def my_demo(file_list, model_path):
Net_OK = ['Res101_SFCN', 'LCN']
if (cfg.NET not in Net_OK):
print('net is not Res101_SFCN demo not work')
return
net = CrowdCounter(cfg.GPU_ID, cfg.NET)
new_weight_dict = torch.load(model_path)
if (cfg.GPU_ID == [0]):
new_weight_dict = re_name_weight(new_weight_dict)
net.load_state_dict(new_weight_dict)
net.cuda()
net.eval()
print('net eval is ok=================')
f1 = plt.figure(1)
for filename in file_list:
print(filename)
img = Image.open(filename)
if img.mode == 'L':
img = img.convert('RGB')
img = img_transform(img)
with torch.no_grad():
img = Variable(img[None, :, :, :]).cuda()
start = time.time()
for i in range(1000):
pred_map = net.test_forward(img)
pred_map.cpu()
end = time.time()
density_pre = pred_map.squeeze().cpu().numpy() / 100.
num_people = int(np.sum(density_pre))
print('in this picture,there are ', num_people, ' people')
print('Do once forward need {:.3f}ms '.format(
(end - start) * 1000 / 100.0))
def main(params):
H, W = params['image_size']
mean_std = ([0.452016860247, 0.447249650955, 0.431981861591],
[0.23242045939, 0.224925786257, 0.221840232611])
data_transform = transforms.Compose([
transforms.Resize((H, W)),
transforms.ToTensor(),
transforms.Normalize(*mean_std)
])
net = CrowdCounter([0], params['model'])
net.load_state_dict(torch.load(params['model_path']))
net.cuda()
net.eval()
video_list = np.sort(glob(params['dataset_path'] + '/*'))
for v in video_list:
print(v)
outputdir = params['outputdir_prefix'] + '/%d_%d/' % (H, W)
os.makedirs(outputdir, exist_ok=True)
file_list = np.sort(glob(v + '/*.jpg'))
imgs = torch.zeros(len(file_list), 3, H, W)
for i, f in enumerate(tqdm(file_list)):
imgs[i] = data_transform(Image.open(f))
train_dataset = torch.utils.data.TensorDataset(
imgs, torch.zeros(len(file_list)))
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=params['batch_size'], shuffle=False)
pred_map = []
for x, y in tqdm(train_loader):
tmp = net.test_forward(x.cuda()).squeeze().detach().cpu().numpy()
if (len(tmp.shape) == 2):
tmp = tmp[np.newaxis]
pred_map.append(tmp)
pred_map = np.concatenate(pred_map)
np.savez_compressed(outputdir + os.path.basename(v), pred_map)
def main():
cfg_file = open('./config.py',"r")
cfg_lines = cfg_file.readlines()
with open(log_txt, 'a') as f:
f.write(''.join(cfg_lines) + '\n\n\n\n')
if len(cfg.TRAIN.GPU_ID)==1:
torch.cuda.set_device(cfg.TRAIN.GPU_ID[0])
torch.backends.cudnn.benchmark = True
net = CrowdCounter(ce_weights=train_set.wts).cuda()
if len(cfg.TRAIN.GPU_ID)>1:
net = torch.nn.DataParallel(net, device_ids=cfg.TRAIN.GPU_ID).cuda()
else:
net=net.cuda()
net.train()
optimizer = optim.Adam([
{'params': [param for name, param in net.named_parameters() if 'seg' in name], 'lr': cfg.TRAIN.SEG_LR},
{'params': [param for name, param in net.named_parameters() if 'seg' not in name], 'lr': cfg.TRAIN.LR}
])
i_tb = 0
for epoch in range(cfg.TRAIN.MAX_EPOCH):
_t['train time'].tic()
i_tb,model_path = train(train_loader, net, optimizer, epoch, i_tb)
_t['train time'].toc(average=False)
print 'train time of one epoch: {:.2f}s'.format(_t['train time'].diff)
if epoch%cfg.VAL.FREQ!=0:
continue
_t['val time'].tic()
validate(val_loader, model_path, epoch, restore_transform)
_t['val time'].toc(average=False)
print 'val time of one epoch: {:.2f}s'.format(_t['val time'].diff)
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), strict=False)
net.cuda()
net.eval()
f1 = plt.figure(1)
gts = []
preds = []
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 = 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
def test(file_list, model_path):
net = CrowdCounter(cfg.GPU_ID, 'RAZ_loc')
net.cuda()
net.load_state_dict(torch.load(model_path))
net.eval()
gts = []
preds = []
record = open('submmited_raz_loc_0.5-0512.txt', 'w+')
for infos in file_list:
filename = infos.split()[0]
imgname = os.path.join(dataRoot, 'img', filename + '.jpg')
img = Image.open(imgname)
ori_img = Image.open(os.path.join(ori_data, filename + '.jpg'))
ori_w,ori_h = ori_img.size
w,h = img.size
ratio_w = ori_w/w
ratio_h = ori_h/h
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_pred = F.softmax(crop_pred,dim=1).data[0,1,:,:]
crop_pred = crop_pred[None,:,:]
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 = F.avg_pool2d(pred_map,3,1,1)
maxm = F.max_pool2d(pred_map,3,1,1)
maxm = torch.eq(maxm,pred_map)
pred_map = maxm*pred_map
pred_map[pred_map<0.5]=0
pred_map = pred_map.bool().long()
pred_map = pred_map.cpu().data.numpy()[0,0,:,:]
ids = np.array(np.where(pred_map==1)) #y,x
ori_ids_y = ids[0,:]*ratio_h
ori_ids_x = ids[1,:]*ratio_w
ids = np.vstack((ori_ids_x,ori_ids_y)).astype(np.int16)#x,y
loc_str = ''
for i_id in range(ids.shape[1]):
loc_str = loc_str + ' ' + str(ids[0][i_id]) + ' ' + str(ids[1][i_id]) # x, y
pred = ids.shape[1]
print(f'{filename} {pred:d}{loc_str}', file=record)
print(f'{filename} {pred:d}')
record.close()
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()
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()
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(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]
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
# 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))
dst.paste(im2, (im1.width, 0))
return dst
cm = plt.get_cmap('jet')
file_folder = []
file_name = []
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()
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):
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 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()
class Trainer():
def __init__(self, dataloader, cfg_data, pwd):
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.net_name = cfg.NET
self.net = CrowdCounter(cfg.GPU_ID, self.net_name).cuda()
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.timer = {
'iter time': Timer(),
'train time': Timer(),
'val time': Timer()
}
self.epoch = 0
self.i_tb = 0
self.mae = 1e5
self.mse = 1e5
self.ep = 0 # record which epoch gets the better performance
if cfg.PRE_GCC:
self.net.load_state_dict(torch.load(cfg.PRE_GCC_MODEL))
self.train_loader, self.val_loader, self.restore_transform = dataloader
if cfg.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', resume=cfg.RESUME)
def forward(self):
# self.validate_V3()
for epoch in range(self.epoch, cfg.MAX_EPOCH):
self.epoch = epoch
if epoch > cfg.LR_DECAY_START:
self.scheduler.step()
# training
self.timer['train time'].tic()
self.train()
self.timer['train time'].toc(average=False)
print('train time: {:.2f}s'.format(self.timer['train time'].diff))
print('=' * 20)
# validation
if (epoch % cfg.VAL_FREQ == 0
and epoch > 0) or epoch > cfg.VAL_DENSE_START:
self.timer['val time'].tic()
if self.data_mode in [
'SHHA', 'SHHB', 'QNRF', 'UCF50', 'Mall', 'FDST'
]:
self.validate_V1(epoch)
elif self.data_mode is 'WE':
self.validate_V2()
elif self.data_mode is 'GCC':
self.validate_V3()
self.timer['val time'].toc(average=False)
print('val time: {:.2f}s'.format(self.timer['val time'].diff))
torch.save(self.net.cpu().state_dict(),
"./weights/Pre_model_{}.pth".format(epoch + 1))
self.net.cuda()
print('Best model:', self.ep, 'MAE:', self.mae, 'MSE:', self.mse)
def train(self): # training for all datasets
self.net.train()
for i, data in enumerate(self.train_loader, 0):
self.timer['iter time'].tic()
img, gt_map, img_p, gt_map_p = data
img = Variable(img).cuda()
gt_map = Variable(gt_map).cuda()
img_p = Variable(img_p).cuda()
gt_map_p = Variable(gt_map_p).cuda()
self.optimizer.zero_grad()
pred_map = self.net(img, gt_map, img_p)
loss = self.net.loss
loss.backward()
self.optimizer.step()
if (i + 1) % cfg.PRINT_FREQ == 0:
self.i_tb += 1
# self.writer.add_scalar('train_loss', loss.item(), self.i_tb)
self.timer['iter time'].toc(average=False)
print( '[ep %d][it %d][loss %.4f][lr %.4f][%.2fs]' % \
(self.epoch + 1, i + 1, loss.item(), self.optimizer.param_groups[0]['lr']*10000, self.timer['iter time'].diff) )
print(' [cnt: gt: %.1f pred: %.2f]' %
(gt_map[0].sum().data / self.cfg_data.LOG_PARA,
pred_map[0].sum().data / self.cfg_data.LOG_PARA))
def validate_V1(self,
epoch): # validate_V1 for SHHA, SHHB, UCF-QNRF, UCF50
self.net.eval()
losses = AverageMeter()
maes = AverageMeter()
mses = AverageMeter()
for vi, data in enumerate(self.val_loader, 0):
img, gt_map, img_p, gt_map_p = data
with torch.no_grad():
img = Variable(img).cuda()
gt_map = Variable(gt_map).cuda()
img_p = Variable(img_p).cuda()
gt_map_p = Variable(gt_map_p).cuda()
pred_map = self.net.forward(img, gt_map, img_p)
pred_map = pred_map.data.cpu().numpy()
gt_map = gt_map.data.cpu().numpy()
for i_img in range(pred_map.shape[0]):
pred_cnt = np.sum(pred_map[i_img]) / self.cfg_data.LOG_PARA
gt_count = np.sum(gt_map[i_img]) / self.cfg_data.LOG_PARA
losses.update(self.net.loss.item())
maes.update(abs(gt_count - pred_cnt))
mses.update((gt_count - pred_cnt) * (gt_count - pred_cnt))
# if vi==0:
# vis_results(self.exp_name, self.epoch, self.writer, self.restore_transform, img, pred_map, gt_map)
mae = maes.avg
mse = np.sqrt(mses.avg)
if mae < self.mae:
self.mae = mae
self.ep = epoch
if mse < self.mse:
self.mse = mse
loss = losses.avg
print('[ep %d][loss %.4f][MAE %.4f][MSE %.4f][lr %.4f]' % \
(self.epoch + 1, loss, mae, mse, self.optimizer.param_groups[0]['lr']*10000))
# self.writer.add_scalar('val_loss', loss, self.epoch + 1)
# self.writer.add_scalar('mae', mae, self.epoch + 1)
# self.writer.add_scalar('mse', mse, self.epoch + 1)
# self.train_record = update_model(self.net,self.optimizer,self.scheduler,self.epoch,self.i_tb,self.exp_path,self.exp_name, \
# [mae, mse, loss],self.train_record)
# print_summary(self.exp_name,[mae, mse, loss],self.train_record)
def validate_V2(self): # validate_V2 for WE
self.net.eval()
losses = AverageCategoryMeter(5)
maes = AverageCategoryMeter(5)
roi_mask = []
from datasets.WE.setting import cfg_data
from scipy import io as sio
for val_folder in cfg_data.VAL_FOLDER:
roi_mask.append(
sio.loadmat(
os.path.join(cfg_data.DATA_PATH, 'test',
val_folder + '_roi.mat'))['BW'])
for i_sub, i_loader in enumerate(self.val_loader, 0):
mask = roi_mask[i_sub]
for vi, data in enumerate(i_loader, 0):
img, gt_map = data
with torch.no_grad():
img = Variable(img).cuda()
gt_map = Variable(gt_map).cuda()
pred_map = self.net.forward(img, gt_map)
pred_map = pred_map.data.cpu().numpy()
gt_map = gt_map.data.cpu().numpy()
for i_img in range(pred_map.shape[0]):
pred_cnt = np.sum(
pred_map[i_img]) / self.cfg_data.LOG_PARA
gt_count = np.sum(
gt_map[i_img]) / self.cfg_data.LOG_PARA
losses.update(self.net.loss.item(), i_sub)
maes.update(abs(gt_count - pred_cnt), i_sub)
# if vi==0:
# vis_results(self.exp_name, self.epoch, self.writer, self.restore_transform, img, pred_map, gt_map)
mae = np.average(maes.avg)
loss = np.average(losses.avg)
# self.writer.add_scalar('val_loss', loss, self.epoch + 1)
# self.writer.add_scalar('mae', mae, self.epoch + 1)
# self.writer.add_scalar('mae_s1', maes.avg[0], self.epoch + 1)
# self.writer.add_scalar('mae_s2', maes.avg[1], self.epoch + 1)
# self.writer.add_scalar('mae_s3', maes.avg[2], self.epoch + 1)
# self.writer.add_scalar('mae_s4', maes.avg[3], self.epoch + 1)
# self.writer.add_scalar('mae_s5', maes.avg[4], self.epoch + 1)
self.train_record = update_model(self.net,self.optimizer,self.scheduler,self.epoch,self.i_tb,self.exp_path,self.exp_name, \
[mae, 0, loss],self.train_record)
# print_WE_summary(self.log_txt,self.epoch,[mae, 0, loss],self.train_record,maes)
def validate_V3(self): # validate_V3 for GCC
self.net.eval()
losses = AverageMeter()
maes = AverageMeter()
mses = AverageMeter()
c_maes = {
'level': AverageCategoryMeter(9),
'time': AverageCategoryMeter(8),
'weather': AverageCategoryMeter(7)
}
c_mses = {
'level': AverageCategoryMeter(9),
'time': AverageCategoryMeter(8),
'weather': AverageCategoryMeter(7)
}
for vi, data in enumerate(self.val_loader, 0):
img, gt_map, attributes_pt = data
with torch.no_grad():
img = Variable(img).cuda()
gt_map = Variable(gt_map).cuda()
pred_map = self.net.forward(img, gt_map)
pred_map = pred_map.data.cpu().numpy()
gt_map = gt_map.data.cpu().numpy()
for i_img in range(pred_map.shape[0]):
pred_cnt = np.sum(pred_map[i_img]) / self.cfg_data.LOG_PARA
gt_count = np.sum(gt_map[i_img]) / self.cfg_data.LOG_PARA
s_mae = abs(gt_count - pred_cnt)
s_mse = (gt_count - pred_cnt) * (gt_count - pred_cnt)
losses.update(self.net.loss.item())
maes.update(s_mae)
mses.update(s_mse)
attributes_pt = attributes_pt.squeeze()
c_maes['level'].update(s_mae, attributes_pt[i_img][0])
c_mses['level'].update(s_mse, attributes_pt[i_img][0])
c_maes['time'].update(s_mae, attributes_pt[i_img][1] / 3)
c_mses['time'].update(s_mse, attributes_pt[i_img][1] / 3)
c_maes['weather'].update(s_mae, attributes_pt[i_img][2])
c_mses['weather'].update(s_mse, attributes_pt[i_img][2])
# if vi==0:
# vis_results(self.exp_name, self.epoch, self.writer, self.restore_transform, img, pred_map, gt_map)
loss = losses.avg
mae = maes.avg
mse = np.sqrt(mses.avg)
# self.writer.add_scalar('val_loss', loss, self.epoch + 1)
# self.writer.add_scalar('mae', mae, self.epoch + 1)
# self.writer.add_scalar('mse', mse, self.epoch + 1)
self.train_record = update_model(self.net,self.optimizer,self.scheduler,self.epoch,self.i_tb,self.exp_path,self.exp_name, \
[mae, mse, loss],self.train_record)
print_GCC_summary(self.log_txt, self.epoch, [mae, mse, loss],
self.train_record, c_maes, c_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()
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, '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)