def main():
# Parse arguments
args = parser.add_cmdline_args(classifier.init_classifier_compression_arg_parser(True)).parse_args()
app = ClassifierCompressorSampleApp(args, script_dir=os.path.dirname(__file__))
if app.handle_subapps():
return
init_knowledge_distillation(app.args, app.model, app.compression_scheduler)
app.run_training_loop()
# Finally run results on the test set
return app.test()
def main(individual):
# Parse arguments
#print(individual)
args = parser.add_cmdline_args(
classifier.init_classifier_compression_arg_parser(True)).parse_args()
args.compress = 'FB_gene1.yaml'
idx = 0
num = 0 #initial 5%
yaml_list = []
for i in range(int(len(individual) / 7)): #各ビット数/7
num = 0
for j in range(7): #長さ
if (individual[idx] == 1):
num += pow(2, j)
idx += 1
if num >= 100:
num = 90
elif i == 59 and num <= 30:
num = 31
elif num <= 5:
num = 6
yaml_list.append(num / 100)
print("yaml_list")
print(yaml_list)
write_yaml(yaml_list)
app = ClassifierCompressorSampleApp(args,
script_dir=os.path.dirname(__file__))
if app.handle_subapps():
return
init_knowledge_distillation(app.args, app.model, app.compression_scheduler)
app.run_training_loop()
# Finally run results on the test set
# return top1, top5, losssesが来る
loaded_array = np.load(
'/home/oza/pre-experiment/speeding/distiller/distiller/apputils/simple_gene.npz'
)
accuracy = loaded_array['array_1']
sparce = loaded_array['array_2']
print("accuracy: " + str(accuracy))
print("sparce: " + str(sparce))
accuracy /= 100
sparce /= 100
#return app.test()
score = accuracy * sparce
print("score: " + str(score))
global max
if (score > max):
max = score
print("max score: " + str(score))
print("max individual: " + str(yaml_list))
return accuracy * sparce
def main():
# Parse arguments
args = parser.add_cmdline_args(
classifier.init_classifier_compression_arg_parser(True)).parse_args()
performance_tracker = apputils.SparsityAccuracyTracker(
args.num_best_scores)
#args.compress = 'OZA'
#print(args.compress)
#quit()
app = ClassifierCompressorSampleApp(args,
script_dir=os.path.dirname(__file__))
if app.handle_subapps():
return
init_knowledge_distillation(app.args, app.model, app.compression_scheduler)
app.run_training_loop()
# Finally run results on the test set
# return top1, top5, losssesが来る
return app.test()
