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)
def __init__(self, config, opt_config):
self.config = config
self.opt_config = opt_config
self.models = []
for cmodel in config.models:
with open(cmodel.config) as config_f:
mconfig = Struct(**yaml.load(config_f))
model = models.build_model(mconfig.model, mconfig.opt)
model.load(cmodel.weights)
self.models.append(model)
self.n_models = len(self.models)
self.apollo_net = ApolloNet()
#!/usr/bin/env python2
import caffe
import apollocaffe
from apollocaffe import ApolloNet, layers
import numpy as np
import timeit
#caffe.set_mode_gpu()
apollocaffe.set_device(0)
net = ApolloNet()
batch_size = 64
data = np.random.random(size=(batch_size, 512, 20, 20)).astype(np.float32)
labels = np.random.randint(10, size=(batch_size, )).astype(np.int32).astype(
np.float32)
#print data.dtype
#print labels.dtype
#def load_mem():
# net.clear_forward()
# net.f(layers.MemoryData(
# "mem", data, labels, tops=["input_top", "label_top"],
# batch_size=batch_size, channels=512, width=20, height=20))
#
#def load_np():
# net.clear_forward()
# net.f(layers.NumpyData("np", data))
#
#load_mem()
def __init__(self):
self.net = ApolloNet()
self.experiences = []
def __init__(self):
self.net = ApolloNet()
self.transitions = []
