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dataloadercelebA.py
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dataloadercelebA.py
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import torch
import torchvision.transforms as transforms
import torch.utils.data as data
import os
import pickle
import numpy as np
import nltk
from PIL import Image
import cv2
import linecache as lc
from skimage import io
import matplotlib.pyplot as plt
from matplotlib.patches import Circle
import re
class NormalizeImageDict(object):
def __init__(self, image_keys, normalizeRange=True):
self.image_keys = image_keys
self.normalizeRange = normalizeRange
#self.normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
self.normalize = transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
def __call__(self, sample):
for key in self.image_keys:
if self.normalizeRange:
sample[key] /= 255
sample[key] = self.normalize(sample[key])
return sample
def getlinenumber(imgfile):
with open(imgfile) as lmfile:
lineNum = sum(1 for _ in lmfile)
return lineNum-1
class MyDataSet(data.Dataset):
NumFileList = 0
def __init__(self, filelist, transform=None):
self.filelist = filelist
self.transform = transform
with open(filelist) as lmfile:
self.NumFileList = sum(1 for _ in lmfile)
def __len__(self):
#return getlinenumber(self.filelist) # too slow
return self.NumFileList # one time calc
def __getitem__(self, idx):
line = lc.getline(self.filelist, idx+1)
line = line.rstrip('\n')
file = line.split(' ')
ImgName = file[0]
iAttractive = []
iEyeGlasses = []
iMale = []
iMouthOpen = []
iSmiling = []
iYoung = []
iAttractive.append(float(file[3]))
iEyeGlasses.append(float(file[16]))
iMale.append(float(file[21]))
iMouthOpen.append(float(file[22]))
iSmiling.append(float(file[32]))
iYoung.append(float(file[40]))
input = io.imread(ImgName)
if input.ndim < 3:
input = cv2.cvtColor(input, cv2.COLOR_GRAY2RGB)
inp = cv2.resize(input, (256, 256))
iAttractive = np.asarray(iAttractive)
iEyeGlasses = np.asarray(iEyeGlasses)
iMale = np.asarray(iMale)
iMouthOpen = np.asarray(iMouthOpen)
iSmiling = np.asarray(iSmiling)
iYoung = np.asarray(iYoung)
sample = {'image': inp, 'Attractive': iAttractive, 'EyeGlasses': iEyeGlasses, 'Male': iMale, \
'MouthOpen': iMouthOpen, 'Smiling': iSmiling, 'Young': iYoung}
if self.transform:
sample = self.transform(sample)
return sample
class ToTensorDict(object):
#Convert ndarrays in sample to Tensors.
def __call__(self, sample):
image, Attractive, EyeGlasses, Male, MouthOpen, Smiling, Young = sample['image'], sample['Attractive'],\
sample['EyeGlasses'], sample['Male'], sample['MouthOpen'], sample['Smiling'], sample['Young']
# swap color axis because
# numpy image: H x W x C
# torch image: C X H X W
image = image.transpose((2, 0, 1))
img = torch.from_numpy(image)
Attractive = torch.from_numpy(np.asarray(Attractive))
EyeGlasses = torch.from_numpy(np.asarray(EyeGlasses))
Male = torch.from_numpy(np.asarray(Male))
MouthOpen = torch.from_numpy(np.asarray(MouthOpen))
Smiling = torch.from_numpy(np.asarray(Smiling))
Young = torch.from_numpy(np.asarray(Young))
return {'image': img.float(), 'Attractive': Attractive.float(), 'EyeGlasses': EyeGlasses.float(), 'Male': Male.float(), \
'MouthOpen': MouthOpen.float(), 'Smiling': Smiling.float(), 'Young': Young.float()}
"""
transformed_dataset = MyDataSet(filelist ='celebATrain',
transform=transforms.Compose([
ToTensorDict(),
NormalizeImageDict(['image'])
]))
dataloader = data.DataLoader(transformed_dataset, batch_size=4, shuffle=True, num_workers=1)
for i_batch, sample_batched in enumerate(dataloader):
print(i_batch, sample_batched['image'].size(), sample_batched['Attractive'].size())
print(sample_batched['Attractive'])
print(sample_batched['EyeGlasses'])
if i_batch == 1:
break
testdataset = MyDataSet("celebATrain")
for i in range(1, len(testdataset)):
fig = plt.figure()
sample = agedataset[i]
print(i, sample['image'].shape)
ax = fig.add_subplot(1,1,1)
ax.imshow(sample['image'])
plt.show()
"""