def test2(clothes_type,net1_path, net2_path):
global predicts
test_sel = 'all'
num_keypoints = len(FashionData.class_points[clothes_type])
normalize = transforms.Normalize(mean=FashionData.mean_std[clothes_type]['mean'],
std=FashionData.mean_std[clothes_type]['std'])
transform = transforms.Compose(
[transforms.ToTensor(), normalize])
testSet = FashionData.fashionSet(selected=test_sel, classname=clothes_type,
root='/home/wuxiaodong/fa/test/Images/',
train='test', transform=transform)
testloader = torch.utils.data.DataLoader(testSet, batch_size=40, shuffle=False,num_workers=2)
net1 = kres152.get_kres152(state_dict=torch.load(net1_path, map_location=lambda storage, loc: storage),num_keypoints=num_keypoints)
net2 = kres152.get_kres152(state_dict=torch.load(net2_path, map_location=lambda storage, loc: storage),num_keypoints=num_keypoints)
net1.cuda()
net2.cuda()
net1.eval()
net2.eval()
for batch_idx, (data, label) in enumerate(testloader):
batch_num = label.size()[0]
batch_size = testloader.batch_size
data = Variable(data.cuda(), volatile=True)
output1 = net1(data)[:, :2*num_keypoints]
output2 = net2(data)[:, :2*num_keypoints]
output = output1 + output2
output = output.cpu().data
for i in range(batch_num):
image_id = 'Images/{}/{}'.format(clothes_type, testSet.selected_imgs[batch_idx*batch_size+i])
predict = {}
for j in range(num_keypoints):
point_name = FashionData.class_points[clothes_type][j]
pre_new_x = output[i, 2*j]
pre_new_y = output[i, 2*j+1]
ori_resize = label[i, 0]
ori_scale = label[i, 1]
ori_left_pad = label[i, 2]
ori_top_pad = label[i, 3]
ori_x = int(((pre_new_x+0.5)*ori_resize - ori_left_pad)/ori_scale)
ori_y = int(((pre_new_y+0.5)*ori_resize - ori_top_pad)/ori_scale)
width = int((ori_resize-ori_left_pad)/ori_scale)
height = int((ori_resize-ori_top_pad)/ori_scale)
if ori_x<0 or ori_x>width:
ori_x = int(width/2)
if ori_y<0 or ori_y>height:
ori_y = int(height/2)
predict[point_name] = '{}_{}_{}'.format(ori_x, ori_y, 1)
predicts[image_id]=predict
import torchvision.transforms as transforms
from torch.autograd import Variable
import kres152
import FashionData
from train import *
# losswei[1] must be 0
losswei = [1, 0]
viswei = [0, 0, 0]
LR = 0.01
postfix = 'stage2_dense161_'
net1_path = "/home/wuxiaodong/fa/models/resnet152/snapshots/dense161__epoch280.pth"
net1 = kres152.get_kres152(state_dict=torch.load(net1_path),
num_keypoints=num_keypoints)
net1.cuda()
net2 = kres152.get_kres152(state_dict=torch.load(net1_path),
num_keypoints=num_keypoints)
net2.cuda()
criterion = kres152.KEYPointLoss(weight=losswei,
vis_weight=viswei,
num_keypoints=num_keypoints).cuda()
trainloader = torch.utils.data.DataLoader(trainSet,
batch_size=20,
shuffle=True,
num_workers=2)
valloader = torch.utils.data.DataLoader(valSet,
net.eval()
for batch_idx, (data, label) in enumerate(valloader):
data = Variable(data.cuda(), volatile=True)
label = Variable(label.cuda())
output = net(data)
# loss = criterion(output, label)
loss = criterion(output[:, :2 * num_keypoints],
label[:, :2 * num_keypoints])
loss_sum += loss.data[0]
del loss, output, label
print('test loss = {}'.format(loss_sum / batch_idx))
if __name__ == '__main__':
net = kres152.get_kres152(
num_keypoints=len(FashionData.class_points[clothes_type]))
net.cuda()
criterion = kres152.KEYPointLoss(
weight=losswei,
vis_weight=viswei,
num_keypoints=len(FashionData.class_points[clothes_type])).cuda()
for epoch in range(300):
if epoch == 10:
losswei = [1, 0.01]
viswei = [10, 10, 1]
LR = LR * 0.1
if epoch == 100:
losswei = [1, 0.001]
viswei = [10, 10, 1]
LR = LR * 0.1
shuffle=False,
num_workers=2)
state = {}
state['loss_window'] = [0] * 200
state['total_batch_num'] = 0
state['epoch'] = 0
state['loss_avg'] = 0.0
state['ne'] = 0.0
state['test_loss'] = 0.0
net1_path = "/home/wuxiaodong/fa/models/trousers/snapshots/trousers_e2estage2_net1_epoch60.pth"
net2_path = "/home/wuxiaodong/fa/models/trousers/snapshots/trousers_e2estage2_net2_epoch60.pth"
net1 = kres152.get_kres152(state_dict=torch.load(
net1_path, map_location=lambda storage, loc: storage),
num_keypoints=num_keypoints)
net2 = kres152.get_kres152(state_dict=torch.load(
net2_path, map_location=lambda storage, loc: storage),
num_keypoints=num_keypoints)
net1.cuda()
net2.cuda()
def train2():
net1.train()
net2.train()
optimizer = torch.optim.SGD([
{
'params': net2.parameters(),
'lr': LR