From a3ddca6797e47dae88d38575dc9cdfc7e064a6e7 Mon Sep 17 00:00:00 2001
From: tgupta6 <tgupta6@illinois.edu>
Date: Fri, 9 Sep 2016 11:57:40 -0500
Subject: [PATCH] change constants in visualize_relevance and constants_crunchy
---
answer_classifier_cached_features/train.py | 1 +
constants_crunchy.py | 12 ++++++------
visual_util/visualize_relevance.py | 6 +++---
3 files changed, 10 insertions(+), 9 deletions(-)
diff --git a/answer_classifier_cached_features/train.py b/answer_classifier_cached_features/train.py
index 1db0b33..e6de431 100644
--- a/answer_classifier_cached_features/train.py
+++ b/answer_classifier_cached_features/train.py
@@ -476,6 +476,7 @@ def create_vqa_batch_generator():
resnet_feat_dim=constants.resnet_feat_dim)
num_train_subset_questions = len(data_mgr.qids)
+ print num_train_subset_questions
index_generator = tftools.data.random(
constants.answer_batch_size,
diff --git a/constants_crunchy.py b/constants_crunchy.py
index 0998551..6d46dad 100644
--- a/constants_crunchy.py
+++ b/constants_crunchy.py
@@ -5,7 +5,7 @@ def mkdir_if_not_exists(dir_name):
if not os.path.exists(dir_name):
os.mkdir(dir_name)
-experiment_name = 'QA_joint_pretrain_genome_split'
+experiment_name = 'object_attribute_classifier_large_images_vqa_split' #'QA_joint_pretrain_genome_split'
# Global output directory (all subexperiments will be saved here)
global_output_dir = '/home/tanmay/Code/GenVQA/Exp_Results/VQA'
@@ -128,7 +128,7 @@ region_model_accuracies_txt = os.path.join(
'model_accuracies.txt')
# Object Attribute Classifier Evaluation Params
-region_eval_on = 'train_subset' # One of {'test','train_held_out','train_subset'}
+region_eval_on = 'train_held_out' # One of {'test','train_held_out','train_subset'}
region_model_to_eval = region_model + '-' + '80000'
region_attribute_scores_dirname = os.path.join(
@@ -189,13 +189,13 @@ vqa_answer_vocab_json = os.path.join(
# Answer classifier training params
answer_batch_size = 50
-answer_num_epochs = 4
+answer_num_epochs = 6
answer_offset = 0
answer_obj_atr_loss_wt = 0.1
answer_regularization_coeff = 1e-5
answer_queue_size = 500
answer_embedding_dim = 600
-answer_lr = 1e-3
+answer_lr = 1e-4
answer_log_every_n_iter = 500
answer_output_dir = os.path.join(
global_experiment_dir,
@@ -220,7 +220,7 @@ answer_fine_tune_from = answer_model + '-' + str(answer_fine_tune_from_iter)
# Answer eval params
answer_eval_on = 'val'
-answer_model_to_eval = answer_model + '-18500'
+answer_model_to_eval = answer_model + '-42000'
vqa_results_dir = os.path.join(
answer_output_dir,
@@ -238,7 +238,7 @@ answer_eval_results_json = os.path.join(
# Select best model
models_dir = answer_output_dir
-start_model = 1000
+start_model = 40000
step_size = 2000
model_accuracies_txt = os.path.join(
answer_output_dir,
diff --git a/visual_util/visualize_relevance.py b/visual_util/visualize_relevance.py
index 72b07c7..17b947e 100644
--- a/visual_util/visualize_relevance.py
+++ b/visual_util/visualize_relevance.py
@@ -200,13 +200,13 @@ if __name__=='__main__':
'mscoco_val2014_annotations_with_parsed_questions.json')
exp_dir = '/home/tanmay/Code/GenVQA/Exp_Results/VQA/' + \
- 'QA_explicit_dot_joint_training_pretrained_same_lr/'
+ 'QA_joint_pretrain_genome_split/'
eval_data_json = os.path.join(
exp_dir,
- 'answer_classifiers/eval_val_rest_data.json')
+ 'answer_classifiers/Results/eval_val_data.json')
- output_dir = os.path.join(exp_dir, 'qual_results_val_rest_conf')
+ output_dir = os.path.join(exp_dir, 'qual_results_val_conf')
if not os.path.exists(output_dir):
os.mkdir(output_dir)
--
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