def __init__(self,
detector_cfg_path,
detector_weights_path,
bert_cfg_path,
bert_weights_path,
object_vocab_path,
tacotron_weights_path,
waveglow_cfg_path,
waveglow_weights_path,
cpu_device,
gpu_device,
fc_layer=0,
max_caption_length=67,
sampling_rate=22050):
"""
args:
detector_cfg_path: path to the detector config
detector_weights_path: path to the detector weights
bert_cfg_path: path to the bert decoder config
bert_weights_path: path to the bert decoder weights
tacotron_weights_path: path to the tacotron weights
waveglow_weights_path: path to the waveglow weights
cpu_device: The cpu device to run some parts of visualization
gpu_device: The gpu device to run the bulk of computations, currently requires at least 1 GPU device
fc_layer: the fully connected layer from the detector to extract features from, 0-indexed
max_caption_length: the maximum number of tokens the caption can be
sampling_rate: the rate that audio representations are sampled per second
"""
self.captioner = Captioner(detector_cfg_path, detector_weights_path,
bert_cfg_path, bert_weights_path,
object_vocab_path, cpu_device, gpu_device,
fc_layer, max_caption_length)
device = gpu_device if gpu_device else cpu_device
self.tts = TTS(tacotron_weights_path, waveglow_cfg_path,
waveglow_weights_path, device, sampling_rate)
def prepare_captioner(self):
config = yaml.load(open('config.yaml', 'r'), Loader=yaml.FullLoader)
checkpoint_path = os.path.join(config['project_root_dir'],
config['checkpoint_path'])
vocab_file_path = os.path.join(config['project_root_dir'],
config['vocab_file_path'])
self.captioner = Captioner(self.sess, checkpoint_path, vocab_file_path)
def build_captioner(model_name,
image_net,
LM_net,
dataset_name='coco',
split_name='val',
vocab='vocabulary',
precomputed_h5=None,
gpu=0,
prev_word_restriction=True):
model_files = ['%s.caffemodel' % (mf) for mf in model_name]
if image_net:
image_net_file = home_dir + image_net
else:
image_net_file = None
lstm_net_file = home_dir + LM_net
vocab_file = '%s/%s.txt' % (determine_vocab_folder(dataset_name,
split_name), vocab)
device_id = gpu
with open(vocab_file, 'r') as vocab_file_read:
vocab = [line.strip() for line in vocab_file_read.readlines()]
anno_path = determine_anno_path(dataset_name, split_name)
image_root = determine_image_pattern(dataset_name, split_name)
sg = build_sequence_generator(anno_path,
50,
image_root,
vocab=vocab,
max_words=50,
align=False,
shuffle=False,
gt_captions=True,
pad=True,
truncate=True,
split_ids=None)
dataset = {}
for image_path, sentence in sg.image_sentence_pairs:
if image_path not in dataset:
dataset[image_path] = []
dataset[image_path].append((sg.line_to_stream(sentence), sentence))
print 'Original dataset contains %d images' % len(dataset.keys())
captioner = Captioner(model_files,
image_net_file,
lstm_net_file,
vocab_file,
device_id=device_id,
precomputed_feats=precomputed_h5,
prev_word_restriction=prev_word_restriction)
return captioner, sg, dataset
def main(image_path, nsamples, temperature):
# Generate questions
sess = gpt2.start_tf_sess()
gpt2.load_gpt2(sess)
config = yaml.load(open('config.yaml', 'r'), Loader=yaml.FullLoader)
checkpoint_path = os.path.join(config['project_root_dir'],
config['checkpoint_path'])
vocab_file_path = os.path.join(config['project_root_dir'],
config['vocab_file_path'])
captioner = Captioner(sess, checkpoint_path, vocab_file_path)
caption = captioner.caption(image_path)
questions = gpt2_gen_questions(sess,
caption,
nsamples=nsamples,
temperature=temperature)
# Print generated questions
print('----------\nQuestions:')
for i, question in enumerate(questions):
print('%d. %s' % (i + 1, question))
def main():
MAX_IMAGES = -1 # -1 to use all images
TAG = 'coco_2layer_factored'
if MAX_IMAGES >= 0:
TAG += '_%dimages' % MAX_IMAGES
eval_on_test = False
if eval_on_test:
ITER = 100000
MODEL_FILENAME = 'lrcn_finetune_trainval_stepsize40k_iter_%d' % ITER
DATASET_NAME = 'test'
else: # eval on val
ITER = 50000
MODEL_FILENAME = 'lrcn_finetune_iter_%d' % ITER
DATASET_NAME = 'val'
TAG += '_%s' % DATASET_NAME
MODEL_DIR = './examples/coco_caption'
MODEL_FILE = '%s/%s.caffemodel' % (MODEL_DIR, MODEL_FILENAME)
IMAGE_NET_FILE = './models/bvlc_reference_caffenet/deploy.prototxt'
LSTM_NET_FILE = './examples/coco_caption/lrcn_word_to_preds.deploy.prototxt'
NET_TAG = '%s_%s' % (TAG, MODEL_FILENAME)
DATASET_SUBDIR = '%s/%s_ims' % (DATASET_NAME,
str(MAX_IMAGES) if MAX_IMAGES >= 0 else 'all')
DATASET_CACHE_DIR = './retrieval_cache/%s/%s' % (DATASET_SUBDIR, MODEL_FILENAME)
VOCAB_FILE = './examples/coco_caption/h5_data/buffer_100/vocabulary.txt'
DEVICE_ID = 0
with open(VOCAB_FILE, 'r') as vocab_file:
vocab = [line.strip() for line in vocab_file.readlines()]
coco = COCO(COCO_ANNO_PATH % DATASET_NAME)
image_root = COCO_IMAGE_PATTERN % DATASET_NAME
sg = CocoSequenceGenerator(coco, BUFFER_SIZE, image_root, vocab=vocab,
align=False, shuffle=False)
dataset = {}
for image_path, sentence in sg.image_sentence_pairs:
if image_path not in dataset:
dataset[image_path] = []
dataset[image_path].append((sg.line_to_stream(sentence), sentence))
print 'Original dataset contains %d images' % len(dataset.keys())
if 0 <= MAX_IMAGES < len(dataset.keys()):
all_keys = dataset.keys()
perm = np.random.permutation(len(all_keys))[:MAX_IMAGES]
chosen_keys = set([all_keys[p] for p in perm])
for key in all_keys:
if key not in chosen_keys:
del dataset[key]
print 'Reduced dataset to %d images' % len(dataset.keys())
if MAX_IMAGES < 0: MAX_IMAGES = len(dataset.keys())
captioner = Captioner(MODEL_FILE, IMAGE_NET_FILE, LSTM_NET_FILE, VOCAB_FILE,
device_id=DEVICE_ID)
beam_size = 1
generation_strategy = {'type': 'beam', 'beam_size': beam_size}
if generation_strategy['type'] == 'beam':
strategy_name = 'beam%d' % generation_strategy['beam_size']
elif generation_strategy['type'] == 'sample':
strategy_name = 'sample%f' % generation_strategy['temp']
else:
raise Exception('Unknown generation strategy type: %s' % generation_strategy['type'])
CACHE_DIR = '%s/%s' % (DATASET_CACHE_DIR, strategy_name)
experimenter = CaptionExperiment(captioner, dataset, DATASET_CACHE_DIR, CACHE_DIR, sg)
captioner.set_image_batch_size(min(100, MAX_IMAGES))
experimenter.generation_experiment(generation_strategy)
captioner.set_caption_batch_size(min(MAX_IMAGES * 5, 1000))
experimenter.retrieval_experiment()
from captioner import Captioner
import retriever
import math
#import image_convert
#im_file = './demo_data/test2.jpg'
pretrained_weights_path = '../models/two_layer_LSTM.caffemodel'
gpu_id = 0
# Initialize the retrieval model
image_net_proto = '../prototxt/VGG_ILSVRC_16_layers_deploy.prototxt'
lstm_net_proto = '../prototxt/scrc_word_to_preds_full.prototxt'
vocab_file = '../data/vocabulary.txt'
# utilize the captioner module from LRCN
captioner = Captioner(pretrained_weights_path, image_net_proto, lstm_net_proto,
vocab_file, gpu_id)
captioner.set_image_batch_size(
50) # decrease the number if your GPU memory is small
vocab_dict = retriever.build_vocab_dict_from_captioner(captioner)
while (1):
sum_candidate_box = []
sum_score_box = []
query = raw_input("type the input query: ")
#query = 'bike on the red house'
print("query =", query)
print("Find best candidate..!")
for i in range(8):
im_file = './splited_image/test' + str(i) + '.jpg'
def main(model_name='',
image_net='',
LM_net='',
dataset_name='val',
vocab='vocabulary',
precomputed_feats=None,
feats_bool_in=False,
precomputed_h5=None,
experiment={'type': 'generation'},
prev_word_restriction=False,
gpu=0):
#model_name is the trained model: path relative to /home/lisa/caffe-LSTM-video
#image_net is the model to extract length 1000 image features: path relative to snapshots folder; do not need to include "caffemodel"
#dataset_name indicates which dataset to look at
#vocab indicates which vocabulary file to look at
#feats_bool is whether or not the images are saved as pickle feature files or if they are normal images
#experiment: dict which has all info needed for experiments. Must have field type which will indicate madlib versus generation expt.
if not precomputed_feats:
precomputed_feats = model_name
MAX_IMAGES = -1 # -1 to use all images
TAG = 'coco_2layer_factored'
if MAX_IMAGES >= 0:
TAG += '_%dimages' % MAX_IMAGES
eval_on_test = False
if eval_on_test:
ITER = 100000
MODEL_FILENAME = 'lrcn_finetune_trainval_stepsize40k_iter_%d' % ITER
DATASET_NAME = 'test'
else: # eval on val
MODEL_FILENAME = model_name
DATASET_NAME = dataset_name
TAG += '_%s' % DATASET_NAME
#MODEL_DIR = home_dir + '/examples/coco_caption/snapshots'
MODEL_DIR = ''
MODEL_FILE = ['%s.caffemodel' % (MF) for MF in MODEL_FILENAME]
#IMAGE_NET_FILE = home_dir + '/models/bvlc_reference_caffenet/deploy.prototxt'
IMAGE_NET_FILE = home_dir + image_net
#LSTM_NET_FILE = home_dir + '/examples/coco_caption/lrcn_word_to_preds.deploy.prototxt'
LSTM_NET_FILE = home_dir + LM_net
DATASET_SUBDIR = '%s/%s_ims' % (DATASET_NAME, str(MAX_IMAGES)
if MAX_IMAGES >= 0 else 'all')
#DATASET_CACHE_DIR = home_dir + '/retrieval_cache/%s/%s' % (DATASET_SUBDIR, MODEL_FILENAME)
DATASET_CACHE_DIR = '/x/lisaanne/retrieval_cache/%s/%s' % (
DATASET_SUBDIR, '_'.join(MODEL_FILENAME))
FEATURE_CACHE_DIR = '/x/lisaanne/retrieval_cache/%s/%s' % (
DATASET_SUBDIR, precomputed_feats)
VOCAB_FILE = '../../examples/coco_caption/h5_data/buffer_100/%s.txt' % vocab
DEVICE_ID = gpu
with open(VOCAB_FILE, 'r') as vocab_file:
vocab = [line.strip() for line in vocab_file.readlines()]
coco = COCO(COCO_ANNO_PATH % DATASET_NAME)
#COCO_IMAGE_PATTERN = '/y/lisaanne/coco/images/%s2014'
COCO_IMAGE_PATTERN = '../../data/coco/coco/images/%s2014'
#COCO_IMAGE_PATTERN = '/y/lisaanne/coco/images2/%s2014'
#image_root = COCO_IMAGE_PATTERN % DATASET_NAME
image_root = COCO_IMAGE_PATTERN % 'val'
#image_root = '/z/lisaanne/imageData/imagenet/'
sg = CocoSequenceGenerator(coco,
BUFFER_SIZE,
image_root,
vocab=vocab,
max_words=MAX_WORDS,
align=False,
shuffle=False,
gt_captions=True,
pad=True,
truncate=True,
split_ids=None,
feats_bool=feats_bool_in)
dataset = {}
for image_path, sentence in sg.image_sentence_pairs:
if image_path not in dataset:
dataset[image_path] = []
dataset[image_path].append((sg.line_to_stream(sentence), sentence))
print 'Original dataset contains %d images' % len(dataset.keys())
if 0 <= MAX_IMAGES < len(dataset.keys()):
all_keys = dataset.keys()
perm = np.random.permutation(len(all_keys))[:MAX_IMAGES]
chosen_keys = set([all_keys[p] for p in perm])
for key in all_keys:
if key not in chosen_keys:
del dataset[key]
print 'Reduced dataset to %d images' % len(dataset.keys())
if MAX_IMAGES < 0: MAX_IMAGES = len(dataset.keys())
captioner = Captioner(MODEL_FILE,
IMAGE_NET_FILE,
LSTM_NET_FILE,
VOCAB_FILE,
device_id=DEVICE_ID,
precomputed_feats=precomputed_h5,
prev_word_restriction=prev_word_restriction)
if 'beam_size' in experiment.keys():
beam_size = experiment['beam_size']
else:
beam_size = 1
generation_strategy = {'type': 'beam', 'beam_size': beam_size}
if generation_strategy['type'] == 'beam':
strategy_name = 'beam%d' % generation_strategy['beam_size']
elif generation_strategy['type'] == 'sample':
strategy_name = 'sample%f' % generation_strategy['temp']
else:
raise Exception('Unknown generation strategy type: %s' %
generation_strategy['type'])
CACHE_DIR = '%s/%s' % (DATASET_CACHE_DIR, strategy_name)
experimenter = CaptionExperiment(captioner, dataset, FEATURE_CACHE_DIR,
CACHE_DIR, sg, feats_bool_in)
captioner.set_image_batch_size(min(100, MAX_IMAGES))
if experiment['type'] == 'madlib':
all_mean_index = []
all_mean_prob = []
all_top_words = []
for fw in experiment['fill_words']:
for cw in experiment['cooccur_words']:
mean_index, mean_prob, top_words = experimenter.madlib_experiment(
fw, [cw])
all_mean_index.append(mean_index)
all_mean_prob.append(mean_prob)
all_top_words.append(top_words)
return all_mean_index, all_mean_prob, all_top_words
if experiment['type'] == 'generation':
experimenter.generation_experiment(generation_strategy, 1000)
if experiment['type'] == 'score_generation':
if 'read_file' in experiment.keys():
read_file = experiment['read_file']
else:
read_file = True
experimenter.score_generation(experiment['json_file'], read_file)
import caffe
import util
from captioner import Captioner
vgg_weights_path = './models/VGG_ILSVRC_16_layers.caffemodel'
gpu_id = 0
image_dir = './datasets/ReferIt/ImageCLEF/images/'
cached_context_features_dir = './data/referit_context_features/'
image_net_proto = './prototxt/VGG_ILSVRC_16_layers_deploy.prototxt'
lstm_net_proto = './prototxt/scrc_word_to_preds_full.prototxt'
vocab_file = './data/vocabulary.txt'
captioner = Captioner(vgg_weights_path, image_net_proto, lstm_net_proto,
vocab_file, gpu_id)
batch_size = 100
captioner.set_image_batch_size(batch_size)
imlist = util.io.load_str_list('./data/split/referit_all_imlist.txt')
num_im = len(imlist)
# Load all images into memory
loaded_images = []
for n_im in range(num_im):
if n_im % 200 == 0:
print('loading image %d / %d into memory' % (n_im, num_im))
im = skimage.io.imread(image_dir + imlist[n_im] + '.jpg')
# Gray scale to RGB
if im.ndim == 2:
def __init__(self, image_model, image_net_proto, lstm_net_proto, vocab): self.captioner = Captioner(image_model, image_net_proto, lstm_net_proto, vocab,-1) self.captioner.set_image_batch_size(1)
def main():
parser = argparse.ArgumentParser()
# Model Setup
parser.add_argument("--detector_config", default=None, type=str,
help="detector config file path.")
parser.add_argument("--detector_weights", default=None, type=str,
help="pretrained detector weights.")
parser.add_argument("--decoder_config", default=None, type=str,
help="Bert decoder config file path.")
parser.add_argument("--decoder_weights", default=None, type=str,
help="pretrained Bert decoder weights.")
parser.add_argument("--object_vocab", default=None, type=str,
help="object vocabulary, maps object ids to object names")
# For COCO
parser.add_argument('--coco_root', type=str, default='~/Datasets/coco')
parser.add_argument("--coco_data_info", default='annotations/dataset_coco.json', type=str,
help="The input data file name.")
parser.add_argument("--coco_ann_file", default='annotations/captions_val2014.json', type=str,
help="caption annotations file (i.e. answer key)")
parser.add_argument('--valid_jpgs_file', default='annotations/coco_valid_jpgs.json', type=str,
help="lists the valid jpgs")
# For data pipeline
parser.add_argument('--batch_size', type=int, default=1,
help="Batch size for decoding. Highly recommended to be a multiple of 8")
parser.add_argument('--dl_workers', type=int, default=0, help="Number of dataloader workers")
# For reproducibility
parser.add_argument('--seed', type=int, default=-1, help="random seed for initialization")
args = parser.parse_args()
assert(torch.cuda.is_available())
cpu_device = torch.device("cpu")
gpu_device = torch.device("cuda:0")
n_gpu = torch.cuda.device_count()
# fix random seed (optional)
if args.seed != -1:
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
with torch.no_grad():
captioner = Captioner(args.detector_config, args.detector_weights, args.decoder_config, args.decoder_weights,
args.object_vocab, cpu_device, gpu_device)
# TODO: optimize for amp, data-parallel
torch.cuda.empty_cache() # Empty everything
valid_dataset = CocoCaptionsKarpathyValidImgs(args.coco_root)
valid_dl = DataLoader(valid_dataset, batch_size=args.batch_size, collate_fn=ccc_karpathy_valid_collate,
num_workers=args.dl_workers, pin_memory=True)
total_batch = math.ceil(len(valid_dataset) / args.batch_size)
predictions = []
print('start the caption evaluation...')
with tqdm(total=total_batch) as pbar:
for img_ids, img_npys in valid_dl:
captions = captioner.forward(img_npys)
for img_id, caption in zip(img_ids, captions):
predictions.append({'image_id': img_id, 'caption': caption})
pbar.update(1)
language_eval(preds=predictions, annFile=os.path.join(args.coco_root, args.coco_ann_file),
model_id='0', split='val')