Illegal memory address ошибка

Здравствуйте! Помогите пожалуйста разобраться в проблеме.

Используя фреймворк IRIS написал REST службу. Использую 1.11.5 версию Golang. При обращении к определенному URL пытаюсь вернуть все данные из таблицы reports (база данных PostgreSQL) c помощью библиотеки GORM. В момент запроса к базе данных возникает ошибка:

[WARN] 2019/06/06 13:31 Recovered from a route's Handler('constructor/controllers.glob..func1')
At Request: 200 /api/report/d5f9c639-13e6-42c1-9043-30783981724b GET ::1
Trace: runtime error: invalid memory address or nil pointer dereference

C:/Go/src/runtime/asm_amd64.s:522
C:/Go/src/runtime/panic.go:513
C:/Go/src/runtime/panic.go:82
C:/Go/src/runtime/signal_windows.go:204
C:/Users/NNogerbek/go/src/github.com/jinzhu/gorm/main.go:768
C:/Users/NNogerbek/go/src/github.com/jinzhu/gorm/main.go:179
C:/Users/NNogerbek/go/src/github.com/jinzhu/gorm/main.go:322
C:/Users/NNogerbek/go/src/constructor/controllers/report.go:14
C:/Users/NNogerbek/go/src/github.com/kataras/iris/context/context.go:1208
C:/Users/NNogerbek/go/src/github.com/kataras/iris/context/context.go:1217
C:/Users/NNogerbek/go/src/github.com/kataras/iris/middleware/logger/logger.go:50
C:/Users/NNogerbek/go/src/github.com/kataras/iris/middleware/logger/logger.go:31
C:/Users/NNogerbek/go/src/github.com/kataras/iris/context/context.go:1208
C:/Users/NNogerbek/go/src/github.com/kataras/iris/context/context.go:1217
C:/Users/NNogerbek/go/src/github.com/kataras/iris/middleware/recover/recover.go:56
C:/Users/NNogerbek/go/src/github.com/kataras/iris/context/context.go:922
C:/Users/NNogerbek/go/src/github.com/kataras/iris/context/context.go:1094
C:/Users/NNogerbek/go/src/github.com/kataras/iris/core/router/handler.go:227
C:/Users/NNogerbek/go/src/github.com/kataras/iris/core/router/router.go:84
C:/Users/NNogerbek/go/src/github.com/kataras/iris/core/router/router.go:161
C:/Go/src/net/http/server.go:2741
C:/Go/src/net/http/server.go:1847
C:/Go/src/runtime/asm_amd64.s:1333

Ниже представлен полный код проекта. Подскажите пожалуйста в каком месте сделал упущение?

main.go:

package main

import (
	"constructor/database"
	"constructor/routes"
	"github.com/joho/godotenv"
	"github.com/kataras/iris"
)

func main()  {
	// Check the connection to remote PostgreSQL database with the help of the "gorm".
	database.ConnectPostgreSQL()
	defer database.DisconnectPostgreSQL()

	// Create an application with default logger and recovery middleware.
	application := iris.Default()

	// Define the list of all available routes of the application.
	routes.Handle(application)

	// Start to listen and serve the application.
	application.Run(iris.Addr(":8000" + port), iris.WithoutServerError(iris.ErrServerClosed))
}

database.go:

package database

import (
	"constructor/utils"
	"fmt"
	"github.com/jinzhu/gorm"
	_ "github.com/jinzhu/gorm/dialects/postgres"
)

var DBGORM *gorm.DB

func ConnectPostgreSQL() {
	// Initialize variables related with the remote PostgreSQL database.
	databaseUser := utils.CheckEnvironmentVariable("PostgreSQL_USER")
	databasePassword := utils.CheckEnvironmentVariable("PostgreSQL_PASSWORD")
	databaseHost := utils.CheckEnvironmentVariable("PostgreSQL_HOST")
	databaseName := utils.CheckEnvironmentVariable("PostgreSQL_DATABASE_NAME")

	// Define the connection string for the remote PostgreSQL database with the help of the "gorm" package.
	databaseURL:= fmt.Sprintf("host=%s user=%s dbname=%s password=%s sslmode=disable", databaseHost, databaseUser, databaseName, databasePassword)

	// Create connection pool to remote PostgreSQL database with the help of the "gorm" package.
	DBGORM, err := gorm.Open("postgres", databaseURL); if err != nil {
		utils.Logger().Println(err)
		panic(err)
	}

	// Ping the remote PostgreSQL database with the help of "gorm" package.
	err = DBGORM.DB().Ping(); if err != nil {
		utils.Logger().Println(err)
		panic(err)
	}

	// Enable logging mode of "gorm" package.
	DBGORM.LogMode(true)
}

func DisconnectPostgreSQL() error {
	return DBGORM.Close()
}

routes.go:

package routes

import (
	"constructor/controllers"
	"github.com/kataras/iris"
)

func Handle(application *iris.Application)  {
	application.Get("/api/report/{report_id:string}", controllers.GetReport)
}

controllers/report.go:

package controllers
import (
	"constructor/database"
	"constructor/utils"
	"github.com/kataras/iris"
)

type Report struct {
	ID int `gorm:"primary_key" json:"report_id"`
	Name string `json:"report_name"`
}

var GetReport = func(ctx iris.Context) {
	// Initialize variable.
	reportID := ctx.Params().Get("report_id")

	if utils.IsValidUUID(reportID) {
		var reports []Report
		database.DBGORM.Find(&reports) // <-ERROR
		ctx.JSON(reports)
	} else {
		ctx.StatusCode(iris.StatusBadRequest)
		ctx.JSON(iris.Map{"STATUS": "ERROR", "DESCRIPTION": "BAD REQUEST",})
	}
}

I am trying to setup a deep learning machine with dual rtx 3070 gpus in Ubuntu 20.04. I have installed Nvidia drivers 460,CUDA 11.2 and Cudnn 8.1. When i try to test the gpu with a sample tensorflow code i am getting CUDA_ERROR_ILLEGAL_ADDRESS on both GPUs. Can someone let me know what the issue is?

Hitting this issue in Python3.8 and 3.9 and also in tensorflow 2.5.0,2.9.0

Mon Jun 27 14:10:22 2022
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 460.91.03    Driver Version: 460.91.03    CUDA Version: 11.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  GeForce RTX 3070    Off  | 00000000:09:00.0 Off |                  N/A |
| 57%   46C    P8    28W / 270W |     15MiB /  7979MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+
|   1  GeForce RTX 3070    Off  | 00000000:0A:00.0 Off |                  N/A |
|  0%   48C    P8    23W / 270W |      5MiB /  7982MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A      1264      G   /usr/lib/xorg/Xorg                  9MiB |
|    0   N/A  N/A      1463      G   /usr/bin/gnome-shell                3MiB |
|    1   N/A  N/A      1264      G   /usr/lib/xorg/Xorg                  4MiB |
+-----------------------------------------------------------------------------+

nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2021 NVIDIA Corporation
Built on Sun_Feb_14_21:12:58_PST_2021
Cuda compilation tools, release 11.2, V11.2.152
Build cuda_11.2.r11.2/compiler.29618528_0

2022-06-27 13:59:11.843491: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0
2022-06-27 13:59:12.901104: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcuda.so.1
2022-06-27 13:59:12.943243: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-06-27 13:59:12.943685: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties:
pciBusID: 0000:09:00.0 name: GeForce RTX 3070 computeCapability: 8.6
coreClock: 1.815GHz coreCount: 46 deviceMemorySize: 7.79GiB deviceMemoryBandwidth: 417.29GiB/s
2022-06-27 13:59:12.943725: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-06-27 13:59:12.944127: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 1 with properties:
pciBusID: 0000:0a:00.0 name: GeForce RTX 3070 computeCapability: 8.6
coreClock: 1.815GHz coreCount: 46 deviceMemorySize: 7.79GiB deviceMemoryBandwidth: 417.29GiB/s
2022-06-27 13:59:12.944141: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0
2022-06-27 13:59:12.945421: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11
2022-06-27 13:59:12.945447: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11
2022-06-27 13:59:12.945900: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcufft.so.10
2022-06-27 13:59:12.946021: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcurand.so.10
2022-06-27 13:59:12.946360: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusolver.so.11
2022-06-27 13:59:12.946647: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcusparse.so.11
2022-06-27 13:59:12.946717: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudnn.so.8
2022-06-27 13:59:12.946758: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-06-27 13:59:12.947192: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-06-27 13:59:12.947610: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-06-27 13:59:12.948028: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-06-27 13:59:12.948421: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0, 1
2022-06-27 13:59:12.948662: 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 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2022-06-27 13:59:13.250592: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-06-27 13:59:13.250986: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 0 with properties:
pciBusID: 0000:09:00.0 name: GeForce RTX 3070 computeCapability: 8.6
coreClock: 1.815GHz coreCount: 46 deviceMemorySize: 7.79GiB deviceMemoryBandwidth: 417.29GiB/s
2022-06-27 13:59:13.251025: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-06-27 13:59:13.251384: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1733] Found device 1 with properties:
pciBusID: 0000:0a:00.0 name: GeForce RTX 3070 computeCapability: 8.6
coreClock: 1.815GHz coreCount: 46 deviceMemorySize: 7.79GiB deviceMemoryBandwidth: 417.29GiB/s
2022-06-27 13:59:13.251420: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-06-27 13:59:13.251794: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-06-27 13:59:13.252260: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-06-27 13:59:13.252633: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-06-27 13:59:13.252984: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1871] Adding visible gpu devices: 0, 1
2022-06-27 13:59:13.253013: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcudart.so.11.0
2022-06-27 13:59:13.721007: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1258] Device interconnect StreamExecutor with strength 1 edge matrix:
2022-06-27 13:59:13.721033: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1264]      0 1
2022-06-27 13:59:13.721041: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 0:   N N
2022-06-27 13:59:13.721045: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1277] 1:   N N
2022-06-27 13:59:13.721180: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-06-27 13:59:13.721614: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-06-27 13:59:13.722002: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-06-27 13:59:13.722382: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-06-27 13:59:13.722757: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-06-27 13:59:13.723125: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6114 MB memory) -> physical GPU (device: 0, name: GeForce RTX 3070, pci bus id: 0000:09:00.0, compute capability: 8.6)
2022-06-27 13:59:13.723332: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:937] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2022-06-27 13:59:13.723699: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1418] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:1 with 6126 MB memory) -> physical GPU (device: 1, name: GeForce RTX 3070, pci bus id: 0000:0a:00.0, compute capability: 8.6)
2022-06-27 13:59:14.182153: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:176] None of the MLIR Optimization Passes are enabled (registered 2)
2022-06-27 13:59:14.201622: I tensorflow/core/platform/profile_utils/cpu_utils.cc:114] CPU Frequency: 3700310000 Hz
Epoch 1/10
2022-06-27 13:59:14.370438: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublas.so.11
2022-06-27 13:59:14.763996: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Successfully opened dynamic library libcublasLt.so.11
2022-06-27 13:59:14.764035: I tensorflow/stream_executor/cuda/cuda_blas.cc:1838] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.
1563/1563 [==============================] - 3s 1ms/step - loss: 1.8131 - accuracy: 0.3549
Epoch 2/10
 500/1563 [========>.....................] - ETA: 1s - loss: 1.6540 - accuracy: 0.4167Traceback (most recent call last):
  File "/home/vicky/testtf/testf.py", line 24, in <module>
    model_gpu.fit(X_train_scaled, y_train_encoded, epochs = 10)
  File "/home/vicky/testtf/tf/lib/python3.9/site-packages/tensorflow/python/keras/engine/training.py", line 1188, in fit
    callbacks.on_train_batch_end(end_step, logs)
  File "/home/vicky/testtf/tf/lib/python3.9/site-packages/tensorflow/python/keras/callbacks.py", line 457, in on_train_batch_end
    self._call_batch_hook(ModeKeys.TRAIN, 'end', batch, logs=logs)
  File "/home/vicky/testtf/tf/lib/python3.9/site-packages/tensorflow/python/keras/callbacks.py", line 317, in _call_batch_hook
    self._call_batch_end_hook(mode, batch, logs)
  File "/home/vicky/testtf/tf/lib/python3.9/site-packages/tensorflow/python/keras/callbacks.py", line 337, in _call_batch_end_hook
    self._call_batch_hook_helper(hook_name, batch, logs)
  File "/home/vicky/testtf/tf/lib/python3.9/site-packages/tensorflow/python/keras/callbacks.py", line 375, in _call_batch_hook_helper
    hook(batch, logs)
  File "/home/vicky/testtf/tf/lib/python3.9/site-packages/tensorflow/python/keras/callbacks.py", line 1029, in on_train_batch_end
    self._batch_update_progbar(batch, logs)
  File "/home/vicky/testtf/tf/lib/python3.9/site-packages/tensorflow/python/keras/callbacks.py", line 1101, in _batch_update_progbar
    logs = tf_utils.sync_to_numpy_or_python_type(logs)
  File "/home/vicky/testtf/tf/lib/python3.9/site-packages/tensorflow/python/keras/utils/tf_utils.py", line 519, in sync_to_numpy_or_python_type
    return nest.map_structure(_to_single_numpy_or_python_type, tensors)
  File "/home/vicky/testtf/tf/lib/python3.9/site-packages/tensorflow/python/util/nest.py", line 867, in map_structure
    structure[0], [func(*x) for x in entries],
  File "/home/vicky/testtf/tf/lib/python3.9/site-packages/tensorflow/python/util/nest.py", line 867, in <listcomp>
    structure[0], [func(*x) for x in entries],
  File "/home/vicky/testtf/tf/lib/python3.9/site-packages/tensorflow/python/keras/utils/tf_utils.py", line 515, in _to_single_numpy_or_python_type
    x = t.numpy()
  File "/home/vicky/testtf/tf/lib/python3.9/site-packages/tensorflow/python/framework/ops.py", line 1094, in numpy
    maybe_arr = self._numpy()  # pylint: disable=protected-access
  File "/home/vicky/testtf/tf/lib/python3.9/site-packages/tensorflow/python/framework/ops.py", line 1062, in _numpy
    six.raise_from(core._status_to_exception(e.code, e.message), None)  # pylint: disable=protected-access
  File "<string>", line 3, in raise_from
tensorflow.python.framework.errors_impl.InternalError: Could not synchronize CUDA stream: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered

This is the sample code i am running

import tensorflow as tf
from tensorflow import keras
import numpy as np
(X_train, y_train), (X_test, y_test) = keras.datasets.cifar10.load_data()
# scaling image values between 0-1
X_train_scaled = X_train/255
X_test_scaled = X_test/255
# one hot encoding labels
y_train_encoded = keras.utils.to_categorical(y_train, num_classes = 10, dtype = 'float32')
y_test_encoded = keras.utils.to_categorical(y_test, num_classes = 10, dtype = 'float32')
def get_model():
    model = keras.Sequential([
        keras.layers.Flatten(input_shape=(32,32,3)),
        keras.layers.Dense(3000, activation='relu'),
        keras.layers.Dense(1000, activation='relu'),
        keras.layers.Dense(10, activation='sigmoid')
    ])
    model.compile(optimizer='SGD',
              loss='categorical_crossentropy',
              metrics=['accuracy'])
    return model
with tf.device('/GPU:0'):
    model_gpu = get_model()
    model_gpu.fit(X_train_scaled, y_train_encoded, epochs = 10)

windows10 pro, NVIDIA GeForce RTX 2080 SUPER, GPU Memory2,4/39,9 GB, deeplabcut2.2b8

Hi all,

So I am a bit frustrated with this error I am getting. I start to train the network and then suddenly I get this weird error (below). It doesn’t matter if the iteration was 24,000 or 500, I get it over and over again.

 `Selecting multi-animal trainer
Config:
{'all_joints': [[0], [1], [2], [3], [4]],
 'all_joints_names': ['snout', 'ear1', 'ear2', 'tail base', 'tail end'],
 'batch_size': 8,
 'crop_pad': 0,
 'cropratio': 0.4,
 'dataset': 'training-datasets\iteration-0\UnaugmentedDataSet_MULTINov29\MULTI_ornaim95shuffle5.pickle',
 'dataset_type': 'multi-animal-imgaug',
 'deterministic': False,
 'display_iters': 500,
 'fg_fraction': 0.25,
 'global_scale': 0.8,
 'init_weights': 'C:\Users\orna\.conda\envs\dlc-gpu\lib\site-packages\deeplabcut\pose_estimation_tensorflow\models\pretrained\resnet_v1_50.ckpt',
 'intermediate_supervision': False,
 'intermediate_supervision_layer': 12,
 'location_refinement': True,
 'locref_huber_loss': True,
 'locref_loss_weight': 0.05,
 'locref_stdev': 7.2801,
 'log_dir': 'log',
 'max_input_size': 1500,
 'mean_pixel': [123.68, 116.779, 103.939],
 'metadataset': 'training-datasets\iteration-0\UnaugmentedDataSet_MULTINov29\Documentation_data-MULTI_95shuffle5.pickle',
 'min_input_size': 64,
 'mirror': False,
 'multi_step': [[0.0001, 7500], [5e-05, 12000], [1e-05, 200000]],
 'net_type': 'resnet_50',
 'num_joints': 5,
 'num_limbs': 10,
 'optimizer': 'adam',
 'pafwidth': 20,
 'pairwise_huber_loss': False,
 'pairwise_loss_weight': 0.1,
 'pairwise_predict': False,
 'partaffinityfield_graph': [[0, 1],
                             [1, 2],
                             [1, 3],
                             [1, 4],
                             [2, 3],
                             [0, 4],
                             [2, 4],
                             [0, 3],
                             [3, 4],
                             [0, 2]],
 'partaffinityfield_predict': True,
 'pos_dist_thresh': 17,
 'project_path': 'C:\Users\orna\Desktop\dlc_projects\MULTI-ornaim-2020-11-29',
 'regularize': False,
 'rotation': 25,
 'rotratio': 0.4,
 'save_iters': 10000,
 'scale_jitter_lo': 0.5,
 'scale_jitter_up': 1.25,
 'scoremap_dir': 'test',
 'shuffle': True,
 'snapshot_prefix': 'C:\Users\orna\Desktop\dlc_projects\MULTI-ornaim-2020-11-29\dlc-models\iteration-0\MULTINov29-trainset95shuffle5\train\snapshot',
 'stride': 8.0,
 'weigh_negatives': False,
 'weigh_only_present_joints': False,
 'weigh_part_predictions': False,
 'weight_decay': 0.0001}
Activating limb prediction...
Starting with multi-animal imaug + adam pose-dataset loader.
Batch Size is 8
Getting specs multi-animal-imgaug 10 5
Initializing ResNet
Loading ImageNet-pretrained resnet_50
2020-11-29 20:42:42.928549: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2020-11-29 20:42:43.043267: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties:
name: GeForce RTX 2080 SUPER major: 7 minor: 5 memoryClockRate(GHz): 1.815
pciBusID: 0000:01:00.0
totalMemory: 8.00GiB freeMemory: 6.55GiB
2020-11-29 20:42:43.047534: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0
2020-11-29 20:42:43.316876: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:
2020-11-29 20:42:43.319096: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0
2020-11-29 20:42:43.320361: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N
2020-11-29 20:42:43.321758: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6279 MB memory) -> physical GPU (device: 0, name: GeForce RTX 2080 SUPER, pci bus id: 0000:01:00.0, compute capability: 7.5)
Max_iters overwritten as 15000
Display_iters overwritten as 10
Save_iters overwritten as 500
Training parameters:
{'stride': 8.0, 'weigh_part_predictions': False, 'weigh_negatives': False, 'fg_fraction': 0.25, 'mean_pixel': [123.68, 116.779, 103.939], 'shuffle': True, 'snapshot_prefix': 'C:\Users\orna\Desktop\dlc_projects\MULTI-ornaim-2020-11-29\dlc-models\iteration-0\MULTINov29-trainset95shuffle5\train\snapshot', 'log_dir': 'log', 'global_scale': 0.8, 'location_refinement': True, 'locref_stdev': 7.2801, 'locref_loss_weight': 0.05, 'locref_huber_loss': True, 'optimizer': 'adam', 'intermediate_supervision': False, 'intermediate_supervision_layer': 12, 'regularize': False, 'weight_decay': 0.0001, 'crop_pad': 0, 'scoremap_dir': 'test', 'batch_size': 8, 'dataset_type': 'multi-animal-imgaug', 'deterministic': False, 'mirror': False, 'pairwise_huber_loss': False, 'weigh_only_present_joints': False, 'partaffinityfield_predict': True, 'pairwise_predict': True, 'all_joints': [[0], [1], [2], [3], [4]], 'all_joints_names': ['snout', 'ear1', 'ear2', 'tail base', 'tail end'], 'cropratio': 0.4, 'dataset': 'training-datasets\iteration-0\UnaugmentedDataSet_MULTINov29\MULTI_ornaim95shuffle5.pickle', 'display_iters': 500, 'init_weights': 'C:\Users\orna\.conda\envs\dlc-gpu\lib\site-packages\deeplabcut\pose_estimation_tensorflow\models\pretrained\resnet_v1_50.ckpt', 'max_input_size': 1500, 'metadataset': 'training-datasets\iteration-0\UnaugmentedDataSet_MULTINov29\Documentation_data-MULTI_95shuffle5.pickle', 'min_input_size': 64, 'multi_step': [[0.0001, 7500], [5e-05, 12000], [1e-05, 200000]], 'net_type': 'resnet_50', 'num_joints': 5, 'num_limbs': 10, 'pafwidth': 20, 'pairwise_loss_weight': 0.1, 'partaffinityfield_graph': [[0, 1], [1, 2], [1, 3], [1, 4], [2, 3], [0, 4], [2, 4], [0, 3], [3, 4], [0, 2]], 'pos_dist_thresh': 17, 'project_path': 'C:\Users\orna\Desktop\dlc_projects\MULTI-ornaim-2020-11-29', 'rotation': 25, 'rotratio': 0.4, 'save_iters': 10000, 'scale_jitter_lo': 0.5, 'scale_jitter_up': 1.25}
Starting multi-animal training....
iteration: 10 loss: 0.3964 scmap loss: 0.3097 locref loss: 0.0162 limb loss: 0.0704 lr: 0.0001
iteration: 20 loss: 0.0842 scmap loss: 0.0600 locref loss: 0.0052 limb loss: 0.0190 lr: 0.0001
...
iteration: 990 loss: 0.0153 scmap loss: 0.0141 locref loss: 0.0003 limb loss: 0.0009 lr: 0.0001
iteration: 1000 loss: 0.0146 scmap loss: 0.0135 locref loss: 0.0003 limb loss: 0.0008 lr: 0.0001
2020-11-29 20:45:27.262728: E tensorflow/stream_executor/cuda/cuda_event.cc:48] Error polling for event status: failed to query event: CUDA_ERROR_ILLEGAL_ADDRESS: an illegal memory access was encountered
2020-11-29 20:45:27.266438: F tensorflow/core/common_runtime/gpu/gpu_event_mgr.cc:274] Unexpected Event status: 1`

What could be the case?
Thanks!

Почему эта программа паникует?

type Point struct {
    X, Y float64
}

func (p *Point) Abs() float64 {
    return math.Sqrt(p.X*p.X + p.Y*p.Y)
}

func main() {
    var p *Point
    fmt.Println(p.Abs())
}

panic: runtime error: invalid memory address or nil pointer dereference
[signal SIGSEGV: segmentation violation code=0xffffffff addr=0x0 pc=0xd2c5a]

goroutine 1 [running]:
main.(*Point).Abs(...)
 ../main.go:6
main.main()
 ../main.go:11 +0x1a

Ответ

Неинициализированный указатель p в main функции равен nil, и вы не можете следовать за нулевым указателем.

Если x равен nil, попытка выполнить *x вызовет панику во время выполнения. Спецификация языка программирования Go: адресные операторы.

Вам необходимо создать Point. С помощью new:

func main() {
    var p *Point = new(Point)
    fmt.Println(p.Abs())
}

Либо с помощи литерала с оператором ссылки:

func main() {
    var p *Point = &Point{}
    fmt.Println(p.Abs())
}

Поскольку методы с получателями указателей принимают либо значение, либо указатель, вы также можете вообще пропустить указатель:

func main() {
    var p Point // имеет нулевое значение Point{X:0, Y:0}
    fmt.Println(p.Abs())
}

Читайте также:

• Спецификация Go: типы указателя (pointer types), типы функций (function types)
• Go FAQ: Следует ли определять методы для значений или указателей?
• Основы Go: указатели
• Указатели по-простому в Golang