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import torch
import torch.nn as nn
import os
import numpy as np
from models import spinal_net
import decoder
import loss
from dataset import BaseDataset
def collater(data):
out_data_dict = {}
for name in data[0]:
out_data_dict[name] = []
for sample in data:
for name in sample:
out_data_dict[name].append(torch.from_numpy(sample[name]))
for name in out_data_dict:
out_data_dict[name] = torch.stack(out_data_dict[name], dim=0)
return out_data_dict
class Network(object):
def __init__(self, args):
torch.manual_seed(317)
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
heads = {'hm': args.num_classes,
'reg': 2*args.num_classes,
'wh': 2*4,}
self.model = spinal_net.SpineNet(heads=heads,
pretrained=True,
down_ratio=args.down_ratio,
final_kernel=1,
head_conv=256)
self.num_classes = args.num_classes
self.decoder = decoder.DecDecoder(K=args.K, conf_thresh=args.conf_thresh)
self.dataset = {'spinal': BaseDataset}
def save_model(self, path, epoch, model):
if isinstance(model, torch.nn.DataParallel):
state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
data = {'epoch': epoch, 'state_dict': state_dict}
torch.save(data, path)
def load_model(self, model, resume, strict=True):
checkpoint = torch.load(resume, map_location=lambda storage, loc: storage)
print('loaded weights from {}, epoch {}'.format(resume, checkpoint['epoch']))
state_dict_ = checkpoint['state_dict']
state_dict = {}
for k in state_dict_:
if k.startswith('module') and not k.startswith('module_list'):
state_dict[k[7:]] = state_dict_[k]
else:
state_dict[k] = state_dict_[k]
model_state_dict = model.state_dict()
if not strict:
for k in state_dict:
if k in model_state_dict:
if state_dict[k].shape != model_state_dict[k].shape:
print('Skip loading parameter {}, required shape{}, ' \
'loaded shape{}.'.format(k, model_state_dict[k].shape, state_dict[k].shape))
state_dict[k] = model_state_dict[k]
else:
print('Drop parameter {}.'.format(k))
for k in model_state_dict:
if not (k in state_dict):
print('No param {}.'.format(k))
state_dict[k] = model_state_dict[k]
model.load_state_dict(state_dict, strict=False)
return model
def train_network(self, args):
save_path = 'weights_'+args.dataset
if not os.path.exists(save_path):
os.mkdir(save_path)
self.optimizer = torch.optim.Adam(self.model.parameters(), args.init_lr)
scheduler = torch.optim.lr_scheduler.ExponentialLR(self.optimizer, gamma=0.96, last_epoch=-1)
if args.ngpus>0:
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
self.model = nn.DataParallel(self.model)
self.model.to(self.device)
criterion = loss.LossAll()
print('Setting up data...')
dataset_module = self.dataset[args.dataset]
dsets = {x: dataset_module(data_dir=args.data_dir,
phase=x,
input_h=args.input_h,
input_w=args.input_w,
down_ratio=args.down_ratio)
for x in ['train', 'val']}
dsets_loader = {'train': torch.utils.data.DataLoader(dsets['train'],
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True,
collate_fn=collater),
'val':torch.utils.data.DataLoader(dsets['val'],
batch_size=1,
shuffle=False,
num_workers=1,
pin_memory=True,
collate_fn=collater)}
print('Starting training...')
train_loss = []
val_loss = []
for epoch in range(1, args.num_epoch+1):
print('-'*10)
print('Epoch: {}/{} '.format(epoch, args.num_epoch))
epoch_loss = self.run_epoch(phase='train',
data_loader=dsets_loader['train'],
criterion=criterion)
train_loss.append(epoch_loss)
scheduler.step(epoch)
epoch_loss = self.run_epoch(phase='val',
data_loader=dsets_loader['val'],
criterion=criterion)
val_loss.append(epoch_loss)
np.savetxt(os.path.join(save_path, 'train_loss.txt'), train_loss, fmt='%.6f')
np.savetxt(os.path.join(save_path, 'val_loss.txt'), val_loss, fmt='%.6f')
if epoch % 10 == 0 or epoch ==1:
self.save_model(os.path.join(save_path, 'model_{}.pth'.format(epoch)), epoch, self.model)
if len(val_loss)>1:
if val_loss[-1]<np.min(val_loss[:-1]):
self.save_model(os.path.join(save_path, 'model_last.pth'), epoch, self.model)
def run_epoch(self, phase, data_loader, criterion):
if phase == 'train':
self.model.train()
else:
self.model.eval()
running_loss = 0.
for data_dict in data_loader:
for name in data_dict:
data_dict[name] = data_dict[name].to(device=self.device)
if phase == 'train':
self.optimizer.zero_grad()
with torch.enable_grad():
pr_decs = self.model(data_dict['input'])
loss = criterion(pr_decs, data_dict)
loss.backward()
self.optimizer.step()
else:
with torch.no_grad():
pr_decs = self.model(data_dict['input'])
loss = criterion(pr_decs, data_dict)
running_loss += loss.item()
epoch_loss = running_loss / len(data_loader)
print('{} loss: {}'.format(phase, epoch_loss))
return epoch_loss
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