96 lines
2.1 KiB
Python
96 lines
2.1 KiB
Python
import torch
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import torch.nn.functional as F
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import torch.nn as nn
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import torch
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import torch.nn as nn
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# 假设输入是一个 batch 的序列数据，形状为 (batch_size, seq_len, hidden_dim)
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batch_size, seq_len, hidden_dim = 2, 2, 4
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input_tensor = torch.randn(batch_size, seq_len, hidden_dim)
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# 定义 LayerNorm 层
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layer_norm1 = nn.LayerNorm(hidden_dim)
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layer_norm2 = nn.LayerNorm(hidden_dim)
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# 应用 LayerNorm
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output = layer_norm1(input_tensor)
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print(input_tensor.numpy())
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print("\n")
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print("\n")
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print(output.detach().numpy())
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output = layer_norm2(output)
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print("\n")
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print("\n")
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print(output.detach().numpy())
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x1 = torch.empty((1, 7, 768), dtype=float)
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time_shift = nn.ZeroPad2d((1, 1, -1, 1))
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xx = time_shift(x1)
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x1 = torch.tensor([[1, 2]], dtype=float)
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x2 = torch.tensor([[5, 6], [7, 8]], dtype=float)
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y = x1 @ x2 # torch.matmul(x1 , x2)
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x_inverse = torch.inverse(x2, out=None)
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y_inverse = y @ x_inverse
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y_inverse = y_inverse.permute(1, 0)
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x = torch.tensor([[1, 2], [3, 4]], dtype=float)
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print(x)
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print("x.tile((2)) -> ", x.tile((2)).shape)
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print(x.tile((2)))
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print()
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print("x.tile((2, 1)) -> ", x.tile((2, 1)).shape)
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print(x.tile((2, 1)))
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print()
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print("x.tile((2, 1, 2)) -> ", x.tile((2, 1, 2)).shape)
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print(x.tile((2, 1, 2)))
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print()
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print("x.tile((2, 1, 1)) -> ", x.tile((2, 1, 1)).shape)
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print(x.tile((2, 1, 1)))
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print()
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y = torch.tensor([[2, 1], [3, 4]])
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print(y.ne(x))
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print()
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print(x.prod(1))
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print(x.prod(0))
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print()
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print(x.unsqueeze(1).shape)
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print(x.unsqueeze(1).squeeze(1).shape)
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x = torch.tensor([[1, 2], [3, 4]]).to(float)
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print(x.mean(1))
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print(x.mean(0))
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print(x.mean(0, keepdim=True))
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print()
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print()
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x = torch.tensor([[1, 2], [3, 4]])
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print(x.flatten(0))
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x = torch.tensor([[1, 2], [3, 4]])
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print(torch.stack((x, x), 1))
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print(torch.cat((x, x), 1))
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# So if A and B are of shape (3, 4):
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# torch.cat([A, B], dim=0) will be of shape (6, 4)
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# torch.stack([A, B], dim=0) will be of shape (2, 3, 4)
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x = torch.ones([1, 32, 6, 128])
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y = torch.ones([1, 32, 128, 6])
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z = torch.ones([1, 32, 6, 128])
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att = torch.matmul(x, y)
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mm = torch.matmul(att, z)
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print(mm.shape)
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