def test_deeplabv3_1p():
start_time = time.time()
epoch_size = 100
args_opt = argparse.Namespace(base_size=513, crop_size=513, batch_size=2)
args_opt.base_size = config.crop_size
args_opt.crop_size = config.crop_size
args_opt.batch_size = config.batch_size
train_dataset = create_dataset(args_opt, data_url, 1, config.batch_size,
usage="eval")
dataset_size = train_dataset.get_dataset_size()
callback = LossCallBack(dataset_size)
net = deeplabv3_resnet50(config.seg_num_classes, [config.batch_size, 3, args_opt.crop_size, args_opt.crop_size],
infer_scale_sizes=config.eval_scales, atrous_rates=config.atrous_rates,
decoder_output_stride=config.decoder_output_stride, output_stride=config.output_stride,
fine_tune_batch_norm=config.fine_tune_batch_norm, image_pyramid=config.image_pyramid)
net.set_train()
model_fine_tune(net, 'layer')
loss = OhemLoss(config.seg_num_classes, config.ignore_label)
opt = Momentum(filter(lambda x: 'beta' not in x.name and 'gamma' not in x.name and 'depth' not in x.name and 'bias' not in x.name, net.trainable_params()), learning_rate=config.learning_rate, momentum=config.momentum, weight_decay=config.weight_decay)
model = Model(net, loss, opt)
model.train(epoch_size, train_dataset, callback)
print(time.time() - start_time)
print("expect loss: ", callback.loss)
print("expect time: ", callback.time)
expect_loss = 0.92
expect_time = 40
assert callback.loss.asnumpy() <= expect_loss
assert callback.time <= expect_time
load_param_into_net(train_net, param_dict)
for para in train_net.trainable_params():
if fix_weight_layer in para.name:
para.requires_grad = False
if __name__ == "__main__":
start_time = time.time()
epoch_size = 3
args_opt.base_size = config.crop_size
args_opt.crop_size = config.crop_size
train_dataset = create_dataset(args_opt, args_opt.data_url, 1, config.batch_size,
usage="train", shuffle=False)
dataset_size = train_dataset.get_dataset_size()
callback = LossCallBack(dataset_size)
net = deeplabv3_resnet50(config.seg_num_classes, [config.batch_size, 3, args_opt.crop_size, args_opt.crop_size],
infer_scale_sizes=config.eval_scales, atrous_rates=config.atrous_rates,
decoder_output_stride=config.decoder_output_stride, output_stride=config.output_stride,
fine_tune_batch_norm=config.fine_tune_batch_norm, image_pyramid=config.image_pyramid)
net.set_train()
model_fine_tune(args_opt, net, 'layer')
loss = OhemLoss(config.seg_num_classes, config.ignore_label)
opt = Momentum(filter(lambda x: 'beta' not in x.name and 'gamma' not in x.name and 'depth' not in x.name and 'bias' not in x.name, net.trainable_params()), learning_rate=config.learning_rate, momentum=config.momentum, weight_decay=config.weight_decay)
model = Model(net, loss, opt)
model.train(epoch_size, train_dataset, callback)
print(time.time() - start_time)
print("expect loss: ", callback.loss / 3)
print("expect time: ", callback.time)
expect_loss = 0.5
expect_time = 35
assert callback.loss.asnumpy() / 3 <= expect_loss
assert callback.time <= expect_time
