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#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File : dataset.py
@Time : 2023/04/06 22:39:31
@Author : BQH
@Version : 1.0
@Contact : raogx.vip@hotmail.com
@License : (C)Copyright 2017-2018, Liugroup-NLPR-CASIA
@Desc : None
'''
# here put the import lib
import os
import json
import torch
import numpy as np
import random
from PIL import Image
from PIL import ImageOps
from copy import deepcopy
from .aug_strategy import imgaug_mask
from .aug_strategy import pipe_sequential_rotate
from .aug_strategy import pipe_sequential_translate
from .aug_strategy import pipe_sequential_scale
from .aug_strategy import pipe_someof_flip
from .aug_strategy import pipe_someof_blur
from .aug_strategy import pipe_sometimes_mpshear
from .aug_strategy import pipe_someone_contrast
from .NuImages.nuimages import NuImages
def imresize(im, size, interp='bilinear'):
if interp == 'nearest':
resample = Image.NEAREST
elif interp == 'bilinear':
resample = Image.BILINEAR
elif interp == 'bicubic':
resample = Image.BICUBIC
else:
raise Exception('resample method undefined!')
return im.resize(size, resample)
class BaseDataset(torch.utils.data.Dataset):
def __init__(self, odgt, opt, **kwargs):
# parse options
self.imgSizes = opt.INPUT.CROP.SIZE
self.imgMaxSize = opt.INPUT.CROP.MAX_SIZE
# max down sampling rate of network to avoid rounding during conv or pooling
self.padding_constant = 2**5 # resnet 总共下采样5次
# parse the input list
if odgt is not None:
self.parse_input_list(odgt, **kwargs)
self.pixel_mean = np.array(opt.DATASETS.PIXEL_MEAN)
self.pixel_std = np.array(opt.DATASETS.PIXEL_STD)
def parse_input_list(self, odgt, max_sample=-1, start_idx=-1, end_idx=-1):
if isinstance(odgt, list):
self.list_sample = odgt
elif isinstance(odgt, str):
self.list_sample = [json.loads(x.rstrip()) for x in open(odgt, 'r')]
if max_sample > 0:
self.list_sample = self.list_sample[0:max_sample]
if start_idx >= 0 and end_idx >= 0: # divide file list
self.list_sample = self.list_sample[start_idx:end_idx]
self.num_sample = len(self.list_sample)
assert self.num_sample > 0
print('# samples: {}'.format(self.num_sample))
def img_transform(self, img):
# 0-255 to 0-1
img = np.float32(np.array(img)) / 255.
img = (img - self.pixel_mean) / self.pixel_std
img = img.transpose((2, 0, 1)) # [c, h, w]
return img
def segm_transform(self, segm: np.ndarray):
# to tensor, -1 to 149
segm = torch.from_numpy(np.array(segm)).long()
return segm
# Round x to the nearest multiple of p and x' >= x
def round2nearest_multiple(self, x, p):
return ((x - 1) // p + 1) * p
def get_img_ratio(self, img_size, target_size):
img_rate = np.max(img_size) / np.min(img_size)
target_rate = np.max(target_size) / np.min(target_size)
if img_rate > target_rate:
# 按长边缩放
ratio = max(target_size) / max(img_size)
else:
ratio = min(target_size) / min(img_size)
return ratio
def resize_padding(self, img, outsize, Interpolation=Image.BILINEAR):
w, h = img.size
target_w, target_h = outsize[0], outsize[1]
ratio = self.get_img_ratio([w, h], outsize)
ow, oh = round(w * ratio), round(h * ratio)
img = img.resize((ow, oh), Interpolation)
dh, dw = target_h - oh, target_w - ow
top, bottom = dh // 2, dh - (dh // 2)
left, right = dw // 2, dw - (dw // 2)
img = ImageOps.expand(img, border=(left, top, right, bottom), fill=0) # 左 顶 右 底 顺时针
return img
class ADE200kDataset(BaseDataset):
def __init__(self, odgt, opt, dynamic_batchHW=False, **kwargs):
super(ADE200kDataset, self).__init__(odgt, opt, **kwargs)
self.root_dataset = opt.DATASETS.ROOT_DIR
# down sampling rate of segm labe
self.segm_downsampling_rate = opt.MODEL.SEM_SEG_HEAD.COMMON_STRIDE # 网络输出相对于输入缩小的倍数
self.dynamic_batchHW = dynamic_batchHW # 是否动态调整batchHW, cswin_transformer需要使用固定image size
self.num_querys = opt.MODEL.MASK_FORMER.NUM_OBJECT_QUERIES
# self.visualize = ADEVisualize()
self.aug_pipe = self.get_data_aug_pipe()
def get_data_aug_pipe(self):
pipe_aug = []
if random.random() > 0.5:
aug_list = [pipe_sequential_rotate, pipe_sequential_scale, pipe_sequential_translate, pipe_someof_blur,
pipe_someof_flip, pipe_sometimes_mpshear, pipe_someone_contrast]
index = np.random.choice(a=[0, 1, 2, 3, 4, 5, 6],
p=[0.05, 0.25, 0.20, 0.25, 0.15, 0.05, 0.05])
if (index == 0 or index == 4 or index == 5) and random.random() < 0.5: # 会稍微削弱旋转 但是会极大增强其他泛化能力
index2 = np.random.choice(a=[1, 2, 3], p=[0.4, 0.3, 0.3])
pipe_aug = [aug_list[index], aug_list[index2]]
else:
pipe_aug = [aug_list[index]]
return pipe_aug
def get_batch_size(self, batch_records):
batch_width, batch_height = self.imgMaxSize[0], self.imgMaxSize[1]
if self.dynamic_batchHW:
if isinstance(self.imgSizes, list) or isinstance(self.imgSizes, tuple):
this_short_size = np.random.choice(self.imgSizes)
else:
this_short_size = self.imgSizes
batch_widths = np.zeros(len(batch_records), np.int32)
batch_heights = np.zeros(len(batch_records), np.int32)
for i, item in enumerate(batch_records):
img_height, img_width = item['image'].shape[0], item['image'].shape[1]
this_scale = min(
this_short_size / min(img_height, img_width), \
self.imgMaxSize / max(img_height, img_width))
batch_widths[i] = img_width * this_scale
batch_heights[i] = img_height * this_scale
batch_width = np.max(batch_widths)
batch_height = np.max(batch_heights)
batch_width = int(self.round2nearest_multiple(batch_width, self.padding_constant))
batch_height = int(self.round2nearest_multiple(batch_height, self.padding_constant))
return batch_width, batch_height
def __getitem__(self, index):
this_record = self.list_sample[index]
# load image and label
image_path = os.path.join(self.root_dataset, this_record['fpath_img'])
segm_path = os.path.join(self.root_dataset, this_record['fpath_segm'])
img = Image.open(image_path).convert('RGB')
segm = Image.open(segm_path).convert('L')
# data augmentation
img = np.array(img)
segm = np.array(segm)
for seq in self.aug_pipe:
img, segm = imgaug_mask(img, segm, seq)
output = dict()
output['image'] = img
output['mask'] = segm
return output
def collate_fn(self, batch):
batch_width, batch_height = self.get_batch_size(batch)
out = {}
images = []
masks = []
raw_images = []
for item in batch:
img = deepcopy(item['image'])
segm = item['mask']
img = Image.fromarray(img)
segm = Image.fromarray(segm)
img = self.resize_padding(img, (batch_width, batch_height))
img = self.img_transform(img)
segm = self.resize_padding(segm, (batch_width, batch_height), Image.NEAREST)
segm = segm.resize((batch_width // self.segm_downsampling_rate, batch_height // self.segm_downsampling_rate), Image.NEAREST)
images.append(torch.from_numpy(img).float())
masks.append(torch.from_numpy(np.array(segm)).long())
raw_images.append(item['image'])
out['images'] = torch.stack(images)
out['masks'] = torch.stack(masks)
out['raw_img'] = raw_images
return out
def __len__(self):
return self.num_sample
class LaneDetec(ADE200kDataset):
def __init__(self, odgt, opt, dynamic_batchHW=False, **kwargs):
super(LaneDetec, self).__init__(odgt, opt, dynamic_batchHW, **kwargs)
def __getitem__(self, index):
this_record = self.list_sample[index]
# load image and label
image_path = os.path.join(self.root_dataset, this_record['fpath_img'])
segm_path = os.path.join(self.root_dataset, this_record['fpath_segm'])
img = Image.open(image_path).convert('RGB')
segm = Image.open(segm_path).convert('L')
# data augmentation
img = np.array(img)[800:, :, :] # 移除图片上方的天空部分
segm = np.array(segm)[800:, :]
for seq in self.aug_pipe:
img, segm = imgaug_mask(img, segm, seq)
output = dict()
output['image'] = img
output['mask'] = segm
return output
# 用于nuImages数据集的Dataset类
class NuImagesDataset(ADE200kDataset):
def __init__(self, data_root, opt, version='v1.0-train', **kwargs):
super(NuImagesDataset, self).__init__(None, opt, **kwargs)
self.nuim = NuImages(dataroot=data_root, version=version, lazy=False)
self.num_sample = len(self.nuim.sample)
print(f'Load {self.num_sample} samples from {version}')
def __getitem__(self, index):
sample = self.nuim.sample[index]
sd_token = sample['key_camera_token']
sample_data = self.nuim.get('sample_data', sd_token)
im_path = os.path.join(self.nuim.dataroot, sample_data['filename'])
img = Image.open(im_path).convert('RGB')
img = np.array(img)
semseg_mask, instanceseg_mask = self.nuim.get_segmentation(sd_token)
semseg_mask[semseg_mask==31] = 0 # 31是vehicle.ego, 不做预测
output = dict()
output['image'] = img
output['mask'] = semseg_mask
output['ins_mask'] = instanceseg_mask
# self.nuim.render_image(sd_token, annotation_type='all', with_category=True, with_attributes=True, out_path='/home/dataset/nuImages/ImageData/out_test.png')
return output
def __len__(self):
return self.num_sample
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