Flowers in PyTorch

from torchvision.datasets import Flowers102

train_data = Flowers102(
    root="data"
)

train_data = Flowers102(
    root="data",
    split="train",
    transform=None,
    target_transform=None,
    download=False
)

val_data = Flowers102(
    root="data",
    split="val"
)

test_data = Flowers102(
    root="data",
    split="test"
)

len(train_data), len(val_data), len(test_data)
# (1020, 1020, 6149)

train_data
# Dataset Flowers102
#     Number of datapoints: 1020
#     Root location: data
#     split=train

train_data.root
# 'data'

train_data._split
# 'train'

print(train_data.transform)
# None

print(train_data.target_transform)
# None

train_data.download
# <bound method Flowers102.download of Dataset Flowers102
#     Number of datapoints: 1020
#     Root location: data
#     split=train>

len(set(train_data._labels)), train_data._labels
# (102,
#  [0, 0, 0, ..., 1, ..., 2, ..., 3, ..., 4, ..., 5, ..., 6, ..., 101])

train_data[0]
# (<PIL.Image.Image image mode=RGB size=754x500>, 0)

train_data[1]
# (<PIL.Image.Image image mode=RGB size=624x500>, 0)

train_data[2]
# (<PIL.Image.Image image mode=RGB size=667x500>, 0)

train_data[10]
# (<PIL.Image.Image image mode=RGB size=500x682>, 1)

train_data[20]
# (<PIL.Image.Image image mode=RGB size=667x500>, 2)

val_data[0]
# (<PIL.Image.Image image mode=RGB size=606x500>, 0)

val_data[1]
# (<PIL.Image.Image image mode=RGB size=667x500>, 0)

val_data[2]
# (<PIL.Image.Image image mode=RGB size=500x628>, 0)

val_data[10]
# (<PIL.Image.Image image mode=RGB size=500x766>, 1)

val_data[20]
# (<PIL.Image.Image image mode=RGB size=624x500>, 2)

test_data[0]
# (<PIL.Image.Image image mode=RGB size=523x500>, 0)

test_data[1]
# (<PIL.Image.Image image mode=RGB size=666x500>, 0)

test_data[2]
# (<PIL.Image.Image image mode=RGB size=595x500>, 0)

test_data[20]
# (<PIL.Image.Image image mode=RGB size=500x578>, 1)

test_data[60]
# (<PIL.Image.Image image mode=RGB size=500x625>, 2)

import matplotlib.pyplot as plt

def show_images(data, ims, main_title=None):
    plt.figure(figsize=(10, 5))
    plt.suptitle(t=main_title, y=1.0, fontsize=14)
    for i, j in enumerate(ims, start=1):
        plt.subplot(2, 5, i)
        im, lab = data[j]
        plt.imshow(X=im)
        plt.title(label=lab)
    plt.tight_layout()
    plt.show()

train_ims = (0, 1, 2, 10, 20, 30, 40, 50, 60, 70)
val_ims = (0, 1, 2, 10, 20, 30, 40, 50, 60, 70)
test_ims = (0, 1, 2, 20, 60, 80, 116, 161, 186, 206)

show_images(data=train_data, ims=train_ims, main_title="train_data")
show_images(data=train_data, ims=val_ims, main_title="val_data")
show_images(data=test_data, ims=test_ims, main_title="test_data")