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* explains .
can use Places365 dataset as shown below:
*Memos:
- The 1st argument is root(Required-Type:str or pathlib.Path). *An absolute or relative path is possible.
- The 2nd argument is split(Optional-Default:"train-standard"-Type:str). *"train-standard"(1,803,460 images), "train-challenge"(8,026,628 images) or "val"(36,500 images) can be set to it. "test"(328,500 images) isn't supported so I requested the feature on .
- The 3rd argument is small(Optional-Default:False-Type:bool).
- The 4th argument is download(Optional-Default:False-Type:bool): *Memos:
- If it's True, the dataset is downloaded from the internet and extracted(unzipped) to root.
- If it's True and the dataset is already downloaded, it's extracted.
- If it's True and the dataset is already downloaded and extracted, error occurs because the extracted folders exist. *Deleting the extracted folders doesn't get error.
- It should be False if the dataset is already downloaded and extracted not to get error.
- From :
- for split="train-standard" and small=False, you can manually download and extract the dataset filelist_places365-standard.tar and train_large_places365standard.tar to data/ and data/data_large_standard/ respectively.
- for split="train-standard" and small=True, you can manually download and extract the dataset filelist_places365-standard.tar and train_256_places365standard.tar to data/ and data/data_256_standard/ respectively.
- for split="train-challenge" and small=False, you can manually download and extract the dataset filelist_places365-challenge.tar and train_large_places365challenge.tar to data/ and data/data_large/ respectively.
- for split="train-challenge" and small=True, you can manually download and extract the dataset filelist_places365-challenge.tar and train_256_places365challenge.tar to data/ and data/data_256_challenge/ respectively.
- for split="val" and small=False, you can manually download and extract the dataset filelist_places365-standard.tar and val_large.tar to data/ and data/val_large/ respectively.
- for split="val" and small=True, you can manually download and extract the dataset filelist_places365-standard.tar and val_large.tar to data/ and data/val_256/ respectively.
- The 5th argument is transform(Optional-Default:None-Type:callable).
- The 6th argument is target_transform(Optional-Default:None-Type:callable).
- The 7th argument is loader(Optional-Default:torchvision.datasets.folder.default_loader-Type:callable).
- About the label from the classes for the "train-standard" image indices, airfield(0) is 0~4999, airplane_cabin(1) is 5000~9999, airport_terminal(2) is 10000~14999, alcove(3) is 15000~19999, alley(4) is 20000~24999, amphitheater(5) is 25000~29999, amusement_arcade(6) is 30000~34999, amusement_park(7) is 35000~39999, apartment_building/outdoor(8) is 40000~44999, aquarium(9) is 45000~49999, etc.
- About the label from the classes for the "train-challenge" image indices, airfield(0) is 0~38566, airplane_cabin(1) is 38567~47890, airport_terminal(2) is 47891~74901, alcove(3) is 74902~98482, alley(4) is 98483~137662, amphitheater(5) is 137663~150034, amusement_arcade(6) is 150035~161051, amusement_park(7) is 161052~201051, apartment_building/outdoor(8) is 201052~227872, aquarium(9) is 227873~267872, etc.
from torchvision.datasets import Places365
from torchvision.datasets.folder import default_loader
trainstd_large_data = Places365(
root="data"
)
trainstd_large_data = Places365(
root="data",
split="train-standard",
small=False,
download=False,
transform=None,
target_transform=None,
loader=default_loader
)
trainstd_small_data = Places365(
root="data",
split="train-standard",
small=True
)
trainchal_large_data = Places365(
root="data",
split="train-challenge",
small=False
)
trainchal_small_data = Places365(
root="data",
split="train-challenge",
small=True
)
val_large_data = Places365(
root="data",
split="val",
small=False
)
val_small_data = Places365(
root="data",
split="val",
small=True
)
len(trainstd_large_data), len(trainstd_small_data)
# (1803460, 1803460)
len(trainchal_large_data), len(trainchal_small_data)
# (8026628, 8026628)
len(val_large_data), len(val_small_data)
# (36500, 36500)
trainstd_large_data
# Dataset Places365
# Number of datapoints: 1803460
# Root location: data
# Split: train-standard
# Small: False
trainstd_large_data.root
# 'data'
trainstd_large_data.split
# 'train-standard'
trainstd_large_data.small
# False
trainstd_large_data.download_devkit
trainstd_large_data.download_images
# <bound method Places365.download_devkit of Dataset Places365
# Number of datapoints: 1803460
# Root location: data
# Split: train-standard
# Small: False>
print(trainstd_large_data.transform)
# None
print(trainstd_large_data.target_transform)
# None
trainstd_large_data.loader
# <function torchvision.datasets.folder.default_loader(path: str) -> Any>
len(trainstd_large_data.classes), trainstd_large_data.classes
# (365,
# ['/a/airfield', '/a/airplane_cabin', '/a/airport_terminal',
# '/a/alcove', '/a/alley', '/a/amphitheater', '/a/amusement_arcade',
# '/a/amusement_park', '/a/apartment_building/outdoor',
# '/a/aquarium', '/a/aqueduct', '/a/arcade', '/a/arch',
# '/a/archaelogical_excavation', ..., '/y/youth_hostel', '/z/zen_garden'])
trainstd_large_data[0]
# (<PIL.Image.Image image mode=RGB size=683x512>, 0)
trainstd_large_data[1]
# (<PIL.Image.Image image mode=RGB size=768x512>, 0)
trainstd_large_data[2]
# (<PIL.Image.Image image mode=RGB size=718x512>, 0)
trainstd_large_data[5000]
# (<PIL.Image.Image image mode=RGB size=512x683 at 0x1E7834F4770>, 1)
trainstd_large_data[10000]
# (<PIL.Image.Image image mode=RGB size=683x512 at 0x1E7834A8110>, 2)
trainstd_small_data[0]
# (<PIL.Image.Image image mode=RGB size=256x256>, 0)
trainstd_small_data[1]
# (<PIL.Image.Image image mode=RGB size=256x256>, 0)
trainstd_small_data[2]
# (<PIL.Image.Image image mode=RGB size=256x256>, 0)
trainstd_small_data[5000]
# (<PIL.Image.Image image mode=RGB size=256x256>, 1)
trainstd_small_data[10000]
# (<PIL.Image.Image image mode=RGB size=256x256>, 2)
trainchal_large_data[0]
# (<PIL.Image.Image image mode=RGB size=683x512 at 0x156E22BB680>, 0)
trainchal_large_data[1]
# (<PIL.Image.Image image mode=RGB size=768x512 at 0x156DF8213D0>, 0)
trainchal_large_data[2]
# (<PIL.Image.Image image mode=RGB size=718x512 at 0x156DF8213D0>, 0)
trainchal_large_data[38567]
# (<PIL.Image.Image image mode=RGB size=512x683 at 0x156DF8213D0>, 1)
trainchal_large_data[47891]
# (<PIL.Image.Image image mode=RGB size=683x512 at 0x156DF8213D0>, 2)
trainchal_small_data[0]
# (<PIL.Image.Image image mode=RGB size=256x256 at 0x2955B625CA0>, 0)
trainchal_small_data[1]
# (<PIL.Image.Image image mode=RGB size=256x256 at 0x2950D2A8350>, 0)
trainchal_small_data[2]
# (<PIL.Image.Image image mode=RGB size=256x256 at 0x2950D2A82C0>, 0)
trainchal_small_data[38567]
# (<PIL.Image.Image image mode=RGB size=256x256 at 0x2955B3BF6B0>, 1)
trainchal_small_data[47891]
# (<PIL.Image.Image image mode=RGB size=256x256 at 0x2955B3DD4F0>, 2)
val_large_data[0]
# (<PIL.Image.Image image mode=RGB size=512x772 at 0x295408DA750>, 165)
val_large_data[1]
# (<PIL.Image.Image image mode=RGB size=600x493 at 0x29561D468D0>, 358)
val_large_data[2]
# (<PIL.Image.Image image mode=RGB size=763x512 at 0x2955E09DD60>, 93)
val_large_data[3]
# (<PIL.Image.Image image mode=RGB size=827x512 at 0x29540938A70>, 164)
val_large_data[4]
# (<PIL.Image.Image image mode=RGB size=772x512 at 0x29562600650>, 289)
val_small_data[0]
# (<PIL.Image.Image image mode=RGB size=256x256 at 0x2950D34C500>, 165)
val_small_data[1]
# (<PIL.Image.Image image mode=RGB size=256x256 at 0x29540892870>, 358)
val_small_data[2]
# (<PIL.Image.Image image mode=RGB size=256x256 at 0x2954085DBB0>, 93)
val_small_data[3]
# (<PIL.Image.Image image mode=RGB size=256x256 at 0x29561E348C0>, 164)
val_small_data[4]
# (<PIL.Image.Image image mode=RGB size=256x256 at 0x29560A415B0>, 289)
import matplotlib.pyplot as plt
def show_images(data, ims, main_title=None):
plt.figure(figsize=(12, 6))
plt.suptitle(t=main_title, y=1.0, fontsize=14)
for i, j in enumerate(iterable=ims, start=1):
plt.subplot(2, 5, i)
im, lab = data[j]
plt.imshow(X=im)
plt.title(label=lab)
plt.tight_layout(h_pad=3.0)
plt.show()
trainstd_ims = (0, 1, 2, 5000, 10000, 15000, 20000, 25000, 30000, 35000)
trainchal_ims = (0, 1, 2, 38567, 47891, 74902, 98483, 137663, 150035, 161052)
val_ims = (0, 1, 2, 3, 4, 5, 6, 7, 8, 9)
show_images(data=trainstd_large_data, ims=trainstd_ims,
main_title="trainstd_large_data")
show_images(data=trainstd_small_data, ims=trainstd_ims,
main_title="trainstd_small_data")
show_images(data=trainchal_large_data, ims=trainchal_ims,
main_title="trainchal_large_data")
show_images(data=trainchal_small_data, ims=trainchal_ims,
main_title="trainchal_small_data")
show_images(data=val_large_data, ims=val_ims,
main_title="val_large_data")
show_images(data=val_small_data, ims=val_ims,
main_title="val_small_data")