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import copy
import logging

import numpy as np
import torch
from PIL import Image, ImageDraw
from detectron2.data import MetadataCatalog
from detectron2.evaluation import DatasetEvaluator
from detectron2.structures import Instances
from detectron2.utils import comm

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@ROI_MASK_HEAD_REGISTRY.register()
class CustomMaskRCNNConvUpsampleHead(MaskRCNNConvUpsampleHead):

    @configurable
    def __init__(self, input_shape: ShapeSpec, *, num_classes, conv_dims, conv_norm="", **kwargs):
        super().__init__(input_shape, num_classes=num_classes, conv_dims=conv_dims, conv_norm=conv_norm, **kwargs)
        self.vis_period = 0

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logger.info("Starting training from iteration {}".format(start_iter))
    with EventStorage(start_iter) as storage:
        previous_val_map = 0  # ADDED NEW
        no_improvement_for_epochs = 0  # ADDED NEW
        for data, iteration in zip(data_loader, range(start_iter, max_iter)):
            storage.iter = iteration
            ...
            if (
                    cfg.TEST.EVAL_PERIOD > 0
                    and (iteration + 1) % cfg.TEST.EVAL_PERIOD == 0

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# Only 'ADDED NEW' commented statements are added for validation loss calculation.
validation_data_loader = prepare_validation_loader(cfg) # ADDED NEW

logger.info("Starting training from iteration {}".format(start_iter))
with EventStorage(start_iter) as storage:
    for data, iteration in zip(data_loader, range(start_iter, max_iter)):
        storage.iter = iteration

        loss_dict = model(data)
        losses = sum(loss_dict.values())

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def calculate_validation_loss(validation_dataset_loader, model, storage):
    data = next(validation_dataset_loader)
    with torch.no_grad():
        loss_dict = model(data)

        losses = sum(loss_dict.values())
        assert torch.isfinite(losses).all(), loss_dict

        loss_dict_reduced = {"val_" + k: v.item() for k, v in
                             comm.reduce_dict(loss_dict).items()}

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def prepare_validation_loader(cfg):
    val_cfg = cfg.clone()
    val_cfg.defrost()
    val_cfg.DATASETS.TRAIN = val_cfg.DATASETS.TEST
    val_cfg.freeze()
    val_mapper = DatasetMapper(val_cfg, True, augmentations=[])
    return iter(build_detection_train_loader(val_cfg, mapper=val_mapper))

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#!/bin/bash

# Usage
# Make install_kafka.sh and check_consumers.sh executable via `chmod +x _file_`.
# Then, run the script:
# ./check_consumers.sh -g 'consumer.group.retry' -b 'localhost:9092,localhost:9093'

while getopts g:b: flag
do

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"use strict";

const ImageData = require("./ImageData");
const ImageConverterOperator = require("./ImageConverterOperator");
const gm = require("gm").subClass({imageMagick: true})

/**
 * Get enable to use memory size in ImageMagick
 * Typically we determine to us 90% of max memory size
 * @see https://docs.aws.amazon.com/lambda/latest/dg/lambda-environment-variables.html

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Mount S3 Bucket to AWS EC2 Instance

1. [Connect][ec2-connect] to the EC2 instance.
2. Update package manager to be able to find s3fs: sudo apt-get update
3. Install s3fs and awscli: sudo apt install s3fs awscli -y

   which s3fs
   /usr/bin/s3fs

4. Setup access(if you do NOT have credentials please check the [Give permission to EC2 to access S3][permission] section below ):

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Angerer Timo: 12.7, Baumann Maximilian: 12.7, Botos Arpad: 12.7, Cankaya Faruk: 12.7, Cervieri Carrau Valentin: 12.7, Chen Junpeng: 12.7, Coffey Aubrey: 12.7, Coutinho Manuel: 12.7, Dalkilic Sinem: 12.7, Danner Katharina: 12.7, Derkacz-Bogner Daniel: 12.7, Eberhard Eike: 12.7, Egawa Mayumi: 12.7, Elfering Felix: 12.7, Flesch Lukas: 12.7, Frank Fabian: 12.7, Frings Andreas: 12.7, Fritsch Jonas: 12.7, Fuchsloch Leonardo: 12.7, Gedik Erenay: 12.7, Germes Tanae: 12.7, Ginter Pascal: 12.7, Gräßle Sarah: 12.7, Gu Yiyu: 12.7, Guan Haowen: 12.7, Guerrero Gutierrez Victor Hugo: 12.7, Gültekin Bora: 12.7, Guo Mingxiao: 12.7, Hakobyan Astghik: 12.7, Hamdy Hoda: 12.7, Harlander Julia: 12.7, Hekim David: 12.7, Hlubek Kamil: 12.7, Holzner Andreas: 12.7, Hontheim Henning: 12.7, Huber Lukas: 12.7, Hug Michael: 12.7, Iyidogan Atacan: 12.7, Izrailevitch Valentina: 12.7, Johler Marc: 12.7, Karabulut Erkan: 12.7, Karali Fatma: 12.7, Kim Hyunsung: 12.7, Kiray Mert: 12.7, Köbele Till: 12.7, Kobiella Robert: 12.7, Kolb Katharina: 1