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Commit 99ac4c72 authored by tgupta6's avatar tgupta6
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Modified code with variable name scoping

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classifiers/attribute_classifiers/__init__.py 0 → 100644
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classifiers/attribute_classifiers/__init__.pyc 0 → 100644
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classifiers/attribute_classifiers/atr_data_io_helper.py 0 → 100644
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import json
import sys
import os
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import tensorflow as tf
from scipy import misc
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)
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):
image_name = os.path.join(image_dir, str(i) + '.jpg')
image = misc.imresize(mpimg.imread(image_name),(img_height, img_width), interp='nearest')
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']
counter = 0;
for grid_row in range(0,3):
for grid_col in range(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
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)
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):
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/max_iter
return mean_image
class html_atr_table_writer():
def __init__(self, filename):
self.filename = filename
self.html_file = open(self.filename, 'w')
self.html_file.write("""<!DOCTYPE html>\n<html>\n<body>\n<table border="1" style="width:100%"> \n""")
def add_element(self, col_dict):
self.html_file.write(' <tr>\n')
for key in range(len(col_dict)):
self.html_file.write(""" <td>{}</td>\n""".format(col_dict[key]))
self.html_file.write(' </tr>\n')
def image_tag(self, image_path, height, width):
return """<img src="{}" alt="IMAGE NOT FOUND!" height={} width={}>""".format(image_path,height,width)
def close_file(self):
self.html_file.write('</table>\n</body>\n</html>')
self.html_file.close()
if __name__=="__main__":
html_writer = html_atr_table_writer('/home/tanmay/Code/GenVQA/Exp_Results/Atr_Classifier_v_1/trial.html')
col_dict={0: 'sam', 1: html_writer.image_tag('something.png',25,25)}
html_writer.add_element(col_dict)
html_writer.close_file()
classifiers/attribute_classifiers/atr_data_io_helper.pyc 0 → 100644
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classifiers/attribute_classifiers/eval_atr_classifier.py 0 → 100644
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import sys
import os
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
from scipy import misc
import tensorflow as tf
import atr_data_io_helper as atr_data_loader
import tf_graph_creation_helper as graph_creator
import plot_helper as plotter
def eval(eval_params):
sess=tf.InteractiveSession()
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)
saver = tf.train.Saver()
saver.restore(sess, eval_params['model_name'] + '-' + str(eval_params['global_step']))
mean_image = np.load(os.path.join(eval_params['out_dir'], 'mean_image.npy'))
test_json_filename = eval_params['test_json']
image_dir = eval_params['image_dir']
html_dir = eval_params['html_dir']
if not os.path.exists(html_dir):
os.mkdir(html_dir)
html_writer = atr_data_loader.html_atr_table_writer(os.path.join(html_dir, 'index.html'))
col_dict = {
0: 'Grount Truth',
1: 'Prediction',
2: 'Image'}
html_writer.add_element(col_dict)
color_dict = {
0: 'red', # blanks are treated as red
1: 'green',
2: 'blue'}
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)
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
print(correct)
for row in range(batch_size*9):
gt_id = np.argmax(test_batch[1][row,:])
pred_id = np.argmax(result[1][row, :])
if not gt_id==pred_id:
img_filename = os.path.join(html_dir,'{}_{}.png'.format(i,row))
misc.imsave(img_filename, test_batch[0][row,:,:,:] + mean_image)
col_dict = {
0: color_dict[gt_id],
1: color_dict[pred_id],
2: html_writer.image_tag('{}_{}.png'.format(i,row), 25, 25)}
html_writer.add_element(col_dict)
html_writer.close_file()
print('Test Accuracy: {}'.format(correct / 5000))
sess.close()
tf.reset_default_graph()
classifiers/attribute_classifiers/eval_atr_classifier.pyc 0 → 100644
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classifiers/attribute_classifiers/train_atr_classifier.py
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......@@ -5,36 +5,33 @@ import matplotlib.image as mpimg
import numpy as np
import tensorflow as tf
import atr_data_io_helper as atr_data_loader
import tf_graph_creation_helper as graph_creator
import plot_helper as plotter
def train():
# Start session
def train(train_params):
sess = tf.InteractiveSession()
x, y, keep_prob = placeholder_inputs()
y_pred = comp_graph_v_2(x, y, keep_prob)
# Specify loss
cross_entropy = -tf.reduce_sum(y*tf.log(y_pred))
# Specify training method
train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)
# Evaluator
accuracy = evaluation(y, y_pred)
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])
cross_entropy = graph_creator.loss(y, y_pred)
vars_to_opt = tf.get_collection(tf.GraphKeys.VARIABLES, scope='atr')
all_vars = tf.get_collection(tf.GraphKeys.VARIABLES)
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)
accuracy = graph_creator.evaluation(y, y_pred)
# Merge summaries and write them to ~/Code/Tensorflow_Exp/logDir
merged = tf.merge_all_summaries()
# Output dir
outdir = '/home/tanmay/Code/GenVQA/Exp_Results/Atr_Classifier_v_1/'
outdir = train_params['out_dir']
if not os.path.exists(outdir):
os.mkdir(outdir)
# Training Data
img_width = 75
img_height = 75
train_json_filename = '/home/tanmay/Code/GenVQA/GenVQA/shapes_dataset/train_anno.json'
image_dir = '/home/tanmay/Code/GenVQA/GenVQA/shapes_dataset/images'
train_json_filename = train_params['train_json']
image_dir = train_params['image_dir']
mean_image = atr_data_loader.mean_image(train_json_filename, image_dir, 1000, 100, img_height, img_width)
np.save(os.path.join(outdir, 'mean_image.npy'), mean_image)
......@@ -44,12 +41,17 @@ def train():
# Session Saver
saver = tf.train.Saver()
# Start Training
sess.run(tf.initialize_all_variables())
batch_size = 100
max_epoch = 10
max_iter = 95
# Restore obj network parameters
saver.restore(sess, train_params['obj_model_name'] + '-' + str(train_params['obj_global_step']))
batch_size = 10
max_epoch = 2
max_iter = 950
val_acc_array_iter = np.empty([max_iter*max_epoch])
val_acc_array_epoch = np.zeros([max_epoch])
train_acc_array_epoch = np.zeros([max_epoch])
......@@ -57,24 +59,20 @@ def train():
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)
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]))
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'))
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]
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,'obj_classifier_{}.ckpt'.format(epoch)))
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)
sess.close()
tf.reset_default_graph()
if __name__=='__main__':
train()
classifiers/attribute_classifiers/train_atr_classifier.pyc 0 → 100644
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classifiers/object_classifiers/eval_obj_classifier.py
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......@@ -5,7 +5,7 @@ import matplotlib.image as mpimg
import numpy as np
from scipy import misc
import tensorflow as tf
import object_classifiers.obj_data_io_helper as shape_data_loader
import obj_data_io_helper as shape_data_loader
import tf_graph_creation_helper as graph_creator
import plot_helper as plotter
......@@ -25,32 +25,42 @@ def eval(eval_params):
if not os.path.exists(html_dir):
os.mkdir(html_dir)
html_writer = shape_data_loader.html_obj_table_writer(os.path.join(html_dir, 'index.html'))
col_dict = {0: 'Grount Truth',
1: 'Prediction',
2: 'Image'}
col_dict = {
0: 'Grount Truth',
1: 'Prediction',
2: 'Image'}
html_writer.add_element(col_dict)
shape_dict = {0: 'blank',
1: 'rectangle',
2: 'triangle',
3: 'circle'}
shape_dict = {
0: 'blank',
1: 'rectangle',
2: 'triangle',
3: 'circle'}
batch_size = 100
correct = 0
for i in range(1):
for i in range(50):
test_batch = shape_data_loader.obj_mini_batch_loader(test_json_filename, image_dir, mean_image, 10000 + i * batch_size, batch_size, 75, 75)
#print(test_batch[0].dtype)
# print([np.amax(test_batch[0][0,:, :, :]), np.amax(mean_image)])
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
print correct
for row in range(batch_size * 9):
gt_id = np.argmax(test_batch[1][row, :])
pred_id = np.argmax(result[1][row, :])
if not gt_id == pred_id:
img_filename = os.path.join(html_dir, '{}_{}.png'.format(i, row))
misc.imsave(img_filename, test_batch[0][row, :, :, :])
col_dict = {0: shape_dict[gt_id],
1: shape_dict[pred_id],
2: html_writer.image_tag('{}_{}.png'.format(i, row), 25, 25)}
misc.imsave(img_filename, test_batch[0][row, :, :, :] + mean_image)
col_dict = {
0: shape_dict[gt_id],
1: shape_dict[pred_id],
2: html_writer.image_tag('{}_{}.png'.format(i, row), 25, 25)}
html_writer.add_element(col_dict)
html_writer.close_file()
print 'Test Accuracy: {}'.format(correct / 5000)
sess.close()
tf.reset_default_graph()
classifiers/object_classifiers/eval_obj_classifier.pyc
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classifiers/object_classifiers/obj_data_io_helper.py
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#Embedded file name: /home/tanmay/Code/GenVQA/GenVQA/classifiers/object_classifiers/obj_data_io_helper.py
import json
import sys
import os
......@@ -9,19 +8,20 @@ import tensorflow as tf
from scipy import misc
def obj_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)
obj_images = np.empty(shape=[9 * batch_size,
img_height / 3,
img_width / 3,
channels])
obj_images = np.empty(shape=[9 * batch_size, img_height / 3, img_width / 3, channels])
obj_labels = np.zeros(shape=[9 * batch_size, 4])
for i in range(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')
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']
counter = 0
for grid_row in range(0, 3):
for grid_col in range(0, 3):
......@@ -31,7 +31,7 @@ def obj_mini_batch_loader(json_filename, image_dir, mean_image, start_index, bat
if np.ndim(mean_image) == 0:
obj_images[9 * (i - start_index) + counter, :, :, :] = cropped_image / 254.0
else:
obj_images[9 * (i - start_index) + counter, :, :, :] = (cropped_image - mean_image) / 254
obj_images[9 * (i - start_index) + counter, :, :, :] = (cropped_image / 254.0) - mean_image
obj_labels[9 * (i - start_index) + counter, grid_config[6 * grid_row + 2 * grid_col]] = 1
counter = counter + 1
......@@ -51,7 +51,6 @@ def mean_image(json_filename, image_dir, num_images, batch_size, img_height = 10
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 / max_iter
tmp_mean_image = mean_image * 254
return mean_image
......
classifiers/object_classifiers/obj_data_io_helper.pyc
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classifiers/object_classifiers/train_obj_classifier.py
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......@@ -21,21 +21,26 @@ def train(train_params):
if not os.path.exists(outdir):
os.mkdir(outdir)
# Training Data
img_width = 75
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)
np.save(os.path.join(outdir, 'mean_image.npy'), mean_image)
# Val Data
val_batch = shape_data_loader.obj_mini_batch_loader(train_json_filename, 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())
batch_size = 10
max_epoch = 2
max_iter = 1
max_iter = 950
val_acc_array_iter = np.empty([max_iter * max_epoch])
val_acc_array_epoch = np.zeros([max_epoch])
train_acc_array_epoch = np.zeros([max_epoch])
......
classifiers/object_classifiers/train_obj_classifier.pyc
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classifiers/train_classifiers.py
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......@@ -6,35 +6,59 @@ import numpy as np
import tensorflow as tf
import object_classifiers.train_obj_classifier as obj_trainer
import object_classifiers.eval_obj_classifier as obj_evaluator
import attribute_classifiers.train_atr_classifier as atr_trainer
import attribute_classifiers.eval_atr_classifier as atr_evaluator
workflow = {
'train_obj': False,
'eval_obj': False,
'train_atr': True,
'eval_atr': True,
}
train_params = {
obj_classifier_train_params = {
'out_dir': '/home/tanmay/Code/GenVQA/Exp_Results/Obj_Classifier',
'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',
}
eval_params = {
obj_classifier_eval_params = {
'out_dir': '/home/tanmay/Code/GenVQA/Exp_Results/Obj_Classifier',
'model_name': '/home/tanmay/Code/GenVQA/Exp_Results/Obj_Classifier/obj_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',
'html_dir': '/home/tanmay/Code/GenVQA/Exp_Results/Obj_Classifier/html_dir',
'create_graph': False,
}
atr_classifier_train_params = {
'out_dir': '/home/tanmay/Code/GenVQA/Exp_Results/Atr_Classifier',
'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',
'obj_model_name': obj_classifier_eval_params['model_name'],
'obj_global_step': 1,
}
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',
'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',
'html_dir': '/home/tanmay/Code/GenVQA/Exp_Results/Atr_Classifier/html_dir',
}
if __name__=='__main__':
if workflow['train_obj']:
obj_trainer.train(train_params)
obj_trainer.train(obj_classifier_train_params)
obj_feat = tf.get_collection('obj_feat', scope='obj/conv2')
print(obj_feat)
if workflow['eval_obj']:
obj_evaluator.eval(eval_params)
obj_evaluator.eval(obj_classifier_eval_params)
if workflow['train_atr']:
atr_trainer.train(atr_classifier_train_params)
if workflow['eval_atr']:
atr_evaluator.eval(atr_classifier_eval_params)
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