def make_birds():
train_data, val_data, test_data = corpus.load_birds()
by_bird = defaultdict(list)
all_birds = []
for datum in val_data:
id_parts = datum.image_id.split("_")
bird_name = "_".join(id_parts[:-2])
by_bird[bird_name].append(datum)
all_birds.append(datum)
counter = 0
with open("experiments/all_same/birds.ids.txt", "w") as id_f:
for bird in by_bird:
if counter == 100:
break
birds = by_bird[bird]
np.random.shuffle(birds)
bird1 = birds[0]
for bird2 in birds:
if bird2.image_id == bird1.image_id:
continue
print >> id_f, "%s,%s,0" % (bird1.image_id, bird2.image_id)
counter += 1
with open("experiments/one_different/birds.ids.txt", "w") as id_f:
for i in range(100):
bird1 = all_birds[np.random.randint(len(all_birds))]
bird2 = all_birds[np.random.randint(len(all_birds))]
print >> id_f, "%s,%s,0" % (bird1.image_id, bird2.image_id)
with open("experiments/by_similarity/birds.ids.txt", "w") as id_f:
pass
def make_birds():
train_data, val_data, test_data = corpus.load_birds()
by_bird = defaultdict(list)
all_birds = []
for datum in val_data:
id_parts = datum.image_id.split("_")
bird_name = "_".join(id_parts[:-2])
by_bird[bird_name].append(datum)
all_birds.append(datum)
counter = 0
with open("experiments/all_same/birds.ids.txt", "w") as id_f:
for bird in by_bird:
if counter == 100:
break
birds = by_bird[bird]
np.random.shuffle(birds)
bird1 = birds[0]
for bird2 in birds:
if bird2.image_id == bird1.image_id:
continue
print >>id_f, "%s,%s,0" % (bird1.image_id, bird2.image_id)
counter += 1
with open("experiments/one_different/birds.ids.txt", "w") as id_f:
for i in range(100):
bird1 = all_birds[np.random.randint(len(all_birds))]
bird2 = all_birds[np.random.randint(len(all_birds))]
print >>id_f, "%s,%s,0" % (bird1.image_id, bird2.image_id)
with open("experiments/by_similarity/birds.ids.txt", "w") as id_f:
pass
def main():
apollocaffe.set_device(0)
#apollocaffe.set_cpp_loglevel(0)
apollocaffe.set_random_seed(0)
np.random.seed(0)
job = sys.argv[1]
corpus_name = sys.argv[2]
config = util.Struct(**yaml.load(CONFIG))
if corpus_name == "abstract":
train_scenes, dev_scenes, test_scenes = corpus.load_abstract()
else:
assert corpus_name == "birds"
train_scenes, dev_scenes, test_scenes = corpus.load_birds()
apollo_net = ApolloNet()
print "loaded data"
print "%d training examples" % len(train_scenes)
listener0_model = Listener0Model(apollo_net, config.model)
speaker0_model = Speaker0Model(apollo_net, config.model)
sampling_speaker1_model = SamplingSpeaker1Model(apollo_net, config.model)
compiled_speaker1_model = CompiledSpeaker1Model(apollo_net, config.model)
if job == "train.base":
train(train_scenes, dev_scenes, listener0_model, apollo_net, config.opt)
train(train_scenes, dev_scenes, speaker0_model, apollo_net, config.opt)
apollo_net.save("models/%s.base.caffemodel" % corpus_name)
exit()
if job == "train.compiled":
apollo_net.load("models/%s.base.caffemodel" % corpus_name)
print "loaded model"
train(train_scenes, dev_scenes, compiled_speaker1_model, apollo_net,
config.opt)
apollo_net.save("models/%s.compiled.caffemodel" % corpus_name)
exit()
if job in ("sample.base", "sample.compiled"):
if job == "sample.base":
apollo_net.load("models/%s.base.caffemodel" % corpus_name)
else:
apollo_net.load("models/%s.compiled.caffemodel" % corpus_name)
print "loaded model"
if job == "sample.base":
models = {
"sampling_speaker1": sampling_speaker1_model,
}
elif job == "sample.compiled":
models = {
"compiled_speaker1": compiled_speaker1_model,
}
name = job.split(".")[1]
run_experiment("one_different", corpus_name, name, models, dev_scenes)
run_experiment("by_similarity", corpus_name, name, models, dev_scenes)
run_experiment("all_same", corpus_name, name, models, dev_scenes)
