
pin_memory=True)
test.test(test_loader, model, opt, test_data.class_names)