import os
import time
import random
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torchvision.models as models
import torch.backends.cudnn as cudnn
from data_loader import get_loader
from args import get_parser
from models import *
from torch.optim import lr_scheduler
from tqdm import tqdm
import pdb
import torch.nn.functional as F
from triplet_loss import *
import pickle
from build_vocab import Vocabulary
from torch.autograd import Variable
from torch.nn.utils.rnn import pack_padded_sequence
import torchvision.utils as vutils
# =============================================================================
parser = get_parser()
opts = parser.parse_args()
device = [0]
with open(opts.vocab_path, 'rb') as f:
vocab = pickle.load(f)
# =============================================================================
##load models
image_model = torch.nn.DataParallel(ImageEmbedding().cuda(), device_ids=device)
recipe_model = torch.nn.DataParallel(TextEmbedding().cuda(), device_ids=device)
netG = torch.nn.DataParallel(G_NET().cuda(), device_ids=device)
multi_label_net = torch.nn.DataParallel(MultiLabelNet().cuda(), device_ids=device)
cm_discriminator = torch.nn.DataParallel(cross_modal_discriminator().cuda(), device_ids=device)
text_discriminator = torch.nn.DataParallel(text_emb_discriminator().cuda(), device_ids=device)
netsD = torch.nn.DataParallel(D_NET128().cuda(), device_ids=device)
## load loss functions
triplet_loss = TripletLoss(device, margin=0.3)
img2text_criterion = nn.MultiLabelMarginLoss().cuda()
weights_class = torch.Tensor(opts.numClasses).fill_(1)
weights_class[0] = 0
class_criterion = nn.CrossEntropyLoss(weight=weights_class).cuda()
GAN_criterion = nn.BCELoss().cuda()
nz = opts.Z_DIM
noise = Variable(torch.FloatTensor(opts.batch_size, nz)).cuda()
fixed_noise = Variable(torch.FloatTensor(opts.batch_size, nz).normal_(0, 1)).cuda()
real_labels = Variable(torch.FloatTensor(opts.batch_size).fill_(1)).cuda()
fake_labels = Variable(torch.FloatTensor(opts.batch_size).fill_(0)).cuda()
fc_sia = nn.Sequential(
nn.Linear(opts.embDim, opts.embDim),
nn.BatchNorm1d(opts.embDim),
nn.Tanh(),
).cuda()
model_list = [image_model, recipe_model, netG, multi_label_net, cm_discriminator, text_discriminator, netsD, fc_sia]
optimizer = torch.optim.Adam([
{'params': image_model.parameters()},
{'params': recipe_model.parameters()},
{'params': netG.parameters()},
{'params': multi_label_net.parameters()}
], lr=opts.lr, betas=(0.5, 0.999))
optimizers_imgD = torch.optim.Adam(netsD.parameters(), lr=opts.lr, betas=(0.5, 0.999))
optimizer_cmD = torch.optim.Adam(cm_discriminator.parameters(), lr=opts.lr, betas=(0.5, 0.999))
label = list(range(0, opts.batch_size))
label.extend(label)
label = np.array(label)
label = torch.tensor(label).cuda().long()
method = 'acme'
save_folder = method
os.makedirs(save_folder, exist_ok=True)
epoch_trace_f_dir = os.path.join(save_folder, "trace_" + method + ".csv")
with open(epoch_trace_f_dir, "w") as f:
f.write("epoch,lr,I2R,R@1,R@5,R@10,R2I,R@1,R@5,R@10\n")
def main():
# data preparation, loaders
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
train_transform = transforms.Compose([
transforms.Resize(256),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip()])
val_transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224)])
cudnn.benchmark = True
# preparing the training laoder
train_loader = get_loader(opts.img_path, train_transform, vocab, opts.data_path, partition='train',
batch_size=opts.batch_size, shuffle=True,
num_workers=opts.workers, pin_memory=True)
print('Training loader prepared.')
# preparing validation loader
val_loader = get_loader(opts.img_path, val_transform, vocab, opts.data_path, partition='test',
batch_size=opts.batch_size, shuffle=False,
num_workers=opts.workers, pin_memory=True)
print('Validation loader prepared.')
best_val_i2t = {1:0.0,5:0.0,10:0.0}
best_val_t2i = {1:0.0,5:0.0,10:0.0}
best_epoch_i2t = 0
best_epoch_t2i = 0
for epoch in range(0, opts.epochs):
train(train_loader, epoch, val_loader)
recall_i2t, recall_t2i, medR_i2t, medR_t2i = validate(val_loader)
with open(epoch_trace_f_dir, "a") as f:
lr = optimizer.param_groups[1]['lr']
f.write("{},{},{},{},{},{},{},{},{},{}\n".format\
(epoch,lr,medR_i2t,recall_i2t[1],recall_i2t[5],recall_i2t[10],\
medR_t2i,recall_t2i[1],recall_t2i[5],recall_t2i[10]))
for keys in best_val_i2t:
if recall_i2t[keys] > best_val_i2t[keys]:
best_val_i2t = recall_i2t
best_epoch = epoch+1
model_num = 1
for model_n in model_list:
filename = save_folder + '/model_e%03d_v%d.pkl' % (epoch+1, model_num)
torch.save(model_n.state_dict(), filename)
model_num += 1
break
print("best: ", best_epoch, best_val_i2t)
print('params lr: %f' % optimizer.param_groups[1]['lr'])
if epoch == 30:
optimizer.param_groups[0]['lr'] = 0.00001
optimizer.param_groups[1]['lr'] = 0.00001
optimizer.param_groups[2]['lr'] = 0.00001
optimizer.param_groups[3]['lr'] = 0.00001
optimizers_imgD.param_groups[0]['lr'] = 0.00001
optimizer_cmD.param_groups[0]['lr'] = 0.00001
def train_Dnet(idx, real_imgs, fake_imgs, mu, label_class):
netD = netsD
real_imgs = real_imgs[idx]
fake_imgs = fake_imgs[idx]
real_logits = netD(real_imgs, mu.detach())
fake_logits = netD(fake_imgs.detach(), mu.detach())
lossD_real = GAN_criterion(real_logits[0], real_labels)
lossD_fake = GAN_criterion(fake_logits[0], fake_labels)
lossD = lossD_real + lossD_fake
return lossD
def KL_loss(mu, logvar):
KLD_element = mu.pow(2).add_(logvar.exp()).mul_(-1).add_(1).add_(logvar)
KLD = torch.mean(KLD_element).mul_(-0.5)
return KLD
def train_Gnet(idx, real_imgs, fake_imgs, mu, logvar, label_class):
netD = netsD
real_imgs = real_imgs[idx]
fake_imgs = fake_imgs[idx]
real_logits = netD(real_imgs, mu)
fake_logits = netD(fake_imgs, mu)
lossG_fake = GAN_criterion(fake_logits[0], real_labels)
lossG_real_cond = class_criterion(real_logits[1], label_class)
lossG_fake_cond = class_criterion(fake_logits[1], label_class)
lossG_cond = lossG_real_cond + lossG_fake_cond
lossG = lossG_fake + lossG_cond
kl_loss = KL_loss(mu, logvar) * 2
lossG = kl_loss + lossG
return lossG
def compute_gradient_penalty(D, real_samples, fake_samples):
"""Calculates the gradient penalty loss for WGAN GP"""
# Random weight term for interpolation between real and fake samples
alpha = torch.cuda.FloatTensor(np.random.random((real_samples.size(0), 1)))
# Get random interpolation between real and fake samples
interpolates = (alpha * real_samples + ((1 - alpha) * fake_samples)).requires_grad_(True)
d_interpolates = D(interpolates)
fake = torch.autograd.Variable(torch.cuda.FloatTensor(real_samples.shape[0], 1).fill_(1.0), requires_grad=False)
# Get gradient w.r.t. interpolates
gradients = torch.autograd.grad(
outputs=d_interpolates, # fack samples
inputs=interpolates, # real samples
grad_outputs=fake,
create_graph=True,
retain_graph=True,
only_inputs=True,
)[0]
gradients = gradients.view(gradients.size(0), -1)
gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()
return gradient_penalty
def train(train_loader, epoch, val_loader):
tri_losses = AverageMeter()
img_losses = AverageMeter()
text_losses = AverageMeter()
cmG_losses = AverageMeter()
image_model.train()
recipe_model.train()
for i, data in enumerate(tqdm(train_loader)):
img_emd_modal = image_model(data[0][0].cuda())
recipe_emb_modal = recipe_model(data[0][1].cuda(), data[0][2].cuda(), data[0][3].cuda(), data[0][4].cuda())
################################################################
# modal-level fusion
################################################################
real_validity = cm_discriminator(img_emd_modal.detach())
fake_validity = cm_discriminator(recipe_emb_modal.detach())
gradient_penalty = compute_gradient_penalty(cm_discriminator, img_emd_modal.detach(), recipe_emb_modal.detach())
loss_cmD = -torch.mean(real_validity) + torch.mean(fake_validity) + 10 * gradient_penalty
optimizer_cmD.zero_grad()
loss_cmD.backward()
optimizer_cmD.step()
g_fake_validity = cm_discriminator(recipe_emb_modal)
loss_cmG = -torch.mean(g_fake_validity)
################################################################
# cross-modal retrieval
################################################################
img_id_fea = norm(fc_sia(img_emd_modal))
rec_id_fea = norm(fc_sia(recipe_emb_modal))
tri_loss = global_loss(triplet_loss, torch.cat((img_id_fea, rec_id_fea)), label)[0]
################################################################
# translation consistency
label_class = data[1][7].cuda()
real_imgs = []
real_imgs.append(data[1][8].cuda())
ingr_cap = data[1][5].cuda()
lengths = torch.tensor(data[1][6]).cuda()
targets = pack_padded_sequence(ingr_cap, lengths, batch_first=True)[0]
one_hot_cap = data[1][9].cuda().long()
################################################################
# img2text
################################################################
recipe_out = multi_label_net(img_id_fea)
loss_i2t = img2text_criterion(recipe_out[0], one_hot_cap)
loss_t_class = class_criterion(recipe_out[1], label_class)
loss_text = loss_i2t + loss_t_class
###############################################################
# text2img
###############################################################
noise.data.normal_(0, 1)
fake_imgs, mu, logvar = netG(noise, rec_id_fea)
lossD_total = 0
lossD = train_Dnet(0, real_imgs, fake_imgs, mu, label_class)
optimizers_imgD.zero_grad()
lossD.backward()
optimizers_imgD.step()
lossG = train_Gnet(0, real_imgs, fake_imgs, mu, logvar, label_class)
loss_img = lossG
if loss_text.item() < loss_img.item():
loss_img = (loss_text.item()/loss_img.item()) * loss_img
else:
loss_text = (loss_img.item()/loss_text.item()) * loss_text
loss_g = loss_img + loss_text
###############################################################
# back-propogate
###############################################################
loss = tri_loss + 0.005 * loss_cmG + 0.002 * loss_g
tri_losses.update(tri_loss.item(), data[0][0].size(0))
img_losses.update(loss_img.item(), data[0][0].size(0))
text_losses.update(loss_text.item(), data[0][0].size(0))
cmG_losses.update(loss_cmG.item(), data[0][0].size(0))
optimizer.zero_grad()
loss.backward()
optimizer.step()
print(epoch)
print('Epoch: {0} '
'tri loss {tri_loss.val:.4f} ({tri_loss.avg:.4f}), '
'cm loss {loss_cmG.val:.4f} ({loss_cmG.avg:.4f}), '
'img loss {img_losses.val:.4f} ({img_losses.avg:.4f}), '
'text loss {loss_text.val:.4f} ({loss_text.avg:.4f})'
.format(
epoch, tri_loss=tri_losses, loss_cmG=cmG_losses,
img_losses=img_losses, loss_text=text_losses))
def validate(val_loader):
# switch to evaluate mode
image_model.eval()
recipe_model.eval()
end = time.time()
for i, data in enumerate(tqdm(val_loader)):
with torch.no_grad():
img_emd_modal = image_model(data[0][0].cuda())
recipe_emb_modal = recipe_model(data[0][1].cuda(), data[0][2].cuda(), data[0][3].cuda(), data[0][4].cuda())
img_emd_modal = norm(fc_sia(img_emd_modal))
recipe_emb_modal = norm(fc_sia(recipe_emb_modal))
if i==0:
data0 = img_emd_modal.data.cpu().numpy()
data1 = recipe_emb_modal.data.cpu().numpy()
else:
data0 = np.concatenate((data0,img_emd_modal.data.cpu().numpy()),axis=0)
data1 = np.concatenate((data1,recipe_emb_modal.data.cpu().numpy()),axis=0)
medR_i2t, recall_i2t = rank_i2t(opts, data0, data1)
print('I2T Val medR {medR:.4f}\t'
'Recall {recall}'.format(medR=medR_i2t, recall=recall_i2t))
medR_t2i, recall_t2i = rank_t2i(opts, data0, data1)
print('T2I Val medR {medR:.4f}\t'
'Recall {recall}'.format(medR=medR_t2i, recall=recall_t2i))
return recall_i2t, recall_t2i, medR_i2t, medR_t2i
def rank_i2t(opts, img_embeds, rec_embeds):
random.seed(opts.seed)
im_vecs = img_embeds
instr_vecs = rec_embeds
# Ranker
N = 1000
idxs = range(N)
glob_rank = []
glob_recall = {1:0.0,5:0.0,10:0.0}
for i in range(10):
ids = random.sample(range(0,len(img_embeds)), N)
im_sub = im_vecs[ids,:]
instr_sub = instr_vecs[ids,:]
med_rank = []
recall = {1:0.0,5:0.0,10:0.0}
for ii in idxs:
distance = {}
for j in range(N):
distance[j] = np.linalg.norm(im_sub[ii] - instr_sub[j])
distance_sorted = sorted(distance.items(), key=lambda x:x[1])
pos = np.where(np.array(distance_sorted) == distance[ii])[0][0]
if (pos+1) == 1:
recall[1]+=1
if (pos+1) <=5:
recall[5]+=1
if (pos+1)<=10:
recall[10]+=1
# store the position
med_rank.append(pos+1)
for i in recall.keys():
recall[i]=recall[i]/N
med = np.median(med_rank)
for i in recall.keys():
glob_recall[i]+=recall[i]
glob_rank.append(med)
for i in glob_recall.keys():
glob_recall[i] = glob_recall[i]/10
return np.average(glob_rank), glob_recall
def rank_t2i(opts, img_embeds, rec_embeds):
random.seed(opts.seed)
im_vecs = img_embeds
instr_vecs = rec_embeds
# Ranker
N = 1000
idxs = range(N)
glob_rank = []
glob_recall = {1:0.0,5:0.0,10:0.0}
for i in range(10):
ids = random.sample(range(0,len(img_embeds)), N)
im_sub = im_vecs[ids,:]
instr_sub = instr_vecs[ids,:]
med_rank = []
recall = {1:0.0,5:0.0,10:0.0}
for ii in idxs:
distance = {}
for j in range(N):
distance[j] = np.linalg.norm(instr_sub[ii] - im_sub[j])
distance_sorted = sorted(distance.items(), key=lambda x:x[1])
pos = np.where(np.array(distance_sorted) == distance[ii])[0][0]
if (pos+1) == 1:
recall[1]+=1
if (pos+1) <=5:
recall[5]+=1
if (pos+1)<=10:
recall[10]+=1
# store the position
med_rank.append(pos+1)
for i in recall.keys():
recall[i]=recall[i]/N
med = np.median(med_rank)
for i in recall.keys():
glob_recall[i]+=recall[i]
glob_rank.append(med)
for i in glob_recall.keys():
glob_recall[i] = glob_recall[i]/10
return np.average(glob_rank), glob_recall
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__':
main()
