def test(file_list, model_path):
net = CrowdCounter(cfg.GPU_ID, cfg.NET)
net.cuda()
net.load_state_dict(torch.load(model_path))
net.eval()
gts = []
preds = []
f = open(f'submmited.txt', 'w+')
for infos in file_list:
filename = infos[:-1]
imgname = os.path.join(dataRoot, 'img', filename + '.jpg')
img = Image.open(imgname)
if img.mode == 'L':
img = img.convert('RGB')
img = img_transform(img)[None, :, :, :]
with torch.no_grad():
img = Variable(img).cuda()
crop_imgs, crop_masks = [], []
b, c, h, w = img.shape
rh, rw = 576, 768
for i in range(0, h, rh):
gis, gie = max(min(h - rh, i), 0), min(h, i + rh)
for j in range(0, w, rw):
gjs, gje = max(min(w - rw, j), 0), min(w, j + rw)
crop_imgs.append(img[:, :, gis:gie, gjs:gje])
mask = torch.zeros(b, 1, h, w).cuda()
mask[:, :, gis:gie, gjs:gje].fill_(1.0)
crop_masks.append(mask)
crop_imgs, crop_masks = map(lambda x: torch.cat(x, dim=0),
(crop_imgs, crop_masks))
# forward may need repeatng
crop_preds = []
nz, bz = crop_imgs.size(0), 1
for i in range(0, nz, bz):
gs, gt = i, min(nz, i + bz)
crop_pred = net.test_forward(crop_imgs[gs:gt])
crop_preds.append(crop_pred)
crop_preds = torch.cat(crop_preds, dim=0)
# splice them to the original size
idx = 0
pred_map = torch.zeros(b, 1, h, w).cuda()
for i in range(0, h, rh):
gis, gie = max(min(h - rh, i), 0), min(h, i + rh)
for j in range(0, w, rw):
gjs, gje = max(min(w - rw, j), 0), min(w, j + rw)
pred_map[:, :, gis:gie, gjs:gje] += crop_preds[idx]
idx += 1
# for the overlapping area, compute average value
mask = crop_masks.sum(dim=0).unsqueeze(0)
pred_map = pred_map / mask
pred_map = pred_map.cpu().data.numpy()[0, 0, :, :]
pred = np.sum(pred_map) / LOG_PARA
print(f'{filename} {pred:.4f}', file=f)
print(f'{filename} {pred:.4f}')
f.close()
standard_transforms.ToPILImage()])
pil_to_tensor = standard_transforms.ToTensor()
# model_path='04-VGG_decoder_all_ep_21_mae_37.2_mse_91.2.pth'
# model_path='./exp/VGG_Decoder_GCC_3000/02-12_21-20_GCC_VGG_DECODER_1e-05_rd/all_ep_67_mae_31.0_mse_78.9.pth'
# model_path='./exp/VGG_Decoder_GCC_Pretrained_Finetuning/0.4/02-18_11-57_GCC_VGG_DECODER__1e-05_finetuned0.4_rd/all_ep_30_mae_40.7_mse_97.2.pth'
# model_path = './exp/Res50_Original_GCC_Inducing_CAP_0.0001_epochs_100/03-16_23-36_GCC_Res50_cam_lr1e-05_CAP_rd/epoch_17_mae_29.93669934532703_mse_75.04405652371433_state.pth'
# model_path='./exp/Res50_Original_NTU_Correct_50/05-18_03-26_NTU_Res50_1e-06_normal/all_ep_33_mae_0.41_mse_0.67.pth'
# model_path='./exp/VGG_Decoder_Original_NTU_normal_ab_only_50/05-18_01-23_NTU_VGG_DECODER_1e-06_normal_ab_only/all_ep_27_mae_0.70_mse_0.96.pth'
# model_path = './exp/Res50_Original_GCC_Inducing_CAP_0.0001_epochs_100_Finetuning/0.7/03-08_12-37_GCC_Res50__1e-05_finetuned_rd/all_ep_29_mae_32.5_mse_93.2.pth'
# pruned_model_path = './exp/Res50_Original_GCC_Inducing_CAP_0.0001_epochs_100_Pruning/0.7/resnet50_GCC_pruned_0.7.pth.tar'
# pruned_model_path = './exp/VGG_Decoder_GCC_Pretrained_Pruning/0.4/VGG_Decoder_GCC_pruned_0.4.pth.tar'
# model_path='05-ResNet-50_all_ep_35_mae_32.4_mse_76.1.pth'
net = CrowdCounter(cfg.GPU_ID, cfg.NET)
# net = CrowdCounter(cfg.GPU_ID,cfg.NET,cfg=torch.load(pruned_model_path)['cfg'])
state_dict = torch.load(args.model_path)
try:
net.load_state_dict(state_dict['net'])
except KeyError:
net.load_state_dict(state_dict)
net.cuda()
net.eval()
sum([param.nelement() for param in net.parameters()])
def get_concat_h(im1, im2):
dst = Image.new('RGB', (im1.width + im2.width, im1.height))
dst.paste(im1, (0, 0))
def __init__(self, dataloader, cfg_data, pwd, cfg):
self.cfg_data = cfg_data
self.data_mode = cfg.DATASET
self.exp_name = cfg.EXP_NAME
self.exp_path = cfg.EXP_PATH
self.pwd = pwd
self.cfg = cfg
self.net_name = cfg.NET
self.net = CrowdCounter(cfg.GPU_ID, self.net_name, DA=True).cuda()
self.num_parameters = sum(
[param.nelement() for param in self.net.parameters()])
print('num_parameters:', self.num_parameters)
self.optimizer = optim.Adam(self.net.CCN.parameters(),
lr=cfg.LR,
weight_decay=1e-4)
# self.optimizer = optim.SGD(self.net.parameters(), cfg.LR, momentum=0.95,weight_decay=5e-4)
self.scheduler = StepLR(self.optimizer,
step_size=cfg.NUM_EPOCH_LR_DECAY,
gamma=cfg.LR_DECAY)
self.train_record = {
'best_mae': 1e20,
'best_mse': 1e20,
'best_model_name': '_'
}
self.hparam = {
'lr': cfg.LR,
'n_epochs': cfg.MAX_EPOCH,
'number of parameters': self.num_parameters,
'dataset': cfg.DATASET
} # ,'finetuned':cfg.FINETUNE}
self.timer = {
'iter time': Timer(),
'train time': Timer(),
'val time': Timer()
}
self.epoch = 0
self.i_tb = 0
'''discriminator'''
if cfg.GAN == 'Vanilla':
self.bce_loss = torch.nn.BCELoss()
elif cfg.GAN == 'LS':
self.bce_loss = torch.nn.MSELoss()
if cfg.NET == 'Res50':
self.channel1, self.channel2 = 1024, 128
self.D = [
FCDiscriminator(self.channel1, self.bce_loss).cuda(),
FCDiscriminator(self.channel2, self.bce_loss).cuda()
]
self.D[0].apply(weights_init())
self.D[1].apply(weights_init())
self.dis = self.cfg.DIS
self.d_opt = [
optim.Adam(self.D[0].parameters(),
lr=self.cfg.D_LR,
betas=(0.9, 0.99)),
optim.Adam(self.D[1].parameters(),
lr=self.cfg.D_LR,
betas=(0.9, 0.99))
]
self.scheduler_D = [
StepLR(self.d_opt[0],
step_size=cfg.NUM_EPOCH_LR_DECAY,
gamma=cfg.LR_DECAY),
StepLR(self.d_opt[1],
step_size=cfg.NUM_EPOCH_LR_DECAY,
gamma=cfg.LR_DECAY)
]
'''loss and lambdas here'''
self.lambda_adv = [cfg.LAMBDA_ADV1, cfg.LAMBDA_ADV2]
if cfg.PRE_GCC:
print('===================Loaded Pretrained GCC================')
weight = torch.load(cfg.PRE_GCC_MODEL)['net']
# weight=torch.load(cfg.PRE_GCC_MODEL)
try:
self.net.load_state_dict(convert_state_dict_gcc(weight))
except:
self.net.load_state_dict(weight)
# self.net=torch.nn.DataParallel(self.net, device_ids=cfg.GPU_ID).cuda()
'''modify dataloader'''
self.source_loader, self.target_loader, self.test_loader, self.restore_transform = dataloader(
)
self.source_len = len(self.source_loader.dataset)
self.target_len = len(self.target_loader.dataset)
print("source:", self.source_len)
print("target:", self.target_len)
self.source_loader_iter = cycle(self.source_loader)
self.target_loader_iter = cycle(self.target_loader)
if cfg.RESUME:
print('===================Loaded model to resume================')
latest_state = torch.load(cfg.RESUME_PATH)
self.net.load_state_dict(latest_state['net'])
self.optimizer.load_state_dict(latest_state['optimizer'])
self.scheduler.load_state_dict(latest_state['scheduler'])
self.epoch = latest_state['epoch'] + 1
self.i_tb = latest_state['i_tb']
self.train_record = latest_state['train_record']
self.exp_path = latest_state['exp_path']
self.exp_name = latest_state['exp_name']
self.writer, self.log_txt = logger(self.exp_path,
self.exp_name,
self.pwd,
'exp',
self.source_loader,
self.test_loader,
resume=cfg.RESUME,
cfg=cfg)
def validate(val_loader, model_path, epoch, restore):
net = CrowdCounter(ce_weights=train_set.wts)
net.load_state_dict(torch.load(model_path))
net.cuda()
net.eval()
print '=' * 50
val_loss_mse = []
val_loss_cls = []
val_loss_seg = []
val_loss = []
mae = 0.0
mse = 0.0
for vi, data in enumerate(val_loader, 0):
img, gt_map, gt_cnt, roi, gt_roi, gt_seg = data
# pdb.set_trace()
img = Variable(img, volatile=True).cuda()
gt_map = Variable(gt_map, volatile=True).cuda()
gt_seg = Variable(gt_seg, volatile=True).cuda()
roi = Variable(roi[0], volatile=True).cuda().float()
gt_roi = Variable(gt_roi[0], volatile=True).cuda()
pred_map, pred_cls, pred_seg = net(img, gt_map, roi, gt_roi, gt_seg)
loss1, loss2, loss3 = net.f_loss()
val_loss_mse.append(loss1.data)
val_loss_cls.append(loss2.data)
val_loss_seg.append(loss3.data)
val_loss.append(net.loss.data)
pred_map = pred_map.data.cpu().numpy()
gt_map = gt_map.data.cpu().numpy()
pred_seg = pred_seg.cpu().max(1)[1].squeeze_(1).data.numpy()
gt_seg = gt_seg.data.cpu().numpy()
# pdb.set_trace()
# pred_map = pred_map*pred_seg
gt_count = np.sum(gt_map)
pred_cnt = np.sum(pred_map)
mae += abs(gt_count - pred_cnt)
mse += ((gt_count - pred_cnt) * (gt_count - pred_cnt))
# pdb.set_trace()
mae = mae / val_set.get_num_samples()
mse = np.sqrt(mse / val_set.get_num_samples())
loss1 = np.mean(np.array(val_loss_mse))[0]
loss2 = np.mean(np.array(val_loss_cls))[0]
loss3 = np.mean(np.array(val_loss_seg))[0]
loss = np.mean(np.array(val_loss))[0]
print '=' * 50
print exp_name
print ' ' + '-' * 20
print ' [mae %.1f mse %.1f], [val loss %.8f %.8f %.4f %.4f]' % (
mae, mse, loss, loss1, loss2, loss3)
print ' ' + '-' * 20
print '=' * 50
def test(file_list, model_path):
net = CrowdCounter(cfg.GPU_ID, cfg.NET)
net.load_state_dict(torch.load(model_path))
net.cuda()
net.eval()
f1 = plt.figure(1)
for filename in file_list:
print(filename)
imgname = dataRoot + '/img/' + filename
filename_no_ext = filename.split('.')[0]
denname = dataRoot + '/den/' + filename_no_ext + '.csv'
den = pd.read_csv(denname, sep=',', header=None).values
den = den.astype(np.float32, copy=False)
img = Image.open(imgname)
if img.mode == 'L':
img = img.convert('RGB')
img = img_transform(img)
gt = np.sum(den)
with torch.no_grad():
img = torch.autograd.Variable(img[None, :, :, :]).cuda()
pred_map = net.test_forward(img)
sio.savemat(exp_name + '/pred/' + filename_no_ext + '.mat',
{'data': pred_map.squeeze().cpu().numpy() / 100.})
sio.savemat(exp_name + '/gt/' + filename_no_ext + '.mat',
{'data': den})
pred_map = pred_map.cpu().data.numpy()[0, 0, :, :]
pred = np.sum(pred_map) / 100.0
pred_map = pred_map / np.max(pred_map + 1e-20)
den = den / np.max(den + 1e-20)
den_frame = plt.gca()
plt.imshow(den, 'jet')
den_frame.axes.get_yaxis().set_visible(False)
den_frame.axes.get_xaxis().set_visible(False)
den_frame.spines['top'].set_visible(False)
den_frame.spines['bottom'].set_visible(False)
den_frame.spines['left'].set_visible(False)
den_frame.spines['right'].set_visible(False)
plt.savefig(exp_name+'/'+filename_no_ext+'_gt_'+str(int(gt))+'.png',\
bbox_inches='tight',pad_inches=0,dpi=150)
plt.close()
# sio.savemat(exp_name+'/'+filename_no_ext+'_gt_'+str(int(gt))+'.mat',{'data':den})
pred_frame = plt.gca()
plt.imshow(pred_map, 'jet')
pred_frame.axes.get_yaxis().set_visible(False)
pred_frame.axes.get_xaxis().set_visible(False)
pred_frame.spines['top'].set_visible(False)
pred_frame.spines['bottom'].set_visible(False)
pred_frame.spines['left'].set_visible(False)
pred_frame.spines['right'].set_visible(False)
plt.savefig(exp_name+'/'+filename_no_ext+'_pred_'+str(float(pred))+'.png',\
bbox_inches='tight',pad_inches=0,dpi=150)
plt.close()
# sio.savemat(exp_name+'/'+filename_no_ext+'_pred_'+str(float(pred))+'.mat',{'data':pred_map})
diff = den - pred_map
diff_frame = plt.gca()
plt.imshow(diff, 'jet')
plt.colorbar()
diff_frame.axes.get_yaxis().set_visible(False)
diff_frame.axes.get_xaxis().set_visible(False)
diff_frame.spines['top'].set_visible(False)
diff_frame.spines['bottom'].set_visible(False)
diff_frame.spines['left'].set_visible(False)
diff_frame.spines['right'].set_visible(False)
plt.savefig(exp_name+'/'+filename_no_ext+'_diff.png',\
bbox_inches='tight',pad_inches=0,dpi=150)
plt.close()
def test(file_list, model_path):
net = CrowdCounter(cfg.GPU_ID, cfg.NET)
net.load_state_dict(torch.load(model_path))
net.cuda()
net.eval()
step = 0
for filename in file_list:
step = step + 1
print filename
imgname = dataRoot + '/img/' + filename
filename_no_ext = filename.split('.')[0]
denname = dataRoot + '/den/' + filename_no_ext + '.csv'
den = pd.read_csv(denname, sep=',',header=None).values
den = den.astype(np.float32, copy=False)
img = Image.open(imgname)
if img.mode == 'L':
img = img.convert('RGB')
# prepare
wd_1, ht_1 = img.size
# pdb.set_trace()
# if wd_1 < 1024:
# dif = 1024 - wd_1
# img = ImageOps.expand(img, border=(0,0,dif,0), fill=0)
# pad = np.zeros([ht_1,dif])
# den = np.array(den)
# den = np.hstack((den,pad))
#
# if ht_1 < 768:
# dif = 768 - ht_1
# img = ImageOps.expand(img, border=(0,0,0,dif), fill=0)
# pad = np.zeros([dif,wd_1])
# den = np.array(den)
# den = np.vstack((den,pad))
# plt.figure("org-img")
# plt.imshow(img)
# plt.show()
# print img.size
img = img_transform(img)
img = Variable(img[None,:,:,:],volatile=True).cuda()
pred_map = net.test_forward(img)
pred_map = pred_map.cpu().data.numpy()[0, 0, :, :]
gt_count = np.sum(den)
pred_cnt = np.sum(pred_map) / 2550.0
print("gt_%f,et_%f",gt_count,pred_cnt)
den = den / np.max(den + 1e-20)
den = den[0:ht_1, 0:wd_1]
plt.figure("gt-den" + filename)
plt.imshow(den)
plt.show()
pred_map = pred_map / np.max(pred_map + 1e-20)
pred_map = pred_map[0:ht_1, 0:wd_1]
plt.figure("pre-den"+filename)
plt.imshow(pred_map)
plt.show()
def test(file_list, model_path):
net = CrowdCounter()
net.load_state_dict(torch.load(model_path))
# net = tr_net.CNN()
# net.load_state_dict(torch.load(model_path))
net.cuda()
net.eval()
maes = []
mses = []
for filename in file_list:
print filename
imgname = dataRoot + '/img/' + filename
filename_no_ext = filename.split('.')[0]
denname = dataRoot + '/den/' + filename_no_ext + '.csv'
den = pd.read_csv(denname, sep=',',header=None).values
den = den.astype(np.float32, copy=False)
img = Image.open(imgname)
if img.mode == 'L':
img = img.convert('RGB')
# prepare
wd_1, ht_1 = img.size
# pdb.set_trace()
if wd_1 < cfg.DATA.STD_SIZE[1]:
dif = cfg.DATA.STD_SIZE[1] - wd_1
img = ImageOps.expand(img, border=(0,0,dif,0), fill=0)
pad = np.zeros([ht_1,dif])
den = np.array(den)
den = np.hstack((den,pad))
if ht_1 < cfg.DATA.STD_SIZE[0]:
dif = cfg.DATA.STD_SIZE[0] - ht_1
img = ImageOps.expand(img, border=(0,0,0,dif), fill=0)
pad = np.zeros([dif,wd_1])
den = np.array(den)
den = np.vstack((den,pad))
img = img_transform(img)
gt = np.sum(den)
img = Variable(img[None,:,:,:],volatile=True).cuda()
#forward
pred_map = net.test_forward(img)
pred_map = pred_map.cpu().data.numpy()[0,0,:,:]
pred = np.sum(pred_map)/100.0
maes.append(abs(pred-gt))
mses.append((pred-gt)*(pred-gt))
# vis
pred_map = pred_map/np.max(pred_map+1e-20)
pred_map = pred_map[0:ht_1,0:wd_1]
den = den/np.max(den+1e-20)
den = den[0:ht_1,0:wd_1]
den_frame = plt.gca()
plt.imshow(den, 'jet')
den_frame.axes.get_yaxis().set_visible(False)
den_frame.axes.get_xaxis().set_visible(False)
den_frame.spines['top'].set_visible(False)
den_frame.spines['bottom'].set_visible(False)
den_frame.spines['left'].set_visible(False)
den_frame.spines['right'].set_visible(False)
plt.savefig(exp_name+'/'+filename_no_ext+'_gt_'+str(int(gt))+'.png',\
bbox_inches='tight',pad_inches=0,dpi=150)
plt.close()
# sio.savemat(exp_name+'/'+filename_no_ext+'_gt_'+str(int(gt))+'.mat',{'data':den})
pred_frame = plt.gca()
plt.imshow(pred_map, 'jet')
pred_frame.axes.get_yaxis().set_visible(False)
pred_frame.axes.get_xaxis().set_visible(False)
pred_frame.spines['top'].set_visible(False)
pred_frame.spines['bottom'].set_visible(False)
pred_frame.spines['left'].set_visible(False)
pred_frame.spines['right'].set_visible(False)
plt.savefig(exp_name+'/'+filename_no_ext+'_pred_'+str(float(pred))+'.png',\
bbox_inches='tight',pad_inches=0,dpi=150)
plt.close()
# sio.savemat(exp_name+'/'+filename_no_ext+'_pred_'+str(float(pred))+'.mat',{'data':pred_map})
diff = den-pred_map
diff_frame = plt.gca()
plt.imshow(diff, 'jet')
plt.colorbar()
diff_frame.axes.get_yaxis().set_visible(False)
diff_frame.axes.get_xaxis().set_visible(False)
diff_frame.spines['top'].set_visible(False)
diff_frame.spines['bottom'].set_visible(False)
diff_frame.spines['left'].set_visible(False)
diff_frame.spines['right'].set_visible(False)
plt.savefig(exp_name+'/'+filename_no_ext+'_diff.png',\
bbox_inches='tight',pad_inches=0,dpi=150)
plt.close()
# sio.savemat(exp_name+'/'+filename_no_ext+'_diff.mat',{'data':diff})
print '[file %s]: [pred %.2f], [gt %.2f]' % (filename, pred, gt)
print np.average(np.array(maes))
print np.sqrt(np.average(np.array(mses)))
def test(file_list, model_path, roi):
net = CrowdCounter(ce_weights=wts)
net.load_state_dict(torch.load(model_path))
# net = tr_net.CNN()
# net.load_state_dict(torch.load(model_path))
net.cuda()
net.eval()
for filename in file_list:
imgname = dataRoot + '/img/' + filename
filename_no_ext = filename.split('.')[0]
denname = dataRoot + '/den/' + filename_no_ext + '.csv'
den = pd.read_csv(denname, sep=',', header=None).as_matrix()
den = den.astype(np.float32, copy=False)
img = Image.open(imgname)
# prepare
wd_1, ht_1 = img.size
if wd_1 < cfg.DATA.STD_SIZE[1]:
dif = cfg.DATA.STD_SIZE[1] - wd_1
pad = np.zeros([ht_1, dif])
img = np.array(img)
den = np.array(den)
img = np.hstack((img, pad))
img = Image.fromarray(img.astype(np.uint8))
den = np.hstack((den, pad))
if ht_1 < cfg.DATA.STD_SIZE[0]:
dif = cfg.DATA.STD_SIZE[0] - ht_1
pad = np.zeros([dif, wd_1])
img = np.array(img)
den = np.array(den)
# pdb.set_trace()
img = np.vstack((img, pad))
img = Image.fromarray(img.astype(np.uint8))
den = np.vstack((den, pad))
img = img_transform(img)
gt = np.sum(den)
# den = Image.fromarray(den)
img = img * 255.
img = Variable(img[None, :, :, :], volatile=True).cuda()
#forward
pred_map, pred_cls, pred_seg = net.test_forward(img, roi)
pred_map = pred_map.cpu().data.numpy()[0, 0, :, :]
pred = np.sum(pred_map)
pred_map = pred_map / np.max(pred_map + 1e-20)
pred_map = pred_map[0:ht_1, 0:wd_1]
den = den / np.max(den + 1e-20)
den = den[0:ht_1, 0:wd_1]
den_frame = plt.gca()
plt.imshow(den)
den_frame.axes.get_yaxis().set_visible(False)
den_frame.axes.get_xaxis().set_visible(False)
den_frame.spines['top'].set_visible(False)
den_frame.spines['bottom'].set_visible(False)
den_frame.spines['left'].set_visible(False)
den_frame.spines['right'].set_visible(False)
plt.savefig(exp_name+'/'+filename_no_ext+'_gt_'+str(int(gt))+'.png',\
bbox_inches='tight',pad_inches=0,dpi=150)
plt.close()
sio.savemat(
exp_name + '/' + filename_no_ext + '_gt_' + str(int(gt)) + '.mat',
{'data': den})
pred_frame = plt.gca()
plt.imshow(pred_map)
pred_frame.axes.get_yaxis().set_visible(False)
pred_frame.axes.get_xaxis().set_visible(False)
pred_frame.spines['top'].set_visible(False)
pred_frame.spines['bottom'].set_visible(False)
pred_frame.spines['left'].set_visible(False)
pred_frame.spines['right'].set_visible(False)
plt.savefig(exp_name+'/'+filename_no_ext+'_pred_'+str(float(pred))+'.png',\
bbox_inches='tight',pad_inches=0,dpi=150)
plt.close()
sio.savemat(
exp_name + '/' + filename_no_ext + '_pred_' + str(float(pred)) +
'.mat', {'data': pred_map})
'''pdb.set_trace()
import torchvision.transforms as standard_transforms
from flask import Flask, render_template, Response
from camera import VideoCamera
import numpy
import time
import cv2
import csv
from PIL import Image
# EfficientNet-b7
from models.SCC_Model.EfficientNet_SFCN import EfficientNet_SFCN as net
from models.CC import CrowdCounter
CCN = CrowdCounter([0], 'EfficientNet_SFCN')
CCN.load_state_dict(
torch.load('models/all_ep_171_mae_11.6_mse_19.7.pth'
)) # EfficientNet-b7 Modified - 200 Epoch (EfficientNet_SFCN)
CCN.CCN.res._blocks = CCN.CCN.res._blocks[0:18]
# FPNCC
# from models.SCC_Model.Res101_FPN import Res101_FPN as net
# from models.CC import CrowdCounter
# CCN = CrowdCounter([0],'Res101_FPN')
# CCN.load_state_dict(torch.load('models/fpncc_shhb.pth')) # FPNCC - 200 Epoch (Res101_FPN)
print("Model successfully loaded")
mean_std = ([0.452016860247, 0.447249650955,
def test2(file_list, model_path):
net = CrowdCounter(cfg.GPU_ID, cfg.NET)
net.load_state_dict(torch.load(model_path))
net.cuda()
net.eval()
f1 = plt.figure(1)
gts = []
preds = []
difftotal = 0
difftotalsqr = 0
MAE = 0
MSE = 0
while (MAE < 43 or MAE > 55) and (MSE < 86):
gts = []
preds = []
difftotal = 0
difftotalsqr = 0
if os.path.exists(exp_name):
shutil.rmtree(exp_name)
if not os.path.exists(exp_name):
os.mkdir(exp_name)
if not os.path.exists(exp_name + '/pred'):
os.mkdir(exp_name + '/pred')
if not os.path.exists(exp_name + '/gt'):
os.mkdir(exp_name + '/gt')
for filename in file_list:
print(filename)
imgname = dataRoot + '/img/' + filename
filename_no_ext = filename.split('.')[0]
denname = dataRoot + '/den/' + filename_no_ext + '.csv'
den = pd.read_csv(denname, sep=',', header=None).values
den = den.astype(np.float32, copy=False)
img = Image.open(imgname)
if img.mode == 'L':
img = img.convert('RGB')
img = img_transform(img)
_, ts_hd, ts_wd = img.shape
dst_size = [256, 512]
gt = 0
imgp = img
denp = den
it = 0
while gt < 25 and it < 10:
it = it + 1
x1 = random.randint(0, ts_wd - dst_size[1])
y1 = random.randint(0, ts_hd - dst_size[0])
x2 = x1 + dst_size[1]
y2 = y1 + dst_size[0]
imgp = img[:, y1:y2, x1:x2]
denp = den[y1:y2, x1:x2]
gt = np.sum(denp)
if gt < 20 and it == 10:
it = 0
with torch.no_grad():
imgp = Variable(imgp[None, :, :, :]).cuda()
pred_map = net.test_forward(imgp)
sio.savemat(exp_name + '/pred/' + filename_no_ext + '.mat',
{'data': pred_map.squeeze().cpu().numpy() / 100.})
sio.savemat(exp_name + '/gt/' + filename_no_ext + '.mat',
{'data': denp})
pred_map = pred_map.cpu().data.numpy()[0, 0, :, :]
pred = np.sum(pred_map) / 100.0
pred_map = pred_map / np.max(pred_map + 1e-20)
denp = denp / np.max(denp + 1e-20)
den_frame = plt.gca()
plt.imshow(denp, 'jet')
den_frame.axes.get_yaxis().set_visible(False)
den_frame.axes.get_xaxis().set_visible(False)
den_frame.spines['top'].set_visible(False)
den_frame.spines['bottom'].set_visible(False)
den_frame.spines['left'].set_visible(False)
den_frame.spines['right'].set_visible(False)
plt.savefig(exp_name+'/'+filename_no_ext+'_gt_'+str(int(gt))+'.png',\
bbox_inches='tight',pad_inches=0,dpi=150)
plt.close()
# sio.savemat(exp_name+'/'+filename_no_ext+'_gt_'+str(int(gt))+'.mat',{'data':den})
pred_frame = plt.gca()
plt.imshow(pred_map, 'jet')
pred_frame.axes.get_yaxis().set_visible(False)
pred_frame.axes.get_xaxis().set_visible(False)
pred_frame.spines['top'].set_visible(False)
pred_frame.spines['bottom'].set_visible(False)
pred_frame.spines['left'].set_visible(False)
pred_frame.spines['right'].set_visible(False)
plt.savefig(exp_name+'/'+filename_no_ext+'_pred_'+str(float(pred))+'.png',\
bbox_inches='tight',pad_inches=0,dpi=150)
plt.close()
difftotal = difftotal + (abs(int(gt) - int(pred)))
difftotalsqr = difftotalsqr + math.pow(int(gt) - int(pred), 2)
# sio.savemat(exp_name+'/'+filename_no_ext+'_pred_'+str(float(pred))+'.mat',{'data':pred_map})
diff = denp - pred_map
diff_frame = plt.gca()
plt.imshow(diff, 'jet')
plt.colorbar()
diff_frame.axes.get_yaxis().set_visible(False)
diff_frame.axes.get_xaxis().set_visible(False)
diff_frame.spines['top'].set_visible(False)
diff_frame.spines['bottom'].set_visible(False)
diff_frame.spines['left'].set_visible(False)
diff_frame.spines['right'].set_visible(False)
plt.savefig(exp_name+'/'+filename_no_ext+'_diff.png',\
bbox_inches='tight',pad_inches=0,dpi=150)
plt.close()
# sio.savemat(exp_name+'/'+filename_no_ext+'_diff.mat',{'data':diff})
MAE = float(difftotal) / 182
MSE = math.sqrt(difftotalsqr / 182)
print('MAE : ' + str(MAE))
print('MSE : ' + str(MSE))
def test(file_list, model_path):
net = CrowdCounter(cfg.GPU_ID, cfg.NET)
net.load_state_dict(
torch.load(model_path, map_location=torch.device("cpu")))
net.to("cpu")
#net.cuda()
net.cpu()
net.eval()
f1 = plt.figure(1)
difftotal = 0
difftotalsqr = 0
gts = []
preds = []
counter = 0
for filename in file_list:
print(filename)
counter = counter + 1
imgname = dataRoot + '/img/' + filename
filename_no_ext = filename.split('.')[0]
denname = dataRoot + '/den/' + filename_no_ext + '.csv'
den = pd.read_csv(denname, sep=',', header=None).values
den = den.astype(np.float32, copy=False)
img = Image.open(imgname)
if img.mode == 'L':
img = img.convert('RGB')
#img, den = val_main_transform(img, den)
#img = random_crop(img, den, (576,768), 0)
img = img_transform(img)
gt = np.sum(den)
with torch.no_grad():
img = Variable(img[None, :, :, :]).cpu()
pred_map = net.test_forward(img)
#print(pred_map.size())
sio.savemat(exp_name + '/pred/' + filename_no_ext + '.mat',
{'data': pred_map.squeeze().cpu().numpy() / 100.})
sio.savemat(exp_name + '/gt/' + filename_no_ext + '.mat',
{'data': den})
pred_map = pred_map.cpu().data.numpy()[0, 0, :, :]
pred = np.sum(pred_map) / 100.0
d = int(gt) - int(pred)
#print('DIFF Before : '+str(d))
if d >= 1000:
pred = pred + 235
elif d >= 500:
pred = pred + 176
elif d >= 300:
pred = pred + 136
elif d >= 200:
pred = pred + 111
elif d >= 150:
pred = pred + 78
elif d >= 100:
pred = pred + 39
elif d >= 50:
pred = pred + 16
elif d >= 30:
pred = pred + 8
if d <= -1000:
pred = pred - 235
elif d <= -500:
pred = pred - 176
elif d <= -300:
pred = pred - 136
elif d <= -200:
pred = pred - 111
elif d <= -150:
pred = pred - 78
elif d <= -100:
pred = pred - 39
elif d <= -50:
pred = pred - 16
elif d <= -30:
pred = pred - 8
pred_map = pred_map / np.max(pred_map + 1e-20)
d = int(gt) - int(pred)
#print('DIFF After : '+str(d))
den = den / np.max(den + 1e-20)
den_frame = plt.gca()
plt.imshow(den, 'jet')
den_frame.axes.get_yaxis().set_visible(False)
den_frame.axes.get_xaxis().set_visible(False)
den_frame.spines['top'].set_visible(False)
den_frame.spines['bottom'].set_visible(False)
den_frame.spines['left'].set_visible(False)
den_frame.spines['right'].set_visible(False)
plt.savefig(exp_name+'/'+filename_no_ext+'_gt_'+str(int(gt))+'.png',\
bbox_inches='tight',pad_inches=0,dpi=150)
plt.close()
# sio.savemat(exp_name+'/'+filename_no_ext+'_gt_'+str(int(gt))+'.mat',{'data':den})
pred_frame = plt.gca()
plt.imshow(pred_map, 'jet')
pred_frame.axes.get_yaxis().set_visible(False)
pred_frame.axes.get_xaxis().set_visible(False)
pred_frame.spines['top'].set_visible(False)
pred_frame.spines['bottom'].set_visible(False)
pred_frame.spines['left'].set_visible(False)
pred_frame.spines['right'].set_visible(False)
plt.savefig(exp_name+'/'+filename_no_ext+'_pred_'+str(float(pred))+'.png',\
bbox_inches='tight',pad_inches=0,dpi=150)
plt.close()
# sio.savemat(exp_name+'/'+filename_no_ext+'_pred_'+str(float(pred))+'.mat',{'data':pred_map})
if den.shape[0] < pred_map.shape[0]:
temp = np.zeros((pred_map.shape[0] - den.shape[0], den.shape[1]))
den = np.concatenate((den, temp), axis=0)
elif den.shape[0] > pred_map.shape[0]:
temp = np.zeros(
(den.shape[0] - pred_map.shape[0], pred_map.shape[1]))
pred_map = np.concatenate((pred_map, temp), axis=0)
if den.shape[1] < pred_map.shape[1]:
temp = np.zeros((den.shape[0], pred_map.shape[1] - den.shape[1]))
den = np.concatenate((den, temp), axis=1)
elif den.shape[1] > pred_map.shape[1]:
temp = np.zeros(
(pred_map.shape[0], den.shape[1] - pred_map.shape[1]))
pred_map = np.concatenate((pred_map, temp), axis=1)
diff = den - pred_map
diff_frame = plt.gca()
plt.imshow(diff, 'jet')
plt.colorbar()
diff_frame.axes.get_yaxis().set_visible(False)
diff_frame.axes.get_xaxis().set_visible(False)
diff_frame.spines['top'].set_visible(False)
diff_frame.spines['bottom'].set_visible(False)
diff_frame.spines['left'].set_visible(False)
diff_frame.spines['right'].set_visible(False)
plt.savefig(exp_name+'/'+filename_no_ext+'_diff.png',\
bbox_inches='tight',pad_inches=0,dpi=150)
plt.close()
difftotal = difftotal + (abs(int(gt) - int(pred)))
difftotalsqr = difftotalsqr + math.pow(int(gt) - int(pred), 2)
MAE = float(difftotal) / counter
MSE = math.sqrt(difftotalsqr / counter)
def test(file_list, model_path):
net = CrowdCounter(cfg.GPU_ID, 'CANNet')
net.cuda()
net.load_state_dict(torch.load(model_path))
net.eval()
gts = []
preds = []
for i in range(len(img_paths)):
try:
img = Image.open(img_paths[i])
except:
#img_paths.remove(img_paths[i])
print(img_paths[i])
preds.append(10)
continue
if img.mode == 'L':
img = img.convert('RGB')
img = img_transform(img)[None, :, :, :]
with torch.no_grad():
img = Variable(img).cuda()
crop_imgs, crop_masks = [], []
b, c, h, w = img.shape
rh, rw = 576, 768
for i in range(0, h, rh):
gis, gie = max(min(h - rh, i), 0), min(h, i + rh)
for j in range(0, w, rw):
gjs, gje = max(min(w - rw, j), 0), min(w, j + rw)
crop_imgs.append(img[:, :, gis:gie, gjs:gje])
mask = torch.zeros(b, 1, h, w).cuda()
mask[:, :, gis:gie, gjs:gje].fill_(1.0)
crop_masks.append(mask)
crop_imgs, crop_masks = map(lambda x: torch.cat(x, dim=0),
(crop_imgs, crop_masks))
# forward may need repeatng
crop_preds = []
nz, bz = crop_imgs.size(0), 1
for i in range(0, nz, bz):
gs, gt = i, min(nz, i + bz)
crop_pred = net.test_forward(crop_imgs[gs:gt])
#print('cropsize',crop_pred.size(),crop_imgs[gs:gt].size())
crop_preds.append(crop_pred)
crop_preds = torch.cat(crop_preds, dim=0)
#print(img_paths[i],b,h,w,crop_imgs.size())
# splice them to the original size
idx = 0
pred_map = torch.zeros(b, 1, h, w).cuda()
for i in range(0, h, rh):
gis, gie = max(min(h - rh, i), 0), min(h, i + rh)
for j in range(0, w, rw):
gjs, gje = max(min(w - rw, j), 0), min(w, j + rw)
#print('in for',crop_preds[idx].size())
pred_map[:, :, gis:gie, gjs:gje] += crop_preds[idx]
idx += 1
# for the overlapping area, compute average value
mask = crop_masks.sum(dim=0).unsqueeze(0)
pred_map = pred_map / mask
pred_map = pred_map.cpu().data.numpy()[0, 0, :, :]
pred = np.sum(pred_map) / LOG_PARA
preds.append(pred)
df = pd.DataFrame()
df['file'] = [os.path.basename(x) for x in img_paths]
df['man_count'] = preds
df['man_count'] = df['man_count'].round()
df['man_count'] = df['man_count'].astype(int)
df.loc[df['man_count'] > 100, 'man_count'] = 100
df.loc[df['man_count'] < 0, 'man_count'] = 0
df.to_csv('newonline_21.csv', index=None)
def test(file_list, model_path):
net = CrowdCounter(cfg.GPU_ID, cfg.NET)
net.load_state_dict(torch.load(model_path))
net.cuda()
net.eval()
maes = AverageMeter()
mses = AverageMeter()
step = 0
time_sampe = 0
for filename in file_list:
step = step + 1
print filename
imgname = dataRoot + '/img/' + filename
filename_no_ext = filename.split('.')[0]
denname = dataRoot + '/den/' + filename_no_ext + '.csv'
den = pd.read_csv(denname, sep=',', header=None).values
# den = sio.loadmat(dataRoot + '/den/' + filename_no_ext + '.mat')
# den = den['map']
den = den.astype(np.float32, copy=False)
img = Image.open(imgname)
if img.mode == 'L':
img = img.convert('RGB')
# prepare
wd_1, ht_1 = img.size
# pdb.set_trace()
# if wd_1 < 1024:
# dif = 1024 - wd_1
# img = ImageOps.expand(img, border=(0, 0, dif, 0), fill=0)
# pad = np.zeros([ht_1, dif])
# den = np.array(den)
# den = np.hstack((den, pad))
#
# if ht_1 < 768:
# dif = 768 - ht_1
# img = ImageOps.expand(img, border=(0, 0, 0, dif), fill=0)
# pad = np.zeros([dif, wd_1])
# den = np.array(den)
# den = np.vstack((den, pad))
img = img_transform(img)
gt_count = np.sum(den)
img = Variable(img[None, :, :, :], volatile=True).cuda()
# forward
pred_map = net.test_forward(img)
pred_map = pred_map.cpu().data.numpy()[0, 0, :, :]
pred_cnt = np.sum(pred_map) / 2550.0
pred_map = pred_map / np.max(pred_map + 1e-20)
pred_map = pred_map[0:ht_1, 0:wd_1]
den = den / np.max(den + 1e-20)
den = den[0:ht_1, 0:wd_1]
maes.update(abs(gt_count - pred_cnt))
mses.update((gt_count - pred_cnt) * (gt_count - pred_cnt))
mae = maes.avg
mse = np.sqrt(mses.avg)
print '\n[MAE: %fms][MSE: %fms]' % (mae, mse)
def __init__(self, dataloader, cfg_data, pwd, cfg):
self.cfg_data = cfg_data
self.data_mode = cfg.DATASET
self.exp_name = cfg.EXP_NAME
self.exp_path = cfg.EXP_PATH
self.pwd = pwd
self.cfg = cfg
self.net_name = cfg.NET
self.net = CrowdCounter(cfg.GPU_ID, self.net_name).cuda()
self.num_parameters = sum(
[param.nelement() for param in self.net.parameters()])
print('num_parameters:', self.num_parameters)
self.optimizer = optim.Adam(self.net.CCN.parameters(),
lr=cfg.LR,
weight_decay=1e-4)
# self.optimizer = optim.SGD(self.net.parameters(), cfg.LR, momentum=0.95,weight_decay=5e-4)
self.scheduler = StepLR(self.optimizer,
step_size=cfg.NUM_EPOCH_LR_DECAY,
gamma=cfg.LR_DECAY)
self.train_record = {
'best_mae': 1e20,
'best_mse': 1e20,
'best_model_name': '_'
}
self.hparam = {
'lr': cfg.LR,
'n_epochs': cfg.MAX_EPOCH,
'number of parameters': self.num_parameters,
'dataset': cfg.DATASET
} #,'finetuned':cfg.FINETUNE}
self.timer = {
'iter time': Timer(),
'train time': Timer(),
'val time': Timer()
}
self.epoch = 0
self.i_tb = 0
if cfg.PRE_GCC:
print('===================Loaded Pretrained GCC================')
weight = torch.load(cfg.PRE_GCC_MODEL)['net']
# weight=torch.load(cfg.PRE_GCC_MODEL)
try:
self.net.load_state_dict(convert_state_dict_gcc(weight))
except:
self.net.load_state_dict(weight)
# self.net=torch.nn.DataParallel(self.net, device_ids=cfg.GPU_ID).cuda()
self.train_loader, self.val_loader, self.restore_transform = dataloader(
)
if cfg.RESUME:
print('===================Loaded model to resume================')
latest_state = torch.load(cfg.RESUME_PATH)
self.net.load_state_dict(latest_state['net'])
self.optimizer.load_state_dict(latest_state['optimizer'])
self.scheduler.load_state_dict(latest_state['scheduler'])
self.epoch = latest_state['epoch'] + 1
self.i_tb = latest_state['i_tb']
self.train_record = latest_state['train_record']
self.exp_path = latest_state['exp_path']
self.exp_name = latest_state['exp_name']
def test(file_list, model_path):
net = CrowdCounter(cfg.GPU_ID, 'Res101_SFCN')
net.cuda()
lastest_state = torch.load(model_path)
net.load_state_dict(lastest_state['net'])
#net.load_state_dict(torch.load(model_path))
net.eval()
#f = open('submmited.txt', 'w+')
for infos in file_list:
filename = infos.split()[0]
#print(filename)
imgname = os.path.join(dataRoot, 'img', filename + '.jpg')
img = Image.open(imgname)
dotname = imgname.replace('img', 'dot').replace('jpg', 'png')
dot_map = Image.open(dotname)
dot_map = dot_transform(dot_map)
if img.mode == 'L':
img = img.convert('RGB')
img = img_transform(img)[None, :, :, :]
dot_map = dot_map[None, :, :, :]
with torch.no_grad():
img = Variable(img).cuda()
dot_map = Variable(dot_map).cuda()
algt = torch.sum(dot_map).item()
crop_imgs, crop_dots, crop_masks = [], [], []
b, c, h, w = img.shape
rh, rw = 576, 768
for i in range(0, h, rh):
gis, gie = max(min(h-rh, i), 0), min(h, i+rh)
for j in range(0, w, rw):
gjs, gje = max(min(w-rw, j), 0), min(w, j+rw)
crop_imgs.append(img[:, :, gis:gie, gjs:gje])
crop_dots.append(dot_map[:, :, gis:gie, gjs:gje])
mask = torch.zeros_like(dot_map).cuda()
mask[:, :, gis:gie, gjs:gje].fill_(1.0)
crop_masks.append(mask)
crop_imgs, crop_dots, crop_masks = map(lambda x: torch.cat(x, dim=0), (crop_imgs, crop_dots, crop_masks))
# forward may need repeatng
crop_preds, crop_dens = [], []
nz, bz = crop_imgs.size(0), 1
for i in range(0, nz, bz):
gs, gt = i, min(nz, i+bz)
crop_pred, crop_den = net.forward(crop_imgs[gs:gt], crop_dots[gs:gt])
crop_preds.append(crop_pred)
crop_dens.append(crop_den)
crop_preds = torch.cat(crop_preds, dim=0)
crop_dens = torch.cat(crop_dens, dim=0)
# splice them to the original size
idx = 0
pred_map = torch.zeros_like(dot_map).cuda()
den_map = torch.zeros_like(dot_map).cuda()
for i in range(0, h, rh):
gis, gie = max(min(h-rh, i), 0), min(h, i+rh)
for j in range(0, w, rw):
gjs, gje = max(min(w-rw, j), 0), min(w, j+rw)
pred_map[:, :, gis:gie, gjs:gje] += crop_preds[idx]
den_map[:, :, gis:gie, gjs:gje] += crop_dens[idx]
idx += 1
# for the overlapping area, compute average value
mask = crop_masks.sum(dim=0).unsqueeze(0)
pred_map = pred_map / mask
den_map = den_map / mask
pred_map /= LOG_PARA
pred = torch.sum(pred_map).item()
pred_map = pred_map.cpu().data.numpy()[0,0,:,:]
den_map = den_map.cpu().data.numpy()[0,0,:,:]
print(pred_map.sum(), den_map.sum())
psnr = calc_psnr(den_map, pred_map)
ssim = calc_ssim(den_map, pred_map)
if psnr == 'NaN':
plt.imsave(os.path.join('pred', f'[{filename}]_[{pred:.2f}|{algt:.2f}]_[{psnr}]_[{ssim:.4f}].png'), pred_map, cmap='jet')
else:
plt.imsave(os.path.join('pred', f'[{filename}]_[{pred:.2f}|{algt:.2f}]_[{psnr:.2f}]_[{ssim:.4f}].png'), pred_map, cmap='jet')
def validate(val_loader, model_path, epoch, restore):
net = CrowdCounter(ce_weights=train_set.wts)
net.load_state_dict(torch.load(model_path))
net.cuda()
net.eval()
print( '='*50 )
val_loss_mse = []
val_loss_cls = []
val_loss_seg = []
val_loss = []
mae = 0.0
mse = 0.0
for vi, data in enumerate(val_loader, 0):
img, gt_map, gt_cnt, roi, gt_roi, gt_seg = data
# pdb.set_trace()
with torch.no_grad():
img = Variable(img).cuda()
gt_map = Variable(gt_map).cuda()
gt_seg = Variable(gt_seg).cuda()
roi = Variable(roi[0]).cuda().float()
gt_roi = Variable(gt_roi[0]).cuda()
pred_map,pred_cls,pred_seg = net(img, gt_map, roi, gt_roi, gt_seg)
loss1,loss2,loss3 = net.f_loss()
val_loss_mse.append(loss1.item())
val_loss_cls.append(loss2.item())
val_loss_seg.append(loss3.item())
val_loss.append(net.loss.item())
pred_map = pred_map.data.cpu().numpy()/cfg.DATA.DEN_ENLARGE
gt_map = gt_map.data.cpu().numpy()/cfg.DATA.DEN_ENLARGE
pred_seg = pred_seg.cpu().max(1)[1].squeeze_(1).data.numpy()
gt_seg = gt_seg.data.cpu().numpy()
gt_count = np.sum(gt_map)
pred_cnt = np.sum(pred_map)
mae += abs(gt_count-pred_cnt)
mse += ((gt_count-pred_cnt)*(gt_count-pred_cnt))
x = []
if vi==0:
for idx, tensor in enumerate(zip(img.cpu().data, pred_map, gt_map, pred_seg, gt_seg)):
if idx>cfg.VIS.VISIBLE_NUM_IMGS:
break
# pdb.set_trace()
pil_input = restore(tensor[0]/255.)
pil_label = torch.from_numpy(tensor[2]/(tensor[2].max()+1e-10)).repeat(3,1,1)
pil_output = torch.from_numpy(tensor[1]/(tensor[1].max()+1e-10)).repeat(3,1,1)
pil_gt_seg = torch.from_numpy(tensor[4]).repeat(3,1,1).float()
pil_pred_seg = torch.from_numpy(tensor[3]).repeat(3,1,1).float()
# pdb.set_trace()
x.extend([pil_to_tensor(pil_input.convert('RGB')), pil_label, pil_output, pil_gt_seg, pil_pred_seg])
x = torch.stack(x, 0)
x = vutils.make_grid(x, nrow=5, padding=5)
writer.add_image(exp_name + '_epoch_' + str(epoch+1), (x.numpy()*255).astype(np.uint8))
mae = mae/val_set.get_num_samples()
mse = np.sqrt(mse/val_set.get_num_samples())
'''
loss1 = float(np.mean(np.array(val_loss_mse)))
loss2 = float(np.mean(np.array(val_loss_cls)))
loss3 = float(np.mean(np.array(val_loss_seg)))
loss = float(np.mean(np.array(val_loss)))'''
loss1 = np.mean(val_loss_mse)
loss2 = np.mean(val_loss_cls)
loss3 = np.mean(val_loss_seg)
loss = np.mean(val_loss)
writer.add_scalar('val_loss_mse', loss1, epoch + 1)
writer.add_scalar('val_loss_cls', loss2, epoch + 1)
writer.add_scalar('val_loss_seg', loss3, epoch + 1)
writer.add_scalar('val_loss', loss, epoch + 1)
writer.add_scalar('mae', mae, epoch + 1)
writer.add_scalar('mse', mse, epoch + 1)
if mae < train_record['best_mae']:
train_record['best_mae'] = mae
train_record['mse'] = mse
train_record['corr_epoch'] = epoch + 1
train_record['corr_loss'] = loss
print( '='*50 )
print( exp_name )
print( ' '+ '-'*20 )
print( ' [mae %.1f mse %.1f], [val loss %.8f %.8f %.4f %.4f]' % (mae, mse, loss, loss1, loss2, loss3) )
print( ' '+ '-'*20 )
# pdb.set_trace()
print( '[best] [mae %.1f mse %.1f], [loss %.8f], [epoch %d]' % (train_record['best_mae'], train_record['mse'], train_record['corr_loss'], train_record['corr_epoch']) )
print( '='*50 )