vdataset 0.0.6

Load video datasets to PyTorch DataLoader and generate label maps

vdataset

Description

Load video datasets to PyTorch DataLoader. (Custom Video Data set for PyTorch DataLoader)
VDataset can be use to load 20BN-Jester dataset to the PyTorch DataLoader

Required Libraries

• torch
• Pillow
• pandas

Arguments

LableMap Constructor

Argument	Type	Required	Default	Description
labels_csv	str	False	None	The path to the csv file containing the labels and ids.
labels_col_name	str	False	None	The name of the column containing the labels. (Required if labels_csv is not None)
ids_col_name	str/ None	False	None	The name of the column containing the ids.
id_type	type	False	int	The type of the ids.

VDataset Constructor

Argument	Type	Required	Default	Description
csv_file	str	True	-	Path to .csv file
root_dir	str	True	-	Root Directory of the video dataset
file_format	str	False	jpg	File type of the frame images (ex: .jpg, .jpeg, .png)
id_col_name	str	False	video_id	Column name, where id/name of the video on the .csv file
label_col_name	str	False	label	Column name, where label is on the .csv file
frames_limit_mode	str/None	False	None	Mode of the frame count detection ("manual", "csv" or else it auto detects all the frames available)
frames_limit	dict	False	{"start": 0, "end": None}	Number of frames in a video (required if frames_count_mode set to "manual")
frames_limit_col_name	str	False	frames	Column name, where label is on the .csv file (required if frames_count_mode set to "csv")
video_transforms	tuple/None	False	None	Video Transforms (Refer: https://github.com/hassony2/torch_videovision)
label_map	LabelMap/None	False	None	Label Map of the Dataset

Usage

from vdataset import LabelMap, VDataset

from torch.utils.data import DataLoader

from torchvideotransforms.volume_transforms import ClipToTensor # https://github.com/hassony2/torch_videovision
from torchvideotransforms import video_transforms, volume_transforms # https://github.com/hassony2/torch_videovision

# Create Label Map
label_map = LabelMap(labels_csv="/path-to-csv/csv_file.csv", labels_col_name="label") 

print(label_map)
label_map.print() # printing the labels on label-map

# Use Video Transformers
video_transform_list = [video_transforms.RandomRotation(30),
            video_transforms.Resize((100, 100)),
            volume_transforms.ClipToTensor()]
video_transforms = video_transforms.Compose(video_transform_list)

# Create Vdataset (No frame limitation)
full_dataset = VDataset(csv_file='/path-to-csv/csv_file.csv', root_dir='/path-to-root/', video_transforms=video_transforms, label_map=label_map)

# Create Vdataset (Manual frames limitation, remove first 5 frames and last 5 frames)
frames_limited_dataset = VDataset(csv_file='/path-to-csv/csv_file.csv', root_dir='/path-to-root/', video_transforms=video_transforms, frames_limit_mode="manual",  frames_limit={"start": 5, "end": -5} label_map=label_map)

full_dataloader = DataLoader(full_dataset, batch_size=64, shuffle=True, num_workers=2, pin_memory=True)
print(full_dataloader)

frames_limited_dataloader = DataLoader(frames_limited_dataset, batch_size=64, shuffle=True, num_workers=2, pin_memory=True)
print(frames_limited_dataloader)

for image, label in full_dataloader: # Do what do you want in dataset
    print(image, label)
    print(image.size())
    break

for image, label in frames_limited_dataloader: # Do what do you want in dataset
    print(image, label)
    print(image.size())
    break