class EarlyStopping:
def __init__(self, patience=5, delta=0, verbose=True):
self.patience = patience
self.verbose = verbose
self.best_score = None
self.delta = delta
self.counter = 0
self.early_stop = False
self.checkpoint = Checkpoint("model")
def __call__(self, model, val_loss, optimizer, epoch=None):
score = -val_loss
if self.best_score == None or score > self.best_score + self.delta:
self.counter = 0
self.best_score = score
self.checkpoint.save(model, optimizer, val_loss, epoch)
elif score <= self.best_score + self.delta:
self.counter += 1
if self.counter >= self.patience:
self.early_stop = True
# reset the counter
def reset_counter(self):
self.counter = 0
# check if the early stopping criteria is meet
def get_early_stop(self):
return self.early_stop
# return class checkpoint (for loading & saving model)
def get_checkpoint(self):
return self.checkpoint
def __init__(self, patience=5, delta=0, verbose=True):
self.patience = patience
self.verbose = verbose
self.best_score = None
self.delta = delta
self.counter = 0
self.early_stop = False
self.checkpoint = Checkpoint("model")
def main():
opt = TrainOptions().parse()
if opt.sr_dir == '':
print('sr directory is null.')
exit()
sr_pretrain_dir = os.path.join(
opt.exp_dir, opt.exp_id, opt.sr_dir + '-' + opt.load_prefix_pose[0:-1])
if not os.path.isdir(sr_pretrain_dir):
os.makedirs(sr_pretrain_dir)
train_history = ASNTrainHistory()
# print(train_history.lr)
# exit()
checkpoint_hg = Checkpoint()
# visualizer = Visualizer(opt)
# log_name = opt.resume_prefix_pose + 'log.txt'
# visualizer.log_path = sr_pretrain_dir + '/' + log_name
train_distri_path = sr_pretrain_dir + '/' + 'train_rotations.txt'
train_distri_path_2 = sr_pretrain_dir + '/' + 'train_rotations_copy.txt'
# train_distri_path = sr_pretrain_dir + '/' + 'train_rotations.txt'
# train_distri_path_2 = sr_pretrain_dir + '/' + 'train_rotations_copy.txt'
val_distri_path = sr_pretrain_dir + '/' + 'val_rotations.txt'
val_distri_path_2 = sr_pretrain_dir + '/' + 'val_rotations_copy.txt'
# val_distri_path = sr_pretrain_dir + '/' + 'val_rotations.txt'
# val_distri_path_2 = sr_pretrain_dir + '/' + 'val_rotations_copy.txt'
if opt.dataset == 'mpii':
num_classes = 16
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu_id
hg = model.create_hg(num_stacks=2,
num_modules=1,
num_classes=num_classes,
chan=256)
hg = torch.nn.DataParallel(hg).cuda()
if opt.load_prefix_pose == '':
print('please input the checkpoint name of the pose model')
# exit()
# checkpoint_hg.save_prefix = os.path.join(opt.exp_dir, opt.exp_id, opt.resume_prefix_pose)
checkpoint_hg.load_prefix = os.path.join(opt.exp_dir, opt.exp_id,
opt.load_prefix_pose)[0:-1]
checkpoint_hg.load_checkpoint(hg)
print 'collecting training distributions ...\n'
train_distri_list = collect_train_valid_data(train_distri_path,
train_distri_path_2,
hg,
opt,
is_train=True)
print 'collecting validation distributions ...\n'
val_distri_list = collect_train_valid_data(val_distri_path,
val_distri_path_2,
hg,
opt,
is_train=False)
def main():
opt = TrainOptions().parse()
train_history = TrainHistory()
checkpoint = Checkpoint(opt)
visualizer = Visualizer(opt)
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu_ids
cudnn.benchmark = True
"""build graph"""
net = model.CreateNet(opt)
"""optimizer"""
optimizer = model.CreateAdamOptimizer(opt, net)
#net = torch.nn.DataParallel(net).cuda()
net.cuda()
"""optionally resume from a checkpoint"""
checkpoint.load_checkpoint(net, optimizer, train_history)
"""load data"""
train_list = os.path.join(opt.data_dir, opt.train_list)
train_loader = torch.utils.data.DataLoader(ImageLoader(
train_list, transforms.ToTensor(), is_train=True),
batch_size=opt.bs,
shuffle=True,
num_workers=opt.nThreads,
pin_memory=True)
val_list = os.path.join(opt.data_dir, opt.val_list)
val_loader = torch.utils.data.DataLoader(ImageLoader(val_list,
transforms.ToTensor(),
is_train=False),
batch_size=opt.bs,
shuffle=False,
num_workers=opt.nThreads,
pin_memory=True)
"""training and validation"""
for epoch in range(opt.resume_epoch, opt.nEpochs):
model.AdjustLR(opt, optimizer, epoch)
# train for one epoch
train_loss_det, train_loss_reg, tran_loss = \
train(train_loader, net, optimizer, epoch, visualizer)
# evaluate on validation set
val_loss_det, val_loss_reg, val_loss, det_rmse, reg_rmse = \
validate(val_loader, net, epoch, visualizer, is_show=False)
# update training history
e = OrderedDict([('epoch', epoch)])
lr = OrderedDict([('lr', opt.lr)])
loss = OrderedDict([('train_loss_det', train_loss_det),
('train_loss_reg', train_loss_reg),
('val_loss_det', val_loss_det),
('val_loss_reg', val_loss_reg)])
rmse = OrderedDict([('det_rmse', det_rmse), ('val_rmse', reg_rmse)])
train_history.update(e, lr, loss, rmse)
checkpoint.save_checkpoint(net, optimizer, train_history)
visualizer.plot_train_history(train_history)
# plot best validation
if train_history.is_best:
visualizer.imgpts_win_id = 4
validate(val_loader, net, epoch, visualizer, is_show=True)
def main():
par = Params(sys.argv)
random.seed(par.seed)
torch.manual_seed(par.seed)
if torch.cuda.is_available(): torch.cuda.manual_seed_all(par.seed)
if par.trn and par.val:
chk = Checkpoint(par.dir)
if chk.contains_model: ####### resume training ####################################
cfg, mod, opt = chk.load(par) ### also moves to GPU if cfg.cuda
# cfg.update_par(par) ### updates par in cfg
print_time('Learning [resume It={}]...'.format(cfg.n_iters_sofar))
else: ######################## training from scratch ##############################
cfg = Config(par) ### reads cfg and par (reads vocabularies)
mod = Model(cfg)
if cfg.cuda: mod.cuda() ### moves to GPU
opt = Optimizer(cfg, mod) #build Optimizer
print_time('Learning [from scratch]...')
trn = Dataset(par.trn,
cfg.svoc,
cfg.tvoc,
par.batch_size,
par.max_src_len,
par.max_tgt_len,
do_shuffle=True,
do_filter=True,
is_test=False)
val = Dataset(par.val,
cfg.svoc,
cfg.tvoc,
par.batch_size,
par.max_src_len,
par.max_tgt_len,
do_shuffle=True,
do_filter=True,
is_test=True)
Training(cfg, mod, opt, trn, val, chk)
elif par.tst: #################### inference ##########################################
chk = Checkpoint()
cfg, mod, opt = chk.load(par, par.chk)
# cfg.update_par(par) ### updates cfg options with pars
tst = Dataset(par.tst,
cfg.svoc,
cfg.tvoc,
par.batch_size,
0,
0,
do_shuffle=False,
do_filter=False,
is_test=True)
print_time('Inference [model It={}]...'.format(cfg.n_iters_sofar))
Inference(cfg, mod, tst)
def main():
opt = TrainOptions().parse()
train_history = TrainHistory()
checkpoint = Checkpoint(opt)
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu_id
"""Architecture"""
net = MakeLinearModel(1024, 16)
net = torch.nn.DataParallel(net).cuda()
checkpoint.load_checkpoint(net, train_history, '/best-single.pth.tar')
"""Uploading Mean and SD"""
path_to_data = '.../multi-view-pose-estimation/dataset/'
#mean and sd of 2d poses in training dataset
Mean_2D = np.loadtxt(path_to_data + 'Mean_2D.txt')
Mean_2D = Mean_2D.astype('float32')
Mean_2D = torch.from_numpy(Mean_2D)
Mean_Delta = np.loadtxt(path_to_data + 'Mean_Delta.txt')
Mean_Delta = Mean_Delta.astype('float32')
Mean_Delta = torch.from_numpy(Mean_Delta)
Mean_Delta = torch.autograd.Variable(Mean_Delta.cuda(async=True),
requires_grad=False)
Mean_3D = np.loadtxt(path_to_data + 'Mean_3D.txt')
Mean_3D = Mean_3D.astype('float32')
Mean_3D = torch.from_numpy(Mean_3D)
Mean_3D = torch.autograd.Variable(Mean_3D.cuda(async=True),
requires_grad=False)
SD_2D = np.loadtxt(path_to_data + 'SD_2D.txt')
SD_2D = SD_2D.astype('float32')
SD_2D = torch.from_numpy(SD_2D)
SD_Delta = np.loadtxt(path_to_data + 'SD_Delta.txt')
SD_Delta = SD_Delta.astype('float32')
SD_Delta = torch.from_numpy(SD_Delta)
SD_Delta = torch.autograd.Variable(SD_Delta.cuda(async=True),
requires_grad=False)
SD_3D = np.loadtxt(path_to_data + 'SD_3D.txt')
SD_3D = SD_3D.astype('float32')
SD_3D = torch.from_numpy(SD_3D)
SD_3D = torch.autograd.Variable(SD_3D.cuda(async=True),
requires_grad=False)
"""Loading Data"""
train_list = 'train_list_4view.txt'
train_loader = torch.utils.data.DataLoader(data4view.PtsList(
train_list, is_train=True),
batch_size=opt.bs,
shuffle=True,
num_workers=opt.nThreads,
pin_memory=True)
val_list = 'valid_list_4view.txt'
val_loader = torch.utils.data.DataLoader(data4view.PtsList(val_list,
is_train=False),
batch_size=opt.bs,
shuffle=False,
num_workers=opt.nThreads,
pin_memory=True)
demo_list = 'demo_list_4view.txt'
demo_loader = torch.utils.data.DataLoader(data4view.PtsList(
demo_list, is_train=False),
batch_size=1,
shuffle=False,
num_workers=opt.nThreads,
pin_memory=True)
"""Optimizer"""
optimizer = torch.optim.Adam(net.parameters(),
lr=opt.lr,
betas=(0.9, 0.999),
weight_decay=0)
"""Validation"""
if evaluate_mode:
# evaluate on validation set
checkpoint.load_checkpoint(net, train_history, '/best-multi.pth.tar')
val_loss, val_pckh = validate(val_loader, net, Mean_2D, Mean_Delta,
Mean_3D, SD_2D, SD_Delta, SD_3D, 0, opt)
return
"""Demo"""
if demo_mode:
# Grab a random batch to visualize
checkpoint.load_checkpoint(net, train_history, '/best-multi.pth.tar')
demo(demo_loader, net, Mean_2D, Mean_Delta, Mean_3D, SD_2D, SD_Delta,
SD_3D, 0, opt)
return
"""Training"""
for epoch in range(0, opt.nEpochs):
adjust_learning_rate(optimizer, epoch, opt.lr)
# train for one epoch
train_loss = train(train_loader, net, Mean_2D, Mean_Delta, Mean_3D,
SD_2D, SD_Delta, SD_3D, optimizer, epoch, opt)
# evaluate on validation set
val_loss, val_pckh = validate(val_loader, net, Mean_2D, Mean_Delta,
Mean_3D, SD_2D, SD_Delta, SD_3D, epoch,
opt)
# update training history
e = OrderedDict([('epoch', epoch)])
lr = OrderedDict([('lr', opt.lr)])
loss = OrderedDict([('train_loss', train_loss),
('val_loss', val_loss)])
pckh = OrderedDict([('val_pckh', val_pckh)])
train_history.update(e, lr, loss, pckh)
checkpoint.save_checkpoint(net, train_history, 'best-multi.pth.tar')
def train(
self, train_loader, val_loader=None, max_epochs=1000, enable_early_stopping=True
):
if val_loader is None:
enable_early_stopping = False
print()
print("-" * 2, "Training Setup", "-" * 2)
print(f"Maximum Epochs: {max_epochs}")
print(f"Enable Early Stoping: {enable_early_stopping}")
print("-" * 20)
print("*Start Training.")
# model setup
self.model.train().to(self.device)
if self.multi_gpus and torch.cuda.device_count() > 1:
print(f"*Using {torch.cuda.device_count()} GPUs!")
self.model = nn.DataParallel(self.model)
# early stopping instance
if enable_early_stopping:
if self.early_stopping is None:
self.early_stopping = EarlyStopping(patience=5)
else:
self.early_stopping.reset_counter()
# training start!
for epoch in range(1, max_epochs + 1):
running_loss = 0.0
for step, data in enumerate(train_loader, start=1):
inputs, labels = data
inputs, labels = inputs.to(self.device), labels.to(self.device)
# Zero the parameter gradients
self.optimizer.zero_grad()
# forward + backward + optimize
outputs = self.model(inputs)
loss = self.loss_func(outputs, labels)
loss.backward()
self.optimizer.step()
# print statistics
running_loss += loss.item()
if step % 100 == 0 or step == len(train_loader):
print(
f"[{epoch}/{max_epochs}, {step}/{len(train_loader)}] loss: {running_loss / step :.3f}"
)
# train & validation loss
train_loss = running_loss / len(train_loader)
if val_loader is None:
print(f"train loss: {train_loss:.3f}")
else:
# FIXME: fixed the problem that first validation is not correct
val_loss = self.validation(val_loader)
print(f"train loss: {train_loss:.3f}, val loss: {val_loss:.3f}")
if enable_early_stopping:
self.early_stopping(self.model, val_loss, self.optimizer)
if self.early_stopping.get_early_stop() == True:
print("*Early Stopping.")
break
print("*Finished Training!")
if enable_early_stopping:
checkpoint = self.early_stopping.get_checkpoint()
else:
checkpoint = Checkpoint()
checkpoint.tmp_save(self.model, self.optimizer, epoch, val_loss)
self.checkpoint = checkpoint
self.model = checkpoint.load(self.model, self.optimizer)["model"]
return self.model
def main():
# global args, best_prec1
args = parser.parse_args()
print('\n====> Input Arguments')
print(args)
# Tensorboard writer.
global writer
writer = SummaryWriter(log_dir=args.result_path)
# Create dataloader.
print '\n====> Creating dataloader...'
train_loader = get_train_loader(args)
test_loader = get_test_loader(args)
# Load First Glance network.
print '====> Loading the network...'
model = First_Glance(num_classes=args.num_classes, pretrained=True)
"""Load checkpoint and weight of network.
"""
global cp_recorder
if args.checkpoint_dir:
cp_recorder = Checkpoint(args.checkpoint_dir, args.checkpoint_name)
cp_recorder.load_checkpoint(model)
model.cuda()
criterion = nn.CrossEntropyLoss().cuda()
cudnn.benchmark = True
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, weight_decay=args.wd, momentum=args.momentum)
# Train first-glance model.
print '====> Training...'
for epoch in range(cp_recorder.contextual['b_epoch'], args.epoch):
_, _, prec_tri, rec_tri, ap_tri = train_eval(train_loader, test_loader, model, criterion, optimizer, args, epoch)
top1_avg_val, loss_avg_val, prec_val, rec_val, ap_val = validate_eval(test_loader, model, criterion, args, epoch)
# Print result.
writer.add_scalars('mAP (per epoch)', {'train': np.nan_to_num(ap_tri).mean()}, epoch)
writer.add_scalars('mAP (per epoch)', {'valid': np.nan_to_num(ap_val).mean()}, epoch)
print('\n====> Scores')
print('[Epoch {0}]:\n'
' Train:\n'
' Prec@1 {1}\n'
' Recall {2}\n'
' AP {3}\n'
' mAP {4:.3f}\n'
' Valid:\n'
' Prec@1 {5}\n'
' Recall {6}\n'
' AP {7}\n'
' mAP {8:.3f}\n'.format(epoch,
prec_tri, rec_tri, ap_tri, np.nan_to_num(ap_tri).mean(),
prec_val, rec_val, ap_val, np.nan_to_num(ap_val).mean()))
# Record.
writer.add_scalars('Loss (per batch)', {'valid': loss_avg_val}, (epoch+1)*len(train_loader))
writer.add_scalars('Prec@1 (per batch)', {'valid': top1_avg_val}, (epoch+1)*len(train_loader))
writer.add_scalars('mAP (per batch)', {'valid': np.nan_to_num(ap_val).mean()}, (epoch+1)*len(train_loader))
# Save checkpoint.
cp_recorder.record_contextual({'b_epoch': epoch+1, 'b_batch': -1, 'prec': top1_avg_val, 'loss': loss_avg_val,
'class_prec': prec_val, 'class_recall': rec_val, 'class_ap': ap_val, 'mAP': np.nan_to_num(ap_val).mean()})
cp_recorder.save_checkpoint(model)
def main():
opt = TrainOptions().parse()
train_history = TrainHistory()
checkpoint = Checkpoint()
visualizer = Visualizer(opt)
exp_dir = os.path.join(opt.exp_dir, opt.exp_id)
log_name = opt.vis_env + 'log.txt'
visualizer.log_name = os.path.join(exp_dir, log_name)
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu_id
# if opt.dataset == 'mpii':
num_classes = 16
# layer_num = 2
net = create_cu_net(neck_size=4,
growth_rate=32,
init_chan_num=128,
num_classes=num_classes,
layer_num=opt.layer_num,
max_link=1,
inter_loss_num=opt.layer_num)
# num1 = get_n_params(net)
# num2 = get_n_trainable_params(net)
# num3 = get_n_conv_params(net)
# print 'number of params: ', num1
# print 'number of trainalbe params: ', num2
# print 'number of conv params: ', num3
# torch.save(net.state_dict(), 'test-model-size.pth.tar')
# exit()
# device = torch.device("cuda:0")
# net = net.to(device)
net = torch.nn.DataParallel(net).cuda()
global quan_op
quan_op = QuanOp(net)
optimizer = torch.optim.RMSprop(net.parameters(),
lr=opt.lr,
alpha=0.99,
eps=1e-8,
momentum=0,
weight_decay=0)
"""optionally resume from a checkpoint"""
if opt.resume_prefix != '':
# if 'pth' in opt.resume_prefix:
# trunc_index = opt.resume_prefix.index('pth')
# opt.resume_prefix = opt.resume_prefix[0:trunc_index - 1]
# checkpoint.save_prefix = os.path.join(exp_dir, opt.resume_prefix)
checkpoint.save_prefix = exp_dir + '/'
checkpoint.load_prefix = os.path.join(exp_dir, opt.resume_prefix)[0:-1]
checkpoint.load_checkpoint(net, optimizer, train_history)
opt.lr = optimizer.param_groups[0]['lr']
resume_log = True
else:
checkpoint.save_prefix = exp_dir + '/'
resume_log = False
print 'save prefix: ', checkpoint.save_prefix
# model = {'state_dict': net.state_dict()}
# save_path = checkpoint.save_prefix + 'test-model-size.pth.tar'
# torch.save(model, save_path)
# exit()
"""load data"""
train_loader = torch.utils.data.DataLoader(MPII(
'dataset/mpii-hr-lsp-normalizer.json',
'/bigdata1/zt53/data',
is_train=True),
batch_size=opt.bs,
shuffle=True,
num_workers=opt.nThreads,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(MPII(
'dataset/mpii-hr-lsp-normalizer.json',
'/bigdata1/zt53/data',
is_train=False),
batch_size=opt.bs,
shuffle=False,
num_workers=opt.nThreads,
pin_memory=True)
"""optimizer"""
# optimizer = torch.optim.SGD( net.parameters(), lr=opt.lr,
# momentum=opt.momentum,
# weight_decay=opt.weight_decay )
# optimizer = torch.optim.RMSprop(net.parameters(), lr=opt.lr, alpha=0.99,
# eps=1e-8, momentum=0, weight_decay=0)
print type(optimizer)
# idx = range(0, 16)
# idx = [e for e in idx if e not in (6, 7, 8, 9, 12, 13)]
idx = [0, 1, 2, 3, 4, 5, 10, 11, 14, 15]
logger = Logger(os.path.join(opt.exp_dir, opt.exp_id,
'training-summary.txt'),
title='training-summary',
resume=resume_log)
logger.set_names(
['Epoch', 'LR', 'Train Loss', 'Val Loss', 'Train Acc', 'Val Acc'])
if not opt.is_train:
visualizer.log_path = os.path.join(opt.exp_dir, opt.exp_id,
'val_log.txt')
val_loss, val_pckh, predictions = validate(
val_loader, net, train_history.epoch[-1]['epoch'], visualizer, idx,
joint_flip_index, num_classes)
checkpoint.save_preds(predictions)
return
"""training and validation"""
start_epoch = 0
if opt.resume_prefix != '':
start_epoch = train_history.epoch[-1]['epoch'] + 1
for epoch in range(start_epoch, opt.nEpochs):
adjust_lr(opt, optimizer, epoch)
# # train for one epoch
train_loss, train_pckh = train(train_loader, net, optimizer, epoch,
visualizer, idx, opt)
# evaluate on validation set
val_loss, val_pckh, predictions = validate(val_loader, net, epoch,
visualizer, idx,
joint_flip_index,
num_classes)
# visualizer.display_imgpts(imgs, pred_pts, 4)
# exit()
# update training history
e = OrderedDict([('epoch', epoch)])
lr = OrderedDict([('lr', optimizer.param_groups[0]['lr'])])
loss = OrderedDict([('train_loss', train_loss),
('val_loss', val_loss)])
pckh = OrderedDict([('val_pckh', val_pckh)])
train_history.update(e, lr, loss, pckh)
checkpoint.save_checkpoint(net, optimizer, train_history, predictions)
# visualizer.plot_train_history(train_history)
logger.append([
epoch, optimizer.param_groups[0]['lr'], train_loss, val_loss,
train_pckh, val_pckh
])
logger.close()
def main():
opt = TrainOptions().parse()
train_history = TrainHistoryFace()
checkpoint = Checkpoint()
visualizer = Visualizer(opt)
exp_dir = os.path.join(opt.exp_dir, opt.exp_id)
log_name = opt.vis_env + 'log.txt'
visualizer.log_name = os.path.join(exp_dir, log_name)
num_classes = opt.class_num
if not opt.slurm:
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu_id
layer_num = opt.layer_num
order = opt.order
net = create_cu_net(neck_size= 4, growth_rate= 32, init_chan_num= 128,
class_num= num_classes, layer_num= layer_num, order= order,
loss_num= layer_num, use_spatial_transformer= opt.stn,
mlp_tot_layers= opt.mlp_tot_layers, mlp_hidden_units= opt.mlp_hidden_units,
get_mean_from_mlp= opt.get_mean_from_mlp)
# Load the pre-trained model
saved_wt_file = opt.saved_wt_file
if saved_wt_file == "":
print("=> Training from scratch")
else:
print("=> Loading weights from " + saved_wt_file)
checkpoint_t = torch.load(saved_wt_file)
state_dict = checkpoint_t['state_dict']
tt_names=[]
for names in net.state_dict():
tt_names.append(names)
for name, param in state_dict.items():
name = name[7:]
if name not in net.state_dict():
print("=> not load weights '{}'".format(name))
continue
if isinstance(param, Parameter):
param = param.data
if (net.state_dict()[name].shape[0] == param.shape[0]):
net.state_dict()[name].copy_(param)
else:
print("First dim different. Not loading weights {}".format(name))
if (opt.freeze):
print("\n\t\tFreezing basenet parameters\n")
for param in net.parameters():
param.requires_grad = False
"""
for i in range(layer_num):
net.choleskys[i].fc_1.bias.requires_grad = True
net.choleskys[i].fc_1.weight.requires_grad = True
net.choleskys[i].fc_2.bias.requires_grad = True
net.choleskys[i].fc_2.weight.requires_grad = True
net.choleskys[i].fc_3.bias.requires_grad = True
net.choleskys[i].fc_3.weight.requires_grad = True
"""
net.cholesky.fc_1.bias.requires_grad = True
net.cholesky.fc_1.weight.requires_grad = True
net.cholesky.fc_2.bias.requires_grad = True
net.cholesky.fc_2.weight.requires_grad = True
net.cholesky.fc_3.bias.requires_grad = True
net.cholesky.fc_3.weight.requires_grad = True
else:
print("\n\t\tNot freezing anything. Tuning every parameter\n")
for param in net.parameters():
param.requires_grad = True
net = torch.nn.DataParallel(net).cuda() # use multiple GPUs
# Optimizer
if opt.optimizer == "rmsprop":
optimizer = torch.optim.RMSprop(filter(lambda p: p.requires_grad, net.parameters()), lr=opt.lr, alpha=0.99,
eps=1e-8, momentum=0, weight_decay=0)
elif opt.optimizer == "adam":
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, net.parameters()), lr=opt.lr)
else:
print("Unknown Optimizer. Aborting!!!")
sys.exit(0)
print type(optimizer)
# Optionally resume from a checkpoint
if opt.resume_prefix != '':
# if 'pth' in opt.resume_prefix:
# trunc_index = opt.resume_prefix.index('pth')
# opt.resume_prefix = opt.resume_prefix[0:trunc_index - 1]
checkpoint.save_prefix = os.path.join(exp_dir, opt.resume_prefix)
checkpoint.load_prefix = os.path.join(exp_dir, opt.resume_prefix)[0:-1]
checkpoint.load_checkpoint(net, optimizer, train_history)
else:
checkpoint.save_prefix = exp_dir + '/'
print("Save prefix = {}".format(checkpoint.save_prefix))
# Load data
json_path = opt.json_path
train_json = opt.train_json
val_json = opt.val_json
print("Path added to each image path in JSON = {}".format(json_path))
print("Train JSON path = {}".format(train_json))
print("Val JSON path = {}".format(val_json))
if opt.bulat_aug:
# Use Bulat et al Augmentation Scheme
train_loader = torch.utils.data.DataLoader(
FACE(train_json, json_path, is_train= True, scale_factor= 0.2, rot_factor= 50, use_occlusion= True, keep_pts_inside= True),
batch_size=opt.bs, shuffle= True,
num_workers=opt.nThreads, pin_memory= True)
else:
train_loader = torch.utils.data.DataLoader(
FACE(train_json, json_path, is_train= True, keep_pts_inside= True),
batch_size=opt.bs, shuffle= True,
num_workers=opt.nThreads, pin_memory= True)
val_loader = torch.utils.data.DataLoader(
FACE(val_json, json_path, is_train=False),
batch_size=opt.bs, shuffle=False,
num_workers=opt.nThreads, pin_memory=True)
logger = Logger(os.path.join(opt.exp_dir, opt.exp_id, opt.resume_prefix+'face-training-log.txt'),
title='face-training-summary')
logger.set_names(['Epoch', 'LR', 'Train Loss', 'Val Loss', 'Train RMSE', 'Val RMSE', 'Train RMSE Box', 'Val RMSE Box', 'Train RMSE Meta', 'Val RMSE Meta'])
if not opt.is_train:
visualizer.log_path = os.path.join(opt.exp_dir, opt.exp_id, 'val_log.txt')
val_loss, val_rmse, predictions = validate(val_loader, net,
train_history.epoch[-1]['epoch'], visualizer, num_classes, flip_index)
checkpoint.save_preds(predictions)
return
global weights_HG
weights_HG = [float(x) for x in opt.hg_wt.split(",")]
if opt.is_covariance:
print("Covariance used from the heatmap")
else:
print("Covariance calculated from MLP")
if opt.stn:
print("Using spatial transformer on heatmaps")
print ("Postprocessing applied = {}".format(opt.pp))
if (opt.smax):
print("Scaled softmax used with tau = {}".format(opt.tau))
else:
print("No softmax used")
print("Individual Hourglass loss weights")
print(weights_HG)
print("wt_MSE (tradeoff between GLL and MSE in each hourglass)= " + str(opt.wt_mse))
print("wt_gauss_regln (tradeoff between GLL and Gaussian Regularisation in each hourglass)= " + str(opt.wt_gauss_regln))
if opt.bulat_aug:
print("Using Bulat et al, ICCV 2017 Augmentation Scheme")
print("Using Learning Policy {}".format(opt.lr_policy))
chosen_lr_policy = dict_of_functions[opt.lr_policy]
# Optionally resume from a checkpoint
start_epoch = 0
if opt.resume_prefix != '':
start_epoch = train_history.epoch[-1]['epoch'] + 1
# Training and validation
start_epoch = 0
if opt.resume_prefix != '':
start_epoch = train_history.epoch[-1]['epoch'] + 1
train_loss_orig_epoch = []
train_loss_gau_t1_epoch = []
train_loss_gau_t2_epoch = []
train_nme_orig_epoch = []
train_nme_gau_epoch = []
train_nme_new_epoch = []
val_loss_orig_epoch = []
val_loss_gau_t1_epoch = []
val_loss_gau_t2_epoch = []
val_nme_orig_epoch = []
val_nme_gau_epoch = []
val_nme_new_epoch = []
for epoch in range(start_epoch, opt.nEpochs):
chosen_lr_policy(opt, optimizer, epoch)
# Train for one epoch
train_loss, train_loss_mse,train_loss_gau_t1, train_loss_gau_t2,train_rmse_orig, train_rmse_gau, train_rmse_new_gd_box, train_rmse_new_meta_box = train(train_loader, net, optimizer, epoch, visualizer, opt)
#train_loss_gau_epoch.append(train_loss_gau)
train_loss_gau_t1_epoch.append(train_loss_gau_t1)
train_loss_gau_t2_epoch.append(train_loss_gau_t2)
train_nme_orig_epoch.append(train_rmse_orig)
train_nme_gau_epoch.append(train_rmse_gau)
train_loss_orig_epoch.append(train_loss_mse)
# Evaluate on validation set
val_loss, val_loss_mse, val_loss_gau_t1, val_loss_gau_t2 , val_rmse_orig, val_rmse_gau, val_rmse_new_gd_box, val_rmse_new_meta_box, predictions= validate(val_loader, net, epoch, visualizer, opt, num_classes, flip_index)
val_loss_orig_epoch.append(val_loss_mse)
val_loss_gau_t1_epoch.append(val_loss_gau_t1)
val_loss_gau_t2_epoch.append(val_loss_gau_t2)
val_nme_orig_epoch.append(val_rmse_orig)
val_nme_gau_epoch.append(val_rmse_gau)
# Update training history
e = OrderedDict( [('epoch', epoch)] )
lr = OrderedDict( [('lr', optimizer.param_groups[0]['lr'])] )
loss = OrderedDict( [('train_loss', train_loss),('val_loss', val_loss)] )
rmse = OrderedDict( [('val_rmse', val_rmse_gau)] )
train_history.update(e, lr, loss, rmse)
checkpoint.save_checkpoint(net, optimizer, train_history, predictions)
visualizer.plot_train_history_face(train_history)
logger.append([epoch, optimizer.param_groups[0]['lr'], train_loss, val_loss, train_rmse_gau, val_rmse_gau, train_rmse_new_gd_box, val_rmse_new_gd_box, train_rmse_new_meta_box, val_rmse_new_meta_box])
logger.close()
def train():
args = configs.get_args()
use_cuda = args.use_cuda and torch.cuda.is_available()
# prepare dataset
dataset = libs.dataset.MyDataset(min_length=args.min_length)
voc_size = dataset.get_voc_size()
dataloader = DataLoader(dataset, 1, True, drop_last=False)
# prepare model
model = models.TopModuleCNN(voc_size, output_channel=args.output_channel)
if use_cuda:
model = model.cuda()
# load pretrained if asked
if args.resume:
checkpoint_path = Checkpoint.get_certain_checkpoint(
"./experiment/cnn_net", "best")
resume_checkpoint = Checkpoint.load(checkpoint_path)
model = resume_checkpoint.model
optimizer = resume_checkpoint.optimizer
resume_optim = optimizer.optimizer
defaults = resume_optim.param_groups[0]
defaults.pop('params', None)
optimizer.optimizer = resume_optim.__class__(model.parameters(),
**defaults)
start_epoch = resume_checkpoint.epoch
max_ans_acc = resume_checkpoint.max_ans_acc
else:
start_epoch = 1
max_ans_acc = 0
optimizer = NoamOpt(
512, 1, 2000,
optim.Adam(model.parameters(), lr=0, betas=(0.9, 0.98), eps=1e-9))
# define loss
loss = nn.CrossEntropyLoss(weight=torch.tensor([1., 4.]))
if use_cuda:
loss = loss.cuda()
# training
for i in range(start_epoch, args.epochs):
# test the model
if args.resume:
test_ans_acc = max_ans_acc
else:
test_ans_acc = test(DataLoader(dataset, 1, True, drop_last=False),
model, i)
print('For EPOCH {}, total f1: {:.2f}'.format(i, test_ans_acc))
# calculate loss
j = 0
los1 = []
for _, data in enumerate(dataloader):
j += 1
x = data['que'].long()
y = data['ans'].long()
res = data['res'].long()
if use_cuda:
x, y, res = x.cuda(), y.cuda(), res.cuda()
res_pred = model(x, y)
los1.append(loss(res_pred, res).unsqueeze(0))
# apply gradient
if j % args.batch_size == 0:
los1 = torch.cat(los1)
los = los1.sum()
model.zero_grad()
los.backward()
optimizer.step()
los1 = []
print('EPOCH: {}, {} / {}====> LOSS: {:.2f}'.format(
i, j // args.batch_size,
dataloader.__len__() // args.batch_size,
los.item() / args.batch_size))
# save checkpoint
if test_ans_acc > max_ans_acc:
max_ans_acc = test_ans_acc
th_checkpoint = Checkpoint(model=model,
optimizer=optimizer,
epoch=i,
max_ans_acc=max_ans_acc)
th_checkpoint.save_according_name("./experiment/cnn_net", 'best')
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--device',
type=str,
default='gpu',
help='For cpu: \'cpu\', for gpu: \'gpu\'')
parser.add_argument('--chunk_size',
type=int,
default=36,
help='chunk size(sequence length)')
parser.add_argument('--step_size',
type=int,
default=1,
help='sequence split step')
parser.add_argument('--lr', type=float, default=5e-4, help='learning rate')
parser.add_argument('--weight_decay',
type=argtype.check_float,
default='1e-2',
help='weight_decay')
parser.add_argument('--epoch',
type=argtype.epoch,
default='inf',
help='the number of epoch for training')
parser.add_argument('--batch_size',
type=int,
default=256,
help='size of batches for training')
parser.add_argument('--val_ratio',
type=float,
default=.3,
help='validation set ratio')
parser.add_argument('--model_name',
type=str,
default='main_model',
help='model name to save')
parser.add_argument('--transfer',
type=argtype.boolean,
default=False,
help='whether fine tuning or not')
parser.add_argument('--oversample_times',
type=int,
default=30,
help='the times oversampling times for fine tuning')
parser.add_argument('--patience',
type=int,
default=20,
help='patience for early stopping')
parser.add_argument('--c_loss',
type=argtype.boolean,
default=True,
help='whether using custom loss or not')
parser.add_argument('--predict',
type=argtype.boolean,
default=False,
help='predict and save csv file or not')
parser.add_argument('--filename',
type=str,
default='submission',
help='csv file name to save predict result')
parser.add_argument('--Y_list',
type=argtype.str_to_list,
default='Y12,Y15',
help='target Y for pre-training')
parser.add_argument('--window_size',
type=int,
default=1,
help='window size for moving average')
parser.add_argument('--attention',
type=argtype.boolean,
default=True,
help='select model using attention mechanism')
args = parser.parse_args()
data_dir = './data'
if args.device == 'gpu':
args.device = 'cuda'
device = torch.device(args.device)
chunk_size = args.chunk_size
step_size = args.step_size
lr = args.lr
weight_decay = args.weight_decay
EPOCH = args.epoch
batch_size = args.batch_size
val_ratio = args.val_ratio
model_name = args.model_name
transfer_learning = args.transfer
times = args.oversample_times
patience = args.patience
c_loss = args.c_loss
pred = args.predict
filename = args.filename
Y_list = args.Y_list
window_size = args.window_size
attention = args.attention
params = {
'chunk_size': chunk_size,
'step_size': step_size,
'learning_rate': lr,
'weight_decay': weight_decay,
'epoch size': EPOCH,
'batch_size': batch_size,
'valid_ratio': val_ratio,
'model_name': model_name,
'transfer_learning': transfer_learning,
'oversample_times': times,
'early_stopping_patience': patience,
'c_loss': c_loss,
'pred': pred,
'filename': filename,
'Y_list': Y_list,
'window_size': window_size,
'attention': attention
}
Y = ''
for y in Y_list:
Y += y
model_name = f'{model_name}/{Y}'
Dataframe = dataframe.Dataframe(data_dir=data_dir)
input_size = len(Dataframe.feature_cols)
if attention:
model = regressor.Attention_Regressor(input_size).to(device)
else:
model = regressor.BiLSTM_Regressor().to(device)
checkpoint = Checkpoint(model_name=model_name,
transfer_learning=transfer_learning)
early_stopping = Early_stopping(patience=patience)
vis = Custom_Visdom(model_name, transfer_learning)
vis.print_params(params)
if transfer_learning:
dataset_list = []
if attention:
pre_df = Dataframe.get_pretrain_df()\
.iloc[-chunk_size+1:][Dataframe.feature_cols]
df = Dataframe.get_y18_df()
df = pd.concat([pre_df, df], axis=0)
else:
df = Dataframe.get_y18_df()
train_dataset = datasets.CustomSequenceDataset(chunk_size=chunk_size,
df=df,
Y='Y18',
step_size=step_size,
noise=True,
times=times)
dataset_list.append(train_dataset)
dataset = ConcatDataset(dataset_list)
train_loader, valid_loader = datasets.split_dataset(
dataset=dataset,
batch_size=batch_size,
val_ratio=val_ratio,
shuffle=True)
checkpoint.load_model(model)
else:
dataset_list = []
for y in Y_list:
df = Dataframe.get_pretrain_df()
df[y] = df[y].rolling(window=window_size, min_periods=1).mean()
dataset = datasets.CustomSequenceDataset(chunk_size=chunk_size,
df=df,
Y=y,
step_size=step_size,
noise=False,
times=1)
dataset_list.append(dataset)
dataset = ConcatDataset(dataset_list)
train_loader, valid_loader = datasets.split_dataset(
dataset=dataset,
batch_size=batch_size,
val_ratio=val_ratio,
shuffle=True)
optimizer = Adam(model.parameters(),
lr=lr,
weight_decay=float(weight_decay))
if c_loss:
criterion = custom_loss.mse_AIFrenz_torch
else:
criterion = nn.MSELoss()
training_time = time.time()
epoch = 0
y_df = Dataframe.get_pretrain_df()[Y_list]
y18_df = Dataframe.get_y18_df()[['Y18']]
while epoch < EPOCH:
print(f'\r Y: {Y} \
chunk size: {chunk_size} \
transfer: {transfer_learning}')
epoch += 1
train_loss_per_epoch, train_loss_list_per_batch, batch_list = train(
model=model,
train_loader=train_loader,
criterion=criterion,
optimizer=optimizer,
epoch=epoch,
transfer_learning=transfer_learning,
attention=attention,
freeze_name='transfer_layer')
valid_loss = valid(model=model,
valid_loader=valid_loader,
criterion=criterion,
attention=attention)
iter_time = time.time() - training_time
print(
f'\r Epoch: {epoch:3d}/{str(EPOCH):3s}\t',
f'train time: {int(iter_time//60):2d}m {iter_time%60:5.2f}s\t'
f'avg train loss: {train_loss_per_epoch:7.3f}\t'
f'valid loss: {valid_loss:7.3f}')
checkpoint.save_log(batch_list, epoch, train_loss_list_per_batch,
train_loss_per_epoch, valid_loss)
early_stop, is_best = early_stopping(valid_loss)
checkpoint.save_checkpoint(model, optimizer, is_best)
vis.print_training(EPOCH, epoch, training_time, train_loss_per_epoch,
valid_loss, patience, early_stopping.counter)
vis.loss_plot(checkpoint)
print('-----' * 17)
y_true, y_pred, y_idx = predict.trainset_predict(
model=model,
data_dir=data_dir,
Y=Y_list[0],
chunk_size=chunk_size,
attention=attention,
window_size=window_size)
y18_true, y18_pred, y18_idx = predict.trainset_predict(
model=model,
data_dir=data_dir,
Y='Y18',
chunk_size=chunk_size,
attention=attention,
window_size=window_size)
y_df['pred'] = y_pred
y18_df['pred'] = y18_pred
vis.predict_plot(y_df, 'pre')
vis.predict_plot(y18_df, 'trans')
vis.print_error()
if early_stop:
break
if transfer_learning:
checkpoint.load_model(model, transfer_learningd=True)
else:
checkpoint.load_model(model, transfer_learningd=False)
y_true, y_pred, y_idx = predict.trainset_predict(model=model,
data_dir=data_dir,
Y=Y_list[0],
chunk_size=chunk_size,
attention=attention,
window_size=window_size)
y18_true, y18_pred, y18_idx = predict.trainset_predict(
model=model,
data_dir=data_dir,
Y='Y18',
chunk_size=chunk_size,
attention=attention,
window_size=window_size)
y_df['pred'] = y_pred
y18_df['pred'] = y18_pred
vis.predict_plot(y_df, 'pre')
vis.predict_plot(y18_df, 'trans')
vis.print_error()
if pred:
predict.test_predict(model=model,
chunk_size=chunk_size,
filename=filename,
attention=attention)
def main():
opt = TrainOptions().parse()
train_history = TrainHistoryFace()
checkpoint = Checkpoint()
visualizer = Visualizer(opt)
exp_dir = os.path.join(opt.exp_dir, opt.exp_id)
log_name = opt.vis_env + '_val_log.txt'
visualizer.log_name = os.path.join(exp_dir, log_name)
num_classes = opt.class_num
if not opt.slurm:
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu_id
layer_num = opt.layer_num
order = opt.order
net = create_cu_net(neck_size=4,
growth_rate=32,
init_chan_num=128,
class_num=num_classes,
layer_num=layer_num,
order=order,
loss_num=layer_num,
use_spatial_transformer=opt.stn,
mlp_tot_layers=opt.mlp_tot_layers,
mlp_hidden_units=opt.mlp_hidden_units,
get_mean_from_mlp=opt.get_mean_from_mlp)
# Load the pre-trained model
saved_wt_file = opt.saved_wt_file
print("Loading weights from " + saved_wt_file)
checkpoint_t = torch.load(saved_wt_file)
state_dict = checkpoint_t['state_dict']
for name, param in state_dict.items():
name = name[7:]
if name not in net.state_dict():
print("=> not load weights '{}'".format(name))
continue
if isinstance(param, Parameter):
param = param.data
net.state_dict()[name].copy_(param)
net = torch.nn.DataParallel(net).cuda() # use multiple GPUs
# Optimizer
if opt.optimizer == "rmsprop":
optimizer = torch.optim.RMSprop(filter(lambda p: p.requires_grad,
net.parameters()),
lr=opt.lr,
alpha=0.99,
eps=1e-8,
momentum=0,
weight_decay=0)
elif opt.optimizer == "adam":
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad,
net.parameters()),
lr=opt.lr)
else:
print("Unknown Optimizer. Aborting!!!")
sys.exit(0)
print(type(optimizer))
# Optionally resume from a checkpoint
if opt.resume_prefix != '':
# if 'pth' in opt.resume_prefix:
# trunc_index = opt.resume_prefix.index('pth')
# opt.resume_prefix = opt.resume_prefix[0:trunc_index - 1]
checkpoint.save_prefix = os.path.join(exp_dir, opt.resume_prefix)
checkpoint.load_prefix = os.path.join(exp_dir, opt.resume_prefix)[0:-1]
checkpoint.load_checkpoint(net, optimizer, train_history)
else:
checkpoint.save_prefix = exp_dir + '/'
print("Save prefix = {}".format(
checkpoint.save_prefix))
# Load data
json_path = opt.json_path
train_json = opt.train_json
val_json = opt.val_json
print("Path added to each image path in JSON = {}".format(json_path))
print("Train JSON path = {}".format(train_json))
print("Val JSON path = {}".format(val_json))
# This train loader is useless
train_loader = torch.utils.data.DataLoader(FACE(train_json,
json_path,
is_train=True),
batch_size=opt.bs,
shuffle=True,
num_workers=opt.nThreads,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(FACE(val_json,
json_path,
is_train=False),
batch_size=opt.bs,
shuffle=False,
num_workers=opt.nThreads,
pin_memory=True)
if not opt.is_train:
visualizer.log_path = os.path.join(opt.exp_dir, opt.exp_id,
'val_log.txt')
val_loss, val_rmse, predictions = validate(
val_loader, net, train_history.epoch[-1]['epoch'], visualizer,
num_classes, flip_index)
checkpoint.save_preds(predictions)
return
global f_path
global weights_HG
f_path = exp_dir
weights_HG = [float(x) for x in opt.hg_wt.split(",")]
print("Postprocessing applied = {}".format(opt.pp))
if (opt.smax):
print("Scaled softmax used with tau = {}".format(opt.tau))
else:
print("No softmax used")
if opt.is_covariance:
print("Covariance used from the heatmap")
else:
print("Covariance calculated from MLP")
print("Individual Hourglass loss weights")
print(weights_HG)
print("wt_MSE (tradeoff between GLL and MSE in each hourglass)= " +
str(opt.wt_mse))
print(
"wt_gauss_regln (tradeoff between GLL and Gaussian Regularisation in each hourglass)= "
+ str(opt.wt_gauss_regln))
# Optionally resume from a checkpoint
start_epoch = 0
if opt.resume_prefix != '':
start_epoch = train_history.epoch[-1]['epoch'] + 1
# Training and validation
start_epoch = 0
if opt.resume_prefix != '':
start_epoch = train_history.epoch[-1]['epoch'] + 1
train_loss_orig_epoch = []
train_loss_gau_t1_epoch = []
train_loss_gau_t2_epoch = []
train_nme_orig_epoch = []
train_nme_gau_epoch = []
train_nme_new_epoch = []
val_loss_orig_epoch = []
val_loss_gau_t1_epoch = []
val_loss_gau_t2_epoch = []
val_nme_orig_epoch = []
val_nme_gau_epoch = []
val_nme_new_epoch = []
for epoch in range(1):
# Evaluate on validation set
val_loss, val_loss_mse, val_loss_gau_t1, val_loss_gau_t2, val_rmse_orig, val_rmse_gau, val_rmse_new_box, predictions = validate(
val_loader, net, epoch, visualizer, opt, num_classes, flip_index)
val_loss_orig_epoch.append(val_loss_mse)
val_loss_gau_t1_epoch.append(val_loss_gau_t1)
val_loss_gau_t2_epoch.append(val_loss_gau_t2)
val_nme_orig_epoch.append(val_rmse_orig)
val_nme_gau_epoch.append(val_rmse_gau)
"patch_size": 8
})
Dis = Discriminator(args).to(device)
# summary(Dis,(3,32,32,))
optG = optim.AdamW(Gen.parameters(), lr=lr, betas=(beta1, beta2))
optD = optim.AdamW(Dis.parameters(), lr=lr, betas=(beta1, beta2))
img_list = []
G_losses = []
D_losses = []
loss_logs = {"gen_loss": [], "dis_loss": []}
iters = 0
ckp_class = Checkpoint(ckp_folder,
max_epochs=num_epochs,
num_ckps=5,
start_after=0.1)
# check if any existing checkpoint exists, none found hence start_epoch is 0.
# Optimizer states also get saved
Gen, Dis, optG, optD, start_epoch, old_logs = ckp_class.check_if_exists(
Gen, Dis, optG, optD)
loss_logs = old_logs or loss_logs
print(start_epoch) # , loss_logs
# Commented out IPython magic to ensure Python compatibility.
for epoch in range(start_epoch, num_epochs + 1):
for i, data in enumerate(dataset.train_loader):
###########################
def main():
opt = TrainOptions().parse()
if opt.sr_dir == '':
print('sr directory is null.')
exit()
sr_pretrain_dir = os.path.join(
opt.exp_dir, opt.exp_id, opt.sr_dir + '-' + opt.load_prefix_pose[0:-1])
if not os.path.isdir(sr_pretrain_dir):
os.makedirs(sr_pretrain_dir)
train_history = ASNTrainHistory()
# print(train_history.lr)
# exit()
checkpoint_agent = Checkpoint()
visualizer = Visualizer(opt)
visualizer.log_path = sr_pretrain_dir + '/' + 'log.txt'
train_scale_path = sr_pretrain_dir + '/' + 'train_scales.txt'
train_rotation_path = sr_pretrain_dir + '/' + 'train_rotations.txt'
val_scale_path = sr_pretrain_dir + '/' + 'val_scales.txt'
val_rotation_path = sr_pretrain_dir + '/' + 'val_rotations.txt'
# with open(visualizer.log_path, 'a+') as log_file:
# log_file.write(opt.resume_prefix_pose + '.pth.tar\n')
# lost_joint_count_path = os.path.join(opt.exp_dir, opt.exp_id, opt.astn_dir, 'joint-count.txt')
# print("=> log saved to path '{}'".format(visualizer.log_path))
# if opt.dataset == 'mpii':
# num_classes = 16
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu_id
print 'collecting training scale and rotation distributions ...\n'
train_scale_distri = read_grnd_distri_from_txt(train_scale_path)
train_rotation_distri = read_grnd_distri_from_txt(train_rotation_path)
dataset = MPII('dataset/mpii-hr-lsp-normalizer.json',
'/bigdata1/zt53/data',
is_train=True,
grnd_scale_distri=train_scale_distri,
grnd_rotation_distri=train_rotation_distri)
train_loader = torch.utils.data.DataLoader(dataset,
batch_size=opt.bs,
shuffle=True,
num_workers=opt.nThreads,
pin_memory=True)
print 'collecting validation scale and rotation distributions ...\n'
val_scale_distri = read_grnd_distri_from_txt(val_scale_path)
val_rotation_distri = read_grnd_distri_from_txt(val_rotation_path)
dataset = MPII('dataset/mpii-hr-lsp-normalizer.json',
'/bigdata1/zt53/data',
is_train=False,
grnd_scale_distri=val_scale_distri,
grnd_rotation_distri=val_rotation_distri)
val_loader = torch.utils.data.DataLoader(dataset,
batch_size=opt.bs,
shuffle=False,
num_workers=opt.nThreads,
pin_memory=True)
agent = model.create_asn(chan_in=256,
chan_out=256,
scale_num=len(dataset.scale_means),
rotation_num=len(dataset.rotation_means),
is_aug=True)
agent = torch.nn.DataParallel(agent).cuda()
optimizer = torch.optim.RMSprop(agent.parameters(),
lr=opt.lr,
alpha=0.99,
eps=1e-8,
momentum=0,
weight_decay=0)
# optimizer = torch.optim.Adam(agent.parameters(), lr=opt.agent_lr)
if opt.load_prefix_sr == '':
checkpoint_agent.save_prefix = sr_pretrain_dir + '/'
else:
checkpoint_agent.save_prefix = sr_pretrain_dir + '/' + opt.load_prefix_sr
checkpoint_agent.load_prefix = checkpoint_agent.save_prefix[0:-1]
checkpoint_agent.load_checkpoint(agent, optimizer, train_history)
# adjust_lr(optimizer, opt.lr)
# lost_joint_count_path = os.path.join(opt.exp_dir, opt.exp_id, opt.asdn_dir, 'joint-count-finetune.txt')
print 'agent: ', type(optimizer), optimizer.param_groups[0]['lr']
if opt.dataset == 'mpii':
num_classes = 16
hg = model.create_hg(num_stacks=2,
num_modules=1,
num_classes=num_classes,
chan=256)
hg = torch.nn.DataParallel(hg).cuda()
if opt.load_prefix_pose == '':
print('please input the checkpoint name of the pose model')
exit()
checkpoint_hg = Checkpoint()
# checkpoint_hg.save_prefix = os.path.join(opt.exp_dir, opt.exp_id, opt.resume_prefix_pose)
checkpoint_hg.load_prefix = os.path.join(opt.exp_dir, opt.exp_id,
opt.load_prefix_pose)[0:-1]
checkpoint_hg.load_checkpoint(hg)
logger = Logger(sr_pretrain_dir + '/' + 'training-summary.txt',
title='training-summary')
logger.set_names(['Epoch', 'LR', 'Train Loss', 'Val Loss'])
"""training and validation"""
start_epoch = 0
if opt.load_prefix_sr != '':
start_epoch = train_history.epoch[-1]['epoch'] + 1
for epoch in range(start_epoch, opt.nEpochs):
# train for one epoch
train_loss = train(train_loader, hg, agent, optimizer, epoch,
visualizer, opt)
val_loss = validate(val_loader, hg, agent, epoch, visualizer, opt)
# update training history
e = OrderedDict([('epoch', epoch)])
lr = OrderedDict([('lr', optimizer.param_groups[0]['lr'])])
loss = OrderedDict([('train_loss', train_loss),
('val_loss', val_loss)])
# pckh = OrderedDict( [('val_pckh', val_pckh)] )
train_history.update(e, lr, loss)
# print(train_history.lr[-1]['lr'])
checkpoint_agent.save_checkpoint(agent,
optimizer,
train_history,
is_asn=True)
visualizer.plot_train_history(train_history, 'sr')
logger.append(
[epoch, optimizer.param_groups[0]['lr'], train_loss, val_loss])
logger.close()
def main():
# global args, best_prec1
args = parser.parse_args()
print('\n====> Input Arguments')
print(args)
# Tensorboard writer.
global writer
writer = SummaryWriter(log_dir=args.result_path)
# Create dataloader.
print '\n====> Creating dataloader...'
train_loader = get_train_loader(args)
test_loader = get_test_loader(args)
# Load Resnet_a network.
print '====> Loading the network...'
model = Inception_a(num_class=args.num_class,
num_frame=args.num_frame,
pretrained=True)
# Load single frame pretrain.
pretrain_model = torch.load('models/pretrain_inc_sf.pth')
keys = model.state_dict().keys()
new_state_dict = {}
for i, k in enumerate(pretrain_model.keys()):
new_state_dict[keys[i]] = pretrain_model[k]
model.load_state_dict(new_state_dict)
"""Load checkpoint and weight of network.
"""
global cp_recorder
if args.checkpoint_dir:
cp_recorder = Checkpoint(args.checkpoint_dir, args.checkpoint_name)
cp_recorder.load_checkpoint(model)
model = nn.DataParallel(model)
model.cuda()
criterion = nn.CrossEntropyLoss().cuda()
cudnn.benchmark = True
# optimizer = torch.optim.SGD(model.module.classifier.parameters(), lr=args.lr, weight_decay=args.wd, momentum=args.momentum)
optimizer = torch.optim.SGD(model.parameters(),
lr=args.lr,
weight_decay=args.wd,
momentum=args.momentum)
# Train Resnet_a model.
print '====> Training...'
for epoch in range(cp_recorder.contextual['b_epoch'], args.epoch):
_, _, prec_tri, rec_tri, ap_tri = train_eval(train_loader, test_loader,
model, criterion,
optimizer, args, epoch)
top1_avg_val, loss_avg_val, prec_val, rec_val, ap_val = validate_eval(
test_loader, model, criterion, args, epoch)
# Print result.
writer.add_scalars('mAP (per epoch)',
{'train': np.nan_to_num(ap_tri).mean()}, epoch)
writer.add_scalars('mAP (per epoch)',
{'valid': np.nan_to_num(ap_val).mean()}, epoch)
print_score = False
if print_score:
print('\n====> Scores')
print(
'[Epoch {0}]:\n'
' Train:\n'
' Prec@1 {1}\n'
' Recall {2}\n'
' AP {3}\n'
' mAP {4:.3f}\n'
' Valid:\n'
' Prec@1 {5}\n'
' Recall {6}\n'
' AP {7}\n'
' mAP {8:.3f}\n'.format(epoch, prec_tri, rec_tri, ap_tri,
np.nan_to_num(ap_tri).mean(),
prec_val, rec_val, ap_val,
np.nan_to_num(ap_val).mean()))
# Record.
writer.add_scalars('Loss (per batch)', {'valid': loss_avg_val},
(epoch + 1) * len(train_loader))
writer.add_scalars('Prec@1 (per batch)', {'valid': top1_avg_val},
(epoch + 1) * len(train_loader))
writer.add_scalars('mAP (per batch)',
{'valid': np.nan_to_num(ap_val).mean()},
(epoch + 1) * len(train_loader))
# Save checkpoint.
cp_recorder.record_contextual({
'b_epoch': epoch + 1,
'b_batch': -1,
'prec': top1_avg_val,
'loss': loss_avg_val,
'class_prec': prec_val,
'class_recall': rec_val,
'class_ap': ap_val,
'mAP': np.nan_to_num(ap_val).mean()
})
cp_recorder.save_checkpoint(model)
if arguments.fix_a is None and arguments.reg_type == "swd" and arguments.pruning_iterations != 1:
print('Progressive a is not compatible with iterative pruning')
raise ValueError
if arguments.no_ft and arguments.pruning_iterations != 1:
print("You can't specify a pruning_iteration value if there is no fine-tuning at all")
raise ValueError
get_mask = get_mask_function(arguments.pruning_type)
_dataset = get_dataset(arguments)
_targets = [int((n + 1) * (arguments.target / arguments.pruning_iterations)) for n in
range(arguments.pruning_iterations)]
# Train model
print('Train model !')
print(f'Regularization with t-{_targets[0]}')
training_model = Checkpoint(arguments, 'training')
training_model.regularization = Regularization(None, _targets[0], arguments)
training_model.load()
train_model(training_model, arguments, [0, arguments.epochs], _dataset, None, soft_pruning=arguments.soft_pruning)
if arguments.lr_rewinding:
training_model.rewind_lr()
if arguments.no_ft:
print('\nPruning model without fine tuning :')
pruned_model = training_model.clone('pruned')
pruned_model.load()
mask = get_mask(pruned_model.model, arguments.target)
apply_mask(pruned_model.model, mask)
_acc, _top5, _test_loss = test_model(_dataset, pruned_model.model, arguments)
pruned_model.save_results({'epoch': 'before', 'acc': _acc, 'top5': _top5, 'loss': _test_loss,
def main():
opt = TrainOptions().parse()
if opt.joint_dir == '':
print('joint directory is null.')
exit()
joint_dir = os.path.join(opt.exp_dir, opt.exp_id,
opt.joint_dir + '-' + opt.load_prefix_pose[0:-1])
# joint_dir = os.path.join(opt.exp_dir, opt.exp_id,
# opt.joint_dir)
if not os.path.isdir(joint_dir):
os.makedirs(joint_dir)
visualizer = Visualizer(opt)
visualizer.log_path = joint_dir + '/' + 'train-log.txt'
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu_id
# lost_joint_count_path = os.path.join(opt.exp_dir, opt.exp_id,
# opt.joint_dir, 'joint-count.txt')
if opt.dataset == 'mpii':
num_classes = 16
hg = model.create_hg(num_stacks=2,
num_modules=1,
num_classes=num_classes,
chan=256)
hg = torch.nn.DataParallel(hg).cuda()
"""optimizer"""
optimizer_hg = torch.optim.RMSprop(hg.parameters(),
lr=opt.lr,
alpha=0.99,
eps=1e-8,
momentum=0,
weight_decay=0)
if opt.load_prefix_pose == '':
print('please input the checkpoint name of the pose model')
exit()
train_history_pose = PoseTrainHistory()
checkpoint_hg = Checkpoint()
if opt.load_checkpoint:
checkpoint_hg.load_prefix = joint_dir + '/' + opt.load_prefix_pose[0:-1]
checkpoint_hg.load_checkpoint(hg, optimizer_hg, train_history_pose)
else:
checkpoint_hg.load_prefix = os.path.join(opt.exp_dir, opt.exp_id) + \
'/' + opt.load_prefix_pose[0:-1]
checkpoint_hg.load_checkpoint(hg, optimizer_hg, train_history_pose)
for param_group in optimizer_hg.param_groups:
param_group['lr'] = opt.lr
checkpoint_hg.save_prefix = joint_dir + '/pose-'
# trunc_index = checkpoint.save_prefix_pose.index('lr-0.00025-85')
# checkpoint.save_prefix_pose = checkpoint.save_prefix_pose[0:trunc_index]
# print(checkpoint.save_prefix_pose)
print 'hg optimizer: ', type(
optimizer_hg), optimizer_hg.param_groups[0]['lr']
agent_sr = model.create_asn(chan_in=256,
chan_out=256,
scale_num=len(dataset.scale_means),
rotation_num=len(dataset.rotation_means),
is_aug=True)
agent_sr = torch.nn.DataParallel(agent_sr).cuda()
optimizer_sr = torch.optim.RMSprop(agent_sr.parameters(),
lr=opt.agent_lr,
alpha=0.99,
eps=1e-8,
momentum=0,
weight_decay=0)
if opt.load_prefix_sr == '':
print('please input the checkpoint name of the sr agent.')
exit()
train_history_sr = ASNTrainHistory()
checkpoint_sr = Checkpoint()
if opt.load_checkpoint:
checkpoint_sr.load_prefix = joint_dir + '/' + opt.load_prefix_sr[0:-1]
checkpoint_sr.load_checkpoint(agent_sr, optimizer_sr, train_history_sr)
else:
sr_pretrain_dir = os.path.join(
opt.exp_dir, opt.exp_id,
opt.sr_dir + '-' + opt.load_prefix_pose[0:-1])
checkpoint_sr.load_prefix = sr_pretrain_dir + '/' + opt.load_prefix_sr[
0:-1]
checkpoint_sr.load_checkpoint(agent_sr, optimizer_sr, train_history_sr)
for param_group in optimizer_sr.param_groups:
param_group['lr'] = opt.agent_lr
checkpoint_sr.save_prefix = joint_dir + '/agent-'
# trunc_index = checkpoint.save_prefix_asn.index('lr-0.00025-80')
# checkpoint.save_prefix_asn = checkpoint.save_prefix_asn[0:trunc_index]
# print(checkpoint.save_prefix_asn)
# adjust_lr(optimizer_asn, 5e-5)
print 'agent optimizer: ', type(
optimizer_sr), optimizer_sr.param_groups[0]['lr']
train_dataset_hg = MPII('dataset/mpii-hr-lsp-normalizer.json',
'/bigdata1/zt53/data',
is_train=True)
train_loader_hg = torch.utils.data.DataLoader(train_dataset_hg,
batch_size=opt.bs,
shuffle=True,
num_workers=opt.nThreads,
pin_memory=True)
val_dataset_hg = MPII('dataset/mpii-hr-lsp-normalizer.json',
'/bigdata1/zt53/data',
is_train=False)
val_loader_hg = torch.utils.data.DataLoader(val_dataset_hg,
batch_size=opt.bs,
shuffle=False,
num_workers=opt.nThreads,
pin_memory=True)
train_dataset_agent = AGENT('dataset/mpii-hr-lsp-normalizer.json',
'/bigdata1/zt53/data',
separate_s_r=True)
train_loader_agent = torch.utils.data.DataLoader(train_dataset_agent,
batch_size=opt.bs,
shuffle=True,
num_workers=opt.nThreads,
pin_memory=True)
# idx = range(0, 16)
# idx_pckh = [e for e in idx if e not in (6, 7, 8, 9, 12, 13)]
if not opt.is_train:
visualizer.log_path = joint_dir + '/' + 'val-log.txt'
val_loss, val_pckh, predictions = validate(
val_loader_hg, hg, train_history_pose.epoch[-1]['epoch'],
visualizer, num_classes)
checkpoint_hg.save_preds(predictions)
return
logger = Logger(joint_dir + '/' + 'pose-training-summary.txt',
title='pose-training-summary')
logger.set_names(
['Epoch', 'LR', 'Train Loss', 'Val Loss', 'Train PCKh', 'Val PCKh'])
"""training and validation"""
start_epoch_pose = train_history_pose.epoch[-1]['epoch'] + 1
epoch_sr = train_history_sr.epoch[-1]['epoch'] + 1
for epoch in range(start_epoch_pose, opt.nEpochs):
adjust_lr(opt, optimizer_hg, epoch)
# train hg for one epoch
train_loss_pose, train_pckh = train_hg(train_loader_hg, hg,
optimizer_hg, agent_sr, epoch,
visualizer, opt)
# util.save_drop_count(drop_count, lost_joint_count_path)
# evaluate on validation set
val_loss, val_pckh, predictions = validate(val_loader_hg, hg, epoch,
visualizer, num_classes)
# visualizer.display_imgpts(imgs, pred_pts, 4)
# exit()
# update training history
e_pose = OrderedDict([('epoch', epoch)])
lr_pose = OrderedDict([('lr', optimizer_hg.param_groups[0]['lr'])])
loss_pose = OrderedDict([('train_loss', train_loss_pose),
('val_loss', val_loss)])
pckh = OrderedDict([('train_pckh', train_pckh),
('val_pckh', val_pckh)])
train_history_pose.update(e_pose, lr_pose, loss_pose, pckh)
checkpoint_hg.save_checkpoint(hg, optimizer_hg, train_history_pose,
predictions)
visualizer.plot_train_history(train_history_pose)
logger.append([
epoch, optimizer_hg.param_groups[0]['lr'], train_loss_pose,
val_loss, train_pckh, val_pckh
])
# exit()
# if train_history_pose.is_best:
# visualizer.display_imgpts(imgs, pred_pts, 4)
# train agent_sr for one epoch
train_loss_sr = train_agent_sr(train_loader_agent, hg, agent_sr,
optimizer_sr, epoch_sr, visualizer, opt)
e_sr = OrderedDict([('epoch', epoch_sr)])
lr_sr = OrderedDict([('lr', optimizer_sr.param_groups[0]['lr'])])
loss_sr = OrderedDict([('train_loss', train_loss_sr), ('val_loss', 0)])
train_history_sr.update(e_sr, lr_sr, loss_sr)
# print(train_history.lr[-1]['lr'])
checkpoint_sr.save_checkpoint(agent_sr,
optimizer_sr,
train_history_sr,
is_asn=True)
visualizer.plot_train_history(train_history_sr, 'sr')
# exit()
epoch_sr += 1
logger.close()
def main():
opt = TrainOptions().parse()
train_history = PoseTrainHistory()
checkpoint = Checkpoint()
visualizer = Visualizer(opt)
exp_dir = os.path.join(opt.exp_dir, opt.exp_id)
log_name = opt.vis_env + 'log.txt'
visualizer.log_path = os.path.join(exp_dir, log_name)
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu_id
# if opt.dataset == 'mpii':
num_classes = 16
net = create_hg(num_stacks=2,
num_modules=1,
num_classes=num_classes,
chan=256)
# num1 = get_n_params(net)
# num2 = get_n_trainable_params(net)
# num3 = get_n_conv_params(net)
# print('number of params: ', num1)
# print('number of trainalbe params: ', num2)
# print('number of conv params: ', num3)
# exit()
net = torch.nn.DataParallel(net).cuda()
"""optimizer"""
optimizer = torch.optim.RMSprop(net.parameters(),
lr=opt.lr,
alpha=0.99,
eps=1e-8,
momentum=0,
weight_decay=0)
"""optionally resume from a checkpoint"""
if opt.load_prefix_pose != '':
# if 'pth' in opt.resume_prefix:
# trunc_index = opt.resume_prefix.index('pth')
# opt.resume_prefix = opt.resume_prefix[0:trunc_index - 1]
checkpoint.save_prefix = os.path.join(exp_dir, opt.load_prefix_pose)
checkpoint.load_prefix = os.path.join(exp_dir,
opt.load_prefix_pose)[0:-1]
checkpoint.load_checkpoint(net, optimizer, train_history)
# trunc_index = checkpoint.save_prefix.index('lr-0.00025-80')
# checkpoint.save_prefix = checkpoint.save_prefix[0:trunc_index]
# checkpoint.save_prefix = exp_dir + '/'
else:
checkpoint.save_prefix = exp_dir + '/'
print('save prefix: ', checkpoint.save_prefix)
# model = {'state_dict': net.state_dict()}
# save_path = checkpoint.save_prefix + 'test-model-size.pth.tar'
# torch.save(model, save_path)
# exit()
"""load data"""
train_loader = torch.utils.data.DataLoader(MPII(
'dataset/mpii-hr-lsp-normalizer.json',
'/bigdata1/zt53/data',
is_train=True),
batch_size=opt.bs,
shuffle=True,
num_workers=opt.nThreads,
pin_memory=True)
val_loader = torch.utils.data.DataLoader(MPII(
'dataset/mpii-hr-lsp-normalizer.json',
'/bigdata1/zt53/data',
is_train=False),
batch_size=opt.bs,
shuffle=False,
num_workers=opt.nThreads,
pin_memory=True)
print(type(optimizer), optimizer.param_groups[0]['lr'])
# idx = range(0, 16)
# idx = [e for e in idx if e not in (6, 7, 8, 9, 12, 13)]
idx = [0, 1, 2, 3, 4, 5, 10, 11, 14, 15]
# criterion = torch.nn.MSELoss(size_average=True).cuda()
if not opt.is_train:
visualizer.log_path = os.path.join(opt.exp_dir, opt.exp_id,
'val_log.txt')
val_loss, val_pckh, predictions = validate(
val_loader, net, train_history.epoch[-1]['epoch'], visualizer, idx,
num_classes)
checkpoint.save_preds(predictions)
return
"""training and validation"""
start_epoch = 0
if opt.load_prefix_pose != '':
start_epoch = train_history.epoch[-1]['epoch'] + 1
for epoch in range(start_epoch, opt.nEpochs):
adjust_lr(opt, optimizer, epoch)
# # train for one epoch
train_loss, train_pckh = train(train_loader, net, optimizer, epoch,
visualizer, idx, opt)
# evaluate on validation set
val_loss, val_pckh, predictions = validate(val_loader, net, epoch,
visualizer, idx,
num_classes)
# visualizer.display_imgpts(imgs, pred_pts, 4)
# exit()
# update training history
e = OrderedDict([('epoch', epoch)])
lr = OrderedDict([('lr', optimizer.param_groups[0]['lr'])])
loss = OrderedDict([('train_loss', train_loss),
('val_loss', val_loss)])
pckh = OrderedDict([('train_pckh', train_pckh),
('val_pckh', val_pckh)])
train_history.update(e, lr, loss, pckh)
checkpoint.save_checkpoint(net, optimizer, train_history, predictions)
visualizer.plot_train_history(train_history)
def main():
# global args, best_prec1
args = parser.parse_args()
print('\n====> Input Arguments')
print(args)
# Tensorboard writer.
global writer
writer = SummaryWriter(log_dir=args.result_path)
# Create dataloader.
print '\n====> Creating dataloader...'
train_loader = get_train_loader(args)
test_loader = get_test_loader(args)
# Load GRM network.
print '====> Loading the GRM network...'
model = GRM(num_classes=args.num_classes,
adjacency_matrix=args.adjacency_matrix)
# Load First-Glance network.
print '====> Loading the finetune First Glance model...'
if args.fg_finetune and os.path.isfile(args.fg_finetune):
model.fg.load_state_dict(torch.load(args.fg_finetune))
else:
print("No find '{}'".format(args.fg_finetune))
# Load checkpoint and weight of network.
global cp_recorder
if args.checkpoint_dir:
cp_recorder = Checkpoint(args.checkpoint_dir, args.checkpoint_name)
cp_recorder.load_checkpoint(model)
model.fg = torch.nn.DataParallel(model.fg)
model.full_im_net = torch.nn.DataParallel(model.full_im_net)
model.cuda()
criterion = nn.CrossEntropyLoss().cuda()
cudnn.benchmark = True
optimizer_cls = torch.optim.SGD(model.classifier.parameters(),
lr=args.lr,
weight_decay=args.wd,
momentum=args.momentum)
optimizer_ggnn = torch.optim.Adam(model.ggnn.parameters(),
lr=0.00001,
weight_decay=args.wd)
# Train GRM model.
print '====> Training...'
for epoch in range(cp_recorder.contextual['b_epoch'], args.epoch):
_, _, prec_tri, rec_tri, ap_tri = train_eval(train_loader, test_loader,
model, criterion,
optimizer_cls,
optimizer_ggnn, args,
epoch)
top1_avg_val, loss_avg_val, prec_val, rec_val, ap_val = validate_eval(
test_loader, model, criterion, args, epoch)
# Print result.
writer.add_scalars('mAP (per epoch)',
{'train': np.nan_to_num(ap_tri).mean()}, epoch)
writer.add_scalars('mAP (per epoch)',
{'valid': np.nan_to_num(ap_val).mean()}, epoch)
print('\n====> Scores')
print(
'[Epoch {0}]:\n'
' Train:\n'
' Prec@1 {1}\n'
' Recall {2}\n'
' AP {3}\n'
' mAP {4:.3f}\n'
' Valid:\n'
' Prec@1 {5}\n'
' Recall {6}\n'
' AP {7}\n'
' mAP {8:.3f}\n'.format(epoch, prec_tri, rec_tri, ap_tri,
np.nan_to_num(ap_tri).mean(), prec_val,
rec_val, ap_val,
np.nan_to_num(ap_val).mean()))
# Record.
writer.add_scalars('Loss (per batch)', {'valid': loss_avg_val},
(epoch + 1) * len(train_loader))
writer.add_scalars('Prec@1 (per batch)', {'valid': top1_avg_val},
(epoch + 1) * len(train_loader))
writer.add_scalars('mAP (per batch)',
{'valid': np.nan_to_num(ap_val).mean()},
(epoch + 1) * len(train_loader))
# Save checkpoint.
cp_recorder.record_contextual({
'b_epoch': epoch + 1,
'b_batch': -1,
'prec': top1_avg_val,
'loss': loss_avg_val,
'class_prec': prec_val,
'class_recall': rec_val,
'class_ap': ap_val,
'mAP': np.nan_to_num(ap_val).mean()
})
cp_recorder.save_checkpoint(model)