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#!/usr/bin/env python

import rospy
from std_msgs.msg import String
from sensor_msgs.msg import NavSatFix

class publishGPS(object):

	def __init__(self):
		rospy.loginfo("Initialising GPS publishing")

amogh112

#!/usr/bin/env python

import rospy
import sys
from std_msgs.msg import String, Header
from sensor_msgs.msg import CameraInfo
from sensor_msgs.msg import Image

class publishTF(object):

amogh112

import tensorflow as tf

def load_graph(frozen_graph_filename):
    # We load the protobuf file from the disk and parse it to retrieve the 
    # unserialized graph_def
    with tf.gfile.GFile(frozen_graph_filename, "rb") as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())

    # Then, we import the graph_def into a new Graph and returns it 

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import cv2
import numpy as np
import time

drawing = False # true if mouse is pressed
ix,iy = -1,-1

# mouse callback function
def draw_circle(event,x,y,flags,param):
    global ix,iy,drawing

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import numpy as np


def generateData(a, b, num_samples):
    data_x = np.random.uniform(a, b, num_samples)
    data_y = np.random.uniform(a, b, num_samples)
    data = np.array(list(zip(data_x, data_y)))
    labels = np.array([-1 if datap[0] <= datap[1] else 1 for datap in data])
    return data, labels

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import time
from collections import defaultdict
import os
import gc
import torch
from tqdm import trange, tqdm

import masker
import tests
import utils

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class VLBertClassifier(VLBert):

    def __init__(self, cfg, args, tok, num_layers, num_outputs, hidden_units=1024, dim_mlp=384):
        super(VLBertClassifier, self).__init__(cfg, args, tok)

        if num_layers == 2:
            self.final_mlp = torch.nn.Sequential(
                        torch.nn.Dropout(0.1, inplace=False),
                        torch.nn.Linear(dim_mlp, hidden_units),
                        torch.nn.ReLU(inplace=True),

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def print_gc_tensors():

    dict_tensorsize_to_count = defaultdict(int) # Tensor size to number
    dict_device_to_tensors = defaultdict(lambda:  defaultdict(int)) # Device to tensor count
    dict_device_memory =  defaultdict(int) # Device to tensor memory

    total_count = 0
    for obj in gc.get_objects():
        try:
            if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)):

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import tqdm
from multiprocessing import Pool


def process_movie(movie):
    print("Processing movie : ", movie)
    path_movie_videos = os.path.join(path_all_movies, movie)
    list_avi_files = os.listdir(path_movie_videos)

    count=1

amogh112

Command to run:
NCCL_DEBUG=WARN CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 ./scripts/dist_run_single.sh 6 contrastive_pretrain/train_end2end.py ./cfgs/contrastive_pretrain/base_prec_random_movienet_images_4x16G_fp32.yaml ./checkpoints_debug2 | tee debug2.txt

Namespace(cfg='./cfgs/contrastive_pretrain/base_prec_random_movienet_images_4x16G_fp32.yaml', cudnn_off=False, dist=True, do_test=False, log_dir='./checkpoints_debug2/./output/vl-bert/contrastive_random_images/base_prec_random_movienet_images_4x16G_fp32/train_train/tensorboard_logs', model_dir='./checkpoints_debug2', slurm=False)
Namespace(cfg='./cfgs/contrastive_pretrain/base_prec_random_movienet_images_4x16G_fp32.yaml', cudnn_off=False, dist=True, do_test=False, log_dir='./checkpoints_debug2/./output/vl-bert/contrastive_random_images/base_prec_random_movienet_images_4x16G_fp32/train_train/tensorboard_logs', model_dir='./checkpoints_debug2', slurm=False)
Namespace(cfg='./cfgs/contrastive_pretrain/base_prec_random_movienet_images_4x16G_fp32.yaml', cudnn_off=Fa