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eval_baseline_cvc.py
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import os
import time
import argparse
from tqdm import tqdm
import pickle
import random
import numpy as np
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.nn.functional as F
from torch.autograd import Variable
from tensorboardX import SummaryWriter
import sys
from PIL import Image
from datasets import get_dataloder,datasets_dict
from models import get_models,models_dict
from utils import save_checkpoint,calc_parameters_count,get_logger,get_gpus_memory_info
from utils import BinaryIndicatorsMetric,AverageMeter
from utils import BCEDiceLoss,SoftDiceLoss,DiceLoss,BCEDiceLoss
def main(args):
#################### init logger ###################################
args.model_list=["unet","unet++",'attention_unet_v1','multires_unet','r2unet_t3']
for model_name in args.model_list:
if model_name=='unet':
args.model='unet'
model_weight_path='./logs/unet_ep1600/cvc/20200312-143050/model_best.pth.tar'
model=get_models(args)
model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
elif model_name=='unet++':
args.model='unet++'
args.deepsupervision=False
model_weight_path='./logs/unet++_ep1600/cvc/20200312-143358/model_best.pth.tar'
model=get_models(args)
model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
elif model_name == 'attention_unet_v1':
args.model = 'attention_unet_v1'
model_weight_path = './logs/attention_unet_v1_ep1600/cvc/20200312-143413/model_best.pth.tar'
model = get_models(args)
model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
elif model_name == 'multires_unet':
args.model = 'multires_unet'
model_weight_path = './logs/multires_unet_ep1600_t2/20200322-194117/model_best.pth.tar'
model = get_models(args)
model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
# change bn relu order
elif model_name == 'multires_unet_align':
args.model = 'multires_unet'
model_weight_path = './logs/multires_unet_ep1600_chbnrelu/20200327-184457/model_best.pth.tar'
model = get_models(args)
model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
elif model_name == 'r2unet_t3':
args.model = 'r2unet'
args.time_step=3
model_weight_path = './logs/r2unet_ep1600_t2/20200324-032815/model_best.pth.tar'
model = get_models(args)
model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
elif model_name == 'unet_ep800dice':
args.model = 'unet'
model_weight_path = './logs/unet_ep800_bcedice/cvc/20200315-043021/model_best.pth.tar'
model = get_models(args)
model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
elif model_name=='unet++_nodeep_ep800dice':
args.model='unet++'
args.deepsupervision=False
model_weight_path='./logs/unet++_ep800_bcedice/cvc/20200315-043214/model_best.pth.tar'
model=get_models(args)
model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
elif model_name == 'unet++_deep_ep800dice':
args.model = 'unet++'
args.deepsupervision = True
model_weight_path = './logs/unet++_deep_ep800_bcedice/cvc/20200315-043134/model_best.pth.tar'
model = get_models(args)
model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
elif model_name == 'attention_unet_v1_ep800dice':
args.model = 'attention_unet_v1'
args.deepsupervision=False
model_weight_path = './logs/attention_unet_v1_ep800_bcedice/cvc/20200315-043300/model_best.pth.tar'
model = get_models(args)
model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
elif model_name == 'multires_unet_ep800dice':
args.model = 'multires_unet'
args.deepsupervision=False
model_weight_path = './logs/multires_unet_ep800_bcedice/cvc/20200312-173031/model_best.pth.tar'
model = get_models(args)
model.load_state_dict(torch.load(model_weight_path, map_location='cpu')['state_dict'])
else:
raise NotImplementedError()
assert os.path.exists(args.save)
args.model_save_path=os.path.join(args.save,model_name)
logger = get_logger(args.model_save_path)
args.save_images= os.path.join(args.model_save_path,"images")
if not os.path.exists(args.save_images):
os.mkdir(args.save_images)
if args.manualSeed is None:
args.manualSeed = random.randint(1, 10000)
np.random.seed(args.manualSeed)
torch.manual_seed(args.manualSeed)
args.use_cuda = args.gpus > 0 and torch.cuda.is_available()
args.device = torch.device('cuda' if args.use_cuda else 'cpu')
if args.use_cuda:
torch.cuda.manual_seed(args.manualSeed)
cudnn.benchmark = True
val_loader = get_dataloder(args, split_flag="valid")
setting = {k: v for k, v in args._get_kwargs()}
logger.info(setting)
logger.info('param size = %fMB', calc_parameters_count(model))
# init loss
if args.loss == 'bce':
criterion = nn.BCELoss()
elif args.loss == 'bcelog':
criterion = nn.BCEWithLogitsLoss()
elif args.loss == "dice":
criterion = DiceLoss()
elif args.loss == "softdice":
criterion = SoftDiceLoss()
elif args.loss == 'bcedice':
criterion = BCEDiceLoss()
else:
criterion = nn.CrossEntropyLoss()
if args.use_cuda:
logger.info("load model and criterion to gpu !")
model = model.to(args.device)
criterion = criterion.to(args.device)
infer(args, model, criterion, val_loader,logger,args.save_images)
def infer(args, model, criterion, val_loader,logger,path):
OtherVal = BinaryIndicatorsMetric()
val_loss = AverageMeter()
model.eval()
with torch.no_grad():
for step, (input, target,name) in tqdm(enumerate(val_loader)):
input = input.to(args.device)
target = target.to(args.device)
pred = model(input)
if args.deepsupervision:
pred=pred[-1].clone()
# sabe predit mask
# save the mask
file_masks=pred.clone()
file_masks=torch.sigmoid(file_masks).data.cpu().numpy()
n,c,h,w=file_masks.shape
assert n==len(file_masks)
for i in range(len(file_masks)):
file_index=int(name[i].split('.')[0])
file_mask=(file_masks[i][0] > 0.5).astype(np.uint8)
file_mask[file_mask >= 1] = 255
file_mask=Image.fromarray(file_mask)
file_mask.save(os.path.join(path,str(file_index)+".png"))
# compute loss
pred = pred.view(pred.size(0), -1)
target = target.view(target.size(0), -1)
v_loss = criterion(pred, target)
val_loss.update(v_loss.item(), 1)
OtherVal.update(labels=target, preds=pred, n=1)
vmr, vms, vmp, vmf, vmjc, vmd, vmacc = OtherVal.get_avg
# mvmr, mvms, mvmp, mvmf, mvmjc, mvmd, mvmacc = valuev2
logger.info("Val_Loss:{:.5f} Acc:{:.5f} Dice:{:.5f} Jc:{:.5f}".format(val_loss.avg, vmacc, vmd, vmjc))
if __name__ == '__main__':
models_name=models_dict.keys()
datasets_name=datasets_dict.keys()
parser = argparse.ArgumentParser(description='Unet serieas baseline')
# Add default argument
parser.add_argument('--model', type=str, default='unet',choices=models_name,
help='Model to train and evaluation')
parser.add_argument('--note' ,type=str, default='_',
help='model note ')
parser.add_argument('--save',type=str,default='./nas_search_unet/eval/cvc')
parser.add_argument('--dataset',type=str, default='cvc',choices=datasets_name,
help='Model to train and evaluation')
parser.add_argument('--base_size', type=int, default=256, help="resize base size")
parser.add_argument('--crop_size', type=int, default=256, help="crop size")
parser.add_argument('--im_channel', type=int, default=3, help="input image channel ")
parser.add_argument('--class_num', type=int, default=1, help="output feature channel")
parser.add_argument('--epoch', type=int, default=200, help="epochs")
parser.add_argument('--train_batch', type=int, default=8, help="train_batch")
parser.add_argument('--val_batch', type=int, default=1, help="val_batch ")
parser.add_argument('--num_workers', type=int, default=4, help="dataloader numworkers")
parser.add_argument('--init_weight_type',type=str, choices=["kaiming",'normal','xavier','orthogonal'],
default="kaiming",help=" model init mode")
parser.add_argument('--print_freq', type=int, default=100, help=" print freq (iteras) ")
parser.add_argument('--gpus', type=int,default=1, help=" use cuda or not ")
parser.add_argument('--grad_clip',type=int, default=5,help=" grid clip to ignore grad boom")
parser.add_argument('--manualSeed', type=int, default=100, help=" manualSeed ")
#model special
parser.add_argument('--deepsupervision', action='store_true', help=" deepsupervision for unet++")
parser.add_argument('--time_step',type=int, default=3,help=" r2unet use time step !")
parser.add_argument('--alpha', type=float, default=1.67, help=" multires unet channel changg ")
# optimer
parser.add_argument('--loss',type=str, choices=['bce','bcelog','dice','softdice','bcedice'],
default="bcelog",help="loss name ")
args = parser.parse_args()
main(args)