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# PyTorch torch.nn.Conv3d Function [ PyTorch torch.nn Reference Manual](#) * * * `torch.nn.Conv3d` is a 3D convolution module in PyTorch. It processes 3D inputs, such as video or medical images (depth x height x width). ### Function Definition torch.nn.Conv3d(in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True) ### Input Shape (batch, channels, depth, height, width) * * * ## Usage Examples ### Example 1: Basic Usage ## Example import torch import torch.nn as nn # 3D convolution conv3d = nn.Conv3d(in_channels=3, out_channels=32, kernel_size=3, padding=1) # Input: batch=2, channels=3, depth=16, height=32, width=32 x = torch.randn(2,3,16,32,32) output = conv3d(x) print("Input shape:", x.shape) print("Output shape:", output.shape) ### Example 2: Video Classification ## Example import torch import torch.nn as nn class VideoCNN(nn.Module): def __init__ (self, num_classes=10): super(VideoCNN,self). __init__ () self.conv1= nn.Conv3d(3,32, kernel_size=3, padding=1) self.pool= nn.MaxPool3d(2) self.conv2= nn.Conv3d(32,64, kernel_size=3, padding=1) self.gap= nn.AdaptiveAvgPool3d(1) self.fc= nn.Linear(64, num_classes) def forward(self, x): x = torch.relu(self.conv1(x)) x =self.pool(x) x = torch.relu(self.conv2(x)) x =self.gap(x) x = x.view(x.size(0), -1) return self.fc(x) model = VideoCNN() x = torch.randn(4,3,16,112,112)# 16-frame video output = model(x) print("Video:", x.shape,"-> Classes:", output.shape) ### Example 3: Parameter Calculation ## Example import torch import torch.nn as nn conv = nn.Conv3d(64,128, kernel_size=3, padding=1) # Parameter count calculation: out_ch * in_ch * k_d * k_h * k_w + bias params =128 * 64 * 3 * 3 * 3 print("Parameters:", params) print("Weight shape:", conv.weight.shape) * * * ## Use Cases * **Video Processing**: Action recognition * **Medical Imaging**: CT, MRI * **3D Segmentation**: Voxel data >