del images del inp_x_fake del inp_x del label del output torch.cuda.empty_cache() iters += 1
时间: 2024-02-10 12:00:16 浏览: 29
这段代码是用来清除内存中的变量和缓存,以防止出现内存溢出的情况。具体来说,它会删除变量 images、inp_x_fake、inp_x、label 和 output,以及清空PyTorch的缓存(通过调用torch.cuda.empty_cache()函数)。最后,将迭代次数iters加1。这段代码通常在训练模型的循环中被调用,以确保内存不被过度占用。
相关问题
del images del inp_x_fake del inp_x del label del output
这是Python代码,这几行代码的作用是删除变量 images、inp_x_fake、inp_x、label、output,以释放内存空间。通过 del 关键字可以删除Python中的对象,从而释放内存。在这段代码中,可能是在一个循环或函数的末尾,为了避免下一次循环或函数调用时内存占用过高,需要手动删除不再需要的变量。
out = self.inp_prelu(self.inp_snorm(self.inp_conv(x)))
This code represents a neural network layer where an input tensor x is passed through a series of operations:
1. The first operation is inp_conv, which performs a convolution operation on the input tensor with some learnable filters.
2. The output of the convolution operation is then passed through inp_snorm, which performs a spatial normalization operation to normalize the output tensor across channels and spatial dimensions.
3. The normalized output is then passed through inp_prelu, which applies a parametric rectified linear unit (PReLU) activation function to introduce non-linearity.
4. Finally, the output of the PReLU activation function is returned as the output of the layer.
Overall, this layer can be used as a building block for a deeper neural network architecture to learn more complex representations of input data.
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