zubair-irshad

import numpy as np
import torch
import collections
from collections import namedtuple
from abc import ABCMeta
from matplotlib import cm
import xml.etree.ElementTree as ET
import os
from collections import defaultdict

zubair-irshad

import numpy as np
import os
from .kitti360_utils import *
from .ray_utils import *
from PIL import Image
from torchvision import transforms as T
import random


def read_files(file_path):

zubair-irshad

import sys
import argparse
import cv2
import numpy as np

import rosbag
from sensor_msgs.msg import Image
from cv_bridge import CvBridge
import numpy as np

zubair-irshad

    def train_epoch(self, diter, length, batch_size, epoch, writer, train_steps):
        loss, action_loss, aux_loss = 0, 0, 0
        step_id = 0
        
        # high_level_losses=[]
        # low_level_action_losses =[]
        # low_level_stop_losses =[]
        # low_level_total_losses=[]

zubair-irshad

with warnings.catch_warnings():
    warnings.filterwarnings("ignore", category=FutureWarning)
    import tensorflow as tf

# WandB – Login to your wandb account so you can log all your metrics
wandb.login()

wandb.init(project="hierarchical_cma", sync_tensorboard=True)

wb_config = wandb.config

zubair-irshad

class CNN(nn.Module):
    def __init__(self, im_size, hidden_dim,hidden_dim2,hidden_dim3, kernel_size, n_classes):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(3,16,3,padding=1)
        self.bn1 = nn.BatchNorm2d(16)
        self.conv2 = nn.Conv2d(16,32,3,padding=1)
        self.bn2 = nn.BatchNorm2d(32)
        self.conv3 = nn.Conv2d(32,64,3,padding=1)
        self.bn3 = nn.BatchNorm2d(64)
        self.conv4 = nn.Conv2d(64,128,3,padding=1)

zubair-irshad

class CNN(nn.Module):
    def __init__(self, im_size, hidden_dim,hidden_dim2,hidden_dim3, kernel_size, n_classes):
        '''
        Create components of a CNN classifier and initialize their weights.
        Arguments:
            im_size (tuple): A tuple of ints with (channels, height, width)
            hidden_dim (int): Number of hidden activations to use
            kernel_size (int): Width and height of (square) convolution filters
            n_classes (int): Number of classes to score
        '''