redknightlois

class Ralamb(Optimizer):

    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
        defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
        self.buffer = [[None, None, None] for ind in range(10)]
        super(Ralamb, self).__init__(params, defaults)

    def __setstate__(self, state):
        super(Ralamb, self).__setstate__(state)

apivovarov

Intro

This wiki explains how to convert Tensorflow Object Detection SSD models to TFLite format.

Model Zoo: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md

Preparation

These instructions use python3 and pip3.

Ajk4

import numpy as np

def pointcloud_to_depth_map(pointcloud: np.ndarray, theta_res=150, phi_res=32, max_depth=50, phi_min_degrees=60,
                            phi_max_degrees=100) -> np.ndarray:
    """
        All params are set so they match default carla lidar settings
    """
    assert pointcloud.shape[1] == 3, 'Must have (N, 3) shape'
    assert len(pointcloud.shape) == 2, 'Must have (N, 3) shape'

post2web

# from https://stackoverflow.com/questions/42463172/how-to-perform-max-mean-pooling-on-a-2d-array-using-numpy
import numpy as np
def asStride(arr,sub_shape,stride):
    '''Get a strided sub-matrices view of an ndarray.
    See also skimage.util.shape.view_as_windows()
    '''
    s0,s1=arr.strides[:2]
    m1,n1=arr.shape[:2]
    m2,n2=sub_shape
    view_shape=(1+(m1-m2)//stride[0],1+(n1-n2)//stride[1],m2,n2)+arr.shape[2:]

ptschandl

#!/usr/bin/env python3

'''
This script exctracts training variables from all logs from 
tensorflow event files ("event*"), writes them to Pandas 
and finally stores in long-format to a CSV-file including
all (readable) runs of the logging directory.

The magic "5" infers there are only the following v.tags:
[lr, loss, acc, val_loss, val_acc]

chirag1992m

import numpy as np
import torch
import keras

def pyt_to_keras(pytorch_model, keras_model):
  """
    Given a PyTorch model, this method transfers the weight to
    a Keras Model (with backend TensorFlow) with the same architecture.
    Assumptions:
      1. The corresponding layer names in both the models will be the same

jubjamie

import tensorflow as tf
from tensorflow.python.platform import gfile
with tf.Session() as sess:
    model_filename ='PATH_TO_PB.pb'
    with gfile.FastGFile(model_filename, 'rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
        g_in = tf.import_graph_def(graph_def)
LOGDIR='/logs/tests/1/'
train_writer = tf.summary.FileWriter(LOGDIR)

niclin

sudo apt-get update
sudo apt-get install -y libevent-dev libncurses-dev make
wget https://github.com/tmux/tmux/releases/download/2.3/tmux-2.3.tar.gz
tar xvzf tmux-2.3.tar.gz
cd tmux-2.3/
./configure && make
sudo make install

aaronpolhamus

n02119789 1 kit_fox
n02100735 2 English_setter
n02110185 3 Siberian_husky
n02096294 4 Australian_terrier
n02102040 5 English_springer
n02066245 6 grey_whale
n02509815 7 lesser_panda
n02124075 8 Egyptian_cat
n02417914 9 ibex
n02123394 10 Persian_cat

sloria

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

• "Build tools for others that you want to be built for you." - Kenneth Reitz
• "Simplicity is alway better than functionality." - Pieter Hintjens