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| Original file line number | Diff line number | Diff line change |
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| # 记录YoloR工程化 | ||
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| ## 项目地址 | ||
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| * [yolor](https://github.com/WongKinYiu/yolor) | ||
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| ## 转换成ONNX的过程 | ||
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| You must convert the saved SyncBatchNorm in pretrained yolor-xx.pt to BatchNorm first ! if you reload it use CPU. | ||
| ```python | ||
| import argparse | ||
| import sys | ||
| import time | ||
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| sys.path.append('./') # to run '$ python *.py' files in subdirectories | ||
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| import torch | ||
| import torch.nn as nn | ||
| import models | ||
| from models.experimental import attempt_load | ||
| from utils.activations import Hardswish | ||
| from utils.general import set_logging, check_img_size | ||
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| # convert SyncBatchNorm to BatchNorm2d | ||
| def convert_sync_batchnorm_to_batchnorm(module): | ||
| module_output = module | ||
| if isinstance(module, torch.nn.modules.batchnorm.SyncBatchNorm): | ||
| module_output = torch.nn.BatchNorm2d(module.num_features, | ||
| module.eps, module.momentum, | ||
| module.affine, | ||
| module.track_running_stats) | ||
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| if module.affine: | ||
| with torch.no_grad(): | ||
| module_output.weight = module.weight | ||
| module_output.bias = module.bias | ||
| module_output.running_mean = module.running_mean | ||
| module_output.running_var = module.running_var | ||
| module_output.num_batches_tracked = module.num_batches_tracked | ||
| if hasattr(module, "qconfig"): | ||
| module_output.qconfig = module.qconfig | ||
| for name, child in module.named_children(): | ||
| module_output.add_module(name, convert_sync_batchnorm_to_batchnorm(child)) | ||
| del module | ||
| return module_output | ||
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| if __name__ == '__main__': | ||
| parser = argparse.ArgumentParser() | ||
| parser.add_argument('--weights', type=str, default='./yolor-p6.pt', help='weights path') | ||
| parser.add_argument('--img-size', nargs='+', type=int, default=[1280, 1280], help='image size') # height, width | ||
| parser.add_argument('--batch-size', type=int, default=1, help='batch size') | ||
| opt = parser.parse_args() | ||
| opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand | ||
| print(opt) | ||
| set_logging() | ||
| t = time.time() | ||
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| # Load PyTorch model | ||
| model = attempt_load(opt.weights, map_location=torch.device('cpu')) # load FP32 model | ||
| labels = model.names | ||
| model.eval() | ||
| model = model.to("cpu") | ||
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| model = convert_sync_batchnorm_to_batchnorm(model) | ||
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| print(model) | ||
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| # Checks | ||
| gs = int(max(model.stride)) # grid size (max stride) | ||
| opt.img_size = [check_img_size(x, gs) for x in opt.img_size] # verify img_size are gs-multiples | ||
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| # Input | ||
| img = torch.zeros(opt.batch_size, 3, *opt.img_size) # image size(1,3,320,192) iDetection | ||
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| # Update model | ||
| for k, m in model.named_modules(): | ||
| m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility | ||
| if isinstance(m, models.common.Conv) and isinstance(m.act, nn.Hardswish): | ||
| m.act = Hardswish() # assign activation | ||
| # if isinstance(m, models.yolo.Detect): | ||
| # m.forward = m.forward_export # assign forward (optional) | ||
| model.model[-1].export = True # set Detect() layer export=True | ||
| y = model(img) # dry run | ||
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| # TorchScript export | ||
| # try: | ||
| # print('\nStarting TorchScript export with torch %s...' % torch.__version__) | ||
| # f = opt.weights.replace('.pt', '.torchscript.pt') # filename | ||
| # ts = torch.jit.trace(model, img) | ||
| # ts.save(f) | ||
| # print('TorchScript export success, saved as %s' % f) | ||
| # except Exception as e: | ||
| # print('TorchScript export failure: %s' % e) | ||
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| # ONNX export | ||
| try: | ||
| import onnx | ||
| import onnxruntime as ort | ||
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| print('\nStarting ONNX export with onnx %s...' % onnx.__version__) | ||
| f = opt.weights.replace('.pt', f'-{opt.img_size[0]}-{opt.img_size[1]}.onnx') # filename | ||
| torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'], | ||
| output_names=['classes', 'boxes'] if y is None else ['output']) | ||
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| # Checks | ||
| onnx_model = onnx.load(f) # load onnx model | ||
| onnx.checker.check_model(onnx_model) # check onnx model | ||
| print(onnx.helper.printable_graph(onnx_model.graph)) # print a human readable model | ||
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| do_simplify = True | ||
| if do_simplify: | ||
| from onnxsim import simplify | ||
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| onnx_model, check = simplify(onnx_model, check_n=3) | ||
| assert check, 'assert simplify check failed' | ||
| onnx.save(onnx_model, f) | ||
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| session = ort.InferenceSession(f) | ||
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| for ii in session.get_inputs(): | ||
| print("input: ", ii) | ||
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| for oo in session.get_outputs(): | ||
| print("output: ", oo) | ||
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| print('ONNX export success, saved as %s' % f) | ||
| except Exception as e: | ||
| print('ONNX export failure: %s' % e) | ||
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| # CoreML export | ||
| # try: | ||
| # import coremltools as ct | ||
| # | ||
| # print('\nStarting CoreML export with coremltools %s...' % ct.__version__) | ||
| # # convert model from torchscript and apply pixel scaling as per detect.py | ||
| # model = ct.convert(ts, inputs=[ct.ImageType(name='image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])]) | ||
| # f = opt.weights.replace('.pt', '.mlmodel') # filename | ||
| # model.save(f) | ||
| # print('CoreML export success, saved as %s' % f) | ||
| # except Exception as e: | ||
| # print('CoreML export failure: %s' % e) | ||
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| # Finish | ||
| print('\nExport complete (%.2fs). Visualize with https://github.com/lutzroeder/netron.' % (time.time() - t)) | ||
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| """ | ||
| PYTHONPATH=. python3 ./models/export.py --weights ./weights/yolor-p6.pt --img-size 640 | ||
| """ | ||
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| ``` | ||
| also there is a bug in the source code will make the converted onnx get wrong output shape. | ||
| ```python | ||
| class IDetect(nn.Module): | ||
| # ... | ||
| def forward(self, x): | ||
| # x = x.copy() # for profiling | ||
| z = [] # inference output | ||
| # self.training |= self.export # 这句有点问题 error | ||
| self.training = not self.export # change to this line | ||
| print("self.training: ", self.training, self.nl) | ||
| ``` | ||
| my log: | ||
| ```python | ||
| Checking 0/3... | ||
| Checking 1/3... | ||
| Checking 2/3... | ||
| input: NodeArg(name='images', type='tensor(float)', shape=[1, 3, 320, 320]) | ||
| output: NodeArg(name='output', type='tensor(float)', shape=[1, 6375, 85]) | ||
| output: NodeArg(name='910', type='tensor(float)', shape=[1, 3, 40, 40, 85]) | ||
| output: NodeArg(name='944', type='tensor(float)', shape=[1, 3, 20, 20, 85]) | ||
| output: NodeArg(name='978', type='tensor(float)', shape=[1, 3, 10, 10, 85]) | ||
| output: NodeArg(name='1012', type='tensor(float)', shape=[1, 3, 5, 5, 85]) | ||
| ONNX export success, saved as ./weights/yolor-p6-320-320.onnx | ||
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| ``` | ||
| 还有一段逻辑在导出成onnx的时候有问题，直到我修改成和yolov5一样之后，导出的onnx就可以跑出正常的结果了. 也许是pytorch的自动broadcast没有在onnx中执行 | ||
| ```python | ||
| if not self.training: # inference | ||
| # if self.grid[i].shape[2:4] != x[i].shape[2:4]: | ||
| # self.grid[i] = self._make_grid(nx, ny).to(x[i].device) | ||
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| self.grid[i] = self._make_grid(nx, ny).to(x[i].device) | ||
| y = x[i].sigmoid() | ||
| # in yolor | ||
| # y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy | ||
| # y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh | ||
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| # change it as yolov5 | ||
| xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy (bs,na,ny,nx,2) | ||
| wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i].view(1, self.na, 1, 1, 2) # wh (bs,na,ny,nx,2) | ||
| y = torch.cat((xy, wh, y[..., 4:]), -1) # (bs,na,ny,nx,2+2+1+nc=xy+wh+conf+cls_prob) | ||
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| z.append(y.view(bs, -1, self.no)) | ||
| ``` | ||
| 在pytorch中的直接内存操作，在转换onnx后会有问题。似乎onnx还不支持这种操作，虽然能转换，但是效果无法对齐。因此将源码的直接内存操作修改成普通操作，就没有问题了。 | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,81 @@ | ||
| // | ||
| // Created by DefTruth on 2021/8/7. | ||
| // | ||
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| #include "lite/lite.h" | ||
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| static void test_default() | ||
| { | ||
| std::string onnx_path = "../../../hub/onnx/cv/yolov4_tiny_weights_coco.onnx"; | ||
| std::string test_img_path = "../../../examples/lite/resources/test_lite_detection_1.jpg"; | ||
| std::string save_img_path = "../../../logs/test_lite_tiny_yolov4_coco_1.jpg"; | ||
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| // 1. Test Default Engine ONNXRuntime | ||
| lite::cv::detection::TinyYoloV4COCO *tiny_yolov4_coco = new lite::cv::detection::TinyYoloV4COCO(onnx_path); // default | ||
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| std::vector<lite::cv::types::Boxf> detected_boxes; | ||
| cv::Mat img_bgr = cv::imread(test_img_path); | ||
| tiny_yolov4_coco->detect(img_bgr, detected_boxes); | ||
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| lite::cv::utils::draw_boxes_inplace(img_bgr, detected_boxes); | ||
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| cv::imwrite(save_img_path, img_bgr); | ||
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| std::cout << "Default Version Detected Boxes Num: " << detected_boxes.size() << std::endl; | ||
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| delete tiny_yolov4_coco; | ||
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| } | ||
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| static void test_onnxruntime() | ||
| { | ||
| std::string onnx_path = "../../../hub/onnx/cv/yolov4_tiny_weights_coco.onnx"; | ||
| std::string test_img_path = "../../../examples/lite/resources/test_lite_detection_2.jpg"; | ||
| std::string save_img_path = "../../../logs/test_lite_tiny_yolov4_coco_2.jpg"; | ||
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| // 2. Test Specific Engine ONNXRuntime | ||
| lite::onnxruntime::cv::detection::TinyYoloV4COCO *tiny_yolov4_coco = | ||
| new lite::onnxruntime::cv::detection::TinyYoloV4COCO(onnx_path); | ||
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| std::vector<lite::onnxruntime::cv::types::Boxf> detected_boxes; | ||
| cv::Mat img_bgr = cv::imread(test_img_path); | ||
| tiny_yolov4_coco->detect(img_bgr, detected_boxes); | ||
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| lite::onnxruntime::cv::utils::draw_boxes_inplace(img_bgr, detected_boxes); | ||
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| cv::imwrite(save_img_path, img_bgr); | ||
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| std::cout << "ONNXRuntime Version Detected Boxes Num: " << detected_boxes.size() << std::endl; | ||
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| delete tiny_yolov4_coco; | ||
| } | ||
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| static void test_mnn() | ||
| { | ||
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| } | ||
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| static void test_ncnn() | ||
| { | ||
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| } | ||
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| static void test_tnn() | ||
| { | ||
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| } | ||
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| static void test_lite() | ||
| { | ||
| test_default(); | ||
| test_onnxruntime(); | ||
| test_mnn(); | ||
| test_ncnn(); | ||
| test_tnn(); | ||
| } | ||
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| int main(__unused int argc, __unused char *argv[]) | ||
| { | ||
| test_lite(); | ||
| return 0; | ||
| } |
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