提交 421266a7 编写于 作者: J jiangjiajun

add BasicTransforms

上级 7acf4f36
......@@ -23,6 +23,7 @@ import random
import platform
import chardet
import paddlex.utils.logging as logging
from paddlex.cv.transforms.template import TemplateTransforms
class EndSignal():
......@@ -209,8 +210,8 @@ def GenerateMiniBatch(batch_data):
padding_batch = []
for data in batch_data:
im_c, im_h, im_w = data[0].shape[:]
padding_im = np.zeros((im_c, max_shape[1], max_shape[2]),
dtype=np.float32)
padding_im = np.zeros(
(im_c, max_shape[1], max_shape[2]), dtype=np.float32)
padding_im[:, :im_h, :im_w] = data[0]
padding_batch.append((padding_im, ) + data[1:])
return padding_batch
......@@ -226,12 +227,14 @@ class Dataset:
if num_workers == 'auto':
import multiprocessing as mp
num_workers = mp.cpu_count() // 2 if mp.cpu_count() // 2 < 8 else 8
if platform.platform().startswith(
"Darwin") or platform.platform().startswith("Windows"):
if platform.platform().startswith("Darwin") or platform.platform(
).startswith("Windows"):
parallel_method = 'thread'
if transforms is None:
raise Exception("transform should be defined.")
self.transforms = transforms
if isinstance(transforms, TemplateTransforms):
self.transforms = transforms.transforms
self.num_workers = num_workers
self.buffer_size = buffer_size
self.parallel_method = parallel_method
......
......@@ -15,3 +15,4 @@
from . import cls_transforms
from . import det_transforms
from . import seg_transforms
from . import template
......@@ -18,6 +18,7 @@ import random
import os.path as osp
import numpy as np
from PIL import Image, ImageEnhance
from .template import TemplateTransforms
class ClsTransform:
......@@ -461,3 +462,55 @@ class ArrangeClassifier(ClsTransform):
else:
outputs = (im, )
return outputs
class BasicClsTransforms(TemplateTransforms):
""" 分类模型的基础Transforms流程,具体如下
训练阶段:
1. 随机从图像中crop一块子图,并resize成crop_size大小
2. 将1的输出按0.5的概率随机进行水平翻转
3. 将图像进行归一化
验证/预测阶段:
1. 将图像按比例Resize,使得最小边长度为crop_size[0] * 1.14
2. 从图像中心crop出一个大小为crop_size的图像
3. 将图像进行归一化
Args:
mode(str): 图像处理流程所处阶段,训练/验证/预测,分别对应'train', 'eval', 'test'
crop_size(int|list): 输入模型里的图像大小
mean(list): 图像均值
std(list): 图像方差
"""
def __init__(self,
mode,
crop_size=[224, 224],
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]):
super(TemplateClsTransforms, self).__init__(mode=mode)
width = crop_size
if isinstance(crop_size, list):
if shape[0] != shape[1]:
raise Exception(
"In classifier model, width and height should be equal")
width = crop_size[0]
if width % 32 != 0:
raise Exception(
"In classifier model, width and height should be multiple of 32, e.g 224、256、320...."
)
if self.mode == 'train':
# 训练时的transforms,包含数据增强
self.transforms = transforms.Compose([
transforms.RandomCrop(crop_size=width),
transforms.RandomHorizontalFlip(prob=0.5),
transforms.Normalize(
mean=mean, std=std)
])
else:
# 验证/预测时的transforms
self.transforms = transforms.Compose([
transforms.ReiszeByShort(short_size=int(width * 1.14)),
transforms.CenterCrop(crop_size=width), transforms.Normalize(
mean=mean, std=std)
])
......@@ -27,6 +27,7 @@ from PIL import Image, ImageEnhance
from .imgaug_support import execute_imgaug
from .ops import *
from .box_utils import *
from .template import TemplateTransforms
class DetTransform:
......@@ -1227,3 +1228,109 @@ class ArrangeYOLOv3(DetTransform):
im_shape = im_info['image_shape']
outputs = (im, im_shape)
return outputs
class BasicRCNNTransforms(TemplateTransforms):
""" RCNN模型(faster-rcnn/mask-rcnn)图像处理流程,具体如下,
训练阶段:
1. 随机以0.5的概率将图像水平翻转
2. 图像归一化
3. 图像按比例Resize,scale计算方式如下
scale = min_max_size[0] / short_size_of_image
if max_size_of_image * scale > min_max_size[1]:
scale = min_max_size[1] / max_size_of_image
4. 将3步骤的长宽进行padding,使得长宽为32的倍数
验证阶段:
1. 图像归一化
2. 图像按比例Resize,scale计算方式同上训练阶段
3. 将2步骤的长宽进行padding,使得长宽为32的倍数
Args:
mode(str): 图像处理流程所处阶段,训练/验证/预测,分别对应'train', 'eval', 'test'
min_max_size(list): 图像在缩放时,最小边和最大边的约束条件
mean(list): 图像均值
std(list): 图像方差
"""
def __init__(self,
mode,
min_max_size=[800, 1333],
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]):
super(RCNNTransforms, self).__init__(mode=mode)
if self.mode == 'train':
# 训练时的transforms,包含数据增强
self.transforms = transforms.Compose([
transforms.RandomHorizontalFlip(prob=0.5),
transforms.Normalize(
mean=mean, std=std), transforms.ResizeByShort(
short_size=min_max_size[0], max_size=min_max_size[1]),
transforms.Padding(coarsest_stride=32)
])
else:
# 验证/预测时的transforms
self.transforms = transforms.Compose([
transforms.Normalize(
mean=mean, std=std), transforms.ResizeByShort(
short_size=min_max_size[0], max_size=min_max_size[1]),
transforms.Padding(coarsest_stride=32)
])
class BasicYOLOTransforms(TemplateTransforms):
"""YOLOv3模型的图像预处理流程,具体如下,
训练阶段:
1. 在前mixup_epoch轮迭代中,使用MixupImage策略,见https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/det_transforms.html#mixupimage
2. 对图像进行随机扰动,包括亮度,对比度,饱和度和色调
3. 随机扩充图像,见https://paddlex.readthedocs.io/zh_CN/latest/apis/transforms/det_transforms.html#randomexpand
4. 随机裁剪图像
5. 将4步骤的输出图像Resize成shape参数的大小
6. 随机0.5的概率水平翻转图像
7. 图像归一化
验证/预测阶段:
1. 将图像Resize成shape参数大小
2. 图像归一化
Args:
mode(str): 图像处理流程所处阶段,训练/验证/预测,分别对应'train', 'eval', 'test'
shape(list): 输入模型中图像的大小,输入模型的图像会被Resize成此大小
mixup_epoch(int): 模型训练过程中,前mixup_epoch会使用mixup策略
mean(list): 图像均值
std(list): 图像方差
"""
def __init__(self,
mode,
shape=[608, 608],
mixup_epoch=250,
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]):
super(YOLOTransforms, self).__init__(mode=mode)
width = shape
if isinstance(shape, list):
if shape[0] != shape[1]:
raise Exception(
"In YOLOv3 model, width and height should be equal")
width = shape[0]
if width % 32 != 0:
raise Exception(
"In YOLOv3 model, width and height should be multiple of 32, e.g 224、256、320...."
)
if self.mode == 'train':
# 训练时的transforms,包含数据增强
self.transforms = transforms.Compose([
transforms.MixupImage(mixup_epoch=mixup_epoch),
transforms.RandomDistort(), transforms.RandomExpand(),
transforms.RandomCrop(), transforms.Resize(
target_size=width, interp='RANDOM'),
transforms.RandomHorizontalFlip(), transforms.Normalize(
mean=mean, std=std)
])
else:
# 验证/预测时的transforms
self.transforms = transforms.Compose([
transforms.Resize(
target_size=width, interp='CUBIC'), transforms.Normalize(
mean=mean, std=std)
])
......@@ -21,6 +21,7 @@ import numpy as np
from PIL import Image
import cv2
from collections import OrderedDict
from .template import TemplateTransforms
class SegTransform:
......@@ -1088,3 +1089,42 @@ class ArrangeSegmenter(SegTransform):
return (im, im_info)
else:
return (im, )
class BasicSegTransforms(TemplateTransforms):
""" 语义分割模型(UNet/DeepLabv3p)的图像处理流程,具体如下
训练阶段:
1. 随机对图像以0.5的概率水平翻转
2. 按不同的比例随机Resize原图
3. 从原图中随机crop出大小为train_crop_size大小的子图,如若crop出来的图小于train_crop_size,则会将图padding到对应大小
4. 图像归一化
预测阶段:
1. 图像归一化
Args:
mode(str): 图像处理所处阶段,训练/验证/预测,分别对应'train', 'eval', 'test'
train_crop_size(list): 模型训练阶段,随机从原图crop的大小
mean(list): 图像均值
std(list): 图像方差
"""
def __init__(self,
mode,
train_crop_size=[769, 769],
mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5]):
super(TemplateSegTransforms, self).__init__(mode=mode)
if self.mode == 'train':
# 训练时的transforms,包含数据增强
self.transforms = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.ResizeStepScaling(),
transforms.RandomPaddingCrop(crop_size=train_crop_size),
transforms.Normalize(
mean=mean, std=std)
])
else:
# 验证/预测时的transforms
self.transforms = transforms.Compose(
[transforms.Normalize(
mean=mean, std=std)])
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
class TemplateTransforms:
def __init__(self, mode):
assert mode in [
'train', 'eval', 'test'
], "Parameter mode in TemplateTransforms should be one of ['train', 'eval', 'test']"
self.mode = mode
def add_augmenters(self, augmenters):
if not isinstance(augmenters, list):
raise Exception(
"augmenters should be list type in func add_augmenters()")
assert mode == 'train', "There should be exists augmenters while on train mode"
self.transforms = augmenters + self.transforms.transforms
......@@ -97,8 +97,6 @@ class Predictor:
config.disable_glog_info()
if memory_optimize:
config.enable_memory_optim()
else:
config.diable_memory_optim()
# 开启计算图分析优化,包括OP融合等
config.switch_ir_optim(True)
......
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