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eval_unetr18bn_smoke100k.py
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eval_unetr18bn_smoke100k.py
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import os
import time
import numpy as np
import argparse
import torch
import torch.nn as nn
import cv2
from models.unet import UNet2D, UNetResNet18_BN_Scaled, UNetResNet18_BN_Scaled28, ResNet34_BN_Scaled
from dataloader.dataloader import zDataLoader
from dataloader.dataset import Basic_Dataset, SmokeCloud_Dataset_V2
from checkpoint.checkpoint import CheckpointMgr
from models.loss import FocalLoss_BCE
pj = os.path.join
def arg_parse():
parser = argparse.ArgumentParser()
# parser.add_argument('--result', default='/project/data/smoke_cloud/result/')
parser.add_argument('--output',default='./pth/smoke_cloud_unetr18_smoke100k/')
parser.add_argument('--lr',default=0.001,type=float)
parser.add_argument('--max_epoch',default=100,type=int)
parser.add_argument('--batchsize',default=8,type=int)
parser.add_argument('--view_interval',default=50,type=int)
parser.add_argument('--ckpt_interval',default=1000,type=int)
parser.add_argument('--devnum',default=1,type=int)
parser.add_argument('--mode', type=int, default=0)
args = parser.parse_args()
return args
def val(args):
print('#'*10, 'EVAL' , '#'*10)
# if not os.path.exists(args.result):
# os.makedirs(args.result, exist_ok=True)
# n_cuda_device = torch.cuda.device_count()
n_cuda_device = min([args.devnum,4])
if args.mode == 0:
model = UNetResNet18_BN_Scaled(n_classes=1, use_bn=True)
mask_size = (56, 56)
args.output = './pth/smoke_cloud_unetr18_smoke100k/'
print("UNetResNet18_BN_Scaled,", mask_size)
elif args.mode == 1:
model = UNetResNet18_BN_Scaled28(n_classes=1, use_bn=True)
mask_size = (28,28)
args.output = './pth/smoke_cloud_unetr18_smoke100k_hw28/'
print("UNetResNet18_BN_Scaled28,", mask_size)
elif args.mode == 2:
model =ResNet34_BN_Scaled(n_classes=1, use_bn=True)
mask_size = (14, 14)
args.output = './pth/smoke_cloud_r34_smoke100k_hw14/'
print("ResNet34_BN_Scaled,", mask_size)
else:
model = UNetResNet18_BN_Scaled(n_classes=1, use_bn=True)
mask_size = (56,56)
args.output = './pth/smoke_cloud_unetr18_smoke100k/'
print("UNetResNet18_BN_Scaled,", mask_size)
# dataset_test = Basic_Dataset(datapath=args.datapath)
dataset_test = SmokeCloud_Dataset_V2(ssspath=None, s100kpath='/project/data/smoke_cloud/smoke100k',mode='test', mask_size=mask_size)
dataloader = zDataLoader(imgs_per_gpu=args.batchsize,workers_per_gpu=8 if n_cuda_device > 1 else 16,
num_gpus=n_cuda_device,dist=False,shuffle=False,pin_memory=True,verbose=True)(dataset_test)
# # model = UNet2D(n_channels=3, n_classes=1)
# model = UNetResNet18_BN_Scaled(n_classes=1, use_bn=True)
checkpoint_op = CheckpointMgr(ckpt_dir=args.output)
checkpoint_op.load_checkpoint(model,map_location='cpu')
model = model.cuda()
if n_cuda_device > 1:
# model.forward = model.inference
model = nn.DataParallel(model)
model.eval()
cnt = 0
m_iou = 0
m_t = 0
mp_iou = 0
mr_iou = 0
m_fp_ratio = 0
m_non_t = 0
with torch.no_grad():
for ind,batch in enumerate(dataloader):
process_line = ind/len(dataloader)*100
if m_t > 1:
m_iou_score = m_iou/m_t
mp_iou_score = mp_iou/m_t
mr_iou_score = mr_iou/m_t
else:
m_iou_score = 0
mp_iou_score = 0
mr_iou_score = 0
if m_non_t > 1:
m_fp_score = m_fp_ratio / m_non_t
else:
m_fp_score = 0
print('{:.2f}% done, with miou = {:.4f}, mriou = {:.4f}, mpiou = {:.4f}, m_fp = {:.4f}'\
.format(process_line, m_iou_score, mr_iou_score, mp_iou_score, m_fp_score), end='\r')
# if process_line > 2:
# break
imgs = batch['X']#[b,c,h,w]
ori_imgs = batch['img']#[b,h,w,c]
gt_masks = batch['y']#[b,h,w]
# print(gt_masks.shape)
imgs = imgs.cuda()
gt_masks = gt_masks.cuda()
preds = model.inference(imgs) # [b,1,h,w]
preds = preds.squeeze()
_gt_masks = (gt_masks > 0.5) #[b,h,w]
_preds = (preds > 0.5)
joint = (_gt_masks&_preds).float()
union = (_gt_masks|_preds).float()
_non_obj_ = gt_masks.sum(dim=[1,2]) == 0#[b]
_obj_ = ~_non_obj_
_joint_obj_ = joint[_obj_]
_union_obj_ = union[_obj_]
_preds_obj_ = _preds[_obj_]
_gt_masks_obj_ = _gt_masks[_obj_]
if _joint_obj_.shape[0] > 0:
_joint_obj_ = torch.sum(_joint_obj_, dim=[1,2]).cpu().data.numpy()
_union_obj_ = torch.sum(_union_obj_, dim=[1, 2]).cpu().data.numpy()
iou = _joint_obj_/(_union_obj_+1e-3) #[b]
p_iou = _joint_obj_/ (torch.sum(_preds_obj_.float(),dim=[1,2]).cpu().data.numpy()+1e-3)
r_iou = _joint_obj_/ (torch.sum(_gt_masks_obj_.float(),dim=[1,2]).cpu().data.numpy()+1e-3)
m_t += _joint_obj_.shape[0]
m_iou += iou.sum()
mp_iou += p_iou.sum()
mr_iou += r_iou.sum()
_gt_masks_non_ = _gt_masks[_non_obj_]
if _gt_masks_non_.shape[0] > 0:
_preds_non_ = _preds[_non_obj_]
fp_ratio = _preds_non_.sum(dim=[1,2]).cpu().data.numpy()/(224*224)
m_fp_ratio += fp_ratio.sum()
m_non_t += _gt_masks_non_.shape[0]
# print(joint)
# print(union)
#
# print(iou.sum(), iou)
# break
#
# for img,pred, gt_mask in zip(ori_imgs, preds, gt_masks):
# img = img.cpu().data.numpy().astype(np.uint8) #[h,w,3] uint8
# pred = pred.cpu().data.numpy().squeeze() #[h,w] fp32
# gt_mask = gt_mask.cpu().data.numpy().squeeze() #uint8
#
# hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# hsv[...,-1] = (hsv[...,-1]*pred).astype(np.uint8)
# img2 = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)
# img = np.concatenate([img,img2], axis=1)
#
# # r_ch = img[...,-1]*1
# # # upred = pred*255
# # # r_ch[pred>0.1] = upred[pred>0.1]
# # r_ch[pred>0.3] = 255
# # img[...,-1] = r_ch.astype(np.uint8)
#
# cv2.imwrite( pj(args.result,'{:04d}.png'.format(cnt)), img)
# cnt += 1
print('#' * 10, 'EVAL END', '#' * 10)
m_iou_score = m_iou/m_t
m_fp_score = m_fp_ratio/m_non_t
print('m_iou = {:.4f}, mriou = {:.4f}, mpiou = {:.4f}, mfp={:.4f}'.format(m_iou_score, mr_iou/m_t, mp_iou/m_t, m_fp_score))
if __name__ == '__main__':
args = arg_parse()
val(args)