class CaptionExperiment:
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 getCaption(self, image):
row = image
bytes = array.array("b", row.image).tostring()
im = Image.open(io.BytesIO(bytes))
image = np.array(im, dtype=np.uint8)
dataset = [image]
descriptors = self.captioner.compute_descriptors(dataset)
images = dataset
num_images = len(images)
batch_size = num_images
# Generate captions for all images.
all_captions = [None] * num_images
for image_index in xrange(0, num_images, batch_size):
batch_end_index = min(image_index + batch_size, num_images)
output_captions, output_probs = self.captioner.sample_captions(
descriptors[image_index:batch_end_index], temp=float("inf")
)
for batch_index, output in zip(range(image_index, batch_end_index), output_captions):
all_captions[batch_index] = output
#
# # Collect model/reference captions, formatting the model's captions and
# # each set of reference captions as a list of len(self.images) strings.
# # For each image, write out the highest probability caption.
model_captions = [""] * len(images)
for image_index, image in enumerate(images):
caption = self.captioner.sentence(all_captions[image_index])
model_captions[image_index] = caption
generation_result = [
Row(row.id, model_captions[image_index]) for (image_index, image_path) in enumerate(images)
]
return generation_result
class CaptionExperiment():
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 getCaption(self, image):
row=image
bytes = array.array('b', row.image).tostring()
im = Image.open(io.BytesIO(bytes))
image = np.array(im,dtype=np.uint8)
dataset = [image]
descriptors = self.captioner.compute_descriptors(dataset)
images = dataset
num_images = len(images)
batch_size = num_images
#Generate captions for all images.
all_captions = [None] * num_images
for image_index in xrange(0, num_images, batch_size):
batch_end_index = min(image_index + batch_size, num_images)
output_captions, output_probs = self.captioner.sample_captions(
descriptors[image_index:batch_end_index], temp=float('inf'))
for batch_index, output in zip(range(image_index, batch_end_index),
output_captions):
all_captions[batch_index] = output
#
# # Collect model/reference captions, formatting the model's captions and
# # each set of reference captions as a list of len(self.images) strings.
# # For each image, write out the highest probability caption.
model_captions = [''] * len(images)
for image_index, image in enumerate(images):
caption = self.captioner.sentence(all_captions[image_index])
model_captions[image_index] = caption
generation_result = [Row(row.id,model_captions[image_index]) for (image_index, image_path) in enumerate(images)]
return generation_result
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
vgg_weights_path = './models/VGG_ILSVRC_16_layers.caffemodel'
gpu_id = 0
image_dir = './data/resized_imcrop/'
cached_local_features_dir = './data/referit_local_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/training/train_imcrop_list.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])
# Gray scale to RGB
if im.ndim == 2:
im = np.tile(im[..., np.newaxis], (1, 1, 3))
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'
edgebox_file = './proposal_box/selective_box' + str(
i) + '.txt' # pre-extracted EdgeBox proposals
if distractor_set == "kitchen":
distractor_dir = image_dir
distractor_imlist_file = tst_imlist_file
else:
distractor_dir = './datasets/Kitchen/images/ImageNET/'
distractor_imlist_file = './data/split/kitchen_imagenet_imlist.txt'
query_file = './data/metadata/kitchen_query_dict.json'
vocab_file = './data/vocabulary.txt'
# utilize the captioner module from LRCN
lstm_net_proto = './prototxt/scrc_word_to_preds_no_spatial_no_context.prototxt'
image_net_proto = './prototxt/VGG_ILSVRC_16_layers_deploy.prototxt'
captioner = Captioner(pretrained_weights_path, image_net_proto, lstm_net_proto,
vocab_file, gpu_id)
captioner.set_image_batch_size(50)
vocab_dict = retriever.build_vocab_dict_from_captioner(captioner)
# Load image and caption list
imlist = util.io.load_str_list(tst_imlist_file)
num_im = len(imlist)
query_dict = util.io.load_json(query_file)
# Load distractors
distractor_list = util.io.load_str_list(distractor_imlist_file)
num_distractors = len(distractor_list)
# Sample distractor images for each test image
distractor_ids_per_im = {}
np.random.seed(3) # fix random seed for test repeatibility
for imname in imlist:
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)
################################################################################
image_dir = './datasets/ReferIt/ImageCLEF/images/'
proposal_dir = './data/referit_edgeboxes_top100/'
cached_context_features_dir = './data/referit_context_features/'
imcrop_dict_file = './data/metadata/referit_imcrop_dict.json'
imcrop_bbox_dict_file = './data/metadata/referit_imcrop_bbox_dict.json'
query_file = './data/metadata/referit_query_dict.json'
vocab_file = './data/vocabulary.txt'
# utilize the captioner module from LRCN
image_net_proto = './prototxt/VGG_ILSVRC_16_layers_deploy.prototxt'
captioner = Captioner(pretrained_weights_path, image_net_proto, lstm_net_proto,
vocab_file, gpu_id)
captioner.set_image_batch_size(50)
vocab_dict = retriever.build_vocab_dict_from_captioner(captioner)
# Load image and caption list
imlist = util.io.load_str_list(tst_imlist_file)
num_im = len(imlist)
query_dict = util.io.load_json(query_file)
imcrop_dict = util.io.load_json(imcrop_dict_file)
imcrop_bbox_dict = util.io.load_json(imcrop_bbox_dict_file)
# Load candidate regions (bounding boxes)
load_proposal = (candidate_regions == 'proposal_regions')
candidate_boxes_dict = {imname: None for imname in imlist}
for n_im in range(num_im):
if n_im % 1000 == 0:
print('loading candidate regions %d / %d' % (n_im, num_im))
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:
im = np.tile(im[..., np.newaxis], (1, 1, 3))
# RGBA to RGB
