def main(data_root, result_root, split, seed, feat_window_size):
result_root += "-s-%d-%d" % (split, seed)
### read label2index mapping and index2label mapping ###########################
label2index = dict()
index2label = dict()
with open(os.path.join(data_root, 'mapping.txt'), 'r') as f:
content = f.read().split('\n')[0:-1]
for line in content:
label2index[line.split()[1]] = int(line.split()[0])
index2label[int(line.split()[0])] = line.split()[1]
### read test data #############################################################
#with open('data/split1.test', 'r') as f:
with open(os.path.join(data_root, 'split%d.test' % split), 'r') as f:
video_list = f.read().split('\n')[0:-1]
dataset = Dataset(data_root, video_list, label2index, shuffle=False)
# load prior, length model, grammar, and network
load_iteration = NUM_ITERS
log_prior = np.log(
np.loadtxt('%s/prior.iter-' % result_root + str(load_iteration) +
'.txt'))
grammar = PathGrammar('%s/grammar.txt' % result_root, label2index)
length_model = PoissonModel('%s/lengths.iter-' % result_root +
str(load_iteration) + '.txt',
max_length=2000)
forwarder = Forwarder(dataset.input_dimension,
dataset.n_classes,
feat_window_size=feat_window_size)
forwarder.load_model('%s/network.iter-' % result_root +
str(load_iteration) + '.net')
# parallelization
n_threads = 4
# Viterbi decoder
viterbi_decoder = Viterbi(grammar,
length_model,
frame_sampling=30,
max_hypotheses=np.inf)
# forward each video
log_probs = dict()
queue = mp.Queue()
for i, data in enumerate(dataset):
sequence, _ = data
video = list(dataset.features.keys())[i]
queue.put(video)
log_probs[video] = forwarder.forward(sequence) - log_prior
log_probs[video] = log_probs[video] - np.max(log_probs[video])
# Viterbi decoding
procs = []
for i in range(n_threads):
p = mp.Process(target=decode,
args=(queue, log_probs, viterbi_decoder, index2label,
result_root))
procs.append(p)
p.start()
for p in procs:
p.join()
def infer(label2index, index2label, n_threads):
# load models
log_prior = np.log(np.loadtxt('results/prior'))
grammar = PathGrammar('results/grammar', label2index)
length_model = PoissonModel('results/mean_lengths', max_length=2000)
forwarder = Forwarder('results/net.model')
# Viterbi decoder (max_hypotheses = n: at each time step, prune all hypotheses worse than the top n)
viterbi_decoder = Viterbi(grammar,
length_model,
frame_sampling=30,
max_hypotheses=50000)
# create list of test videos
with open('data/split1.test', 'r') as f:
video_list = f.read().split('\n')[0:-1]
# forward each video
log_probs = dict()
queue = mp.Queue()
for video in video_list:
queue.put(video)
dataset = Dataset('data', [video], label2index)
log_probs[video] = forwarder.forward(dataset) - log_prior
log_probs[video] = log_probs[video] - np.max(log_probs[video])
# Viterbi decoding
procs = []
for i in range(n_threads):
p = mp.Process(target=decode,
args=(queue, log_probs, viterbi_decoder, index2label))
procs.append(p)
p.start()
for p in procs:
p.join()
content = f.read().split('\n')[0:-1]
for line in content:
label2index[line.split()[1]] = int(line.split()[0])
index2label[int(line.split()[0])] = line.split()[1]
### read test data #############################################################
with open(args.decoded_path, 'r') as f:
video_list = f.read().split('\n')[0:-1]
dataset = Dataset('data', video_list, label2index, shuffle=False)
# load prior, length model, grammar, and network
load_iteration = 10000
log_prior = np.log(
np.loadtxt(args.result_path + 'prior.iter-' + str(load_iteration) +
'.txt'))
grammar = PathGrammar(args.result_path + 'grammar.txt', label2index)
length_model = PoissonModel(args.result_path + 'lengths.iter-' +
str(load_iteration) + '.txt',
max_length=2000)
forwarder = Forwarder(dataset.input_dimension, dataset.n_classes)
forwarder.load_model(args.result_path + 'network.iter-' + str(load_iteration) +
'.net')
# parallelization
n_threads = 4
# Viterbi decoder
viterbi_decoder = Viterbi(grammar,
length_model,
frame_sampling=30,
max_hypotheses=np.inf)
label2index[line.split()[1]] = int(line.split()[0])
index2label[int(line.split()[0])] = line.split()[1]
### read test data #############################################################
with open('/scratch/liju2/nn_viterbi/data/split1.test', 'r') as f:
video_list = f.read().split('\n')[0:-1]
dataset = Dataset('/scratch/liju2/nn_viterbi/data',
video_list,
label2index,
shuffle=False)
# load prior, length model, grammar, and network
load_iteration = 100000
log_prior = np.log(
np.loadtxt('results/prior.iter-' + str(load_iteration) + '.txt'))
grammar = PathGrammar('results/grammar.txt', label2index)
length_model = PoissonModel('results/lengths.iter-' + str(load_iteration) +
'.txt',
max_length=2000)
forwarder = Forwarder(dataset.input_dimension, dataset.n_classes)
forwarder.load_model('results/network.iter-' + str(load_iteration) + '.net')
window = 10
step = 5
# parallelization
n_threads = 4
# Viterbi decoder
viterbi_decoder = Viterbi(grammar, length_model, frame_sampling=30)
# forward each video
log_probs = dict()
# read label2index mapping and index2label mapping
label2index = dict()
index2label = dict()
with open(file_dict, 'r') as f:
content = f.read().split('\n')[0:-1]
idx_count = 0
for line in content:
line = line.strip()
label2index[line] = idx_count
index2label[idx_count] = line
idx_count = idx_count + 1
# load grammar
print('Running: ' + file_grammar)
grammar = PathGrammar(file_grammar, label2index)
# load lm
length_model = PoissonModel(file_length, max_length=5000)
# load your data here (must be in log space!!!):
# Note that you might want to remove a prior first.
log_probs = np.loadtxt(file_probs, dtype=np.float32)
# sanity check
print(np.max(log_probs))
print(np.min(log_probs))
# scale down if out of range
if np.max(log_probs) > 0:
log_probs = log_probs - (2 * np.max(log_probs))
