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import os
import paddlers as pdrs
from paddlers import transforms as T
# download dataset
data_dir = 'sar_ship_1'
if not os.path.exists(data_dir):
dataset_url = 'https://paddleseg.bj.bcebos.com/dataset/sar_ship_1.tar.gz'
pdrs.utils.download_and_decompress(dataset_url, path='./')
# define transforms
train_transforms = T.Compose([
T.RandomDistort(),
T.RandomExpand(),
T.RandomCrop(),
T.RandomHorizontalFlip(),
T.BatchRandomResize(
target_sizes=[320, 352, 384, 416, 448, 480, 512, 544, 576, 608],
interp='RANDOM'),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
eval_transforms = T.Compose([
T.Resize(target_size=608, interp='CUBIC'),
T.Normalize(
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
# define dataset
train_file_list = os.path.join(data_dir, 'train.txt')
val_file_list = os.path.join(data_dir, 'valid.txt')
label_file_list = os.path.join(data_dir, 'labels.txt')
train_dataset = pdrs.datasets.VOCDetection(
data_dir=data_dir,
file_list=train_file_list,
label_list=label_file_list,
transforms=train_transforms,
shuffle=True)
eval_dataset = pdrs.datasets.VOCDetection(
data_dir=data_dir,
file_list=train_file_list,
label_list=label_file_list,
transforms=eval_transforms,
shuffle=False)
# define models
num_classes = len(train_dataset.labels)
model = pdrs.tasks.FasterRCNN(num_classes=num_classes)
# train
model.train(
num_epochs=60,
train_dataset=train_dataset,
train_batch_size=2,
eval_dataset=eval_dataset,
pretrain_weights='COCO',
learning_rate=0.005 / 12,
warmup_steps=10,
warmup_start_lr=0.0,
save_interval_epochs=5,
lr_decay_epochs=[20, 40],
save_dir='output/faster_rcnn_sar_ship',
use_vdl=True)