def run_test():
model = getattr(models, opt.model)(use_imp=opt.use_imp, n=opt.feat_num)
global use_data_parallel
if opt.use_gpu:
# model.cuda() # ???? model.cuda() or model = model.cuda() all is OK
model, use_data_parallel = multiple_gpu_process(model)
normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
test_batch_size = 1
test_data_transforms_crops = transforms.Compose([
transforms.CenterCrop(128), # center crop 128x128
transforms.ToTensor(),
normalize
# transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
test_data_transforms_full = transforms.Compose([
transforms.ToTensor(), normalize
# transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
test_data_transforms = test_data_transforms_full if test_batch_size == 1 else test_data_transforms_crops
# test_data_transforms = test_data_transforms_crops
test_ckpt = opt.resume
load_epoch = (model.module if use_data_parallel == 1 else model).load(
None, test_ckpt)
print('Load epoch %d for test!' % load_epoch)
test_data = ImageFilelist(flist=opt.test_data_list,
transform=test_data_transforms)
test_dataloader = DataLoader(test_data, test_batch_size, shuffle=False)
mse_loss = t.nn.MSELoss(size_average=False)
if opt.use_imp:
rate_loss = RateLoss(opt.rate_loss_threshold, opt.rate_loss_weight)
else:
rate_loss = None
test(model, test_dataloader, test_batch_size, mse_loss, rate_loss)
def train(**kwargs):
opt.parse(kwargs)
# log file
logfile_name = "Cmpr_with_YOLOv2_" + opt.exp_desc + time.strftime(
"_%m_%d_%H:%M:%S") + ".log.txt"
ps = PlotSaver(logfile_name)
# step1: Model
model = getattr(models, opt.model)(
use_imp=opt.use_imp,
n=opt.feat_num,
input_4_ch=opt.input_4_ch,
model_name="Cmpr_yolo_imp_" + opt.exp_desc + "_r={r}_gama={w}".format(
r=opt.rate_loss_threshold, w=opt.rate_loss_weight)
if opt.use_imp else "Cmpr_yolo_no_imp_" + opt.exp_desc)
# pdb.set_trace()
if opt.use_gpu:
model = multiple_gpu_process(model)
cudnn.benchmark = True
# step2: Data
normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
train_data_transforms = transforms.Compose([
# transforms.RandomHorizontalFlip(), TODO: try to reimplement by myself to simultaneous operate on label and data
transforms.ToTensor(),
normalize
])
val_data_transforms = transforms.Compose(
[transforms.ToTensor(), normalize])
train_data = ImageCropWithBBoxMaskDataset(
opt.train_data_list,
train_data_transforms,
contrastive_degree=opt.contrastive_degree,
mse_bbox_weight=opt.input_original_bbox_weight)
val_data = ImageCropWithBBoxMaskDataset(
opt.val_data_list,
val_data_transforms,
contrastive_degree=opt.contrastive_degree,
mse_bbox_weight=opt.input_original_bbox_weight)
train_dataloader = DataLoader(train_data,
opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
pin_memory=True)
val_dataloader = DataLoader(val_data,
opt.batch_size,
shuffle=False,
num_workers=opt.num_workers,
pin_memory=True)
# step3: criterion and optimizer
mse_loss = t.nn.MSELoss(size_average=False)
if opt.use_imp:
# TODO: new rate loss
rate_loss = RateLoss(opt.rate_loss_threshold, opt.rate_loss_weight)
# rate_loss = LimuRateLoss(opt.rate_loss_threshold, opt.rate_loss_weight)
def weighted_mse_loss(input, target, weight):
# weight[weight!=opt.mse_bbox_weight] = 1
# weight[weight==opt.mse_bbox_weight] = opt.mse_bbox_weight
# print('max val', weight.max())
# return mse_loss(input, target)
# weight_clone = weight.clone()
# weight_clone[weight_clone == opt.input_original_bbox_weight] = 0
# return t.sum(weight_clone * (input - target) ** 2)
weight_clone = t.ones_like(weight)
weight_clone[weight ==
opt.input_original_bbox_inner] = opt.mse_bbox_weight
return t.sum(weight_clone * (input - target)**2)
def yolo_rate_loss(imp_map, mask_r):
return rate_loss(imp_map)
# V2 contrastive_degree must be 0!
# return YoloRateLossV2(mask_r, opt.rate_loss_threshold, opt.rate_loss_weight)(imp_map)
lr = opt.lr
optimizer = t.optim.Adam(model.parameters(), lr=lr, betas=(0.9, 0.999))
start_epoch = 0
decay_file_create_time = -1 # 为了避免同一个文件反复衰减学习率, 所以判断修改时间
if opt.resume:
if use_data_parallel:
start_epoch = model.module.load(
None if opt.finetune else optimizer, opt.resume, opt.finetune)
else:
start_epoch = model.load(None if opt.finetune else optimizer,
opt.resume, opt.finetune)
if opt.finetune:
print('Finetune from model checkpoint file', opt.resume)
else:
print('Resume training from checkpoint file', opt.resume)
print('Continue training at epoch %d.' % start_epoch)
# step4: meters
mse_loss_meter = AverageValueMeter()
if opt.use_imp:
rate_loss_meter = AverageValueMeter()
rate_display_meter = AverageValueMeter()
total_loss_meter = AverageValueMeter()
previous_loss = 1e100
tolerant_now = 0
same_lr_epoch = 0
# ps init
ps.new_plot('train mse loss',
opt.print_freq,
xlabel="iteration",
ylabel="train_mse_loss")
ps.new_plot('val mse loss', 1, xlabel="epoch", ylabel="val_mse_loss")
if opt.use_imp:
ps.new_plot('train rate value',
opt.print_freq,
xlabel="iteration",
ylabel="train_rate_value")
ps.new_plot('train rate loss',
opt.print_freq,
xlabel="iteration",
ylabel="train_rate_loss")
ps.new_plot('train total loss',
opt.print_freq,
xlabel="iteration",
ylabel="train_total_loss")
ps.new_plot('val rate value',
1,
xlabel="iteration",
ylabel="val_rate_value")
ps.new_plot('val rate loss',
1,
xlabel="iteration",
ylabel="val_rate_loss")
ps.new_plot('val total loss',
1,
xlabel="iteration",
ylabel="val_total_loss")
for epoch in range(start_epoch + 1, opt.max_epoch + 1):
same_lr_epoch += 1
# per epoch avg loss meter
mse_loss_meter.reset()
if opt.use_imp:
rate_display_meter.reset()
rate_loss_meter.reset()
total_loss_meter.reset()
else:
total_loss_meter = mse_loss_meter
# cur_epoch_loss refresh every epoch
# vis.refresh_plot('cur epoch train mse loss')
ps.new_plot("cur epoch train mse loss",
opt.print_freq,
xlabel="iteration in cur epoch",
ylabel="train_mse_loss")
# progress_bar = tqdm(enumerate(train_dataloader), total=len(train_dataloader), ascii=True)
# progress_bar.set_description('epoch %d/%d, loss = 0.00' % (epoch, opt.max_epoch))
# Init val
if (epoch == start_epoch + 1) and opt.init_val:
print('Init validation ... ')
if opt.use_imp:
mse_val_loss, rate_val_loss, total_val_loss, rate_val_display = val(
model, val_dataloader, weighted_mse_loss, yolo_rate_loss,
ps)
else:
mse_val_loss = val(model, val_dataloader, weighted_mse_loss,
None, ps)
ps.add_point('val mse loss', mse_val_loss)
if opt.use_imp:
ps.add_point('val rate value', rate_val_display)
ps.add_point('val rate loss', rate_val_loss)
ps.add_point('val total loss', total_val_loss)
ps.make_plot('val mse loss')
if opt.use_imp:
ps.make_plot('val rate value')
ps.make_plot('val rate loss')
ps.make_plot('val total loss')
# log sth.
if opt.use_imp:
ps.log(
'Init Val @ Epoch:{epoch}, lr:{lr}, val_mse_loss: {val_mse_loss}, val_rate_loss: {val_rate_loss}, val_total_loss: {val_total_loss}, val_rate_display: {val_rate_display} '
.format(epoch=epoch,
lr=lr,
val_mse_loss=mse_val_loss,
val_rate_loss=rate_val_loss,
val_total_loss=total_val_loss,
val_rate_display=rate_val_display))
else:
ps.log(
'Init Val @ Epoch:{epoch}, lr:{lr}, val_mse_loss:{val_mse_loss}'
.format(epoch=epoch, lr=lr, val_mse_loss=mse_val_loss))
if opt.only_init_val:
print('Only Init Val Over!')
return
model.train()
if epoch == start_epoch + 1:
print('Start training, please inspect log file %s!' % logfile_name)
# mask is the detection bounding box mask
for idx, (data, mask, o_mask) in enumerate(train_dataloader):
# pdb.set_trace()
data = Variable(data)
mask = Variable(mask)
o_mask = Variable(o_mask, requires_grad=False)
if opt.use_gpu:
data = data.cuda(async=True)
mask = mask.cuda(async=True)
o_mask = o_mask.cuda(async=True)
# pdb.set_trace()
optimizer.zero_grad()
reconstructed, imp_mask_sigmoid = model(data, mask, o_mask)
# print ('imp_mask_height', model.imp_mask_height)
# pdb.set_trace()
# print ('type recons', type(reconstructed.data))
loss = weighted_mse_loss(reconstructed, data, o_mask)
# loss = mse_loss(reconstructed, data)
caffe_loss = loss / (2 * opt.batch_size)
if opt.use_imp:
rate_loss_display = imp_mask_sigmoid
# rate_loss_ = rate_loss(rate_loss_display)
rate_loss_ = yolo_rate_loss(rate_loss_display, mask)
total_loss = caffe_loss + rate_loss_
else:
total_loss = caffe_loss
total_loss.backward()
optimizer.step()
mse_loss_meter.add(caffe_loss.data[0])
if opt.use_imp:
rate_loss_meter.add(rate_loss_.data[0])
rate_display_meter.add(rate_loss_display.data.mean())
total_loss_meter.add(total_loss.data[0])
if idx % opt.print_freq == opt.print_freq - 1:
ps.add_point(
'train mse loss',
mse_loss_meter.value()[0]
if opt.print_smooth else caffe_loss.data[0])
ps.add_point(
'cur epoch train mse loss',
mse_loss_meter.value()[0]
if opt.print_smooth else caffe_loss.data[0])
if opt.use_imp:
ps.add_point(
'train rate value',
rate_display_meter.value()[0]
if opt.print_smooth else rate_loss_display.data.mean())
ps.add_point(
'train rate loss',
rate_loss_meter.value()[0]
if opt.print_smooth else rate_loss_.data[0])
ps.add_point(
'train total loss',
total_loss_meter.value()[0]
if opt.print_smooth else total_loss.data[0])
if not opt.use_imp:
ps.log('Epoch %d/%d, Iter %d/%d, loss = %.2f, lr = %.8f' %
(epoch, opt.max_epoch, idx, len(train_dataloader),
total_loss_meter.value()[0], lr))
else:
ps.log(
'Epoch %d/%d, Iter %d/%d, loss = %.2f, mse_loss = %.2f, rate_loss = %.2f, rate_display = %.2f, lr = %.8f'
%
(epoch, opt.max_epoch, idx, len(train_dataloader),
total_loss_meter.value()[0],
mse_loss_meter.value()[0], rate_loss_meter.value()[0],
rate_display_meter.value()[0], lr))
# 进入debug模式
if os.path.exists(opt.debug_file):
pdb.set_trace()
if epoch % opt.save_interval == 0:
print('save checkpoint file of epoch %d.' % epoch)
if use_data_parallel:
model.module.save(optimizer, epoch)
else:
model.save(optimizer, epoch)
ps.make_plot('train mse loss')
ps.make_plot('cur epoch train mse loss', epoch)
if opt.use_imp:
ps.make_plot("train rate value")
ps.make_plot("train rate loss")
ps.make_plot("train total loss")
if epoch % opt.eval_interval == 0:
print('Validating ...')
# val
if opt.use_imp:
mse_val_loss, rate_val_loss, total_val_loss, rate_val_display = val(
model, val_dataloader, weighted_mse_loss, yolo_rate_loss,
ps)
else:
mse_val_loss = val(model, val_dataloader, weighted_mse_loss,
None, ps)
ps.add_point('val mse loss', mse_val_loss)
if opt.use_imp:
ps.add_point('val rate value', rate_val_display)
ps.add_point('val rate loss', rate_val_loss)
ps.add_point('val total loss', total_val_loss)
ps.make_plot('val mse loss')
if opt.use_imp:
ps.make_plot('val rate value')
ps.make_plot('val rate loss')
ps.make_plot('val total loss')
# log sth.
if opt.use_imp:
ps.log(
'Epoch:{epoch}, lr:{lr}, train_mse_loss: {train_mse_loss}, train_rate_loss: {train_rate_loss}, train_total_loss: {train_total_loss}, train_rate_display: {train_rate_display} \n\
val_mse_loss: {val_mse_loss}, val_rate_loss: {val_rate_loss}, val_total_loss: {val_total_loss}, val_rate_display: {val_rate_display} '
.format(epoch=epoch,
lr=lr,
train_mse_loss=mse_loss_meter.value()[0],
train_rate_loss=rate_loss_meter.value()[0],
train_total_loss=total_loss_meter.value()[0],
train_rate_display=rate_display_meter.value()[0],
val_mse_loss=mse_val_loss,
val_rate_loss=rate_val_loss,
val_total_loss=total_val_loss,
val_rate_display=rate_val_display))
else:
ps.log(
'Epoch:{epoch}, lr:{lr}, train_mse_loss:{train_mse_loss}, val_mse_loss:{val_mse_loss}'
.format(epoch=epoch,
lr=lr,
train_mse_loss=mse_loss_meter.value()[0],
val_mse_loss=mse_val_loss))
# Adaptive adjust lr
# 每个lr，如果有opt.tolerant_max次比上次的val_loss还高，
# update learning rate
# if loss_meter.value()[0] > previous_loss:
if opt.use_early_adjust:
if total_loss_meter.value()[0] > previous_loss:
tolerant_now += 1
if tolerant_now == opt.tolerant_max:
tolerant_now = 0
same_lr_epoch = 0
lr = lr * opt.lr_decay
for param_group in optimizer.param_groups:
param_group['lr'] = lr
print('Due to early stop anneal lr to %.10f at epoch %d' %
(lr, epoch))
ps.log('Due to early stop anneal lr to %.10f at epoch %d' %
(lr, epoch))
else:
tolerant_now -= 1
if epoch % opt.lr_anneal_epochs == 0:
# if same_lr_epoch and same_lr_epoch % opt.lr_anneal_epochs == 0:
same_lr_epoch = 0
tolerant_now = 0
lr = lr * opt.lr_decay
for param_group in optimizer.param_groups:
param_group['lr'] = lr
print('Anneal lr to %.10f at epoch %d due to full epochs.' %
(lr, epoch))
ps.log('Anneal lr to %.10f at epoch %d due to full epochs.' %
(lr, epoch))
if opt.use_file_decay_lr and os.path.exists(opt.lr_decay_file):
cur_mtime = os.path.getmtime(opt.lr_decay_file)
if cur_mtime > decay_file_create_time:
decay_file_create_time = cur_mtime
lr = lr * opt.lr_decay
for param_group in optimizer.param_groups:
param_group['lr'] = lr
print(
'Anneal lr to %.10f at epoch %d due to decay-file indicator.'
% (lr, epoch))
ps.log(
'Anneal lr to %.10f at epoch %d due to decay-file indicator.'
% (lr, epoch))
previous_loss = total_loss_meter.value()[0]
def train(**kwargs):
global batch_model_id
opt.parse(kwargs)
if opt.use_batch_process:
max_bp_times = len(opt.exp_ids)
if batch_model_id >= max_bp_times:
print('Batch Processing Done!')
return
else:
print('Cur Batch Processing ID is %d/%d.' %
(batch_model_id + 1, max_bp_times))
opt.r = opt.r_s[batch_model_id]
opt.exp_id = opt.exp_ids[batch_model_id]
opt.exp_desc = opt.exp_desc_LUT[opt.exp_id]
opt.plot_path = "plot/plot_%d" % opt.exp_id
print('Cur Model(exp_%d) r = %f, desc = %s. ' %
(opt.exp_id, opt.r, opt.exp_desc))
opt.make_new_dirs()
# log file
EvalVal = opt.only_init_val and opt.init_val and not opt.test_test
EvalTest = opt.only_init_val and opt.init_val and opt.test_test
EvalSuffix = ""
if EvalVal:
EvalSuffix = "_val"
if EvalTest:
EvalSuffix = "_test"
logfile_name = opt.exp_desc + time.strftime(
"_%m_%d_%H:%M:%S") + EvalSuffix + ".log.txt"
ps = PlotSaver(logfile_name, log_to_stdout=opt.log_to_stdout)
# step1: Model
# model = getattr(models, opt.model)(use_imp = opt.use_imp, n = opt.feat_num, model_name=opt.exp_desc + ("_r={r}_gm={w}".format(
# r=opt.rate_loss_threshold,
# w=opt.rate_loss_weight)
# if opt.use_imp else "_no_imp"))
model = getattr(models, opt.model)(use_imp=opt.use_imp,
n=opt.feat_num,
model_name=opt.exp_desc)
# print (model)
# pdb.set_trace()
global use_data_parallel
if opt.use_gpu:
model, use_data_parallel = multiple_gpu_process(model)
# real use gpu or cpu
opt.use_gpu = opt.use_gpu and use_data_parallel >= 0
cudnn.benchmark = True
# step2: Data
normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
train_data_transforms = transforms.Compose([
# transforms.RandomHorizontalFlip(), TODO: try to reimplement by myself to simultaneous operate on label and data
transforms.RandomCrop(128),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize
])
val_data_transforms = transforms.Compose(
[transforms.CenterCrop(128),
transforms.ToTensor(), normalize])
caffe_data_transforms = transforms.Compose([transforms.CenterCrop(128)])
# transforms || data
train_data = ImageFilelist(
flist=opt.train_data_list,
transform=train_data_transforms,
)
val_data = ImageFilelist(
flist=opt.val_data_list,
prefix=opt.val_data_prefix,
transform=val_data_transforms,
)
val_data_caffe = ImageFilelist(flist=opt.val_data_list,
transform=caffe_data_transforms)
test_data_caffe = ImageFilelist(flist=opt.test_data_list,
transform=caffe_data_transforms)
if opt.make_caffe_data:
save_caffe_data(test_data_caffe)
print('Make caffe dataset over!')
return
# train_data = ImageCropWithBBoxMaskDataset(
# opt.train_data_list,
# train_data_transforms,
# contrastive_degree = opt.contrastive_degree,
# mse_bbox_weight = opt.input_original_bbox_weight
# )
# val_data = ImageCropWithBBoxMaskDataset(
# opt.val_data_list,
# val_data_transforms,
# contrastive_degree = opt.contrastive_degree,
# mse_bbox_weight = opt.input_original_bbox_weight
# )
train_dataloader = DataLoader(train_data,
opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
pin_memory=True)
val_dataloader = DataLoader(val_data,
opt.batch_size,
shuffle=False,
num_workers=opt.num_workers,
pin_memory=True)
# step3: criterion and optimizer
mse_loss = t.nn.MSELoss(size_average=False)
if opt.use_imp:
# TODO: new rate loss
rate_loss = RateLoss(opt.rate_loss_threshold, opt.rate_loss_weight)
# rate_loss = LimuRateLoss(opt.rate_loss_threshold, opt.rate_loss_weight)
def weighted_mse_loss(input, target, weight):
# weight[weight!=opt.mse_bbox_weight] = 1
# weight[weight==opt.mse_bbox_weight] = opt.mse_bbox_weight
# print('max val', weight.max())
# return mse_loss(input, target)
# weight_clone = weight.clone()
# weight_clone[weight_clone == opt.input_original_bbox_weight] = 0
# return t.sum(weight_clone * (input - target) ** 2)
weight_clone = t.ones_like(weight)
weight_clone[weight ==
opt.input_original_bbox_inner] = opt.mse_bbox_weight
return t.sum(weight_clone * (input - target)**2)
def yolo_rate_loss(imp_map, mask_r):
return rate_loss(imp_map)
# V2 contrastive_degree must be 0!
# return YoloRateLossV2(mask_r, opt.rate_loss_threshold, opt.rate_loss_weight)(imp_map)
start_epoch = 0
decay_file_create_time = -1 # 为了避免同一个文件反复衰减学习率, 所以判断修改时间
previous_loss = 1e100
tolerant_now = 0
same_lr_epoch = 0
lr = opt.lr
optimizer = t.optim.Adam(model.parameters(), lr=lr, betas=(0.9, 0.999))
if opt.resume:
start_epoch = (model.module if use_data_parallel == 1 else model).load(
None if opt.finetune else optimizer, opt.resume, opt.finetune)
if opt.finetune:
print('Finetune from model checkpoint file', opt.resume)
else:
print('Resume training from checkpoint file', opt.resume)
print('Continue training from epoch %d.' % start_epoch)
same_lr_epoch = start_epoch % opt.lr_anneal_epochs
decay_times = start_epoch // opt.lr_anneal_epochs
lr = opt.lr * (opt.lr_decay**decay_times)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
print('Decay lr %d times, now lr is %e.' % (decay_times, lr))
# step4: meters
mse_loss_meter = AverageValueMeter()
if opt.use_imp:
rate_loss_meter = AverageValueMeter()
rate_display_meter = AverageValueMeter()
total_loss_meter = AverageValueMeter()
# ps init
ps.new_plot('train mse loss',
opt.print_freq,
xlabel="iteration",
ylabel="train_mse_loss")
ps.new_plot('val mse loss', 1, xlabel="epoch", ylabel="val_mse_loss")
if opt.use_imp:
ps.new_plot('train rate value',
opt.print_freq,
xlabel="iteration",
ylabel="train_rate_value")
ps.new_plot('train rate loss',
opt.print_freq,
xlabel="iteration",
ylabel="train_rate_loss")
ps.new_plot('train total loss',
opt.print_freq,
xlabel="iteration",
ylabel="train_total_loss")
ps.new_plot('val rate value',
1,
xlabel="iteration",
ylabel="val_rate_value")
ps.new_plot('val rate loss',
1,
xlabel="iteration",
ylabel="val_rate_loss")
ps.new_plot('val total loss',
1,
xlabel="iteration",
ylabel="val_total_loss")
# 如果测试时是600，max_epoch也是600
if opt.only_init_val and opt.max_epoch <= start_epoch:
opt.max_epoch = start_epoch + 2
for epoch in range(start_epoch + 1, opt.max_epoch + 1):
same_lr_epoch += 1
# per epoch avg loss meter
mse_loss_meter.reset()
if opt.use_imp:
rate_display_meter.reset()
rate_loss_meter.reset()
total_loss_meter.reset()
else:
total_loss_meter = mse_loss_meter
# cur_epoch_loss refresh every epoch
# vis.refresh_plot('cur epoch train mse loss')
ps.new_plot("cur epoch train mse loss",
opt.print_freq,
xlabel="iteration in cur epoch",
ylabel="train_mse_loss")
# progress_bar = tqdm(enumerate(train_dataloader), total=len(train_dataloader), ascii=True)
# progress_bar.set_description('epoch %d/%d, loss = 0.00' % (epoch, opt.max_epoch))
# Init val
if (epoch == start_epoch + 1) and opt.init_val:
print('Init validation ... ')
if opt.use_imp:
mse_val_loss, rate_val_loss, total_val_loss, rate_val_display = val(
model, val_dataloader, mse_loss, rate_loss, ps)
else:
mse_val_loss = val(model, val_dataloader, mse_loss, None, ps)
ps.add_point('val mse loss', mse_val_loss)
if opt.use_imp:
ps.add_point('val rate value', rate_val_display)
ps.add_point('val rate loss', rate_val_loss)
ps.add_point('val total loss', total_val_loss)
ps.make_plot('val mse loss')
if opt.use_imp:
ps.make_plot('val rate value')
ps.make_plot('val rate loss')
ps.make_plot('val total loss')
# log sth.
if opt.use_imp:
ps.log(
'Init Val @ Epoch:{epoch}, lr:{lr}, val_mse_loss: {val_mse_loss}, val_rate_loss: {val_rate_loss}, val_total_loss: {val_total_loss}, val_rate_display: {val_rate_display} '
.format(epoch=epoch,
lr=lr,
val_mse_loss=mse_val_loss,
val_rate_loss=rate_val_loss,
val_total_loss=total_val_loss,
val_rate_display=rate_val_display))
else:
ps.log(
'Init Val @ Epoch:{epoch}, lr:{lr}, val_mse_loss: {val_mse_loss}'
.format(epoch=epoch, lr=lr, val_mse_loss=mse_val_loss))
if opt.only_init_val:
print('Only Init Val Over!')
return
model.train()
# if epoch == start_epoch + 1:
# print ('Start training, please inspect log file %s!' % logfile_name)
# mask is the detection bounding box mask
for idx, data in enumerate(train_dataloader):
# pdb.set_trace()
data = Variable(data)
# mask = Variable(mask)
# o_mask = Variable(o_mask, requires_grad=False)
if opt.use_gpu:
data = data.cuda(async=True)
# mask = mask.cuda(async = True)
# o_mask = o_mask.cuda(async = True)
# pdb.set_trace()
optimizer.zero_grad()
if opt.use_imp:
reconstructed, imp_mask_sigmoid = model(data)
else:
reconstructed = model(data)
# print ('imp_mask_height', model.imp_mask_height)
# pdb.set_trace()
# print ('type recons', type(reconstructed.data))
loss = mse_loss(reconstructed, data)
# loss = mse_loss(reconstructed, data)
caffe_loss = loss / (2 * opt.batch_size)
if opt.use_imp:
rate_loss_ = rate_loss(imp_mask_sigmoid)
# rate_loss_ = yolo_rate_loss(imp_mask_sigmoid, mask)
total_loss = caffe_loss + rate_loss_
else:
total_loss = caffe_loss
total_loss.backward()
optimizer.step()
mse_loss_meter.add(caffe_loss.data[0])
if opt.use_imp:
rate_loss_meter.add(rate_loss_.data[0])
rate_display_meter.add(imp_mask_sigmoid.data.mean())
total_loss_meter.add(total_loss.data[0])
if idx % opt.print_freq == opt.print_freq - 1:
ps.add_point(
'train mse loss',
mse_loss_meter.value()[0]
if opt.print_smooth else caffe_loss.data[0])
ps.add_point(
'cur epoch train mse loss',
mse_loss_meter.value()[0]
if opt.print_smooth else caffe_loss.data[0])
if opt.use_imp:
ps.add_point(
'train rate value',
rate_display_meter.value()[0]
if opt.print_smooth else imp_mask_sigmoid.data.mean())
ps.add_point(
'train rate loss',
rate_loss_meter.value()[0]
if opt.print_smooth else rate_loss_.data[0])
ps.add_point(
'train total loss',
total_loss_meter.value()[0]
if opt.print_smooth else total_loss.data[0])
if not opt.use_imp:
ps.log('Epoch %d/%d, Iter %d/%d, loss = %.2f, lr = %.8f' %
(epoch, opt.max_epoch, idx, len(train_dataloader),
total_loss_meter.value()[0], lr))
else:
ps.log(
'Epoch %d/%d, Iter %d/%d, loss = %.2f, mse_loss = %.2f, rate_loss = %.2f, rate_display = %.2f, lr = %.8f'
%
(epoch, opt.max_epoch, idx, len(train_dataloader),
total_loss_meter.value()[0],
mse_loss_meter.value()[0], rate_loss_meter.value()[0],
rate_display_meter.value()[0], lr))
# 进入debug模式
if os.path.exists(opt.debug_file):
pdb.set_trace()
if epoch % opt.save_interval == 0:
print('Save checkpoint file of epoch %d.' % epoch)
if use_data_parallel == 1:
model.module.save(optimizer, epoch)
else:
model.save(optimizer, epoch)
ps.make_plot('train mse loss')
ps.make_plot('cur epoch train mse loss', epoch)
if opt.use_imp:
ps.make_plot("train rate value")
ps.make_plot("train rate loss")
ps.make_plot("train total loss")
if epoch % opt.eval_interval == 0:
print('Validating ...')
# val
if opt.use_imp:
mse_val_loss, rate_val_loss, total_val_loss, rate_val_display = val(
model, val_dataloader, mse_loss, rate_loss, ps)
else:
mse_val_loss = val(model, val_dataloader, mse_loss, None, ps)
ps.add_point('val mse loss', mse_val_loss)
if opt.use_imp:
ps.add_point('val rate value', rate_val_display)
ps.add_point('val rate loss', rate_val_loss)
ps.add_point('val total loss', total_val_loss)
ps.make_plot('val mse loss')
if opt.use_imp:
ps.make_plot('val rate value')
ps.make_plot('val rate loss')
ps.make_plot('val total loss')
# log sth.
if opt.use_imp:
ps.log(
'Epoch:{epoch}, lr:{lr}, train_mse_loss: {train_mse_loss}, train_rate_loss: {train_rate_loss}, train_total_loss: {train_total_loss}, train_rate_display: {train_rate_display} \n\
val_mse_loss: {val_mse_loss}, val_rate_loss: {val_rate_loss}, val_total_loss: {val_total_loss}, val_rate_display: {val_rate_display} '
.format(epoch=epoch,
lr=lr,
train_mse_loss=mse_loss_meter.value()[0],
train_rate_loss=rate_loss_meter.value()[0],
train_total_loss=total_loss_meter.value()[0],
train_rate_display=rate_display_meter.value()[0],
val_mse_loss=mse_val_loss,
val_rate_loss=rate_val_loss,
val_total_loss=total_val_loss,
val_rate_display=rate_val_display))
else:
ps.log(
'Epoch:{epoch}, lr:{lr}, train_mse_loss:{train_mse_loss}, val_mse_loss:{val_mse_loss}'
.format(epoch=epoch,
lr=lr,
train_mse_loss=mse_loss_meter.value()[0],
val_mse_loss=mse_val_loss))
# Adaptive adjust lr
# 每个lr，如果有opt.tolerant_max次比上次的val_loss还高，
# update learning rate
# if loss_meter.value()[0] > previous_loss:
if opt.use_early_adjust:
if total_loss_meter.value()[0] > previous_loss:
tolerant_now += 1
if tolerant_now == opt.tolerant_max:
tolerant_now = 0
same_lr_epoch = 0
lr = lr * opt.lr_decay
for param_group in optimizer.param_groups:
param_group['lr'] = lr
print('Due to early stop anneal lr to %.10f at epoch %d' %
(lr, epoch))
ps.log('Due to early stop anneal lr to %.10f at epoch %d' %
(lr, epoch))
else:
# tolerant_now -= 1
tolerant_now = 0
if epoch % opt.lr_anneal_epochs == 0:
# if same_lr_epoch and same_lr_epoch % opt.lr_anneal_epochs == 0:
same_lr_epoch = 0
tolerant_now = 0
lr = lr * opt.lr_decay
for param_group in optimizer.param_groups:
param_group['lr'] = lr
print('Anneal lr to %.10f at epoch %d due to full epochs.' %
(lr, epoch))
ps.log('Anneal lr to %.10f at epoch %d due to full epochs.' %
(lr, epoch))
if opt.use_file_decay_lr and os.path.exists(opt.lr_decay_file):
cur_mtime = os.path.getmtime(opt.lr_decay_file)
if cur_mtime > decay_file_create_time:
decay_file_create_time = cur_mtime
lr = lr * opt.lr_decay
for param_group in optimizer.param_groups:
param_group['lr'] = lr
print(
'Anneal lr to %.10f at epoch %d due to decay-file indicator.'
% (lr, epoch))
ps.log(
'Anneal lr to %.10f at epoch %d due to decay-file indicator.'
% (lr, epoch))
previous_loss = total_loss_meter.value()[0]
if opt.use_batch_process:
batch_model_id += 1
train(kwargs)