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DA.py
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DA.py
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import random
from data import ImageDetectionsField, TextField, RawField
from data import COCO, DataLoader
import evaluation
from evaluation import PTBTokenizer, Cider
from models.transformer.transformer_orig import Transformer
from models.transformer import MemoryAugmentedEncoder, MeshedDecoder, ScaledDotProductAttentionMemory
import torch
from torch.optim import Adam
from torch.optim.lr_scheduler import LambdaLR
from torch.nn import NLLLoss
import torch.nn.functional as F
from tqdm import tqdm
import argparse, os, pickle
import numpy as np
import itertools
import multiprocessing
from shutil import copyfile
import warnings
warnings.filterwarnings("ignore")
import os, json
# lines below to make the training reproducible.
seed = 1234
random.seed(seed)
torch.manual_seed(seed)
np.random.seed(seed)
torch.cuda.manual_seed_all(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def evaluate_loss(model, dataloader, loss_fn, text_field):
# Validation loss
model.eval()
running_loss = .0
with tqdm(desc='Epoch %d - validation' % e, unit='it', total=len(dataloader)) as pbar:
with torch.no_grad():
for it, (detections, captions) in enumerate(dataloader):
detections, captions = detections.to(device), captions.to(device)
out = model(detections, captions)
captions = captions[:, 1:].contiguous()
out = out[:, :-1].contiguous()
loss = loss_ls_v2(out, captions)
this_loss = loss.item()
running_loss += this_loss
pbar.set_postfix(loss=running_loss / (it + 1))
pbar.update()
val_loss = running_loss / len(dataloader)
return val_loss
def evaluate_metrics(model, dataloader, text_field):
import itertools
model.eval()
gen = {}
gts = {}
with tqdm(desc='Epoch %d - evaluation' % e, unit='it', total=len(dataloader)) as pbar:
for it, (images, caps_gt) in enumerate(iter(dataloader)):
images = images.to(device)
with torch.no_grad():
out, _ = model.beam_search(images, 20, text_field.vocab.stoi['<eos>'], 5, out_size=1)
caps_gen = text_field.decode(out, join_words=False)
for i, (gts_i, gen_i) in enumerate(zip(caps_gt, caps_gen)):
gen_i = ' '.join([k for k, g in itertools.groupby(gen_i)])
gen['%d_%d' % (it, i)] = [gen_i, ]
gts['%d_%d' % (it, i)] = gts_i
pbar.update()
gts = evaluation.PTBTokenizer.tokenize(gts)
gen = evaluation.PTBTokenizer.tokenize(gen)
scores, _ = evaluation.compute_scores(gts, gen)
return scores
def train_xe(model, dataloader, optim, text_field):
# Training with cross-entropy
model.train()
scheduler.step()
running_loss = .0
with tqdm(desc='Epoch %d - train' % e, unit='it', total=len(dataloader)) as pbar:
for it, (detections, captions) in enumerate(dataloader):
detections, captions = detections.to(device), captions.to(device)
out = model(detections, captions)
optim.zero_grad()
captions_gt = captions[:, 1:].contiguous()
out = out[:, :-1].contiguous()
loss_labelsmoothing = loss_ls_v2(out, captions_gt)
loss_labelsmoothing.backward()
optim.step()
this_loss = loss_labelsmoothing.item()
running_loss += this_loss
pbar.set_postfix(loss=running_loss / (it + 1))
pbar.update()
scheduler.step()
loss = running_loss / len(dataloader)
return loss
class CELossWithLS(torch.nn.Module):
def __init__(self, classes=None, smoothing=0.1, gamma=3.0, isCos=True, ignore_index=-1):
super(CELossWithLS, self).__init__()
self.complement = 1.0 - smoothing
self.smoothing = smoothing
self.cls = classes
self.log_softmax = torch.nn.LogSoftmax(dim=1)
self.gamma = gamma
self.ignore_index = ignore_index
def forward(self, logits, target):
with torch.no_grad():
oh_labels = F.one_hot(target.to(torch.int64), num_classes = self.cls).permute(0,1,2).contiguous()
smoothen_ohlabel = oh_labels * self.complement + self.smoothing / self.cls
logs = self.log_softmax(logits[target!=self.ignore_index])
pt = torch.exp(logs)
return -torch.sum((1-pt).pow(self.gamma)*logs * smoothen_ohlabel[target!=self.ignore_index], dim=1).mean()
if __name__ == '__main__':
device = torch.device('cuda')
parser = argparse.ArgumentParser(description='Meshed-Memory Transformer')
parser.add_argument('--exp_name', type=str, default='m2_transformer')
parser.add_argument('--batch_size', type=int, default=10)
parser.add_argument('--workers', type=int, default=0)
parser.add_argument('--m', type=int, default=40)
parser.add_argument('--head', type=int, default=8)
parser.add_argument('--warmup', type=int, default=10000)
parser.add_argument('--features_path', type=str)
parser.add_argument('--annotation_folder', type=str)
args = parser.parse_args()
print(args)
print('Training')
# Pipeline for image regions
image_field = ImageDetectionsField(detections_path=args.features_path, max_detections=6, load_in_tmp=False)
# Pipeline for text
text_field = TextField(init_token='<bos>', eos_token='<eos>', lower=True, tokenize='spacy',
remove_punctuation=True, nopoints=False)
# Create the dataset
dataset = COCO(image_field, text_field, args.features_path, args.annotation_folder, args.annotation_folder)
train_dataset, val_dataset = dataset.splits
print("-"*100)
print(len(train_dataset))
print(len(val_dataset))
if not os.path.isfile('vocab_%s.pkl' % args.exp_name):
print("Building vocabulary")
text_field.build_vocab(train_dataset, val_dataset, min_freq=2)
pickle.dump(text_field.vocab, open('vocab_%s.pkl' % args.exp_name, 'wb'))
else:
text_field.vocab = pickle.load(open('vocab_%s.pkl' % args.exp_name, 'rb'))
print(len(text_field.vocab))
print(text_field.vocab.stoi)
# Model and dataloaders
encoder = MemoryAugmentedEncoder(3, 0, attention_module=ScaledDotProductAttentionMemory,
attention_module_kwargs={'m': args.m})
decoder = MeshedDecoder(len(text_field.vocab), 54, 3, text_field.vocab.stoi['<pad>'])
model = Transformer(text_field.vocab.stoi['<bos>'], encoder, decoder).to(device)
dict_dataset_train = train_dataset.image_dictionary({'image': image_field, 'text': RawField()})
print(len(dict_dataset_train))
ref_caps_train = list(train_dataset.text)
cider_train = Cider(PTBTokenizer.tokenize(ref_caps_train))
dict_dataset_val = val_dataset.image_dictionary({'image': image_field, 'text': RawField()})
print(len(dict_dataset_val))
def lambda_lr(s):
warm_up = args.warmup
s += 1
return (model.d_model ** -.5) * min(s ** -.5, s * warm_up ** -1.5)
# Load the pretrained model
pretrained_params = torch.load('saved_best_checkpoints/4_save_models_oldfeatures_baseGRLLS/m2_transformer_best.pth')
# pretrained_params = torch.load('saved_best_checkpoints/MICCAI_SGH_Without_LS/ResNet/m2_transformer_best.pth')
model.load_state_dict(pretrained_params['state_dict'], strict = False)
print("Epoch %d" % pretrained_params['epoch'])
print(pretrained_params['best_cider'])
# Initial conditions
optim = Adam(model.parameters(), lr=1, betas=(0.9, 0.98))
scheduler = LambdaLR(optim, lambda_lr)
loss_fn = NLLLoss(ignore_index=text_field.vocab.stoi['<pad>'])
loss_ls_v2 = CELossWithLS(classes=len(text_field.vocab), smoothing=0.0, gamma=0.0, isCos=False, ignore_index=text_field.vocab.stoi['<pad>'])
use_rl = False
best_cider = .0
best_bleu = .0
patience = 0
start_epoch = 0
best_epoch = 0
print("Training starts")
for e in range(start_epoch, start_epoch + 50):
dataloader_train = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.workers,
drop_last=True)
dataloader_val = DataLoader(val_dataset, batch_size=args.batch_size, shuffle=False, num_workers=args.workers)
if (args.annotation_folder == 'annotations/annotations_DA_few_shot') or (args.annotation_folder == 'annotations/annotations_DA_one_shot'):
dict_dataloader_train = DataLoader(dict_dataset_train, batch_size=args.batch_size // 5, shuffle=True,
num_workers=args.workers)
dict_dataloader_val = DataLoader(dict_dataset_val, batch_size=args.batch_size // 5)
elif (args.annotation_folder == 'annotations/annotations_DA_zero_shot'):
dict_dataloader_train = DataLoader(dict_dataset_train, batch_size=args.batch_size, shuffle=True,
num_workers=args.workers)
dict_dataloader_val = DataLoader(dict_dataset_val, batch_size=args.batch_size)
# train model with a word-level cross-entropy loss(xe)
if not use_rl:
train_loss = train_xe(model, dataloader_train, optim, text_field)
# Validation loss
val_loss = evaluate_loss(model, dataloader_val, loss_fn, text_field)
# Validation scores
scores = evaluate_metrics(model, dict_dataloader_val, text_field)
val_cider = scores['CIDEr']
# Prepare for next epoch
best = False
if val_cider >= best_cider:
best_bleu = scores['BLEU'][0]
best_cider = val_cider
best_epoch = e
best = True
print("Validation scores", scores, 'Best epoch',best_epoch,'Best bleu:%.4f, cider:%.4f'%(best_bleu,best_cider))
torch.save({
'torch_rng_state': torch.get_rng_state(),
'cuda_rng_state': torch.cuda.get_rng_state(),
'numpy_rng_state': np.random.get_state(),
'random_rng_state': random.getstate(),
'epoch': e,
'val_loss': val_loss,
'val_cider': val_cider,
'state_dict': model.state_dict(),
'optimizer': optim.state_dict(),
'scheduler': scheduler.state_dict(),
'patience': patience,
'best_cider': best_cider,
'use_rl': use_rl,
}, 'saved_models/%s_last.pth' % args.exp_name)
if best:
print('saving best epoch...!')
copyfile('saved_models/%s_last.pth' % args.exp_name, 'saved_models/%s_best.pth' % args.exp_name)
data = torch.load('saved_models/m2_transformer_best.pth')
model.load_state_dict(data['state_dict'])
print("Epoch %d" % data['epoch'])
print(data['best_cider'])