Question

我在自定义数据集中训练了StarGAN模型。我需要将此模型从.pth（Pytorch）转换为.pb以便在Android Studio上使用。我进行了很多搜索，发现了一些转换方法。但是，所有解决方案都不适用于我的情况。

我尝试在仅包含nn.Linear层的小型网络上进行尝试。在此网络上，解决方案效果很好！

我认为，我的网络包括Conv2D层和MaxPooling2D层，因此转换处理不起作用。

首先，这是我的网络（StarGAN）。

import torch
import torch.nn as nn
import numpy as np


class ResidualBlock(nn.Module):
    def __init__(self, dim_in, dim_out):
        super(ResidualBlock, self).__init__()
        self.main = nn.Sequential(
            nn.Conv2d(dim_in, dim_out, kernel_size=3, stride=1, padding=1, bias=False),
            nn.InstanceNorm2d(dim_out, affine=True, track_running_stats=True),
            nn.ReLU(inplace=True),
            nn.Conv2d(dim_out, dim_out, kernel_size=3, stride=1, padding=1, bias=False),
            nn.InstanceNorm2d(dim_out, affine=True, track_running_stats=True))

    def forward(self, x):
        return x + self.main(x)


class Generator(nn.Module):
    def __init__(self, conv_dim=64, c_dim=5, repeat_num=6):
        super(Generator, self).__init__()

        layers = []
        layers.append(nn.Conv2d(3 + c_dim, conv_dim, kernel_size=7, stride=1, padding=3, bias=False))
        layers.append(nn.InstanceNorm2d(conv_dim, affine=True, track_running_stats=True))
        layers.append(nn.ReLU(inplace=True))

        curr_dim = conv_dim
        for _ in range(2):
            layers.append(nn.Conv2d(curr_dim, curr_dim * 2, kernel_size=4, stride=2, padding=1, bias=False))
            layers.append(nn.InstanceNorm2d(curr_dim * 2, affine=True, track_running_stats=True))
            layers.append(nn.ReLU(inplace=True))
            curr_dim = curr_dim * 2

        for _ in range(repeat_num):
            layers.append(ResidualBlock(dim_in=curr_dim, dim_out=curr_dim))

        for _ in range(2):
            layers.append(nn.ConvTranspose2d(curr_dim, curr_dim // 2, kernel_size=4, stride=2, padding=1, bias=False))
            layers.append(nn.InstanceNorm2d(curr_dim // 2, affine=True, track_running_stats=True))
            layers.append(nn.ReLU(inplace=True))
            curr_dim = curr_dim // 2

        layers.append(nn.Conv2d(curr_dim, 3, kernel_size=7, stride=1, padding=3, bias=False))
        layers.append(nn.Tanh())
        self.main = nn.Sequential(*layers)

    def forward(self, x, c):
        c = c.view(c.size(0), c.size(1), 1, 1)
        c = c.repeat(1, 1, x.size(2), x.size(3))
        x = torch.cat([x, c], dim=1)
        return self.main(x)


class Discriminator(nn.Module):
    def __init__(self, image_size=128, conv_dim=64, c_dim=5, repeat_num=6):
        super(Discriminator, self).__init__()
        layers = []
        layers.append(nn.Conv2d(3, conv_dim, kernel_size=4, stride=2, padding=1))
        layers.append(nn.LeakyReLU(0.01))

        curr_dim = conv_dim
        for _ in range(1, repeat_num):
            layers.append(nn.Conv2d(curr_dim, curr_dim * 2, kernel_size=4, stride=2, padding=1))
            layers.append(nn.LeakyReLU(0.01))
            curr_dim = curr_dim * 2

        kernel_size = int(image_size / np.power(2, repeat_num))
        self.main = nn.Sequential(*layers)
        self.conv1 = nn.Conv2d(curr_dim, 1, kernel_size=3, stride=1, padding=1, bias=False)
        self.conv2 = nn.Conv2d(curr_dim, c_dim, kernel_size=kernel_size, bias=False)

    def forward(self, x):
        h = self.main(x)
        out_src = self.conv1(h)
        out_cls = self.conv2(h)
        return out_src, out_cls.view(out_cls.size(0), out_cls.size(1))

这是错误消息。

TypeError: object of type 'torch._C.Value' has no len() (occurred when translating repeat)

有什么转换方法吗？救救我。

Answer 1

尝试使用TensorboardX生成模型图时，我遇到相同的问题。

我认为该错误是由运营商torch.onnx当前支持的错误引起的。您可以检查此链接：
https://pytorch.org/docs/stable/onnx.html
在受支持的运营商部分，您将看到未列出repeat。

要回答您的问题，看来您当前无法使用repeat和torch.onnx来转换模型。

有什么方法可以将预训练的模型从PyTorch转换为ONNX？

1 个答案: