Question

In the original Caffe framework, there was an executable under caffe/build/tools called convert_imageset, which took a directory of JPEG images and a text file with labels for each image, and output an LMDB that could be fed to a Caffe model to train, test, etc.

What is the best way to convert raw JPEG images and labels to an LMDB that Caffe2 can ingest using the AddInput() function from this MNIST tutorial？

根据我的研究，您不能简单地使用此工具创建LMDB文件并提供Caffe2模型。

教程脚本只下载两个LMDB（mnist-train-nchw-lmdb和mnist-test-nchw-lmdb）并将它们传递给AddInput()，但不了解LMDB的创建方式。

Answer 1

有一个名为make_image_db.cc的二进制文件，它正是您所描述的内容。它位于caffe2/build/bin/make_image_db：

// This script converts an image dataset to a database.
//
// caffe2::FLAGS_input_folder is the root folder that holds all the images
//
// caffe2::FLAGS_list_file is the path to a file containing a list of files
// and their labels, as follows:
//
//   subfolder1/file1.JPEG 7
//   subfolder1/file2.JPEG 7
//   subfolder2/file1.JPEG 8
//   ...

如https://github.com/caffe2/caffe2/issues/1755中所述，您可以按以下方式使用二进制文件（也使用较少的参数）：

caffe2/build/bin/make_image_db -color -db lmdb -input_folder ./some_input_folder
-list_file ./labels_file -num_threads 10 -output_db_name ./some_output_folder -raw -scale 256 -shuffle

关于如何创建和读取lmdb数据库（用于随机图像）的完整Caffe2示例可以在官方github存储库中找到，并且可以用作框架以适应您自己的图像https://github.com/caffe2/caffe2/blob/master/caffe2/python/examples/lmdb_create_example.py。由于我还没有使用过这种方法，我只想复制一下这个例子。为了创建数据库，可以使用：

import argparse
import numpy as np

import lmdb
from caffe2.proto import caffe2_pb2
from caffe2.python import workspace, model_helper

def create_db(output_file):
    print(">>> Write database...")
    LMDB_MAP_SIZE = 1 << 40   # MODIFY
    env = lmdb.open(output_file, map_size=LMDB_MAP_SIZE)

    checksum = 0
    with env.begin(write=True) as txn:
        for j in range(0, 128):
            # MODIFY: add your own data reader / creator
            label = j % 10
            width = 64
            height = 32

            img_data = np.random.rand(3, width, height)
            # ...

            # Create TensorProtos
            tensor_protos = caffe2_pb2.TensorProtos()
            img_tensor = tensor_protos.protos.add()
            img_tensor.dims.extend(img_data.shape)
            img_tensor.data_type = 1

            flatten_img = img_data.reshape(np.prod(img_data.shape))
            img_tensor.float_data.extend(flatten_img)

            label_tensor = tensor_protos.protos.add()
            label_tensor.data_type = 2
            label_tensor.int32_data.append(label)
            txn.put(
                '{}'.format(j).encode('ascii'),
                tensor_protos.SerializeToString()
            )

            checksum += np.sum(img_data) * label
            if (j % 16 == 0):
                print("Inserted {} rows".format(j))

    print("Checksum/write: {}".format(int(checksum)))
    return checksum

然后可以通过以下方式加载数据库：

def read_db_with_caffe2(db_file, expected_checksum):
    print(">>> Read database...")
    model = model_helper.ModelHelper(name="lmdbtest")
    batch_size = 32
    data, label = model.TensorProtosDBInput(
        [], ["data", "label"], batch_size=batch_size,
        db=db_file, db_type="lmdb")

    checksum = 0

    workspace.RunNetOnce(model.param_init_net)
    workspace.CreateNet(model.net)

    for _ in range(0, 4):
        workspace.RunNet(model.net.Proto().name)

        img_datas = workspace.FetchBlob("data")
        labels = workspace.FetchBlob("label")
        for j in range(batch_size):
            checksum += np.sum(img_datas[j, :]) * labels[j]

    print("Checksum/read: {}".format(int(checksum)))
    assert np.abs(expected_checksum - checksum < 0.1), \
        "Read/write checksums dont match"

最后但同样重要的是，还有一个关于如何创建minidb数据库的教程：https://github.com/caffe2/caffe2/blob/master/caffe2/python/tutorials/create_your_own_dataset.ipynb。为此，可以使用以下功能：

def write_db(db_type, db_name, features, labels):
    db = core.C.create_db(db_type, db_name, core.C.Mode.write)
    transaction = db.new_transaction()
    for i in range(features.shape[0]):
        feature_and_label = caffe2_pb2.TensorProtos()
        feature_and_label.protos.extend([
            utils.NumpyArrayToCaffe2Tensor(features[i]),
            utils.NumpyArrayToCaffe2Tensor(labels[i])])
        transaction.put(
            'train_%03d'.format(i),
            feature_and_label.SerializeToString())
    # Close the transaction, and then close the db.
    del transaction
    del db

特征是一个张量，包含你的图像为numpy数组。标签是功能的相应真实标签。然后，您只需将该函数称为

write_db("minidb", "train_images.minidb", train_features, train_labels)

最后，您将通过

加载数据库中的图像

net_proto = core.Net("example_reader")
dbreader = net_proto.CreateDB([], "dbreader", db="train_images.minidb", db_type="minidb")
net_proto.TensorProtosDBInput([dbreader], ["X", "Y"], batch_size=16)

Answer 2

用于lmbd中的create database：创建列车数据文件夹创建train.txt文件，包含文件名标签创建验证数据文件夹创建val.txt文件contatining filename和label

编辑此文件

gedit examples/imagenet/create_imagenet.sh

EXAMPLE= path to where *.lmbd folder wil be stored
DATA= path where val.txt and train.txt is present
TOOLS=build/tools

TRAIN_DATA_ROOT=test/make_caffe_data/train/ # path to trainfiles
VAL_DATA_ROOT=test/make_caffe_data/val/ # path to test_files

设置RESIZE=true以将图像大小调整为256x256。如果图片有，请保留为false 已经使用其他工具调整了大小。 RESIZE=true

./examples/imagenet/create_imagenet.sh

Creating an image LMDB for Caffe2

2 个答案: