- sudo pip3 install awscli
- cd ~/
- mkdir ~/.aws
- vim ~/.aws/config
-
[default]
raw_output = net.layers['conv6'] | |
# Step1 : | |
raw_prediction = tf.reshape(raw_output, [-1, args.num_classes]) | |
label_proc = prepare_label(label_batch, tf.stack(raw_output.get_shape()[1:3]), num_classes=args.num_classes, one_hot=False) # [batch_size, h, w] | |
raw_gt = tf.reshape(label_proc, [-1,]) | |
indices = tf.squeeze(tf.where(tf.less_equal(raw_gt, args.num_classes - 1)), 1) | |
# Ste2 : | |
gt = tf.cast(tf.gather(raw_gt, indices), tf.int32) |
def mrcnn_mask_loss_graph(target_masks, target_class_ids, pred_masks): | |
"""Mask binary cross-entropy loss for the masks head. | |
target_masks: [batch, num_rois, height, width]. | |
A float32 tensor of values 0 or 1. Uses zero padding to fill array. | |
target_class_ids: [batch, num_rois]. Integer class IDs. Zero padded. | |
pred_masks: [batch, proposals, height, width, num_classes] float32 tensor | |
with values from 0 to 1. | |
""" | |
# Step1.1 : Reshape for simplicity. Merge first two dimensions into one. | |
target_class_ids = K.reshape(target_class_ids, (-1,)) |
# from scapy3k.all import * | |
import sys | |
print (' - Encoding : ', sys.getdefaultencoding()) | |
# print (' - Version : ', sys.version) | |
import math | |
import pprint | |
import datetime | |
import pandas as pd | |
from scapy.all import * #https://scapy.readthedocs.io/en/latest/installation.html |
import h5py | |
import numpy as np | |
filename = 'filename.h5' | |
data = h5py.File(filename) | |
nodes = list(data.items()) | |
data_path= '/a/b' #mention the node you are interested in | |
#Convert to .csv | |
filename_save = 'filename.csv' |
import os | |
import re | |
import sys | |
import copy | |
import struct | |
import pprint | |
import traceback | |
def pcd_read(filename, verbose=0, verbose_steps=0): | |
# Refer this to unpack - https://docs.python.org/3/library/struct.html#format-characters |
import time | |
import tqdm | |
import colorama # for multiple tqdm bars on Windows | |
import numpy as np | |
dataloader = np.random.random((10)) | |
with tqdm.tqdm(total=len(dataloader), leave=True, disable=False, position=0) as pbar: | |
for val in dataloader: | |
pbar.update(1) |
mkdir .jupyterhub
cd .jupyterhub
jupyterhub --generate-config
openssl rand -hex 8 > jupyterhub_cookie_secret
conda create -y -n XOSight-Pattern2 "python=3.8" "mamba>=0.22.1" -c conda-forge
conda activate XOSight-Pattern2
mamba install ros-noetic-desktop -c robostack -c robostack-experimental -c conda-forge --override-channels
mamba install vs2019_win-64
conda deactivate
conda activate XOSight-Pattern2
mamba install rosdep