Conv3d() in PyTorch

#pytorch #conv3d #convolutionallayer #layer

*Memos:

My post explains Convolutional Layer.
My post explains Conv1d().
My post explains Conv2d().
My post explains manual_seed().
My post explains requires_grad.

Conv3d() can get the 4D or 5D tensor of the one or more elements computed by 3D convolution from the 4D or 5D tensor of one or more elements as shown below:

*Memos:

The 1st argument for initialization is in_channels(Required-Type:float). *It must be 1 <= x.
The 2nd argument for initialization is out_channels(Required-Type:float). *It must be 1 <= x.
The 3rd argument for initialization is kernel_size(Required-Type:int or tuple or list of int). *It must be 1 <= x.
The 4th argument for initialization is stride(Optional-Default:1-Type:int or tuple or list of int). *It must be 1 <= x.
The 5th argument for initialization is padding(Optional-Default:0-Type:int, str or tuple or list of int): *Memos:
- It must be 0 <= x if not str.
- It must be either 'valid' or 'same' for str.
The 6th argument for initialization is dilation(Optional-Default:1-Type:int, tuple or list of int). *It must be 1 <= x.
The 7th argument for initialization is groups(Optional-Default:1-Type:int). *It must be 1 <= x.
The 8th argument for initialization is bias(Optional-Default:True-Type:bool). *If it's False, None is set.
The 9th argument for initialization is padding_mode(Optional-Default:'zeros'-Type:str). *'zeros', 'reflect', 'replicate' or 'circular' can be selected.
The 10th argument for initialization is device(Optional-Type:str, int or device()). *Memos:
- If device is not given, get_default_device() is used. *My post explains get_default_device() and set_default_device().
- device= can be omitted.
- My post explains device argument.
The 11th argument for initialization is dtype(Optional-Type:int). *Memos:
- If dtype is not given, get_default_dtype() is used. *My post explains get_default_dtype() and set_default_dtype().
- dtype= can be omitted.
- My post explains dtype argument.
The 1st argument is input(Required-Type:tensor of float or complex). *complex must be set to dtype of Conv3d() to use a complex tensor.
The tensor's requires_grad which is False by default is set to True by Conv3d().
Input tensor's device and dtype must be same as Conv3d()'s device and dtype respectively.
conv3d1.device and conv3d1.dtype don't work.

import torch
from torch import nn

tensor1 = torch.tensor([[[[8., -3., 0., 1., 5., -2.]]]])

tensor1.requires_grad
# False

torch.manual_seed(42)

conv3d1 = nn.Conv3d(in_channels=1, out_channels=3, kernel_size=1)
tensor2 = conv3d1(input=tensor1)
tensor2
# tensor([[[[7.0349, -1.3750, 0.9186, 1.6831, 4.7413, -0.6105]]],
#         [[[6.4210, -2.7091, -0.2191, 0.6109, 3.9309, -1.8791]]],
#         [[[-1.6724, 0.9046, 0.2018, -0.0325, -0.9696, 0.6703]]]],
#        grad_fn=<SqueezeBackward1>)

tensor2.requires_grad
# True

conv3d1
# Conv3d(1, 3, kernel_size=(1, 1, 1), stride=(1, 1, 1))

conv3d1.in_channels
# 1

conv3d1.out_channels
# 3

conv3d1.kernel_size
# (1, 1, 1)

conv3d1.stride
# (1, 1, 1)

conv3d1.padding
# (0, 0, 0)

conv3d1.dilation
# (1, 1, 1)

conv3d1.groups
# 1

conv3d1.bias
# Parameter containing:
# tensor([0.9186, -0.2191, 0.2018], requires_grad=True)

conv3d1.padding_mode
# 'zeros'

conv3d1.weight
# Parameter containing:
# tensor([[[[[0.7645]]]], [[[[0.8300]]]], [[[[-0.2343]]]]],
# requires_grad=True)

torch.manual_seed(42)

conv3d2 = nn.Conv3d(in_channels=3, out_channels=3, kernel_size=1)
conv3d2(input=tensor2)
# tensor([[[[5.9849, -2.4511, -0.1504, 0.6165, 3.6841, -1.6842]]],
#         [[[3.2258, 0.2207, 1.0403, 1.3134, 2.4062, 0.4939]]],
#         [[[-0.5434, 0.0364, -0.1217, -0.1744, -0.3853, -0.0163]]]],
#        grad_fn=<SqueezeBackward1>)

torch.manual_seed(42)

conv3d = nn.Conv3d(in_channels=1, out_channels=3, kernel_size=1, stride=1, 
                   padding=0, dilation=1, groups=1, bias=True,
                   padding_mode='zeros', device=None, dtype=None)
conv3d(input=tensor1)
# tensor([[[[7.0349, -1.3750, 0.9186, 1.6831, 4.7413, -0.6105]]],
#         [[[6.4210, -2.7091, -0.2191, 0.6109, 3.9309, -1.8791]]],
#         [[[-1.6724, 0.9046, 0.2018, -0.0325, -0.9696, 0.6703]]]],
#        grad_fn=<SqueezeBackward1>)

my_tensor = torch.tensor([[[[8., -3., 0.],
                            [1., 5., -2.]]]])
torch.manual_seed(42)

conv3d = nn.Conv3d(in_channels=1, out_channels=3, kernel_size=1)
conv3d(input=my_tensor)
# tensor([[[[7.0349, -1.3750, 0.9186], [1.6831, 4.7413, -0.6105]]],
#         [[[6.4210, -2.7091, -0.2191], [0.6109, 3.9309, -1.8791]]],
#         [[[-1.6724, 0.9046, 0.2018], [-0.0325, -0.9696, 0.6703]]]],
#        grad_fn=<SqueezeBackward1>)

my_tensor = torch.tensor([[[[8.], [-3.], [0.],
                            [1.], [5.], [-2.]]]])
torch.manual_seed(42)

conv3d = nn.Conv3d(in_channels=1, out_channels=3, kernel_size=1)
conv3d(input=my_tensor)
# tensor([[[[7.0349], [-1.3750], [0.9186], [1.6831], [4.7413], [-0.6105]]],
#         [[[6.4210], [-2.7091], [-0.2191], [0.6109], [3.9309], [-1.8791]]],
#         [[[-1.6724], [0.9046], [0.2018], [-0.0325], [-0.9696], [0.6703]]]],
#        grad_fn=<SqueezeBackward1>)

my_tensor = torch.tensor([[[[[8.], [-3.], [0.]],
                            [[1.], [5.], [-2.]]]]])
torch.manual_seed(42)

conv3d = nn.Conv3d(in_channels=1, out_channels=3, kernel_size=1)
conv3d(input=my_tensor)
# tensor([[[[[7.0349], [-1.3750], [0.9186]],
#           [[1.6831], [4.7413], [-0.6105]]],
#          [[[6.4210], [-2.7091], [-0.2191]]
#           [[0.6109], [3.9309], [-1.8791]]],
#          [[[-1.6724], [0.9046], [0.2018]],
#           [[-0.0325], [-0.9696], [0.6703]]]]],
#        grad_fn=<ConvolutionBackward0>)

my_tensor = torch.tensor([[[[[8.+0.j], [-3.+0.j], [0.+0.j]],
                            [[1.+0.j], [5.+0.j], [-2.+0.j]]]]])
torch.manual_seed(42)

conv3d = nn.Conv3d(in_channels=1, out_channels=3, kernel_size=1, 
                   dtype=torch.complex64)
conv3d(input=my_tensor)
# tensor([[[[[5.6295+7.2273j], [-2.7805-1.9027j], [-0.4869+0.5873j]],
#           [[0.2777+1.4173j], [3.3358+4.7373j], [-2.0159-1.0727j]]],
#          [[[-0.9926+6.6153j], [1.5844-3.4895j], [0.8815-0.7336j]],
#           [[0.6473+0.1850j], [-0.2898+3.8594j], [1.3501-2.5709j]]],
#          [[[-0.8836+1.8015j], [1.5265-0.4182j], [0.8692+0.1872j]],
#           [[0.6501+0.3889j], [-0.2263+1.1961j], [1.3074-0.2164j]]]]],
#        grad_fn=<AddBackward0>)

DEV Community

Conv3d() in PyTorch

Top comments (0)

Read next

Salesforce Apex Triggers

How to Use Shadow DOM and Honeypots to Deter Crawlers

Conhecendo objetos em JavaScript

My Journey as a Game Developer: From Single-Player Adventures to Multiplayer Worlds