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visualize.py
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visualize.py
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import torch
import torch.nn as nn
import torchvision
import numpy as np
import torch.nn.functional as F
from functools import partial
import math
from facenet_pytorch import InceptionResnetV1
import torchvision.models as models
import functools
import torchvision.models as models
import random
from Models.BaseModel import BaseModel
#from Models.resnet import *
class Generator(torch.nn.Module):
def __init__(self,d_model):
super(Generator, self).__init__()
self.d_model = d_model
up = nn.Upsample(scale_factor=2, mode='bilinear')
dconv1 = nn.Conv2d(self.d_model, self.d_model//2, 3, 1, 1) # 2*2 512
dconv2 = nn.Conv2d(self.d_model//2, self.d_model//2, 3, 1, 1) # 4*4 256
dconv3 = nn.Conv2d(self.d_model//2, self.d_model//2, 3, 1, 1) # 16*16 256
dconv4 = nn.Conv2d(self.d_model//2, self.d_model//2, 3, 1, 1) # 32 * 32 * 256
dconv5 = nn.Conv2d(self.d_model//2, self.d_model//4, 3, 1, 1) # 64 * 64 *128
#dconv6 = nn.Conv2d(self.d_model//4, self.d_model//8, 3, 1, 1) # 128 * 128 *32
dconv7 = nn.Conv2d(self.d_model//4, 3, 3, 1, 1)
# batch_norm2_1 = nn.BatchNorm2d(self.d_model//8)
batch_norm4_1 = nn.BatchNorm2d(self.d_model//4)
batch_norm8_4 = nn.BatchNorm2d(self.d_model//2)
batch_norm8_5 = nn.BatchNorm2d(self.d_model//2)
batch_norm8_6 = nn.BatchNorm2d(self.d_model//2)
batch_norm8_7 = nn.BatchNorm2d(self.d_model//2)
relu = nn.ReLU()
tanh = nn.Tanh()
self.model = torch.nn.Sequential(relu, up, dconv1, batch_norm8_4, \
relu, up, dconv2, batch_norm8_5, relu,
up, dconv3, batch_norm8_6, relu, up, dconv4,
batch_norm8_7, relu, up, dconv5, batch_norm4_1,
relu, up, dconv7, tanh)
def forward(self,x):
x = x.unsqueeze(dim=2).unsqueeze(dim=3)
out = self.model(x)
return out
class selfattention(nn.Module):
def __init__(self, inplanes):
super(selfattention, self).__init__()
self.interchannel = inplanes
self.inplane = inplanes
self.g = nn.Conv2d(inplanes, inplanes, kernel_size=1, stride=1, padding=0)
self.theta = nn.Conv2d(inplanes, self.interchannel, kernel_size=1, stride=1, padding=0)
self.phi = nn.Conv2d(inplanes, self.interchannel, kernel_size=1, stride=1, padding=0)
self.act = nn.LeakyReLU(0.1)
def forward(self, x):
b, c, h, w = x.size()
g_y = self.g(x).view(b, c, -1) # BXcXN
theta_x = self.theta(x).view(b, self.interchannel, -1)
theta_x = F.softmax(theta_x, dim=-1) # softmax on N
theta_x = theta_x.permute(0, 2, 1).contiguous() # BXNXC'
phi_x = self.phi(x).view(b, self.interchannel, -1) # BXC'XN
similarity = torch.bmm(phi_x, theta_x) # BXc'Xc'
g_y = F.softmax(g_y, dim=1)
attention = torch.bmm(similarity, g_y) # BXCXN
attention = attention.view(b, c, h, w).contiguous()
y = self.act(x + attention)
return y
class BasicBlockNormal(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlockNormal, self).__init__()
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.conv1 = nn.Conv2d(inplanes,planes,3,stride,1)
self.relu = nn.LeakyReLU(negative_slope=0.1,inplace=True)
self.conv2 = nn.Conv2d(planes,planes,3,1,1)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.relu(out)
out = self.conv2(out)
#out = self.relu(out)
if self.downsample is not None:
identity = self.downsample(x)
out = (out + identity)
return self.relu(out)
class FaceCycleBackbone(torch.nn.Module):
def __init__(self):
super(FaceCycleBackbone, self).__init__()
self.conv1 = nn.Sequential(nn.Conv2d(3, 64, 7, 2, 3, bias=True),
nn.LeakyReLU(negative_slope=0.1),
nn.Conv2d(64, 64, 3, 1, 1, bias=True),
nn.LeakyReLU(negative_slope=0.1))
self.layer1 = nn.Sequential(nn.Conv2d(64, 64, 3, 1, 1, bias=True),
nn.LeakyReLU(negative_slope=0.1),
selfattention(64),
nn.Conv2d(64, 64, 3, 1, 1, bias=True),
nn.LeakyReLU(negative_slope=0.1)) # 64
self.layer2_1 = nn.Sequential(nn.Conv2d(64, 128, 3, 2, 1, bias=True),
nn.LeakyReLU(negative_slope=0.1),
selfattention(128),
nn.Conv2d(128, 128, 3, 1, 1, bias=True),
nn.LeakyReLU(negative_slope=0.1), ) # 64
self.resblock1 = BasicBlockNormal(128, 128)
self.resblock2 = BasicBlockNormal(128, 128)
self.layer2_2 = nn.Sequential(nn.Conv2d(128, 128, 3, 2, 1, bias=True),
nn.LeakyReLU(negative_slope=0.1),
nn.Conv2d(128, 128, 3, 1, 1, bias=True),
nn.LeakyReLU(negative_slope=0.1), ) # 64
self.layer3_1 = nn.Sequential(nn.Conv2d(128, 256, 3, 2, 1, bias=True),
nn.LeakyReLU(negative_slope=0.1),
nn.Conv2d(256, 256, 3, 1, 1, bias=True),
nn.LeakyReLU(negative_slope=0.1), ) # 64
self.layer3_2 = nn.Sequential(nn.Conv2d(256, 256, 3, 1, 1, bias=True), # stride 2 for 128x128
nn.LeakyReLU(negative_slope=0.1),
nn.Conv2d(256, 128, 3, 1, 1, bias=True),
nn.LeakyReLU(negative_slope=0.1)) # 64
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
if m.bias is not None:
m.bias.data.zero_()
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
elif isinstance(m, nn.Linear):
m.weight.data.normal_(0, 0.01)
m.bias.data.zero_()
def forward(self, x):
#encoder
'''
:param x: [batch,3,64,64]
:return:
'''
out_1 = self.conv1(x) # [batch,64,32,32]
out_1 = self.layer1(out_1) # [batch,64,32,32]
out_2 = self.layer2_1(out_1) # [batch,128,16,16]
out_2 = self.resblock1(out_2) # [batch,128,16,16]
out_2 = self.resblock2(out_2) # [batch,128,16,16]
out_2 = self.layer2_2(out_2) # [batch,128,8,8]
out_3 = self.layer3_1(out_2) # [batch,256,4,4]
out_3 = self.layer3_2(out_3) # [batch,128,4,4]
out_3 = out_3.view(x.size()[0],-1) # [batch,2048]
#print(out_3.size())
# expcode = self.fc(out_3) # [batch,256]
#out_3 = self.fc(out_3)
#exp_fea = self.exp_fc(out_3)
#pose_fea = self.pose_fc(out_3)
#fea = torch.cat([exp_fea,pose_fea],dim=1)
#fea = exp_fea + pose_fea
#return fea
return out_3
#return exp_fea
#return pose_fea
#return exp_fea,pose_fea
#return expcode
class ExpPoseModel(nn.Module):
def __init__(self):
super(ExpPoseModel, self).__init__()
self.encoder = FaceCycleBackbone()
#self.encoder = resnet18()
#self.encoder = resnet34()
#self.encoder = resnet50()
self.exp_fc = nn.Sequential(nn.Linear(2048, 2048),
nn.ReLU(),
nn.Linear(2048,512),
nn.BatchNorm1d(512))
self.pose_fc = nn.Sequential(nn.Linear(2048, 2048),
nn.ReLU(),
nn.Linear(2048,512),
nn.BatchNorm1d(512))
self.decoder = Generator(d_model=512)
def forward(self,exp_img,normal_img,flip_img):
#exp_fea = self.exp_encoder_fc(self.exp_encoder(exp_img))
#pose_fea = self.pose_encoder_fc(self.pose_encoder(pose_img))
fea = self.encoder(exp_img)
exp_fea = self.exp_fc(fea)
pose_fea = self.pose_fc(fea)
normal_fea = self.encoder(normal_img)
flip_fea = self.encoder(flip_img)
normal_exp_fea_fc = self.exp_fc(normal_fea)
flip_exp_fea_fc = self.exp_fc(flip_fea)
normal_pose_fea_fc = self.pose_fc(normal_fea)
flip_pose_fea_fc = self.pose_fc(flip_fea)
########### test
recon_normal_exp_flip_pose_fea = F.normalize(normal_exp_fea_fc + flip_pose_fea_fc, dim=1)
recon_flip_exp_normal_pose_fea = F.normalize(flip_exp_fea_fc+normal_pose_fea_fc, dim=1)
recon_normal_exp_normal_exp_fea = F.normalize(normal_exp_fea_fc + normal_exp_fea_fc, dim=1)
recon_normal_pose_normal_pose_fea = F.normalize(normal_pose_fea_fc + normal_pose_fea_fc,dim=1)
recon_flip_pose_flip_pose_fea = F.normalize(flip_pose_fea_fc + flip_pose_fea_fc,dim=1)
recon_normal_exp_flip_pose_img = self.decoder(recon_normal_exp_flip_pose_fea)
recon_flip_exp_normal_pose_img = self.decoder(recon_flip_exp_normal_pose_fea)
recon_normal_exp_normal_exp_img = self.decoder(recon_normal_exp_normal_exp_fea)
recon_normal_pose_normal_pose_img = self.decoder(recon_normal_pose_normal_pose_fea)
recon_flip_pose_flip_pose_img = self.decoder(recon_flip_pose_flip_pose_fea)
#
return recon_normal_exp_flip_pose_img,recon_flip_exp_normal_pose_img,recon_normal_exp_normal_exp_img,recon_normal_pose_normal_pose_img,recon_flip_pose_flip_pose_img
class DiffLoss(nn.Module):
def __init__(self):
super(DiffLoss, self).__init__()
def forward(self, input1, input2):
# Zero mean
input1_mean = torch.mean(input1, dim=0, keepdims=True)
input2_mean = torch.mean(input2, dim=0, keepdims=True)
input1 = input1 - input1_mean
input2 = input2 - input2_mean
input1_l2_norm = torch.norm(input1, p=2, dim=1, keepdim=True).detach()
input1_l2 = input1.div(input1_l2_norm.expand_as(input1) + 1e-6)
input2_l2_norm = torch.norm(input2, p=2, dim=1, keepdim=True).detach()
input2_l2 = input2.div(input2_l2_norm.expand_as(input2) + 1e-6)
#diff_loss = torch.mean((input1_l2.t().mm(input2_l2)).pow(2))
diff_loss = torch.mean((input1_l2 * input2_l2).sum(dim=1).pow(2))
return diff_loss
class ExpPose(BaseModel):
def __init__(self,config):
BaseModel.__init__(self,config)
self.temperature = config['T']
self.batch_size = config['batch_size']
self.neg_alpha = config['neg_alpha']
self.pose_alpha = config['pose_alpha']
if not config['eval'] and not config['t_sne']:
self.model = ExpPoseModel()
else:
#face_cycle_backbone = FaceCycleBackbone()
#face_cycle_backbone = SimCLRNetwork()
face_cycle_backbone = FaceCycleBackbone()
self.model = face_cycle_backbone.cuda()
if config['continue_train']:
self.model = torch.nn.DataParallel(self.model).cuda()
self.model.load_state_dict(torch.load(config['load_model'])['state_dict'])
print('load continue model !')
elif config['eval'] or (config['t_sne'] != None and config['t_sne']):
#self.model.last_linear = torch.nn.Identity()
# self.model.fc = torch.nn.Identity()
# # self.model.fc = nn.Sequential(nn.Linear(2048, 2048),
# # nn.ReLU(),
# # nn.Linear(2048,512),
# # nn.BatchNorm1d(512))
if config['eval_mode'] == 'exp': # exp
state_dict = torch.load(config['load_model'])['state_dict']
for k in list(state_dict.keys()):
if k.startswith('module.encoder'):
state_dict[k[len("module.encoder."):]] = state_dict[k]
elif k.startswith('module.exp_fc') or k.startswith('module.pose_fc'):
state_dict[k[len("module."):]] = state_dict[k]
# # if k.startswith('module.exp_fc'):
# # state_dict[k[len("module.exp_"):]] = state_dict[k]
# del state_dict[k]
# if k.startswith('module.exp_encoder'):
# state_dict[k[len("module.exp_encoder."):]] = state_dict[k]
del state_dict[k]
######## simclr
# state_dict = torch.load(config['load_model'])['state_dict']
# if config['eval_mode'] == 'exp':
# self.model = torch.nn.DataParallel(self.model).cuda()
elif config['eval_mode'] == 'pose': # pose
state_dict = torch.load(config['load_model'])['state_dict']
for k in list(state_dict.keys()):
if k.startswith('module.encoder'):
state_dict[k[len("module.encoder."):]] = state_dict[k]
del state_dict[k]
print('pose loaded!')
elif config['eval_mode'] == 'face_cycle':
state_dict = torch.load(config['load_model'])['codegeneration']
for k in list(state_dict.keys()):
if k.startswith('expresscode.'):
del state_dict[k]
elif config['eval_mode'] == 'TCAE':
self.model.fc = nn.Sequential(nn.Linear(32768, 2048),
nn.ReLU(),
nn.Linear(2048,2048),
nn.BatchNorm1d(2048))
state_dict = torch.load(config['load_model'])['state_dict']
for k in list(state_dict.keys()):
if k.startswith('encoder.exp.'):
state_dict['fc.' + k[len("encoder.exp.")]] = state_dict[k]
if k.startswith('encoder.'):
state_dict[k[len("encoder."):]] = state_dict[k]
del state_dict[k]
#self.model = self.model.cuda()
#self.model = torch.nn.DataParallel(self.model).cuda()
msg = self.model.load_state_dict(state_dict,strict=False)
assert set(msg.missing_keys) == set()
print('load model !')
else:
self.model = torch.nn.DataParallel(self.model).cuda()
self.criterion = nn.CrossEntropyLoss().cuda()
self.recon_criterion = nn.L1Loss().cuda()
self.diff_loss = DiffLoss().cuda()
self.optimizer = torch.optim.Adam(self.model.parameters(),lr=config['lr'],weight_decay=config['wd'])
self.scaler = GradScaler()
def get_lambda(self,epoch):
def sigmoid(x):
if x >= 0:
z = math.exp(-x)
sig = 1 / (1 + z)
return sig
else:
z = math.exp(x)
sig = z / (1 + z)
return sig
def exp_decay(x):
z = math.exp(-x)
z = max(1e-10,z)
return z
if epoch < 200:
lam = (sigmoid(epoch/5.0) - 0.5) * 2.0
else:
lam = exp_decay((epoch-200)/5.0)
return lam
def optimize_parameters(self,data):
self.model.train()
img_normal = data['img_normal'].cuda()
img_flip = data['img_flip'].cuda()
cur_epoch = data['epoch']
with autocast():
exp_fea, pose_fea, recon_normal_exp_flip_pose_img, recon_flip_exp_normal_pose_img,recon_normal_exp_normal_pose_img = self.forward(data)
exp_logits,exp_labels = self.neg_inter_info_nce_loss(exp_fea)
exp_contra_loss = self.criterion(exp_logits,exp_labels)
pose_logits,pose_labels = self.neg_inter_info_nce_loss(pose_fea)
pose_contra_loss = self.criterion(pose_logits,pose_labels)
recon_normal_loss = self.recon_criterion(recon_flip_exp_normal_pose_img,img_normal) # || s-s'||
#recon_normal_loss = 0.
recon_flip_loss = self.recon_criterion(recon_normal_exp_flip_pose_img,img_flip) # ||f-f'||
#recon_flip_loss = 0.
recon_orin_loss = self.recon_criterion(recon_normal_exp_normal_pose_img,img_normal) # ||s-s''||
#recon_orin_loss = 0.
recon_weight = self.get_lambda(cur_epoch)
diff_loss = self.diff_loss(exp_fea,pose_fea)
#diff_loss = 0.
loss = exp_contra_loss + pose_contra_loss * self.pose_alpha + diff_loss + recon_weight * (recon_normal_loss + recon_flip_loss + recon_orin_loss)
exp_acc1,exp_acc5 = utils.accuracy(exp_logits,exp_labels,(1,5))
pose_acc1,pose_acc5 = utils.accuracy(pose_logits,pose_labels,(1,5))
self.optimizer.zero_grad()
self.scaler.scale(loss).backward()
self.scaler.step(self.optimizer)
self.scaler.update()
print_img = torch.cat([img_normal[:2],img_flip[:2],recon_flip_exp_normal_pose_img[:2],recon_normal_exp_flip_pose_img[:2],recon_normal_exp_normal_pose_img[:2]],dim=3)
return {'train_acc1_exp':exp_acc1,'train_acc5_exp':exp_acc5,'train_loss':loss,
'train_acc1_pose':pose_acc1,'train_acc5_pose':pose_acc5,
'train_diff_loss': diff_loss,
'train_exp_contra_loss':exp_contra_loss,
'train_pose_contra_loss':pose_contra_loss,
'train_recon_normal_loss':recon_normal_loss,
'train_recon_flip_loss':recon_flip_loss,
'train_recon_orin_loss':recon_orin_loss,
'train_print_img':print_img,'recon_weight':recon_weight}
def neg_inter_info_nce_loss(self, features):
# time_start = time.time()
b, dim = features.size()
labels = torch.cat([torch.arange(b // 2) for i in range(2)], dim=0)
labels = (labels.unsqueeze(0) == labels.unsqueeze(1)).float()
labels_flag = (1 - labels).bool()
features_expand = features.expand((b, b, dim)) # 512 * 512 * dim
fea_neg_li = list(features_expand[labels_flag].chunk(b, dim=0))
fea_neg_tensor = torch.stack(fea_neg_li, dim=0) # 512 * 510 * dim
# time_alpha = time.time()
neg_mask = np.random.beta(self.neg_alpha, self.neg_alpha,
size=(fea_neg_tensor.shape[0], fea_neg_tensor.shape[1]))
time_alpha_finish = time.time()
# print('cost alpha time: {}'.format(time_alpha_finish - time_alpha))
if isinstance(neg_mask, np.ndarray):
neg_mask = torch.from_numpy(neg_mask).float().cuda()
neg_mask = neg_mask.unsqueeze(dim=2)
indices = torch.randperm(fea_neg_tensor.shape[1])
fea_neg_tensor = fea_neg_tensor * neg_mask + (1 - neg_mask) * fea_neg_tensor[:, indices]
features = F.normalize(features, dim=1)
q, k = features.chunk(2, dim=0)
fea_neg_tensor = F.normalize(fea_neg_tensor, dim=2)
pos = torch.cat(
[torch.einsum('nc,nc->n', [q, k]).unsqueeze(-1), torch.einsum('nc,nc->n', [k, q]).unsqueeze(-1)], dim=0)
fea_neg_tensor = fea_neg_tensor.transpose(2, 1)
neg = torch.bmm(features.view(b, 1, -1), fea_neg_tensor).view(b, -1)
logits = torch.cat([pos, neg], dim=1)
labels = torch.zeros(logits.shape[0], dtype=torch.long).cuda()
logits = logits / self.temperature
# print('cost time: {}'.format(time.time() - time_start))
return logits, labels
def forward(self,data):
exp_images = data['exp_images']
img_normal = data['img_normal'].cuda()
img_flip = data['img_flip'].cuda()
exp_images = torch.cat(exp_images,dim=0).cuda()
exp_fea, pose_fea, recon_normal_exp_flip_pose_img, recon_flip_exp_normal_pose_img,recon_normal_exp_normal_pose_img = self.model(exp_images,img_normal,img_flip)
return exp_fea, pose_fea, recon_normal_exp_flip_pose_img, recon_flip_exp_normal_pose_img,recon_normal_exp_normal_pose_img
def linear_forward(self,data):
img = data['img_normal'].cuda()
fea = self.model(img)
return fea
def linear_forward_id(self,data):
img1 = data['img_normal1'].cuda()
fea1 = self.model(img1)
img2 = data['img_normal2'].cuda()
fea2 = self.model(img2)
return fea1,fea2
def linear_eval_id(self,data):
self.model.eval()
with torch.no_grad():
fea1,fea2 = self.linear_forward_id(data)
return fea1,fea2
def metric_better(self,cur,best):
ans = best
flag = False
if best == None or cur < best:
flag = True
ans = cur
return flag,ans
def eval(self,data):
pass
def linear_eval(self,data):
self.model.eval()
with torch.no_grad():
fea = self.linear_forward(data)
return fea
def set_requires_grad(self, nets, requires_grad=False):
"""Set requies_grad=Fasle for all the networks to avoid unnecessary computations
Parameters:
nets (network list) -- a list of networks
requires_grad (bool) -- whether the networks require gradients or not
"""
if not isinstance(nets, list):
nets = [nets]
for net in nets:
if net is not None:
for param in net.parameters():
param.requires_grad = requires_grad
if __name__ == '__main__':
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
model = ExpPoseModel()
model = torch.nn.DataParallel(model).cuda()
model.load_state_dict(torch.load('./checkpoints/visualize_rafdb/best.pth')['state_dict'])
rafdb_root = r'./dataset/RAFDB/img/aligned/'
save_root = r'./save_imgs/'
model = model.eval()
from torchvision.transforms import transforms
trans = transforms.Compose([
transforms.Resize((64,64)),
transforms.ToTensor()
])
flip = transforms.RandomHorizontalFlip(1.0)
to_img = transforms.Compose([transforms.ToPILImage(),transforms.Resize((100,100))])
#gray = transforms.Grayscale(num_output_channels=3)
from PIL import Image
path_list = os.listdir(rafdb_root)
img_list = []
for p in path_list:
if p.startswith('test'):
img_list.append(p)
count = 0
for img_name in img_list:
img = Image.open(os.path.join(rafdb_root,img_name)).convert('RGB')
img_flip = flip(img)
tensor_flip = trans(img_flip).unsqueeze(dim=0).cuda()
tensor_normal = trans(img).unsqueeze(dim=0).cuda()
recon_normal_exp_flip_pose_img,recon_flip_exp_normal_pose_img,recon_normal_exp_normal_pose_img,recon_normal_pose_normal_pose_img,recon_flip_pose_flip_pose_img = model(tensor_normal,tensor_normal,tensor_flip)
recon_normal_exp_flip_pose_img = to_img(recon_normal_exp_flip_pose_img[0])
recon_flip_exp_normal_pose_img = to_img(recon_flip_exp_normal_pose_img[0])
recon_normal_exp_normal_exp_img = to_img(recon_normal_exp_normal_pose_img[0])
recon_normal_pose_normal_pose_img = to_img(recon_normal_pose_normal_pose_img[0])
recon_flip_pose_flip_pose_img = to_img(recon_flip_pose_flip_pose_img[0])
if not os.path.exists(save_root):
os.makedirs(save_root)
recon_normal_exp_flip_pose_img.save(os.path.join(save_root,img_name + '_' + 'recon_normal_exp_flip_pose.jpg'))
recon_flip_exp_normal_pose_img.save(os.path.join(save_root,img_name + '_' + 'recon_flip_exp_normal_pose.jpg'))
recon_normal_exp_normal_exp_img.save(os.path.join(save_root,img_name + '_' + 'recon_normal_exp_normal_exp.jpg'))
recon_normal_pose_normal_pose_img.save(os.path.join(save_root,img_name + '_' + 'recon_normal_pose_normal_pose.jpg'))
recon_flip_pose_flip_pose_img.save(os.path.join(save_root,img_name + '_' + 'recon_flip_pose_flip_pose.jpg'))