718c41634f
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
82 lines
3.0 KiB
Python
82 lines
3.0 KiB
Python
# Tencent is pleased to support the open source community by making ncnn available.
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#
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# Copyright (C) 2021 THL A29 Limited, a Tencent company. All rights reserved.
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#
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# Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
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# in compliance with the License. You may obtain a copy of the License at
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#
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# https://opensource.org/licenses/BSD-3-Clause
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#
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# Unless required by applicable law or agreed to in writing, software distributed
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# under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
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# CONDITIONS OF ANY KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations under the License.
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class Model(nn.Module):
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def __init__(self):
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super(Model, self).__init__()
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self.attention_0_0 = nn.MultiheadAttention(embed_dim=64, num_heads=4)
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self.attention_0_1 = nn.MultiheadAttention(embed_dim=64, num_heads=8, bias=False, add_bias_kv=False, add_zero_attn=False)
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self.attention_0_2 = nn.MultiheadAttention(embed_dim=64, num_heads=16, bias=True, add_bias_kv=True, add_zero_attn=True)
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if torch.__version__ >= '1.9':
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self.attention_1_0 = nn.MultiheadAttention(embed_dim=40, num_heads=4, batch_first=True)
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self.attention_1_1 = nn.MultiheadAttention(embed_dim=40, num_heads=8, bias=False, add_bias_kv=False, add_zero_attn=False, batch_first=True)
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self.attention_1_2 = nn.MultiheadAttention(embed_dim=40, num_heads=10, bias=True, add_bias_kv=True, add_zero_attn=True, batch_first=True)
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def forward(self, xq, xk, xv, yq, yk, yv):
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x0, x0w = self.attention_0_0(xq, xk, xv)
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x1, x1w = self.attention_0_1(xq, xk, xv)
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x2, x2w = self.attention_0_2(xq, xk, xv)
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if torch.__version__ < '1.9':
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return x0, x0w, x1, x1w, x2, x2w
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y0, y0w = self.attention_1_0(yq, yk, yv)
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y1, y1w = self.attention_1_1(yq, yk, yv)
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y2, y2w = self.attention_1_2(yq, yk, yv)
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return x0, x0w, x1, x1w, x2, x2w, y0, y0w, y1, y1w, y2, y2w
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def test():
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net = Model()
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net.eval()
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torch.manual_seed(0)
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xq = torch.rand(20, 1, 64)
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xk = torch.rand(20, 1, 64)
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xv = torch.rand(20, 1, 64)
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yq = torch.rand(1, 15, 40)
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yk = torch.rand(1, 24, 40)
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yv = torch.rand(1, 24, 40)
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a = net(xq, xk, xv, yq, yk, yv)
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# export torchscript
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mod = torch.jit.trace(net, (xq, xk, xv, yq, yk, yv))
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mod.save("test_nn_MultiheadAttention.pt")
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# torchscript to pnnx
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import os
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os.system("../src/pnnx test_nn_MultiheadAttention.pt inputshape=[20,1,64],[20,1,64],[20,1,64],[1,15,40],[1,24,40],[1,24,40]")
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# pnnx inference
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import test_nn_MultiheadAttention_pnnx
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b = test_nn_MultiheadAttention_pnnx.test_inference()
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for a0, b0 in zip(a, b):
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if not torch.equal(a0, b0):
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return False
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return True
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if __name__ == "__main__":
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if test():
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exit(0)
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else:
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exit(1)
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