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classifiers/attribute_classifiers/#eval_atr_classifier.py# deleted 100644 → 0
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import sys
import json
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)
g = tf.get_default_graph()
obj_feat = g.get_operation_by_name('obj/conv2/obj_feat')
y_pred = graph_creator.atr_comp_graph(x, keep_prob, obj_feat.outputs[0])
accuracy = graph_creator.evaluation(y, y_pred)
# Object model restorer
vars_to_restore = tf.get_collection(tf.GraphKeys.VARIABLES, scope='obj') + \
tf.get_collection(tf.GraphKeys.VARIABLES, scope='atr')
print('Variables to restore:')
print([var.name for var in vars_to_restore])
saver = tf.train.Saver(vars_to_restore)
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']
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):
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',
1: 'green',
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_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
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.py deleted 120000 → 0
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tanmay@crunchy.29101:1450461082
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classifiers/attribute_classifiers/eval_atr_classifier.py
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......@@ -55,7 +55,7 @@ def eval(eval_params):
html_writer.add_element(col_dict)
color_dict = {
0: 'red', # blanks are treated as red
0: 'red',
1: 'green',
2: 'blue',
3: 'blank'}
......
color_classifiers/__init__.py deleted 100644 → 0
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color_classifiers/atr_data_io_helper.py deleted 100644 → 0
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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')
# image.resize((img_height, img_width, 3))
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]
# print([start_row, start_col])
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.
else:
atr_images[9*(i-start_index)+counter,:,:,:] = (cropped_image-mean_image)/254
atr_labels[9*(i-start_index)+counter, grid_config[6*grid_row+2*grid_col+1]] = 1
counter = counter + 1
# imgplot = plt.imshow(obj_images[0,:,:,:].astype(np.uint8))
# plt.show()
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
tmp_mean_image = mean_image*254
# imgplot = plt.imshow(tmp_mean_image.astype(np.uint8))
# plt.show()
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()
color_classifiers/atr_data_io_helper.pyc deleted 100644 → 0
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color_classifiers/eval_atr_classifier.py deleted 100644 → 0
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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
from train_atr_classifier import placeholder_inputs, comp_graph_v_2, evaluation
sess=tf.InteractiveSession()
x, y, keep_prob = placeholder_inputs()
y_pred = comp_graph_v_2(x, y, keep_prob)
accuracy = evaluation(y, y_pred)
saver = tf.train.Saver()
saver.restore(sess, '/home/tanmay/Code/GenVQA/Exp_Results/Atr_Classifier_v_1/obj_classifier_1.ckpt')
mean_image = np.load('/home/tanmay/Code/GenVQA/Exp_Results/Atr_Classifier_v_1/mean_image.npy')
# Test Data
test_json_filename = '/home/tanmay/Code/GenVQA/GenVQA/shapes_dataset/test_anno.json'
image_dir = '/home/tanmay/Code/GenVQA/GenVQA/shapes_dataset/images'
# Base dir for html visualizer
html_dir = '/home/tanmay/Code/GenVQA/Exp_Results/Atr_Classifier_v_1/html'
if not os.path.exists(html_dir):
os.mkdir(html_dir)
# HTML file writer
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)
shape_dict = {
0: 'blank',
1: 'rectangle',
2: 'triangle',
3: 'circle'}
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,:,:,:])
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))
color_classifiers/train_atr_classifier.py deleted 100644 → 0
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import sys
import os
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import tensorflow as tf
import atr_data_io_helper as atr_data_loader
def plot_accuracy(xdata, ydata, xlim=None, ylim=None, savePath=None):
fig, ax = plt.subplots( nrows=1, ncols=1 )
ax.plot(xdata, ydata)
plt.xlabel('Iterations')
plt.ylabel('Accuracy')
if not xlim==None:
plt.xlim(xlim)
if not ylim==None:
plt.ylim(ylim)
if not savePath==None:
fig.savefig(savePath)
plt.close(fig)
def plot_accuracies(xdata, ydata_train, ydata_val, xlim=None, ylim=None, savePath=None):
fig, ax = plt.subplots( nrows=1, ncols=1 )
ax.plot(xdata, ydata_train, xdata, ydata_val)
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend(['Train', 'Val'], loc='lower right')
if not xlim==None:
plt.xlim(xlim)
if not ylim==None:
plt.ylim(ylim)
if not savePath==None:
fig.savefig(savePath)
plt.close(fig)
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
def max_pool_4x4(x):
return tf.nn.max_pool(x, ksize=[1, 4, 4, 1], strides=[1, 4, 4, 1], padding='SAME')
def placeholder_inputs():
# Specify placeholder_inputs
x = tf.placeholder(tf.float32, shape=[None, 25, 25, 3])
y = tf.placeholder(tf.float32, shape=[None, 4])
keep_prob = tf.placeholder(tf.float32)
return x, y, keep_prob
def comp_graph_v_1(x, y, keep_prob):
# Specify computation graph
W_conv1 = weight_variable([5, 5, 3, 10])
b_conv1 = bias_variable([10])
h_conv1 = tf.nn.relu(conv2d(x, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
#print(tf.Tensor.get_shape(h_pool1))
W_fc1 = weight_variable([13*13*10, 4])
b_fc1 = bias_variable([4])
h_pool1_flat = tf.reshape(h_pool1, [-1, 13*13*10])
h_pool1_flat_drop = tf.nn.dropout(h_pool1_flat, keep_prob)
y_pred = tf.nn.softmax(tf.matmul(h_pool1_flat_drop,W_fc1) + b_fc1)
return y_pred
def comp_graph_v_2(x, y, keep_prob):
# Specify computation graph
W_conv1 = weight_variable([5, 5, 3, 4])
b_conv1 = bias_variable([4])
h_conv1 = tf.nn.relu(conv2d(x, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
h_conv1_drop = tf.nn.dropout(h_pool1, keep_prob)
W_conv2 = weight_variable([3, 3, 4, 8])
b_conv2 = bias_variable([8])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
W_fc1 = weight_variable([7*7*8, 4])
b_fc1 = bias_variable([4])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*8])
h_pool2_flat_drop = tf.nn.dropout(h_pool2_flat, keep_prob)
y_pred = tf.nn.softmax(tf.matmul(h_pool2_flat_drop,W_fc1) + b_fc1)
return y_pred
def evaluation(y, y_pred):
# Evaluation function
correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_pred,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
#tf.scalar_summary("accuracy", accuracy)
return accuracy
def train():
# Start session
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)
# 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/'
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'
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)
# Val Data
val_batch = atr_data_loader.atr_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 = 100
max_epoch = 10
max_iter = 95
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])
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)
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)
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)))
sess.close()
if __name__=='__main__':
train()
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object_classifiers/#eval_obj_classifier.py# deleted 100644 → 0
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import sys
import os
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import tensorflow as tf
import obj_data_io_helper as shape_data_loader
from train_obj_classifier import placeholder_inputs, comp_graph_v_1, evaluation
sess=tf.InteractiveSession()
x, y, keep_prob = placeholder_inputs()
y_pred = comp_graph_v_1(x, y, keep_prob)
accuracy = evaluation(y, y_pred)
saver = tf.train.Saver()
saver.restore(sess, '/home/tanmay/Code/GenVQA/Exp_Results/Shape_Classifier_v_1/obj_classifier_9.ckpt')
mean_image = np.load('/home/tanmay/Code/GenVQA/Exp_Results/Shape_Classifier_v_1/mean_image.npy')
# Test Data
test_json_filename = '/home/tanmay/Code/GenVQA/GenVQA/shapes_dataset/test_anno.json'
image_dir = '/home/tanmay/Code/GenVQA/GenVQA/shapes_dataset/images'
# HTML file writer
html_writer = shape_data_loader.html_obj_table_writer('/home/tanmay/Code/GenVQA/Exp_Results/Shape_Classifier_v_1/trial.html')
batch_size = 100
correct = 0
for i in range(1): #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)
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):
col_dict = {
0: test_batch[1][row,:],
1: y_pred[row, :]}
html_writer.add_element(col_dict)
html_writer.close_file()
print('Test Accuracy: {}'.format(correct/5000))
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object_classifiers/eval_obj_classifier.py deleted 100644 → 0
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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 obj_data_io_helper as shape_data_loader
from train_obj_classifier import placeholder_inputs, comp_graph_v_1, comp_graph_v_2, evaluation
sess=tf.InteractiveSession()
x, y, keep_prob = placeholder_inputs()
y_pred = comp_graph_v_2(x, y, keep_prob)
accuracy = evaluation(y, y_pred)
saver = tf.train.Saver()
saver.restore(sess, '/home/tanmay/Code/GenVQA/Exp_Results/Shape_Classifier_v_2/obj_classifier_1.ckpt')
mean_image = np.load('/home/tanmay/Code/GenVQA/Exp_Results/Shape_Classifier_v_2/mean_image.npy')
# Test Data
test_json_filename = '/home/tanmay/Code/GenVQA/GenVQA/shapes_dataset/test_anno.json'
image_dir = '/home/tanmay/Code/GenVQA/GenVQA/shapes_dataset/images'
# Base dir for html visualizer
html_dir = '/home/tanmay/Code/GenVQA/Exp_Results/Shape_Classifier_v_2/html'
if not os.path.exists(html_dir):
os.mkdir(html_dir)
# HTML file writer
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'}
html_writer.add_element(col_dict)
shape_dict = {
0: 'blank',
1: 'rectangle',
2: 'triangle',
3: 'circle'}
batch_size = 100
correct = 0
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)
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)}
html_writer.add_element(col_dict)
html_writer.close_file()
print('Test Accuracy: {}'.format(correct/5000))
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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 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_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')
# image.resize((img_height, img_width, 3))
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]
# print([start_row, start_col])
cropped_image = image[start_row:start_row+crop_shape[0], start_col:start_col+crop_shape[1], :]
if np.ndim(mean_image)==0:
obj_images[9*(i-start_index)+counter,:,:,:] = cropped_image/254.
else:
obj_images[9*(i-start_index)+counter,:,:,:] = (cropped_image-mean_image)/254
obj_labels[9*(i-start_index)+counter, grid_config[6*grid_row+2*grid_col]] = 1
counter = counter + 1
# imgplot = plt.imshow(obj_images[0,:,:,:].astype(np.uint8))
# plt.show()
return (obj_images, obj_labels)
def mean_image_batch(json_filename, image_dir, start_index, batch_size, img_height=100, img_width=100, channels=3):
batch = obj_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
tmp_mean_image = mean_image*254
# imgplot = plt.imshow(tmp_mean_image.astype(np.uint8))
# plt.show()
return mean_image
class html_obj_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_obj_table_writer('/home/tanmay/Code/GenVQA/Exp_Results/Shape_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()
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import json
import sys
import os
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
def obj_mini_batch_loader(json_filename, batch_size, start_index):
with open(json_filename, 'r') as json_file:
json_data = json.load(json_file)
obj_images = []
obj_labels = []
for i in range(start_index, start_index + batch_size):
image_name = os.path.join(image_dir, str(i+1) + '.jpg')
image = mpimg.imread(image_name)
crop_shape = [image.shape[0]/3, image.shape[1]/3]
selected_anno = [q for q in json_data if q['image_id']==1]
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]
print([start_row, start_col])
obj_images.append(image[start_row:start_row+crop_shape[0]-1, start_col:start_col+crop_shape[1]-1, :])
obj_labels.append(grid_config[6*grid_row+2*grid_col])
# imgplot = plt.imshow(obj_images[counter])
# plt.show()
counter = counter + 1
if __name__=="__main__":
json_filename = '/home/tanmay/Code/GenVQA/GenVQA/shapes_dataset/train_anno.json'
batch_size = 1
start_index = 0
image_dir = '/home/tanmay/Code/GenVQA/GenVQA/shapes_dataset/images'
obj_mini_batch_loader(json_filename, batch_size, start_index)
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import sys
import os
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import tensorflow as tf
import obj_data_io_helper as shape_data_loader
def plot_accuracy(xdata, ydata, xlim=None, ylim=None, savePath=None):
fig, ax = plt.subplots( nrows=1, ncols=1 )
ax.plot(xdata, ydata)
plt.xlabel('Iterations')
plt.ylabel('Accuracy')
if not xlim==None:
plt.xlim(xlim)
if not ylim==None:
plt.ylim(ylim)
if not savePath==None:
fig.savefig(savePath)
plt.close(fig)
def plot_accuracies(xdata, ydata_train, ydata_val, xlim=None, ylim=None, savePath=None):
fig, ax = plt.subplots( nrows=1, ncols=1 )
ax.plot(xdata, ydata_train, xdata, ydata_val)
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend(['Train', 'Val'], loc='lower right')
if not xlim==None:
plt.xlim(xlim)
if not ylim==None:
plt.ylim(ylim)
if not savePath==None:
fig.savefig(savePath)
plt.close(fig)
def weight_variable(shape):
initial = tf.truncated_normal(shape, stddev=0.1)
return tf.Variable(initial)
def bias_variable(shape):
initial = tf.constant(0.1, shape=shape)
return tf.Variable(initial)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
def max_pool_4x4(x):
return tf.nn.max_pool(x, ksize=[1, 4, 4, 1], strides=[1, 4, 4, 1], padding='SAME')
def placeholder_inputs():
# Specify placeholder_inputs
x = tf.placeholder(tf.float32, shape=[None, 25, 25, 3])
y = tf.placeholder(tf.float32, shape=[None, 4])
keep_prob = tf.placeholder(tf.float32)
return x, y, keep_prob
def comp_graph_v_1(x, y, keep_prob):
# Specify computation graph
W_conv1 = weight_variable([5, 5, 3, 10])
b_conv1 = bias_variable([10])
h_conv1 = tf.nn.relu(conv2d(x, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
#print(tf.Tensor.get_shape(h_pool1))
W_fc1 = weight_variable([13*13*10, 4])
b_fc1 = bias_variable([4])
h_pool1_flat = tf.reshape(h_pool1, [-1, 13*13*10])
h_pool1_flat_drop = tf.nn.dropout(h_pool1_flat, keep_prob)
y_pred = tf.nn.softmax(tf.matmul(h_pool1_flat_drop,W_fc1) + b_fc1)
return y_pred
def comp_graph_v_2(x, y, keep_prob):
# Specify computation graph
W_conv1 = weight_variable([5, 5, 3, 4])
b_conv1 = bias_variable([4])
h_conv1 = tf.nn.relu(conv2d(x, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
h_conv1_drop = tf.nn.dropout(h_pool1, keep_prob)
W_conv2 = weight_variable([3, 3, 4, 8])
b_conv2 = bias_variable([8])
h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)
W_fc1 = weight_variable([7*7*8, 4])
b_fc1 = bias_variable([4])
h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*8])
h_pool2_flat_drop = tf.nn.dropout(h_pool2_flat, keep_prob)
y_pred = tf.nn.softmax(tf.matmul(h_pool2_flat_drop,W_fc1) + b_fc1)
return y_pred
def evaluation(y, y_pred):
# Evaluation function
correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(y_pred,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
#tf.scalar_summary("accuracy", accuracy)
return accuracy
def train():
# Start session
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)
# Merge summaries and write them to ~/Code/Tensorflow_Exp/logDir
merged = tf.merge_all_summaries()
#writer = tf.train.SummaryWriter("/home/tanmay/Code/GenVQA/Exp_Results/Shape_Classifier", graph_def=tf.GraphDef())
# Output dir
outdir = '/home/tanmay/Code/GenVQA/Exp_Results/Shape_Classifier_v_2/'
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'
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 = 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])
for epoch in range(max_epoch):
for i in range(max_iter):
train_batch = shape_data_loader.obj_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)
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)))
sess.close()
if __name__=='__main__':
train()
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