def __init__(self, input_size, embedding_size, hidden_size, vocab_size,
num_layer):
super(Model, self).__init__()
self.encoder = model.EncoderCNN(input_size, embedding_size)
self.decoder = model.DecoderRNN(embedding_size, hidden_size,
vocab_size, num_layer)
self.criterion = nn.CrossEntropyLoss()
def main(args):
transform = transforms.Compose([
transforms.Resize(256),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
with open(args.vocab_path, "rb") as f1, \
open(args.batched_file_path, "rb") as f2:
vocab = pickle.load(f1)
batched_val_set = pickle.load(f2)
coco_caps = COCO(args.caption_path)
batched_val_loader = get_loader(args.image_dir,
args.caption_path,
batched_val_set,
vocab,
transform,
shuffle=True,
num_workers=3)
encoder = model.EncoderCNN()
decoder = model.DecoderRNN(512, 196, 512, 512, len(vocab), 1)
if torch.cuda.is_available():
encoder = encoder.cuda()
decoder = decoder.cuda()
checkpoint = torch.load(args.load_checkpoint)
decoder.load_state_dict(checkpoint["state_dict"])
checkpoint = None
torch.cuda.empty_cache()
for i, (images, captions, lengths, ids) in enumerate(batched_val_loader):
if i == args.num_runs:
break
print("\nactual captions for batch " + str(i) + " are: ")
annIds = coco_caps.getAnnIds(imgIds=ids)
anns = coco_caps.loadAnns(annIds)
for ann in anns:
print(ann["caption"])
images = to_var(images, volatile=True)
captions = to_var(captions, volatile=True)
features = encoder(images)
results = decoder.sample(features, args.beam_size)
print("predicted captions are: ")
for result in results:
candidate = [vocab(i) for i in result[1][:-1]]
references = [nltk.tokenize.word_tokenize(ann["caption"].lower()) for ann in anns]
score = bleu_score.sentence_bleu(references, candidate)
print("probability: %5.4f, BLEU score: %5.4f, caption: %s" %(result[0], score, caption_id_to_string(result[1], vocab)))
with open('vocabSet.pkl', 'rb') as f:
vocabularySet = pickle.load(f)
print("Loaded Vocabulary Set")
with open('vocabSet2.pkl', 'rb') as f:
vocabularySet2 = pickle.load(f)
print("Loaded Reverse Vocabulary Set")
modelsPath = "LSTM4Models/"
imagesPath = "../data/val2014/"
captionsPath = "../data/annotations/captions_val.json"
cnnEn = model.EncoderCNN(wordEmbeddings).eval()
lstmDe = model.DecoderRNN(wordEmbeddings, lstmHiddenStates, len(vocabularySet),
lstmLayers)
cnnEn = cnnEn.to(device)
lstmDe = lstmDe.to(device)
valData = COCO(captionsPath)
#Exploiting Pycocotools to get insights about data
print("Total Annotations: " + str(len(valData.anns.keys())))
print("Total Images: " + str(len(valData.imgs.keys())))
#Visualise
print(valData.imgToAnns[393212])
for (i, key) in enumerate(valData.imgToAnns.keys()):
batch_size = args.bs
imgh = args.imh
imgw = args.imw
embed_dim = args.embed_size
hidden_dim = args.nhid
attention_dim =args.attention_dim
transform = transforms.Compose([transforms.Resize((imgh, imgw)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))
])
fine_tune_encoder = False
encoder = model.EncoderCNN().to(device)
encoder.fine_tune(fine_tune_encoder)
decoder = model.DecoderRNN(ntokens, embed_dim, hidden_dim, idx2word, word2idx).to(device)
loss_fn = nn.CrossEntropyLoss().to(device)
decoder_optimizer = t.optim.Adam(params=filter(lambda p: p.requires_grad, decoder.parameters()),
lr=decoder_lr)
encoder_optimizer = t.optim.Adam(params=filter(lambda p: p.requires_grad, encoder.parameters()),
lr=encoder_lr) if fine_tune_encoder else None
# def prepare_sequence(seq, to_ix):
# idxs = [to_ix[w] for w in seq]
# return t.tensor(idxs, dtype=t.long, device = device)
def batchify(data, bs):
## DataLoaders provide various ways to get batches of examples.
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
collate_fn=dataload.collate_fn,
**kwargs)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.batch_size,
shuffle=True,
collate_fn=dataload.collate_fn,
**kwargs)
## Load the proper neural network model.
if args.model == 'Pretrained':
model.encoder = model.EncoderCNN(args.embed_dim)
model.decoder = model.DecoderRNN(embed_size=args.embed_dim,
hidden_size=args.hidden_dim,
vocab_size=vocab_size,
num_layers=1,
max_seq_length=10)
else:
raise Exception('Unknown model {}'.format(args.model))
## the loss function -cross-entropy.
criterion = functional.cross_entropy
## Activate CUDA if specified and available.
if args.cuda:
def main(args):
transform = transforms.Compose([
transforms.Resize(256),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
useCuda = not args.disable_cuda
with open(args.vocab_path,
'rb') as f1, open(args.batched_train_path,
'rb') as f2, open(args.batched_val_path,
'rb') as f3:
vocab = pickle.load(f1)
batched_train_set = pickle.load(f2)
batched_val_set = pickle.load(f3)
batched_train_loader = get_loader(args.train_image_dir,
args.train_caption_path,
batched_train_set,
vocab,
transform,
shuffle=True,
num_workers=3)
batched_val_loader = get_loader(args.val_image_dir,
args.val_caption_path,
batched_val_set,
vocab,
transform,
shuffle=True,
num_workers=1)
random_val_loader = get_loader(args.val_image_dir,
args.val_caption_path,
batched_val_set,
vocab,
transform,
shuffle=True,
num_workers=1)
encoder_cnn = model.EncoderCNN(args.is_normalized, useCuda=useCuda)
decoder_rnn = model.DecoderRNN(args.embedding_dim,
args.hidden_size,
len(vocab),
args.batch_size,
dropout=args.dropout,
useCuda=useCuda)
if torch.cuda.is_available() and useCuda:
decoder_rnn.cuda()
loss_function = nn.NLLLoss()
#loss_function = nn.CrossEntropyLoss()
params = list(decoder_rnn.parameters())
optimizer = optim.Adam(params, lr=args.encoder_lr)
#scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', patience=1)
output_train_file = open(args.output_train_name, 'w')
output_val_file = open(args.output_val_name, 'w')
start_epoch = 0
save_name = file_namer.make_checkpoint_name(args.batch_size, args.min_occurrences, args.num_epochs, \
args.dropout, args.decoder_lr, args.encoder_lr, args.embedding_dim, args.hidden_size, args.grad_clip, \
args.is_normalized) if args.load_checkpoint == "" else args.load_checkpoint
checkpoint_name = file_namer.get_checkpoint(save_name)
if checkpoint_name is not None:
print("loading from checkpoint " + checkpoint_name)
checkpoint = torch.load(checkpoint_name) if useCuda else torch.load(
checkpoint_name, map_location=lambda storage, loc: storage)
start_epoch = checkpoint['epoch']
decoder_rnn.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
args.load_checkpoint = checkpoint_name
checkpoint = None
torch.cuda.empty_cache()
else:
print("No existing checkpoints, starting from scratch")
args.load_checkpoint = "No checkpoint found"
full_return_index = mp.Value('i', 0)
full_return_value = mp.Value('d', 0.0)
full_val_processes = None
for epoch in range(start_epoch, args.num_epochs):
val_processes = None
return_index = mp.Value('i', 0)
return_value = mp.Value('d', 0.0)
train_progress_bar = tqdm(iterable=batched_train_loader,
desc='Epoch [%i/%i] (Train)' %
(epoch, args.num_epochs))
train_sum_loss = 0
for i, (images, captions, _) in enumerate(train_progress_bar):
train_sum_loss += trainer.train(encoder_cnn, decoder_rnn,
loss_function, optimizer, images,
captions, args.grad_clip, useCuda)
train_progress_bar.set_postfix(loss=train_sum_loss /
((i % 100) + 1))
if i % 100 == 0:
output_train_file.write(
"%d, %5.4f\n" %
(epoch * len(batched_train_loader) + i,
train_sum_loss / 100 if i > 0 else train_sum_loss))
if i % 1000 == 0:
if val_processes is not None:
val_processes.join()
output_val_file.write(
"%d, %5.4f\n" %
(return_index.value, return_value.value))
val_processes = mp.Process(
target=validate,
args=(random_val_loader, encoder_cnn, decoder_rnn,
loss_function, useCuda,
epoch * len(batched_train_loader) + i,
return_index, return_value))
val_processes.start()
train_sum_loss = 0
if full_val_processes is not None:
full_val_processes.join()
#scheduler.step(full_return_value.value)
output_val_file.write(
"End of Epoch\n%d, %5.4f\n" %
(full_return_index.value, full_return_value.value))
full_val_processes = mp.Process(
target=validate_full,
args=(batched_val_loader, encoder_cnn, decoder_rnn, loss_function,
useCuda, epoch, args.num_epochs, len(batched_train_loader),
full_return_index, full_return_value))
full_val_processes.start()
torch.save({'epoch': epoch + 1,
'state_dict': decoder_rnn.state_dict(),
'optimizer': optimizer.state_dict()},
file_namer.make_checkpoint_name(args.batch_size, args.min_occurrences, epoch + 1, args.dropout, \
args.decoder_lr, args.encoder_lr, args.embedding_dim, args.hidden_size, args.grad_clip, args.is_normalized))
if full_val_processes is not None:
full_val_processes.join()
output_val_file.write(
"End of Epoch\n%d, %5.4f\n" %
(full_return_index.value, full_return_value.value))
full_val_processes = None
output_train_file.close()
output_val_file.close()
if args.plot:
args.train_files.append(args.output_train_name)
args.val_files.append(args.output_val_name)
plot(args)
args.png_files = [args.plot_name]
if args.send_email:
args.txt_files = [args.output_train_name, args.output_val_name]
f = open('arguments.txt', 'w')
for arg in sorted(vars(args)):
# arguments we don't want sent in the email
ignore_args = [
'user', 'password', 'to', 'plot_name', 'train_image_dir',
'val_image_dir', 'send_email', 'plot', 'plot_name',
'train_caption_path', 'val_caption_path', 'png_files',
'txt_files', 'disable_cuda', 'body', 'output_train_name',
'output_val_name', 'show', 'subject', 'max_batched_set_size'
]
if not arg in ignore_args:
f.write("%s: %s\n" % (arg, getattr(args, arg)))
f.close()
if not args.body:
args.body = 'arguments.txt'
else:
args.txt_files.append('arguments.txt')
send_email(args)
collate_fn=dataload.collate_fn,
**kwargs)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=args.batch_size,
shuffle=True,
collate_fn=dataload.collate_fn,
**kwargs)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=True,
collate_fn=dataload.val_collate,
**kwargs)
## Load the proper neural network model.
if args.model == 'Pretrained':
# Problem 2 (no hidden layer, input -> output)
model.encoder = model.EncoderCNN(10)
model.decoder = model.DecoderRNN(encoder_dim=2048,
decoder_dim=512,
attention_dim=512,
embed_size=512,
hidden_size=args.hidden_dim,
vocab_size=vocab_size,
num_layers=1,
max_seq_length=15)
#elif args.model == 'resnet_common':
# Problem 5 (multiple hidden layers, input -> hidden layers -> output)
# print("sruthi check 1")
# model = models.resnetcommon.ResnetCommon(im_size, args.hidden_dim, args.kernel_size, n_classes)
else:
raise Exception('Unknown model {}'.format(args.model))
vocabularySet2 = pickle.load(f)
print("Loaded Reverse Vocabulary Set")
modelPath = "models/"
imagesPath = "../data/images/"
captionsPath = "../data/annotations/captions_train.json"
#Hyper Parameters - TUNABLE
lstmLayers = 3
lstmHiddenStates = 512
wordEmbeddings = 256
epochs = 5
batchSize = 64
learningRate = 0.001
cnn = model.EncoderCNN(wordEmbeddings).to(device)
lstm = model.DecoderRNN(wordEmbeddings, lstmHiddenStates, len(vocabularySet),
lstmLayers).to(device)
criterion = torch.nn.CrossEntropyLoss()
parameters = list(lstm.parameters()) + list(cnn.linear.parameters()) + list(
cnn.bn.parameters())
optimizer = torch.optim.Adam(parameters, lr=learningRate)
#Preprocessing of Image data
transform = transforms.Compose([
transforms.RandomCrop(224),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
with open("../ImageCaptioner/data/vocab/vocab_occurrence_5.pkl", 'rb') as f1,\
open("../ImageCaptioner/data/batched_data/val_batch_1.pkl", "rb") as f2:
vocab = pickle.load(f1)
batched_val_set = pickle.load(f2)
coco_caps = COCO("../ImageCaptioner/data/annotations/captions_val2014.json")
batched_val_loader = get_loader(
"../ImageCaptioner/data/val2014",
"../ImageCaptioner/data/annotations/captions_val2014.json",
batched_val_set,
vocab,
transform,
shuffle=True,
num_workers=3)
encoder = model.EncoderCNN()
decoder = model.DecoderRNN(512, 196, 512, 512, len(vocab), 1)
if torch.cuda.is_available():
encoder = encoder.cuda()
decoder = decoder.cuda()
checkpoint = torch.load(
"noNorm/model_batch_100_dims_512x512_lr_0.0001/checkpoint_25.pt")
decoder.load_state_dict(checkpoint['state_dict'])
checkpoint = None
torch.cuda.empty_cache()
for i, (images, captions, lengths, ids) in enumerate(batched_val_loader):
if i == 1:
break
print("actual captions are: ")
def main(args):
transform = transforms.Compose([
transforms.Resize(256),
transforms.RandomCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
useCuda = not args.disable_cuda
with open(args.vocab_path, 'rb') as vocab_path, \
open(args.batched_train_path, 'rb') as batched_train_path, \
open(args.batched_val_path, 'rb') as batched_val_path:
vocab = pickle.load(vocab_path)
batched_train_set = pickle.load(batched_train_path)
batched_val_set = pickle.load(batched_val_path)
batched_train_loader = get_loader(args.train_image_dir,
args.train_caption_path,
batched_train_set,
vocab,
transform,
shuffle=True,
num_workers=3)
batched_val_loader = get_loader(args.val_image_dir,
args.val_caption_path,
batched_val_set,
vocab,
transform,
shuffle=True,
num_workers=1)
batched_val_loader_full = get_loader(args.val_image_dir,
args.val_caption_path,
batched_val_set,
vocab,
transform,
shuffle=True,
num_workers=1)
encoder_cnn = model.EncoderCNN()
decoder_rnn = model.DecoderRNN(512,
196,
args.embedding_dim,
args.hidden_dim,
len(vocab),
args.num_layers,
args.dropout,
useCuda=useCuda)
if torch.cuda.is_available() and useCuda:
encoder_cnn.cuda()
decoder_rnn.cuda()
loss_function = nn.NLLLoss()
params = list(decoder_rnn.parameters())
optimizer = optim.Adam(params, lr=args.lr)
output_train_file = open(
args.output_dir + "/train_" + str(args.num_epochs) + ".txt", 'w')
output_val_file = open(
args.output_dir + "/val_" + str(args.num_epochs) + ".txt", 'w')
start_epoch = 0
if args.load_checkpoint is not None:
checkpoint = torch.load(
args.load_checkpoint) if useCuda else torch.load(
args.load_checkpoint,
map_location=lambda storage, loc: storage)
print("loading from checkpoint " + str(args.load_checkpoint))
start_epoch = checkpoint['epoch']
decoder_rnn.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
checkpoint = None
torch.cuda.empty_cache()
for epoch in range(start_epoch, args.num_epochs):
progress_bar = tqdm(iterable=batched_train_loader,
desc='Epoch [%i/%i] (Train)' %
(epoch, args.num_epochs))
train_sum_loss = 0
for i, (images, captions, lengths, ids) in enumerate(progress_bar, 1):
loss = train(images, captions, encoder_cnn, decoder_rnn,
loss_function, optimizer, args.grad_clip, useCuda)
train_sum_loss += loss.data.select(0, 0)
progress_bar.set_postfix(loss=train_sum_loss / ((i % 100) + 1))
if i % 100 == 0:
output_train_file.write("%d, %5.4f\n" %
(epoch * len(batched_train_loader) + i,
train_sum_loss / 100))
train_sum_loss = 0
if i % 1000 == 0:
temp_loss = validate(batched_val_loader, encoder_cnn,
decoder_rnn, loss_function, useCuda)
output_val_file.write(
"%d, %5.4f\n" %
(epoch * len(batched_train_loader) + i, temp_loss))
# end of batch
output_train_file.write(
"%d, %5.4f\n" % ((epoch + 1) * len(batched_train_loader),
train_sum_loss / len(batched_train_loader) / 100))
val_sum_loss = 0
val_progress_bar = tqdm(iterable=batched_val_loader_full,
desc='Epoch [%i/%i] (Val)' %
(epoch, args.num_epochs))
for i, (images, captions, lengths,
ids) in enumerate(val_progress_bar, 1):
loss = evaluate(images, captions, encoder_cnn, decoder_rnn,
loss_function, optimizer, useCuda)
val_sum_loss += loss.data.select(0, 0)
val_progress_bar.set_postfix(loss=val_sum_loss / i)
output_val_file.write("%d, %5.4f\n" %
((epoch + 1) * len(batched_train_loader),
val_sum_loss / len(batched_val_loader_full)))
torch.save(
{
'epoch': epoch + 1,
'state_dict': decoder_rnn.state_dict(),
'optimizer': optimizer.state_dict()
}, args.output_dir + "/checkpoint_" + str(epoch + 1) + ".pt")
output_train_file.close()
output_val_file.close()
def main():
VOCAB = vocabulary.load_vocab(vocab_dict_path)
print(len(VOCAB))
dataset_raw = preprocessing.PreprocessedData(tagg_toy, im_toy,
dataset_new_folder, nlp,
VOCAB)
# dataset_raw = preprocessing.PreprocessedData(tagged_files_paths, im_paths, dataset_new_folder, nlp, VOCAB)
print("\nDataset lengths.... data / labels")
print(len(dataset_raw.train_data), len(dataset_raw.labels))
dataset_processed = dataset.ImageTextDataset(dataset_raw)
print('\nLength of dataset')
print(len(dataset_processed))
U = utils.Utils()
train_n, val_n = U.partition_numbers(.8, len(dataset_processed))
train_set, val_set = torch.utils.data.random_split(dataset_processed,
[train_n, val_n])
print('\nTrainset {}, valset {}'.format(train_set, val_set))
########################### HYPERPARAMETERS ##########################################################
num_workers = 8 if CUDA else 0
batch_size = 32
embedding_dim = 64
num_hidden_nodes = 512
size_of_vocab = len(VOCAB)
num_output_nodes = size_of_vocab
num_layers = 3
bidirection = True
dropout = 0
nepochs = 20
lr = 0.001
weight_decay = 0.00001
#####################################################################################################
train_dataloader = DataLoader(train_set,
batch_size=batch_size,
collate_fn=dataset.collate,
shuffle=True,
num_workers=num_workers,
drop_last=False)
val_dataloader = DataLoader(val_set,
batch_size=batch_size,
collate_fn=dataset.collate,
shuffle=True,
num_workers=num_workers,
drop_last=True)
# Instantiate
encoder = model.EncoderCNN(embedding_dim)
decoder = model.DECODER(size_of_vocab,
embedding_dim,
num_hidden_nodes,
num_output_nodes,
num_layers,
bidirectional=bidirection,
dropout=dropout)
# Criterion & Optimizer
criterion = nn.CrossEntropyLoss(ignore_index=0)
optimizer = optim.Adam(decoder.parameters(),
lr=lr,
weight_decay=weight_decay)
# Train
train_losses, test_losses, train_perplexities, test_perplexities = train_test.run_epochs(
encoder, decoder, optimizer, criterion, train_dataloader,
val_dataloader, nepochs)
# Generate
generate.generate_labels(dataset_raw.train_data[0:20])
