Converting to TFLite: Invalid quantization params for op MAXIMUM at index 4 in subgraph 0

System information

OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 18.04
TensorFlow installed from (source or binary): binary
TensorFlow version (or github SHA if from source): 1.15.0-gpu

Command used to run the converter or code if you’re using the Python API
import os

import tensorflow as tf
import numpy as np
from PIL import Image

dataset = []
directory_images = './data_test'
directory_saved = './models'
for img in os.listdir(directory_images):
	data = Image.open(os.path.join(directory_images,img)))
	data = np.asarray(data, dtype=np.float32)[np.newaxis, :]
	dataset.append(data)

def representative_dataset_gen():
    for input_value in dataset:
        yield [input_value]

converter = tf.lite.TFLiteConverter.from_saved_model(directory_saved + "/saved")
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_dataset_gen
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.uint8
converter.inference_output_type = tf.uint8

tflite_model = converter.convert()

name = directory_saved + "/tflite_model"
open(name + ".tflite", "wb").write(tflite_model)

The output from the converter invocation
Traceback (most recent call last):
  File "saved2lite.py", line 34, in <module>
    tflite_model = converter.convert()
  File "/home/ds017/.pyenv/versions/takehome/lib/python3.6/site-packages/tensorflow_core/lite/python/lite.py", line 993, in convert
    inference_output_type)
  File "/home/ds017/.pyenv/versions/takehome/lib/python3.6/site-packages/tensorflow_core/lite/python/lite.py", line 239, in _calibrate_quantize_model
    inference_output_type, allow_float)
  File "/home/ds017/.pyenv/versions/takehome/lib/python3.6/site-packages/tensorflow_core/lite/python/optimize/calibrator.py", line 78, in calibrate_and_quantize
    np.dtype(output_type.as_numpy_dtype()).num, allow_float)
  File "/home/ds017/.pyenv/versions/takehome/lib/python3.6/site-packages/tensorflow_core/lite/python/optimize/tensorflow_lite_wrap_calibration_wrapper.py", line 115, in QuantizeModel
    return _tensorflow_lite_wrap_calibration_wrapper.CalibrationWrapper_QuantizeModel(self, input_py_type, output_py_type, allow_float)
RuntimeError: Invalid quantization params for op MAXIMUM at index 4 in subgraph 0

Also, please include a link to the saved model or GraphDef
https://www.dropbox.com/s/tj96fsm6t6rq8ye/model-r100-arcface-ms1m-refine-v2.zip?dl=0

Failure details
Conversion fails.
Any other info / logs
Full log:
2019-12-19 11:49:10.789835: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1
2019-12-19 11:49:10.810583: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:10.811068: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1050 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.392
pciBusID: 0000:01:00.0
2019-12-19 11:49:10.811245: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2019-12-19 11:49:10.812226: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
2019-12-19 11:49:10.813094: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0
2019-12-19 11:49:10.813351: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0
2019-12-19 11:49:10.814559: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0
2019-12-19 11:49:10.815596: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0
2019-12-19 11:49:10.817880: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2019-12-19 11:49:10.818002: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:10.818618: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:10.819072: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2019-12-19 11:49:10.819465: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-12-19 11:49:10.843162: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2019-12-19 11:49:10.843971: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55724cd4c080 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2019-12-19 11:49:10.844031: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2019-12-19 11:49:10.926515: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:10.927095: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55724cdae1a0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2019-12-19 11:49:10.927112: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): GeForce GTX 1050 Ti, Compute Capability 6.1
2019-12-19 11:49:10.927282: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:10.927748: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1050 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.392
pciBusID: 0000:01:00.0
2019-12-19 11:49:10.927774: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2019-12-19 11:49:10.927782: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
2019-12-19 11:49:10.927790: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0
2019-12-19 11:49:10.927797: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0
2019-12-19 11:49:10.927804: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0
2019-12-19 11:49:10.927811: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0
2019-12-19 11:49:10.927819: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2019-12-19 11:49:10.927857: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:10.928283: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:10.928683: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2019-12-19 11:49:10.928703: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2019-12-19 11:49:10.929442: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-12-19 11:49:10.929452: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0
2019-12-19 11:49:10.929458: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N
2019-12-19 11:49:10.929674: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:10.930285: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:10.930708: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2934 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
WARNING:tensorflow:From /home/ds017/.pyenv/versions/takehome/lib/python3.6/site-packages/tensorflow_core/lite/python/convert_saved_model.py:60: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.loader.load or tf.compat.v1.saved_model.load. There will be a new function for importing SavedModels in Tensorflow 2.0.
2019-12-19 11:49:13.849255: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:13.849669: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1050 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.392
pciBusID: 0000:01:00.0
2019-12-19 11:49:13.849711: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2019-12-19 11:49:13.849721: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
2019-12-19 11:49:13.849729: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0
2019-12-19 11:49:13.849737: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0
2019-12-19 11:49:13.849744: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0
2019-12-19 11:49:13.849752: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0
2019-12-19 11:49:13.849759: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2019-12-19 11:49:13.849796: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:13.850140: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:13.850456: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2019-12-19 11:49:13.850478: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-12-19 11:49:13.850484: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0
2019-12-19 11:49:13.850488: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N
2019-12-19 11:49:13.850595: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:13.850985: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:13.851332: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2934 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
2019-12-19 11:49:16.749682: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:16.750076: I tensorflow/core/grappler/devices.cc:55] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0
2019-12-19 11:49:16.750135: I tensorflow/core/grappler/clusters/single_machine.cc:356] Starting new session
2019-12-19 11:49:16.750521: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:16.750902: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1050 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.392
pciBusID: 0000:01:00.0
2019-12-19 11:49:16.750927: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2019-12-19 11:49:16.750936: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
2019-12-19 11:49:16.750945: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0
2019-12-19 11:49:16.750952: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0
2019-12-19 11:49:16.750959: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0
2019-12-19 11:49:16.750966: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0
2019-12-19 11:49:16.750974: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2019-12-19 11:49:16.751007: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:16.751348: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:16.751663: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2019-12-19 11:49:16.751682: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-12-19 11:49:16.751688: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0
2019-12-19 11:49:16.751692: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N
2019-12-19 11:49:16.751831: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:16.752173: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:16.752495: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2934 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
2019-12-19 11:49:17.182263: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:786] Optimization results for grappler item: graph_to_optimize
2019-12-19 11:49:17.182289: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:788]   function_optimizer: function_optimizer did nothing. time = 0.003ms.
2019-12-19 11:49:17.182737: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:788]   function_optimizer: function_optimizer did nothing. time = 0ms.
WARNING:tensorflow:From /home/ds017/.pyenv/versions/takehome/lib/python3.6/site-packages/tensorflow_core/lite/python/util.py:249: convert_variables_to_constants (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.compat.v1.graph_util.convert_variables_to_constants`
WARNING:tensorflow:From /home/ds017/.pyenv/versions/takehome/lib/python3.6/site-packages/tensorflow_core/python/framework/graph_util_impl.py:277: extract_sub_graph (from tensorflow.python.framework.graph_util_impl) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.compat.v1.graph_util.extract_sub_graph`
2019-12-19 11:49:18.713005: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:18.713562: I tensorflow/core/grappler/devices.cc:55] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0
2019-12-19 11:49:18.713618: I tensorflow/core/grappler/clusters/single_machine.cc:356] Starting new session
2019-12-19 11:49:18.714027: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:18.714386: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1050 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.392
pciBusID: 0000:01:00.0
2019-12-19 11:49:18.714424: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0
2019-12-19 11:49:18.714433: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0
2019-12-19 11:49:18.714441: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0
2019-12-19 11:49:18.714448: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0
2019-12-19 11:49:18.714455: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0
2019-12-19 11:49:18.714462: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0
2019-12-19 11:49:18.714470: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7
2019-12-19 11:49:18.714501: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:18.714882: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:18.715214: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2019-12-19 11:49:18.715235: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-12-19 11:49:18.715242: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0
2019-12-19 11:49:18.715261: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N
2019-12-19 11:49:18.715411: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:18.715760: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-19 11:49:18.716119: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2934 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1050 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
2019-12-19 11:49:20.750755: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:786] Optimization results for grappler item: graph_to_optimize
2019-12-19 11:49:20.750831: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:788]   constant_folding: Graph size after: 1230 nodes (-2206), 1329 edges (-2360), time = 1332.40198ms.
2019-12-19 11:49:20.750850: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:788]   constant_folding: Graph size after: 1230 nodes (0), 1329 edges (0), time = 370.552ms.
Traceback (most recent call last):
  File "saved2lite.py", line 34, in <module>
    tflite_model = converter.convert()
  File "/home/ds017/.pyenv/versions/takehome/lib/python3.6/site-packages/tensorflow_core/lite/python/lite.py", line 993, in convert
    inference_output_type)
  File "/home/ds017/.pyenv/versions/takehome/lib/python3.6/site-packages/tensorflow_core/lite/python/lite.py", line 239, in _calibrate_quantize_model
    inference_output_type, allow_float)
  File "/home/ds017/.pyenv/versions/takehome/lib/python3.6/site-packages/tensorflow_core/lite/python/optimize/calibrator.py", line 78, in calibrate_and_quantize
    np.dtype(output_type.as_numpy_dtype()).num, allow_float)
  File "/home/ds017/.pyenv/versions/takehome/lib/python3.6/site-packages/tensorflow_core/lite/python/optimize/tensorflow_lite_wrap_calibration_wrapper.py", line 115, in QuantizeModel
    return _tensorflow_lite_wrap_calibration_wrapper.CalibrationWrapper_QuantizeModel(self, input_py_type, output_py_type, allow_float)
RuntimeError: Invalid quantization params for op MAXIMUM at index 4 in subgraph 0

@miaout17 Could you please take a look?

Hi There,
We are checking to see if you still need help on this, as you are using an older version of tensorflow which is officially considered end of life . We recommend that you upgrade to the latest 2.x version and let us know if the issue still persists in newer versions. Please open a new issue for any help you need against 2.x, and we will get you the right help.
This issue will be closed automatically 7 days from now. If you still need help with this issue, please provide us with more information.


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It's in there in the latest release :)

Simply great. saw it already! :)


Consensus block entry saving calling append entry with delayed indexes

When there is a new block entry to be sent to append entry log on append_entry_to_peer, it is always being called with around 20 indexes of delay as shown in the below example log. This suggests there might be some underlying operation that is taking significant duration and is causing retries and overwrite of unnecessary log entries frequently.
2024-07-15T14:38:28.992908Z  INFO stratus::eth::consensus: sending transaction executions to peer peer_clone.next_index=0 peer_clone.match_index=0
2024-07-15T14:38:28.992930Z  INFO stratus::eth::consensus: appending entry to peer: current_term: 1, target_index: 771, next_index: 0
2024-07-15T14:38:28.992949Z  INFO stratus::eth::consensus: appending entry to peer: current_term: 1, prev_log_term: 1, prev_log_index: 769, target_index: 771, next_index: 770
2024-07-15T14:38:28.992958Z  INFO stratus::eth::consensus: sending append request. term: 1, prev_log_index: 769, prev_log_term: 1
2024-07-15T14:38:28.993621Z  INFO stratus::eth::consensus: successfully appended entry to peer: match_index: 770, next_index: 771
2024-07-15T14:38:29.006699Z  INFO stratus::eth::miner::miner::interval_miner: interval mining block lag_us=1444
2024-07-15T14:38:29.006759Z  INFO miner::commit{block_number="#31"}: stratus::eth::miner::miner: commiting block block_number=#31 transactions_len=0
2024-07-15T14:38:29.006933Z  INFO stratus::eth::consensus: Leader received block to send to followers number=31
2024-07-15T14:38:29.007004Z  INFO stratus::eth::consensus: sending block to peer: peer.match_index: 747, peer.next_index: 748
2024-07-15T14:38:29.007017Z  INFO stratus::eth::consensus: appending entry to peer: current_term: 1, target_index: 772, next_index: 748
2024-07-15T14:38:29.007036Z  INFO stratus::eth::consensus: appending entry to peer: current_term: 1, prev_log_term: 1, prev_log_index: 747, target_index: 772, next_index: 748
2024-07-15T14:38:29.007044Z  INFO stratus::eth::consensus: sending append request. term: 1, prev_log_index: 747, prev_log_term: 1
2024-07-15T14:38:29.007584Z  INFO stratus::eth::consensus: successfully appended entry to peer: match_index: 748, next_index: 749
2024-07-15T14:38:29.007597Z  INFO stratus::eth::consensus: appending entry to peer: current_term: 1, prev_log_term: 1, prev_log_index: 748, target_index: 772, next_index: 749
2024-07-15T14:38:29.007605Z  INFO stratus::eth::consensus: sending append request. term: 1, prev_log_index: 748, prev_log_term: 1
2024-07-15T14:38:29.007930Z  INFO stratus::eth::consensus: successfully appended entry to peer: match_index: 749, next_index: 750

How to reproduce:
Run the below command and observe this log line compared to the other index lines:
just run-chaos-experiment stratus 2 1 false leader-election

We've shelved raft for now, closing.


Evaluate savevars early

In g:startify_session_savevars I have a variable that contains the actual layout of all windows, but the value written in session file always contained an extra window what I didn't expect (that window had the session file opened).
The problem is that split triggers BufWinEnter/Leave auto commands that I use to update my variable:
https://github.com/mhinz/vim-startify/blob/53c8799ad035ce1649d4bc6ab6cd4297d304a374/autoload/startify.vim#L302
This PR tries to evaluate savevars as early as possible.

Thanks! ✨


[RevEng] Updating some existing logging messages plus adding Trace messages

For the SQL Server and Sqlite RevEng providers. Fix for #3456.

@Providers. This doesn't break you, but you might want to consider providing similar logging messages in your own providers.

cc @natemcmaster - you'll probably want to check the changes to the sqlite provider.

I'm concerned we are over-logging. Unless I'm mistaken, I believe all of these trace level log messages will appear when a user executes commands with -Verbose. @bricelam can you verify what commands logging does with trace level messages?

Trace will only be written if --verbose is specified. Here is the console and PowerShell loggers.

@natemcmaster I've force-pushed with the updated changes.

Nice! :shipit:

Checked in with commit 2d14e47.


e2e master is broken

https://jenkins-etcd.prod.coreos.systems/view/operator/job/etcd-operator-master/563/console
https://jenkins-etcd.prod.coreos.systems/view/operator/job/etcd-operator-master/564/console

So I run stress testing on e2e and it seems fine. Above failures is temporary. Closing this now


Why We Captured the Damage Attribute From Source?

On line 21 of GSDamageExecutionCalc.cpp
DEFINE_ATTRIBUTE_CAPTUREDEF(UGSAttributeSetBase, Damage, Source, true);
According to your description, 'Damage' is a meta attribute, it can distribute the value to own attributes, Eg. health or armor.
Well, my question is why we need to captured the 'Damage' from the 'source'?
I think 'Source' is the GE's instigator. If  A was injured by B, Then Why A need to care about B's meta attribute 'Damage'.

A doesn't know about B's meta attribute Damage. All sources of damage in the damage GE are collected into B's Damage attribute. The server then takes this value, does whatever calculations it needs to, then outputs it to A's Damage attribute. A then reads from its own Damage attribute.
This is GAS convention initially copied from Epic's ActionRPG sample project. Epic's Lyra fixes a potential bug by creating a separate outgoing damage meta attribute BaseDamage, but otherwise follows the same pattern of reading the damage meta attribute from the source.


clipboard not work

Hello. I'm using the latest versions of primefaces and when I enabled the security option primefaces.CSP it stopped working <p: ajax event = "success" listener = "# {bean.successListener}" />.
the option to copy if it works but not the listener.

I will investigate. Thanks for reporting.

Hi, try onclick = "return false;", and it worked.
I'm sorry I wasted your time


Optimizing with given initial points

Hi everyone.
I would like to apply the command "skopt.optimizer()" to minimize a function with given initial points.
However, I could not find any option for inserting given initial values for parameters as can be seen in the following:
class skopt.optimizer.Optimizer(dimensions, base_estimator='gp', n_random_starts=None, n_initial_points=10, initial_point_generator='random', n_jobs=1, acq_func='gp_hedge', acq_optimizer='auto', random_state=None, model_queue_size=None, acq_func_kwargs=None, acq_optimizer_kwargs=None)
Would you please let me know how to insert initial values using the command.
Thanks

I believe you can tell the optimizer instance about your initial points using its tell method. I think initialization requires an integer n_initial_points > 0, but as long as you tell it more observations than that number, you should be good to expect a next parameter set that is determined by the acquisition function and not the initial point generation scheme.


Manage LCPL file (EPUB acquisition / download from LCP license rel=publication link)

Download publication linked to this LCPL file

I believe this is already implemented. To be confirmed.

implemented.


Add support for preprocessors to optimize datashade operations

When datashading one or more elements the data usually has to be converted into a format that can be passed to datashader, e.g. a path consisting of a number of NumPy arrays has to be concatenated or a trimesh has to merge the vertices and simplexes. These concatenation or merge operations can often be slow so it is not ideal for them to be run on every zoom event when interacting with a datashaded plot.
It would therefore be desirable for rasterization operations to cache the result of these concatenation/merge operations so they can be reused. This could fairly easily be achieved by adding some preprocessors to these operations that convert the data into a format that can be datashaded directly.

Now implemented.


PackageId:CPython39.Exe.x64;PackageAction:Install;ReturnCode:1603;

This issue has been moved from a ticket on Developer Community.

Describe your issue here
===DO NOT EDIT BELOW THIS LINE===
PackageId:CPython39.Exe.x64;PackageAction:Install;ReturnCode:1603;

Original Comments
Dominik Sobota on 12/5/2022, 03:12 PM:
(private comment, text removed)
Feedback Bot on 12/5/2022, 07:17 PM:
(private comment, text removed)
Graham Wheeler [MSFT] on 12/14/2022, 02:55 PM:
(private comment, text removed)

Original Solutions
(no solutions)

duplicate of https://github.com/microsoft/PTVS/issues/7238


Documentation around containerTemplate and events

Currently the documentation around containerTemplate can be confusing to new users (it was for me).  The examples given don't reproduce behavior similar to the defaultContainerTemplate, and omit some details that would probably be necessary for any production use of this feature.
A few suggestions to improve:

Specify what is in the opts parameter.  Most importantly, event is not in the documentation, and that is crucial to swapping the prerender frame out for the main one.
Document what props are passed in.  For example, cspNonce is used in the default template, some documentation around this would be appreciated.
Document the different types of events and when they fire.  I saw there was another question around RESIZE that probably could've been answered by this documentation.
Ideally, the examples given would reproduce the behavior of the default template.  Alternatively, it might be good to include the default template right there as a more robust sample, instead of having to dig into the source.

I'd be happy to help out with updating this, however I'm still new to this library so I might need some guidance on some of this.

#243

Thanks for taking care of this one!


Discovery doesn't show waiting when using .enableRegistration

When adding a device the discovery shows a loading screen. when using .enableRegistration the loading screen is not shown but states no devices found. when devices are found the display is updated accordingly without user interaction.
https://www.youtube.com/watch?v=-6ypaMicnhE

The current version of .enableRegistration currently has a bug, I'm hopping we can fix it on the SDK side for the @next release (which should still be compatible with previous Brain versions).
There will also be an example of how to use it!


x/tools/cmd/goimports: does not handle package unsafe properly

Please answer these questions before submitting your issue. Thanks!
I believe goimports does not automatically include package unsafe even if referenced in the source, which is fine, but when package unsafe is imported it can confuse goimports and produces syntactically correct, but unexpected output, such as:
import (
	"reflect"
	"unsafe"
)
import "fmt"

What version of Go are you using (go version)?

go version go1.6 linux/amd64

What operating system and processor architecture are you using (go env)?

GOARCH="amd64"
GOBIN=""
GOEXE=""
GOHOSTARCH="amd64"
GOHOSTOS="linux"
GOOS="linux"
GORACE=""
GOROOT="/usr/lib/google-golang"
GOTOOLDIR="/usr/lib/google-golang/pkg/tool/linux_amd64"
GO15VENDOREXPERIMENT="1"
CC="gcc"
GOGCCFLAGS="-fPIC -m64 -pthread -fmessage-length=0 -gno-record-gcc-switches -fdebug-prefix-map=/tmp/go-build262021310=/tmp/go-build"
CXX="g++"
CGO_ENABLED="1"

What did you do?

Ran goimports on the source code http://play.golang.org/p/Ppc325UINU
package code

import "unsafe"
import "fmt"

var _ = fmt.Print
var _ = unsafe.Sizeof
var _ = reflect.Value

What did you expect to see?

http://play.golang.org/p/ySR9QrdVlp
package code

import (
	"fmt"
	"reflect"
	"unsafe"
)

var _ = fmt.Print
var _ = unsafe.Sizeof
var _ = reflect.Value

What did you see instead?

http://play.golang.org/p/i49dfImPxA
package code

import (
	"reflect"
	"unsafe"
)
import "fmt"

var _ = fmt.Print
var _ = unsafe.Sizeof
var _ = reflect.Value

CC @bradfitz

This is a duplicate of #14075.
There is a fix waiting for review: https://golang.org/cl/19828.