From 2c4d564e6c49de311eb8bbcb36ee1093e50f1135 Mon Sep 17 00:00:00 2001
From: tgupta6 <tgupta6@illinois.edu>
Date: Mon, 7 Nov 2016 12:36:37 -0600
Subject: [PATCH] add new constants_crunchy
---
constants_crunchy.py | 31 ++++++++++++++++++++-----------
1 file changed, 20 insertions(+), 11 deletions(-)
diff --git a/constants_crunchy.py b/constants_crunchy.py
index add67d3..5776a30 100644
--- a/constants_crunchy.py
+++ b/constants_crunchy.py
@@ -5,14 +5,14 @@ def mkdir_if_not_exists(dir_name):
if not os.path.exists(dir_name):
os.mkdir(dir_name)
-experiment_name = 'trial_new_rel_feat'
+experiment_name = 'ans_through_obj_atr_pretrained_no_rel_bin_feats'
##########################################################################
# Machine Specific Paths #
##########################################################################
# Global output directory (all subexperiments will be saved here)
-global_output_dir = '/home/tanmay/Code/GenVQA/Exp_Results/VQA'
+global_output_dir = '/home/tanmay/Code/GenVQA/Exp_Results/models_cvpr/'
global_experiment_dir = os.path.join(
global_output_dir,
@@ -42,9 +42,9 @@ word2vec_binary = '/home/tanmay/Code/word2vec/word2vec-api-master/' + \
vqa_basedir = '/home/ssd/VQA/'
# Pretrained obj atr model to be restored
-pretrained_model = '/home/tanmay/Code/GenVQA/Exp_Results/VQA/' + \
- 'object_attribute_classifier_wordvec_xform/' + \
- 'object_attribute_classifiers/model-102000'
+pretrained_model = '/home/tanmay/Code/GenVQA/Exp_Results/models_cvpr/' + \
+ 'obj_atr_through_none_single_feat/answer_classifiers/' + \
+ 'model-72000'
##########################################################################
# Model Parameters #
@@ -93,6 +93,10 @@ regions_json = os.path.join(
data_absolute_path,
'restructured/region_with_hypernym_labels.json')
+eval_regions_json = os.path.join(
+ data_absolute_path,
+ 'restructured/region_with_labels.json')
+
mean_image_filename = os.path.join(
data_absolute_path,
'restructured/mean_image.jpg')
@@ -132,7 +136,7 @@ region_model = os.path.join(
# Object Attribute Classifier Training Params
region_batch_size = 200
region_num_epochs = 20
-region_queue_size = 400
+region_queue_size = 300
region_regularization_coeff = 1e-5
region_lr = 1e-3
@@ -169,7 +173,12 @@ region_attribute_scores_dirname = os.path.join(
region_output_dir,
'attribute_scores')
+region_object_scores_dirname = os.path.join(
+ region_output_dir,
+ 'object_scores')
+
mkdir_if_not_exists(region_attribute_scores_dirname)
+mkdir_if_not_exists(region_object_scores_dirname)
##########################################################################
# VQA Parameters #
@@ -253,11 +262,11 @@ answer_model = os.path.join(
'model')
# Answer classifier training params
-answer_train_from_scratch = True
+answer_train_from_scratch = False
answer_batch_size = 50
answer_num_epochs = 20
-answer_queue_size = 500
+answer_queue_size = 200
answer_regularization_coeff = 1e-5
answer_lr = 1e-3
@@ -285,8 +294,8 @@ model_accuracies_txt = os.path.join(
'model_accuracies.txt')
# Answer eval params
-answer_eval_on = 'testdev'
-answer_model_to_eval = answer_model + '-43000'
+answer_eval_on = 'val'
+answer_model_to_eval = answer_model + '-68000'
vqa_results_dir = os.path.join(
answer_output_dir,
@@ -315,6 +324,6 @@ raw_vqa_test_ques_json = os.path.join(
vqa_basedir,
'MultipleChoice_mscoco_test2015_questions.json')
-raw_vqa_val_anno_json = os.path.join(
+raw_vqa_test_anno_json = os.path.join(
vqa_basedir,
'mscoco_test2015_annotations.json')
--
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