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See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: README.md ================================================ # Improving Visual-Semantic Embeddings with Hard Negatives Code for the image-caption retrieval methods from **[VSE++: Improving Visual-Semantic Embeddings with Hard Negatives](https://arxiv.org/abs/1707.05612)** *, F. Faghri, D. J. Fleet, J. R. Kiros, S. Fidler, Proceedings of the British Machine Vision Conference (BMVC), 2018. (BMVC Spotlight)* ## Dependencies We recommended to use Anaconda for the following packages. * Python 2.7 (Checkout branch `python3`) * [PyTorch](http://pytorch.org/) (>0.2) (Checkout branch `pytorch4.1`) * [NumPy](http://www.numpy.org/) (>1.12.1) * [TensorBoard](https://github.com/TeamHG-Memex/tensorboard_logger) * [pycocotools](https://github.com/cocodataset/cocoapi) * [torchvision]() * [matplotlib]() * Punkt Sentence Tokenizer: ```python import nltk nltk.download() > d punkt ``` ## Download data Download the dataset files and pre-trained models. We use splits produced by [Andrej Karpathy](http://cs.stanford.edu/people/karpathy/deepimagesent/). The precomputed image features are from [here](https://github.com/ryankiros/visual-semantic-embedding/) and [here](https://github.com/ivendrov/order-embedding). To use full image encoders, download the images from their original sources [here](http://nlp.cs.illinois.edu/HockenmaierGroup/Framing_Image_Description/KCCA.html), [here](http://shannon.cs.illinois.edu/DenotationGraph/) and [here](http://mscoco.org/). ```bash wget http://www.cs.toronto.edu/~faghri/vsepp/vocab.tar wget http://www.cs.toronto.edu/~faghri/vsepp/data.tar wget http://www.cs.toronto.edu/~faghri/vsepp/runs.tar ``` We refer to the path of extracted files for `data.tar` as `$DATA_PATH` and files for `models.tar` as `$RUN_PATH`. Extract `vocab.tar` to `./vocab` directory. *Update: The vocabulary was originally built using all sets (including test set captions). Please see issue #29 for details. Please consider not using test set captions if building up on this project.* ## Evaluate pre-trained models ```python python -c "\ from vocab import Vocabulary import evaluation evaluation.evalrank('$RUN_PATH/coco_vse++/model_best.pth.tar', data_path='$DATA_PATH', split='test')" ``` To do cross-validation on MSCOCO, pass `fold5=True` with a model trained using `--data_name coco`. ## Training new models Run `train.py`: ```bash python train.py --data_path "$DATA_PATH" --data_name coco_precomp --logger_name runs/coco_vse++ --max_violation ``` Arguments used to train pre-trained models: | Method | Arguments | | :-------: | :-------: | | VSE0 | `--no_imgnorm` | | VSE++ | `--max_violation` | | Order0 | `--measure order --use_abs --margin .05 --learning_rate .001` | | Order++ | `--measure order --max_violation` | ## Reference If you found this code useful, please cite the following paper: @article{faghri2018vse++, title={VSE++: Improving Visual-Semantic Embeddings with Hard Negatives}, author={Faghri, Fartash and Fleet, David J and Kiros, Jamie Ryan and Fidler, Sanja}, booktitle = {Proceedings of the British Machine Vision Conference ({BMVC})}, url = {https://github.com/fartashf/vsepp}, year={2018} } ## License [Apache License 2.0](http://www.apache.org/licenses/LICENSE-2.0) ================================================ FILE: data.py ================================================ import torch import torch.utils.data as data import torchvision.transforms as transforms import os import nltk from PIL import Image from pycocotools.coco import COCO import numpy as np import json as jsonmod def get_paths(path, name='coco', use_restval=False): """ Returns paths to images and annotations for the given datasets. For MSCOCO indices are also returned to control the data split being used. The indices are extracted from the Karpathy et al. splits using this snippet: >>> import json >>> dataset=json.load(open('dataset_coco.json','r')) >>> A=[] >>> for i in range(len(D['images'])): ... if D['images'][i]['split'] == 'val': ... A+=D['images'][i]['sentids'][:5] ... :param name: Dataset names :param use_restval: If True, the the `restval` data is included in train. """ roots = {} ids = {} if 'coco' == name: imgdir = os.path.join(path, 'images') capdir = os.path.join(path, 'annotations') roots['train'] = { 'img': os.path.join(imgdir, 'train2014'), 'cap': os.path.join(capdir, 'captions_train2014.json') } roots['val'] = { 'img': os.path.join(imgdir, 'val2014'), 'cap': os.path.join(capdir, 'captions_val2014.json') } roots['test'] = { 'img': os.path.join(imgdir, 'val2014'), 'cap': os.path.join(capdir, 'captions_val2014.json') } roots['trainrestval'] = { 'img': (roots['train']['img'], roots['val']['img']), 'cap': (roots['train']['cap'], roots['val']['cap']) } ids['train'] = np.load(os.path.join(capdir, 'coco_train_ids.npy')) ids['val'] = np.load(os.path.join(capdir, 'coco_dev_ids.npy'))[:5000] ids['test'] = np.load(os.path.join(capdir, 'coco_test_ids.npy')) ids['trainrestval'] = ( ids['train'], np.load(os.path.join(capdir, 'coco_restval_ids.npy'))) if use_restval: roots['train'] = roots['trainrestval'] ids['train'] = ids['trainrestval'] elif 'f8k' == name: imgdir = os.path.join(path, 'images') cap = os.path.join(path, 'dataset_flickr8k.json') roots['train'] = {'img': imgdir, 'cap': cap} roots['val'] = {'img': imgdir, 'cap': cap} roots['test'] = {'img': imgdir, 'cap': cap} ids = {'train': None, 'val': None, 'test': None} elif 'f30k' == name: imgdir = os.path.join(path, 'images') cap = os.path.join(path, 'dataset_flickr30k.json') roots['train'] = {'img': imgdir, 'cap': cap} roots['val'] = {'img': imgdir, 'cap': cap} roots['test'] = {'img': imgdir, 'cap': cap} ids = {'train': None, 'val': None, 'test': None} return roots, ids class CocoDataset(data.Dataset): """COCO Custom Dataset compatible with torch.utils.data.DataLoader.""" def __init__(self, root, json, vocab, transform=None, ids=None): """ Args: root: image directory. json: coco annotation file path. vocab: vocabulary wrapper. transform: transformer for image. """ self.root = root # when using `restval`, two json files are needed if isinstance(json, tuple): self.coco = (COCO(json[0]), COCO(json[1])) else: self.coco = (COCO(json),) self.root = (root,) # if ids provided by get_paths, use split-specific ids if ids is None: self.ids = list(self.coco.anns.keys()) else: self.ids = ids # if `restval` data is to be used, record the break point for ids if isinstance(self.ids, tuple): self.bp = len(self.ids[0]) self.ids = list(self.ids[0]) + list(self.ids[1]) else: self.bp = len(self.ids) self.vocab = vocab self.transform = transform def __getitem__(self, index): """This function returns a tuple that is further passed to collate_fn """ vocab = self.vocab root, caption, img_id, path, image = self.get_raw_item(index) if self.transform is not None: image = self.transform(image) # Convert caption (string) to word ids. tokens = nltk.tokenize.word_tokenize( str(caption).lower().decode('utf-8')) caption = [] caption.append(vocab('')) caption.extend([vocab(token) for token in tokens]) caption.append(vocab('')) target = torch.Tensor(caption) return image, target, index, img_id def get_raw_item(self, index): if index < self.bp: coco = self.coco[0] root = self.root[0] else: coco = self.coco[1] root = self.root[1] ann_id = self.ids[index] caption = coco.anns[ann_id]['caption'] img_id = coco.anns[ann_id]['image_id'] path = coco.loadImgs(img_id)[0]['file_name'] image = Image.open(os.path.join(root, path)).convert('RGB') return root, caption, img_id, path, image def __len__(self): return len(self.ids) class FlickrDataset(data.Dataset): """ Dataset loader for Flickr30k and Flickr8k full datasets. """ def __init__(self, root, json, split, vocab, transform=None): self.root = root self.vocab = vocab self.split = split self.transform = transform self.dataset = jsonmod.load(open(json, 'r'))['images'] self.ids = [] for i, d in enumerate(self.dataset): if d['split'] == split: self.ids += [(i, x) for x in range(len(d['sentences']))] def __getitem__(self, index): """This function returns a tuple that is further passed to collate_fn """ vocab = self.vocab root = self.root ann_id = self.ids[index] img_id = ann_id[0] caption = self.dataset[img_id]['sentences'][ann_id[1]]['raw'] path = self.dataset[img_id]['filename'] image = Image.open(os.path.join(root, path)).convert('RGB') if self.transform is not None: image = self.transform(image) # Convert caption (string) to word ids. tokens = nltk.tokenize.word_tokenize( str(caption).lower().decode('utf-8')) caption = [] caption.append(vocab('')) caption.extend([vocab(token) for token in tokens]) caption.append(vocab('')) target = torch.Tensor(caption) return image, target, index, img_id def __len__(self): return len(self.ids) class PrecompDataset(data.Dataset): """ Load precomputed captions and image features Possible options: f8k, f30k, coco, 10crop """ def __init__(self, data_path, data_split, vocab): self.vocab = vocab loc = data_path + '/' # Captions self.captions = [] with open(loc+'%s_caps.txt' % data_split, 'rb') as f: for line in f: self.captions.append(line.strip()) # Image features self.images = np.load(loc+'%s_ims.npy' % data_split) self.length = len(self.captions) # rkiros data has redundancy in images, we divide by 5, 10crop doesn't if self.images.shape[0] != self.length: self.im_div = 5 else: self.im_div = 1 # the development set for coco is large and so validation would be slow if data_split == 'dev': self.length = 5000 def __getitem__(self, index): # handle the image redundancy img_id = index/self.im_div image = torch.Tensor(self.images[img_id]) caption = self.captions[index] vocab = self.vocab # Convert caption (string) to word ids. tokens = nltk.tokenize.word_tokenize( str(caption).lower().decode('utf-8')) caption = [] caption.append(vocab('')) caption.extend([vocab(token) for token in tokens]) caption.append(vocab('')) target = torch.Tensor(caption) return image, target, index, img_id def __len__(self): return self.length def collate_fn(data): """Build mini-batch tensors from a list of (image, caption) tuples. Args: data: list of (image, caption) tuple. - image: torch tensor of shape (3, 256, 256). - caption: torch tensor of shape (?); variable length. Returns: images: torch tensor of shape (batch_size, 3, 256, 256). targets: torch tensor of shape (batch_size, padded_length). lengths: list; valid length for each padded caption. """ # Sort a data list by caption length data.sort(key=lambda x: len(x[1]), reverse=True) images, captions, ids, img_ids = zip(*data) # Merge images (convert tuple of 3D tensor to 4D tensor) images = torch.stack(images, 0) # Merget captions (convert tuple of 1D tensor to 2D tensor) lengths = [len(cap) for cap in captions] targets = torch.zeros(len(captions), max(lengths)).long() for i, cap in enumerate(captions): end = lengths[i] targets[i, :end] = cap[:end] return images, targets, lengths, ids def get_loader_single(data_name, split, root, json, vocab, transform, batch_size=100, shuffle=True, num_workers=2, ids=None, collate_fn=collate_fn): """Returns torch.utils.data.DataLoader for custom coco dataset.""" if 'coco' in data_name: # COCO custom dataset dataset = CocoDataset(root=root, json=json, vocab=vocab, transform=transform, ids=ids) elif 'f8k' in data_name or 'f30k' in data_name: dataset = FlickrDataset(root=root, split=split, json=json, vocab=vocab, transform=transform) # Data loader data_loader = torch.utils.data.DataLoader(dataset=dataset, batch_size=batch_size, shuffle=shuffle, pin_memory=True, num_workers=num_workers, collate_fn=collate_fn) return data_loader def get_precomp_loader(data_path, data_split, vocab, opt, batch_size=100, shuffle=True, num_workers=2): """Returns torch.utils.data.DataLoader for custom coco dataset.""" dset = PrecompDataset(data_path, data_split, vocab) data_loader = torch.utils.data.DataLoader(dataset=dset, batch_size=batch_size, shuffle=shuffle, pin_memory=True, collate_fn=collate_fn) return data_loader def get_transform(data_name, split_name, opt): normalizer = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) t_list = [] if split_name == 'train': t_list = [transforms.RandomResizedCrop(opt.crop_size), transforms.RandomHorizontalFlip()] elif split_name == 'val': t_list = [transforms.Resize(256), transforms.CenterCrop(224)] elif split_name == 'test': t_list = [transforms.Resize(256), transforms.CenterCrop(224)] t_end = [transforms.ToTensor(), normalizer] transform = transforms.Compose(t_list + t_end) return transform def get_loaders(data_name, vocab, crop_size, batch_size, workers, opt): dpath = os.path.join(opt.data_path, data_name) if opt.data_name.endswith('_precomp'): train_loader = get_precomp_loader(dpath, 'train', vocab, opt, batch_size, True, workers) val_loader = get_precomp_loader(dpath, 'dev', vocab, opt, batch_size, False, workers) else: # Build Dataset Loader roots, ids = get_paths(dpath, data_name, opt.use_restval) transform = get_transform(data_name, 'train', opt) train_loader = get_loader_single(opt.data_name, 'train', roots['train']['img'], roots['train']['cap'], vocab, transform, ids=ids['train'], batch_size=batch_size, shuffle=True, num_workers=workers, collate_fn=collate_fn) transform = get_transform(data_name, 'val', opt) val_loader = get_loader_single(opt.data_name, 'val', roots['val']['img'], roots['val']['cap'], vocab, transform, ids=ids['val'], batch_size=batch_size, shuffle=False, num_workers=workers, collate_fn=collate_fn) return train_loader, val_loader def get_test_loader(split_name, data_name, vocab, crop_size, batch_size, workers, opt): dpath = os.path.join(opt.data_path, data_name) if opt.data_name.endswith('_precomp'): test_loader = get_precomp_loader(dpath, split_name, vocab, opt, batch_size, False, workers) else: # Build Dataset Loader roots, ids = get_paths(dpath, data_name, opt.use_restval) transform = get_transform(data_name, split_name, opt) test_loader = get_loader_single(opt.data_name, split_name, roots[split_name]['img'], roots[split_name]['cap'], vocab, transform, ids=ids[split_name], batch_size=batch_size, shuffle=False, num_workers=workers, collate_fn=collate_fn) return test_loader ================================================ FILE: evaluation.py ================================================ from __future__ import print_function import os import pickle import numpy from data import get_test_loader import time import numpy as np from vocab import Vocabulary # NOQA import torch from model import VSE, order_sim from collections import OrderedDict 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=0): self.val = val self.sum += val * n self.count += n self.avg = self.sum / (.0001 + self.count) def __str__(self): """String representation for logging """ # for values that should be recorded exactly e.g. iteration number if self.count == 0: return str(self.val) # for stats return '%.4f (%.4f)' % (self.val, self.avg) class LogCollector(object): """A collection of logging objects that can change from train to val""" def __init__(self): # to keep the order of logged variables deterministic self.meters = OrderedDict() def update(self, k, v, n=0): # create a new meter if previously not recorded if k not in self.meters: self.meters[k] = AverageMeter() self.meters[k].update(v, n) def __str__(self): """Concatenate the meters in one log line """ s = '' for i, (k, v) in enumerate(self.meters.iteritems()): if i > 0: s += ' ' s += k + ' ' + str(v) return s def tb_log(self, tb_logger, prefix='', step=None): """Log using tensorboard """ for k, v in self.meters.iteritems(): tb_logger.log_value(prefix + k, v.val, step=step) def encode_data(model, data_loader, log_step=10, logging=print): """Encode all images and captions loadable by `data_loader` """ batch_time = AverageMeter() val_logger = LogCollector() # switch to evaluate mode model.val_start() end = time.time() # numpy array to keep all the embeddings img_embs = None cap_embs = None for i, (images, captions, lengths, ids) in enumerate(data_loader): # make sure val logger is used model.logger = val_logger # compute the embeddings img_emb, cap_emb = model.forward_emb(images, captions, lengths, volatile=True) # initialize the numpy arrays given the size of the embeddings if img_embs is None: img_embs = np.zeros((len(data_loader.dataset), img_emb.size(1))) cap_embs = np.zeros((len(data_loader.dataset), cap_emb.size(1))) # preserve the embeddings by copying from gpu and converting to numpy img_embs[ids] = img_emb.data.cpu().numpy().copy() cap_embs[ids] = cap_emb.data.cpu().numpy().copy() # measure accuracy and record loss model.forward_loss(img_emb, cap_emb) # measure elapsed time batch_time.update(time.time() - end) end = time.time() if i % log_step == 0: logging('Test: [{0}/{1}]\t' '{e_log}\t' 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' .format( i, len(data_loader), batch_time=batch_time, e_log=str(model.logger))) del images, captions return img_embs, cap_embs def evalrank(model_path, data_path=None, split='dev', fold5=False): """ Evaluate a trained model on either dev or test. If `fold5=True`, 5 fold cross-validation is done (only for MSCOCO). Otherwise, the full data is used for evaluation. """ # load model and options checkpoint = torch.load(model_path) opt = checkpoint['opt'] if data_path is not None: opt.data_path = data_path # load vocabulary used by the model with open(os.path.join(opt.vocab_path, '%s_vocab.pkl' % opt.data_name), 'rb') as f: vocab = pickle.load(f) opt.vocab_size = len(vocab) # construct model model = VSE(opt) # load model state model.load_state_dict(checkpoint['model']) print('Loading dataset') data_loader = get_test_loader(split, opt.data_name, vocab, opt.crop_size, opt.batch_size, opt.workers, opt) print('Computing results...') img_embs, cap_embs = encode_data(model, data_loader) print('Images: %d, Captions: %d' % (img_embs.shape[0] / 5, cap_embs.shape[0])) if not fold5: # no cross-validation, full evaluation r, rt = i2t(img_embs, cap_embs, measure=opt.measure, return_ranks=True) ri, rti = t2i(img_embs, cap_embs, measure=opt.measure, return_ranks=True) ar = (r[0] + r[1] + r[2]) / 3 ari = (ri[0] + ri[1] + ri[2]) / 3 rsum = r[0] + r[1] + r[2] + ri[0] + ri[1] + ri[2] print("rsum: %.1f" % rsum) print("Average i2t Recall: %.1f" % ar) print("Image to text: %.1f %.1f %.1f %.1f %.1f" % r) print("Average t2i Recall: %.1f" % ari) print("Text to image: %.1f %.1f %.1f %.1f %.1f" % ri) else: # 5fold cross-validation, only for MSCOCO results = [] for i in range(5): r, rt0 = i2t(img_embs[i * 5000:(i + 1) * 5000], cap_embs[i * 5000:(i + 1) * 5000], measure=opt.measure, return_ranks=True) print("Image to text: %.1f, %.1f, %.1f, %.1f, %.1f" % r) ri, rti0 = t2i(img_embs[i * 5000:(i + 1) * 5000], cap_embs[i * 5000:(i + 1) * 5000], measure=opt.measure, return_ranks=True) if i == 0: rt, rti = rt0, rti0 print("Text to image: %.1f, %.1f, %.1f, %.1f, %.1f" % ri) ar = (r[0] + r[1] + r[2]) / 3 ari = (ri[0] + ri[1] + ri[2]) / 3 rsum = r[0] + r[1] + r[2] + ri[0] + ri[1] + ri[2] print("rsum: %.1f ar: %.1f ari: %.1f" % (rsum, ar, ari)) results += [list(r) + list(ri) + [rsum, ar, ari]] print("-----------------------------------") print("Mean metrics: ") mean_metrics = tuple(np.array(results).mean(axis=0).flatten()) print("rsum: %.1f" % (mean_metrics[10] * 6)) print("Average i2t Recall: %.1f" % mean_metrics[11]) print("Image to text: %.1f %.1f %.1f %.1f %.1f" % mean_metrics[:5]) print("Average t2i Recall: %.1f" % mean_metrics[12]) print("Text to image: %.1f %.1f %.1f %.1f %.1f" % mean_metrics[5:10]) torch.save({'rt': rt, 'rti': rti}, 'ranks.pth.tar') def i2t(images, captions, npts=None, measure='cosine', return_ranks=False): """ Images->Text (Image Annotation) Images: (5N, K) matrix of images Captions: (5N, K) matrix of captions """ if npts is None: npts = images.shape[0] / 5 index_list = [] ranks = numpy.zeros(npts) top1 = numpy.zeros(npts) for index in range(npts): # Get query image im = images[5 * index].reshape(1, images.shape[1]) # Compute scores if measure == 'order': bs = 100 if index % bs == 0: mx = min(images.shape[0], 5 * (index + bs)) im2 = images[5 * index:mx:5] d2 = order_sim(torch.Tensor(im2).cuda(), torch.Tensor(captions).cuda()) d2 = d2.cpu().numpy() d = d2[index % bs] else: d = numpy.dot(im, captions.T).flatten() inds = numpy.argsort(d)[::-1] index_list.append(inds[0]) # Score rank = 1e20 for i in range(5 * index, 5 * index + 5, 1): tmp = numpy.where(inds == i)[0][0] if tmp < rank: rank = tmp ranks[index] = rank top1[index] = inds[0] # Compute metrics r1 = 100.0 * len(numpy.where(ranks < 1)[0]) / len(ranks) r5 = 100.0 * len(numpy.where(ranks < 5)[0]) / len(ranks) r10 = 100.0 * len(numpy.where(ranks < 10)[0]) / len(ranks) medr = numpy.floor(numpy.median(ranks)) + 1 meanr = ranks.mean() + 1 if return_ranks: return (r1, r5, r10, medr, meanr), (ranks, top1) else: return (r1, r5, r10, medr, meanr) def t2i(images, captions, npts=None, measure='cosine', return_ranks=False): """ Text->Images (Image Search) Images: (5N, K) matrix of images Captions: (5N, K) matrix of captions """ if npts is None: npts = images.shape[0] / 5 ims = numpy.array([images[i] for i in range(0, len(images), 5)]) ranks = numpy.zeros(5 * npts) top1 = numpy.zeros(5 * npts) for index in range(npts): # Get query captions queries = captions[5 * index:5 * index + 5] # Compute scores if measure == 'order': bs = 100 if 5 * index % bs == 0: mx = min(captions.shape[0], 5 * index + bs) q2 = captions[5 * index:mx] d2 = order_sim(torch.Tensor(ims).cuda(), torch.Tensor(q2).cuda()) d2 = d2.cpu().numpy() d = d2[:, (5 * index) % bs:(5 * index) % bs + 5].T else: d = numpy.dot(queries, ims.T) inds = numpy.zeros(d.shape) for i in range(len(inds)): inds[i] = numpy.argsort(d[i])[::-1] ranks[5 * index + i] = numpy.where(inds[i] == index)[0][0] top1[5 * index + i] = inds[i][0] # Compute metrics r1 = 100.0 * len(numpy.where(ranks < 1)[0]) / len(ranks) r5 = 100.0 * len(numpy.where(ranks < 5)[0]) / len(ranks) r10 = 100.0 * len(numpy.where(ranks < 10)[0]) / len(ranks) medr = numpy.floor(numpy.median(ranks)) + 1 meanr = ranks.mean() + 1 if return_ranks: return (r1, r5, r10, medr, meanr), (ranks, top1) else: return (r1, r5, r10, medr, meanr) ================================================ FILE: model.py ================================================ import torch import torch.nn as nn import torch.nn.init import torchvision.models as models from torch.autograd import Variable from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence import torch.backends.cudnn as cudnn from torch.nn.utils.clip_grad import clip_grad_norm import numpy as np from collections import OrderedDict def l2norm(X): """L2-normalize columns of X """ norm = torch.pow(X, 2).sum(dim=1, keepdim=True).sqrt() X = torch.div(X, norm) return X def EncoderImage(data_name, img_dim, embed_size, finetune=False, cnn_type='vgg19', use_abs=False, no_imgnorm=False): """A wrapper to image encoders. Chooses between an encoder that uses precomputed image features, `EncoderImagePrecomp`, or an encoder that computes image features on the fly `EncoderImageFull`. """ if data_name.endswith('_precomp'): img_enc = EncoderImagePrecomp( img_dim, embed_size, use_abs, no_imgnorm) else: img_enc = EncoderImageFull( embed_size, finetune, cnn_type, use_abs, no_imgnorm) return img_enc # tutorials/09 - Image Captioning class EncoderImageFull(nn.Module): def __init__(self, embed_size, finetune=False, cnn_type='vgg19', use_abs=False, no_imgnorm=False): """Load pretrained VGG19 and replace top fc layer.""" super(EncoderImageFull, self).__init__() self.embed_size = embed_size self.no_imgnorm = no_imgnorm self.use_abs = use_abs # Load a pre-trained model self.cnn = self.get_cnn(cnn_type, True) # For efficient memory usage. for param in self.cnn.parameters(): param.requires_grad = finetune # Replace the last fully connected layer of CNN with a new one if cnn_type.startswith('vgg'): self.fc = nn.Linear(self.cnn.classifier._modules['6'].in_features, embed_size) self.cnn.classifier = nn.Sequential( *list(self.cnn.classifier.children())[:-1]) elif cnn_type.startswith('resnet'): self.fc = nn.Linear(self.cnn.module.fc.in_features, embed_size) self.cnn.module.fc = nn.Sequential() self.init_weights() def get_cnn(self, arch, pretrained): """Load a pretrained CNN and parallelize over GPUs """ if pretrained: print("=> using pre-trained model '{}'".format(arch)) model = models.__dict__[arch](pretrained=True) else: print("=> creating model '{}'".format(arch)) model = models.__dict__[arch]() if arch.startswith('alexnet') or arch.startswith('vgg'): model.features = nn.DataParallel(model.features) model.cuda() else: model = nn.DataParallel(model).cuda() return model def load_state_dict(self, state_dict): """ Handle the models saved before commit pytorch/vision@989d52a """ if 'cnn.classifier.1.weight' in state_dict: state_dict['cnn.classifier.0.weight'] = state_dict[ 'cnn.classifier.1.weight'] del state_dict['cnn.classifier.1.weight'] state_dict['cnn.classifier.0.bias'] = state_dict[ 'cnn.classifier.1.bias'] del state_dict['cnn.classifier.1.bias'] state_dict['cnn.classifier.3.weight'] = state_dict[ 'cnn.classifier.4.weight'] del state_dict['cnn.classifier.4.weight'] state_dict['cnn.classifier.3.bias'] = state_dict[ 'cnn.classifier.4.bias'] del state_dict['cnn.classifier.4.bias'] super(EncoderImageFull, self).load_state_dict(state_dict) def init_weights(self): """Xavier initialization for the fully connected layer """ r = np.sqrt(6.) / np.sqrt(self.fc.in_features + self.fc.out_features) self.fc.weight.data.uniform_(-r, r) self.fc.bias.data.fill_(0) def forward(self, images): """Extract image feature vectors.""" features = self.cnn(images) # normalization in the image embedding space features = l2norm(features) # linear projection to the joint embedding space features = self.fc(features) # normalization in the joint embedding space if not self.no_imgnorm: features = l2norm(features) # take the absolute value of the embedding (used in order embeddings) if self.use_abs: features = torch.abs(features) return features class EncoderImagePrecomp(nn.Module): def __init__(self, img_dim, embed_size, use_abs=False, no_imgnorm=False): super(EncoderImagePrecomp, self).__init__() self.embed_size = embed_size self.no_imgnorm = no_imgnorm self.use_abs = use_abs self.fc = nn.Linear(img_dim, embed_size) self.init_weights() def init_weights(self): """Xavier initialization for the fully connected layer """ r = np.sqrt(6.) / np.sqrt(self.fc.in_features + self.fc.out_features) self.fc.weight.data.uniform_(-r, r) self.fc.bias.data.fill_(0) def forward(self, images): """Extract image feature vectors.""" # assuming that the precomputed features are already l2-normalized features = self.fc(images) # normalize in the joint embedding space if not self.no_imgnorm: features = l2norm(features) # take the absolute value of embedding (used in order embeddings) if self.use_abs: features = torch.abs(features) return features def load_state_dict(self, state_dict): """Copies parameters. overwritting the default one to accept state_dict from Full model """ own_state = self.state_dict() new_state = OrderedDict() for name, param in state_dict.items(): if name in own_state: new_state[name] = param super(EncoderImagePrecomp, self).load_state_dict(new_state) # tutorials/08 - Language Model # RNN Based Language Model class EncoderText(nn.Module): def __init__(self, vocab_size, word_dim, embed_size, num_layers, use_abs=False): super(EncoderText, self).__init__() self.use_abs = use_abs self.embed_size = embed_size # word embedding self.embed = nn.Embedding(vocab_size, word_dim) # caption embedding self.rnn = nn.GRU(word_dim, embed_size, num_layers, batch_first=True) self.init_weights() def init_weights(self): self.embed.weight.data.uniform_(-0.1, 0.1) def forward(self, x, lengths): """Handles variable size captions """ # Embed word ids to vectors x = self.embed(x) packed = pack_padded_sequence(x, lengths, batch_first=True) # Forward propagate RNN out, _ = self.rnn(packed) # Reshape *final* output to (batch_size, hidden_size) padded = pad_packed_sequence(out, batch_first=True) I = torch.LongTensor(lengths).view(-1, 1, 1) I = Variable(I.expand(x.size(0), 1, self.embed_size)-1).cuda() out = torch.gather(padded[0], 1, I).squeeze(1) # normalization in the joint embedding space out = l2norm(out) # take absolute value, used by order embeddings if self.use_abs: out = torch.abs(out) return out def cosine_sim(im, s): """Cosine similarity between all the image and sentence pairs """ return im.mm(s.t()) def order_sim(im, s): """Order embeddings similarity measure $max(0, s-im)$ """ YmX = (s.unsqueeze(1).expand(s.size(0), im.size(0), s.size(1)) - im.unsqueeze(0).expand(s.size(0), im.size(0), s.size(1))) score = -YmX.clamp(min=0).pow(2).sum(2).sqrt().t() return score class ContrastiveLoss(nn.Module): """ Compute contrastive loss """ def __init__(self, margin=0, measure=False, max_violation=False): super(ContrastiveLoss, self).__init__() self.margin = margin if measure == 'order': self.sim = order_sim else: self.sim = cosine_sim self.max_violation = max_violation def forward(self, im, s): # compute image-sentence score matrix scores = self.sim(im, s) diagonal = scores.diag().view(im.size(0), 1) d1 = diagonal.expand_as(scores) d2 = diagonal.t().expand_as(scores) # compare every diagonal score to scores in its column # caption retrieval cost_s = (self.margin + scores - d1).clamp(min=0) # compare every diagonal score to scores in its row # image retrieval cost_im = (self.margin + scores - d2).clamp(min=0) # clear diagonals mask = torch.eye(scores.size(0)) > .5 I = Variable(mask) if torch.cuda.is_available(): I = I.cuda() cost_s = cost_s.masked_fill_(I, 0) cost_im = cost_im.masked_fill_(I, 0) # keep the maximum violating negative for each query if self.max_violation: cost_s = cost_s.max(1)[0] cost_im = cost_im.max(0)[0] return cost_s.sum() + cost_im.sum() class VSE(object): """ rkiros/uvs model """ def __init__(self, opt): # tutorials/09 - Image Captioning # Build Models self.grad_clip = opt.grad_clip self.img_enc = EncoderImage(opt.data_name, opt.img_dim, opt.embed_size, opt.finetune, opt.cnn_type, use_abs=opt.use_abs, no_imgnorm=opt.no_imgnorm) self.txt_enc = EncoderText(opt.vocab_size, opt.word_dim, opt.embed_size, opt.num_layers, use_abs=opt.use_abs) if torch.cuda.is_available(): self.img_enc.cuda() self.txt_enc.cuda() cudnn.benchmark = True # Loss and Optimizer self.criterion = ContrastiveLoss(margin=opt.margin, measure=opt.measure, max_violation=opt.max_violation) params = list(self.txt_enc.parameters()) params += list(self.img_enc.fc.parameters()) if opt.finetune: params += list(self.img_enc.cnn.parameters()) self.params = params self.optimizer = torch.optim.Adam(params, lr=opt.learning_rate) self.Eiters = 0 def state_dict(self): state_dict = [self.img_enc.state_dict(), self.txt_enc.state_dict()] return state_dict def load_state_dict(self, state_dict): self.img_enc.load_state_dict(state_dict[0]) self.txt_enc.load_state_dict(state_dict[1]) def train_start(self): """switch to train mode """ self.img_enc.train() self.txt_enc.train() def val_start(self): """switch to evaluate mode """ self.img_enc.eval() self.txt_enc.eval() def forward_emb(self, images, captions, lengths, volatile=False): """Compute the image and caption embeddings """ # Set mini-batch dataset images = Variable(images, volatile=volatile) captions = Variable(captions, volatile=volatile) if torch.cuda.is_available(): images = images.cuda() captions = captions.cuda() # Forward img_emb = self.img_enc(images) cap_emb = self.txt_enc(captions, lengths) return img_emb, cap_emb def forward_loss(self, img_emb, cap_emb, **kwargs): """Compute the loss given pairs of image and caption embeddings """ loss = self.criterion(img_emb, cap_emb) self.logger.update('Le', loss.data[0], img_emb.size(0)) return loss def train_emb(self, images, captions, lengths, ids=None, *args): """One training step given images and captions. """ self.Eiters += 1 self.logger.update('Eit', self.Eiters) self.logger.update('lr', self.optimizer.param_groups[0]['lr']) # compute the embeddings img_emb, cap_emb = self.forward_emb(images, captions, lengths) # measure accuracy and record loss self.optimizer.zero_grad() loss = self.forward_loss(img_emb, cap_emb) # compute gradient and do SGD step loss.backward() if self.grad_clip > 0: clip_grad_norm(self.params, self.grad_clip) self.optimizer.step() ================================================ FILE: train.py ================================================ import pickle import os import time import shutil import torch import data from vocab import Vocabulary # NOQA from model import VSE from evaluation import i2t, t2i, AverageMeter, LogCollector, encode_data import logging import tensorboard_logger as tb_logger import argparse def main(): # Hyper Parameters parser = argparse.ArgumentParser() parser.add_argument('--data_path', default='/w/31/faghri/vsepp_data/', help='path to datasets') parser.add_argument('--data_name', default='precomp', help='{coco,f8k,f30k,10crop}_precomp|coco|f8k|f30k') parser.add_argument('--vocab_path', default='./vocab/', help='Path to saved vocabulary pickle files.') parser.add_argument('--margin', default=0.2, type=float, help='Rank loss margin.') parser.add_argument('--num_epochs', default=30, type=int, help='Number of training epochs.') parser.add_argument('--batch_size', default=128, type=int, help='Size of a training mini-batch.') parser.add_argument('--word_dim', default=300, type=int, help='Dimensionality of the word embedding.') parser.add_argument('--embed_size', default=1024, type=int, help='Dimensionality of the joint embedding.') parser.add_argument('--grad_clip', default=2., type=float, help='Gradient clipping threshold.') parser.add_argument('--crop_size', default=224, type=int, help='Size of an image crop as the CNN input.') parser.add_argument('--num_layers', default=1, type=int, help='Number of GRU layers.') parser.add_argument('--learning_rate', default=.0002, type=float, help='Initial learning rate.') parser.add_argument('--lr_update', default=15, type=int, help='Number of epochs to update the learning rate.') parser.add_argument('--workers', default=10, type=int, help='Number of data loader workers.') parser.add_argument('--log_step', default=10, type=int, help='Number of steps to print and record the log.') parser.add_argument('--val_step', default=500, type=int, help='Number of steps to run validation.') parser.add_argument('--logger_name', default='runs/runX', help='Path to save the model and Tensorboard log.') parser.add_argument('--resume', default='', type=str, metavar='PATH', help='path to latest checkpoint (default: none)') parser.add_argument('--max_violation', action='store_true', help='Use max instead of sum in the rank loss.') parser.add_argument('--img_dim', default=4096, type=int, help='Dimensionality of the image embedding.') parser.add_argument('--finetune', action='store_true', help='Fine-tune the image encoder.') parser.add_argument('--cnn_type', default='vgg19', help="""The CNN used for image encoder (e.g. vgg19, resnet152)""") parser.add_argument('--use_restval', action='store_true', help='Use the restval data for training on MSCOCO.') parser.add_argument('--measure', default='cosine', help='Similarity measure used (cosine|order)') parser.add_argument('--use_abs', action='store_true', help='Take the absolute value of embedding vectors.') parser.add_argument('--no_imgnorm', action='store_true', help='Do not normalize the image embeddings.') parser.add_argument('--reset_train', action='store_true', help='Ensure the training is always done in ' 'train mode (Not recommended).') opt = parser.parse_args() print(opt) logging.basicConfig(format='%(asctime)s %(message)s', level=logging.INFO) tb_logger.configure(opt.logger_name, flush_secs=5) # Load Vocabulary Wrapper vocab = pickle.load(open(os.path.join( opt.vocab_path, '%s_vocab.pkl' % opt.data_name), 'rb')) opt.vocab_size = len(vocab) # Load data loaders train_loader, val_loader = data.get_loaders( opt.data_name, vocab, opt.crop_size, opt.batch_size, opt.workers, opt) # Construct the model model = VSE(opt) # optionally resume from a checkpoint if opt.resume: if os.path.isfile(opt.resume): print("=> loading checkpoint '{}'".format(opt.resume)) checkpoint = torch.load(opt.resume) start_epoch = checkpoint['epoch'] best_rsum = checkpoint['best_rsum'] model.load_state_dict(checkpoint['model']) # Eiters is used to show logs as the continuation of another # training model.Eiters = checkpoint['Eiters'] print("=> loaded checkpoint '{}' (epoch {}, best_rsum {})" .format(opt.resume, start_epoch, best_rsum)) validate(opt, val_loader, model) else: print("=> no checkpoint found at '{}'".format(opt.resume)) # Train the Model best_rsum = 0 for epoch in range(opt.num_epochs): adjust_learning_rate(opt, model.optimizer, epoch) # train for one epoch train(opt, train_loader, model, epoch, val_loader) # evaluate on validation set rsum = validate(opt, val_loader, model) # remember best R@ sum and save checkpoint is_best = rsum > best_rsum best_rsum = max(rsum, best_rsum) save_checkpoint({ 'epoch': epoch + 1, 'model': model.state_dict(), 'best_rsum': best_rsum, 'opt': opt, 'Eiters': model.Eiters, }, is_best, prefix=opt.logger_name + '/') def train(opt, train_loader, model, epoch, val_loader): # average meters to record the training statistics batch_time = AverageMeter() data_time = AverageMeter() train_logger = LogCollector() # switch to train mode model.train_start() end = time.time() for i, train_data in enumerate(train_loader): if opt.reset_train: # Always reset to train mode, this is not the default behavior model.train_start() # measure data loading time data_time.update(time.time() - end) # make sure train logger is used model.logger = train_logger # Update the model model.train_emb(*train_data) # measure elapsed time batch_time.update(time.time() - end) end = time.time() # Print log info if model.Eiters % opt.log_step == 0: logging.info( 'Epoch: [{0}][{1}/{2}]\t' '{e_log}\t' 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' 'Data {data_time.val:.3f} ({data_time.avg:.3f})\t' .format( epoch, i, len(train_loader), batch_time=batch_time, data_time=data_time, e_log=str(model.logger))) # Record logs in tensorboard tb_logger.log_value('epoch', epoch, step=model.Eiters) tb_logger.log_value('step', i, step=model.Eiters) tb_logger.log_value('batch_time', batch_time.val, step=model.Eiters) tb_logger.log_value('data_time', data_time.val, step=model.Eiters) model.logger.tb_log(tb_logger, step=model.Eiters) # validate at every val_step if model.Eiters % opt.val_step == 0: validate(opt, val_loader, model) def validate(opt, val_loader, model): # compute the encoding for all the validation images and captions img_embs, cap_embs = encode_data( model, val_loader, opt.log_step, logging.info) # caption retrieval (r1, r5, r10, medr, meanr) = i2t(img_embs, cap_embs, measure=opt.measure) logging.info("Image to text: %.1f, %.1f, %.1f, %.1f, %.1f" % (r1, r5, r10, medr, meanr)) # image retrieval (r1i, r5i, r10i, medri, meanri) = t2i( img_embs, cap_embs, measure=opt.measure) logging.info("Text to image: %.1f, %.1f, %.1f, %.1f, %.1f" % (r1i, r5i, r10i, medri, meanri)) # sum of recalls to be used for early stopping currscore = r1 + r5 + r10 + r1i + r5i + r10i # record metrics in tensorboard tb_logger.log_value('r1', r1, step=model.Eiters) tb_logger.log_value('r5', r5, step=model.Eiters) tb_logger.log_value('r10', r10, step=model.Eiters) tb_logger.log_value('medr', medr, step=model.Eiters) tb_logger.log_value('meanr', meanr, step=model.Eiters) tb_logger.log_value('r1i', r1i, step=model.Eiters) tb_logger.log_value('r5i', r5i, step=model.Eiters) tb_logger.log_value('r10i', r10i, step=model.Eiters) tb_logger.log_value('medri', medri, step=model.Eiters) tb_logger.log_value('meanri', meanri, step=model.Eiters) tb_logger.log_value('rsum', currscore, step=model.Eiters) return currscore def save_checkpoint(state, is_best, filename='checkpoint.pth.tar', prefix=''): torch.save(state, prefix + filename) if is_best: shutil.copyfile(prefix + filename, prefix + 'model_best.pth.tar') def adjust_learning_rate(opt, optimizer, epoch): """Sets the learning rate to the initial LR decayed by 10 every 30 epochs""" lr = opt.learning_rate * (0.1 ** (epoch // opt.lr_update)) for param_group in optimizer.param_groups: param_group['lr'] = lr def accuracy(output, target, topk=(1,)): """Computes the precision@k for the specified values of k""" maxk = max(topk) batch_size = target.size(0) _, pred = output.topk(maxk, 1, True, True) pred = pred.t() correct = pred.eq(target.view(1, -1).expand_as(pred)) res = [] for k in topk: correct_k = correct[:k].view(-1).float().sum(0) res.append(correct_k.mul_(100.0 / batch_size)) return res if __name__ == '__main__': main() ================================================ FILE: vocab.py ================================================ # Create a vocabulary wrapper import nltk import pickle from collections import Counter from pycocotools.coco import COCO import json import argparse import os annotations = { 'coco_precomp': ['train_caps.txt', 'dev_caps.txt'], 'coco': ['annotations/captions_train2014.json', 'annotations/captions_val2014.json'], 'f8k_precomp': ['train_caps.txt', 'dev_caps.txt'], '10crop_precomp': ['train_caps.txt', 'dev_caps.txt'], 'f30k_precomp': ['train_caps.txt', 'dev_caps.txt'], 'f8k': ['dataset_flickr8k.json'], 'f30k': ['dataset_flickr30k.json'], } class Vocabulary(object): """Simple vocabulary wrapper.""" def __init__(self): self.word2idx = {} self.idx2word = {} self.idx = 0 def add_word(self, word): if word not in self.word2idx: self.word2idx[word] = self.idx self.idx2word[self.idx] = word self.idx += 1 def __call__(self, word): if word not in self.word2idx: return self.word2idx[''] return self.word2idx[word] def __len__(self): return len(self.word2idx) def from_coco_json(path): coco = COCO(path) ids = coco.anns.keys() captions = [] for i, idx in enumerate(ids): captions.append(str(coco.anns[idx]['caption'])) return captions def from_flickr_json(path): dataset = json.load(open(path, 'r'))['images'] captions = [] for i, d in enumerate(dataset): captions += [str(x['raw']) for x in d['sentences']] return captions def from_txt(txt): captions = [] with open(txt, 'rb') as f: for line in f: captions.append(line.strip()) return captions def build_vocab(data_path, data_name, jsons, threshold): """Build a simple vocabulary wrapper.""" counter = Counter() for path in jsons[data_name]: full_path = os.path.join(os.path.join(data_path, data_name), path) if data_name == 'coco': captions = from_coco_json(full_path) elif data_name == 'f8k' or data_name == 'f30k': captions = from_flickr_json(full_path) else: captions = from_txt(full_path) for i, caption in enumerate(captions): tokens = nltk.tokenize.word_tokenize( caption.lower().decode('utf-8')) counter.update(tokens) if i % 1000 == 0: print("[%d/%d] tokenized the captions." % (i, len(captions))) # Discard if the occurrence of the word is less than min_word_cnt. words = [word for word, cnt in counter.items() if cnt >= threshold] # Create a vocab wrapper and add some special tokens. vocab = Vocabulary() vocab.add_word('') vocab.add_word('') vocab.add_word('') vocab.add_word('') # Add words to the vocabulary. for i, word in enumerate(words): vocab.add_word(word) return vocab def main(data_path, data_name): vocab = build_vocab(data_path, data_name, jsons=annotations, threshold=4) with open('./vocab/%s_vocab.pkl' % data_name, 'wb') as f: pickle.dump(vocab, f, pickle.HIGHEST_PROTOCOL) print("Saved vocabulary file to ", './vocab/%s_vocab.pkl' % data_name) if __name__ == '__main__': parser = argparse.ArgumentParser() parser.add_argument('--data_path', default='/w/31/faghri/vsepp_data/') parser.add_argument('--data_name', default='coco', help='{coco,f8k,f30k,10crop}_precomp|coco|f8k|f30k') opt = parser.parse_args() main(opt.data_path, opt.data_name)