def train():
from paddlex.cls import transforms
train_transforms = transforms.Compose([
transforms.RandomCrop(crop_size=224),
transforms.RandomHorizontalFlip(),
transforms.Normalize()
])
eval_transforms = transforms.Compose([
transforms.ResizeByShort(short_size=256),
transforms.CenterCrop(crop_size=224),
transforms.Normalize()
])
train_dataset = pdx.datasets.ImageNet(
data_dir=f'{data_dir}/mini_imagenet_veg',
file_list=f'{data_dir}/mini_imagenet_veg/train_list.txt',
label_list=f'{data_dir}/mini_imagenet_veg/labels.txt',
transforms=train_transforms)
eval_dataset = pdx.datasets.ImageNet(
data_dir=f'{data_dir}/mini_imagenet_veg',
file_list=f'{data_dir}/mini_imagenet_veg/val_list.txt',
label_list=f'{data_dir}/mini_imagenet_veg/labels.txt',
transforms=eval_transforms)
num_classes = len(train_dataset.labels)
model = pdx.cls.MobileNetV3_large_ssld(num_classes=num_classes)
model.train(num_epochs=12,
train_dataset=train_dataset,
train_batch_size=32,
eval_dataset=eval_dataset,
lr_decay_epochs=[6, 8],
save_interval_epochs=1,
learning_rate=0.00625,
save_dir=f'{output_dir}/output/mobilenetv3_large_ssld',
use_vdl=True)
def train():
from paddlex.cls import transforms
train_transforms = transforms.Compose([
transforms.RandomCrop(crop_size=224),
transforms.RandomHorizontalFlip(),
transforms.Normalize()
])
eval_transforms = transforms.Compose([
transforms.ResizeByShort(short_size=256),
transforms.CenterCrop(crop_size=224),
transforms.Normalize()
])
train_dataset = pdx.datasets.ImageNet(
data_dir=f'{data_dir}/vegetables_cls',
file_list=f'{data_dir}/vegetables_cls/train_list.txt',
label_list=f'{data_dir}/vegetables_cls/labels.txt',
transforms=train_transforms,
shuffle=True)
eval_dataset = pdx.datasets.ImageNet(
data_dir=f'{data_dir}/vegetables_cls',
file_list=f'{data_dir}/vegetables_cls/val_list.txt',
label_list=f'{data_dir}/vegetables_cls/labels.txt',
transforms=eval_transforms)
num_classes = len(train_dataset.labels)
model = pdx.cls.MobileNetV2(num_classes=num_classes)
model.train(num_epochs=10,
train_dataset=train_dataset,
train_batch_size=32,
eval_dataset=eval_dataset,
lr_decay_epochs=[4, 6, 8],
save_interval_epochs=1,
learning_rate=0.025,
save_dir=f'{output_dir}/mobilenetv2',
use_vdl=True)
def train(model_dir=None, sensitivities_file=None, eval_metric_loss=0.05):
# 下载和解压蔬菜分类数据集
veg_dataset = 'https://bj.bcebos.com/paddlex/datasets/vegetables_cls.tar.gz'
pdx.utils.download_and_decompress(veg_dataset, path='./')
# 定义训练和验证时的transforms
train_transforms = transforms.Compose([
transforms.RandomCrop(crop_size=224),
transforms.RandomHorizontalFlip(),
transforms.Normalize()
])
eval_transforms = transforms.Compose([
transforms.ResizeByShort(short_size=256),
transforms.CenterCrop(crop_size=224),
transforms.Normalize()
])
# 定义训练和验证所用的数据集
train_dataset = pdx.datasets.ImageNet(
data_dir='vegetables_cls',
file_list='vegetables_cls/train_list.txt',
label_list='vegetables_cls/labels.txt',
transforms=train_transforms,
shuffle=True)
eval_dataset = pdx.datasets.ImageNet(
data_dir='vegetables_cls',
file_list='vegetables_cls/val_list.txt',
label_list='vegetables_cls/labels.txt',
transforms=eval_transforms)
num_classes = len(train_dataset.labels)
model = pdx.cls.MobileNetV2(num_classes=num_classes)
if model_dir is None:
# 使用imagenet数据集预训练模型权重
pretrain_weights = "IMAGENET"
else:
# 使用传入的model_dir作为预训练模型权重
assert os.path.isdir(model_dir), "Path {} is not a directory".format(
model_dir)
pretrain_weights = model_dir
save_dir = './output/mobilenetv2'
if sensitivities_file is not None:
# DEFAULT 指使用模型预置的参数敏感度信息作为裁剪依据
if sensitivities_file != "DEFAULT":
assert os.path.exists(
sensitivities_file), "Path {} not exist".format(
sensitivities_file)
save_dir = './output/mobilenetv2_prune'
model.train(
num_epochs=10,
train_dataset=train_dataset,
train_batch_size=32,
eval_dataset=eval_dataset,
lr_decay_epochs=[4, 6, 8],
learning_rate=0.025,
pretrain_weights=pretrain_weights,
save_dir=save_dir,
use_vdl=True,
sensitivities_file=sensitivities_file,
eval_metric_loss=eval_metric_loss)
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import paddle.fluid as fluid
from paddlex.cls import transforms
import paddlex as pdx
# 下载和解压蔬菜分类数据集
veg_dataset = 'https://bj.bcebos.com/paddlex/datasets/vegetables_cls.tar.gz'
pdx.utils.download_and_decompress(veg_dataset, path='./')
# 定义训练和验证时的transforms
train_transforms = transforms.Compose(
[transforms.RandomCrop(crop_size=224),
transforms.Normalize()])
eval_transforms = transforms.Compose([
transforms.ResizeByShort(short_size=256),
transforms.CenterCrop(crop_size=224),
transforms.Normalize()
])
# 定义训练和验证所用的数据集
train_dataset = pdx.datasets.ImageNet(
data_dir='vegetables_cls',
file_list='vegetables_cls/train_list.txt',
label_list='vegetables_cls/labels.txt',
transforms=train_transforms,
shuffle=True)
eval_dataset = pdx.datasets.ImageNet(data_dir='vegetables_cls',
file_list='vegetables_cls/val_list.txt',
label_list='vegetables_cls/labels.txt',
transforms=eval_transforms)
from paddlex.cls import transforms
import paddlex
import cv2
base = './data'
train_transforms = transforms.Compose([
transforms.ResizeByShort(256),
transforms.RandomCrop(crop_size=224),
transforms.RandomRotate(rotate_range=30, prob=0.5),
transforms.RandomDistort(),
transforms.Normalize()
])
train_dataset = paddlex.datasets.ImageNet(data_dir=base,
file_list='train_list.txt',
label_list='labels.txt',
transforms=train_transforms,
shuffle=True)
model = paddlex.load_model('weights/final')
im = cv2.imread('test.jpg')
print(model.evaluate(eval_dataset=train_dataset))