Source code for mmdet.core.export.pytorch2onnx

from functools import partial

import mmcv
import numpy as np
import torch
from mmcv.runner import load_checkpoint

[docs]def generate_inputs_and_wrap_model(config_path, checkpoint_path, input_config, cfg_options=None): """Prepare sample input and wrap model for ONNX export. The ONNX export API only accept args, and all inputs should be torch.Tensor or corresponding types (such as tuple of tensor). So we should call this function before exporting. This function will: 1. generate corresponding inputs which are used to execute the model. 2. Wrap the model's forward function. For example, the MMDet models' forward function has a parameter ``return_loss:bool``. As we want to set it as False while export API supports neither bool type or kwargs. So we have to replace the forward method like ``model.forward = partial(model.forward, return_loss=False)``. Args: config_path (str): the OpenMMLab config for the model we want to export to ONNX checkpoint_path (str): Path to the corresponding checkpoint input_config (dict): the exactly data in this dict depends on the framework. For MMSeg, we can just declare the input shape, and generate the dummy data accordingly. However, for MMDet, we may pass the real img path, or the NMS will return None as there is no legal bbox. Returns: tuple: (model, tensor_data) wrapped model which can be called by ``model(*tensor_data)`` and a list of inputs which are used to execute the model while exporting. """ model = build_model_from_cfg( config_path, checkpoint_path, cfg_options=cfg_options) one_img, one_meta = preprocess_example_input(input_config) tensor_data = [one_img] model.forward = partial( model.forward, img_metas=[[one_meta]], return_loss=False) # pytorch has some bug in pytorch1.3, we have to fix it # by replacing these existing op opset_version = 11 # put the import within the function thus it will not cause import error # when not using this function try: from mmcv.onnx.symbolic import register_extra_symbolics except ModuleNotFoundError: raise NotImplementedError('please update mmcv to version>=v1.0.4') register_extra_symbolics(opset_version) return model, tensor_data
[docs]def build_model_from_cfg(config_path, checkpoint_path, cfg_options=None): """Build a model from config and load the given checkpoint. Args: config_path (str): the OpenMMLab config for the model we want to export to ONNX checkpoint_path (str): Path to the corresponding checkpoint Returns: torch.nn.Module: the built model """ from mmdet.models import build_detector cfg = mmcv.Config.fromfile(config_path) if cfg_options is not None: cfg.merge_from_dict(cfg_options) # import modules from string list. if cfg.get('custom_imports', None): from mmcv.utils import import_modules_from_strings import_modules_from_strings(**cfg['custom_imports']) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True cfg.model.pretrained = None = True # build the model cfg.model.train_cfg = None model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg')) checkpoint = load_checkpoint(model, checkpoint_path, map_location='cpu') if 'CLASSES' in checkpoint.get('meta', {}): model.CLASSES = checkpoint['meta']['CLASSES'] else: from mmdet.datasets import DATASETS dataset = DATASETS.get(['type']) assert (dataset is not None) model.CLASSES = dataset.CLASSES model.cpu().eval() return model
[docs]def preprocess_example_input(input_config): """Prepare an example input image for ``generate_inputs_and_wrap_model``. Args: input_config (dict): customized config describing the example input. Returns: tuple: (one_img, one_meta), tensor of the example input image and \ meta information for the example input image. Examples: >>> from mmdet.core.export import preprocess_example_input >>> input_config = { >>> 'input_shape': (1,3,224,224), >>> 'input_path': 'demo/demo.jpg', >>> 'normalize_cfg': { >>> 'mean': (123.675, 116.28, 103.53), >>> 'std': (58.395, 57.12, 57.375) >>> } >>> } >>> one_img, one_meta = preprocess_example_input(input_config) >>> print(one_img.shape) torch.Size([1, 3, 224, 224]) >>> print(one_meta) {'img_shape': (224, 224, 3), 'ori_shape': (224, 224, 3), 'pad_shape': (224, 224, 3), 'filename': '<demo>.png', 'scale_factor': 1.0, 'flip': False} """ input_path = input_config['input_path'] input_shape = input_config['input_shape'] one_img = mmcv.imread(input_path) one_img = mmcv.imresize(one_img, input_shape[2:][::-1]) show_img = one_img.copy() if 'normalize_cfg' in input_config.keys(): normalize_cfg = input_config['normalize_cfg'] mean = np.array(normalize_cfg['mean'], dtype=np.float32) std = np.array(normalize_cfg['std'], dtype=np.float32) to_rgb = normalize_cfg.get('to_rgb', True) one_img = mmcv.imnormalize(one_img, mean, std, to_rgb=to_rgb) one_img = one_img.transpose(2, 0, 1) one_img = torch.from_numpy(one_img).unsqueeze(0).float().requires_grad_( True) (_, C, H, W) = input_shape one_meta = { 'img_shape': (H, W, C), 'ori_shape': (H, W, C), 'pad_shape': (H, W, C), 'filename': '<demo>.png', 'scale_factor': np.ones(4, dtype=np.float32), 'flip': False, 'show_img': show_img, 'flip_direction': None } return one_img, one_meta