text stringlengths 1 93.6k |
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"""
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M_ImageCaptioner2_Prompt = """
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Describe this image in detail please.
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The language of reply is English only!!!
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Starts with "In the image,"
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"""
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ImageCaptionerPostProcessing_System = """
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I want you to write me a detailed list of tips for Content.
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Write a very short description of the scene and put it in the 'short_describes' field
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Write complete [moods, styles, lights, elements, objects] of the word array and put it in the '$_tags' field
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Don't include anything that isn't in Content.
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The language of reply is English only!!!
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"""
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# <FILESEP>
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# Loading the RDT into a hash table:
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# Noe that this is less memory and cpu efficient
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# as compared to using rdt.pkl (based on marisa_trie and numpy)
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import codecs
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from collections import defaultdict
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from traceback import format_exc
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from time import time
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import gzip
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from os.path import splitext
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# enter path to graph of words here
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dt_fpath = "all.norm-sz500-w10-cb0-it3-min5.w2v.vocab_1100000_similar250.gz"
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VERBOSE = False
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SEP = "\t"
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SEP_SCORE = ":"
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SEP_LIST = ","
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UNSEP = "_"
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MIN_SIM = 0.0
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tic = time()
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with gzip.open(dt_fpath) if splitext(dt_fpath)[-1] == ".gz" else codecs.open(dt_fpath,"r","utf-8") as input_file:
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dt = defaultdict(lambda: defaultdict(float))
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rel_num = 0
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for i, line in enumerate(input_file):
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#if i > 10: break
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try:
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word_i, neighbors = line.split(SEP)
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word_i = word_i.replace(SEP, UNSEP)
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for word_j_sim_ij in neighbors.split(SEP_LIST):
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word_j, sim_ij = word_j_sim_ij.split(SEP_SCORE)
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word_j = word_j.replace(SEP, UNSEP)
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sim_ij = float(sim_ij)
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if sim_ij < MIN_SIM: continue
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rel_num += 1
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dt[word_i][word_j] = sim_ij
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except:
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print(format_exc())
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if VERBOSE: print("bad line:", i, line)
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print(time()-tic, "sec.")
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print("Sample entries:")
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i = 0
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for w1 in dt:
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for w2 in dt[w1]:
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print(w1, w2, dt[w1][w2])
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i += 1
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if i > 1000: break
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# <FILESEP>
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"""
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main.py: Main code to drive LSC-CNN
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Authors : svp, mns, dbs
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"""
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import argparse
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import random
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from data_reader import DataReader
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import matplotlib
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from matplotlib import pyplot as plt
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import cv2
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import numpy as np
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import os
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import random, string
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import math
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import pickle
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from collections import OrderedDict
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import torch
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from torch import nn as nn, optim as optim
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from torch.autograd import Variable
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import datetime
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from error_function import offset_sum
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from scipy.misc import imsave, imresize
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from utils import apply_nms
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from network import LSCCNN
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from utils.logging_tools import *
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from utils.loss_weights import *
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################ Architecture Hyper-parameters ################
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