I appreciate it if someone can help me regarding what im doing wrong.
My dataset get the path of the main folder, and then based on the index goes to the corresponding subfolders that belong to that video and read all the frames and stack them to gather and the output is like CxFxHxW, where C is the channel number = 3 and F is number of all the frames for that video.
e.g. if a video has 20 frames with size of 300x300 in rgb, the output will be 3x20x300x300
im not shure if i should add the batch size now to make it 1x3x20x300x300 or later…
Now lets go to the dataloader.
I build my dataloader as follow:
import torch
from torch.utils import data
import os
import torch.utils.data as data
from PIL import Image
import os
import os.path
import torchvision.transforms as transforms
def pil_loader(path):
# open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('RGB')
def accimage_loader(path):
import accimage
try:
return accimage.Image(path)
except IOError:
# Potentially a decoding problem, fall back to PIL.Image
return pil_loader(path)
def default_loader(path):
from torchvision import get_image_backend
if get_image_backend() == 'accimage':
return accimage_loader(path)
else:
return pil_loader(path)
class VideoDataset(data.Dataset):
# path is the path that all the video folders are there, meaning that for each video we have a folder that includes all the frame for that folder
def __init__(self, path):
filenames= os.listdir(path) # get all files' and folders' names in the current directory
filenames.sort()
NameOfAllTheVideos = []
for filename in filenames: # loop through all the files and folders
if os.path.isdir(os.path.join(os.path.abspath(PATH), filename)): # check whether the current object is a folder or not
NameOfAllTheVideos.append(os.path.join(os.path.abspath(PATH), filename)) #create the address to read each video
NameOfAllTheVideos.sort() #Name/Address of video folder that includes the frame inside of them
JPEGFilesForEachVideo = {} #each key is the name of the folder and the value is the name of Frames
for i in NameOfAllTheVideos:
AllJPEGFiles = os.listdir(i + '/')
AllJPEGFiles.sort()
JPEGFilesForEachVideo[i] = AllJPEGFiles # give all the freames to that corresponding video
self.JPEGFilesForEachVideo = JPEGFilesForEachVideo # so here i have the name/address of each video andthe name/address of their corresponding frames
def __len__(self):
return len(self.JPEGFilesForEachVideo)
def __getitem__(self, index):
# here o read the frames for that specific video that corresponds to index and save the frames in a tensor
JPEGAddresses = list(self.JPEGFilesForOneVideo.keys())
for FrameIndex in range(len(self.JPEGFilesForOneVideo[JPEGAddresses[index]])):
JPEGPath = JPEGAddresses[index] + '/' + self.JPEGFilesForOneVideo[JPEGAddresses[index]][FrameIndex]
Frame = default_loader(JPEGPath)
SIZE = [300,300]
transform = transforms.Compose(
[transforms.Resize(SIZE),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
FrameTransformed = torch.unsqueeze(transform(Frame),1)
if FrameIndex == 0:
FramesForOneVideo = FrameTransformed
else:
FramesForOneVideo =torch.cat((FramesForOneVideo, FrameTransformed), 1)
return torch.unsqueeze(FramesForOneVideo,0) # so it will have the size BxCxFxDxD
if i give the path to it i will have:
PATH = '/home/alireza/Desktop/Datasets/VID/ILSVRC-New/Data/VID/train/'
Training = VideoDataset(PATH)
print(Training)
<__main__.VideoDataset object at 0x7fcff04fdd68>
How can i check to see if im doing it right or not?
I am not sure if it is right or not???
if i give it to Dataloader it gives me:
import torch
import numpy as np
from torch.utils.data import Dataset, DataLoader
train_loader = data.DataLoader(Training,
batch_size=2,
shuffle=False,
num_workers=2)
print(train_loader)
<torch.utils.data.dataloader.DataLoader object at 0x7fcff0c9da90>
Again please let me know how i can check its correctness…
but when i want to iterate through it, it gives me this error:
for epoch in range(1):
for i, data in enumerate(train_loader, 0):
# get the inputs
inputs, labels = data
print(epoch, i, "inputs", inputs.data, "labels", labels.data)
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-39-3e3a8304e478> in <module>()
15
16 for epoch in range(1):
---> 17 for i, data in enumerate(train_loader, 0):
18 # get the inputs
19 inputs, labels = data
~/anaconda3/lib/python3.6/site-packages/torch/utils/data/dataloader.py in __next__(self)
284 self.reorder_dict[idx] = batch
285 continue
--> 286 return self._process_next_batch(batch)
287
288 next = __next__ # Python 2 compatibility
~/anaconda3/lib/python3.6/site-packages/torch/utils/data/dataloader.py in _process_next_batch(self, batch)
305 self._put_indices()
306 if isinstance(batch, ExceptionWrapper):
--> 307 raise batch.exc_type(batch.exc_msg)
308 return batch
309
AttributeError: Traceback (most recent call last):
File "/home/alireza/anaconda3/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 57, in _worker_loop
samples = collate_fn([dataset[i] for i in batch_indices])
File "/home/alireza/anaconda3/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 57, in <listcomp>
samples = collate_fn([dataset[i] for i in batch_indices])
File "<ipython-input-35-d5c268d5dcaa>", line 56, in __getitem__
JPEGAddresses = list(self.JPEGFilesForOneVideo.keys())
AttributeError: 'VideoDataset' object has no attribute 'JPEGFilesForOneVideo'
