win10+maskrcnn-benchmark+cocodataset+GPU

windows不能让maskrcnn-benchmark跑自己的训练集吗？不可能的，都是操作系统，只是网上的资料都是在Linux下面搞的。

首先声明：显存2G或者内存小于12G的滚一边去，嗯，我的就是显存2G的，一个同学4G的显存8G的内存显示内存不够，给他加了一个4G的内存条后没有显示显存不够，也没有显示内存不够，就是报了个index out of range的错误，我也不知道为什么

 

数据集准备

COCO数据集，自己的数据集也行，但是我没试过，可以看其它文章，虽然他们都是Linux的，但是，都是操作系统，有什么区别，数据集优先下载val2017、train2017和2017 Train/Val annotations，其它的就挂在那里下载吧，指不定什么时候要用

 

修改.yaml文件

这个肯定是要修改的，怎么改你们自己搞，用哪个也自己搞，但是

DATASETS:
  TRAIN: ("coco_2014_train", "coco_2014_valminusminival")
  TEST: ("coco_2014_minival",)

这个是必须要改的，因为它规定了你用的是哪个训练集和测试集，而里面写的coco_2014_train、coco_2014_valminusminival、coco_2014_minival来自/maskrcnn_benchmark/config/paths_catalog.py，代码如下

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""Centralized catalog of paths."""

import os


class DatasetCatalog(object):
    DATA_DIR = "datasets"
    DATASETS = {
        "coco_2017_train": {
            "img_dir": "coco/train2017",
            "ann_file": "coco/annotations/instances_train2017.json"
        },
        "coco_2017_val": {
            "img_dir": "coco/val2017",
            "ann_file": "coco/annotations/instances_val2017.json"
        },
        "coco_2014_train": {
            "img_dir": "coco/train2014",
            "ann_file": "coco/annotations/instances_train2014.json"
        },
        "coco_2014_val": {
            "img_dir": "coco/val2014",
            "ann_file": "coco/annotations/instances_val2014.json"
        },
        "coco_2014_minival": {
            "img_dir": "coco/val2014",
            "ann_file": "coco/annotations/instances_minival2014.json"
        },
        "coco_2014_valminusminival": {
            "img_dir": "coco/val2014",
            "ann_file": "coco/annotations/instances_valminusminival2014.json"
        },
        "keypoints_coco_2014_train": {
            "img_dir": "coco/train2014",
            "ann_file": "coco/annotations/person_keypoints_train2014.json",
        },
        "keypoints_coco_2014_val": {
            "img_dir": "coco/val2014",
            "ann_file": "coco/annotations/person_keypoints_val2014.json"
        },
        "keypoints_coco_2014_minival": {
            "img_dir": "coco/val2014",
            "ann_file": "coco/annotations/person_keypoints_minival2014.json",
        },
        "keypoints_coco_2014_valminusminival": {
            "img_dir": "coco/val2014",
            "ann_file": "coco/annotations/person_keypoints_valminusminival2014.json",
        },
        "voc_2007_train": {
            "data_dir": "voc/VOC2007",
            "split": "train"
        },
        "voc_2007_train_cocostyle": {
            "img_dir": "voc/VOC2007/JPEGImages",
            "ann_file": "voc/VOC2007/Annotations/pascal_train2007.json"
        },
        "voc_2007_val": {
            "data_dir": "voc/VOC2007",
            "split": "val"
        },
        "voc_2007_val_cocostyle": {
            "img_dir": "voc/VOC2007/JPEGImages",
            "ann_file": "voc/VOC2007/Annotations/pascal_val2007.json"
        },
        "voc_2007_test": {
            "data_dir": "voc/VOC2007",
            "split": "test"
        },
        "voc_2007_test_cocostyle": {
            "img_dir": "voc/VOC2007/JPEGImages",
            "ann_file": "voc/VOC2007/Annotations/pascal_test2007.json"
        },
        "voc_2012_train": {
            "data_dir": "voc/VOC2012",
            "split": "train"
        },
        "voc_2012_train_cocostyle": {
            "img_dir": "voc/VOC2012/JPEGImages",
            "ann_file": "voc/VOC2012/Annotations/pascal_train2012.json"
        },
        "voc_2012_val": {
            "data_dir": "voc/VOC2012",
            "split": "val"
        },
        "voc_2012_val_cocostyle": {
            "img_dir": "voc/VOC2012/JPEGImages",
            "ann_file": "voc/VOC2012/Annotations/pascal_val2012.json"
        },
        "voc_2012_test": {
            "data_dir": "voc/VOC2012",
            "split": "test"
            # PASCAL VOC2012 doesn't made the test annotations available, so there's no json annotation
        },
        "cityscapes_fine_instanceonly_seg_train_cocostyle": {
            "img_dir": "cityscapes/images",
            "ann_file": "cityscapes/annotations/instancesonly_filtered_gtFine_train.json"
        },
        "cityscapes_fine_instanceonly_seg_val_cocostyle": {
            "img_dir": "cityscapes/images",
            "ann_file": "cityscapes/annotations/instancesonly_filtered_gtFine_val.json"
        },
        "cityscapes_fine_instanceonly_seg_test_cocostyle": {
            "img_dir": "cityscapes/images",
            "ann_file": "cityscapes/annotations/instancesonly_filtered_gtFine_test.json"
        }
    }

    @staticmethod
    def get(name):
        if "coco" in name:
            data_dir = DatasetCatalog.DATA_DIR
            attrs = DatasetCatalog.DATASETS[name]
            args = dict(
                root=os.path.join(data_dir, attrs["img_dir"]),
                ann_file=os.path.join(data_dir, attrs["ann_file"]),
            )
            return dict(
                factory="COCODataset",
                args=args,
            )
        elif "voc" in name:
            data_dir = DatasetCatalog.DATA_DIR
            attrs = DatasetCatalog.DATASETS[name]
            args = dict(
                data_dir=os.path.join(data_dir, attrs["data_dir"]),
                split=attrs["split"],
            )
            return dict(
                factory="PascalVOCDataset",
                args=args,
            )
        raise RuntimeError("Dataset not available: {}".format(name))


class ModelCatalog(object):
    S3_C2_DETECTRON_URL = "https://dl.fbaipublicfiles.com/detectron"
    C2_IMAGENET_MODELS = {
        "MSRA/R-50": "ImageNetPretrained/MSRA/R-50.pkl",
        "MSRA/R-50-GN": "ImageNetPretrained/47261647/R-50-GN.pkl",
        "MSRA/R-101": "ImageNetPretrained/MSRA/R-101.pkl",
        "MSRA/R-101-GN": "ImageNetPretrained/47592356/R-101-GN.pkl",
        "FAIR/20171220/X-101-32x8d": "ImageNetPretrained/20171220/X-101-32x8d.pkl",
    }

    C2_DETECTRON_SUFFIX = "output/train/{}coco_2014_train%3A{}coco_2014_valminusminival/generalized_rcnn/model_final.pkl"
    C2_DETECTRON_MODELS = {
        "35857197/e2e_faster_rcnn_R-50-C4_1x": "01_33_49.iAX0mXvW",
        "35857345/e2e_faster_rcnn_R-50-FPN_1x": "01_36_30.cUF7QR7I",
        "35857890/e2e_faster_rcnn_R-101-FPN_1x": "01_38_50.sNxI7sX7",
        "36761737/e2e_faster_rcnn_X-101-32x8d-FPN_1x": "06_31_39.5MIHi1fZ",
        "35858791/e2e_mask_rcnn_R-50-C4_1x": "01_45_57.ZgkA7hPB",
        "35858933/e2e_mask_rcnn_R-50-FPN_1x": "01_48_14.DzEQe4wC",
        "35861795/e2e_mask_rcnn_R-101-FPN_1x": "02_31_37.KqyEK4tT",
        "36761843/e2e_mask_rcnn_X-101-32x8d-FPN_1x": "06_35_59.RZotkLKI",
        "37129812/e2e_mask_rcnn_X-152-32x8d-FPN-IN5k_1.44x": "09_35_36.8pzTQKYK",
        # keypoints
        "37697547/e2e_keypoint_rcnn_R-50-FPN_1x": "08_42_54.kdzV35ao"
    }

    @staticmethod
    def get(name):
        if name.startswith("Caffe2Detectron/COCO"):
            return ModelCatalog.get_c2_detectron_12_2017_baselines(name)
        if name.startswith("ImageNetPretrained"):
            return ModelCatalog.get_c2_imagenet_pretrained(name)
        raise RuntimeError("model not present in the catalog {}".format(name))

    @staticmethod
    def get_c2_imagenet_pretrained(name):
        prefix = ModelCatalog.S3_C2_DETECTRON_URL
        name = name[len("ImageNetPretrained/"):]
        name = ModelCatalog.C2_IMAGENET_MODELS[name]
        url = "/".join([prefix, name])
        return url

    @staticmethod
    def get_c2_detectron_12_2017_baselines(name):
        # Detectron C2 models are stored following the structure
        # prefix//2012_2017_baselines/.yaml./suffix
        # we use as identifiers in the catalog Caffe2Detectron/COCO//
        prefix = ModelCatalog.S3_C2_DETECTRON_URL
        dataset_tag = "keypoints_" if "keypoint" in name else ""
        suffix = ModelCatalog.C2_DETECTRON_SUFFIX.format(dataset_tag, dataset_tag)
        # remove identification prefix
        name = name[len("Caffe2Detectron/COCO/"):]
        # split in  and 
        model_id, model_name = name.split("/")
        # parsing to make it match the url address from the Caffe2 models
        model_name = "{}.yaml".format(model_name)
        signature = ModelCatalog.C2_DETECTRON_MODELS[name]
        unique_name = ".".join([model_name, signature])
        url = "/".join([prefix, model_id, "12_2017_baselines", unique_name, suffix])
        return url

看到那个DATASETS了么，那就是coco_2014_train、coco_2014_valminusminival、coco_2014_minival的来源，你把训练集和测试集改成自己想要的，保险起见，名字还是沿用DATASETS的比较好，然后把.yaml中的名字改成自己想要的DATASETS中的名字就行了，比如我是这样改的：

DATASETS:
  TRAIN: ("coco_2017_train",)
  TEST: ("coco_2017_val",)

然后把paths_catalog.py中的DATASETS中的对应的训练集和测试集的路径改成自己的，虽然他用的是什么coco/......，但是我们windows就有我们windows的粗暴路径，比如这样的

O:/Users/xxx/Anaconda3/envs/maskrcnn_benchmark/maskrcnn-benchmark/datasets/coco

记得斜杠的方向

 

训练命令

这个其实官方就有提供，查看官方指南会好一点

我这里就提供几个能用的，记得在Anaconda Prompt (Anaconda3)里面cd到maskrcnn_benchmark里面

单GPU：

python tools/train_net.py --config-file O:/Users/XXX/Anaconda3/envs/maskrcnn_benchmark/maskrcnn-benchmark/configs/e2e_mask_rcnn_R_101_FPN_1x.yaml SOLVER.IMS_PER_BATCH 1 SOLVER.BASE_LR 0.0025 SOLVER.MAX_ITER 60000 SOLVER.STEPS "(30000, 40000)" TEST.IMS_PER_BATCH 1 MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN 2000

python tools/train_net.py --config-file O:/Users/XXX/Anaconda3/envs/maskrcnn_benchmark/maskrcnn-benchmark/configs/e2e_mask_rcnn_R_101_FPN_1x.yaml

python tools/train_net.py --config-file O:/Users/XXX/Anaconda3/envs/maskrcnn_benchmark/maskrcnn-benchmark/configs/e2e_mask_rcnn_R_101_FPN_1x.yaml SOLVER.IMS_PER_BATCH 2 SOLVER.BASE_LR 0.0025 SOLVER.MAX_ITER 60000 SOLVER.STEPS "(30000, 40000)" TEST.IMS_PER_BATCH 1 MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN 2000

python tools/train_net.py --config-file O:/Users/XXX/Anaconda3/envs/maskrcnn_benchmark/maskrcnn-benchmark/configs/e2e_mask_rcnn_R_101_FPN_1x.yaml SOLVER.IMS_PER_BATCH 2 SOLVER.BASE_LR 0.0025 SOLVER.MAX_ITER 720000 SOLVER.STEPS "(480000, 640000)" TEST.IMS_PER_BATCH 1 MODEL.RPN.FPN_POST_NMS_TOP_N_TRAIN 2000

多GPU直接看官方的吧

 

 

在添加两张照片：

单GPU第一个命令：

单GPU第四个命令（基于官方参数修改）：

 

从图中可以看出，显存最低4G，最低4G，最低4G，内存若干，8G肯定不行

单GPU第二个命令，显存16G也不行