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import torch
import numpy as np
from models import spinal_net
import decoder
import os
from dataset import BaseDataset
import time
import cobb_evaluate
def apply_mask(image, mask, alpha=0.5):
"""Apply the given mask to the image.
"""
color = np.random.rand(3)
for c in range(3):
image[:, :, c] = np.where(mask == 1,
image[:, :, c] *
(1 - alpha) + alpha * color[c] * 255,
image[:, :, c])
return image
class Network(object):
def __init__(self, args):
torch.manual_seed(317)
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
heads = {'hm': args.num_classes, # cen, tl, tr, bl, br
'reg': 2*args.num_classes,
'wh': 2*4,}
self.model = spinal_net.SpineNet(heads=heads,
pretrained=True,
down_ratio=args.down_ratio,
final_kernel=1,
head_conv=256)
self.num_classes = args.num_classes
self.decoder = decoder.DecDecoder(K=args.K, conf_thresh=args.conf_thresh)
self.dataset = {'spinal': BaseDataset}
def load_model(self, model, resume):
checkpoint = torch.load(resume, map_location=lambda storage, loc: storage)
print('loaded weights from {}, epoch {}'.format(resume, checkpoint['epoch']))
state_dict_ = checkpoint['state_dict']
model.load_state_dict(state_dict_, strict=False)
return model
def eval(self, args, save):
save_path = 'weights_'+args.dataset
self.model = self.load_model(self.model, os.path.join(save_path, args.resume))
self.model = self.model.to(self.device)
self.model.eval()
dataset_module = self.dataset[args.dataset]
dsets = dataset_module(data_dir=args.data_dir,
phase='test',
input_h=args.input_h,
input_w=args.input_w,
down_ratio=args.down_ratio)
data_loader = torch.utils.data.DataLoader(dsets,
batch_size=1,
shuffle=False,
num_workers=1,
pin_memory=True)
total_time = []
landmark_dist = []
pr_cobb_angles = []
gt_cobb_angles = []
for cnt, data_dict in enumerate(data_loader):
begin_time = time.time()
images = data_dict['images'][0]
img_id = data_dict['img_id'][0]
images = images.to('cuda')
print('processing {}/{} image ...'.format(cnt, len(data_loader)))
with torch.no_grad():
output = self.model(images)
hm = output['hm']
wh = output['wh']
reg = output['reg']
torch.cuda.synchronize(self.device)
pts2 = self.decoder.ctdet_decode(hm, wh, reg) # 17, 11
pts0 = pts2.copy()
pts0[:,:10] *= args.down_ratio
x_index = range(0,10,2)
y_index = range(1,10,2)
ori_image = dsets.load_image(dsets.img_ids.index(img_id)).copy()
h,w,c = ori_image.shape
pts0[:, x_index] = pts0[:, x_index]/args.input_w*w
pts0[:, y_index] = pts0[:, y_index]/args.input_h*h
# sort the y axis
sort_ind = np.argsort(pts0[:,1])
pts0 = pts0[sort_ind]
pr_landmarks = []
for i, pt in enumerate(pts0):
pr_landmarks.append(pt[2:4])
pr_landmarks.append(pt[4:6])
pr_landmarks.append(pt[6:8])
pr_landmarks.append(pt[8:10])
pr_landmarks = np.asarray(pr_landmarks, np.float32) #[68, 2]
end_time = time.time()
total_time.append(end_time-begin_time)
gt_landmarks = dsets.load_gt_pts(dsets.load_annoFolder(img_id))
for pr_pt, gt_pt in zip(pr_landmarks, gt_landmarks):
landmark_dist.append(np.sqrt((pr_pt[0]-gt_pt[0])**2+(pr_pt[1]-gt_pt[1])**2))
pr_cobb_angles.append(cobb_evaluate.cobb_angle_calc(pr_landmarks, ori_image))
gt_cobb_angles.append(cobb_evaluate.cobb_angle_calc(gt_landmarks, ori_image))
pr_cobb_angles = np.asarray(pr_cobb_angles, np.float32)
gt_cobb_angles = np.asarray(gt_cobb_angles, np.float32)
out_abs = abs(gt_cobb_angles - pr_cobb_angles)
out_add = gt_cobb_angles + pr_cobb_angles
term1 = np.sum(out_abs, axis=1)
term2 = np.sum(out_add, axis=1)
SMAPE = np.mean(term1 / term2 * 100)
print('mse of landmarkds is {}'.format(np.mean(landmark_dist)))
print('SMAPE is {}'.format(SMAPE))
total_time = total_time[1:]
print('avg time is {}'.format(np.mean(total_time)))
print('FPS is {}'.format(1./np.mean(total_time)))
def SMAPE_single_angle(self, gt_cobb_angles, pr_cobb_angles):
out_abs = abs(gt_cobb_angles - pr_cobb_angles)
out_add = gt_cobb_angles + pr_cobb_angles
term1 = out_abs
term2 = out_add
term2[term2==0] += 1e-5
SMAPE = np.mean(term1 / term2 * 100)
return SMAPE
def eval_three_angles(self, args, save):
save_path = 'weights_'+args.dataset
self.model = self.load_model(self.model, os.path.join(save_path, args.resume))
self.model = self.model.to(self.device)
self.model.eval()
dataset_module = self.dataset[args.dataset]
dsets = dataset_module(data_dir=args.data_dir,
phase='test',
input_h=args.input_h,
input_w=args.input_w,
down_ratio=args.down_ratio)
data_loader = torch.utils.data.DataLoader(dsets,
batch_size=1,
shuffle=False,
num_workers=1,
pin_memory=True)
total_time = []
landmark_dist = []
pr_cobb_angles = []
gt_cobb_angles = []
for cnt, data_dict in enumerate(data_loader):
begin_time = time.time()
images = data_dict['images'][0]
img_id = data_dict['img_id'][0]
images = images.to('cuda')
print('processing {}/{} image ...'.format(cnt, len(data_loader)))
with torch.no_grad():
output = self.model(images)
hm = output['hm']
wh = output['wh']
reg = output['reg']
torch.cuda.synchronize(self.device)
pts2 = self.decoder.ctdet_decode(hm, wh, reg) # 17, 11
pts0 = pts2.copy()
pts0[:,:10] *= args.down_ratio
x_index = range(0,10,2)
y_index = range(1,10,2)
ori_image = dsets.load_image(dsets.img_ids.index(img_id)).copy()
h,w,c = ori_image.shape
pts0[:, x_index] = pts0[:, x_index]/args.input_w*w
pts0[:, y_index] = pts0[:, y_index]/args.input_h*h
# sort the y axis
sort_ind = np.argsort(pts0[:,1])
pts0 = pts0[sort_ind]
pr_landmarks = []
for i, pt in enumerate(pts0):
pr_landmarks.append(pt[2:4])
pr_landmarks.append(pt[4:6])
pr_landmarks.append(pt[6:8])
pr_landmarks.append(pt[8:10])
pr_landmarks = np.asarray(pr_landmarks, np.float32) #[68, 2]
end_time = time.time()
total_time.append(end_time-begin_time)
gt_landmarks = dsets.load_gt_pts(dsets.load_annoFolder(img_id))
for pr_pt, gt_pt in zip(pr_landmarks, gt_landmarks):
landmark_dist.append(np.sqrt((pr_pt[0]-gt_pt[0])**2+(pr_pt[1]-gt_pt[1])**2))
pr_cobb_angles.append(cobb_evaluate.cobb_angle_calc(pr_landmarks, ori_image))
gt_cobb_angles.append(cobb_evaluate.cobb_angle_calc(gt_landmarks, ori_image))
pr_cobb_angles = np.asarray(pr_cobb_angles, np.float32)
gt_cobb_angles = np.asarray(gt_cobb_angles, np.float32)
print('SMAPE1 is {}'.format(self.SMAPE_single_angle(gt_cobb_angles[:,0], pr_cobb_angles[:,0])))
print('SMAPE2 is {}'.format(self.SMAPE_single_angle(gt_cobb_angles[:,1], pr_cobb_angles[:,1])))
print('SMAPE3 is {}'.format(self.SMAPE_single_angle(gt_cobb_angles[:,2], pr_cobb_angles[:,2])))
print('mse of landmarkds is {}'.format(np.mean(landmark_dist)))
total_time = total_time[1:]
print('avg time is {}'.format(np.mean(total_time)))
print('FPS is {}'.format(1./np.mean(total_time)))
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