def main():
model = im2recipe(inst_emb=opts.inst_emb)
model.visionMLP = torch.nn.DataParallel(model.visionMLP)
model.to(device)
# define loss function (criterion) and optimizer
# cosine similarity between embeddings -> input1, input2, target
cosine_crit = nn.CosineEmbeddingLoss(0.1).to(device)
if opts.semantic_reg:
weights_class = torch.Tensor(opts.numClasses).fill_(1)
weights_class[0] = 0 # the background class is set to 0, i.e. ignore
# CrossEntropyLoss combines LogSoftMax and NLLLoss in one single class
class_crit = nn.CrossEntropyLoss(weight=weights_class).to(device)
# we will use two different criteria
criterion = [cosine_crit, class_crit]
else:
criterion = cosine_crit
print("=> loading checkpoint '{}'".format(opts.model_path))
if device.type == 'cpu':
checkpoint = torch.load(opts.model_path,
encoding='latin1',
map_location='cpu')
else:
checkpoint = torch.load(opts.model_path, encoding='latin1')
opts.start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
opts.model_path, checkpoint['epoch']))
# data preparation, loaders
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
# preparing test loader
test_loader = torch.utils.data.DataLoader(
ImagerLoader(
opts.img_path,
transforms.Compose([
transforms.Resize(
256
), # rescale the image keeping the original aspect ratio
transforms.CenterCrop(
224), # we get only the center of that rescaled
transforms.ToTensor(),
normalize,
]),
data_path=data_path,
sem_reg=opts.semantic_reg,
partition='test',
n_samples=opts.n_samples),
batch_size=opts.batch_size,
shuffle=False,
num_workers=opts.workers,
pin_memory=True)
print('Test loader prepared.')
# run test
test(test_loader, model, criterion)
def main():
im_path = opts.test_image_path
ext = os.path.basename(im_path).split('.')[-1]
if ext not in ['jpeg', 'jpg', 'png']:
raise Exception("Wrong image format.")
# create model
model = im2recipe()
model.visionMLP = torch.nn.DataParallel(model.visionMLP)
model.to(device)
# load checkpoint
print("=> loading checkpoint '{}'".format(opts.model_path))
if device.type == 'cpu':
checkpoint = torch.load(opts.model_path,
encoding='latin1',
map_location='cpu')
else:
checkpoint = torch.load(opts.model_path, encoding='latin1')
opts.start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
opts.model_path, checkpoint['epoch']))
# data preparation, loaders
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transform = transforms.Compose([
transforms.Scale(
256), # rescale the image keeping the original aspect ratio
transforms.CenterCrop(224), # we get only the center of that rescaled
transforms.ToTensor(),
normalize
])
# load image
im = Image.open(im_path).convert('RGB')
im = transform(im)
im = im.view((1, ) + im.shape)
# get model output
output = model.visionMLP(im)
output = norm(output)
output = output.data.cpu().numpy()
# save output
with open(im_path.replace(ext, 'pkl'), 'wb') as f:
pickle.dump(output, f)
def main():
model = im2recipe()
model.visionMLP = torch.nn.DataParallel(model.visionMLP, device_ids=[0])
if not opts.no_cuda:
model.cuda()
print("=> loading checkpoint '{}'".format(opts.model_path))
checkpoint = torch.load(opts.model_path)
opts.start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
opts.model_path, checkpoint['epoch']))
# data preparation, loaders
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
# preparing test loader
test_loader = torch.utils.data.DataLoader(
ImagerLoader(
opts.img_path,
transforms.Compose([
transforms.Scale(
256
), # rescale the image keeping the original aspect ratio
transforms.CenterCrop(
224), # we get only the center of that rescaled
transforms.ToTensor(),
normalize,
]),
data_path=opts.data_path,
sem_reg=opts.semantic_reg,
partition='temp'),
batch_size=opts.batch_size,
shuffle=False,
num_workers=opts.workers,
pin_memory=(not opts.no_cuda))
print('Test loader prepared.')
# run test
test(test_loader, model)
def main():
model = im2recipe()
#model.visionMLP = torch.nn.DataParallel(model.visionMLP, device_ids=[0,1,2,3])
# barelo:
model.visionMLP = torch.nn.DataParallel(model.visionMLP, device_ids=[0])
# model.visionMLP = torch.nn.DataParallel(model.visionMLP, device_ids=[0,1])
if not opts.no_cuda:
model.cuda()
# define loss function (criterion) and optimizer
# cosine similarity between embeddings -> input1, input2, target
cosine_crit = nn.CosineEmbeddingLoss(0.1)
if not opts.no_cuda:
cosine_crit.cuda()
# cosine_crit = nn.CosineEmbeddingLoss(0.1)
if opts.semantic_reg:
weights_class = torch.Tensor(opts.numClasses).fill_(1)
weights_class[0] = 0 # the background class is set to 0, i.e. ignore
# CrossEntropyLoss combines LogSoftMax and NLLLoss in one single class
class_crit = nn.CrossEntropyLoss(weight=weights_class)
if not opts.no_cuda:
class_crit.cuda()
# class_crit = nn.CrossEntropyLoss(weight=weights_class)
# we will use two different criteria
criterion = [cosine_crit, class_crit]
else:
criterion = cosine_crit
print("=> loading checkpoint '{}'".format(opts.model_path))
checkpoint = torch.load(opts.model_path)
opts.start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(
opts.model_path, checkpoint['epoch']))
# data preparation, loaders
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transform = transforms.Compose([
transforms.Scale(
256), # rescale the image keeping the original aspect ratio
transforms.CenterCrop(224), # we get only the center of that rescaled
transforms.ToTensor(),
normalize,
])
dataset = dataset_h5(opts.data_path + '/' + opts.emb_h5_input_file + '.h5',
transform=transform)
assert dataset
print("starting")
# preparing test loader
test_loader = torch.utils.data.DataLoader(dataset,
batch_size=opts.batch_size,
shuffle=False,
num_workers=opts.workers,
pin_memory=(not opts.no_cuda))
print 'Test loader prepared.'
# run test
test(test_loader, model, criterion)
def main():
model = im2recipe(inst_emb=opts.inst_emb)
model.visionMLP = torch.nn.DataParallel(model.visionMLP)
model.to(device)
# define loss function (criterion) and optimizer
# cosine similarity between embeddings -> input1, input2, target
cosine_crit = nn.CosineEmbeddingLoss(0.1).to(device)
if opts.semantic_reg:
weights_class = torch.Tensor(opts.numClasses).fill_(1)
weights_class[0] = 0 # the background class is set to 0, i.e. ignore
# CrossEntropyLoss combines LogSoftMax and NLLLoss in one single class
class_crit = nn.CrossEntropyLoss(weight=weights_class).to(device)
# we will use two different criteria
criterion = [cosine_crit, class_crit]
else:
criterion = cosine_crit
# # creating different parameter groups
vision_params = list(map(id, model.visionMLP.parameters()))
base_params = filter(lambda p: id(p) not in vision_params, model.parameters())
# optimizer - with lr initialized accordingly
optimizer = torch.optim.Adam([
{'params': base_params},
{'params': model.visionMLP.parameters(), 'lr': opts.lr*opts.freeVision }
], lr=opts.lr*opts.freeRecipe)
if opts.resume:
if os.path.isfile(opts.resume):
print("=> loading checkpoint '{}'".format(opts.resume))
checkpoint = torch.load(opts.resume)
opts.start_epoch = checkpoint['epoch']
best_val = checkpoint['best_val']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(opts.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(opts.resume))
best_val = float('inf')
else:
best_val = float('inf')
# models are save only when their loss obtains the best value in the validation
valtrack = 0
print('There are %d parameter groups' % len(optimizer.param_groups))
print('Initial base params lr: %f' % optimizer.param_groups[0]['lr'])
print('Initial vision params lr: %f' % optimizer.param_groups[1]['lr'])
# data preparation, loaders
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
cudnn.benchmark = True
# preparing the training laoder
train_loader = torch.utils.data.DataLoader(
ImagerLoader(opts.img_path,
transforms.Compose([
transforms.Resize(256), # rescale the image keeping the original aspect ratio
transforms.CenterCrop(256), # we get only the center of that rescaled
transforms.RandomCrop(224), # random crop within the center crop
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]),data_path=data_path,sem_reg=opts.semantic_reg,partition='train',n_samples=opts.n_samples),
batch_size=opts.batch_size, shuffle=True,
num_workers=opts.workers, pin_memory=True)
print('Training loader prepared.')
# preparing validation loader
val_loader = torch.utils.data.DataLoader(
ImagerLoader(opts.img_path,
transforms.Compose([
transforms.Resize(256), # rescale the image keeping the original aspect ratio
transforms.CenterCrop(224), # we get only the center of that rescaled
transforms.ToTensor(),
normalize,
]),data_path=data_path,sem_reg=opts.semantic_reg,partition='val',n_samples=opts.n_samples),
batch_size=opts.batch_size, shuffle=False,
num_workers=opts.workers, pin_memory=True)
print('Validation loader prepared.')
# run epochs
for epoch in range(opts.start_epoch, opts.epochs):
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
if (epoch+1) % opts.valfreq == 0 and epoch != 0:
val_loss = validate(val_loader, model, criterion, epoch)
# check patience
if val_loss >= best_val:
valtrack += 1
else:
valtrack = 0
if valtrack >= opts.patience:
# we switch modalities
opts.freeVision = opts.freeRecipe; opts.freeRecipe = not(opts.freeVision)
# change the learning rate accordingly
adjust_learning_rate(optimizer, epoch, opts)
valtrack = 0
# save the best model
is_best = val_loss < best_val
best_val = min(val_loss, best_val)
save_checkpoint({
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_val': best_val,
'optimizer': optimizer.state_dict(),
'valtrack': valtrack,
'freeVision': opts.freeVision,
'curr_val': val_loss,
}, is_best)
print('** Validation: %f (best) - %d (valtrack)' % (best_val, valtrack))
def main():
model = im2recipe()
model.visionMLP = torch.nn.DataParallel(model.visionMLP)
# model = torch.nn.DataParallel(model)
model.to(device)
# define loss function (criterion) and optimizer
# cosine similarity between embeddings -> input1, input2, target
cosine_crit = nn.CosineEmbeddingLoss(0.1).to(device)
criterion = cosine_crit
# # creating different parameter groups
vision_params = list(map(id, model.visionMLP.parameters()))
base_params = filter(lambda p: id(p) not in vision_params,
model.parameters())
# optimizer - with lr initialized accordingly
optimizer = torch.optim.Adam([{
'params': base_params
}, {
'params': model.visionMLP.parameters(),
'lr': opts.lr * opts.freeVision
}],
lr=opts.lr * opts.freeRecipe)
if opts.resume:
if os.path.isfile(opts.resume):
print("=> loading checkpoint '{}'".format(opts.resume))
checkpoint = torch.load(opts.resume)
opts.start_epoch = checkpoint['epoch']
best_val = checkpoint['best_val']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
opts.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(opts.resume))
best_val = float('-inf')
else:
best_val = float('-inf')
optimizer = torch.optim.AdamW([{
'params': base_params
}, {
'params': model.visionMLP.parameters()
}])
optimizer.param_groups[0]['lr'] = opts.lr
optimizer.param_groups[1]['lr'] = 0
# models are save only when their loss obtains the best value in the validation
valtrack = 0 # lr decay
print('There are %d parameter groups' % len(optimizer.param_groups))
print('Initial base params lr: {}'.format(optimizer.param_groups[0]['lr']))
print('Initial vision params lr: {}'.format(
optimizer.param_groups[1]['lr']))
# data preparation, loaders
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
cudnn.benchmark = True
# random_sampler = torch.utils.data.RandomSampler(range(100), replacement=True)
# batch_sampler = torch.utils.data.BatchSampler(random_sampler, batch_size=opts.batch_size, drop_last=True)
train_loader = torch.utils.data.DataLoader(
ImagerLoader(
opts.img_path,
transforms.Compose([
transforms.Resize(
256
), # rescale the image keeping the original aspect ratio
transforms.CenterCrop(
256), # we get only the center of that rescaled
transforms.RandomCrop(
224), # random crop within the center crop
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
]),
data_path=opts.data_path,
partition='train',
sem_reg=opts.semantic_reg),
num_workers=opts.workers,
pin_memory=True,
batch_size=opts.batch_size,
shuffle=True,
drop_last=True,
collate_fn=collate_fn)
# 238459 recipes
print('Training loader prepared, {} recipes'.format(
len(train_loader.dataset)))
# preparing validation loader
val_loader = torch.utils.data.DataLoader(
ImagerLoader(
opts.img_path,
transforms.Compose([
transforms.Resize(
256
), # rescale the image keeping the original aspect ratio
transforms.CenterCrop(
224), # we get only the center of that rescaled
transforms.ToTensor(),
normalize,
]),
data_path=opts.data_path,
partition='val'),
num_workers=opts.workers,
pin_memory=True,
batch_size=100,
shuffle=True,
drop_last=True,
collate_fn=collate_fn)
# 51129 recipes
print('Validation loader prepared, {} recipes'.format(
len(val_loader.dataset)))
# run epochs
torch.cuda.empty_cache()
for epoch in range(opts.start_epoch, opts.epochs):
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch)
# evaluate on validation set
if (epoch + 1) % opts.valfreq == 0 and epoch != 0:
# sum_score = r1i + r5i + r10i + r1t + r5t + r10t
sum_score = 0
for i in range(3):
sum_score += validate(val_loader, model, criterion)
sum_score /= 3
print('Average_score: {}\n'.format(sum_score))
write_log('Average_score: {}\n'.format(sum_score))
if sum_score < best_val:
valtrack += 1
if valtrack >= opts.patience:
# we switch modalities
opts.freeVision = opts.freeRecipe
# opts.freeRecipe = not (opts.freeVision)
# change the learning rate accordingly
adjust_learning_rate(optimizer, epoch, opts)
valtrack = 0
# save the best model
last_name = opts.snapshots + '{}_lr{}_margin{}_e{}_v-{:.3f}.pth.tar'.format(
opts.model, opts.lr, opts.margin, epoch, best_val)
is_best = sum_score > best_val
best_val = max(sum_score, best_val)
save_checkpoint(
{
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'best_val': best_val,
'optimizer': optimizer.state_dict(),
'valtrack': valtrack,
'freeVision': opts.freeVision,
'curr_val': sum_score,
'lr': opts.lr,
'margin': opts.margin,
'last_name': last_name
}, is_best)
print('** Validation: Epoch: {}, Sum_scores: {}, Best: {}'.format(
epoch, sum_score, best_val))
def test_oneimage(image_path):
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
im = Image.open(image_path).convert('RGB')
transform_test_image = transforms.Compose([
transforms.Resize(256), # rescale the image keeping the original aspect ratio
transforms.CenterCrop(256), # we get only the center of that rescaled
transforms.ToTensor(),
normalize,
])
im = transform_test_image(im)
with torch.no_grad():
#im = torch.autograd.Variable(im.cuda())
im = torch.autograd.Variable(im)
im = torch.unsqueeze(im,0)
with open('./data/index_from_id.json', 'r') as f:
index_from_id = json.load(f)
with open('./data/classes1M.pkl', 'rb') as f:
class_dict = pickle.load(f)
classindex = pickle.load(f)
#checkpoint = torch.load(opts.model_path,encoding='latin1')
checkpoint = torch.load(opts.model_path,encoding='latin1',map_location='cpu')
model = im2recipe()
#device = torch.device('cpu')
#model.visionMLP = torch.nn.DataParallel(model.visionMLP)
#model.cuda()
#print (checkpoint['state_dict'])
'''
check_point
optimizer
state_dict
valtrack
best_val
epoch
freeVision
curr_val
'''
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in checkpoint['state_dict'].items():
#print (k)
name = k.replace(".module","") # remove `module.`
new_state_dict[name] = v
# load params
model.load_state_dict(new_state_dict)
#model.load_state_dict(checkpoint['state_dict'])
#model.cpu()
model.eval()
output = model.visionMLP(im)
output = output.view(output.size(0), -1)
#print (output.shape,type(output))
output = model.visual_embedding(output)
output = norm(output)
output = model.semantic_branch(output)
#print ("outputsize",output.size())
#im = torch.Tensor(1, 3, 224, 224)
#im=resnet(im)
#im = torch.tensor(1, 3, 224, 224)
#print(resnet(im))
f**k = nn.functional.softmax(output, 1)
#print (f**k)
maxk = max((1, ))
_, pred = f**k.topk(maxk, 1, True, True)
pred = pred.data.cpu().numpy()
batch_pre = []
for ii in range(len(pred)):
now_pre = []
for j in pred[ii]:
now_pre.append(classindex[j])
batch_pre.append(now_pre)
for ii in range(len(pred)):
for j in range(len(batch_pre[ii])):
pred_name = batch_pre[ii][j]
bianhao = pred[ii][j]
'''
print (pred_name_top1,target_recipe_top1)
print(index_from_id[target_recipe_top1]["title"])
print(index_from_id[target_recipe_top1]["ingredients"])
print(index_from_id[target_recipe_top1]["instructions"])
print("......................................")
'''
for kk, key in enumerate(index_from_id):
if class_dict[key] == bianhao:
print(index_from_id[key]["title"])
print (index_from_id[key]["ingredients"])
print (index_from_id[key]["instructions"])
print("************************************")
print("......................................")
def main():
model = im2recipe()
model.visionMLP = torch.nn.DataParallel(model.visionMLP)
# model = torch.nn.DataParallel(model)
model.to(device)
# define loss function (criterion) and optimizer
# cosine similarity between embeddings -> input1, input2, target
cosine_crit = nn.CosineEmbeddingLoss(0.1).to(device)
criterion = cosine_crit
# # creating different parameter groups
vision_params = list(map(id, model.visionMLP.parameters()))
base_params = filter(lambda p: id(p) not in vision_params,
model.parameters())
# optimizer - with lr initialized accordingly
optimizer = torch.optim.Adam([{
'params': base_params
}, {
'params': model.visionMLP.parameters(),
'lr': opts.lr * opts.freeVision
}],
lr=opts.lr * opts.freeRecipe)
if opts.resume:
if os.path.isfile(opts.resume):
print("=> loading checkpoint '{}'".format(opts.resume))
checkpoint = torch.load(opts.resume)
opts.start_epoch = checkpoint['epoch']
best_val = checkpoint['best_val']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
opts.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(opts.resume))
best_val = float('-inf')
else:
best_val = float('-inf')
# data preparation, loaders
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
val_loader = torch.utils.data.DataLoader(
ImagerLoader(
opts.img_path,
transforms.Compose([
# rescale the image keeping the original aspect ratio
transforms.Scale(256),
# we get only the center of that rescaled
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
]),
data_path=opts.data_path,
partition='val'),
num_workers=opts.workers,
pin_memory=True,
batch_size=100,
shuffle=True,
drop_last=True,
collate_fn=collate_fn)
# 51129 recipes
print('Validation loader prepared, {} recipes'.format(
len(val_loader.dataset)))
res = np.zeros(11)
for i in range(10):
res += validate(val_loader, model, criterion)
res /= 10
i2t_info = "Average Image to text: {:.1f}, {:.1f}, {:.1f}, {:.1f}, {:.1f}".format(
res[0], res[1], res[2], res[3], res[4])
print(i2t_info)
t2i_info = "Average Text to image: {:.1f}, {:.1f}, {:.1f}, {:.1f}, {:.1f}".format(
res[5],
res[6],
res[7],
res[8],
res[9],
)
print(t2i_info)
print('Average Sum Score: ', res[10])
