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2 years ago 👋 Hello @chinaandylee, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.
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Requirements
Python>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started:
git clone https://github.com/ultralytics/yolov5 # clone
cd yolov5
pip install -r requirements.txt # installEnvironments
YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
- Google Colab and Kaggle notebooks with free GPU:
- Google Cloud Deep Learning VM. See GCP Quickstart Guide
- Amazon Deep Learning AMI. See AWS Quickstart Guide
- Docker Image. See Docker Quickstart Guide
Status
If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), validation (val.py), inference (detect.py) and export (export.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.
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chinaandylee
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AntoineChauviere
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Question
When I export tflite model with opt --nms python export.py --weights yolov5s.pt --include tflite --nms I got the following error: ..... tf.expand_dims (TFOpLambda) (1, 25200, 1, 4) 0 tf.concat[0][0]
tf.math.multiply (TFOpLambda) (1, 25200, 2) 0 tf.operators.getitem_1[0][0] tf.operators.getitem_2[0][0]
tf.image.combined_non_max_suppr CombinedNonMaxSuppre 0 tf.expand_dims[0][0] tf.math.multiply[0][0]
Total params: 7,015,519 Trainable params: 0 Non-trainable params: 7,015,519
2022-02-27 14:40:41.858021: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider a voiding using them. Found untraced functions such as tf_conv_2_layer_call_fn, tf_conv_2_layer_call_and_return_conditional_losses, tf_conv_3_layer_call_fn, tf_conv_3_layer_call_and _return_conditional_losses, tf_conv_4_layer_call_fn while saving (showing 5 of 520). These functions will not be directly callable after loading. Found untraced functions such as tf_conv_2_layer_call_fn, tf_conv_2_layer_call_and_return_conditional_losses, tf_conv_3_layer_call_fn, tf_conv_3_layer_call_and _return_conditional_losses, tf_conv_4_layer_call_fn while saving (showing 5 of 520). These functions will not be directly callable after loading. Assets written to: last_saved_model\assets TensorFlow SavedModel: export success, saved as last_saved_model (238.6 MB)
TensorFlow Lite: starting export with tensorflow 2.4.0... Found untraced functions such as tf_conv_2_layer_call_fn, tf_conv_2_layer_call_and_return_conditional_losses, tf_conv_3_layer_call_fn, tf_conv_3_layer_call_and _return_conditional_losses, tf_conv_4_layer_call_fn while saving (showing 5 of 520). These functions will not be directly callable after loading. Found untraced functions such as tf_conv_2_layer_call_fn, tf_conv_2_layer_call_and_return_conditional_losses, tf_conv_3_layer_call_fn, tf_conv_3_layer_call_and _return_conditional_losses, tf_conv_4_layer_call_fn while saving (showing 5 of 520). These functions will not be directly callable after loading. Assets written to: C:\Users\kk\AppData\Local\Temp\tmp7pgu55vr\assets 2022-02-27 14:41:35.278574: I tensorflow/core/grappler/devices.cc:69] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0 2022-02-27 14:41:35.281165: I tensorflow/core/grappler/clusters/single_machine.cc:356] Starting new session 2022-02-27 14:41:35.283499: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set 2022-02-27 14:41:35.297874: 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.
2022-02-27 14:41:36.286075: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:316] Ignored output_format. 2022-02-27 14:41:36.288583: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:319] Ignored drop_control_dependency. 2022-02-27 14:41:36.360661: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set loc(callsite("model/tf.image.combined_non_max_suppression/combined_non_max_suppression/CombinedNonMaxSuppression"("C:\Users\kk\Anaconda3\envs\pytorch_gpu\lib\s ite-packages\tensorflow\python\eager\def_function.py":1205:0) at callsite("C:\Users\kk\Anaconda3\envs\pytorch_gpu\lib\site-packages\tensorflow\python\eager\def _function.py":1299:0 at callsite("C:\Users\kk\Anaconda3\envs\pytorch_gpu\lib\site-packages\tensorflow\lite\python\lite.py":847:0 at callsite("export.py":324:0 at callsite("export.py":488:0 at callsite("C:\Users\kk\Anaconda3\envs\pytorch_gpu\lib\site-packages\torch\autograd\grad_mode.py":28:0 at callsite("export.py":5 39:0 at "export.py":544:0)))))))): error: 'tf.CombinedNonMaxSuppression' op is neither a custom op nor a flex op error: failed while converting: 'main': Ops that can be supported by the flex runtime (enabled via setting the -emit-select-tf-ops flag): tf.CombinedNonMaxSuppression {clip_boxes = false, device = "", pad_per_class = false}
TensorFlow Lite: export failure: C:\Users\kk\Anaconda3\envs\pytorch_gpu\lib\site-packages\tensorflow\python\eager\def_function.py:1205:0: error: 'tf.CombinedNo nMaxSuppression' op is neither a custom op nor a flex op C:\Users\kk\Anaconda3\envs\pytorch_gpu\lib\site-packages\tensorflow\python\eager\def_function.py:1299:0: note: called from C:\Users\kk\Anaconda3\envs\pytorch_gpu\lib\site-packages\tensorflow\lite\python\lite.py:847:0: note: called from export.py:324:0: note: called from export.py:488:0: note: called from C:\Users\kk\Anaconda3\envs\pytorch_gpu\lib\site-packages\torch\autograd\gradmode.py:28:0: note: called from export.py:539:0: note: called from export.py:544:0: note: called from **###:0: error: failed while converting: 'main': Ops that can be supported by the flex runtime (enabled via setting the -emit-select-tf-ops flag):
tf.CombinedNonMaxSuppression {clip_boxes = false, device = "", pad_perclass = false}**
Export complete (65.58s) Results saved to E:\python_resource\code_resource\pytorch\yolov5-master Detect: python detect.py --weights last_saved_model PyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', 'last_saved_model') Validate: python val.py --weights last_saved_model Visualize: https://netron.app
Any help will be appreciated!
Additional
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