model_name = 'efficientnet-b0'
print(model_name)
model = EfficientNet.from_name(model_name)
#model = EfficientNet.from_pretrained(model_name, num_classes=350)
#summary(model,input_size=(3,224,224))
else:
model = Baseline(args.hidden_size, args.output_size)
optimizer = optim.Adam(model.parameters(), args.learning_rate)
lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer,
patience=1,
verbose=True)
criterion = nn.CrossEntropyLoss() #multi-class classification task
model = model.to(device)
model.train()
# DONOTCHANGE: They are reserved for nsml
bind_model(model)
# below the nsml load
nsml.load(checkpoint='15', session='team_62/airush1/40')
nsml.save('stillgoing')
if args.pause:
nsml.paused(scope=locals())
if args.mode == "train":
# Warning: Do not load data before this line
dataloader = train_dataloader(args.input_size, args.batch_size,
args.num_workers)
for epoch_idx in range(1, args.epochs + 1):
def infer(test_image_data_path, test_meta_data_path):
# DONOTCHANGE This Line
test_meta_data = pd.read_csv(test_meta_data_path,
delimiter=',',
header=0)
device = 0
models = args.models.split(",")
model_weights = [float(w) for w in args.model_weights.split(",")]
nsml_sessionss = args.nsml_sessionss.split(",")
nsml_checkpoints = args.nsml_checkpoints.split(",")
loss_types = args.loss_types.split(",")
transform_random_crop = args.transform_random_crop.split(",")
transform_random_sized_crop = args.transform_random_sized_crop.split(
",")
transform_norm = args.transform_norm.split(",")
infer_transform_center_crop = args.infer_transform_center_crop.split(
",")
total_output_probs = None
for i, model_name in enumerate(models):
batch_size = batch_size_map[model_name] // 2
infer_transform_list = []
if infer_transform_center_crop[i] == "True":
infer_transform_list.append(transforms.Resize((248, 248)))
infer_transform_list.append(
transforms.CenterCrop((args.input_size, args.input_size)))
infer_transform_list.append(transforms.ToTensor())
if transform_norm[i] == "True":
infer_transform_list.append(
transforms.Normalize(
[0.44097832, 0.44847423, 0.42528335],
[0.25748107, 0.26744914, 0.30532702]))
else:
if transform_random_crop[i] == "True":
infer_transform_list.append(transforms.Resize((256, 256)))
infer_transform_list.append(
transforms.CenterCrop(
(args.input_size, args.input_size)))
elif transform_random_sized_crop[i] == "True":
infer_transform_list.append(transforms.Resize((256, 256)))
infer_transform_list.append(
transforms.CenterCrop(
(args.input_size, args.input_size)))
else:
infer_transform_list.append(
transforms.Resize((args.input_size, args.input_size)))
infer_transform_list.append(transforms.ToTensor())
if transform_norm[i] == "True":
infer_transform_list.append(
transforms.Normalize(
[0.44097832, 0.44847423, 0.42528335],
[0.25748107, 0.26744914, 0.30532702]))
print("transform", infer_transform_list)
dataloader = DataLoader(
AIRushDataset(
test_image_data_path,
test_meta_data,
label_path=None,
transform=transforms.Compose(infer_transform_list)
), #[transforms.Resize((args.input_size, args.input_size)), transforms.ToTensor()])),
batch_size=batch_size,
shuffle=False,
num_workers=0,
pin_memory=True)
if model_name == "Resnet18":
model = Resnet18(args.output_size)
elif model_name == "Resnet152":
model = Resnet152(args.output_size)
elif model_name == "baseline":
model = Baseline(args.hidden_size, args.output_size)
elif model_name.split("-")[0] == "efficientnet":
model = EfficientNet.from_pretrained(args.model,
args.output_size)
else:
raise Exception("model type is invalid : " + args.model)
model.to(device)
def load_fn(dir_name):
save_state_path = os.path.join(dir_name, 'state_dict.pkl')
state = torch.load(save_state_path)
model.load_state_dict(state['model'])
print("model loaded", dir_name)
model.eval()
nsml.load(checkpoint=nsml_checkpoints[i],
load_fn=load_fn,
session="team_13/airush1/" + nsml_sessionss[i])
output_probs = None
for batch_idx, image in enumerate(dataloader):
image = image.to(device)
output = model(image).double()
if loss_types[i] == "cross_entropy":
output_prob = F.softmax(output, dim=1)
else:
output_prob = torch.sigmoid(output)
if output_probs is None:
output_probs = to_np(output_prob)
else:
output_probs = np.concatenate(
[output_probs, to_np(output_prob)], axis=0)
if total_output_probs is None:
total_output_probs = output_probs * model_weights[i]
else:
total_output_probs += (output_probs * model_weights[i])
predict = np.argmax(total_output_probs, axis=1)
return predict # this return type should be a numpy array which has shape of (138343)