forked from ccf-ai-infra/GPUCodeForces
40 lines
1.1 KiB
Python
40 lines
1.1 KiB
Python
import torch
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import torch.nn as nn
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class Model(nn.Module):
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"""
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Model that performs 2D convolution operation.
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"""
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def __init__(self, weight, bias=None):
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super(Model, self).__init__()
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self.weight = nn.Parameter(weight)
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self.bias = nn.Parameter(bias) if bias is not None else None
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Performs 2D convolution.
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Args:
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x (torch.Tensor): Input tensor of shape [batch_size, in_channels, height, width]
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Returns:
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torch.Tensor: Output tensor of shape [batch_size, out_channels, out_height, out_width]
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"""
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return torch.nn.functional.conv2d(x, self.weight, self.bias, stride=1, padding=0)
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# Hyperparameters
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batch_size = 4
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in_channels = 3
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out_channels = 64
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height = 32
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width = 32
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kernel_size = 3
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def get_inputs():
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x = torch.randn(batch_size, in_channels, height, width)
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return [x]
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def get_init_inputs():
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weight = torch.randn(out_channels, in_channels, kernel_size, kernel_size)
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bias = torch.randn(out_channels)
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return [weight, bias] |