def prepare_optimizee(args, sgd_in_names, obs_shape, hidden_size, actor_critic,
current_optimizee_step, prev_optimizee_step):
prev_optimizee_step += current_optimizee_step
current_optimizee_step = 0
model = resnet101(pretrained=True)
num_ftrs = model.fc.in_features
fc_layers = nn.Sequential(
nn.Linear(num_ftrs, 512),
nn.ReLU(inplace=True),
nn.Linear(512, args.num_class),
)
model.fc_new = fc_layers
train_blocks = args.train_blocks.split('.')
# default turn-off fc, turn-on fc_new
for param in model.fc.parameters():
param.requires_grad = False
##### Freeze several bottom layers (Optional) #####
non_train_blocks = [
'conv1', 'bn1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc'
]
for name in train_blocks:
try:
non_train_blocks.remove(name)
except Exception:
print(
"cannot find block name %s\nAvailable blocks are: conv1, bn1, layer1, layer2, layer3, layer4, fc"
% name)
for name in non_train_blocks:
for param in getattr(model, name).parameters():
param.requires_grad = False
# Setup optimizer
sgd_in = []
for name in train_blocks:
if name != 'fc':
sgd_in.append({'params': get_params(model, [name]), 'lr': args.lr})
else:
sgd_in.append({
'params': get_params(model, ["fc_new"]),
'lr': args.lr
})
base_lrs = [group['lr'] for group in sgd_in]
optimizer = SGD(sgd_in,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
model = model.cuda()
model.eval()
return model, optimizer, current_optimizee_step, prev_optimizee_step
def settings(*args, **kwargs):
'''
修改配置
'''
username = kwargs.get('username')
params = utils.get_params(
['former_show_col', 'future_show_col', 'currency', 'currency_rate_rmb2ab', 'currency_rate_rmb2dollar'])
pwd_old = request.params.get('pwd_old')
pwd_new1 = request.params.get('pwd_new1')
pwd_new2 = request.params.get('pwd_new2')
if pwd_old and pwd_new1 and pwd_new2:
if pwd_new2 != pwd_new1:
return {'retcode': 0, 'msg': '两次新密码不相同'}
if len(pwd_new1) > 20:
return {'retcode': 0, 'msg': '密码长度不能大于20位'}
sql = get_s_sql('user', ['*'], {'username': username, 'pwd': hashlib.md5(pwd_old).hexdigest()})
r = sql_conn.execute(sql)
if not r:
return {'retcode': 0, 'msg': '原密码错误'}
params['pwd'] = hashlib.md5(pwd_new1).hexdigest()
sql = get_u_sql('user', params, {'username': username})
sql_conn.execute(sql)
sql_conn.commit()
return {'retcode': 1}
def i_cashier_post(*args, **kwargs):
'''
插入数据
'''
params = utils.get_params(cashier_keys)
type = request.params.get('type') # pay or get
params['actual_money'] = abs(float(params['actual_money'])) if type == 'get' else -abs(
float(params['actual_money']))
def _insert(date):
params['date'] = date
sql = get_i_sql('cashier', params)
sql_conn.execute(sql)
days = request.params.get('days')
future_date = request.params.get('future_date')
if days and future_date:
try:
days = int(days)
cur_date = datetime.strptime(params['date'], '%Y-%m-%d')
futute_date = datetime.strptime(future_date, '%Y-%m-%d')
except:
return {'retcode': 0}
while cur_date <= futute_date:
_insert(cur_date.strftime('%Y-%m-%d'))
cur_date += timedelta(days=days)
else:
_insert(params['date'])
sql_conn.commit()
return {'retcode': 1}
def u_cashier(*args, **kwargs):
'''
更新数据
'''
param = utils.get_params(cashier_keys )
sql = get_u_sql('cashier', param,{'cashier_id':request.params.get('cashier_id')})
sql_conn.execute(sql)
return {'retcode': 1}
def prepare_optimizee(args, sgd_in_names, obs_shape, hidden_size, actor_critic, current_optimizee_step, prev_optimizee_step):
prev_optimizee_step += current_optimizee_step
current_optimizee_step = 0
##### Vgg16 #####
vgg = vgg16(pretrained=True)
model = FCN_Vgg(n_class=args.num_class)
model.copy_params_from_vgg16(vgg)
###################
# Setup optimizer
sgd_in = [
{'params': get_params(model, ["conv1_1", "conv1_2"]), 'lr': args.lr},
{'params': get_params(model, ["conv2_1", "conv2_2"]), 'lr': args.lr},
{'params': get_params(model, ["conv3_1", "conv3_2", "conv3_3"]), 'lr': args.lr},
{'params': get_params(model, ["conv4_1", "conv4_2", "conv4_3"]), 'lr': args.lr},
{'params': get_params(model, ["conv5_1", "conv5_2", "conv5_3"]), 'lr': args.lr},
{'params': get_params(model, ["fc6", "fc7"]), 'lr': args.lr},
{'params': get_params(model, ["score_fr", "score_pool3", "score_pool4", "upscore2", "upscore8", "upscore_pool4"]), 'lr': args.lr},
]
optimizer = torch.optim.SGD(sgd_in, lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
# Specify which GPUs to use
model = model.cuda()
model.eval()
return model, optimizer, current_optimizee_step, prev_optimizee_step
def i_user(*args, **kwargs):
'''
插入用户
'''
params = utils.get_params(['username', 'pwd', 'group_id'])
params['pwd']=hashlib.md5(params['pwd']).hexdigest()
sql = get_i_sql('user', params)
sql_conn.execute(sql)
return {'retcode': 1}
def d_cashier(*args, **kwargs):
'''
删除数据
'''
param = utils.get_params(['cashier_id'])
sql = get_d_sql('cashier', param)
sql_conn.execute(sql)
sql_conn.commit()
return {'retcode': 1}
def new_account(*args, **kwargs):
'''
新增账户
'''
cd = utils.get_params(['account_name', 'account_desc', 'account_currency', 'account_about', 'account_label'])
sql = get_i_sql('account', cd)
sql_conn.execute(sql)
sql_conn.commit()
item_id = sql_conn.last_id()
return {'retcode': 1, 'item_id': str(item_id)}
def s_cashier_list_post(*args, **kwargs):
'''
返回明细的内容
'''
param = utils.get_params(['date1', 'date2'])
is_output_xls = request.params.get('is_output_xls') # 标记是否导出到xls
user_cond = '' if 'admin' in kwargs.get('access') else 'pay_user="******" and '% kwargs.get('username')
#支出还是收入的筛选
type=request.params.get('type')
type_cond={'get':'actual_money>=0 and ','pay':' actual_money<=0 and '}[type] if type else ''
keys = request.params.get('show_cols').split(';')
cashier_keys_ = cashier_keys + ['client_name', 'account_name', 'proof_name', 'paytype_name']
keys = filter(lambda x: x in keys, cashier_keys_)
order_type='desc' if request.params.get('ctrl_typ')=='list' else ''
def _get_sql(keys, index=None):
limit_sql = ' limit {0},20'.format(index) if index != None else ''
sql = get_s_sql('cashier,client,proof,account,paytype', keys, {}) + \
" where {user_cond} {type_cond} cashier.client_id=client.client_id and \
cashier.proof_id=proof.proof_id and cashier.account_id=account.account_id and " \
"cashier.paytype_id=paytype.paytype_id and date>='{date1}' and date<= '{date2}'" \
" order by date {order_type} {limit_sql}".\
format(limit_sql=limit_sql,order_type=order_type,user_cond=user_cond,
type_cond=type_cond,**param)
return sql
sql_index = utils.get_sql_index()
total_num = 0
if sql_index == 0 and not is_output_xls:
# 计算数据的条数
sql = _get_sql(['count(*)'])
r = sql_conn.execute(sql, 1)
total_num = r[0]
key_ = keys + ['cashier_id', 'account_currency']
sql = _get_sql(key_, None) if is_output_xls else _get_sql(key_, sql_index)
r = sql_conn.execute(sql)
data = utils.sql_result(r, keys + ['cashier_id', 'account_currency'])
data = cashier_utils.exchage_data(data, kwargs)
if is_output_xls:
url = cashier_utils.output_xls(data, cashier_keys_2ch, keys, param['date1'], param['date2'])
return {'retcode': 1, 'url': url}
print keys
return {'retcode': 1,
'data': template('cashier_list_include', data=data, cashier_keys_2ch=cashier_keys_2ch, keys=keys),
'total_num': total_num}
def s_cashier_detail(*args, **kwargs):
'''
获取一条收纳数据的明细
'''
param = utils.get_params(['cashier_id'])
if 'admin' not in kwargs.get('access'):
param['pay_user'] = kwargs.get('username')
keys = copy(cashier_keys) + ['cashier_id', 'account_currency', 'cashier.account_id']
keys.remove('account_id')
sql = get_s_sql('cashier,account', keys, param) + ' and account.account_id=cashier.account_id'
r = sql_conn.execute(sql)
if not r: return '系统出错'
cashier_data = utils.sql_result(r, keys)[0]
cashier_data = cashier_utils.exchage_data(cashier_data, kwargs)
data = cashier_utils.get_data_in_detail(sql_conn, kwargs)
return template('cashier_detail', cashier_data=cashier_data, data=data, ctrl_type='update')
model = Segception.Efficient(num_classes=n_classes, weights='imagenet', input_shape=(None, None, channels))
# optimizer
learning_rate = tfe.Variable(lr)
optimizer = tf.train.AdamOptimizer(learning_rate)
# Init models (optional, just for get_params function)
init_model(model, input_shape=(batch_size, width, height, channels))
variables_to_restore = model.variables
variables_to_save = model.variables
variables_to_optimize = model.variables
# Init saver. can use also ckpt = tfe.Checkpoint((model=model, optimizer=optimizer,learning_rate=learning_rate, global_step=global_step)
saver_model = tfe.Saver(var_list=variables_to_save)
restore_model = tfe.Saver(var_list=variables_to_restore)
# restore if model saved and show number of params
restore_state(restore_model, name_best_model)
get_params(model)
train(loader=loader, optimizer=optimizer, model=model, epochs=epochs, batch_size=batch_size, augmenter='segmentation', lr=learning_rate,
init_lr=lr, saver=saver_model, variables_to_optimize=variables_to_optimize, name_best_model=name_best_model,
evaluation=True, aux_loss=False, preprocess_mode=preprocess_mode)
# Test best model
print('Testing model')
test_acc, test_miou = get_metrics(loader, model, loader.n_classes, train=False, flip_inference=True, scales=[0.75, 1, 1.5],
write_images=True, preprocess_mode=preprocess_mode, time_exect=False)
print('Test accuracy: ' + str(test_acc.numpy()))
print('Test miou: ' + str(test_miou))
def main():
global args, best_prec1
PID = os.getpid()
args = parser.parse_args()
prepare_seed(args.rand_seed)
if args.timestamp == 'none':
args.timestamp = "{:}".format(time.strftime('%h-%d-%C_%H-%M-%s', time.gmtime(time.time())))
# Log outputs
if args.evaluate:
args.save_dir = args.save_dir + "/Visda17-Res101-evaluate" + \
"%s/%s"%('/'+args.resume if args.resume != 'none' else '', args.timestamp)
else:
args.save_dir = args.save_dir + \
"/Visda17-Res101-%s-train.%s-LR%.2E-epoch%d-batch%d-seed%d"%(
"LWF%.2f"%args.lwf if args.lwf > 0 else "XE", args.train_blocks, args.lr, args.epochs, args.batch_size, args.rand_seed) + \
"%s/%s"%('/'+args.resume if args.resume != 'none' else '', args.timestamp)
logger = prepare_logger(args)
data_transforms = {
'train': transforms.Compose([
transforms.Resize(224),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
]),
'val': transforms.Compose([
transforms.Resize(224),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
]),
}
kwargs = {'num_workers': 20, 'pin_memory': True}
trainset = VisDA17(txt_file=os.path.join(args.data, "train/image_list.txt"), root_dir=os.path.join(args.data, "train"), transform=data_transforms['train'])
valset = VisDA17(txt_file=os.path.join(args.data, "validation/image_list.txt"), root_dir=os.path.join(args.data, "validation"), transform=data_transforms['val'], label_one_hot=True)
train_loader = DataLoader(trainset, batch_size=args.batch_size, shuffle=True, **kwargs)
val_loader = DataLoader(valset, batch_size=args.batch_size, shuffle=False, **kwargs)
model = resnet101(pretrained=True)
num_ftrs = model.fc.in_features
fc_layers = nn.Sequential(
nn.Linear(num_ftrs, 512),
nn.ReLU(inplace=True),
nn.Linear(512, args.num_class),
)
model.fc_new = fc_layers
train_blocks = args.train_blocks.split('.')
# default turn-off fc, turn-on fc_new
for param in model.fc.parameters():
param.requires_grad = False
##### Freeze several bottom layers (Optional) #####
non_train_blocks = ['conv1', 'bn1', 'layer1', 'layer2', 'layer3', 'layer4', 'fc']
for name in train_blocks:
try:
non_train_blocks.remove(name)
except Exception:
print("cannot find block name %s\nAvailable blocks are: conv1, bn1, layer1, layer2, layer3, layer4, fc"%name)
for name in non_train_blocks:
for param in getattr(model, name).parameters():
param.requires_grad = False
# Setup optimizer
factor = 0.1
sgd_in = []
for name in train_blocks:
if name != 'fc':
sgd_in.append({'params': get_params(model, [name]), 'lr': factor*args.lr})
else:
sgd_in.append({'params': get_params(model, ["fc_new"]), 'lr': args.lr})
base_lrs = [ group['lr'] for group in sgd_in ]
optimizer = SGD(sgd_in, lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
# Optionally resume from a checkpoint
if args.resume != 'none':
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch']))
else:
print("=ImageClassdata> no checkpoint found at '{}'".format(args.resume))
model = model.cuda()
model_old = None
if args.lwf > 0:
# create a fixed model copy for Life-long learning
model_old = resnet101(pretrained=True)
for param in model_old.parameters():
param.requires_grad = False
model_old.eval()
model_old.cuda()
if args.evaluate:
prec1 = validate(val_loader, model)
print(prec1)
exit(0)
# Main training loop
iter_max = args.epochs * len(train_loader)
iter_stat = IterNums(iter_max)
for epoch in range(args.start_epoch, args.epochs):
print("<< ============== JOB (PID = %d) %s ============== >>"%(PID, args.save_dir))
logger.log("Epoch: %d"%(epoch+1))
# train for one epoch
train(train_loader, model, optimizer, base_lrs, iter_stat, epoch, logger.writer, model_old=model_old, adjust_lr=True)
# evaluate on validation set
prec1 = validate(val_loader, model)
logger.writer.add_scalar("prec", prec1, epoch)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint(args.save_dir, {
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
}, is_best)
logging.info('Best accuracy: {prec1:.3f}'.format(prec1=best_prec1))
def train(n_classes=11,
batch_size=16,
epochs=100,
width=960,
height=720,
crop_factor_x=2,
crop_factor_y=1.25,
init_lr=1e-4,
median_frequency=.15,
zoom_augmentation=.2,
dataset_path='datasets/endoscopy',
weights_path='weights/endoscopy/model',
preprocess='imagenet'):
CONFIG = {}
CONFIG['n_classes'] = n_classes
CONFIG['batch_size'] = batch_size
CONFIG['epochs'] = epochs
CONFIG['width'] = width
CONFIG['height'] = height
CONFIG['crop_factor_x'] = crop_factor_x
CONFIG['crop_factor_y'] = crop_factor_y
CONFIG['width_train'] = int(
CONFIG['width'] /
CONFIG['crop_factor_x']) # will be cropped from width_test size
CONFIG['height_train'] = int(
CONFIG['height'] /
CONFIG['crop_factor_y']) # will be cropped from height_test size
CONFIG['init_lr'] = init_lr
CONFIG['median_frequency'] = median_frequency
CONFIG['zoom_augmentation'] = zoom_augmentation
CONFIG['dataset_path'] = dataset_path
CONFIG['weights_path'] = weights_path
CONFIG['preprocess'] = preprocess
assert CONFIG['width'] * (
1 - CONFIG['zoom_augmentation']) >= CONFIG['width_train']
assert CONFIG['height'] * (
1 - CONFIG['zoom_augmentation']) >= CONFIG['height_train']
# GPU to use
n_gpu = 0
os.environ["CUDA_VISIBLE_DEVICES"] = str(n_gpu)
# Loader
loader = Loader.Loader(dataFolderPath=CONFIG['dataset_path'],
n_classes=CONFIG['n_classes'],
width=CONFIG['width'],
height=CONFIG['height'],
median_frequency=CONFIG['median_frequency'])
print('Dataset loaded...')
# build model
#model = MiniNetv2.MiniNetv2p(num_classes=CONFIG['n_classes'])
model = ResNet50.ResNet50Seg(CONFIG['n_classes'],
input_shape=(None, None, 3),
weights='imagenet')
# optimizer
learning_rate = tf.Variable(CONFIG['init_lr'])
optimizer = tf.keras.optimizers.Adam(learning_rate)
loss_function = tf.keras.losses.CategoricalCrossentropy()
# restore if model saved and show number of params
restore_state(model, CONFIG['weights_path'])
init_model(model, (1, CONFIG['width'], CONFIG['height'], 3))
get_params(model)
# Train
print('Training...')
_train(loader=loader,
optimizer=optimizer,
loss_function=loss_function,
model=model,
config=CONFIG,
lr=learning_rate,
name_best_model=CONFIG['weights_path'],
evaluation=True,
preprocess_mode=CONFIG['preprocess'])
print('Testing model')
test_acc, test_miou = get_metrics(loader,
model,
loader.n_classes,
train=False,
flip_inference=True,
scales=[1, 2, 1.5, 0.5, 0.75],
write_images=True,
preprocess_mode=CONFIG['preprocess'],
time_exect=True)
print('Test accuracy: ' + str(test_acc.numpy()))
print('Test miou: ' + str(test_miou.numpy()))
def main():
global args, best_mIoU
PID = os.getpid()
args = parser.parse_args()
prepare_seed(args.rand_seed)
device = torch.device("cuda:" + str(args.gpus))
if args.timestamp == 'none':
args.timestamp = "{:}".format(
time.strftime('%h-%d-%C_%H-%M-%s', time.gmtime(time.time())))
switch_model = args.switch_model
assert switch_model in ["deeplab50", "deeplab101"]
# Log outputs
if args.evaluate:
args.save_dir = args.save_dir + "/GTA5-%s-evaluate"%switch_model + \
"%s/%s"%('/'+args.resume if args.resume != 'none' else '', args.timestamp)
else:
args.save_dir = args.save_dir + \
"/GTA5_512x512-{model}-LWF.stg{csg_stages}.w{csg_weight}-APool.{apool}-Aug.{augment}-chunk{chunks}-mlp{mlp}.K{csg_k}-LR{lr}.bone{factor}-epoch{epochs}-batch{batch_size}-seed{seed}".format(
model=switch_model,
csg_stages=args.csg_stages,
mlp=args.mlp,
csg_weight=args.csg,
apool=args.apool,
augment=args.augment,
chunks=args.chunks,
csg_k=args.csg_k,
lr="%.2E"%args.lr,
factor="%.1f"%args.factor,
epochs=args.epochs,
batch_size=args.batch_size,
seed=args.rand_seed
) + \
"%s/%s"%('/'+args.resume if args.resume != 'none' else '', args.timestamp)
logger = prepare_logger(args)
from config_seg import config as data_setting
data_setting.batch_size = args.batch_size
train_loader = get_train_loader(data_setting,
GTA5,
test=False,
augment=args.augment)
args.stages = [int(stage) for stage in args.csg_stages.split('.')
] if len(args.csg_stages) > 0 else []
chunks = [int(chunk) for chunk in args.chunks.split('.')
] if len(args.chunks) > 0 else []
assert len(chunks) == 1 or len(chunks) == len(args.stages)
if len(chunks) < len(args.stages):
chunks = [chunks[0]] * len(args.stages)
if switch_model == 'deeplab50':
layers = [3, 4, 6, 3]
elif switch_model == 'deeplab101':
layers = [3, 4, 23, 3]
model = csg_builder.CSG(deeplab,
get_head=None,
K=args.csg_k,
stages=args.stages,
chunks=chunks,
task='new-seg',
apool=args.apool,
mlp=args.mlp,
base_encoder_kwargs={
'num_seg_classes': args.num_classes,
'layers': layers
})
threds = 3
evaluator = SegEvaluator(
Cityscapes(data_setting, 'val', None),
args.num_classes,
np.array([0.485, 0.456, 0.406]),
np.array([0.229, 0.224, 0.225]),
model.encoder_q, [
1,
],
False,
devices=args.gpus,
config=data_setting,
threds=threds,
verbose=False,
save_path=None,
show_image=False
) # just calculate mIoU, no prediction file is generated
# verbose=False, save_path="./prediction_files", show_image=True, show_prediction=True) # generate prediction files
# Setup optimizer
factor = args.factor
sgd_in = [
{
'params': get_params(model.encoder_q, ["conv1"]),
'lr': factor * args.lr
},
{
'params': get_params(model.encoder_q, ["bn1"]),
'lr': factor * args.lr
},
{
'params': get_params(model.encoder_q, ["layer1"]),
'lr': factor * args.lr
},
{
'params': get_params(model.encoder_q, ["layer2"]),
'lr': factor * args.lr
},
{
'params': get_params(model.encoder_q, ["layer3"]),
'lr': factor * args.lr
},
{
'params': get_params(model.encoder_q, ["layer4"]),
'lr': factor * args.lr
},
{
'params': get_params(model.encoder_q, ["fc_new"]),
'lr': args.lr
},
]
base_lrs = [group['lr'] for group in sgd_in]
optimizer = SGD(sgd_in,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# Optionally resume from a checkpoint
if args.resume != 'none':
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume,
map_location=lambda storage, loc: storage)
args.start_epoch = checkpoint['epoch']
best_mIoU = checkpoint['best_mIoU']
msg = model.load_state_dict(checkpoint['state_dict'])
print("resume weights: ", msg)
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=ImageClassdata> no checkpoint found at '{}'".format(
args.resume))
model = model.to(device)
if args.evaluate:
mIoU = validate(evaluator, model, -1)
print(mIoU)
exit(0)
# Main training loop
iter_max = args.epochs * len(train_loader)
iter_stat = IterNums(iter_max)
for epoch in range(args.start_epoch, args.epochs):
print("<< ============== JOB (PID = %d) %s ============== >>" %
(PID, args.save_dir))
logger.log("Epoch: %d" % (epoch + 1))
# train for one epoch
train(args,
train_loader,
model,
optimizer,
base_lrs,
iter_stat,
epoch,
logger,
device,
adjust_lr=epoch < args.epochs)
# evaluate on validation set
torch.cuda.empty_cache()
mIoU = validate(evaluator, model, epoch)
logger.writer.add_scalar("mIoU", mIoU, epoch + 1)
logger.log("mIoU: %f" % mIoU)
# remember best mIoU and save checkpoint
is_best = mIoU > best_mIoU
best_mIoU = max(mIoU, best_mIoU)
save_checkpoint(
args.save_dir, {
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_mIoU': best_mIoU,
}, is_best)
logging.info('Best accuracy: {mIoU:.3f}'.format(mIoU=best_mIoU))
def main():
global args, best_mIoU
args = parser.parse_args()
pid = os.getpid()
# Log outputs
args.name = "GTA5_Vgg16_batch%d_512x512_Poly_LR%.1e_1to%.1f_all_lwf.%d_epoch%d" % (
args.batch_size, args.lr, args.factor, args.lwf, args.epochs)
if args.resume:
args.name += "_resumed"
directory = "runs/%s/" % (args.name)
if not os.path.exists(directory):
os.makedirs(directory)
filename = directory + 'train.log'
for handler in logging.root.handlers[:]:
logging.root.removeHandler(handler)
rootLogger = logging.getLogger()
logFormatter = logging.Formatter(
"%(asctime)s [%(levelname)-5.5s] %(message)s")
fileHandler = logging.FileHandler(filename)
fileHandler.setFormatter(logFormatter)
rootLogger.addHandler(fileHandler)
consoleHandler = logging.StreamHandler()
consoleHandler.setFormatter(logFormatter)
rootLogger.addHandler(consoleHandler)
rootLogger.setLevel(logging.INFO)
writer = SummaryWriter(directory)
from config_seg import config as data_setting
data_setting.batch_size = args.batch_size
train_loader = get_train_loader(data_setting, GTA5, test=False)
##### Vgg16 #####
vgg = vgg16(pretrained=True)
model = FCN_Vgg(n_class=args.num_class)
model.copy_params_from_vgg16(vgg)
###################
threds = 1
evaluator = SegEvaluator(Cityscapes(data_setting, 'val', None),
args.num_class,
np.array([0.485, 0.456, 0.406]),
np.array([0.229, 0.224, 0.225]),
model, [
1,
],
False,
devices=args.gpus,
config=data_setting,
threds=threds,
verbose=False,
save_path=None,
show_image=False)
# Setup optimizer
##### Vgg16 #####
sgd_in = [
{
'params': get_params(model, ["conv1_1", "conv1_2"]),
'lr': args.factor * args.lr
},
{
'params': get_params(model, ["conv2_1", "conv2_2"]),
'lr': args.factor * args.lr
},
{
'params': get_params(model, ["conv3_1", "conv3_2", "conv3_3"]),
'lr': args.factor * args.lr
},
{
'params': get_params(model, ["conv4_1", "conv4_2", "conv4_3"]),
'lr': args.factor * args.lr
},
{
'params': get_params(model, ["conv5_1", "conv5_2", "conv5_3"]),
'lr': args.factor * args.lr
},
{
'params': get_params(model, ["fc6", "fc7"]),
'lr': args.factor * args.lr
},
{
'params':
get_params(model, [
"score_fr", "score_pool3", "score_pool4", "upscore2",
"upscore8", "upscore_pool4"
]),
'lr':
args.lr
},
]
base_lrs = [group['lr'] for group in sgd_in]
optimizer = torch.optim.SGD(sgd_in,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# Optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
args.start_epoch = checkpoint['epoch']
best_mIoU = checkpoint['best_mIoU']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=ImageClassdata> no checkpoint found at '{}'".format(
args.resume))
model = model.cuda()
model_old = None
if args.lwf > 0:
# create a fixed model copy for Life-long learning
model_old = vgg16(pretrained=True)
###################
for param in model_old.parameters():
param.requires_grad = False
model_old.eval()
model_old.cuda()
if args.evaluate:
mIoU = validate(evaluator, model)
print(mIoU)
# Main training loop
iter_max = args.epochs * math.ceil(len(train_loader) / args.iter_size)
iter_stat = IterNums(iter_max)
for epoch in range(args.start_epoch, args.epochs):
logging.info("============= " + args.name + " ================")
logging.info("============= PID: " + str(pid) + " ================")
logging.info("Epoch: %d" % (epoch + 1))
# train for one epoch
train(args,
train_loader,
model,
optimizer,
base_lrs,
iter_stat,
epoch,
writer,
model_old=model_old,
adjust_lr=epoch < args.epochs)
# evaluate on validation set
torch.cuda.empty_cache()
mIoU = validate(evaluator, model)
writer.add_scalar("mIoU", mIoU, epoch)
# remember best mIoU and save checkpoint
is_best = mIoU > best_mIoU
best_mIoU = max(mIoU, best_mIoU)
save_checkpoint(
directory, {
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_mIoU': best_mIoU,
}, is_best)
logging.info('Best accuracy: {mIoU:.3f}'.format(mIoU=best_mIoU))
STACK_NAME = os.environ.get('STACK_NAME')
INSTANCE_ID = get_param(f"/ct-manager/{STACK_NAME}/instance-id")
INSTANCE_ALIAS = get_param(f"/ct-manager/{STACK_NAME}/instance-alias")
SOURCE_PHONE = get_param(f"/ct-manager/{STACK_NAME}/source-phone")
REGION = get_param(f"/ct-manager/{STACK_NAME}/region")
BEEP_PROMPT = get_param(f"/ct-manager/{STACK_NAME}/beep-prompt")
DEFAULT_AGENT =get_param(f"/ct-manager/{STACK_NAME}/default-agent")
PRESIGNED_URL_DURATION = get_param(f"/ct-manager/{STACK_NAME}/presigned-url-duration")
TAGS = config['tags']
SSM_PARAMS = get_params('/ct-manager/{}'.format(STACK_NAME))
assert len(SSM_PARAMS), "No hay parametros previos, asegure de implementar primero el stack base"
client = boto3.client('connect')
if INSTANCE_ID == 'None':
print(f'Buscando {INSTANCE_ALIAS} en {REGION}')
INSTANCE_ID = find_instance_id(client, INSTANCE_ALIAS)
if SOURCE_PHONE == 'None':
print(f'Buscando {INSTANCE_ALIAS} en {REGION}')
SOURCE_PHONE_LIST = find_source_phone(client, INSTANCE_ID)
if len(SOURCE_PHONE_LIST):
SOURCE_PHONE = SOURCE_PHONE_LIST[0]['PhoneNumber']
def main():
global args, best_prec1
PID = os.getpid()
args = parser.parse_args()
prepare_seed(args.rand_seed)
if args.timestamp == 'none':
args.timestamp = "{:}".format(
time.strftime('%h-%d-%C_%H-%M-%s', time.gmtime(time.time())))
# Log outputs
if args.evaluate:
args.save_dir = args.save_dir + "/Visda17-Res101-evaluate" + \
"%s/%s"%('/'+args.resume.replace('/', '+') if args.resume != 'none' else '', args.timestamp)
else:
args.save_dir = args.save_dir + \
"/VisDA-Res101-CSG.stg{csg_stages}.w{csg_weight}-APool.{apool}-Aug.{augment}-chunk{chunks}-mlp{mlp}.K{csg_k}-LR{lr}.bone{factor}-epoch{epochs}-batch{batch_size}-seed{seed}".format(
csg_stages=args.csg_stages,
mlp=args.mlp,
csg_weight=args.csg,
apool=args.apool,
augment=args.augment,
chunks=args.chunks,
csg_k=args.csg_k,
lr="%.2E"%args.lr,
factor="%.1f"%args.factor,
epochs=args.epochs,
batch_size=args.batch_size,
seed=args.rand_seed
) + \
"%s/%s"%('/'+args.resume.replace('/', '+') if args.resume != 'none' else '', args.timestamp)
logger = prepare_logger(args)
data_transforms = {
'val':
transforms.Compose([
transforms.Resize(224),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
]),
}
if args.augment:
data_transforms['train'] = transforms.Compose([
RandAugment(1, 6., augment_list),
transforms.Resize(224),
transforms.RandomCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
else:
data_transforms['train'] = transforms.Compose([
transforms.Resize(224),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
kwargs = {'num_workers': 20, 'pin_memory': True}
if args.augment:
# two source
trainset = VisDA17(txt_file=os.path.join(args.data,
"train/image_list.txt"),
root_dir=os.path.join(args.data, "train"),
transform=TwoCropsTransform(
data_transforms['train'],
data_transforms['train']))
else:
# one source
trainset = VisDA17(txt_file=os.path.join(args.data,
"train/image_list.txt"),
root_dir=os.path.join(args.data, "train"),
transform=data_transforms['train'])
train_loader = DataLoader(trainset,
batch_size=args.batch_size,
shuffle=True,
drop_last=True,
**kwargs)
valset = VisDA17(txt_file=os.path.join(args.data,
"validation/image_list.txt"),
root_dir=os.path.join(args.data, "validation"),
transform=data_transforms['val'],
label_one_hot=True)
val_loader = DataLoader(valset,
batch_size=args.batch_size,
shuffle=False,
**kwargs)
args.stages = [int(stage) for stage in args.csg_stages.split('.')
] if len(args.csg_stages) > 0 else []
chunks = [int(chunk) for chunk in args.chunks.split('.')
] if len(args.chunks) > 0 else []
assert len(chunks) == 1 or len(chunks) == len(args.stages)
if len(chunks) < len(args.stages):
chunks = [chunks[0]] * len(args.stages)
def get_head(num_ftrs, num_classes):
_dim = 512
return nn.Sequential(
nn.Linear(num_ftrs, _dim),
nn.ReLU(inplace=False),
nn.Linear(_dim, num_classes),
)
model = csg_builder.CSG(
resnet101,
get_head=get_head,
K=args.csg_k,
stages=args.stages,
chunks=chunks,
apool=args.apool,
mlp=args.mlp,
)
train_blocks = "conv1.bn1.layer1.layer2.layer3.layer4.fc"
train_blocks = train_blocks.split('.')
# Setup optimizer
factor = args.factor
sgd_in = []
for name in train_blocks:
if name != 'fc':
sgd_in.append({
'params': get_params(model.encoder_q, [name]),
'lr': factor * args.lr
})
else:
# no update to fc but to fc_new
sgd_in.append({
'params': get_params(model.encoder_q, ["fc_new"]),
'lr': args.lr
})
if model.mlp:
sgd_in.append({
'params': get_params(model.encoder_q, ["fc_csg"]),
'lr': args.lr
})
base_lrs = [group['lr'] for group in sgd_in]
optimizer = SGD(sgd_in,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# Optionally resume from a checkpoint
if args.resume != 'none':
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume,
map_location=lambda storage, loc: storage)
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
msg = model.load_state_dict(checkpoint['state_dict'], strict=False)
print("resume weights: ", msg)
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=ImageClassdata> no checkpoint found at '{}'".format(
args.resume))
model = model.cuda()
if args.evaluate:
prec1 = validate(val_loader, model, args, 0)
print(prec1)
exit(0)
# Main training loop
iter_max = args.epochs * len(train_loader)
iter_stat = IterNums(iter_max)
for epoch in range(args.start_epoch, args.epochs):
print("<< ============== JOB (PID = %d) %s ============== >>" %
(PID, args.save_dir))
logger.log("Epoch: %d" % (epoch + 1))
train(train_loader,
model,
optimizer,
base_lrs,
iter_stat,
epoch,
logger,
args,
adjust_lr=epoch < args.epochs)
prec1 = validate(val_loader, model, args, epoch)
logger.writer.add_scalar("prec", prec1, epoch + 1)
logger.log("prec: %f" % prec1)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint(args.save_dir, {
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_prec1': best_prec1,
},
is_best,
keep_last=1)
logging.info('Best accuracy: {prec1:.3f}'.format(prec1=best_prec1))