ToDtype in PyTorch (2)

from torchvision.datasets import OxfordIIITPet from torchvision.transforms.v2 import ToImage, ToDtype import torch import numpy as np PILImage_data = OxfordIIITPet( root="data", transform=None ) Image_data = OxfordIIITPet( root="data", transform=ToImage() ) td = ToDtype(dtype=torch.float32, scale=False) td(PILImage_data) # It's still PIL Image. # Dataset OxfordIIITPet # Number of datapoints: 3680 # Root location: data  td(PILImage_data[0]) # (<PIL.Image.Image image mode=RGB size=394x500>, 0)  list(td(PILImage_data[0][0]).getdata()) # [(37, 20, 12), # (35, 18, 10), # (36, 19, 11), # (36, 19, 11), # (37, 18, 11), # ...]  td(Image_data[0]) # (Image([[[37., 35., 36., ..., 247., 249., 249.], # [35., 35., 37., ..., 246., 248., 249.], # ..., # [28., 28., 27., ..., 59., 65., 76.]], # [[20., 18., 19., ..., 248., 248., 248.], # [18., 18., 20., ..., 247., 247., 248.], # ..., # [27., 27., 27., ..., 94., 106., 117.]], # [[12., 10., 11., ..., 253., 253., 253.], # [10., 10., 12., ..., 251., 252., 253.], # ..., # [35., 35., 35., ..., 214., 232., 223.]]],), 0)  td(Image_data[0][0]) # Image([[[37, 35, 36, ..., 247, 249, 249], # [35, 35, 37, ..., 246, 248, 249], # ..., # [28, 28, 27, ..., 59, 65, 76]], # [[20, 18, 19, ..., 248, 248, 248], # [18, 18, 20, ..., 247, 247, 248], # ..., # [27, 27, 27, ..., 94, 106, 117]], # [[12, 10, 11, ..., 253, 253, 253], # [10, 10, 12, ..., 251, 252, 253], # ..., # [35, 35, 35, ..., 214, 232, 223]]],)  td((torch.tensor(3), 0)) # int64 td((torch.tensor(3, dtype=torch.int64), 0)) # (tensor(3.), 0)  td(torch.tensor(3)) # tensor(3.)  td((torch.tensor([0, 1, 2, 3]), 0)) # (tensor([0., 1., 2., 3.]), 0)  td(torch.tensor([0, 1, 2, 3])) # tensor([0., 1., 2., 3.])  td((torch.tensor([[0, 1, 2, 3]]), 0)) # (tensor([[0., 1., 2., 3.]]), 0)  td(torch.tensor([[0, 1, 2, 3]])) # tensor([[0., 1., 2., 3.]])  td((torch.tensor([[[0, 1, 2, 3]]]), 0)) # (tensor([[[0., 1., 2., 3.]]]), 0)  td(torch.tensor([[[0, 1, 2, 3]]])) # tensor([[[0., 1., 2., 3.]]])  td((torch.tensor([[[[0, 1, 2, 3]]]]), 0)) # (tensor([[[[0., 1., 2., 3.]]]]), 0)  td(torch.tensor([[[[0, 1, 2, 3]]]])) # tensor([[[[0., 1., 2., 3.]]]])  td((torch.tensor([[[[[0, 1, 2, 3]]]]]), 0)) # (tensor([[[[[0., 1., 2., 3.]]]]]), 0)  td(torch.tensor([[[[[0, 1, 2, 3]]]]])) # tensor([[[[[0., 1., 2., 3.]]]]])  td((torch.tensor([[0, 1, 2, 3]], dtype=torch.int32), 0)) td((torch.tensor([[0., 1., 2., 3.]]), 0)) # float32 td((torch.tensor([[0., 1., 2., 3.]], dtype=torch.float32), 0)) td((torch.tensor([[0., 1., 2., 3.]], dtype=torch.float64), 0)) td((torch.tensor([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]), 0)) # complex64 td((torch.tensor([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]], dtype=torch.complex64), 0)) td((torch.tensor([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]], dtype=torch.complex32), 0)) # (tensor([[0., 1., 2., 3.]]), 0)  td((torch.tensor([[True, False, True, False]]), 0)) # bool td((torch.tensor([[True, False, True, False]], dtype=torch.bool), 0)) # (tensor([[1., 0., 1., 0.]]), 0)  td((np.array(3), 0)) # int32 td((np.array(3, dtype=np.int32), 0)) # (array(3), 0)  td(np.array(3)) # array(3)  td((np.array([0, 1, 2, 3]), 0)) # (array([0, 1, 2, 3]), 0)  td(np.array([0, 1, 2, 3])) # array([0, 1, 2, 3])  td((np.array([[0, 1, 2, 3]]), 0)) # (array([[0, 1, 2, 3]]), 0)  td(np.array([[0, 1, 2, 3]])) # array([[0, 1, 2, 3]])  td((np.array([[[0, 1, 2, 3]]]), 0)) # (array([[[0, 1, 2, 3]]]), 0)  td(np.array([[[0, 1, 2, 3]]])) # array([[[0, 1, 2, 3]]])  td((np.array([[[[0, 1, 2, 3]]]]), 0)) # (array([[[[0, 1, 2, 3]]]]), 0)  td(np.array([[[[0, 1, 2, 3]]]])) # array([[[[0, 1, 2, 3]]]])  td((np.array([[[[[0, 1, 2, 3]]]]]), 0)) # (array([[[[[0, 1, 2, 3]]]]]), 0)  td(np.array([[[[[0, 1, 2, 3]]]]])) # array([[[[[0, 1, 2, 3]]]]])  td((np.array([[0, 1, 2, 3]], dtype=np.int64), 0)) # (array([[0, 1, 2, 3]], dtype=int64), 0)  td((np.array([[0., 1., 2., 3.]]), 0)) # float64 td((np.array([[0., 1., 2., 3.]], dtype=np.float64), 0)) # (array([[0., 1., 2., 3.]]), 0)  td((np.array([[0., 1., 2., 3.]], dtype=np.float32), 0)) # (array([[0., 1., 2., 3.]], dtype=float32), 0)  td((np.array([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]), 0)) # complex128 td((np.array([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]], dtype=np.complex128), 0)) # (array([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]), 0)  td((np.array([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]], dtype=np.complex64), 0)) # (array([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]], dtype=complex64), 0)  td((np.array([[True, False, True, False]]), 0)) # bool td((np.array([[True, False, True, False]], dtype=bool), 0)) # (array([[True, False, True, False]]), 0)  td = ToDtype(dtype=torch.complex64, scale=False) td(PILImage_data) # It's still PIL Image. # Dataset OxfordIIITPet # Number of datapoints: 3680 # Root location: data  td(PILImage_data[0]) # (<PIL.Image.Image image mode=RGB size=394x500>, 0)  list(td(PILImage_data[0][0]).getdata()) # [(37, 20, 12), # (35, 18, 10), # (36, 19, 11), # (36, 19, 11), # (37, 18, 11), # ...]  td(Image_data[0]) # (Image([[[37.+0.j, 35.+0.j, 36.+0.j, ..., 247.+0.j, 249.+0.j, 249.+0.j], # [35.+0.j, 35.+0.j, 37.+0.j, ..., 246.+0.j, 248.+0.j, 249.+0.j], # ..., # [28.+0.j, 28.+0.j, 27.+0.j, ..., 59.+0.j, 65.+0.j, 76.+0.j]], # [[20.+0.j, 18.+0.j, 19.+0.j, ..., 248.+0.j, 248.+0.j, 248.+0.j], # [18.+0.j, 18.+0.j, 20.+0.j, ..., 247.+0.j, 247.+0.j, 248.+0.j], # ..., # [27.+0.j, 27.+0.j, 27.+0.j, ..., 94.+0.j, 106.+0.j, 117.+0.j]], # [[12.+0.j, 10.+0.j, 11.+0.j, ..., 253.+0.j, 253.+0.j, 253.+0.j], # [10.+0.j, 10.+0.j, 12.+0.j, ..., 251.+0.j, 252.+0.j, 253.+0.j], # ..., # [35.+0.j, 35.+0.j, 35.+0.j, ..., 214.+0.j, 232.+0.j, 223.+0.j]]],), # 0)  td(Image_data[0][0]) # Image([[[37.+0.j, 35.+0.j, 36.+0.j, ..., 247.+0.j, 249.+0.j, 249.+0.j], # [35.+0.j, 35.+0.j, 37.+0.j, ..., 246.+0.j, 248.+0.j, 249.+0.j], # ..., # [28.+0.j, 28.+0.j, 27.+0.j, ..., 59.+0.j, 65.+0.j, 76.+0.j]], # [[20.+0.j, 18.+0.j, 19.+0.j, ..., 248.+0.j, 248.+0.j, 248.+0.j], # [18.+0.j, 18.+0.j, 20.+0.j, ..., 247.+0.j, 247.+0.j, 248.+0.j], # ..., # [27.+0.j, 27.+0.j, 27.+0.j, ..., 94.+0.j, 106.+0.j, 117.+0.j]], # [[12.+0.j, 10.+0.j, 11.+0.j, ..., 253.+0.j, 253.+0.j, 253.+0.j], # [10.+0.j, 10.+0.j, 12.+0.j, ..., 251.+0.j, 252.+0.j, 253.+0.j], # ..., # [35.+0.j, 35.+0.j, 35.+0.j, ..., 214.+0.j, 232.+0.j, 223.+0.j]]],)  td((torch.tensor(3), 0)) # int64 td((torch.tensor(3, dtype=torch.int64), 0)) # (tensor(3.+0.j), 0)  td(torch.tensor(3)) # tensor(3.+0.j)  td((torch.tensor([0, 1, 2, 3]), 0)) # (tensor([0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]), 0)  td(torch.tensor([0, 1, 2, 3])) # tensor([0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j])  td((torch.tensor([[0, 1, 2, 3]]), 0)) # (tensor([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]), 0)  td(torch.tensor([[0, 1, 2, 3]])) # tensor([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]])  td((torch.tensor([[[0, 1, 2, 3]]]), 0)) # (tensor([[[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]]), 0)  td(torch.tensor([[[0, 1, 2, 3]]])) # tensor([[[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]])  td((torch.tensor([[[[0, 1, 2, 3]]]]), 0)) # (tensor([[[[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]]]), 0)  td(torch.tensor([[[[0, 1, 2, 3]]]])) # tensor([[[[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]]])  td((torch.tensor([[[[[0, 1, 2, 3]]]]]), 0)) # (tensor([[[[[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]]]]), 0)  td(torch.tensor([[[[[0, 1, 2, 3]]]]])) # tensor([[[[[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]]]])  td((torch.tensor([[0, 1, 2, 3]], dtype=torch.int32), 0)) # (tensor([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]), 0)  td((torch.tensor([[0., 1., 2., 3.]]), 0)) # float32 td((torch.tensor([[0., 1., 2., 3.]], dtype=torch.float32), 0)) td((torch.tensor([[0., 1., 2., 3.]], dtype=torch.float64), 0)) # (tensor([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]), 0)  td((torch.tensor([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]), 0)) # complex64 td((torch.tensor([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]], dtype=torch.complex64), 0)) td((torch.tensor([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]], dtype=torch.complex32), 0)) # (tensor([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]), 0)  td((torch.tensor([[True, False, True, False]]), 0)) # bool td((torch.tensor([[True, False, True, False]], dtype=torch.bool), 0)) # (tensor([[1.+0.j, 0.+0.j, 1.+0.j, 0.+0.j]]), 0)  td((np.array(3), 0)) # int32 td((np.array(3, dtype=np.int32), 0)) # (array(3), 0)  td(np.array(3)) # array(3)  td((np.array([0, 1, 2, 3]), 0)) # (array([0, 1, 2, 3]), 0)  td(np.array([0, 1, 2, 3])) # array([0, 1, 2, 3])  td((np.array([[0, 1, 2, 3]]), 0)) # (array([[0, 1, 2, 3]]), 0)  td(np.array([[0, 1, 2, 3]])) # array([[0, 1, 2, 3]])  td((np.array([[[0, 1, 2, 3]]]), 0)) # (array([[[0, 1, 2, 3]]]), 0)  td(np.array([[[0, 1, 2, 3]]])) # array([[[0, 1, 2, 3]]])  td((np.array([[0, 1, 2, 3]], dtype=np.int64), 0)) # (array([[0, 1, 2, 3]], dtype=int64), 0)  td((np.array([[0., 1., 2., 3.]]), 0)) # float64 td((np.array([[0., 1., 2., 3.]], dtype=np.float64), 0)) # (array([[0., 1., 2., 3.]]), 0)  td((np.array([[0., 1., 2., 3.]], dtype=np.float32), 0)) # (array([[0., 1., 2., 3.]], dtype=float32), 0)  td((np.array([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]), 0)) # complex128 td((np.array([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]], dtype=np.complex128), 0)) # (array([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]), 0)  td((np.array([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]], dtype=np.complex64), 0)) # (array([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]], dtype=complex64), 0)  td((np.array([[True, False, True, False]]), 0)) # bool td((np.array([[True, False, True, False]], dtype=bool), 0)) # (array([[True, False, True, False]]), 0)  td = ToDtype(dtype=torch.bool, scale=False) td(PILImage_data) # It's still PIL Image. # Dataset OxfordIIITPet # Number of datapoints: 3680 # Root location: data  td(PILImage_data[0]) # (<PIL.Image.Image image mode=RGB size=394x500>, 0)  list(td(PILImage_data[0][0]).getdata()) # [(37, 20, 12), # (35, 18, 10), # (36, 19, 11), # (36, 19, 11), # (37, 18, 11), # ...]  td(Image_data[0]) # (Image([[[True, True, True, ..., True, True, True], # [True, True, True, ..., True, True, True], # ..., # [True, True, True, ..., True, True, True]], # [[True, True, True, ..., True, True, True], # [True, True, True, ..., True, True, True], # ..., # [True, True, True, ..., True, True, True]], # [[True, True, True, ..., True, True, True], # [True, True, True, ..., True, True, True], # ..., # [True, True, True, ..., True, True, True]]],), 0)  td(Image_data[0][0]) # Image([[[True, True, True, ..., True, True, True], # [True, True, True, ..., True, True, True], # ..., # [True, True, True, ..., True, True, True]], # [[True, True, True, ..., True, True, True], # [True, True, True, ..., True, True, True], # ..., # [True, True, True, ..., True, True, True]], # [[True, True, True, ..., True, True, True], # [True, True, True, ..., True, True, True], # ..., # [True, True, True, ..., True, True, True]]],)  td((torch.tensor(3), 0)) # int64 td((torch.tensor(3, dtype=torch.int64), 0)) # (tensor(True), 0)  td(torch.tensor(3)) # tensor(True)  td((torch.tensor([0, 1, 2, 3]), 0)) # (tensor([False, True, True, True]), 0)  td(torch.tensor([0, 1, 2, 3])) # tensor([False, True, True, True])  td((torch.tensor([[0, 1, 2, 3]]), 0)) # (tensor([[False, True, True, True]]), 0)  td(torch.tensor([[0, 1, 2, 3]])) # tensor([[False, True, True, True]])  td((torch.tensor([[[0, 1, 2, 3]]]), 0)) # (tensor([[[False, True, True, True]]]), 0)  td(torch.tensor([[[0, 1, 2, 3]]])) # tensor([[[False, True, True, True]]])  td((torch.tensor([[[[0, 1, 2, 3]]]]), 0)) # (tensor([[[[False, True, True, True]]]]), 0)  td(torch.tensor([[[[0, 1, 2, 3]]]])) # tensor([[[[False, True, True, True]]]])  td((torch.tensor([[[[[0, 1, 2, 3]]]]]), 0)) # (tensor([[[[[False, True, True, True]]]]]), 0)  td(torch.tensor([[[[[0, 1, 2, 3]]]]])) # tensor([[[[[False, True, True, True]]]]])  td((torch.tensor([[0, 1, 2, 3]], dtype=torch.int32), 0)) # (tensor([[False, True, True, True]]), 0)  td((torch.tensor([[0., 1., 2., 3.]]), 0)) # float32 td((torch.tensor([[0., 1., 2., 3.]], dtype=torch.float32), 0)) td((torch.tensor([[0., 1., 2., 3.]], dtype=torch.float64), 0)) # (tensor([[False, True, True, True]]), 0)  td((torch.tensor([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]), 0)) # complex64 td((torch.tensor([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]], dtype=torch.complex64), 0)) td((torch.tensor([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]], dtype=torch.complex32), 0)) # (tensor([[False, True, True, True]]), 0)  td((torch.tensor([[True, False, True, False]]), 0)) # bool td((torch.tensor([[True, False, True, False]], dtype=torch.bool), 0)) # (tensor([[True, False, True, False]]), 0)  td((np.array(3), 0)) # int32 td((np.array(3, dtype=np.int32), 0)) # (array(3), 0)  td(np.array(3)) # array(3)  td((np.array([0, 1, 2, 3]), 0)) # (array([0, 1, 2, 3]), 0)  td(np.array([0, 1, 2, 3])) # array([0, 1, 2, 3])  td((np.array([[0, 1, 2, 3]]), 0)) # (array([[0, 1, 2, 3]]), 0)  td(np.array([[0, 1, 2, 3]])) # array([[0, 1, 2, 3]])  td((np.array([[[0, 1, 2, 3]]]), 0)) # (array([[[0, 1, 2, 3]]]), 0)  td(np.array([[[0, 1, 2, 3]]])) # array([[[0, 1, 2, 3]]])  td((np.array([[0, 1, 2, 3]], dtype=np.int64), 0)) # (array([[0, 1, 2, 3]], dtype=int64), 0)  td((np.array([[0., 1., 2., 3.]]), 0)) # float64 td((np.array([[0., 1., 2., 3.]], dtype=np.float64), 0)) # (array([[0., 1., 2., 3.]]), 0)  td((np.array([[0., 1., 2., 3.]], dtype=np.float32), 0)) # (array([[0., 1., 2., 3.]], dtype=float32), 0)  td((np.array([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]), 0)) # complex128 td((np.array([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]], dtype=np.complex128), 0)) # (array([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]]), 0)  td((np.array([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]], dtype=np.complex64), 0)) # (array([[0.+0.j, 1.+0.j, 2.+0.j, 3.+0.j]], dtype=complex64), 0)  td((np.array([[True, False, True, False]]), 0)) # bool td((np.array([[True, False, True, False]], dtype=bool), 0)) # (array([[True, False, True, False]]), 0)