mirror of
https://github.com/we0091234/Car_recognition.git
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162 lines
6.7 KiB
Python
162 lines
6.7 KiB
Python
"""Exports a YOLOv5 *.pt model to ONNX and TorchScript formats
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Usage:
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$ export PYTHONPATH="$PWD" && python models/export.py --weights ./weights/yolov5s.pt --img 640 --batch 1
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"""
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import argparse
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import sys
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import time
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sys.path.append('./') # to run '$ python *.py' files in subdirectories
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import torch
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import torch.nn as nn
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import models
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from models.experimental import attempt_load
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from utils.activations import Hardswish, SiLU
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from utils.general import set_logging, check_img_size
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import onnx
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path') # from yolov5/models/
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parser.add_argument('--img_size', nargs='+', type=int, default=[640, 640], help='image size') # height, width
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parser.add_argument('--batch_size', type=int, default=1, help='batch size')
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parser.add_argument('--dynamic', action='store_true', default=False, help='enable dynamic axis in onnx model')
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parser.add_argument('--onnx2pb', action='store_true', default=False, help='export onnx to pb')
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parser.add_argument('--onnx_infer', action='store_true', default=True, help='onnx infer test')
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#=======================TensorRT=================================
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parser.add_argument('--onnx2trt', action='store_true', default=False, help='export onnx to tensorrt')
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parser.add_argument('--fp16_trt', action='store_true', default=False, help='fp16 infer')
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#================================================================
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opt = parser.parse_args()
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opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand
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print(opt)
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set_logging()
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t = time.time()
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# Load PyTorch model
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model = attempt_load(opt.weights, map_location=torch.device('cpu')) # load FP32 model
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delattr(model.model[-1], 'anchor_grid')
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model.model[-1].anchor_grid=[torch.zeros(1)] * 3 # nl=3 number of detection layers
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model.model[-1].export_cat = True
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model.eval()
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labels = model.names
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# Checks
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gs = int(max(model.stride)) # grid size (max stride)
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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
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img = torch.zeros(opt.batch_size, 3, *opt.img_size) # image size(1,3,320,192) iDetection
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# Update model
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for k, m in model.named_modules():
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m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
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if isinstance(m, models.common.Conv): # assign export-friendly activations
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if isinstance(m.act, nn.Hardswish):
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m.act = Hardswish()
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elif isinstance(m.act, nn.SiLU):
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m.act = SiLU()
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# elif isinstance(m, models.yolo.Detect):
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# m.forward = m.forward_export # assign forward (optional)
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if isinstance(m, models.common.ShuffleV2Block):#shufflenet block nn.SiLU
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for i in range(len(m.branch1)):
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if isinstance(m.branch1[i], nn.SiLU):
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m.branch1[i] = SiLU()
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for i in range(len(m.branch2)):
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if isinstance(m.branch2[i], nn.SiLU):
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m.branch2[i] = SiLU()
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if isinstance(m, models.common.BlazeBlock):#shufflenet block nn.SiLU
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if isinstance(m.relu, nn.SiLU):
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m.relu = SiLU()
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if isinstance(m, models.common.DoubleBlazeBlock):#shufflenet block nn.SiLU
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if isinstance(m.relu, nn.SiLU):
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m.relu = SiLU()
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for i in range(len(m.branch1)):
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if isinstance(m.branch1[i], nn.SiLU):
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m.branch1[i] = SiLU()
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# for i in range(len(m.branch2)):
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# if isinstance(m.branch2[i], nn.SiLU):
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# m.branch2[i] = SiLU()
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y = model(img) # dry run
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# ONNX export
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print('\nStarting ONNX export with onnx %s...' % onnx.__version__)
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f = opt.weights.replace('.pt', '.onnx') # filename
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model.fuse() # only for ONNX
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input_names=['input']
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output_names=['output']
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#tensorrt 7
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# grid = model.model[-1].anchor_grid
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# model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid]
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#tensorrt 7
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torch.onnx.export(model, img, f, verbose=False, opset_version=12,
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input_names=input_names,
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output_names=output_names,
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dynamic_axes = {'input': {0: 'batch'},
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'output': {0: 'batch'}
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} if opt.dynamic else None)
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# model.model[-1].anchor_grid = grid
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# Checks
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onnx_model = onnx.load(f) # load onnx model
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onnx.checker.check_model(onnx_model) # check onnx model
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print('ONNX export success, saved as %s' % f)
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# Finish
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print('\nExport complete (%.2fs). Visualize with https://github.com/lutzroeder/netron.' % (time.time() - t))
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# onnx infer
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if opt.onnx_infer:
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import onnxruntime
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import numpy as np
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providers = ['CPUExecutionProvider']
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session = onnxruntime.InferenceSession(f, providers=providers)
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im = img.cpu().numpy().astype(np.float32) # torch to numpy
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y_onnx = session.run([session.get_outputs()[0].name], {session.get_inputs()[0].name: im})[0]
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print("pred's shape is ",y_onnx.shape)
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print("max(|torch_pred - onnx_pred|） =",abs(y.cpu().numpy()-y_onnx).max())
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# TensorRT export
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if opt.onnx2trt:
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from torch2trt.trt_model import ONNX_to_TRT
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print('\nStarting TensorRT...')
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ONNX_to_TRT(onnx_model_path=f,trt_engine_path=f.replace('.onnx', '.trt'),fp16_mode=opt.fp16_trt)
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# PB export
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if opt.onnx2pb:
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print('download the newest onnx_tf by https://github.com/onnx/onnx-tensorflow/tree/master/onnx_tf')
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from onnx_tf.backend import prepare
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import tensorflow as tf
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outpb = f.replace('.onnx', '.pb') # filename
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# strict=True maybe leads to KeyError: 'pyfunc_0', check: https://github.com/onnx/onnx-tensorflow/issues/167
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tf_rep = prepare(onnx_model, strict=False) # prepare tf representation
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tf_rep.export_graph(outpb) # export the model
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out_onnx = tf_rep.run(img) # onnx output
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# check pb
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with tf.Graph().as_default():
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graph_def = tf.GraphDef()
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with open(outpb, "rb") as f:
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graph_def.ParseFromString(f.read())
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tf.import_graph_def(graph_def, name="")
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with tf.Session() as sess:
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init = tf.global_variables_initializer()
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input_x = sess.graph.get_tensor_by_name(input_names[0]+':0') # input
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outputs = []
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for i in output_names:
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outputs.append(sess.graph.get_tensor_by_name(i+':0'))
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out_pb = sess.run(outputs, feed_dict={input_x: img})
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print(f'out_pytorch {y}')
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print(f'out_onnx {out_onnx}')
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print(f'out_pb {out_pb}')
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