def cal_mae(img_root,gt_dmap_root,model_param_path):
'''
Calculate the MAE of the test data.
img_root: the root of test image data.
gt_dmap_root: the root of test ground truth density-map data.
model_param_path: the path of specific mcnn parameters.
'''
model=CANNet()
model.load_state_dict(torch.load(model_param_path))
model.cuda()
dataset=CrowdDataset(img_root,gt_dmap_root,8,phase='test')
dataloader=torch.utils.data.DataLoader(dataset,batch_size=1,shuffle=False)
model.eval()
mae=0
with torch.no_grad():
for i,(img,gt_dmap) in enumerate(tqdm(dataloader)):
img=img.cuda()
gt_dmap=gt_dmap.cuda()
# forward propagation
et_dmap=model(img)
mae+=abs(et_dmap.data.sum()-gt_dmap.data.sum()).item()
del img,gt_dmap,et_dmap
print("model_param_path:"+model_param_path+" mae:"+str(mae/len(dataloader)))
@time: 6/27/19 2:37 PM
@desc: test videos for serials frame
'''
import torch
import cv2
import random
import matplotlib.pyplot as plt
import matplotlib.cm as CM
from torchvision import transforms
from cannet import CANNet
from my_dataset import CrowdDataset
model = CANNet()
model_param_path = './checkpoints/cvpr2019CAN_353model.pth'
model.load_state_dict(torch.load(model_param_path))
model.cuda()
model.eval()
torch.backends.cudnn.enabled = False
def read_img(img, gt_downsample):
img = img / 255
if len(img.shape) == 2:
img = img[:, :, np.newaxis]
img = np.concatenate((img, img, img), 2)
if gt_downsample > 1:
ds_rows = int(img.shape[0] // gt_downsample)
ds_cols = int(img.shape[1] // gt_downsample)
img = cv2.resize(img,
(ds_cols * gt_downsample, ds_rows * gt_downsample))
