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class NetLong(nn.Module):
    def __init__(self):
        super(NetLong, self).__init__()
        self.fcs = self.make_layers()        
        
    def forward(self, x):        
        x = self.fcs(x)        
        return x
    
    def make_layers(self):

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import _pickle as pickle

class Pickle:
    def __init__(self):
        pass
        
    def save_data(self, data, fpath):
        with open(fpath, 'wb') as hndl:
            pickle.dump(data, hndl, -1)

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In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install NVIDIA Graphics Driver via runfile
  • Remove Previous Installations (Important)
  • Download the Driver
  • Install Dependencies
  • Creat Blacklist for Nouveau Driver

• Stop lightdm/gdm/kdm

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def plot_us(imgs, titles=None):
    fig, axs = plt.subplots(nrows=1, ncols=len(imgs), figsize=(15, 15))
    axs = np.array(axs).reshape(1, -1)
    for i, img in enumerate(imgs):
        axs[0, i].imshow(img)
        if titles not None:
            axs[0, i].set_title(titles[i])
        axs[0, i].set_axis_off()
    plt.grid()
    plt.show()

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import numpy as np
import h5py

with h5py.File('data.h5', 'w') as hndl:
    hndl['mystring'] = 'hello world!'

with h5py.File('data.h5', 'r') as hndl:
    print (hndl['mystring'].value)

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import matplotlib
matplotlib.use('TkAgg')

import ntpath
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf

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img = image_np.copy()/255.0
im_height, im_width, _ = img.shape
print (width, height)
for boxes, classes, scores in zip(output_dict['detection_boxes'], output_dict['detection_classes'], output_dict['detection_scores']):

    if classes == 1 and scores >= 0.5:
        ymin, xmin, ymax, xmax = boxes
        xmin, xmax, ymin, ymax = xmin * im_width, xmax * im_width, ymin * im_height, ymax * im_height
        xmin, xmax, ymin, ymax = int(xmin), int(xmax), int(ymin), int(ymax)
        print (left, right, top, bottom)

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def draw_rectangle(path, x1, y1, x2, y2):
    im = np.array(Image.open(path), dtype=np.uint8)
    fig,ax = plt.subplots(1)
    ax.imshow(im)
    rect = patches.Rectangle((x1,y1),x2-x1,y2-y1,linewidth=1,edgecolor='r',facecolor='none')
    ax.add_patch(rect)
    plt.show()

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def run_inference_for_multiple_images2(images, graph):
    output_dicts = []
    with graph.as_default():
        with tf.Session() as sess:      
            for image in images:
#                 print (image)
                ops = tf.get_default_graph().get_operations()                
                all_tensor_names = {output.name for op in ops for output in op.outputs}
                
                tensor_dict = {}

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def show_img_from_batch(batch):
    img = transforms.ToPILImage()(batch[0])
    plt.figure()
    plt.imshow(img)
    plt.show()