PSPNet编译心得

贴上具体的编译过程吧：

step1：clone下caffe，按照caffe官方教程编译caffe（要编译python接口）

step2：进入到PSPNet里面进行编译，编译过程和caffe的编译差不多，在这里我使用了python接口，所以进行以下语句的编译即可：

make pycaffe -j4
make all -j4
make test -j4

在编译pycaffe是可能会出现以下错误：

    如果不适用GPU的话在interp.hpp直接注释掉这条语句即可；

还可能出现下面这个错误：

sudo apt-get install libmatio-dev安装一下就好。

step3：在PSPNet下面建立demo文件夹

下载相应的caffemodel和prototxt文件，然后修改demo.py就可以用训练好的模型进行测试：

import sys
import time
import getopt
import os
import numpy as np
from PIL import Image as PILImage

# Path of the Caffe installation.
_CAFFE_ROOT = "/home/chen/PSPNet/"

# Model definition and model file paths
_MODEL_DEF_FILE = "pspnet50_ADE20K_473.prototxt"  # Contains the network definition
_MODEL_FILE = "pspnet50_ADE20K.caffemodel"  # Contains the trained weights.

sys.path.insert(0, _CAFFE_ROOT + "python")
import caffe

_MAX_DIM = 473


def get_palette(num_cls):
    """ Returns the color map for visualizing the segmentation mask.

    Args:
        num_cls: Number of classes

    Returns:
        The color map
    """

    n = num_cls
    palette = [0] * (n * 3)
    for j in xrange(0, n):
        lab = j
        palette[j * 3 + 0] = 0
        palette[j * 3 + 1] = 0
        palette[j * 3 + 2] = 0
        i = 0
        while lab:
            palette[j * 3 + 0] |= (((lab >> 0) & 1) << (7 - i))
            palette[j * 3 + 1] |= (((lab >> 1) & 1) << (7 - i))
            palette[j * 3 + 2] |= (((lab >> 2) & 1) << (7 - i))
            i += 1
            lab >>= 3
    return palette


def crfrnn_segmenter(model_def_file, model_file, gpu_device, inputs):
    """ Returns the segmentation of the given image.

    Args:
        model_def_file: File path of the Caffe model definition prototxt file
        model_file: File path of the trained model file (contains trained weights)
        gpu_device: ID of the GPU device. If using the CPU, set this to -1
        inputs: List of images to be segmented 

    Returns:
        The segmented image
    """
    
    assert os.path.isfile(model_def_file), "File {} is missing".format(model_def_file)
    assert os.path.isfile(model_file), ("File {} is missing. Please download it using "
                                        "./download_trained_model.sh").format(model_file)

    if gpu_device >= 0:
        caffe.set_device(gpu_device)
        caffe.set_mode_gpu()
    else:
        caffe.set_mode_cpu()

    net = caffe.Net(model_def_file, model_file, caffe.TEST)

    num_images = len(inputs)
    num_channels = inputs[0].shape[2]
    assert num_channels == 3, "Unexpected channel count. A 3-channel RGB image is exptected."
    
    caffe_in = np.zeros((num_images, num_channels, _MAX_DIM, _MAX_DIM), dtype=np.float32)
    for ix, in_ in enumerate(inputs):
        caffe_in[ix] = in_.transpose((2, 0, 1))

    start_time = time.time()
    out = net.forward_all(**{net.inputs[0]: caffe_in})
    end_time = time.time()

    print("Time taken to run the network: {:.4f} seconds".format(end_time - start_time))
    predictions = out[net.outputs[0]]

    return predictions[0].argmax(axis=0).astype(np.uint8)


def run_crfrnn(input_file, output_file, gpu_device):
    """ Runs the CRF-RNN segmentation on the given RGB image and saves the segmentation mask.

    Args:
        input_file: Input RGB image file (e.g. in JPEG format)
        output_file: Path to save the resulting segmentation in PNG format
        gpu_device: ID of the GPU device. If using the CPU, set this to -1
    """

    input_image = 255 * caffe.io.load_image(input_file)
    input_image = resize_image(input_image)

    image = PILImage.fromarray(np.uint8(input_image))
    image = np.array(image)

    palette = get_palette(256)
    #PIL reads image in the form of RGB, while cv2 reads image in the form of BGR, mean_vec = [R,G,B] 
    mean_vec = np.array([123.68, 116.779, 103.939], dtype=np.float32)
    mean_vec = mean_vec.reshape(1, 1, 3)

    # Rearrange channels to form BGR
    im = image[:, :, ::-1]
    # Subtract mean
    im = im - mean_vec

    # Pad as necessary
    cur_h, cur_w, cur_c = im.shape
    pad_h = _MAX_DIM - cur_h
    pad_w = _MAX_DIM - cur_w
    im = np.pad(im, pad_width=((0, pad_h), (0, pad_w), (0, 0)), mode='constant', constant_values=0)

    # Get predictions
    segmentation = crfrnn_segmenter(_MODEL_DEF_FILE, _MODEL_FILE, gpu_device, [im])
    segmentation = segmentation[0:cur_h, 0:cur_w]

    output_im = PILImage.fromarray(segmentation)
    output_im.putpalette(palette)
    output_im.save(output_file)


def resize_image(image):
    """ Resizes the image so that the largest dimension is not larger than 500 pixels.
        If the image's largest dimension is already less than 500, no changes are made.

    Args:
        Input image

    Returns:
        Resized image where the largest dimension is less than 500 pixels
    """

    width, height = image.shape[0], image.shape[1]
    max_dim = max(width, height)

    if max_dim > _MAX_DIM:
        if height > width:
            ratio = float(_MAX_DIM) / height
        else:
            ratio = float(_MAX_DIM) / width
        image = PILImage.fromarray(np.uint8(image))
        image = image.resize((int(height * ratio), int(width * ratio)), resample=PILImage.BILINEAR)
        image = np.array(image)

    return image


def main(argv):
    """ Main entry point to the program. """
	
    input_file = "/home/chen/PSPNet/demo/test2.jpg"
    output_file = "/home/chen/PSPNet/demo/test_rs.png"
    gpu_device = -1  # Use -1 to run only on the CPU, use 0-3[7] to run on the GPU
    try:
        opts, args = getopt.getopt(argv, 'hi:o:g:', ["ifile=", "ofile=", "gpu="])
    except getopt.GetoptError:
        print("crfasrnn_demo.py -i <input_file> -o <output_file> -g <gpu_device>")
        sys.exit(2)

    for opt, arg in opts:
        if opt == '-h':
            print("crfasrnn_demo.py -i <inputfile> -o <outputfile> -g <gpu_device>")
            sys.exit()
        elif opt in ("-i", "ifile"):
            input_file = arg
        elif opt in ("-o", "ofile"):
            output_file = arg
        elif opt in ("-g", "gpudevice"):
            gpu_device = int(arg)

    print("Input file: {}".format(input_file))
    print("Output file: {}".format(output_file))
    if gpu_device >= 0:
        print("GPU device ID: {}".format(gpu_device))
    else:
        print("Using the CPU (set parameters appropriately to use the GPU)")
    run_crfrnn(input_file, output_file, gpu_device)


if __name__ == "__main__":
    main(sys.argv[1:])

////------------------------------------------------------------------------------------------------   

    复现PSPNet真的好曲折...这里贴一些复现过程的心得吧。

这是PSPNet整个工程的文件结构，在src文件里包含了修改过的caffe的源码，include包含了新增的一些layer的头文件什么的，具体看一下，然后和caffe-master做个对比，这样以后自己要利用caffe搭一个炼丹炉也知道这么弄。

在caffe/src/caffe/layers里面有127个.cpp文件，而在pspnet/src/caffe/layers里面有131个.cpp文件，所以多出来的4个应该是实现PSPNet自己定义的四个layer的实现。

然后proto文件夹,solvers文件夹都一样，util文件夹里PSPNet的刚好也比caffe的多7个文件。

最后是PSPNet/include/caffe/layers里面包含了一些实现caffe中layers的头文件，PSPNet的肯定也是更多的。

/////----------------------------------------------------------------------------------------------------------------------------