def evaluate_model(checkpoint_path, data_loader):
print('Evaluation starts!!!')
net = MCNN()
net.load_state_dict(torch.load(checkpoint_path))
net.to(device)
net.eval()
mae = 0.0
mse = 0.0
for blob in data_loader:
im_data = blob['data'][0]
gt_data = blob['gt_density'][0]
#im_data = Variable(torch.from_numpy(im_data))
density_map = net(im_data)
density_map = density_map.data.cpu().numpy()
gt_data = gt_data.cpu().numpy()
gt_count = np.sum(gt_data)
et_count = np.sum(density_map)
mae += abs(gt_count-et_count)
mse += ((gt_count-et_count)*(gt_count-et_count))
mae = mae/len(val_dataset)
mse = np.sqrt(mse/len(val_dataset))
return mae,mse
output_dir = './output/'
model_name = os.path.basename(model_path).split('.')[0]
file_results = os.path.join(output_dir,'results_' + model_name + '_.txt')
if not os.path.exists(output_dir):
os.mkdir(output_dir)
output_dir = os.path.join(output_dir, 'density_maps_' + model_name)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
net = MCNN()
net.load_state_dict(torch.load(model_path))
net.to(device)
net.eval()
mae = 0.0
mse = 0.0
#load test data
test_dataset = Shanghai_Dataset(data_path, gt_path, gt_downsample=True, pre_load=True)
test_data_loader = DataLoader(test_dataset, batch_size=1, shuffle=False, num_workers=0)
for it,blob in enumerate(test_data_loader):
print('{}/{}'.format(it,len(test_dataset)))
im_data = blob['data'][0]
gt_data = blob['gt_density'][0]
density_map = net(im_data)
density_map = density_map.data.cpu().numpy()
gt_data = gt_data.data.cpu().numpy()
gt_count = np.sum(gt_data)