def evaluate_model(trained_model, data_loader, netname='CrowdCounter'):
if (netname is 'CrowdCounter'):
net = CrowdCounter()
elif (netname is 'MCNNNet'):
net = MCNNNet()
elif (netname is 'AmendNet'):
net = AmendNet()
elif (netname is 'CrowdCounter_MSCNN'):
net = CrowdCounter_MSCNN()
else:
raise (
'netname should be one of ["CrowdCounter", "MCNNNet", "AmendNet", "CrowdCounter_MSCNN"],\
but we got ', netname)
network.load_net(trained_model, net)
net.cuda()
net.eval()
mae = 0.0
mse = 0.0
for blob in data_loader:
im_data = blob['data']
gt_data = blob['gt_density']
density_map = net(im_data, gt_data)
density_map = density_map.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 / data_loader.get_num_samples()
mse = np.sqrt(mse / data_loader.get_num_samples())
return mae, mse
def evaluate_model(trained_model, data_loader):
net = CrowdCounter()
network.load_net(trained_model, net)
net.cuda()
net.eval()
mae = 0.0
mse = 0.0
for blob in data_loader:
im_data = blob['data']
gt_data = blob['gt_density']
density_map = net(im_data, gt_data)
density_map = density_map.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 = np.float(mae / data_loader.get_num_samples())
mse = np.sqrt(mse / data_loader.get_num_samples())
return mae, mse
model_path = './saved_models/mcnn_shtechA_110.h5'
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 = CrowdCounter()
trained_model = os.path.join(model_path)
network.load_net(trained_model, net)
net.cuda()
net.eval()
mae = 0.0
mse = 0.0
mape = 0.0
total_count = 0.0
#load test data
data_loader = ImageDataLoader(data_path,
gt_path,
shuffle=False,
gt_downsample=True,
pre_load=True)
for blob in data_loader:
im_data = blob['data']
gt_data = blob['gt_density']
把一个取值范围是[0,255]的PIL.Image或者shape为(H,W,C)的numpy.ndarray，转换成形状为[C,H,W]，取值范围是[0,1.0]的torch.FloadTensor
class torchvision.transforms.Normalize(mean, std)
给定均值：(R,G,B) 方差：（R，G，B），将会把Tensor正则化。即：Normalized_image=(image-mean)/std。
'''
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224,
0.225]),
])
net = CrowdCounter()
#defining the model
net = net.cuda()
#loading the trained weights
model_path = 'dataset/Shanghai/cmtl_shtechA_204.h5'
trained_model = os.path.join(model_path)
network.load_net(trained_model, net)
net.cuda()
net.eval()
data_loader = ImageDataLoader(
'dataset/Shanghai/part_A_final/test_data/images/',
'dataset/Shanghai/part_A_final/test_data/ground_truth',
shuffle=False,
gt_downsample=True,
pre_load=True)