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