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Caffe学习:Blobs, Layers, and Nets

-注意:网络结构是设备无关的,Blob和Layer=隐藏了模型定义的具体实现细节。定义网络结构后,可以通过Caffe::mode()或者Caffe::set_m...

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Caffe2 - (三) Blobs,Workspace,Tensors等概念

Blobs,Workspace,TensorsCaffe2 的 Data 是以 blobs 的形式组织的. blob 即是内存中被命名的 data chunk(数据块). blobs 一般包含一个 tensor (可以看做是多维数组),在 Python 中的存在形式是 numpy arrays.Workspace 存储所有的 blobs. 如下例,展示了将 blobs 送入 workspace (Feed) 以及从 workspace 读取 blobs 的方法.Workspaces 在开始使用时会自动初始化.from caffe2.python 生成随机 fills 后,即可将 fc ops 添加到模型,并使用创建的 weights 和 bias blobs,可以根据其名字进行调用.Caffe2 中,FC op 包括三部分: input blob ;实际应用中,从相应的 database 来加载读取. data 和 label blobs 的 first dim=16,即 batchsize=16.

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    Caffe2 - (三十二) Detectron 之 roi_data - 模型 minibatch blobs

    , lvl_min, lvl_max ) # Add per FPN level roi blobs named like: _fpn fpn.add_multilevel_roi_blobs(blobs dict with Mask R-CNN blobs blobs = rois_fg blobs = roi_has_mask blobs = masks def _expand_to_class_specific_mask_targets , roidb, fg_rois_per_image, fg_inds, im_scale, batch_idx): 添加 Mask R-CNN keypoint 相关的 blobs 到给定的 blobs (blobs, valid): 当所有的 minibatch 图片 blobs 处理完以后,定型 minibatch. += fg_num blobs += bg_num blobs = blobs.astype(np.float32) blobs = blobs.astype(np.float32) N = len(

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    Registry GC源码分析

    run-dry GC demorun-dry will print the blobs to be deleted, but will not be deleted truely. hello-world marked are not to deleted, the blobs eligible are to delete.In the example above, you can see 4 blobs marked, 5 blobs eligible for deletion. When we make GC scheme in pro, we can decide whether to trigger GC according the number of blobs marked eligible. for example, when the number of blobs marked eligible more than 500, trigger the Registry

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    Caffe2 - (三十三) Detectron 之 roi_data - data loader

    来构建 minibatches, 其共享 minibatch 队列(quene).每一个 GPU 有一个队列线程,从 minibatch 队列中拉取一个 minibatch,并将该 minibatch blobs 送入 workspace;然后运行 EnqueueBlobsOp 将 minibatch blobs 放入 GPU 的 blobs 队列.from __future__ import absolute_importfrom ) return blobs def _shuffle_roidb_inds(self): 随机打乱训练 roidb. _output_names def enqueue_blobs(self, gpu_id, blob_names, blobs): 将 mini-batch 放入 BlobsQueue. 根据图片生成 blobs,并连接为的单个 tensor. 因此,初始化每个 blob 为空列表.

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    Caffe2 - (二十七) Detectron 之 modeling - detector

    - blobs_out: - (blobs 的 variable set): 返回模型训练需要的 blobs. 通过查询 data loader 来返回需要的 blobs 列表list. 训练阶段使用时,Input Blobs 还包括:. - Output blobs: - 其中, rois_fpn - FPN level i 的 RPN proposals. rois_idx_restore - 所有 rois_fpn, i=min...max 组合的排列序列, 用于将 RPN RoIs 恢复到 Input Blobs 原来的顺序. 训练阶段使用时,Output Blobs 还包括: .

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    Using KMeans to cluster data使用K均值来聚类数据

    matplotlib.pyplot as pltHow to do it…怎么做We are going to walk through a simple example that clusters blobs 我们能看到这里有3个清晰的组:f, ax = plt.subplots(figsize=(7.5, 7.5))ax.scatter(blobs, blobs, color=rgb)rgb = np.array ()ax.set_title(Blobs)The output is as follows:输出图形如下:image.pngNow we can use KMeans to find the centers 在第一个例子里,我们假装我们知道这里有三个中心:from sklearn.cluster import KMeanskmean = KMeans(n_clusters=3)kmean.fit(blobs , label=Centers)ax.set_title(Blobs)ax.legend(loc=best)The following screenshot shows the output:以下是输出结果

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    MySQL中需要注意的字段长度问题

    The maximum row size for the used table type, not counting BLOBs, is 65535. The maximum row size for the used table type, not counting BLOBs, is 65535. The maximum row size for the used table type, not counting BLOBs, is 65535. The maximum row size for the used table type, not counting BLOBs, is 65535. The maximum row size for the used table type, not counting BLOBs, is 65535.

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    Caffe2 - (二十六) Detectron 之定制 Python Operators(ops)

    网络层的输入和输出 - Input Blobs & Output Blobs: - Input Blobs: 其中, rpn_rois_fpn - FPN level i 的 RPN proposals 训练阶段使用时,Input Blobs 还包括:. - Output blobs: 其中, rois_fpn - FPN level i 的 RPN proposals. rois_idx_restore - 所有 rois_fpn, i=min...max 组合的排列序列,用于将 RPN RoIs 恢复到 Input Blobs 原来的顺序. 的 roidb entries. - im_info: 参考 GenerateProposals 文档. - 输出blobs - blobs_out: - (blobs 的 variable set) : 返回模型训练需要的 blobs.

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    Caffe2 - (五)Workspace Python API

    CreateNet()定义:def workspace.CreateNet(net_def, input_blobs):如果未给定输入 blobs,则创建空的 net.2. FetchBlobs()定义:def FetchBlobs(names):从 workspace 中拉取多个 blobs 列表. 输入: names:blobs names,一般是字符串形式.输出: 拉取的 blobs 列表.5. GetNameScope()定义:def GetNameScope():返回当前 namescope 字符串,用于拉取 blobs.6. 默认是,workspace blobs.输出: 返回根据 blob name 键值的 (shapes,types) 字典的元组.7.

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    Caffe2 - (二十五) Detectron 之 utils 函数(3)

    in src_blobs: # Backwards compat--dictionary used to be only blobs, now they are # stored under the ,这里进行了保留. # 将这些 blobs 加载到 CPU 内存里的 __preserve__ namescope. # 这些 blobs 也会被保存 model 到 weights file. # 这种处理方式对于 ) save_object(dict(blobs=blobs, cfg=cfg_yaml), weights_file) def broadcast_parameters(model): 从 GPU 0 复制参数到 GPUs1 上对应的参数 blobs, cfg.NUM_GPUS - 1. = ] data = workspace.FetchBlob(blobs) logger.debug(Broadcasting {} to.format(str(blobs))) for i, p in

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    Caffe2 - (二十五) Detectron 之 utils 函数(3)

    in src_blobs: # Backwards compat--dictionary used to be only blobs, now they are # stored under the ,这里进行了保留. # 将这些 blobs 加载到 CPU 内存里的 __preserve__ namescope. # 这些 blobs 也会被保存 model 到 weights file. # 这种处理方式对于 ) save_object(dict(blobs=blobs, cfg=cfg_yaml), weights_file) def broadcast_parameters(model): 从 GPU 0 复制参数到 GPUs1 上对应的参数 blobs, cfg.NUM_GPUS - 1. = ] data = workspace.FetchBlob(blobs) logger.debug(Broadcasting {} to.format(str(blobs))) for i, p in

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    聊聊flink的BlobService

    public interface BlobService extends Closeable {​ ** * Returns a BLOB service for accessing permanent BLOBs PermanentBlobService getPermanentBlobService();​ ** * Returns a BLOB service for accessing transient BLOBs ). * * These may include per-job BLOBs that are covered by high-availability (HA) mode, e.g. a jobs * {@link * org.apache.flink.api.common.cache.DistributedCache}, for example. * * Note: None of these BLOBs This case is covered by BLOBs in the * {@link PermanentBlobService}. * * TODO: change API to not rely

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    和小曼一起走到MySQL行的尽头

    The maximum row size for the used table type, not counting BLOBs, is 65535. You have to change some columns to TEXT or BLOBs大致意思是:行过大,超出了 65535,需要将字段修改为 TEXT 或 BLOBs 。 The maximum row size for the used table type, not counting BLOBs, is 65535. You have to change some columns to TEXT or BLOBs仍然报错,经过反复测试后,新增的 VARCHAR 最大长度只能是 5520:ALTER TABLE t ADD 小曼,那我们是不是按 MySQL 给建议,把字段改成 TEXT 和 BLOBs 就可以跨越限制,不会再出现这个问题?

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    目标检测 - Faster R-CNN 训练过程源码理解

    _cur += cfg.TRAIN.IMS_PER_BATCH return db_inds def _get_next_minibatch(self): Return the blobs to be . blobs = self. _name_to_top_map # Reshape nets input blobs top.reshape(*(blob.shape)) # Copy data into nets input blobs = gt_boxes blobs = np.array( , im_blob.shape, im_scales]], dtype=np.float32) else: # not using RPN # = rois_blob blobs = labels_blob if cfg.TRAIN.BBOX_REG: blobs = bbox_targets_blob blobs = bbox_inside_blob

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    Optimizing the number of centroids最优化形心数量

    How to do it…怎么做To get started well create several blobs that can be used to simulate clusters of data = make_blobs(500, centers=3)from sklearn.cluster import KMeanskmean = KMeans(n_clusters=3)kmean.fit(blobs # first new ground truth首先新的分类准确性>>> blobs, classes = make_blobs(500, centers=10)>>> sillhouette_avgs = []# this could take a while这将花费一定时间for k in range(2, 60): kmean = KMeans(n_clusters=k).fit(blobs) sillhouette_avgs.append(metrics.silhouette_score(blobs,kmean.labels_))f, ax = plt.subplots(figsize=(7

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    Caffe2 - (三十一) Detectron 之 modeling - FPN 与 optimizer

    )) target_lvls = np.clip(target_lvls, k_min, k_max) return target_lvls def add_multilevel_roi_blobs(blobs , blob_prefix, rois, target_lvls, lvl_min, lvl_max): 将 multiple FPN levels 的 RoI blobs 添加到 blobs dict . blobs: blob name 到 numpy ndarray 映射的 dict. blob_prefix: FPN blobs 使用的 name 前缀prefix. rois: rois源,2D = collections.namedtuple(FpnLevelInfo, ) def fpn_level_info_ResNet50_conv5(): return FpnLevelInfo(blobs (0): # 对不同的参数 blobs 进行迭代 for i in range(params_per_gpu): # 对于该参数 blob,所有 GPUs 上的 Gradients from all GPUs

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    Caffe源码理解3:Layer基类与template method设计模式

    explicit Layer(const LayerParameter& param) : layer_param_(param) { Set phase and copy blobs (if there = 0; i < layer_param_.blobs_size(); ++i) { blobs_.reset(new Blob()); blobs_->FromProto(layer_param_.blobs provided as input match the expected numbers specified by the {ExactNum,Min,Max}{Bottom,Top}Blobs() * and making any other necessary adjustments so that the layer can* accommodate the bottom blobs. 参考Blobs, Layers, and Nets: anatomy of a Caffe model虚函数与多态

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    如何优化流水线的镜像同步?速度提高 15 倍!

    .├── blobs│ └── sha256│ ├── 21│ │ └── 21c83c5242199776c232920ddb58cfa2a46b17e42ed831ca9001c8dbc532d22d 39eda93d15866957feaee28f8fc5adb545276a64147445c64992ef69804dbf01# 步骤二:根据 currentlink 文件中的 sha256 值在 blobs 目录:# 将 manifest 文件硬连接到 registry 存储的 blobs 目录下mkdir -p dockerregistryv2blobssha256${manifests_sha256: ${manifests_sha256:0:2}${manifests_sha256}data # 根据镜像的 manifest 文件再将镜像的 layer 和 image config 文件硬链接到 blobs libraryalpine:latest 镜像来讲,它在 registry 中是这样存放的:╭─root@sg-02 varlibregistrydockerregistryv2╰─# tree.├── blobs

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    安全研究 | 如何查看GitLab中的共享敏感数据

    should find# severity: #rating out of 100# scope: #what to search, any combination of the below# - blobs this help message and exit --version show programs version number and exit --all Find everything --blobs Search code blobs --commits Search commits --wiki-blobs Search wiki blobs --issues Search issues --merge-requests

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