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test.py
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test.py
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from __future__ import division
import warnings
from Networks.HR_Net.seg_hrnet import get_seg_model
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, transforms
import dataset
import math
from image import *
from utils import *
import logging
import nni
from nni.utils import merge_parameter
from config import return_args, args
warnings.filterwarnings('ignore')
setup_seed(args.seed)
logger = logging.getLogger('mnist_AutoML')
def main(args):
if args['dataset'] == 'ShanghaiA':
test_file = './npydata/ShanghaiA_test.npy'
elif args['dataset'] == 'ShanghaiB':
test_file = './npydata/ShanghaiB_test.npy'
elif args['dataset'] == 'UCF_QNRF':
test_file = './npydata/qnrf_test.npy'
elif args['dataset'] == 'JHU':
test_file = './npydata/jhu_test.npy'
elif args['dataset'] == 'NWPU':
test_file = './npydata/nwpu_val.npy'
with open(test_file, 'rb') as outfile:
val_list = np.load(outfile).tolist()
os.environ['CUDA_VISIBLE_DEVICES'] = args['gpu_id']
model = get_seg_model()
model = nn.DataParallel(model, device_ids=[0])
model = model.cuda()
optimizer = torch.optim.Adam(
[
{'params': model.parameters(), 'lr': args['lr']},
])
print(args['pre'])
if not os.path.exists(args['save_path']):
os.makedirs(args['save_path'])
if args['pre']:
if os.path.isfile(args['pre']):
print("=> loading checkpoint '{}'".format(args['pre']))
checkpoint = torch.load(args['pre'])
model.load_state_dict(checkpoint['state_dict'], strict=False)
args['start_epoch'] = checkpoint['epoch']
args['best_pred'] = checkpoint['best_prec1']
else:
print("=> no checkpoint found at '{}'".format(args['pre']))
torch.set_num_threads(args['workers'])
print(args['best_pred'], args['start_epoch'])
if args['preload_data'] == True:
test_data = pre_data(val_list, args, train=False)
else:
test_data = val_list
'''inference '''
prec1, visi = validate(test_data, model, args)
is_best = prec1 < args['best_pred']
args['best_pred'] = min(prec1, args['best_pred'])
print('\nThe visualizations are provided in ', args['save_path'])
save_checkpoint({
'arch': args['pre'],
'state_dict': model.state_dict(),
'best_prec1': args['best_pred'],
'optimizer': optimizer.state_dict(),
}, visi, is_best, args['save_path'])
def pre_data(train_list, args, train):
print("Pre_load dataset ......")
data_keys = {}
count = 0
for j in range(len(train_list)):
Img_path = train_list[j]
fname = os.path.basename(Img_path)
# print(fname)
img, fidt_map, kpoint = load_data_fidt(Img_path, args, train)
blob = {}
blob['img'] = img
blob['kpoint'] = np.array(kpoint)
blob['fidt_map'] = fidt_map
blob['fname'] = fname
data_keys[count] = blob
count += 1
return data_keys
def validate(Pre_data, model, args):
print('begin test')
batch_size = 1
test_loader = torch.utils.data.DataLoader(
dataset.listDataset(Pre_data, args['save_path'],
shuffle=False,
transform=transforms.Compose([
transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
]),
args=args, train=False),
batch_size=1)
model.eval()
mae = 0.0
mse = 0.0
visi = []
index = 0
if not os.path.exists('./local_eval/point_files'):
os.makedirs('./local_eval/point_files')
'''output coordinates'''
f_loc = open("./local_eval/point_files/A_localization.txt", "w+")
for i, (fname, img, fidt_map, kpoint) in enumerate(test_loader):
count = 0
img = img.cuda()
if len(img.shape) == 5:
img = img.squeeze(0)
if len(fidt_map.shape) == 5:
fidt_map = fidt_map.squeeze(0)
if len(img.shape) == 3:
img = img.unsqueeze(0)
if len(fidt_map.shape) == 3:
fidt_map = fidt_map.unsqueeze(0)
with torch.no_grad():
d6 = model(img)
'''return counting and coordinates'''
count, pred_kpoint, f_loc = LMDS_counting(d6, i + 1, f_loc, args)
point_map = generate_point_map(pred_kpoint, f_loc, rate=1)
if args['visual'] == True:
if not os.path.exists(args['save_path'] + '_box/'):
os.makedirs(args['save_path'] + '_box/')
ori_img, box_img = generate_bounding_boxes(pred_kpoint, fname)
show_fidt = show_map(d6.data.cpu().numpy())
gt_show = show_map(fidt_map.data.cpu().numpy())
res = np.hstack((ori_img, gt_show, show_fidt, point_map, box_img))
cv2.imwrite(args['save_path'] + '_box/' + fname[0], res)
gt_count = torch.sum(kpoint).item()
mae += abs(gt_count - count)
mse += abs(gt_count - count) * abs(gt_count - count)
if i % 1 == 0:
print('{fname} Gt {gt:.2f} Pred {pred}'.format(fname=fname[0], gt=gt_count, pred=count))
visi.append(
[img.data.cpu().numpy(), d6.data.cpu().numpy(), fidt_map.data.cpu().numpy(),
fname])
index += 1
mae = mae * 1.0 / (len(test_loader) * batch_size)
mse = math.sqrt(mse / (len(test_loader)) * batch_size)
nni.report_intermediate_result(mae)
print(' \n* MAE {mae:.3f}\n'.format(mae=mae), '* MSE {mse:.3f}'.format(mse=mse))
return mae, visi
def LMDS_counting(input, w_fname, f_loc, args):
input_max = torch.max(input).item()
''' find local maxima'''
if args['dataset'] == 'UCF_QNRF':
#input = nn.functional.avg_pool2d(input, (3, 3), stride=1, padding=1)
keep = nn.functional.max_pool2d(input, (3, 3), stride=1, padding=1)
else:
keep = nn.functional.max_pool2d(input, (3, 3), stride=1, padding=1)
keep = (keep == input).float()
input = keep * input
'''set the pixel valur of local maxima as 1 for counting'''
input[input < 100.0 / 255.0 * input_max] = 0
input[input > 0] = 1
''' negative sample'''
if input_max < 0.1:
input = input * 0
count = int(torch.sum(input).item())
kpoint = input.data.squeeze(0).squeeze(0).cpu().numpy()
f_loc.write('{} {} '.format(w_fname, count))
return count, kpoint, f_loc
def generate_point_map(kpoint, f_loc, rate=1):
'''obtain the location coordinates'''
pred_coor = np.nonzero(kpoint)
point_map = np.zeros((int(kpoint.shape[0] * rate), int(kpoint.shape[1] * rate), 3), dtype="uint8") + 255 # 22
# count = len(pred_coor[0])
coord_list = []
for i in range(0, len(pred_coor[0])):
h = int(pred_coor[0][i] * rate)
w = int(pred_coor[1][i] * rate)
coord_list.append([w, h])
cv2.circle(point_map, (w, h), 2, (0, 0, 0), -1)
for data in coord_list:
f_loc.write('{} {} '.format(math.floor(data[0]), math.floor(data[1])))
f_loc.write('\n')
return point_map
def generate_bounding_boxes(kpoint, fname):
'''change the data path'''
Img_data = cv2.imread(
'/home/dkliang/projects/synchronous/dataset/ShanghaiTech/part_A_final/test_data/images/' + fname[0])
ori_Img_data = Img_data.copy()
'''generate sigma'''
pts = np.array(list(zip(np.nonzero(kpoint)[1], np.nonzero(kpoint)[0])))
leafsize = 2048
# build kdtree
tree = scipy.spatial.KDTree(pts.copy(), leafsize=leafsize)
distances, locations = tree.query(pts, k=4)
for index, pt in enumerate(pts):
pt2d = np.zeros(kpoint.shape, dtype=np.float32)
pt2d[pt[1], pt[0]] = 1.
if np.sum(kpoint) > 1:
sigma = (distances[index][1] + distances[index][2] + distances[index][3]) * 0.1
else:
sigma = np.average(np.array(kpoint.shape)) / 2. / 2. # case: 1 point
sigma = min(sigma, min(Img_data.shape[0], Img_data.shape[1]) * 0.05)
if sigma < 6:
t = 2
else:
t = 2
Img_data = cv2.rectangle(Img_data, (int(pt[0] - sigma), int(pt[1] - sigma)),
(int(pt[0] + sigma), int(pt[1] + sigma)), (0, 255, 0), t)
return ori_Img_data, Img_data
def show_map(input):
input[input < 0] = 0
input = input[0][0]
fidt_map1 = input
fidt_map1 = fidt_map1 / np.max(fidt_map1) * 255
fidt_map1 = fidt_map1.astype(np.uint8)
fidt_map1 = cv2.applyColorMap(fidt_map1, 2)
return fidt_map1
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
if __name__ == '__main__':
tuner_params = nni.get_next_parameter()
logger.debug(tuner_params)
params = vars(merge_parameter(return_args, tuner_params))
print(params)
main(params)