diff --git a/classifiers/attribute_classifiers/atr_data_io_helper.py b/classifiers/attribute_classifiers/atr_data_io_helper.py
index 0f707a91e22c77b26389de284fe3e9dc8a13b8e7..572704d7e7ab247f1e921f2f665172efa8a122a7 100644
--- a/classifiers/attribute_classifiers/atr_data_io_helper.py
+++ b/classifiers/attribute_classifiers/atr_data_io_helper.py
@@ -6,47 +6,49 @@ import matplotlib.image as mpimg
import numpy as np
import tensorflow as tf
from scipy import misc
+import time
-def atr_mini_batch_loader(json_filename, image_dir, mean_image, start_index, batch_size, img_height=100, img_width=100, channels=3):
-
- with open(json_filename, 'r') as json_file:
- json_data = json.load(json_file)
+def atr_mini_batch_loader(json_data, image_dir, mean_image, start_index, batch_size, img_height=100, img_width=100, channels=3):
atr_images = np.empty(shape=[9*batch_size, img_height/3, img_width/3, channels])
atr_labels = np.zeros(shape=[9*batch_size, 4])
- for i in range(start_index, start_index + batch_size):
+ for i in xrange(start_index, start_index + batch_size):
image_name = os.path.join(image_dir, str(i) + '.jpg')
image = misc.imresize(mpimg.imread(image_name),(img_height, img_width), interp='nearest')
+ #image = np.zeros(shape=[img_height, img_width, channels])
crop_shape = np.array([image.shape[0], image.shape[1]])/3
- selected_anno = [q for q in json_data if q['image_id']==i]
- grid_config = selected_anno[0]['config']
-
+ grid_config = json_data[i]
counter = 0;
- for grid_row in range(0,3):
- for grid_col in range(0,3):
+ for grid_row in xrange(0,3):
+ for grid_col in xrange(0,3):
start_row = grid_row*crop_shape[0]
start_col = grid_col*crop_shape[1]
cropped_image = image[start_row:start_row+crop_shape[0], start_col:start_col+crop_shape[1], :]
+
if np.ndim(mean_image)==0:
atr_images[9*(i-start_index)+counter,:,:,:] = cropped_image/254.0
else:
atr_images[9*(i-start_index)+counter,:,:,:] = (cropped_image / 254.0) - mean_image
- atr_labels[9*(i-start_index)+counter, grid_config[6*grid_row+2*grid_col+1]] = 1
+
+ if grid_config[6*grid_row+2*grid_col]==0: # if the space is blank
+ atr_labels[9*(i-start_index)+counter,3] = 1
+ else:
+ atr_labels[9*(i-start_index)+counter, grid_config[6*grid_row+2*grid_col+1]] = 1
counter = counter + 1
return (atr_images, atr_labels)
-def mean_image_batch(json_filename, image_dir, start_index, batch_size, img_height=100, img_width=100, channels=3):
- batch = atr_mini_batch_loader(json_filename, image_dir, np.empty([]), start_index, batch_size, img_height, img_width, channels)
+def mean_image_batch(json_data, image_dir, start_index, batch_size, img_height=100, img_width=100, channels=3):
+ batch = atr_mini_batch_loader(json_data, image_dir, np.empty([]), start_index, batch_size, img_height, img_width, channels)
mean_image = np.mean(batch[0], 0)
return mean_image
-def mean_image(json_filename, image_dir, num_images, batch_size, img_height=100, img_width=100, channels=3):
+def mean_image(json_data, image_dir, num_images, batch_size, img_height=100, img_width=100, channels=3):
max_iter = np.floor(num_images/batch_size)
mean_image = np.zeros([img_height/3, img_width/3, channels])
for i in range(max_iter.astype(np.int16)):
- mean_image = mean_image + mean_image_batch(json_filename, image_dir, 1+i*batch_size, batch_size, img_height, img_width, channels)
+ mean_image = mean_image + mean_image_batch(json_data, image_dir, 1+i*batch_size, batch_size, img_height, img_width, channels)
mean_image = mean_image/max_iter
return mean_image
diff --git a/classifiers/attribute_classifiers/atr_data_io_helper.pyc b/classifiers/attribute_classifiers/atr_data_io_helper.pyc
index b958afac0eda1f2f3ff3b1e2e2dc36c210e170a5..457ee8ea56d594f13e29953a5ac035b031255cd4 100644
Binary files a/classifiers/attribute_classifiers/atr_data_io_helper.pyc and b/classifiers/attribute_classifiers/atr_data_io_helper.pyc differ
diff --git a/classifiers/attribute_classifiers/eval_atr_classifier.py b/classifiers/attribute_classifiers/eval_atr_classifier.py
index c3222b1b1495b1bd6813f39d807f773aad9a7b83..643139066de0a3093df31b7496db85a29aa154e2 100644
--- a/classifiers/attribute_classifiers/eval_atr_classifier.py
+++ b/classifiers/attribute_classifiers/eval_atr_classifier.py
@@ -1,4 +1,5 @@
import sys
+import json
import os
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
@@ -15,6 +16,7 @@ def eval(eval_params):
x, y, keep_prob = graph_creator.placeholder_inputs()
_ = graph_creator.obj_comp_graph(x, 1.0)
obj_feat = tf.get_collection('obj_feat', scope='obj/conv2')
+
y_pred = graph_creator.atr_comp_graph(x, keep_prob, obj_feat[0])
accuracy = graph_creator.evaluation(y, y_pred)
@@ -23,6 +25,13 @@ def eval(eval_params):
mean_image = np.load(os.path.join(eval_params['out_dir'], 'mean_image.npy'))
test_json_filename = eval_params['test_json']
+ with open(test_json_filename, 'r') as json_file:
+ raw_json_data = json.load(json_file)
+ test_json_data = dict()
+ for entry in raw_json_data:
+ if entry['image_id'] not in test_json_data:
+ test_json_data[entry['image_id']]=entry['config']
+
image_dir = eval_params['image_dir']
html_dir = eval_params['html_dir']
if not os.path.exists(html_dir):
@@ -36,12 +45,13 @@ def eval(eval_params):
color_dict = {
0: 'red', # blanks are treated as red
1: 'green',
- 2: 'blue'}
+ 2: 'blue',
+ 3: 'blank'}
batch_size = 100
correct = 0
for i in range(50):
- test_batch = atr_data_loader.atr_mini_batch_loader(test_json_filename, image_dir, mean_image, 10000+i*batch_size, batch_size, 75, 75)
+ test_batch = atr_data_loader.atr_mini_batch_loader(test_json_data, image_dir, mean_image, 10000+i*batch_size, batch_size, 75, 75)
feed_dict_test={x: test_batch[0], y: test_batch[1], keep_prob: 1.0}
result = sess.run([accuracy, y_pred], feed_dict=feed_dict_test)
correct = correct + result[0]*batch_size
diff --git a/classifiers/attribute_classifiers/eval_atr_classifier.pyc b/classifiers/attribute_classifiers/eval_atr_classifier.pyc
index 0be14f65263aa832ead7df9c9f5988c223c072a2..960456aa4a4ea6b14e9af628b1864cfd74c89fa3 100644
Binary files a/classifiers/attribute_classifiers/eval_atr_classifier.pyc and b/classifiers/attribute_classifiers/eval_atr_classifier.pyc differ
diff --git a/classifiers/attribute_classifiers/train_atr_classifier.py b/classifiers/attribute_classifiers/train_atr_classifier.py
index 5145bae811398fb383b34a0ad95f7c323e068704..0c59564150a6cfd0ee4f0f6ccee80674ba743ac6 100644
--- a/classifiers/attribute_classifiers/train_atr_classifier.py
+++ b/classifiers/attribute_classifiers/train_atr_classifier.py
@@ -1,5 +1,7 @@
import sys
+import json
import os
+import time
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
@@ -14,15 +16,30 @@ def train(train_params):
x, y, keep_prob = graph_creator.placeholder_inputs()
_ = graph_creator.obj_comp_graph(x, 1.0)
obj_feat = tf.get_collection('obj_feat', scope='obj/conv2')
+
+ # Session Saver
+ obj_saver = tf.train.Saver()
+
+ # Restore obj network parameters
+ obj_saver.restore(sess, train_params['obj_model_name'] + '-' + str(train_params['obj_global_step']))
+
y_pred = graph_creator.atr_comp_graph(x, keep_prob, obj_feat[0])
cross_entropy = graph_creator.loss(y, y_pred)
vars_to_opt = tf.get_collection(tf.GraphKeys.VARIABLES, scope='atr')
+ vars_to_restore = tf.get_collection(tf.GraphKeys.VARIABLES, scope='obj')
+ train_step = tf.train.AdamOptimizer(train_params['adam_lr']).minimize(cross_entropy, var_list=vars_to_opt)
+
+
all_vars = tf.get_collection(tf.GraphKeys.VARIABLES)
+ vars_to_init = [var for var in all_vars if var not in vars_to_restore]
print('Variables that are being optimized: ' + ' '.join([var.name for var in vars_to_opt]))
- print('All variables: ' + ' '.join([var.name for var in all_vars]))
- train_step = tf.train.AdamOptimizer(train_params['adam_lr']).minimize(cross_entropy, var_list=vars_to_opt)
+ print('Variables that will be initialized: ' + ' '.join([var.name for var in vars_to_init]))
accuracy = graph_creator.evaluation(y, y_pred)
+ # Session saver for atr variables
+ atr_saver = tf.train.Saver(vars_to_opt)
+ obj_atr_saver = tf.train.Saver(all_vars)
+
outdir = train_params['out_dir']
if not os.path.exists(outdir):
os.mkdir(outdir)
@@ -31,23 +48,32 @@ def train(train_params):
img_width = 75
img_height = 75
train_json_filename = train_params['train_json']
+ with open(train_json_filename, 'r') as json_file:
+ raw_json_data = json.load(json_file)
+ train_json_data = dict()
+ for entry in raw_json_data:
+ if entry['image_id'] not in train_json_data:
+ train_json_data[entry['image_id']]=entry['config']
+
+
image_dir = train_params['image_dir']
- mean_image = atr_data_loader.mean_image(train_json_filename, image_dir, 1000, 100, img_height, img_width)
+ if train_params['mean_image']=='':
+ print('Computing mean image')
+ mean_image = atr_data_loader.mean_image(train_json_data, image_dir, 1000, 100, img_height, img_width)
+ else:
+ print('Loading mean image')
+ mean_image = np.load(train_params['mean_image'])
np.save(os.path.join(outdir, 'mean_image.npy'), mean_image)
# Val Data
- val_batch = atr_data_loader.atr_mini_batch_loader(train_json_filename, image_dir, mean_image, 9501, 499, img_height, img_width)
+ print('Loading validation data')
+ val_batch = atr_data_loader.atr_mini_batch_loader(train_json_data, image_dir, mean_image, 9501, 499, img_height, img_width)
feed_dict_val={x: val_batch[0], y: val_batch[1], keep_prob: 1.0}
- # Session Saver
- saver = tf.train.Saver()
-
+
# Start Training
- sess.run(tf.initialize_all_variables())
-
- # Restore obj network parameters
- saver.restore(sess, train_params['obj_model_name'] + '-' + str(train_params['obj_global_step']))
-
+ print('Initializing variables')
+ sess.run(tf.initialize_variables(vars_to_init))
batch_size = 10
max_epoch = 2
@@ -57,18 +83,23 @@ def train(train_params):
train_acc_array_epoch = np.zeros([max_epoch])
for epoch in range(max_epoch):
for i in range(max_iter):
- train_batch = atr_data_loader.atr_mini_batch_loader(train_json_filename, image_dir, mean_image, 1+i*batch_size, batch_size, img_height, img_width)
+ print(i)
+ # start = time.time()
+ train_batch = atr_data_loader.atr_mini_batch_loader(train_json_data, image_dir, mean_image, 1+i*batch_size, batch_size, img_height, img_width)
+ # end = time.time()
+ # print(end-start)
feed_dict_train={x: train_batch[0], y: train_batch[1], keep_prob: 0.5}
_, current_train_batch_acc = sess.run([train_step, accuracy], feed_dict=feed_dict_train)
train_acc_array_epoch[epoch] = train_acc_array_epoch[epoch] + current_train_batch_acc
- val_acc_array_iter[i+epoch*max_iter] = accuracy.eval(feed_dict_val)
- plotter.plot_accuracy(np.arange(0,i+1+epoch*max_iter)+1, val_acc_array_iter[0:i+1+epoch*max_iter], xlim=[1, max_epoch*max_iter], ylim=[0, 1.], savePath=os.path.join(outdir,'valAcc_vs_iter.pdf'))
- print('Step: {} Val Accuracy: {}'.format(i+1+epoch*max_iter, val_acc_array_iter[i+epoch*max_iter]))
+ # val_acc_array_iter[i+epoch*max_iter] = accuracy.eval(feed_dict_val)
+ # plotter.plot_accuracy(np.arange(0,i+1+epoch*max_iter)+1, val_acc_array_iter[0:i+1+epoch*max_iter], xlim=[1, max_epoch*max_iter], ylim=[0, 1.], savePath=os.path.join(outdir,'valAcc_vs_iter.pdf'))
+ # print('Step: {} Val Accuracy: {}'.format(i+1+epoch*max_iter, val_acc_array_iter[i+epoch*max_iter]))
train_acc_array_epoch[epoch] = train_acc_array_epoch[epoch] / max_iter
val_acc_array_epoch[epoch] = val_acc_array_iter[i+epoch*max_iter]
plotter.plot_accuracies(xdata=np.arange(0,epoch+1)+1, ydata_train=train_acc_array_epoch[0:epoch+1], ydata_val=val_acc_array_epoch[0:epoch+1], xlim=[1, max_epoch], ylim=[0, 1.], savePath=os.path.join(outdir,'acc_vs_epoch.pdf'))
- save_path = saver.save(sess, os.path.join(outdir,'atr_classifier'), global_step=epoch)
+ _ = atr_saver.save(sess, os.path.join(outdir,'atr_classifier'), global_step=epoch)
+ _ = obj_atr_saver.save(sess, os.path.join(outdir,'obj_atr_classifier'), global_step=epoch)
sess.close()
diff --git a/classifiers/attribute_classifiers/train_atr_classifier.pyc b/classifiers/attribute_classifiers/train_atr_classifier.pyc
index 78a4e66e7a05fc26d316c57a52f11a19907ab03e..500169dccf17b7b3efcb303cb4ea2258248b13b3 100644
Binary files a/classifiers/attribute_classifiers/train_atr_classifier.pyc and b/classifiers/attribute_classifiers/train_atr_classifier.pyc differ
diff --git a/classifiers/object_classifiers/train_obj_classifier.py b/classifiers/object_classifiers/train_obj_classifier.py
index 45d5830b4c22c80b468c4e4d5169ad0692570967..debc76d22ea4565b002b57b77273898abe6e6751 100644
--- a/classifiers/object_classifiers/train_obj_classifier.py
+++ b/classifiers/object_classifiers/train_obj_classifier.py
@@ -26,7 +26,12 @@ def train(train_params):
img_height = 75
train_json_filename = train_params['train_json']
image_dir = train_params['image_dir']
- mean_image = shape_data_loader.mean_image(train_json_filename, image_dir, 1000, 100, img_height, img_width)
+ if train_params['mean_image']=='':
+ print('Computing mean image')
+ mean_image = atr_data_loader.mean_image(train_json_filename, image_dir, 1000, 100, img_height, img_width)
+ else:
+ print('Loading mean image')
+ mean_image = np.load(train_params['mean_image'])
np.save(os.path.join(outdir, 'mean_image.npy'), mean_image)
# Val Data
diff --git a/classifiers/object_classifiers/train_obj_classifier.pyc b/classifiers/object_classifiers/train_obj_classifier.pyc
index eac87e3278c2ff50ee06780b0e6328693ac40b63..b0dd5d511a461b073a33139526028efddda13b3c 100644
Binary files a/classifiers/object_classifiers/train_obj_classifier.pyc and b/classifiers/object_classifiers/train_obj_classifier.pyc differ
diff --git a/classifiers/tf_graph_creation_helper.pyc b/classifiers/tf_graph_creation_helper.pyc
index ba8c1623b0ba07ff0da1f5e5eceedd6f69bfefaa..cc7087d41170050991a04ef6986c615efc6ba662 100644
Binary files a/classifiers/tf_graph_creation_helper.pyc and b/classifiers/tf_graph_creation_helper.pyc differ
diff --git a/classifiers/train_classifiers.py b/classifiers/train_classifiers.py
index d2edabb9bafb5043b1059983373bb402c12e0629..e7d0ba922063e68a0e3e8f6c54e853356a283410 100644
--- a/classifiers/train_classifiers.py
+++ b/classifiers/train_classifiers.py
@@ -12,7 +12,7 @@ import attribute_classifiers.eval_atr_classifier as atr_evaluator
workflow = {
'train_obj': False,
'eval_obj': False,
- 'train_atr': True,
+ 'train_atr': False,
'eval_atr': True,
}
@@ -21,6 +21,8 @@ obj_classifier_train_params = {
'adam_lr': 0.001,
'train_json': '/home/tanmay/Code/GenVQA/GenVQA/shapes_dataset/train_anno.json',
'image_dir': '/home/tanmay/Code/GenVQA/GenVQA/shapes_dataset/images',
+ 'mean_image': '/home/tanmay/Code/GenVQA/Exp_Results/Obj_Classifier/mean_image.npy',
+# 'mean_image': '',
}
obj_classifier_eval_params = {
@@ -39,11 +41,13 @@ atr_classifier_train_params = {
'image_dir': '/home/tanmay/Code/GenVQA/GenVQA/shapes_dataset/images',
'obj_model_name': obj_classifier_eval_params['model_name'],
'obj_global_step': 1,
+ 'mean_image': '/home/tanmay/Code/GenVQA/Exp_Results/Obj_Classifier/mean_image.npy',
+# 'mean_image': '',
}
atr_classifier_eval_params = {
'out_dir': '/home/tanmay/Code/GenVQA/Exp_Results/Atr_Classifier',
- 'model_name': '/home/tanmay/Code/GenVQA/Exp_Results/Atr_Classifier/atr_classifier',
+ 'model_name': '/home/tanmay/Code/GenVQA/Exp_Results/Atr_Classifier/obj_atr_classifier',
'global_step': 1,
'test_json': '/home/tanmay/Code/GenVQA/GenVQA/shapes_dataset/test_anno.json',
'image_dir': '/home/tanmay/Code/GenVQA/GenVQA/shapes_dataset/images',