diff --git a/yolo-second-run/export.py b/yolo-second-run/export.py new file mode 100644 index 0000000..cf918aa --- /dev/null +++ b/yolo-second-run/export.py @@ -0,0 +1,205 @@ +import argparse +import sys +import time +import warnings + +sys.path.append('./') # to run '$ python *.py' files in subdirectories + +import torch +import torch.nn as nn +from torch.utils.mobile_optimizer import optimize_for_mobile + +import models +from models.experimental import attempt_load, End2End +from utils.activations import Hardswish, SiLU +from utils.general import set_logging, check_img_size +from utils.torch_utils import select_device +from utils.add_nms import RegisterNMS + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--weights', type=str, default='./yolor-csp-c.pt', help='weights path') + parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') # height, width + parser.add_argument('--batch-size', type=int, default=1, help='batch size') + parser.add_argument('--dynamic', action='store_true', help='dynamic ONNX axes') + parser.add_argument('--dynamic-batch', action='store_true', help='dynamic batch onnx for tensorrt and onnx-runtime') + parser.add_argument('--grid', action='store_true', help='export Detect() layer grid') + parser.add_argument('--end2end', action='store_true', help='export end2end onnx') + parser.add_argument('--max-wh', type=int, default=None, help='None for tensorrt nms, int value for onnx-runtime nms') + parser.add_argument('--topk-all', type=int, default=100, help='topk objects for every images') + parser.add_argument('--iou-thres', type=float, default=0.45, help='iou threshold for NMS') + parser.add_argument('--conf-thres', type=float, default=0.25, help='conf threshold for NMS') + parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--simplify', action='store_true', help='simplify onnx model') + parser.add_argument('--include-nms', action='store_true', help='export end2end onnx') + parser.add_argument('--fp16', action='store_true', help='CoreML FP16 half-precision export') + parser.add_argument('--int8', action='store_true', help='CoreML INT8 quantization') + opt = parser.parse_args() + opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand + opt.dynamic = opt.dynamic and not opt.end2end + opt.dynamic = False if opt.dynamic_batch else opt.dynamic + print(opt) + set_logging() + t = time.time() + + # Load PyTorch model + device = select_device(opt.device) + model = attempt_load(opt.weights, map_location=device) # load FP32 model + labels = model.names + + # 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 + + # Input + img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device) # image size(1,3,320,192) iDetection + + # 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): # assign export-friendly activations + if isinstance(m.act, nn.Hardswish): + m.act = Hardswish() + elif isinstance(m.act, nn.SiLU): + m.act = SiLU() + # elif isinstance(m, models.yolo.Detect): + # m.forward = m.forward_export # assign forward (optional) + model.model[-1].export = not opt.grid # set Detect() layer grid export + y = model(img) # dry run + if opt.include_nms: + model.model[-1].include_nms = True + y = None + + # 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, strict=False) + ts.save(f) + print('TorchScript export success, saved as %s' % f) + except Exception as e: + print('TorchScript export failure: %s' % e) + + # 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 + ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])]) + bits, mode = (8, 'kmeans_lut') if opt.int8 else (16, 'linear') if opt.fp16 else (32, None) + if bits < 32: + if sys.platform.lower() == 'darwin': # quantization only supported on macOS + with warnings.catch_warnings(): + warnings.filterwarnings("ignore", category=DeprecationWarning) # suppress numpy==1.20 float warning + ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode) + else: + print('quantization only supported on macOS, skipping...') + + f = opt.weights.replace('.pt', '.mlmodel') # filename + ct_model.save(f) + print('CoreML export success, saved as %s' % f) + except Exception as e: + print('CoreML export failure: %s' % e) + + # TorchScript-Lite export + try: + print('\nStarting TorchScript-Lite export with torch %s...' % torch.__version__) + f = opt.weights.replace('.pt', '.torchscript.ptl') # filename + tsl = torch.jit.trace(model, img, strict=False) + tsl = optimize_for_mobile(tsl) + tsl._save_for_lite_interpreter(f) + print('TorchScript-Lite export success, saved as %s' % f) + except Exception as e: + print('TorchScript-Lite export failure: %s' % e) + + # ONNX export + try: + import onnx + + print('\nStarting ONNX export with onnx %s...' % onnx.__version__) + f = opt.weights.replace('.pt', '.onnx') # filename + model.eval() + output_names = ['classes', 'boxes'] if y is None else ['output'] + dynamic_axes = None + if opt.dynamic: + dynamic_axes = {'images': {0: 'batch', 2: 'height', 3: 'width'}, # size(1,3,640,640) + 'output': {0: 'batch', 2: 'y', 3: 'x'}} + if opt.dynamic_batch: + opt.batch_size = 'batch' + dynamic_axes = { + 'images': { + 0: 'batch', + }, } + if opt.end2end and opt.max_wh is None: + output_axes = { + 'num_dets': {0: 'batch'}, + 'det_boxes': {0: 'batch'}, + 'det_scores': {0: 'batch'}, + 'det_classes': {0: 'batch'}, + } + else: + output_axes = { + 'output': {0: 'batch'}, + } + dynamic_axes.update(output_axes) + if opt.grid: + if opt.end2end: + print('\nStarting export end2end onnx model for %s...' % 'TensorRT' if opt.max_wh is None else 'onnxruntime') + model = End2End(model,opt.topk_all,opt.iou_thres,opt.conf_thres,opt.max_wh,device,len(labels)) + if opt.end2end and opt.max_wh is None: + output_names = ['num_dets', 'det_boxes', 'det_scores', 'det_classes'] + shapes = [opt.batch_size, 1, opt.batch_size, opt.topk_all, 4, + opt.batch_size, opt.topk_all, opt.batch_size, opt.topk_all] + else: + output_names = ['output'] + else: + model.model[-1].concat = True + + torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'], + output_names=output_names, + dynamic_axes=dynamic_axes) + + # Checks + onnx_model = onnx.load(f) # load onnx model + onnx.checker.check_model(onnx_model) # check onnx model + + if opt.end2end and opt.max_wh is None: + for i in onnx_model.graph.output: + for j in i.type.tensor_type.shape.dim: + j.dim_param = str(shapes.pop(0)) + + # print(onnx.helper.printable_graph(onnx_model.graph)) # print a human readable model + + # # Metadata + # d = {'stride': int(max(model.stride))} + # for k, v in d.items(): + # meta = onnx_model.metadata_props.add() + # meta.key, meta.value = k, str(v) + # onnx.save(onnx_model, f) + + if opt.simplify: + try: + import onnxsim + + print('\nStarting to simplify ONNX...') + onnx_model, check = onnxsim.simplify(onnx_model) + assert check, 'assert check failed' + except Exception as e: + print(f'Simplifier failure: {e}') + + # print(onnx.helper.printable_graph(onnx_model.graph)) # print a human readable model + onnx.save(onnx_model,f) + print('ONNX export success, saved as %s' % f) + + if opt.include_nms: + print('Registering NMS plugin for ONNX...') + mo = RegisterNMS(f) + mo.register_nms() + mo.save(f) + + except Exception as e: + print('ONNX export failure: %s' % e) + + # Finish + print('\nExport complete (%.2fs). Visualize with https://github.com/lutzroeder/netron.' % (time.time() - t))