# from networks.kd_model import NetModel
from networks.kd_model_wo_paral import NetModel
import logging
import warnings
warnings.filterwarnings("ignore")
from torch.utils import data
from dataset.datasets import CSDataSet
import numpy as np
from tqdm import tqdm
import os
from prefetch_generator import BackgroundGenerator
from PIL import Image as PILImage
from networks.evaluate import id_to_trainid, id_to_trainid, id2trainId, get_palette, get_confusion_matrix
IMG_MEAN = np.array((104.00698793, 116.66876762, 122.67891434),
dtype=np.float32)
args = TrainOptions().initialize()
h, w = map(int, args.input_size.split(','))
# trainloader = data.DataLoader(CSDataSet(args.data_dir, './dataset/list/cityscapes/train.lst', max_iters=args.num_steps*args.batch_size, crop_size=(h, w),
# scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN),
# batch_size=args.batch_size, shuffle=True, num_workers=4, pin_memory=True)
class DataLoaderX(data.DataLoader):
def __iter__(self):
return BackgroundGenerator(super().__iter__())
dataloader = DataLoaderX # 2:10
# dataloader = data.DataLoader #
valset = CSDataSet(args.data_dir,
seed = 0
os.environ['PYTHONHASHSEED'] = str(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed) # if you are using multi-GPU.
np.random.seed(seed) # Numpy module.
random.seed(seed) # Python random module.
torch.manual_seed(seed)
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
IMG_MEAN = np.array((104.00698793, 116.66876762, 122.67891434),
dtype=np.float32)
args = TrainOptions().initialize()
h, w = map(int, args.input_size.split(','))
# class data_prefetcher():
# def __init__(self, loader):
# self.loader = iter(loader)
# self.stream = torch.cuda.Stream()
#
# # With Amp, it isn't necessary to manually convert data to half.
# # if args.fp16:
# # self.mean = self.mean.half()
# # self.std = self.std.half()
# self.preload()
#
# def next(self):
# # torch.cuda.current_stream().wait_stream(self.stream)
import sys
from utils.train_options import TrainOptions
from networks.kd_model import NetModel
import logging
import warnings
warnings.filterwarnings("ignore")
from torch.utils import data
from dataset.datasets import CSDataSet
import numpy as np
IMG_MEAN = np.array((104.00698793,116.66876762,122.67891434), dtype=np.float32)
args = TrainOptions().initialize()
h, w = map(int, args.input_size.split(','))
trainloader = data.DataLoader(CSDataSet(args.data_dir, './dataset/list/cityscapes/train.lst', max_iters=args.num_steps*args.batch_size, crop_size=(h, w),
scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN),
batch_size=args.batch_size, shuffle=True, num_workers=4, pin_memory=True)
valloader = data.DataLoader(CSDataSet(args.data_dir, './dataset/list/cityscapes/val.lst', crop_size=(1024, 2048), mean=IMG_MEAN, scale=False, mirror=False),
batch_size=1, shuffle=False, pin_memory=True)
save_steps = int(2975/args.batch_size)
model = NetModel(args)
for epoch in range(args.start_epoch, args.epoch_nums):
for step, data in enumerate(trainloader, args.last_step+1):
model.adjust_learning_rate(args.lr_g, model.G_solver, step)
model.adjust_learning_rate(args.lr_d, model.D_solver, step)
model.set_input(data)
model.optimize_parameters()
model.print_info(epoch, step)
if (step > 1) and ((step % save_steps == 0) and (step > args.num_steps - 1000)) or (step == args.num_steps - 1):
mean_IU, IU_array = model.evalute_model(model.student, valloader, '0', '512,512', 19, True)
model.save_ckpt(epoch, step, mean_IU, IU_array)
logging.info('[val 512,512] mean_IU:{:.6f} IU_array:{}'.format(mean_IU, IU_array))
import logging
import warnings
warnings.filterwarnings("ignore")
from torch.utils import data
from dataset.datasets import CSDataSet
from dataset.camvid import CamVid
import numpy as np
import os
import torch
import dataset.transforms as my_trans
import torchvision.transforms as transforms
IMG_MEAN = np.array((104.00698793, 116.66876762, 122.67891434),
dtype=np.float32)
args = TrainOptions().initialize()
h, w = map(int, args.input_size.split(','))
# trainloader = torch.utils.data.DataLoader(CSDataSet(args.data_dir, './dataset/list/cityscapes/train.lst', max_iters=args.num_steps*args.batch_size, crop_size=(h, w),
# scale=args.random_scale, mirror=args.random_mirror, mean=IMG_MEAN),
# batch_size=args.batch_size, shuffle=True, num_workers=4, pin_memory=True)
# valloader = torch.utils.data.DataLoader(CSDataSet(args.data_dir, './dataset/list/cityscapes/val.lst', crop_size=(1024, 2048), mean=IMG_MEAN, scale=False, mirror=False),
# batch_size=2, shuffle=False, pin_memory=True)
value_scale = 255
mean = [0.485, 0.456, 0.406]
mean = [item * 255 for item in mean]
std = [0.229, 0.224, 0.225]
std = [item * 255 for item in std]
train_transform = my_trans.Compose([
# my_trans.Resize((args.height, args.width)),
# my_trans.RandScale([0.5, 2.0]),
item['sample_index'] = index
item['dataset_name'] = self.dataset
try:
item['maskname'] = self.maskname[index]
except AttributeError:
item['maskname'] = ''
try:
item['partname'] = self.partname[index]
except AttributeError:
item['partname'] = ''
return item
def __len__(self):
return len(self.imgname)
if __name__ == '__main__':
from utils.train_options import TrainOptions
args = TrainOptions()
options = args.parse_args(['--name', 'hc'])
ds = BaseDataset(options, '3dpw', is_train=False)
print("done")