From 8922eafeee83f2b435df1fe599db6dd1caa9f11d Mon Sep 17 00:00:00 2001
From: tgupta6 <tgupta6@illinois.edu>
Date: Fri, 19 Aug 2016 13:26:38 -0500
Subject: [PATCH] local change in crunchy constants and vqa eval code
---
constants_crunchy.py | 9 +++++---
visual_util/visualize_relevance.py | 33 +++++++++++++++++++++++-------
2 files changed, 32 insertions(+), 10 deletions(-)
diff --git a/constants_crunchy.py b/constants_crunchy.py
index e0b1cac..ac7d315 100644
--- a/constants_crunchy.py
+++ b/constants_crunchy.py
@@ -8,7 +8,7 @@ def mkdir_if_not_exists(dir_name):
experiment_name = 'QA_explicit_dot_joint_training_pretrained_same_lr'
#experiment_name = 'object_attribute_classifier_large_images'
# Global output directory (all subexperiments will be saved here)
-global_output_dir = '/data/tanmay/GenVQA_Exp_Results'
+global_output_dir = '/home/tanmay/Code/GenVQA/Exp_Results/VQA'
global_experiment_dir = os.path.join(
global_output_dir,
@@ -154,7 +154,10 @@ vqa_answer_vocab_json = os.path.join(
# VQA dataset params
num_train_questions = 248349
-num_val_questions = 10000 #121512
+num_val_subset_questions = 10000
+num_val_questions = 121512
+num_val_rest_questions = num_val_questions - num_val_subset_questions
+
num_test_questions = 0
# Answer classifier training params
@@ -189,7 +192,7 @@ answer_fine_tune_from_iter = 19500
answer_fine_tune_from = answer_model + '-' + str(answer_fine_tune_from_iter)
# Answer eval params
-answer_eval_on = 'val'
+answer_eval_on = 'val_rest'
answer_model_to_eval = answer_model + '-39000'
answer_eval_data_json = os.path.join(
diff --git a/visual_util/visualize_relevance.py b/visual_util/visualize_relevance.py
index 521123b..72b07c7 100644
--- a/visual_util/visualize_relevance.py
+++ b/visual_util/visualize_relevance.py
@@ -2,6 +2,7 @@ import ujson
import os
import csv
import numpy as np
+import random
from matplotlib import cm
import image_io
@@ -103,10 +104,13 @@ class RelevanceVisualizer():
box_score_pairs = self.get_box_score_pairs(bboxes, rel_scores)
rel_map = np.zeros(im.shape[0:2])
for box, score in box_score_pairs:
- gauss_map = self.make_gaussian(box, im.shape[0:2])
- rel_map = np.maximum(rel_map, score*gauss_map)
+ box_map = self.make_boxmap(box, im.shape[0:2])
+ rel_map = rel_map + score*box_map
+ # gauss_map = self.make_gaussian(box, im.shape[0:2])
+ # rel_map = np.maximum(rel_map, score*box_map)
rel_map_ =cm.jet(np.uint8(rel_map*255))[:,:,0:3]*255
- im_rel_map = np.uint8(0.3*im+0.7*rel_map_)
+ # im_rel_map = np.uint8(0.5*im+0.5*rel_map_)
+ im_rel_map = np.uint8(0.2*im + 0.8*np.tile(rel_map[:,:,None], [1,1,3])*im)
return rel_map, im_rel_map, ans, ans_score
def make_gaussian(self, box, im_size):
@@ -121,6 +125,16 @@ class RelevanceVisualizer():
g = np.exp(-((xx-cx)**2/(2*sigma_x**2)) - ((yy-cy)**2/(2*sigma_y**2)))
return g
+ def make_boxmap(self, box, im_size):
+ im_h, im_w = im_size
+ x = int(box['x'])
+ y = int(box['y'])
+ w = int(box['w'])
+ h = int(box['h'])
+ map = np.zeros(im_size)
+ map[y-1:y+h-1, x-1:x+w-1] = 1.0
+ return map
+
def write_html(self):
col_dict = {
0: 'Question',
@@ -128,9 +142,13 @@ class RelevanceVisualizer():
2: 'Pos. Relevance',
3: 'Pred. Answer',
4: 'Pred. Relevance',
+ 5: 'Question Id'
}
self.html_writer.add_element(col_dict)
- for qid in rel_vis.eval_data.keys():
+ random.seed(0)
+ qids = sorted(rel_vis.eval_data.keys())
+ random.shuffle(qids)
+ for qid in qids[0:5000]:
question = rel_vis.anno_data[qid]['question']
pred_rel, pred_im_rel, pred_ans, pred_score = rel_vis.create_relevance_map(
@@ -159,11 +177,12 @@ class RelevanceVisualizer():
2 : self.html_writer.image_tag(pos_im_name, im_h, im_w),
3 : pred_ans + ': ' + str(pred_score),
4 : self.html_writer.image_tag(pred_im_name, im_h, im_w),
+ 5 : qid,
}
self.html_writer.add_element(col_dict)
- self.html_writer.close()
+ self.html_writer.close_file()
if __name__=='__main__':
@@ -185,9 +204,9 @@ if __name__=='__main__':
eval_data_json = os.path.join(
exp_dir,
- 'answer_classifiers/eval_val_data.json')
+ 'answer_classifiers/eval_val_rest_data.json')
- output_dir = os.path.join(exp_dir, 'qual_results2')
+ output_dir = os.path.join(exp_dir, 'qual_results_val_rest_conf')
if not os.path.exists(output_dir):
os.mkdir(output_dir)
--
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