Hi seniors,
I am using a medical dataset, containing 2232 .png images of 256x256 resolution, to train my Pytorch based CNN model from scratch. I performed a vertical and a horizontal flip transforms on dataset separately. Now each original, and vertically and horizontally flipped datasets contain 2232 images.
I want to append (or say, make all dataset in one tensor) all the dataset to make of 3*2232 = 6696 images. A code snippet is given below:
orig_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=(0.1565,),std=(0.1668,))
])
HorizFlip = transforms.Compose([
transforms.RandomHorizontalFlip(p=1),
transforms.ToTensor(),
transforms.Normalize(mean=(0.1565,),std=(0.1668,)),
])
VertiFlip = transforms.Compose([
transforms.RandomVerticalFlip(p=1),
transforms.ToTensor(),
transforms.Normalize(mean=(0.1565,),std=(0.1668,)),
])
orig_dataset = MRI_Dataset(csv_file = 'C:/Users/Block-03-EE/AnacondaFiles/RecordWise/tr_file.csv',
root_dir = 'C:/Users/Block-03-EE/AnacondaFiles/RecordWise/training_data',
# root_dir = './tumor_data/MinMax/original_image',
transform = orig_transform
)
verti_dataset = MRI_Dataset(csv_file = 'C:/Users/Block-03-EE/AnacondaFiles/RecordWise/tr_file.csv',
root_dir = 'C:/Users/Block-03-EE/AnacondaFiles/RecordWise/training_data',
# root_dir = './tumor_data/MinMax/original_image',
transform = HorizFlip
)
horiz_dataset = MRI_Dataset(csv_file = 'path/traning_file.csv',
root_dir = 'C:/Users/Block-03-EE/AnacondaFiles/RecordWise/training_data',
# root_dir = './tumor_data/MinMax/original_image',
transform = VertiFlip
)
The MRI_dataset is a class that pics each individual image and itβs corresponding label β with the help of .csv file β form local directory. This code is taken from Alading Persons Youtube video.
I need following magic operation or any idea that would work in my case.
dataset = <magic operation>(orig_dataset, horiz_dataset,verti_dataset)
Please help me in this regards.
Thank you!
PS: I am new to Pytorch