![]() device will be the CPUįor CPU tensor types and the current CUDA device for CUDA tensor types. Layout ( torch.layout, optional) – the desired layout of returned Tensor.ĭevice ( vice, optional) – the desired device of returned tensor.ĭefault: if None, uses the current device for the default tensor type If x is a multi-dimensional array, it is only shuffled along its first index. Out ( Tensor, optional) – the output tensor.ĭtype ( torch.dtype, optional) – the desired data type of returned tensor. Randomly permute a sequence, or return a permuted range. ![]() Generator ( torch.Generator, optional) – a pseudorandom number generator for sampling If x is a multi-dimensional array, it is only shuffled. N ( int) – the upper bound (exclusive) Keyword Arguments : The NumPy random.permutation() function randomly permutes a sequence or an array, and returns it. Returns a random permutation of integers from 0 to n - 1. ![]() randperm ( n, *, generator = None, out = None, dtype = torch.int64, layout = torch.strided, device = None, requires_grad = False, pin_memory = False ) → Tensor ¶ The order of sub-arrays is changed but their contents remains the same. Examples > np.random.permutation(10) array ( 1, 7, 4, 3, 0, 9, 2, 5, 8, 6) random > np.random.permutation( 1, 4, 9, 12, 15) array ( 15, 1, 9, 4, 12) random > arr np.arange(9).reshape( (3, 3)) > np.random. This function only shuffles the array along the first axis of a multi-dimensional array. Extending torch.func with autograd.Function Modify a sequence in-place by shuffling its contents.CPU threading and TorchScript inference It is also important to mention that the random seed in NumPy also affects other methods, such as and it also has a local effect.3, 2, 1 is a permutation of 1, 2, 3 and vice-versa. CUDA Automatic Mixed Precision examples Random Permutations of Elements A permutation refers to an arrangement of elements. ![]()
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