ocr|怎样将paddlepaddleOCR的模型转换为pytorch模型字符识别|模型转换pytorch|padd

目的：将paddlepaddleocr的字符识别模型转换为pytorch的模型
过程：mobilenetv3_small的小模型在转换过程中成功转换了backbone的参数，但是head（两个双向LSTM）转换失败，因为类别数不同，而LSTM中涉及到两个fc层两个lstm层，其中的fc层的参数和类别相关联，所以双向的LSTM参数无法进行转换；
结果：只对backbone的参数进行了转换和拷贝
原始paddlepaddle工程链接：https://github.com/PaddlePaddle/PaddleOCR

简单叙述过程：
【ocr|怎样将paddlepaddleOCR的模型转换为pytorch模型】1）paddlepaddle的模型参数加载；

def _load_state(path): """ 记载paddlepaddle的参数 :param path: :return: """ if os.path.exists(path + '.pdopt'): # XXX another hack to ignore the optimizer state tmp = tempfile.mkdtemp() dst = os.path.join(tmp, os.path.basename(os.path.normpath(path))) shutil.copy(path + '.pdparams', dst + '.pdparams') state = fluid.io.load_program_state(dst) shutil.rmtree(tmp) else: state = fluid.io.load_program_state(path) return state

2）pytorch模型的构建；
3）进行参数拷贝和模型保存；

def init_model(self): for n, m in self.net_pytorch.named_modules(): if isinstance(m, BatchNorm2d): self.bn_init(n, m) elif isinstance(m, Conv2d): self.conv_init(n, m) # elif isinstance(m, Linear): #self.fc_init(n, m) # elif isinstance(m, PReLU): # self.prelu_init(n, m) elif isinstance(m, BatchNorm1d): self.bn_init(n, m)def bn_init(self, layer , m): for key in self.list_layers: if (layer in key) and ('bn' in key): print(key) #, ' -- shape: ', self.state_pp[key].shape) if 'scale' in key: m.weight.data.copy_(torch.FloatTensor(self.state_pp[key])) self.list_layers.remove(key) elif 'offset' in key: m.bias.data.copy_(torch.FloatTensor(self.state_pp[key])) self.list_layers.remove(key) elif 'mean' in key: m.running_mean.copy_(torch.FloatTensor(self.state_pp[key])) self.list_layers.remove(key) elif 'variance' in key: m.running_var.copy_(torch.FloatTensor(self.state_pp[key])) self.list_layers.remove(key)def conv_init(self, layer, m): # for pr in net.params: layer_ = layer+'_' for key in self.list_layers: if (layer_ in key) and ('bn' not in key): print(key) #, ' -- shape: ', self.state_pp[key].shape) if 'weights' in key: m.weight.data.copy_(torch.FloatTensor(self.state_pp[key])) elif 'offset' in key: m.bias.data.copy_(self.state_pp[key]) self.list_layers.remove(key)

技术难点：
1）paddlepaddle中怎样读取每层的输出参数；
2）双向LSTM的参数拷贝；
3）验证参数转换是否成功时，读取图片传入后，每层layer处理后的图片特征；
工程链接：https://github.com/maomaoyuchengzi/paddlepaddle_param_to_pyotrch