def main():
train_dpath = os.path.join(sys.argv[1], "train") # no / ending
valid_dpath = os.path.join(sys.argv[1], "validation") # no / endin
model_fpath = sys.argv[2]
train_dataset = class_model.Dataset(train_dpath, mode="train")
valid_dataset = class_model.Dataset(valid_dpath, mode="validation")
model = AE(img_size=IMG_SIZE).cuda()
model = _train(model, train_dataset, valid_dataset)
torch.save(model.state_dict(), model_fpath)
def main():
root_dpath = sys.argv[1]
model_fpath = sys.argv[2]
train_dataset = class_model.Dataset(os.path.join(root_dpath, "train"),
"train")
valid_dataset = class_model.Dataset(os.path.join(root_dpath, "validation"),
"train")
train_valid_dataset = ConcatDataset([train_dataset, valid_dataset])
model = AE_chu().cuda()
model = train(train_valid_dataset, model)
torch.save(model.state_dict(), model_fpath)
return
def main():
train_dpath = os.path.join(sys.argv[1], "train") # no / ending
valid_dpath = os.path.join(sys.argv[1], "validation") # no / endin
model_fpath = sys.argv[2]
train_dataset = class_model.Dataset(train_dpath, mode="train")
valid_dataset = class_model.Dataset(valid_dpath, mode="validation")
model = class_model.kCNN().cuda()
model = _train(model, train_dataset, valid_dataset)
# fine tune with validation
train_valid_dataset = ConcatDataset([train_dataset, valid_dataset])
model = _train(model, train_valid_dataset, valid_dataset=None)
#save model
torch.save(model.state_dict(), model_fpath)
return
def main():
root_dpath = sys.argv[1]
model_fpath = sys.argv[2]
train_dataset = class_model.Dataset(os.path.join(root_dpath, "train"),
mode="test")
valid_dataset = class_model.Dataset(os.path.join(root_dpath, "validation"),
mode="test")
model = AE().cuda()
model.load_state_dict(torch.load(model_fpath))
model.eval()
train_code = get_encoded_vector(train_dataset, model)
valid_code = get_encoded_vector(valid_dataset, model)
predict_acc(root_dpath, train_code, valid_code)
return
def main():
test_dpath = sys.argv[1]
model_path = sys.argv[2]
output_path = sys.argv[3]
test_dataset = class_model.Dataset(test_dpath, mode="test")
model = class_model.kCNN().cuda()
model.load_state_dict(torch.load(model_path))
#test
ans_list = test(model, test_dataset)
#submit.csv
with open(output_path, 'w') as f:
f.write('image_id,label\n')
for i in range(len(ans_list)):
f.write('{},{}\n'.format(test_dataset.filename_dict[i], chr(ans_list[i]+ord('A'))))
return