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vdataset

python pytorch

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

Metadata

Release files for vdataset 0.0.6

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