import os
import time
import numpy as np
from skimage import io
import time
import torch, gc
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim
import torch.nn.functional as F
from data_loader_cache import get_im_gt_name_dict, create_dataloaders, GOSRandomHFlip, GOSResize, GOSRandomCrop, GOSNormalize #GOSDatasetCache,
from basics import f1_mae_torch #normPRED, GOSPRF1ScoresCache,f1score_torch,
from models import *
device = 'cuda' if torch.cuda.is_available() else 'cpu'
def get_gt_encoder(train_dataloaders, train_datasets, valid_dataloaders, valid_datasets, hypar, train_dataloaders_val, train_datasets_val): #model_path, model_save_fre, max_ite=1000000):
# train_dataloaders, train_datasets = create_dataloaders(train_nm_im_gt_list,
# cache_size = hypar["cache_size"],
# cache_boost = hypar["cache_boost_train"],
# my_transforms = [
# GOSRandomHFlip(),
# # GOSResize(hypar["input_size"]),
# # GOSRandomCrop(hypar["crop_size"]),
# GOSNormalize([0.5,0.5,0.5],[1.0,1.0,1.0]),
# ],
# batch_size = hypar["batch_size_train"],
# shuffle = True)
torch.manual_seed(hypar["seed"])
if torch.cuda.is_available():
torch.cuda.manual_seed(hypar["seed"])
print("define gt encoder ...")
net = ISNetGTEncoder() #UNETGTENCODERCombine()
## load the existing model gt encoder
if(hypar["gt_encoder_model"]!=""):
model_path = hypar["model_path"]+"/"+hypar["gt_encoder_model"]
if torch.cuda.is_available():
net.load_state_dict(torch.load(model_path))
net.cuda()
else:
net.load_state_dict(torch.load(model_path,map_location="cpu"))
print("gt encoder restored from the saved weights ...")
return net ############
if torch.cuda.is_available():
net.cuda()
print("--- define optimizer for GT Encoder---")
optimizer = optim.Adam(net.parameters(), lr=1e-3, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)
model_path = hypar["model_path"]
model_save_fre = hypar["model_save_fre"]
max_ite = hypar["max_ite"]
batch_size_train = hypar["batch_size_train"]
batch_size_valid = hypar["batch_size_valid"]
if(not os.path.exists(model_path)):
os.mkdir(model_path)
ite_num = hypar["start_ite"] # count the total iteration number
ite_num4val = 0 #
running_loss = 0.0 # count the toal loss
running_tar_loss = 0.0 # count the target output loss
last_f1 = [0 for x in range(len(valid_dataloaders))]
train_num = train_datasets[0].__len__()
net.train()
start_last = time.time()
gos_dataloader = train_dataloaders[0]
epoch_num = hypar["max_epoch_num"]
notgood_cnt = 0
for epoch in range(epoch_num): ## set the epoch num as 100000
for i, data in enumerate(gos_dataloader):
if(ite_num >= max_ite):
print("Training Reached the Maximal Iteration Number ", max_ite)
exit()
# start_read = time.time()
ite_num = ite_num + 1
ite_num4val = ite_num4val + 1
# get the inputs
labels = data['label']
if(hypar["model_digit"]=="full"):
labels = labels.type(torch.FloatTensor)
else:
labels = labels.type(torch.HalfTensor)
# wrap them in Variable
if torch.cuda.is_available():
labels_v = Variable(labels.cuda(), requires_grad=False)
else:
labels_v = Variable(labels, requires_grad=False)
# print("time lapse for data preparation: ", time.time()-start_read, ' s')
# y zero the parameter gradients
start_inf_loss_back = time.time()
optimizer.zero_grad()
ds, fs = net(labels_v)#net(inputs_v)
loss2, loss = net.compute_loss(ds, labels_v)
loss.backward()
optimizer.step()
running_loss += loss.item()
running_tar_loss += loss2.item()
# del outputs, loss
del ds, loss2, loss
end_inf_loss_back = time.time()-start_inf_loss_back
print("GT Encoder Training>>>"+model_path.split('/')[-1]+" - [epoch: %3d/%3d, batch: %5d/%5d, ite: %d] train loss: %3f, tar: %3f, time-per-iter: %3f s, time_read: %3f" % (
epoch + 1, epoch_num, (i + 1) * batch_size_train, train_num, ite_num, running_loss / ite_num4val, running_tar_loss / ite_num4val, time.time()-start_last, time.time()-start_last-end_inf_loss_back))
start_last = time.time()
if ite_num % model_save_fre == 0: # validate every 2000 iterations
notgood_cnt += 1
# net.eval()
# tmp_f1, tmp_mae, val_loss, tar_loss, i_val, tmp_time = valid_gt_encoder(net, valid_dataloaders, valid_datasets, hypar, epoch)
tmp_f1, tmp_mae, val_loss, tar_loss, i_val, tmp_time = valid_gt_encoder(net, train_dataloaders_val, train_datasets_val, hypar, epoch)
net.train() # resume train
tmp_out = 0
print("last_f1:",last_f1)
print("tmp_f1:",tmp_f1)
for fi in range(len(last_f1)):
if(tmp_f1[fi]>last_f1[fi]):
tmp_out = 1
print("tmp_out:",tmp_out)
if(tmp_out):
notgood_cnt = 0
last_f1 = tmp_f1
tmp_f1_str = [str(round(f1x,4)) for f1x in tmp_f1]
tmp_mae_str = [str(round(mx,4)) for mx in tmp_mae]
maxf1 = '_'.join(tmp_f1_str)
meanM = '_'.join(tmp_mae_str)
# .cpu().detach().numpy()
model_name = "/GTENCODER-gpu_itr_"+str(ite_num)+\
"_traLoss_"+str(np.round(running_loss / ite_num4val,4))+\
"_traTarLoss_"+str(np.round(running_tar_loss / ite_num4val,4))+\
"_valLoss_"+str(np.round(val_loss /(i_val+1),4))+\
"_valTarLoss_"+str(np.round(tar_loss /(i_val+1),4)) + \
"_maxF1_" + maxf1 + \
"_mae_" + meanM + \
"_time_" + str(np.round(np.mean(np.array(tmp_time))/batch_size_valid,6))+".pth"
torch.save(net.state_dict(), model_path + model_name)
running_loss = 0.0
running_tar_loss = 0.0
ite_num4val = 0
if(tmp_f1[0]>0.99):
print("GT encoder is well-trained and obtained...")
return net
if(notgood_cnt >= hypar["early_stop"]):
print("No improvements in the last "+str(notgood_cnt)+" validation periods, so training stopped !")
exit()
print("Training Reaches The Maximum Epoch Number")
return net
def valid_gt_encoder(net, valid_dataloaders, valid_datasets, hypar, epoch=0):
net.eval()
print("Validating...")
epoch_num = hypar["max_epoch_num"]
val_loss = 0.0
tar_loss = 0.0
tmp_f1 = []
tmp_mae = []
tmp_time = []
start_valid = time.time()
for k in range(len(valid_dataloaders)):
valid_dataloader = valid_dataloaders[k]
valid_dataset = valid_datasets[k]
val_num = valid_dataset.__len__()
mybins = np.arange(0,256)
PRE = np.zeros((val_num,len(mybins)-1))
REC = np.zeros((val_num,len(mybins)-1))
F1 = np.zeros((val_num,len(mybins)-1))
MAE = np.zeros((val_num))
val_cnt = 0.0
i_val = None
for i_val, data_val in enumerate(valid_dataloader):
# imidx_val, inputs_val, labels_val, shapes_val = data_val['imidx'], data_val['image'], data_val['label'], data_val['shape']
imidx_val, labels_val, shapes_val = data_val['imidx'], data_val['label'], data_val['shape']
if(hypar["model_digit"]=="full"):
labels_val = labels_val.type(torch.FloatTensor)
else:
labels_val = labels_val.type(torch.HalfTensor)
# wrap them in Variable
if torch.cuda.is_available():
labels_val_v = Variable(labels_val.cuda(), requires_grad=False)
else:
labels_val_v = Variable(labels_val,requires_grad=False)
t_start = time.time()
ds_val = net(labels_val_v)[0]
t_end = time.time()-t_start
tmp_time.append(t_end)
# loss2_val, loss_val = muti_loss_fusion(ds_val, labels_val_v)
loss2_val, loss_val = net.compute_loss(ds_val, labels_val_v)
# compute F measure
for t in range(hypar["batch_size_valid"]):
val_cnt = val_cnt + 1.0
print("num of val: ", val_cnt)
i_test = imidx_val[t].data.numpy()
pred_val = ds_val[0][t,:,:,:] # B x 1 x H x W
## recover the prediction spatial size to the orignal image size
pred_val = torch.squeeze(F.upsample(torch.unsqueeze(pred_val,0),(shapes_val[t][0],shapes_val[t][1]),mode='bilinear'))
ma = torch.max(pred_val)
mi = torch.min(pred_val)
pred_val = (pred_val-mi)/(ma-mi) # max = 1
# pred_val = normPRED(pred_val)
gt = np.squeeze(io.imread(valid_dataset.dataset["ori_gt_path"][i_test])) # max = 255
if gt.max()==1:
gt=gt*255
with torch.no_grad():
gt = torch.tensor(gt).to(device)
pre,rec,f1,mae = f1_mae_torch(pred_val*255, gt, valid_dataset, i_test, mybins, hypar)
PRE[i_test,:]=pre
REC[i_test,:] = rec
F1[i_test,:] = f1
MAE[i_test] = mae
del ds_val, gt
gc.collect()
torch.cuda.empty_cache()
# if(loss_val.data[0]>1):
val_loss += loss_val.item()#data[0]
tar_loss += loss2_val.item()#data[0]
print("[validating: %5d/%5d] val_ls:%f, tar_ls: %f, f1: %f, mae: %f, time: %f"% (i_val, val_num, val_loss / (i_val + 1), tar_loss / (i_val + 1), np.amax(F1[i_test,:]), MAE[i_test],t_end))
del loss2_val, loss_val
print('============================')
PRE_m = np.mean(PRE,0)
REC_m = np.mean(REC,0)
f1_m = (1+0.3)*PRE_m*REC_m/(0.3*PRE_m+REC_m+1e-8)
# print('--------------:', np.mean(f1_m))
tmp_f1.append(np.amax(f1_m))
tmp_mae.append(np.mean(MAE))
print("The max F1 Score: %f"%(np.max(f1_m)))
print("MAE: ", np.mean(MAE))
# print('[epoch: %3d/%3d, ite: %5d] tra_ls: %3f, val_ls: %3f, tar_ls: %3f, maxf1: %3f, val_time: %6f'% (epoch + 1, epoch_num, ite_num, running_loss / ite_num4val, val_loss/val_cnt, tar_loss/val_cnt, tmp_f1[-1], time.time()-start_valid))
return tmp_f1, tmp_mae, val_loss, tar_loss, i_val, tmp_time
def train(net, optimizer, train_dataloaders, train_datasets, valid_dataloaders, valid_datasets, hypar,train_dataloaders_val, train_datasets_val): #model_path, model_save_fre, max_ite=1000000):
if hypar["interm_sup"]:
print("Get the gt encoder ...")
featurenet = get_gt_encoder(train_dataloaders, train_datasets, valid_dataloaders, valid_datasets, hypar,train_dataloaders_val, train_datasets_val)
## freeze the weights of gt encoder
for param in featurenet.parameters():
param.requires_grad=False
model_path = hypar["model_path"]
model_save_fre = hypar["model_save_fre"]
max_ite = hypar["max_ite"]
batch_size_train = hypar["batch_size_train"]
batch_size_valid = hypar["batch_size_valid"]
if(not os.path.exists(model_path)):
os.mkdir(model_path)
ite_num = hypar["start_ite"] # count the toal iteration number
ite_num4val = 0 #
running_loss = 0.0 # count the toal loss
running_tar_loss = 0.0 # count the target output loss
last_f1 = [0 for x in range(len(valid_dataloaders))]
train_num = train_datasets[0].__len__()
net.train()
start_last = time.time()
gos_dataloader = train_dataloaders[0]
epoch_num = hypar["max_epoch_num"]
notgood_cnt = 0
for epoch in range(epoch_num): ## set the epoch num as 100000
for i, data in enumerate(gos_dataloader):
if(ite_num >= max_ite):
print("Training Reached the Maximal Iteration Number ", max_ite)
exit()
# start_read = time.time()
ite_num = ite_num + 1
ite_num4val = ite_num4val + 1
# get the inputs
inputs, labels = data['image'], data['label']
if(hypar["model_digit"]=="full"):
inputs = inputs.type(torch.FloatTensor)
labels = labels.type(torch.FloatTensor)
else:
inputs = inputs.type(torch.HalfTensor)
labels = labels.type(torch.HalfTensor)
# wrap them in Variable
if torch.cuda.is_available():
inputs_v, labels_v = Variable(inputs.cuda(), requires_grad=False), Variable(labels.cuda(), requires_grad=False)
else:
inputs_v, labels_v = Variable(inputs, requires_grad=False), Variable(labels, requires_grad=False)
# print("time lapse for data preparation: ", time.time()-start_read, ' s')
# y zero the parameter gradients
start_inf_loss_back = time.time()
optimizer.zero_grad()
if hypar["interm_sup"]:
# forward + backward + optimize
ds,dfs = net(inputs_v)
_,fs = featurenet(labels_v) ## extract the gt encodings
loss2, loss = net.compute_loss_kl(ds, labels_v, dfs, fs, mode='MSE')
else:
# forward + backward + optimize
ds,_ = net(inputs_v)
loss2, loss = net.compute_loss(ds, labels_v)
loss.backward()
optimizer.step()
# # print statistics
running_loss += loss.item()
running_tar_loss += loss2.item()
# del outputs, loss
del ds, loss2, loss
end_inf_loss_back = time.time()-start_inf_loss_back
print(">>>"+model_path.split('/')[-1]+" - [epoch: %3d/%3d, batch: %5d/%5d, ite: %d] train loss: %3f, tar: %3f, time-per-iter: %3f s, time_read: %3f" % (
epoch + 1, epoch_num, (i + 1) * batch_size_train, train_num, ite_num, running_loss / ite_num4val, running_tar_loss / ite_num4val, time.time()-start_last, time.time()-start_last-end_inf_loss_back))
start_last = time.time()
if ite_num % model_save_fre == 0: # validate every 2000 iterations
notgood_cnt += 1
net.eval()
tmp_f1, tmp_mae, val_loss, tar_loss, i_val, tmp_time = valid(net, valid_dataloaders, valid_datasets, hypar, epoch)
net.train() # resume train
tmp_out = 0
print("last_f1:",last_f1)
print("tmp_f1:",tmp_f1)
for fi in range(len(last_f1)):
if(tmp_f1[fi]>last_f1[fi]):
tmp_out = 1
print("tmp_out:",tmp_out)
if(tmp_out):
notgood_cnt = 0
last_f1 = tmp_f1
tmp_f1_str = [str(round(f1x,4)) for f1x in tmp_f1]
tmp_mae_str = [str(round(mx,4)) for mx in tmp_mae]
maxf1 = '_'.join(tmp_f1_str)
meanM = '_'.join(tmp_mae_str)
# .cpu().detach().numpy()
model_name = "/gpu_itr_"+str(ite_num)+\
"_traLoss_"+str(np.round(running_loss / ite_num4val,4))+\
"_traTarLoss_"+str(np.round(running_tar_loss / ite_num4val,4))+\
"_valLoss_"+str(np.round(val_loss /(i_val+1),4))+\
"_valTarLoss_"+str(np.round(tar_loss /(i_val+1),4)) + \
"_maxF1_" + maxf1 + \
"_mae_" + meanM + \
"_time_" + str(np.round(np.mean(np.array(tmp_time))/batch_size_valid,6))+".pth"
torch.save(net.state_dict(), model_path + model_name)
running_loss = 0.0
running_tar_loss = 0.0
ite_num4val = 0
if(notgood_cnt >= hypar["early_stop"]):
print("No improvements in the last "+str(notgood_cnt)+" validation periods, so training stopped !")
exit()
print("Training Reaches The Maximum Epoch Number")
def valid(net, valid_dataloaders, valid_datasets, hypar, epoch=0):
net.eval()
print("Validating...")
epoch_num = hypar["max_epoch_num"]
val_loss = 0.0
tar_loss = 0.0
val_cnt = 0.0
tmp_f1 = []
tmp_mae = []
tmp_time = []
start_valid = time.time()
for k in range(len(valid_dataloaders)):
valid_dataloader = valid_dataloaders[k]
valid_dataset = valid_datasets[k]
val_num = valid_dataset.__len__()
mybins = np.arange(0,256)
PRE = np.zeros((val_num,len(mybins)-1))
REC = np.zeros((val_num,len(mybins)-1))
F1 = np.zeros((val_num,len(mybins)-1))
MAE = np.zeros((val_num))
for i_val, data_val in enumerate(valid_dataloader):
val_cnt = val_cnt + 1.0
imidx_val, inputs_val, labels_val, shapes_val = data_val['imidx'], data_val['image'], data_val['label'], data_val['shape']
if(hypar["model_digit"]=="full"):
inputs_val = inputs_val.type(torch.FloatTensor)
labels_val = labels_val.type(torch.FloatTensor)
else:
inputs_val = inputs_val.type(torch.HalfTensor)
labels_val = labels_val.type(torch.HalfTensor)
# wrap them in Variable
if torch.cuda.is_available():
inputs_val_v, labels_val_v = Variable(inputs_val.cuda(), requires_grad=False), Variable(labels_val.cuda(), requires_grad=False)
else:
inputs_val_v, labels_val_v = Variable(inputs_val, requires_grad=False), Variable(labels_val,requires_grad=False)
t_start = time.time()
ds_val = net(inputs_val_v)[0]
t_end = time.time()-t_start
tmp_time.append(t_end)
# loss2_val, loss_val = muti_loss_fusion(ds_val, labels_val_v)
loss2_val, loss_val = net.compute_loss(ds_val, labels_val_v)
# compute F measure
for t in range(hypar["batch_size_valid"]):
i_test = imidx_val[t].data.numpy()
pred_val = ds_val[0][t,:,:,:] # B x 1 x H x W
## recover the prediction spatial size to the orignal image size
pred_val = torch.squeeze(F.upsample(torch.unsqueeze(pred_val,0),(shapes_val[t][0],shapes_val[t][1]),mode='bilinear'))
# pred_val = normPRED(pred_val)
ma = torch.max(pred_val)
mi = torch.min(pred_val)
pred_val = (pred_val-mi)/(ma-mi) # max = 1
if len(valid_dataset.dataset["ori_gt_path"]) != 0:
gt = np.squeeze(io.imread(valid_dataset.dataset["ori_gt_path"][i_test])) # max = 255
if gt.max()==1:
gt=gt*255
else:
gt = np.zeros((shapes_val[t][0],shapes_val[t][1]))
with torch.no_grad():
gt = torch.tensor(gt).to(device)
pre,rec,f1,mae = f1_mae_torch(pred_val*255, gt, valid_dataset, i_test, mybins, hypar)
PRE[i_test,:]=pre
REC[i_test,:] = rec
F1[i_test,:] = f1
MAE[i_test] = mae
del ds_val, gt
gc.collect()
torch.cuda.empty_cache()
# if(loss_val.data[0]>1):
val_loss += loss_val.item()#data[0]
tar_loss += loss2_val.item()#data[0]
print("[validating: %5d/%5d] val_ls:%f, tar_ls: %f, f1: %f, mae: %f, time: %f"% (i_val, val_num, val_loss / (i_val + 1), tar_loss / (i_val + 1), np.amax(F1[i_test,:]), MAE[i_test],t_end))
del loss2_val, loss_val
print('============================')
PRE_m = np.mean(PRE,0)
REC_m = np.mean(REC,0)
f1_m = (1+0.3)*PRE_m*REC_m/(0.3*PRE_m+REC_m+1e-8)
tmp_f1.append(np.amax(f1_m))
tmp_mae.append(np.mean(MAE))
return tmp_f1, tmp_mae, val_loss, tar_loss, i_val, tmp_time
def main(train_datasets,
valid_datasets,
hypar): # model: "train", "test"
### --- Step 1: Build datasets and dataloaders ---
dataloaders_train = []
dataloaders_valid = []
if(hypar["mode"]=="train"):
print("--- create training dataloader ---")
## collect training dataset
train_nm_im_gt_list = get_im_gt_name_dict(train_datasets, flag="train")
## build dataloader for training datasets
train_dataloaders, train_datasets = create_dataloaders(train_nm_im_gt_list,
cache_size = hypar["cache_size"],
cache_boost = hypar["cache_boost_train"],
my_transforms = [
GOSRandomHFlip(), ## this line can be uncommented for horizontal flip augmetation
# GOSResize(hypar["input_size"]),
# GOSRandomCrop(hypar["crop_size"]), ## this line can be uncommented for randomcrop augmentation
GOSNormalize([0.5,0.5,0.5],[1.0,1.0,1.0]),
],
batch_size = hypar["batch_size_train"],
shuffle = True)
train_dataloaders_val, train_datasets_val = create_dataloaders(train_nm_im_gt_list,
cache_size = hypar["cache_size"],
cache_boost = hypar["cache_boost_train"],
my_transforms = [
GOSNormalize([0.5,0.5,0.5],[1.0,1.0,1.0]),
],
batch_size = hypar["batch_size_valid"],
shuffle = False)
print(len(train_dataloaders), " train dataloaders created")
print("--- create valid dataloader ---")
## build dataloader for validation or testing
valid_nm_im_gt_list = get_im_gt_name_dict(valid_datasets, flag="valid")
## build dataloader for training datasets
valid_dataloaders, valid_datasets = create_dataloaders(valid_nm_im_gt_list,
cache_size = hypar["cache_size"],
cache_boost = hypar["cache_boost_valid"],
my_transforms = [
GOSNormalize([0.5,0.5,0.5],[1.0,1.0,1.0]),
# GOSResize(hypar["input_size"])
],
batch_size=hypar["batch_size_valid"],
shuffle=False)
print(len(valid_dataloaders), " valid dataloaders created")
# print(valid_datasets[0]["data_name"])
### --- Step 2: Build Model and Optimizer ---
print("--- build model ---")
net = hypar["model"]#GOSNETINC(3,1)
# convert to half precision
if(hypar["model_digit"]=="half"):
net.half()
for layer in net.modules():
if isinstance(layer, nn.BatchNorm2d):
layer.float()
if torch.cuda.is_available():
net.cuda()
if(hypar["restore_model"]!=""):
print("restore model from:")
print(hypar["model_path"]+"/"+hypar["restore_model"])
if torch.cuda.is_available():
net.load_state_dict(torch.load(hypar["model_path"]+"/"+hypar["restore_model"]))
else:
net.load_state_dict(torch.load(hypar["model_path"]+"/"+hypar["restore_model"],map_location="cpu"))
print("--- define optimizer ---")
optimizer = optim.Adam(net.parameters(), lr=1e-3, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)
### --- Step 3: Train or Valid Model ---
if(hypar["mode"]=="train"):
train(net,
optimizer,
train_dataloaders,
train_datasets,
valid_dataloaders,
valid_datasets,
hypar,
train_dataloaders_val, train_datasets_val)
else:
valid(net,
valid_dataloaders,
valid_datasets,
hypar)
if __name__ == "__main__":
### --------------- STEP 1: Configuring the Train, Valid and Test datasets ---------------
## configure the train, valid and inference datasets
train_datasets, valid_datasets = [], []
dataset_1, dataset_1 = {}, {}
dataset_tr = {"name": "DIS5K-TR",
"im_dir": "../DIS5K/DIS-TR/im",
"gt_dir": "../DIS5K/DIS-TR/gt",
"im_ext": ".jpg",
"gt_ext": ".png",
"cache_dir":"../DIS5K-Cache/DIS-TR"}
dataset_vd = {"name": "DIS5K-VD",
"im_dir": "../DIS5K/DIS-VD/im",
"gt_dir": "../DIS5K/DIS-VD/gt",
"im_ext": ".jpg",
"gt_ext": ".png",
"cache_dir":"../DIS5K-Cache/DIS-VD"}
dataset_te1 = {"name": "DIS5K-TE1",
"im_dir": "../DIS5K/DIS-TE1/im",
"gt_dir": "../DIS5K/DIS-TE1/gt",
"im_ext": ".jpg",
"gt_ext": ".png",
"cache_dir":"../DIS5K-Cache/DIS-TE1"}
dataset_te2 = {"name": "DIS5K-TE2",
"im_dir": "../DIS5K/DIS-TE2/im",
"gt_dir": "../DIS5K/DIS-TE2/gt",
"im_ext": ".jpg",
"gt_ext": ".png",
"cache_dir":"../DIS5K-Cache/DIS-TE2"}
dataset_te3 = {"name": "DIS5K-TE3",
"im_dir": "../DIS5K/DIS-TE3/im",
"gt_dir": "../DIS5K/DIS-TE3/gt",
"im_ext": ".jpg",
"gt_ext": ".png",
"cache_dir":"../DIS5K-Cache/DIS-TE3"}
dataset_te4 = {"name": "DIS5K-TE4",
"im_dir": "../DIS5K/DIS-TE4/im",
"gt_dir": "../DIS5K/DIS-TE4/gt",
"im_ext": ".jpg",
"gt_ext": ".png",
"cache_dir":"../DIS5K-Cache/DIS-TE4"}
### test your own dataset
dataset_demo = {"name": "your-dataset",
"im_dir": "../your-dataset/im",
"gt_dir": "",
"im_ext": ".jpg",
"gt_ext": "",
"cache_dir":"../your-dataset/cache"}
train_datasets = [dataset_tr] ## users can create mutiple dictionary for setting a list of datasets as training set
# valid_datasets = [dataset_vd] ## users can create mutiple dictionary for setting a list of datasets as vaidation sets or inference sets
valid_datasets = [dataset_vd] # dataset_vd, dataset_te1, dataset_te2, dataset_te3, dataset_te4] # and hypar["mode"] = "valid" for inference,
### --------------- STEP 2: Configuring the hyperparamters for Training, validation and inferencing ---------------
hypar = {}
## -- 2.1. configure the model saving or restoring path --
hypar["mode"] = "train"
## "train": for training,
## "valid": for validation and inferening,
## in "valid" mode, it will calculate the accuracy as well as save the prediciton results into the "hypar["valid_out_dir"]", which shouldn't be ""
## otherwise only accuracy will be calculated and no predictions will be saved
hypar["interm_sup"] = False ## in-dicate if activate intermediate feature supervision
if hypar["mode"] == "train":
hypar["valid_out_dir"] = "" ## for "train" model leave it as "", for "valid"("inference") mode: set it according to your local directory
hypar["model_path"] ="../saved_models/IS-Net-test" ## model weights saving (or restoring) path
hypar["restore_model"] = "" ## name of the segmentation model weights .pth for resume training process from last stop or for the inferencing
hypar["start_ite"] = 0 ## start iteration for the training, can be changed to match the restored training process
hypar["gt_encoder_model"] = ""
else: ## configure the segmentation output path and the to-be-used model weights path
hypar["valid_out_dir"] = "../your-results/"##"../DIS5K-Results-test" ## output inferenced segmentation maps into this fold
hypar["model_path"] = "../saved_models/IS-Net" ## load trained weights from this path
hypar["restore_model"] = "isnet.pth"##"isnet.pth" ## name of the to-be-loaded weights
# if hypar["restore_model"]!="":
# hypar["start_ite"] = int(hypar["restore_model"].split("_")[2])
## -- 2.2. choose floating point accuracy --
hypar["model_digit"] = "full" ## indicates "half" or "full" accuracy of float number
hypar["seed"] = 0
## -- 2.3. cache data spatial size --
## To handle large size input images, which take a lot of time for loading in training,
# we introduce the cache mechanism for pre-convering and resizing the jpg and png images into .pt file
hypar["cache_size"] = [1024, 1024] ## cached input spatial resolution, can be configured into different size
hypar["cache_boost_train"] = False ## "True" or "False", indicates wheather to load all the training datasets into RAM, True will greatly speed the training process while requires more RAM
hypar["cache_boost_valid"] = False ## "True" or "False", indicates wheather to load all the validation datasets into RAM, True will greatly speed the training process while requires more RAM
## --- 2.4. data augmentation parameters ---
hypar["input_size"] = [1024, 1024] ## mdoel input spatial size, usually use the same value hypar["cache_size"], which means we don't further resize the images
hypar["crop_size"] = [1024, 1024] ## random crop size from the input, it is usually set as smaller than hypar["cache_size"], e.g., [920,920] for data augmentation
hypar["random_flip_h"] = 1 ## horizontal flip, currently hard coded in the dataloader and it is not in use
hypar["random_flip_v"] = 0 ## vertical flip , currently not in use
## --- 2.5. define model ---
print("building model...")
hypar["model"] = ISNetDIS() #U2NETFASTFEATURESUP()
hypar["early_stop"] = 20 ## stop the training when no improvement in the past 20 validation periods, smaller numbers can be used here e.g., 5 or 10.
hypar["model_save_fre"] = 2000 ## valid and save model weights every 2000 iterations
hypar["batch_size_train"] = 8 ## batch size for training
hypar["batch_size_valid"] = 1 ## batch size for validation and inferencing
print("batch size: ", hypar["batch_size_train"])
hypar["max_ite"] = 10000000 ## if early stop couldn't stop the training process, stop it by the max_ite_num
hypar["max_epoch_num"] = 1000000 ## if early stop and max_ite couldn't stop the training process, stop it by the max_epoch_num
main(train_datasets,
valid_datasets,
hypar=hypar)