在之前的文章《Yolov5 Android tf-lite方式集成》中，导出tf-lite方式的模型使用的是https://github.com/zldrobit/yolov5.git中的tf.py。晚上尝试用yolov5 最新版本的代码的export.py导出，如果不想修改命令行参数，可以字节修改以下代码：
# 需要修改参数 data weights batch-size
def parse_opt():
parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str, default=ROOT / 'data/ads.yaml', help='dataset.yaml path')
parser.add_argument('--weights', type=str, default=ROOT / 'best.pt', help='weights path')
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640, 640], help='image (h, w)')
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--half', action='store_true', help='FP16 half-precision export')
parser.add_argument('--inplace', action='store_true', help='set YOLOv5 Detect() inplace=True')
parser.add_argument('--train', action='store_true', help='model.train() mode')
parser.add_argument('--optimize',default=True, action='store_true', help='TorchScript: optimize for mobile')
parser.add_argument('--int8', action='store_true', help='CoreML/TF INT8 quantization')
parser.add_argument('--dynamic', action='store_true', help='ONNX/TF: dynamic axes')
parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model')
parser.add_argument('--opset', type=int, default=13, help='ONNX: opset version')
parser.add_argument('--topk-per-class', type=int, default=100, help='TF.js NMS: topk per class to keep')
parser.add_argument('--topk-all', type=int, default=100, help='TF.js NMS: topk for all classes to keep')
parser.add_argument('--iou-thres', type=float, default=0.45, help='TF.js NMS: IoU threshold')
parser.add_argument('--conf-thres', type=float, default=0.25, help='TF.js NMS: confidence threshold')
parser.add_argument('--include', nargs='+',
default=['torchscript', 'onnx'],
help='available formats are (torchscript, onnx, coreml, saved_model, pb, tflite, tfjs)')
opt = parser.parse_args()
print_args(FILE.stem, opt)
return opt修改完成后使用下面的命令导出：
python export_ads.py --include tflite导出效果：
(E:\anaconda_dirs\venvs\yolov5_latest) F:\Pycharm_Projects\yolov5_latest>python export_ads.py --include tflite
export_ads: data=F:\Pycharm_Projects\yolov5_latest\data\ads.yaml, weights=F:\Pycharm_Projects\yolov5_latest\best.pt, imgsz=[640, 640], batch_size=16, device=cpu, half=False, inplace=False, train=False, optimize=True, int8=False, dynamic=False, simplify=False, opset=13, topk_per_class=100, topk_all=100, iou_thres=0.45, conf_thres=0.25, include=['tflite']
YOLOv5 v5.0-458-g2c2ef25 torch 1.9.0+cpu CPU
Fusing layers...
Model Summary: 224 layers, 7053910 parameters, 0 gradients, 16.3 GFLOPs
PyTorch: starting from F:\Pycharm_Projects\yolov5_latest\best.pt (14.4 MB)
2021-10-09 21:19:55.779525: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
TensorFlow saved_model: starting export with tensorflow 2.4.1...
from n params module arguments
0 -1 1 3520 models.common.Focus [3, 32, 3]
2021-10-09 21:19:56.879550: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-10-09 21:19:56.880237: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll
2021-10-09 21:19:56.903797: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
2021-10-09 21:19:56.907011: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: obaby-msi-ml
2021-10-09 21:19:56.907167: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: obaby-msi-ml
2021-10-09 21:19:56.907460: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-10-09 21:19:56.908195: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
2 -1 1 18816 models.common.C3 [64, 64, 1]
3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
4 -1 1 156928 models.common.C3 [128, 128, 3]
5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
6 -1 1 625152 models.common.C3 [256, 256, 3]
7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
8 -1 1 656896 models.common.SPP [512, 512, [5, 9, 13]]
9 -1 1 1182720 models.common.C3 [512, 512, 1, False]
10 -1 1 131584 models.common.Conv [512, 256, 1, 1]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 models.common.Concat [1]
13 -1 1 361984 models.common.C3 [512, 256, 1, False]
14 -1 1 33024 models.common.Conv [256, 128, 1, 1]
15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
16 [-1, 4] 1 0 models.common.Concat [1]
17 -1 1 90880 models.common.C3 [256, 128, 1, False]
18 -1 1 147712 models.common.Conv [128, 128, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 1 296448 models.common.C3 [256, 256, 1, False]
21 -1 1 590336 models.common.Conv [256, 256, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 1 1182720 models.common.C3 [512, 512, 1, False]
24 [17, 20, 23] 1 16182 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512], [640, 640]]
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(16, 640, 640, 3)] 0
__________________________________________________________________________________________________
tf_focus (TFFocus) (16, 320, 320, 32) 3488 input_1[0][0]
__________________________________________________________________________________________________
tf_conv_1 (TFConv) (16, 160, 160, 64) 18496 tf_focus[0][0]
__________________________________________________________________________________________________
tf_c3 (TFC3) (16, 160, 160, 64) 18624 tf_conv_1[0][0]
__________________________________________________________________________________________________
tf_conv_7 (TFConv) (16, 80, 80, 128) 73856 tf_c3[0][0]
__________________________________________________________________________________________________
tf_c3_1 (TFC3) (16, 80, 80, 128) 156288 tf_conv_7[0][0]
__________________________________________________________________________________________________
tf_conv_17 (TFConv) (16, 40, 40, 256) 295168 tf_c3_1[0][0]
__________________________________________________________________________________________________
tf_c3_2 (TFC3) (16, 40, 40, 256) 623872 tf_conv_17[0][0]
__________________________________________________________________________________________________
tf_conv_27 (TFConv) (16, 20, 20, 512) 1180160 tf_c3_2[0][0]
__________________________________________________________________________________________________
tfspp (TFSPP) (16, 20, 20, 512) 656128 tf_conv_27[0][0]
__________________________________________________________________________________________________
tf_c3_3 (TFC3) (16, 20, 20, 512) 1181184 tfspp[0][0]
__________________________________________________________________________________________________
tf_conv_35 (TFConv) (16, 20, 20, 256) 131328 tf_c3_3[0][0]
__________________________________________________________________________________________________
tf_upsample (TFUpsample) (16, 40, 40, 256) 0 tf_conv_35[0][0]
__________________________________________________________________________________________________
tf_concat (TFConcat) (16, 40, 40, 512) 0 tf_upsample[0][0]
tf_c3_2[0][0]
__________________________________________________________________________________________________
tf_c3_4 (TFC3) (16, 40, 40, 256) 361216 tf_concat[0][0]
__________________________________________________________________________________________________
tf_conv_41 (TFConv) (16, 40, 40, 128) 32896 tf_c3_4[0][0]
__________________________________________________________________________________________________
tf_upsample_1 (TFUpsample) (16, 80, 80, 128) 0 tf_conv_41[0][0]
__________________________________________________________________________________________________
tf_concat_1 (TFConcat) (16, 80, 80, 256) 0 tf_upsample_1[0][0]
tf_c3_1[0][0]
__________________________________________________________________________________________________
tf_c3_5 (TFC3) (16, 80, 80, 128) 90496 tf_concat_1[0][0]
__________________________________________________________________________________________________
tf_conv_47 (TFConv) (16, 40, 40, 128) 147584 tf_c3_5[0][0]
__________________________________________________________________________________________________
tf_concat_2 (TFConcat) (16, 40, 40, 256) 0 tf_conv_47[0][0]
tf_conv_41[0][0]
__________________________________________________________________________________________________
tf_c3_6 (TFC3) (16, 40, 40, 256) 295680 tf_concat_2[0][0]
__________________________________________________________________________________________________
tf_conv_53 (TFConv) (16, 20, 20, 256) 590080 tf_c3_6[0][0]
__________________________________________________________________________________________________
tf_concat_3 (TFConcat) (16, 20, 20, 512) 0 tf_conv_53[0][0]
tf_conv_35[0][0]
__________________________________________________________________________________________________
tf_c3_7 (TFC3) (16, 20, 20, 512) 1181184 tf_concat_3[0][0]
__________________________________________________________________________________________________
tf_detect (TFDetect) ((16, 25200, 6), [(1 16182 tf_c3_5[0][0]
tf_c3_6[0][0]
tf_c3_7[0][0]
==================================================================================================
Total params: 7,053,910
Trainable params: 0
Non-trainable params: 7,053,910
__________________________________________________________________________________________________
2021-10-09 21:20:02.177675: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
Found untraced functions such as tf_conv_layer_call_and_return_conditional_losses, tf_conv_layer_call_fn, tf_conv_2_layer_call_and_return_conditional_losses, tf_conv_2_layer_call_fn, tf_conv_3_layer_call_and_return_conditional_losses while saving (showing 5 of 550). These functions will not be directly callable after loading.
Found untraced functions such as tf_conv_layer_call_and_return_conditional_losses, tf_conv_layer_call_fn, tf_conv_2_layer_call_and_return_conditional_losses, tf_conv_2_layer_call_fn, tf_conv_3_layer_call_and_return_conditional_losses while saving (showing 5 of 550). These functions will not be directly callable after loading.
Assets written to: F:\Pycharm_Projects\yolov5_latest\best_saved_model\assets
TensorFlow saved_model: export success, saved as F:\Pycharm_Projects\yolov5_latest\best_saved_model (239.7 MB)
TensorFlow Lite: starting export with tensorflow 2.4.1...
Found untraced functions such as tf_conv_layer_call_and_return_conditional_losses, tf_conv_layer_call_fn, tf_conv_2_layer_call_and_return_conditional_losses, tf_conv_2_layer_call_fn, tf_conv_3_layer_call_and_return_conditional_losses while saving (showing 5 of 550). These functions will not be directly callable after loading.
Found untraced functions such as tf_conv_layer_call_and_return_conditional_losses, tf_conv_layer_call_fn, tf_conv_2_layer_call_and_return_conditional_losses, tf_conv_2_layer_call_fn, tf_conv_3_layer_call_and_return_conditional_losses while saving (showing 5 of 550). These functions will not be directly callable after loading.
Assets written to: C:\Users\obaby\AppData\Local\Temp\tmpej3xo4ik\assets
2021-10-09 21:20:50.470748: I tensorflow/core/grappler/devices.cc:69] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0
2021-10-09 21:20:50.471066: I tensorflow/core/grappler/clusters/single_machine.cc:356] Starting new session
2021-10-09 21:20:50.472942: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-10-09 21:20:50.509549: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:928] Optimization results for grappler item: graph_to_optimize
function_optimizer: function_optimizer did nothing. time = 0.001ms.
function_optimizer: function_optimizer did nothing. time = 0ms.
2021-10-09 21:20:51.339012: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:316] Ignored output_format.
2021-10-09 21:20:51.339096: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:319] Ignored drop_control_dependency.
2021-10-09 21:20:51.395311: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
TensorFlow Lite: export success, saved as F:\Pycharm_Projects\yolov5_latest\best-fp16.tflite (14.3 MB)
Export complete (60.24s)
Results saved to F:\Pycharm_Projects\yolov5_latest
Visualize with https://netron.app
(E:\anaconda_dirs\venvs\yolov5_latest) F:\Pycharm_Projects\yolov5_latest>python export_ads.py --include tflite
export_ads: data=F:\Pycharm_Projects\yolov5_latest\data\ads.yaml, weights=F:\Pycharm_Projects\yolov5_latest\best.pt, imgsz=[640, 640], batch_size=1, device=cpu, half=False, inplace=False, train=False, optimize=True, int8=False, dynamic=False, simplify=False, opset=13, topk_per_class=100, topk_all=100, iou_thres=0.45, conf_thres=0.25, include=['tflite']
YOLOv5 v5.0-458-g2c2ef25 torch 1.9.0+cpu CPU
Fusing layers...
Model Summary: 224 layers, 7053910 parameters, 0 gradients, 16.3 GFLOPs
PyTorch: starting from F:\Pycharm_Projects\yolov5_latest\best.pt (14.4 MB)
2021-10-09 21:21:03.907332: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
TensorFlow saved_model: starting export with tensorflow 2.4.1...
from n params module arguments
0 -1 1 3520 models.common.Focus [3, 32, 3]
2021-10-09 21:21:05.007065: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-10-09 21:21:05.007781: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll
2021-10-09 21:21:05.029777: E tensorflow/stream_executor/cuda/cuda_driver.cc:328] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
2021-10-09 21:21:05.032833: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: obaby-msi-ml
2021-10-09 21:21:05.032951: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: obaby-msi-ml
2021-10-09 21:21:05.033353: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-10-09 21:21:05.035414: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
2 -1 1 18816 models.common.C3 [64, 64, 1]
3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
4 -1 1 156928 models.common.C3 [128, 128, 3]
5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
6 -1 1 625152 models.common.C3 [256, 256, 3]
7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
8 -1 1 656896 models.common.SPP [512, 512, [5, 9, 13]]
9 -1 1 1182720 models.common.C3 [512, 512, 1, False]
10 -1 1 131584 models.common.Conv [512, 256, 1, 1]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 0 models.common.Concat [1]
13 -1 1 361984 models.common.C3 [512, 256, 1, False]
14 -1 1 33024 models.common.Conv [256, 128, 1, 1]
15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
16 [-1, 4] 1 0 models.common.Concat [1]
17 -1 1 90880 models.common.C3 [256, 128, 1, False]
18 -1 1 147712 models.common.Conv [128, 128, 3, 2]
19 [-1, 14] 1 0 models.common.Concat [1]
20 -1 1 296448 models.common.C3 [256, 256, 1, False]
21 -1 1 590336 models.common.Conv [256, 256, 3, 2]
22 [-1, 10] 1 0 models.common.Concat [1]
23 -1 1 1182720 models.common.C3 [512, 512, 1, False]
24 [17, 20, 23] 1 16182 models.yolo.Detect [1, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512], [640, 640]]
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(1, 640, 640, 3)] 0
__________________________________________________________________________________________________
tf_focus (TFFocus) (1, 320, 320, 32) 3488 input_1[0][0]
__________________________________________________________________________________________________
tf_conv_1 (TFConv) (1, 160, 160, 64) 18496 tf_focus[0][0]
__________________________________________________________________________________________________
tf_c3 (TFC3) (1, 160, 160, 64) 18624 tf_conv_1[0][0]
__________________________________________________________________________________________________
tf_conv_7 (TFConv) (1, 80, 80, 128) 73856 tf_c3[0][0]
__________________________________________________________________________________________________
tf_c3_1 (TFC3) (1, 80, 80, 128) 156288 tf_conv_7[0][0]
__________________________________________________________________________________________________
tf_conv_17 (TFConv) (1, 40, 40, 256) 295168 tf_c3_1[0][0]
__________________________________________________________________________________________________
tf_c3_2 (TFC3) (1, 40, 40, 256) 623872 tf_conv_17[0][0]
__________________________________________________________________________________________________
tf_conv_27 (TFConv) (1, 20, 20, 512) 1180160 tf_c3_2[0][0]
__________________________________________________________________________________________________
tfspp (TFSPP) (1, 20, 20, 512) 656128 tf_conv_27[0][0]
__________________________________________________________________________________________________
tf_c3_3 (TFC3) (1, 20, 20, 512) 1181184 tfspp[0][0]
__________________________________________________________________________________________________
tf_conv_35 (TFConv) (1, 20, 20, 256) 131328 tf_c3_3[0][0]
__________________________________________________________________________________________________
tf_upsample (TFUpsample) (1, 40, 40, 256) 0 tf_conv_35[0][0]
__________________________________________________________________________________________________
tf_concat (TFConcat) (1, 40, 40, 512) 0 tf_upsample[0][0]
tf_c3_2[0][0]
__________________________________________________________________________________________________
tf_c3_4 (TFC3) (1, 40, 40, 256) 361216 tf_concat[0][0]
__________________________________________________________________________________________________
tf_conv_41 (TFConv) (1, 40, 40, 128) 32896 tf_c3_4[0][0]
__________________________________________________________________________________________________
tf_upsample_1 (TFUpsample) (1, 80, 80, 128) 0 tf_conv_41[0][0]
__________________________________________________________________________________________________
tf_concat_1 (TFConcat) (1, 80, 80, 256) 0 tf_upsample_1[0][0]
tf_c3_1[0][0]
__________________________________________________________________________________________________
tf_c3_5 (TFC3) (1, 80, 80, 128) 90496 tf_concat_1[0][0]
__________________________________________________________________________________________________
tf_conv_47 (TFConv) (1, 40, 40, 128) 147584 tf_c3_5[0][0]
__________________________________________________________________________________________________
tf_concat_2 (TFConcat) (1, 40, 40, 256) 0 tf_conv_47[0][0]
tf_conv_41[0][0]
__________________________________________________________________________________________________
tf_c3_6 (TFC3) (1, 40, 40, 256) 295680 tf_concat_2[0][0]
__________________________________________________________________________________________________
tf_conv_53 (TFConv) (1, 20, 20, 256) 590080 tf_c3_6[0][0]
__________________________________________________________________________________________________
tf_concat_3 (TFConcat) (1, 20, 20, 512) 0 tf_conv_53[0][0]
tf_conv_35[0][0]
__________________________________________________________________________________________________
tf_c3_7 (TFC3) (1, 20, 20, 512) 1181184 tf_concat_3[0][0]
__________________________________________________________________________________________________
tf_detect (TFDetect) ((1, 25200, 6), [(1, 16182 tf_c3_5[0][0]
tf_c3_6[0][0]
tf_c3_7[0][0]
==================================================================================================
Total params: 7,053,910
Trainable params: 0
Non-trainable params: 7,053,910
__________________________________________________________________________________________________
2021-10-09 21:21:08.904313: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
Found untraced functions such as tf_conv_layer_call_fn, tf_conv_layer_call_and_return_conditional_losses, tf_conv_2_layer_call_fn, tf_conv_2_layer_call_and_return_conditional_losses, tf_conv_3_layer_call_fn while saving (showing 5 of 550). These functions will not be directly callable after loading.
Found untraced functions such as tf_conv_layer_call_fn, tf_conv_layer_call_and_return_conditional_losses, tf_conv_2_layer_call_fn, tf_conv_2_layer_call_and_return_conditional_losses, tf_conv_3_layer_call_fn while saving (showing 5 of 550). These functions will not be directly callable after loading.
Assets written to: F:\Pycharm_Projects\yolov5_latest\best_saved_model\assets
TensorFlow saved_model: export success, saved as F:\Pycharm_Projects\yolov5_latest\best_saved_model (239.7 MB)
TensorFlow Lite: starting export with tensorflow 2.4.1...
Found untraced functions such as tf_conv_layer_call_fn, tf_conv_layer_call_and_return_conditional_losses, tf_conv_2_layer_call_fn, tf_conv_2_layer_call_and_return_conditional_losses, tf_conv_3_layer_call_fn while saving (showing 5 of 550). These functions will not be directly callable after loading.
Found untraced functions such as tf_conv_layer_call_fn, tf_conv_layer_call_and_return_conditional_losses, tf_conv_2_layer_call_fn, tf_conv_2_layer_call_and_return_conditional_losses, tf_conv_3_layer_call_fn while saving (showing 5 of 550). These functions will not be directly callable after loading.
Assets written to: C:\Users\obaby\AppData\Local\Temp\tmp3e12zbt2\assets
2021-10-09 21:21:57.639337: I tensorflow/core/grappler/devices.cc:69] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0
2021-10-09 21:21:57.639574: I tensorflow/core/grappler/clusters/single_machine.cc:356] Starting new session
2021-10-09 21:21:57.640650: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-10-09 21:21:57.650914: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:928] Optimization results for grappler item: graph_to_optimize
function_optimizer: function_optimizer did nothing. time = 0.002ms.
function_optimizer: function_optimizer did nothing. time = 0ms.
2021-10-09 21:21:58.471195: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:316] Ignored output_format.
2021-10-09 21:21:58.471422: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:319] Ignored drop_control_dependency.
2021-10-09 21:21:58.529722: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
TensorFlow Lite: export success, saved as F:\Pycharm_Projects\yolov5_latest\best-fp16.tflite (14.3 MB)
Export complete (55.30s)
Results saved to F:\Pycharm_Projects\yolov5_latest
Visualize with https://netron.app通过上面的命令导出的模型，比旧版的tf.py导出的模型大约大了1倍，准确度略有下降，在模拟器上的执行效率却比旧版本的效率快了不少。
☆文章版权声明☆
* 网站名称：obaby@mars * 网址：https://h4ck.org.cn/ * 本文标题： 《Yolov5 tf-lite方式导出》 * 本文链接：https://h4ck.org.cn/2021/10/yolov5-tf-lite%e6%96%b9%e5%bc%8f%e5%af%bc%e5%87%ba/ * 转载文章请标明文章来源，原文标题以及原文链接。请遵从 《署名-非商业性使用-相同方式共享 2.5 中国大陆 (CC BY-NC-SA 2.5 CN) 》许可协议。
分享文章：
相关文章:
- Yolov5 Android tf-lite方式集成
- yolov5 训练速度对比
- Yolov5 Android torchscript方式集成
- Android Skip Ads Yolov5 Project
- Windows 10 yolov5 GPU环境
- Yolov5 不同集成方式 效果对比
- Yolov5 安卓检测效果让人无语
- PyTorch GPU 与虚拟内存
- M1 Mac 安装Tensorflow
- Django admin Foreignkey ManyToMany list_display展示