68 lines
1.7 KiB
Python
68 lines
1.7 KiB
Python
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import torch
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import numpy as np
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#例5，向量内积
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A = torch.randn(10)
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B = torch.randn(10)
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#C=torch.dot(A,B)
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C = torch.einsum("i,i->",A,B)
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print("before:",A.shape, B.shape)
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print("after:",C.shape)
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#例6，向量外积
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A = torch.randn(10)
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B = torch.randn(5)
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#C = torch.outer(A,B)
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C = torch.einsum("i,j->ij",A,B)
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print("before:",A.shape, B.shape)
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print("after:",C.shape)
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#例7，矩阵乘法
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A = torch.randn(5,4)
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B = torch.randn(4,6)
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#C = torch.matmul(A,B)
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C = torch.einsum("ik,kj->ij",A,B)
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print("before:",A.shape, B.shape)
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print("after:",C.shape)
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#例8，张量缩并
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A = torch.randn(3,4,5)
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B = torch.randn(4,3,6)
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#C = torch.tensordot(A,B,dims=[(0,1),(1,0)])
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C = torch.einsum("ijk,jih->kh",A,B)
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print("before:",A.shape, B.shape)
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print("after:",C.shape)
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a = torch.randn(2,3)
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b = torch.randn(5,3,7)
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c = torch.randn(2,7)
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# i = 2, k = 3, j = 5, l = 7
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torch_ein_out = torch.einsum('ik,jkl,il->ij', [a, b, c]).numpy()
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m = torch.nn.Bilinear(3, 7, 5, bias=False)
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m.weight.data = b
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torch_org_out = m(a, c).detach().numpy()
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np_a = a.numpy()
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np_b = b.numpy()
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np_c = c.numpy()
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np_out = np.empty((2, 5), dtype=np.float32)
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# 自由索引外循环 这里是 i 和 j
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for i in range(0, 2):
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for j in range(0, 5):
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# 求和索引内循环 这里是 k 和 l
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sum_result = 0
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for k in range(0, 3):
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for l in range(0, 7):
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sum_result += np_a[i, k] * np_b[j, k, l] * np_c[i, l]
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np_out[i, j] = sum_result
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# print("matrix a:\n", np_a)
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# print("matrix b:\n", np_b)
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print("torch ein out: \n", torch_ein_out)
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print("torch org out: \n", torch_org_out)
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print("numpy out: \n", np_out)
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print("is np_out == torch_ein_out ?", np.allclose(torch_ein_out, np_out))
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print("is torch_org_out == torch_ein_out ?", np.allclose(torch_ein_out, torch_org_out))
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