Closed thangmanhbris closed 3 years ago
arghyaganguly
commented
3 years ago @thangmanhbris , please provide the versions installed for python, TensorFlow, TensorFlow-hub , TensorFlow-serving(if used for inference) and the code snippet used for step -
a)"Then I uploaded saved model and create new model on GCP and tried to infer"
thangmanhbris
commented
3 years ago I run on Google Colab, follow this notebook https://github.com/tensorflow/models/blob/master/research/object_detection/colab_tutorials/inference_from_saved_model_tf2_colab.ipynb
TensorFlow version: 2.4.1 Python version: Python 3.7.10
Code snipper installed like this:
%%bash cd models/research/ protoc object_detection/protos/*.proto --python_out=. cp object_detection/packages/tf2/setup.py . python -m pip install .
Processing /content/models/research Collecting avro-python3 Downloading https://files.pythonhosted.org/packages/cc/97/7a6970380ca8db9139a3cc0b0e3e0dd3e4bc584fb3644e1d06e71e1a55f0/avro-python3-1.10.2.tar.gz Collecting apache-beam Downloading https://files.pythonhosted.org/packages/e1/e7/d6e5a3786d9a037a38af966bf154bcd6cb3cbea2edffda00cf6c417cc9a2/apache_beam-2.28.0-cp37-cp37m-manylinux2010_x86_64.whl (9.0MB) Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (7.1.2) Requirement already satisfied: lxml in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (4.2.6) Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (3.2.2) Requirement already satisfied: Cython in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (0.29.22) Requirement already satisfied: contextlib2 in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (0.5.5) Collecting tf-slim Downloading https://files.pythonhosted.org/packages/02/97/b0f4a64df018ca018cc035d44f2ef08f91e2e8aa67271f6f19633a015ff7/tf_slim-1.1.0-py2.py3-none-any.whl (352kB) Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (1.15.0) Requirement already satisfied: pycocotools in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (2.0.2) Collecting lvis Downloading https://files.pythonhosted.org/packages/72/b6/1992240ab48310b5360bfdd1d53163f43bb97d90dc5dc723c67d41c38e78/lvis-0.5.3-py3-none-any.whl Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (1.4.1) Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from object-detection==0.1) (1.1.5) Collecting tf-models-official Downloading 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requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard~=2.4->tensorflow>=2.4.0->tf-models-official->object-detection==0.1) (3.1.0) Building wheels for collected packages: object-detection, avro-python3, future, dill, seqeval, py-cpuinfo Building wheel for object-detection (setup.py): started Building wheel for object-detection (setup.py): finished with status 'done' Created wheel for object-detection: filename=object_detection-0.1-cp37-none-any.whl size=1643750 sha256=82d6bf77b412849b72a239e0a8ef6134b24687c07f54af881d81d53f5b420e10 Stored in directory: /tmp/pip-ephem-wheel-cache-_uh116og/wheels/94/49/4b/39b051683087a22ef7e80ec52152a27249d1a644ccf4e442ea Building wheel for avro-python3 (setup.py): started Building wheel for avro-python3 (setup.py): finished with status 'done' Created wheel for avro-python3: filename=avro_python3-1.10.2-cp37-none-any.whl size=44011 sha256=3488d0da615252af815d851c8024cb14c551a24b1de50f02385bbb3cd3a4a30c Stored in directory: /root/.cache/pip/wheels/ee/ee/18/c466221ca6900e3efce2f4ea9c329288808679aecdcb2838d3 Building wheel for future (setup.py): started Building wheel for future (setup.py): finished with status 'done' Created wheel for future: filename=future-0.18.2-cp37-none-any.whl size=491058 sha256=768631ce2c0258ab1f2bcac318cf9535a07efa7e9b9019fd490ab5e35efaba6c Stored in directory: /root/.cache/pip/wheels/8b/99/a0/81daf51dcd359a9377b110a8a886b3895921802d2fc1b2397e Building wheel for dill (setup.py): started Building wheel for dill (setup.py): finished with status 'done' Created wheel for dill: filename=dill-0.3.1.1-cp37-none-any.whl size=78532 sha256=98ccdb5687ddac93539aa228bafe6bf8e8b5090eca866fe548b05d7234e79ccd Stored in directory: /root/.cache/pip/wheels/59/b1/91/f02e76c732915c4015ab4010f3015469866c1eb9b14058d8e7 Building wheel for seqeval (setup.py): started Building wheel for seqeval (setup.py): finished with status 'done' Created wheel for seqeval: filename=seqeval-1.2.2-cp37-none-any.whl size=16172 sha256=da2f505f1a0ec6f60b5ae3eb1a820f208142c8186c8a597d7b2c9b4efe5ea3d1 Stored in directory: /root/.cache/pip/wheels/52/df/1b/45d75646c37428f7e626214704a0e35bd3cfc32eda37e59e5f Building wheel for py-cpuinfo (setup.py): started Building wheel for py-cpuinfo (setup.py): finished with status 'done' Created wheel for py-cpuinfo: filename=py_cpuinfo-7.0.0-cp37-none-any.whl size=20070 sha256=6f5cc6216962b970027fa18153828044cc47bbd963d4bffab9015f3584eca83e Stored in directory: /root/.cache/pip/wheels/f1/93/7b/127daf0c3a5a49feb2fecd468d508067c733fba5192f726ad1 Successfully built object-detection avro-python3 future dill seqeval py-cpuinfo Installing collected packages: avro-python3, pbr, mock, future, dill, requests, pyarrow, fastavro, hdfs, apache-beam, tf-slim, lvis, pyyaml, sentencepiece, dataclasses, seqeval, py-cpuinfo, tensorflow-addons, opencv-python-headless, tensorflow-model-optimization, tf-models-official, object-detection Found existing installation: future 0.16.0 Uninstalling future-0.16.0: Successfully uninstalled future-0.16.0 Found existing installation: dill 0.3.3 Uninstalling dill-0.3.3: Successfully uninstalled dill-0.3.3 Found existing installation: requests 2.23.0 Uninstalling requests-2.23.0: Successfully uninstalled requests-2.23.0 Found existing installation: pyarrow 3.0.0 Uninstalling pyarrow-3.0.0: Successfully uninstalled pyarrow-3.0.0 Found existing installation: PyYAML 3.13 Uninstalling PyYAML-3.13: Successfully uninstalled PyYAML-3.13 Successfully installed apache-beam-2.28.0 avro-python3-1.10.2 dataclasses-0.6 dill-0.3.1.1 fastavro-1.3.5 future-0.18.2 hdfs-2.6.0 lvis-0.5.3 mock-2.0.0 object-detection-0.1 opencv-python-headless-4.5.1.48 pbr-5.5.1 py-cpuinfo-7.0.0 pyarrow-2.0.0 pyyaml-5.4.1 requests-2.25.1 sentencepiece-0.1.95 seqeval-1.2.2 tensorflow-addons-0.12.1 tensorflow-model-optimization-0.5.0 tf-models-official-2.4.0 tf-slim-1.1.0
ERROR: multiprocess 0.70.11.1 has requirement dill>=0.3.3, but you'll have dill 0.3.1.1 which is incompatible. ERROR: google-colab 1.0.0 has requirement requests~=2.23.0, but you'll have requests 2.25.1 which is incompatible. ERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible. ERROR: apache-beam 2.28.0 has requirement avro-python3!=1.9.2,<1.10.0,>=1.8.1, but you'll have avro-python3 1.10.2 which is incompatible.
Code snippet used for exporting like this: !python /content/models/research/object_detection/exporter_main_v2.py --input_type encoded_image_string_tensor --pipeline_config_path /content/models/research/object_detection/test_data/faster_rcnn_resnet152_v1_800x1333_coco17_gpu-8/pipeline.config --trained_checkpoint_dir /content/models/research/object_detection/test_data/faster_rcnn_resnet152_v1_800x1333_coco17_gpu-8/checkpoint --output_directory /content/sample_data/exported_models
2021-04-10 04:03:52.164139: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2021-04-10 04:03:54.745870: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-04-10 04:03:54.748051: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1
2021-04-10 04:03:54.754205: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-04-10 04:03:54.754646: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5
coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s
2021-04-10 04:03:54.754682: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2021-04-10 04:03:54.759699: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
2021-04-10 04:03:54.759778: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
2021-04-10 04:03:54.761955: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2021-04-10 04:03:54.762309: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2021-04-10 04:03:54.770766: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
2021-04-10 04:03:54.771671: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
2021-04-10 04:03:54.771857: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
2021-04-10 04:03:54.771992: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-04-10 04:03:54.772472: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-04-10 04:03:54.772823: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-04-10 04:03:54.773226: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-04-10 04:03:54.773346: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-04-10 04:03:54.773711: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5
coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s
2021-04-10 04:03:54.773739: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2021-04-10 04:03:54.773779: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11
2021-04-10 04:03:54.773802: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11
2021-04-10 04:03:54.773818: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10
2021-04-10 04:03:54.773835: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10
2021-04-10 04:03:54.773848: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10
2021-04-10 04:03:54.773861: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11
2021-04-10 04:03:54.773875: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8
2021-04-10 04:03:54.773953: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-04-10 04:03:54.774432: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-04-10 04:03:54.774796: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-04-10 04:03:54.774847: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0
2021-04-10 04:03:55.321904: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-04-10 04:03:55.321976: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267] 0
2021-04-10 04:03:55.321999: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0: N
2021-04-10 04:03:55.322231: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-04-10 04:03:55.322707: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-04-10 04:03:55.323205: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2021-04-10 04:03:55.323596: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
2021-04-10 04:03:55.323649: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6473 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/exporter_lib_v2.py:106: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.
Instructions for updating:
back_prop=False is deprecated. Consider using tf.stop_gradient instead.
Instead of:
results = tf.map_fn(fn, elems, back_prop=False)
Use:
results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))
W0410 04:03:55.454751 140072503203712 deprecation.py:604] From /usr/local/lib/python3.7/dist-packages/object_detection/exporter_lib_v2.py:106: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.
Instructions for updating:
back_prop=False is deprecated. Consider using tf.stop_gradient instead.
Instead of:
results = tf.map_fn(fn, elems, back_prop=False)
Use:
results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))
2021-04-10 04:04:04.685802: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 18874368 exceeds 10% of free system memory.
INFO:tensorflow:depth of additional conv before box predictor: 0
I0410 04:04:06.023887 140072503203712 convolutional_keras_box_predictor.py:154] depth of additional conv before box predictor: 0
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/utils/shape_utils.py:237: calling map_fn (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Use fn_output_signature instead
W0410 04:04:08.961464 140072503203712 deprecation.py:537] From /usr/local/lib/python3.7/dist-packages/object_detection/utils/shape_utils.py:237: calling map_fn (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Use fn_output_signature instead
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:201: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version.
Instructions for updating:
box_ind is deprecated, use box_indices instead
W0410 04:04:11.595384 140072503203712 deprecation.py:537] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:201: calling crop_and_resize_v1 (from tensorflow.python.ops.image_ops_impl) with box_ind is deprecated and will be removed in a future version.
Instructions for updating:
box_ind is deprecated, use box_indices instead
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/utils/model_util.py:57: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
W0410 04:04:12.066478 140072503203712 deprecation.py:339] From /usr/local/lib/python3.7/dist-packages/object_detection/utils/model_util.py:57: Tensor.experimental_ref (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use ref() instead.
WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:201: batch_gather (from tensorflow.python.ops.array_ops) is deprecated and will be removed after 2017-10-25.
Instructions for updating:
tf.batch_gather is deprecated, please use tf.gather with batch_dims=-1 instead.
W0410 04:04:18.554005 140072503203712 deprecation.py:339] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:201: batch_gather (from tensorflow.python.ops.array_ops) is deprecated and will be removed after 2017-10-25.
Instructions for updating:
tf.batch_gather is deprecated, please use tf.gather with batch_dims=-1 instead.
2021-04-10 04:04:19.019204: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)
2021-04-10 04:04:19.019757: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2199995000 Hz
2021-04-10 04:04:26.534352: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 18874368 exceeds 10% of free system memory.
WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.meta_architectures.faster_rcnn_meta_arch.FasterRCNNMetaArch object at 0x7f64ca485490>, because it is not built.
W0410 04:04:27.823537 140072503203712 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.meta_architectures.faster_rcnn_meta_arch.FasterRCNNMetaArch object at 0x7f64ca485490>, because it is not built.
2021-04-10 04:04:48.183030: 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.
W0410 04:05:20.964057 140072503203712 save.py:241] Found untraced functions such as FirstStageBoxPredictor_layer_call_and_return_conditional_losses, FirstStageBoxPredictor_layer_call_fn, mask_rcnn_keras_box_predictor_layer_call_and_return_conditional_losses, mask_rcnn_keras_box_predictor_layer_call_fn, FirstStageBoxPredictor_layer_call_fn while saving (showing 5 of 70). These functions will not be directly callable after loading.
W0410 04:05:23.429476 140072503203712 save.py:241] Found untraced functions such as FirstStageBoxPredictor_layer_call_and_return_conditional_losses, FirstStageBoxPredictor_layer_call_fn, mask_rcnn_keras_box_predictor_layer_call_and_return_conditional_losses, mask_rcnn_keras_box_predictor_layer_call_fn, FirstStageBoxPredictor_layer_call_fn while saving (showing 5 of 70). These functions will not be directly callable after loading.
2021-04-10 04:05:31.886904: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 18874368 exceeds 10% of free system memory.
INFO:tensorflow:Assets written to: /content/sample_data/exported_models/saved_model/assets
I0410 04:05:32.880743 140072503203712 builder_impl.py:775] Assets written to: /content/sample_data/exported_models/saved_model/assets
INFO:tensorflow:Writing pipeline config file to /content/sample_data/exported_models/pipeline.config
I0410 04:05:34.175697 140072503203712 config_util.py:254] Writing pipeline config file to /content/sample_data/exported_models/pipeline.config
thangmanhbris
commented
3 years ago Model configuration on GCP: Python version: 3.7 Framework: Tensorflow, version 2.4 Runtime 2.4 machine type: n1-standard-2 Accelerator type: NVIDIA_TESLA_P4 Accelerator Count: 1
Thanks in advance
arghyaganguly
commented
3 years ago
I followed this: models/research/object_detection/colab_tutorials/inference_from_saved_model_tf2_colab.ipynb. I tried some models such as faster_rcnn_resnet152_v1_800x1333_coco17_gpu-8.tar.gz. Then I exported model using export_main_v2 like this: !python /content/models/research/object_detection/exporter_main_v2.py --input_type encoded_image_string_tensor --pipeline_config_path /content/models/research/object_detection/test_data/faster_rcnn_resnet152_v1_800x1333_coco17_gpu-8/pipeline.config --trained_checkpoint_dir /content/models/research/object_detection/test_data/faster_rcnn_resnet152_v1_800x1333_coco17_gpu-8/checkpoint --output_directory /content/sample_data/exported_models. Then I uploaded saved model and create new model on GCP and tried to infer, but I got error: { "error": { "code": 404, "message": "{\n \"error\": \"{{function_node inference_signature_wrapper_38329}} {{function_node inference_signature_wrapper_38329}} Op type not registered 'DecodeImage' in binary running on frcnb77e01-v10fde29f8-7dff9c8cf8-krqfq. Make sure the Op and Kernel are registered in the binary running in this process. Note that if you are loading a saved graph which used ops from tf.contrib, accessing (e.g.)
tf.contrib.resamplershould be done before importing the graph, as contrib ops are lazily registered when the module is first accessed.\n\t [[{{node StatefulPartitionedCall}}]]\n\t [[StatefulPartitionedCall]]\"\n}", "status": "NOT_FOUND" } }