diff --git a/export.py b/export.py index cf918aa42b..af84e23570 100644 --- a/export.py +++ b/export.py @@ -17,6 +17,7 @@ from utils.add_nms import RegisterNMS if __name__ == '__main__': + default_out_formats = ('torchscript', 'coreml', 'torchscript-lite', 'onnx') 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 @@ -34,10 +35,13 @@ 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') + parser.add_argument('--out-format', action='append', choices=default_out_formats, default=[], + dest='out_formats', help='output format. Can be specified multiple times. Default: all') 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 + opt.out_formats = tuple(opt.out_formats) if opt.out_formats else default_out_formats print(opt) set_logging() t = time.time() @@ -71,135 +75,139 @@ 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) + if 'torchscript' in opt.out_formats: + 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) + if 'coreml' in opt.out_formats: + 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) + if 'torchscript-lite' in opt.out_formats: + 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 'onnx' in opt.out_formats: + 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_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] + 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: - 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) + 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))