mirror of
https://github.com/lordmathis/CUDANet.git
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63 lines
2.8 KiB
Python
63 lines
2.8 KiB
Python
import sys
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import torch
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from torchvision.models.inception import InceptionA
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sys.path.append("../../../tools")
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from utils import print_cpp_vector
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torch.manual_seed(0)
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@torch.no_grad()
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def init_weights(m):
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if isinstance(m, torch.nn.Conv2d):
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torch.nn.init.uniform_(m.weight)
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elif isinstance(m, torch.nn.BatchNorm2d):
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torch.nn.init.uniform_(m.weight)
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torch.nn.init.uniform_(m.bias)
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with torch.no_grad():
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inception_a = InceptionA(3, 6)
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inception_a.apply(init_weights)
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# branch1x1
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print_cpp_vector(torch.flatten(inception_a.branch1x1.conv.weight), "branch1x1_conv_weights")
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print_cpp_vector(torch.flatten(inception_a.branch1x1.bn.weight), "branch1x1_bn_weights")
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print_cpp_vector(torch.flatten(inception_a.branch1x1.bn.bias), "branch1x1_bn_bias")
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# branch5x5
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print_cpp_vector(torch.flatten(inception_a.branch5x5_1.conv.weight), "branch5x5_1_conv_weights")
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print_cpp_vector(torch.flatten(inception_a.branch5x5_1.bn.weight), "branch5x5_1_bn_weights")
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print_cpp_vector(torch.flatten(inception_a.branch5x5_1.bn.bias), "branch5x5_1_bn_bias")
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print_cpp_vector(torch.flatten(inception_a.branch5x5_2.conv.weight), "branch5x5_2_conv_weights")
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print_cpp_vector(torch.flatten(inception_a.branch5x5_2.bn.weight), "branch5x5_2_bn_weights")
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print_cpp_vector(torch.flatten(inception_a.branch5x5_2.bn.bias), "branch5x5_2_bn_bias")
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# branch3x3dbl
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print_cpp_vector(torch.flatten(inception_a.branch3x3dbl_1.conv.weight), "branch3x3dbl_1_conv_weights")
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print_cpp_vector(torch.flatten(inception_a.branch3x3dbl_1.bn.weight), "branch3x3dbl_1_bn_weights")
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print_cpp_vector(torch.flatten(inception_a.branch3x3dbl_1.bn.bias), "branch3x3dbl_1_bn_bias")
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print_cpp_vector(torch.flatten(inception_a.branch3x3dbl_2.conv.weight), "branch3x3dbl_2_conv_weights")
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print_cpp_vector(torch.flatten(inception_a.branch3x3dbl_2.bn.weight), "branch3x3dbl_2_bn_weights")
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print_cpp_vector(torch.flatten(inception_a.branch3x3dbl_2.bn.bias), "branch3x3dbl_2_bn_bias")
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print_cpp_vector(torch.flatten(inception_a.branch3x3dbl_3.conv.weight), "branch3x3dbl_3_conv_weights")
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print_cpp_vector(torch.flatten(inception_a.branch3x3dbl_3.bn.weight), "branch3x3dbl_3_bn_weights")
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print_cpp_vector(torch.flatten(inception_a.branch3x3dbl_3.bn.bias), "branch3x3dbl_3_bn_bias")
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# branchPool
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print_cpp_vector(torch.flatten(inception_a.branch_pool.conv.weight), "branchPool_2_conv_weights")
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print_cpp_vector(torch.flatten(inception_a.branch_pool.bn.weight), "branchPool_2_bn_weights")
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print_cpp_vector(torch.flatten(inception_a.branch_pool.bn.bias), "branchPool_2_bn_bias")
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input_shape = (1, 3, 8, 8)
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input = torch.randn(input_shape)
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print_cpp_vector(torch.flatten(input), "input")
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output = inception_a(input)
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output = torch.flatten(output)
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print_cpp_vector(output)
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