forked from ccf-ai-infra/GPUCodeForces
63 lines
1.6 KiB
Python
63 lines
1.6 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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Simple model that performs InstanceNorm operation.
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"""
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def __init__(self, num_features=64, eps=1e-5, affine=True, track_running_stats=False):
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super(Model, self).__init__()
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self.num_features = num_features
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self.eps = eps
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self.affine = affine
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self.track_running_stats = track_running_stats
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# 创建InstanceNorm层
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self.instance_norm = nn.InstanceNorm2d(
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num_features=num_features,
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eps=eps,
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affine=affine,
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track_running_stats=track_running_stats
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)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Applies InstanceNorm to the input tensor.
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Args:
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x (torch.Tensor): Input tensor of shape [batch_size, num_features, height, width]
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Returns:
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torch.Tensor: Output tensor after instance normalization, same shape as input.
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"""
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return self.instance_norm(x)
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# 参数配置
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batch_size = 16
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num_features = 64
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height = 128
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width = 128
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def get_inputs():
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"""
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生成InstanceNorm的输入张量。
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Returns:
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list: 包含一个形状为 [batch_size, num_features, height, width] 的张量
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"""
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x = torch.randn(batch_size, num_features, height, width)
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return [x]
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def get_init_inputs():
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"""
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获取模型初始化所需的输入（空列表，因为不需要特殊初始化）。
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Returns:
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list: 空列表
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"""
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return [] # No special initialization inputs needed |