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validate_mt.py
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validate_mt.py
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# -*- coding: utf-8 -*-_resnet18_32s
import torch
import os
import argparse
import cv2
import time
import numpy as np
import visdom
from torch.autograd import Variable
from torch.optim.lr_scheduler import ReduceLROnPlateau, StepLR
from torchvision import transforms
from semseg.dataloader.camvid_loader import camvidLoader
from semseg.dataloader.cityscapes_loader import cityscapesLoader
from semseg.dataloader.yolodataset_loader import yoloDataset
from semseg.loss import cross_entropy2d
from semseg.metrics import scores
from semseg.modelloader.drn_a_mt import drnsegmt_a_18
from semseg.schedulers import ConstantLR, PolynomialLR
from semseg.utils.get_class_weights import median_frequency_balancing, ENet_weighing
from semseg.yoloLoss import yoloLoss
def decoder(pred):
'''
pred (tensor) 1x7x7x30
return (tensor) box[[x1,y1,x2,y2]] label[...]
'''
grid_num = 14
boxes=[]
cls_indexs=[]
probs = []
cell_size = 1./grid_num
pred = pred.data
pred = pred.squeeze(0) #7x7x30
contain1 = pred[:,:,4].unsqueeze(2)
contain2 = pred[:,:,9].unsqueeze(2)
contain = torch.cat((contain1,contain2),2)
mask1 = contain > 0.1 #大于阈值
mask2 = (contain==contain.max()) #we always select the best contain_prob what ever it>0.9
mask = (mask1+mask2).gt(0)
# min_score,min_index = torch.min(contain,2) #每个cell只选最大概率的那个预测框
for i in range(grid_num):
for j in range(grid_num):
for b in range(2):
# index = min_index[i,j]
# mask[i,j,index] = 0
if mask[i,j,b] == 1:
#print(i,j,b)
box = pred[i,j,b*5:b*5+4]
contain_prob = torch.FloatTensor([pred[i,j,b*5+4]])
xy = torch.FloatTensor([j,i])*cell_size #cell左上角 up left of cell
box[:2] = box[:2]*cell_size + xy # return cxcy relative to image
box_xy = torch.FloatTensor(box.size())#转换成xy形式 convert[cx,cy,w,h] to [x1,xy1,x2,y2]
box_xy[:2] = box[:2] - 0.5*box[2:]
box_xy[2:] = box[:2] + 0.5*box[2:]
max_prob,cls_index = torch.max(pred[i,j,10:],0)
if float((contain_prob*max_prob)[0]) > 0.1:
boxes.append(box_xy.view(1,4))
cls_indexs.append(cls_index)
# print('cls_index:', cls_index)
probs.append(contain_prob*max_prob)
# print('boxes:', boxes)
if len(boxes) ==0:
boxes = torch.zeros((1,4))
probs = torch.zeros(1)
cls_indexs = torch.zeros(1)
else:
# print('boxes.shape:', len(boxes))
# print('probs.shape:', len(probs))
boxes = torch.cat(boxes,0) #(n,4)
probs = torch.cat(probs,0) #(n,)
cls_indexs = torch.stack(cls_indexs,0) #(n,)
keep = nms(boxes,probs)
return boxes[keep],cls_indexs[keep],probs[keep]
def nms(bboxes,scores,threshold=0.5):
'''
bboxes(tensor) [N,4]
scores(tensor) [N,]
'''
x1 = bboxes[:,0]
y1 = bboxes[:,1]
x2 = bboxes[:,2]
y2 = bboxes[:,3]
areas = (x2-x1) * (y2-y1)
_,order = scores.sort(0,descending=True)
keep = []
while order.numel() > 0:
i = order[0]
keep.append(i)
if order.numel() == 1:
break
xx1 = x1[order[1:]].clamp(min=x1[i])
yy1 = y1[order[1:]].clamp(min=y1[i])
xx2 = x2[order[1:]].clamp(max=x2[i])
yy2 = y2[order[1:]].clamp(max=y2[i])
w = (xx2-xx1).clamp(min=0)
h = (yy2-yy1).clamp(min=0)
inter = w*h
ovr = inter / (areas[i] + areas[order[1:]] - inter)
ids = (ovr<=threshold).nonzero().squeeze()
if ids.numel() == 0:
break
order = order[ids+1]
return torch.LongTensor(keep)
def validate(args):
init_time = str(int(time.time()))
if args.vis:
# start visdom and close all window
vis = visdom.Visdom()
vis.close()
# vis_text_usage = 'Operating in the text window<br>Press s to save data<br>'
# callback_text_usage_window = vis.text(vis_text_usage)
# vis.register_event_handler(type_callback, callback_text_usage_window)
class_weight = None
local_path = os.path.expanduser(args.dataset_path)
train_dst = None
val_dst = None
if args.dataset == 'CamVid':
train_dst = camvidLoader(local_path, is_transform=True, is_augment=args.data_augment, split='train')
val_dst = camvidLoader(local_path, is_transform=True, is_augment=False, split='val')
elif args.dataset == 'CityScapes':
train_dst = cityscapesLoader(local_path, is_transform=True, split='train')
val_dst = cityscapesLoader(local_path, is_transform=True, split='val')
else:
print('{} dataset does not implement'.format(args.dataset))
exit(0)
if args.cuda:
if class_weight is not None:
class_weight = class_weight.cuda()
print('class_weight:', class_weight)
train_loader = torch.utils.data.DataLoader(train_dst, batch_size=args.batch_size, shuffle=True)
val_loader = torch.utils.data.DataLoader(val_dst, batch_size=1, shuffle=True)
yolo_B = 2
yolo_C = 2
yolo_S = 7
yolo_out_tensor_shape = yolo_B * 5 + yolo_C
print('yolo_out_tensor_shape:', yolo_out_tensor_shape)
det_file_root = os.path.expanduser('~/Data/CamVid/train/')
det_train_dst = yoloDataset(root=det_file_root, list_file=['camvid_det.txt'], train=False, transform=[transforms.ToTensor()], yolo_out_tensor_shape=yolo_out_tensor_shape)
det_train_loader = torch.utils.data.DataLoader(det_train_dst, batch_size=1, shuffle=False)
model = drnsegmt_a_18(pretrained=args.init_vgg16, n_classes=args.n_classes, det_tensor_num=yolo_out_tensor_shape)
if args.resume_model_state_dict != '':
pretrained_dict = torch.load(args.resume_model_state_dict, map_location='cpu')
model.load_state_dict(pretrained_dict)
else:
print('missing resume_model_state_dict')
exit()
if args.cuda:
model.cuda()
model.eval()
for epoch in range(0, 1, 1):
# ----for object detection----
for det_i, (det_imgs, det_labels, det_imgs_ori) in enumerate(det_train_loader):
print('det_imgs.shape:', det_imgs.shape)
print('det_labels.shape:', det_labels.shape)
# det_imgs_height = det_imgs.shape[2]
# det_imgs_width = det_imgs.shape[3]
# print('det_imgs_height:', det_imgs_height)
# print('det_imgs_width:', det_imgs_width)
det_imgs = Variable(det_imgs)
det_labels = Variable(det_labels)
if args.cuda:
det_imgs = det_imgs.cuda()
det_labels = det_labels.cuda()
_, outputs_det = model(det_imgs)
# print('outpust_det:', outputs_det.shape)
# det_loss = det_criterion(outputs_det, det_labels)
# det_loss_np = det_loss.cpu().data.numpy()
outputs_det = outputs_det.cpu()
det_boxes, det_cls_indexs, det_probs = decoder(outputs_det)
image_ori = det_imgs_ori[0, ...].cpu().data.numpy()
det_imgs_ori_height = image_ori.shape[0]
det_imgs_ori_width = image_ori.shape[1]
# image = image.transpose(1, 2, 0)
for i, det_box in enumerate(det_boxes):
x1 = int(det_box[0] * det_imgs_ori_width)
x2 = int(det_box[2] * det_imgs_ori_width)
y1 = int(det_box[1] * det_imgs_ori_height)
y2 = int(det_box[3] * det_imgs_ori_height)
det_cls_index = det_cls_indexs[i]
det_cls_index = int(det_cls_index) # convert LongTensor to int
det_prob = det_probs[i]
det_prob = float(det_prob)
if x1<0 or x1>det_imgs_ori_width-1:
continue
if x2<0 or x2>det_imgs_ori_width-1:
continue
if y1<0 or y1>det_imgs_ori_height-1:
continue
if y2<0 or y2>det_imgs_ori_height-1:
continue
# x1 = np.clip(x1, 0, det_imgs_ori_width-1)
# x2 = np.clip(x2, 0, det_imgs_ori_width-1)
# y1 = np.clip(y1, 0, det_imgs_ori_height-1)
# y2 = np.clip(y2, 0, det_imgs_ori_height-1)
if det_prob>0:
print('(x1,y1)->(x2,y2):({},{})->({},{})'.format(x1, y1, x2, y2))
cv2.rectangle(image_ori, (x1, y1), (x2, y2), (0, 0, 255))
cv2.imshow('image_ori', image_ori)
cv2.waitKey()
# ----for object detection----
# # ----for semantic segment----
# for i, (imgs, labels) in enumerate(train_loader):
# # if i==1:
# # break
# # model.train()
#
# # 最后的几张图片可能不到batch_size的数量，比如batch_size=4，可能只剩3张
# imgs_batch = imgs.shape[0]
# if imgs_batch != args.batch_size:
# break
# # iteration_step += 1
#
# imgs = Variable(imgs)
# labels = Variable(labels)
#
# if args.cuda:
# imgs = imgs.cuda()
# labels = labels.cuda()
# outputs_sem, _ = model(imgs)
# # print('outputs_sem.shape:', outputs_sem.shape)
#
# # print('outputs.size:', outputs.size())
# # print('labels.size:', labels.size())
#
# loss = cross_entropy2d(outputs_sem, labels, weight=class_weight)
# loss_np = loss.cpu().data.numpy()
# loss_epoch += loss_np
#
# if args.vis and i%50==0:
# pred_labels = outputs_sem.cpu().data.max(1)[1].numpy()
# label_color = train_dst.decode_segmap(labels.cpu().data.numpy()[0]).transpose(2, 0, 1)
# pred_label_color = train_dst.decode_segmap(pred_labels[0]).transpose(2, 0, 1)
# win = 'label_color'
# vis.image(label_color, win=win, opts=dict(title='Gt', caption='Ground Truth'))
# win = 'pred_label_color'
# vis.image(pred_label_color, win=win, opts=dict(title='Pred', caption='Prediction'))
#
# # 显示一个周期的loss曲线
# if args.vis:
# win = 'loss_iteration'
# loss_np_expand = np.expand_dims(loss_np, axis=0)
# win_res = vis.line(X=np.ones(1)*(i+data_count*(epoch-1)+1), Y=loss_np_expand, win=win, update='append')
# if win_res != win:
# vis.line(X=np.ones(1)*(i+data_count*(epoch-1)+1), Y=loss_np_expand, win=win, opts=dict(title=win, xlabel='iteration', ylabel='loss'))
# # ----for semantic segment----
# best training: python train.py --resume_model fcn32s_camvid_9.pkl --save_model True
# --init_vgg16 True --dataset_path /home/cgf/Data/CamVid --batch_size 1 --vis True
if __name__=='__main__':
# print('train----in----')
parser = argparse.ArgumentParser(description='training parameter setting')
parser.add_argument('--structure', type=str, default='ENetV2', help='use the net structure to segment [ fcn_32s ResNetDUC segnet ENet drn_d_22 ]')
parser.add_argument('--solver', type=str, default='SGD', help='use the solver to optimizer net [ SGD ]')
parser.add_argument('--resume_model', type=str, default='', help='resume model path [ fcn32s_camvid_9.pkl ]')
parser.add_argument('--resume_model_state_dict', type=str, default='', help='resume model state dict path [ fcn32s_camvid_9.pt ]')
parser.add_argument('--save_model', type=bool, default=False, help='save model [ False ]')
parser.add_argument('--save_epoch', type=int, default=1, help='save model after epoch [ 1 ]')
parser.add_argument('--training_epoch', type=int, default=500, help='training epoch end training model [ 30000 ]')
parser.add_argument('--init_vgg16', type=bool, default=False, help='init model using vgg16 weights [ False ]')
parser.add_argument('--dataset', type=str, default='CamVid', help='train dataset [ CamVid CityScapes ]')
parser.add_argument('--dataset_path', type=str, default='~/Data/CamVid', help='train dataset path [ ~/Data/CamVid ~/Data/cityscapes ]')
parser.add_argument('--data_augment', type=bool, default=True, help='enlarge the training data [ True False ]')
parser.add_argument('--class_weighting', type=str, default='MFB', help='weighting class [ MFB ENET ]')
parser.add_argument('--batch_size', type=int, default=1, help='train dataset batch size [ 1 ]')
parser.add_argument('--val_interval', type=int, default=-1, help='val dataset interval unit epoch [ 3 ]')
parser.add_argument('--n_classes', type=int, default=12, help='train class num [ 12 ]')
parser.add_argument('--lr', type=float, default=1e-4, help='train learning rate [ 0.00001 ]')
parser.add_argument('--lr_policy', type=str, default='Polynomial', help='train learning policy [ Constant Polynomial ]')
parser.add_argument('--vis', type=bool, default=False, help='visualize the training results [ False ]')
parser.add_argument('--cuda', type=bool, default=False, help='use cuda [ False ]')
args = parser.parse_args()
print(args)
validate(args)
# print('train----out----')