text stringlengths 1 93.6k |
|---|
else:
|
spamwatch = None
|
# ================================================
|
if not os.path.isdir(Config.TMP_DOWNLOAD_DIRECTORY):
|
os.makedirs(Config.TMP_DOWNLOAD_DIRECTORY)
|
# thumb image
|
if Config.THUMB_IMAGE is not None:
|
check = url(Config.THUMB_IMAGE)
|
if check:
|
try:
|
with open(thumb_image_path, "wb") as f:
|
f.write(requests.get(Config.THUMB_IMAGE).content)
|
except Exception as e:
|
LOGS.info(str(e))
|
def set_key(dictionary, key, value):
|
if key not in dictionary:
|
dictionary[key] = value
|
elif isinstance(dictionary[key], list):
|
if value in dictionary[key]:
|
return
|
dictionary[key].append(value)
|
else:
|
dictionary[key] = [dictionary[key], value]
|
async def make_gif(event, reply, quality=None, fps=None):
|
fps = fps or 1
|
quality = quality or 256
|
result_p = os.path.join("temp", "animation.gif")
|
animation = lottie.parsers.tgs.parse_tgs(reply)
|
with open(result_p, "wb") as result:
|
await _lionutils.run_sync(
|
lottie.exporters.gif.export_gif, animation, result, quality, fps
|
)
|
return result_p
|
# <FILESEP>
|
#############################################
|
## Artemis ##
|
## Copyright (c) 2022-present NAVER Corp. ##
|
## CC BY-NC-SA 4.0 ##
|
#############################################
|
"""
|
This script enables to produce heatmaps for the EM & IS scores of the ARTEMIS
|
model.
|
Change the global parameters below to precise what / how many heatmaps should be
|
generated, and run this script with the same arguments as when evaluating a
|
model, with `--gradcam` in addition.
|
"""
|
import os
|
import cv2
|
import numpy as np
|
import copy
|
import json
|
import torch
|
from torch.autograd import grad
|
import data
|
from vocab import Vocabulary
|
from utils import params_require_grad
|
from artemis_model import ARTEMIS
|
from evaluate import load_model, compute_and_process_compatibility_scores
|
from option import parser, verify_input_args
|
################################################################################
|
# *** GLOBAL PARAMETERS
|
################################################################################
|
# whether to generate heatmaps for the queries yielding the best results (the
|
# ground truth target image is well ranked)
|
ONLY_BEST_RESULTS = True
|
# number of queries to study
|
NUMBER_OF_EXAMPLES = 5
|
# number of coefficients contributing the most to a given score, that should be
|
# considered for backpropagation, in the GradCAM algorithm. If the score is
|
# computed as the dot product of two vectors `a` and `b`, the coefficients are
|
# given by the point-wise product of `a` and `b`.
|
NUMBER_OF_MAIN_COEFF = 3
|
################################################################################
|
# *** GENERATE & SAVE HEATMAPS
|
################################################################################
|
def main_generate_heatmaps(args, model, vocab):
|
"""
|
Potentially find the indices of the most relevant data examples (i.e. data
|
examples whose expected target image is well ranked by the model), and
|
generate heatmaps for them.
|
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