diff --git a/answer_classifier_cached_features/eval.py b/answer_classifier_cached_features/eval.py
index 51d0080ed9c18921e0560fbc5198838321c71d48..046161ce4c9a4438a2eff0937081c8585a51cff7 100644
--- a/answer_classifier_cached_features/eval.py
+++ b/answer_classifier_cached_features/eval.py
@@ -51,6 +51,8 @@ def create_batch_generator(mode):
qids_json,
constants.vocab_json,
constants.vqa_answer_vocab_json,
+ constants.object_labels_json,
+ constants.attribute_labels_json,
constants.image_size,
constants.num_region_proposals,
constants.num_negative_answers,
@@ -267,13 +269,15 @@ if __name__=='__main__':
0,
0,
constants.answer_obj_atr_loss_wt,
+ constants.answer_ans_loss_wt,
+ constants.answer_mil_loss_wt,
resnet_feat_dim=constants.resnet_feat_dim,
training=False)
print 'Starting a session...'
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
- config.gpu_options.per_process_gpu_memory_fraction = 0.5
+ config.gpu_options.per_process_gpu_memory_fraction = 0.9
sess = tf.Session(config=config, graph=graph.tf_graph)
print 'Creating initializer...'
diff --git a/answer_classifier_cached_features/train.py b/answer_classifier_cached_features/train.py
index e2bd786192c4ccca3d472b9416cf6fbce8d098ad..a3705e6541a3e53b8c21199e95bf249fbdbb3004 100644
--- a/answer_classifier_cached_features/train.py
+++ b/answer_classifier_cached_features/train.py
@@ -15,7 +15,7 @@ import numpy as np
import pdb
from itertools import izip
import tensorflow as tf
-
+import gc
class graph_creator():
def __init__(
@@ -484,23 +484,24 @@ class graph_creator():
["accuracy_answer"],
self.moving_average_accuracy)
- # object
- self.object_accuracy = self.add_object_accuracy_computation(
- self.object_scores_with_labels,
- self.plh['object_labels'])
-
- object_accuracy_summary = tf.scalar_summary(
- "accuracy_object",
- self.object_accuracy)
-
- # attributes
- self.attribute_accuracy = self.add_attribute_accuracy_computation(
- self.attribute_scores_with_labels,
- self.plh['attribute_labels'])
-
- attribute_accuracy_summary = tf.scalar_summary(
- "accuracy_attribute",
- self.attribute_accuracy)
+ if self.training:
+ # object
+ self.object_accuracy = self.add_object_accuracy_computation(
+ self.object_scores_with_labels,
+ self.plh['object_labels'])
+
+ object_accuracy_summary = tf.scalar_summary(
+ "accuracy_object",
+ self.object_accuracy)
+
+ # attributes
+ self.attribute_accuracy = self.add_attribute_accuracy_computation(
+ self.attribute_scores_with_labels,
+ self.plh['attribute_labels'])
+
+ attribute_accuracy_summary = tf.scalar_summary(
+ "accuracy_attribute",
+ self.attribute_accuracy)
def add_answer_accuracy_computation(self, scores):
with tf.variable_scope('answer_accuracy'):
@@ -596,6 +597,7 @@ def create_scratch_initializer(graph, sess):
return initializer()
def create_vqa_batch_generator():
+
data_mgr = vqa_data.data(
constants.vqa_train_resnet_feat_dir,
constants.vqa_train_anno,
@@ -617,11 +619,15 @@ def create_vqa_batch_generator():
num_train_subset_questions,
constants.answer_num_epochs,
0)
-
- batch_generator = tftools.data.async_batch_generator(
+
+ batch_generator = tftools.data.batch_generator(
data_mgr,
- index_generator,
- constants.answer_queue_size)
+ index_generator)
+
+ # batch_generator = tftools.data.async_batch_generator(
+ # data_mgr,
+ # index_generator,
+ # constants.answer_queue_size)
return batch_generator
@@ -647,7 +653,7 @@ def create_vgenome_batch_generator():
num_train_subset_regions,
constants.region_num_epochs,
0)
-
+
batch_generator = tftools.data.async_batch_generator(
data_mgr,
index_generator,
@@ -895,11 +901,22 @@ def train(
zip(vars_to_eval_names, eval_vars)}
logger.log(iter, False, eval_vars_dict)
iter+=1
+<<<<<<< HEAD
+
+ if iter%8000==0 and iter!=0:
+ gc.collect()
+=======
+
+ if iter%8000==0 and iter!=0:
+ gc.collect()
+
+>>>>>>> adfc0ab... make ans eval and train compatible with each other
logger.log(iter-1, True, eval_vars_dict)
if __name__=='__main__':
+
print 'Creating batch generator...'
batch_generator = create_batch_generator()
@@ -921,11 +938,13 @@ if __name__=='__main__':
resnet_feat_dim=constants.resnet_feat_dim,
training=True)
+
print 'Attaching optimizer...'
optimizer = attach_optimizer(
graph,
constants.answer_lr)
+
print 'Starting a session...'
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
diff --git a/constants_vision_gpu_1.py b/constants_vision_gpu_1.py
index dd75d168d035dbdc01d161253f0a619cd04dcaf3..24f562690923d7a2e5d1da3f941f025e3fdf8c7e 100644
--- a/constants_vision_gpu_1.py
+++ b/constants_vision_gpu_1.py
@@ -120,9 +120,16 @@ region_model = os.path.join(
region_fine_tune_from_iter = 3000
region_fine_tune_from = region_model + '-' + str(region_fine_tune_from_iter)
+# Object Attribute Model Selection
+region_start_model = 8000
+region_step_size = 8000
+region_model_accuracies_txt = os.path.join(
+ region_output_dir,
+ 'model_accuracies.txt')
+
# Object Attribute Classifier Evaluation Params
-region_eval_on = 'train_held_out' # One of {'train_subset','train_held_out','test'}
-region_model_to_eval = region_model + '-' + '77500'
+region_eval_on = 'test' # One of {'train_subset','train_held_out','test'}
+region_model_to_eval = region_model + '-' + '72000'
region_attribute_scores_dirname = os.path.join(
region_output_dir,
@@ -214,7 +221,7 @@ 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 + '-55000'
+answer_model_to_eval = answer_model + '-72000'
answer_eval_data_json = os.path.join(
answer_output_dir,
diff --git a/data/cropped_regions_cached_features.py b/data/cropped_regions_cached_features.py
index c0f72b1a584243b3b20cfa6eb24099c15a5472b4..8adf16dcfb1d21447e0b8623c158f554b7804e2a 100644
--- a/data/cropped_regions_cached_features.py
+++ b/data/cropped_regions_cached_features.py
@@ -4,7 +4,7 @@ import ujson
import os
import pdb
import time
-import threading
+#import threading
import tftools.data
import image_io
@@ -90,8 +90,9 @@ class data():
batch_list[worker_id] = batch
except Exception, e:
- print 'Error in thread {}: {}'.format(
- threading.current_thread().name, str(e))
+ print 'Error while reading image: {}'.format(str(e))
+ # print 'Error in thread {}: {}'.format(
+ # threading.current_thread().name, str(e))
def get_parallel(self, samples):
diff --git a/data/vqa_cached_features.py b/data/vqa_cached_features.py
index ac04cefef38bd03abaa6f207d379c5621096a689..681356f54689c100f6214eae9ca3506c0a02497a 100644
--- a/data/vqa_cached_features.py
+++ b/data/vqa_cached_features.py
@@ -5,7 +5,7 @@ import re
import pdb
import time
import nltk
-import threading
+#import threading
import tftools.data
import image_io
@@ -210,8 +210,9 @@ class data():
batch_list[worker_id] = region_image
except Exception, e:
- print 'Error in thread {}: {}'.format(
- threading.current_thread().name, str(e))
+ print 'Error while reading image: {}'.format(str(e))
+ # print 'Error in thread {}: {}'.format(
+ # threading.current_thread().name, str(e))
def get_region_image(self, sample, region_number):
question_id = self.sample_to_question_dict[sample]
diff --git a/object_attribute_classifier_cached_features/select_best_model.py b/object_attribute_classifier_cached_features/select_best_model.py
index 198ac93ed468b0413315875de210ea068d77f2ea..2aa5426371a4ac8bfcb75f2f6bfe9b30fe74ba57 100644
--- a/object_attribute_classifier_cached_features/select_best_model.py
+++ b/object_attribute_classifier_cached_features/select_best_model.py
@@ -316,7 +316,8 @@ def model_path_generator(models_dir, start_model, step_size):
if __name__=='__main__':
model_paths = model_path_generator(
- constants.region_output_dir,
+ constants.models_dir,
+ #constants.region_output_dir,
constants.region_start_model,
constants.region_step_size)
diff --git a/split_genome.py b/split_genome.py
index d118ed2ea913f25ad3ac8ba002e4f27b7efc7a3e..d2fc0dd6c255e0ebff139758ce1d21b5aff91251 100644
--- a/split_genome.py
+++ b/split_genome.py
@@ -151,7 +151,8 @@ if __name__=='__main__':
with open(vqa_train2014_held_out_qids_json, 'r') as file:
vqa_train2014_held_out_qids = ujson.load(file)
-
+
+ # Find all image ids in vqa train2014
vqa_train2014_held_out_image_ids = set()
for qid in list(vqa_train2014_held_out_qids):
image_id = str(vqa_train2014_anno[qid]['image_id'])