diff --git a/color_classifiers/__init__.py b/color_classifiers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/color_classifiers/atr_data_io_helper.py b/color_classifiers/atr_data_io_helper.py
new file mode 100644
index 0000000000000000000000000000000000000000..67337b845d916812c3217dfde2ce0c5e18955864
--- /dev/null
+++ b/color_classifiers/atr_data_io_helper.py
@@ -0,0 +1,90 @@
+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 = 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_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()
+
diff --git a/color_classifiers/eval_atr_classifier.py b/color_classifiers/eval_atr_classifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f4120283972d3bd15336d73282440b8f19b8cef
--- /dev/null
+++ b/color_classifiers/eval_atr_classifier.py
@@ -0,0 +1,69 @@
+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, 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'
+
+# Base dir for html visualizer
+html_dir = '/home/tanmay/Code/GenVQA/Exp_Results/Shape_Classifier_v_1/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))
diff --git a/color_classifiers/train_atr_classifier.py b/color_classifiers/train_atr_classifier.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d9c88f9eead9991f8194a7188cb8d54408f65db
--- /dev/null
+++ b/color_classifiers/train_atr_classifier.py
@@ -0,0 +1,197 @@
+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, 10])
+ b_conv1 = bias_variable([10])
+
+ 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([5, 5, 10, 20])
+ b_conv2 = bias_variable([20])
+
+ h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
+ h_pool2 = max_pool_2x2(h_conv2)
+ h_conv2_drop = tf.nn.dropout(h_pool2, keep_prob)
+
+ W_fc1 = weight_variable([7*7*20, 4])
+ b_fc1 = bias_variable([4])
+
+ h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*20])
+ 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()
+
diff --git a/object_classifiers/eval_obj_classifier.py b/object_classifiers/eval_obj_classifier.py
index d9dd4e1ee72649fbfc4dc7d56414be006e953ec8..9853116cdccde23b3e043b83a53044c64d7ae585 100644
--- a/object_classifiers/eval_obj_classifier.py
+++ b/object_classifiers/eval_obj_classifier.py
@@ -3,34 +3,67 @@ 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, evaluation
+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(x, y, keep_prob)
+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/obj_classifier_5.ckpt')
+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/mean_image.npy')
+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)
-
+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}
- correct = correct + accuracy.eval(feed_dict_test)*batch_size
+ 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))
diff --git a/object_classifiers/obj_data_io_helper.py b/object_classifiers/obj_data_io_helper.py
index 3f5484ac87048bd67c3f72d06070c1c218aa0397..d0427abb6d1f3d56ec3d99e5a3b913adc7177959 100644
--- a/object_classifiers/obj_data_io_helper.py
+++ b/object_classifiers/obj_data_io_helper.py
@@ -5,18 +5,20 @@ 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, img_width, 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 = mpimg.imread(image_name)
+ 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']
@@ -46,26 +48,43 @@ def mean_image_batch(json_filename, image_dir, start_index, batch_size, img_heig
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, img_width, channels])
+ 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()
+ # imgplot = plt.imshow(tmp_mean_image.astype(np.uint8))
+ # plt.show()
return mean_image
-if __name__=="__main__":
+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()
- 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'
+if __name__=="__main__":
- batch = obj_mini_batch_loader(json_filename, image_dir, start_index, batch_size)
- print(batch[0].shape)
- print(batch[1].shape)
+ 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()
diff --git a/object_classifiers/obj_data_io_helper.pyc b/object_classifiers/obj_data_io_helper.pyc
index 73a426be9496860d6a43819bf5c4c1506db53ffc..48169a55a825c93a57adb1cf4cc9cfeb258f4b15 100644
Binary files a/object_classifiers/obj_data_io_helper.pyc and b/object_classifiers/obj_data_io_helper.pyc differ
diff --git a/object_classifiers/train_obj_classifier.py b/object_classifiers/train_obj_classifier.py
index a54e8461f4005a1ad942515782f2dacf670a9ddf..19b48a25fa3dcf668d8c2350a0e20538d1eb934e 100644
--- a/object_classifiers/train_obj_classifier.py
+++ b/object_classifiers/train_obj_classifier.py
@@ -57,29 +57,58 @@ def conv2d(x, W):
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, 100, 100, 3])
+ 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(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([50*50*10, 4])
+ W_fc1 = weight_variable([13*13*10, 4])
b_fc1 = bias_variable([4])
- h_pool1_flat = tf.reshape(h_pool1, [-1, 50*50*10])
+ 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,W_fc1) + b_fc1)
+ 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
@@ -97,7 +126,7 @@ def train():
sess = tf.InteractiveSession()
x, y, keep_prob = placeholder_inputs()
- y_pred = comp_graph(x, y, keep_prob)
+ y_pred = comp_graph_v_2(x, y, keep_prob)
# Specify loss
cross_entropy = -tf.reduce_sum(y*tf.log(y_pred))
@@ -112,14 +141,21 @@ def train():
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)
- np.save('/home/tanmay/Code/GenVQA/Exp_Results/Shape_Classifier/mean_image.npy', mean_image)
+ 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)
+ 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
@@ -127,15 +163,15 @@ def train():
# Start Training
sess.run(tf.initialize_all_variables())
- batch_size = 100
- max_epoch = 10
- max_iter = 95
+ 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)
+ 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)
@@ -143,15 +179,15 @@ def 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='/home/tanmay/Code/GenVQA/Exp_Results/Shape_Classifier/valAcc_vs_iter.pdf')
+ 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='/home/tanmay/Code/GenVQA/Exp_Results/Shape_Classifier/acc_vs_epoch.pdf')
+ 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, '/home/tanmay/Code/GenVQA/Exp_Results/Shape_Classifier/obj_classifier_{}.ckpt'.format(epoch))
+ save_path = saver.save(sess, os.path.join(outdir,'obj_classifier_{}.ckpt'.format(epoch)))
sess.close()
diff --git a/object_classifiers/train_obj_classifier.pyc b/object_classifiers/train_obj_classifier.pyc
index cd5b5cafd0d303334de0fb93f98f0f61611f70e1..550d73d4e5c289d02061f24457f8bc52c638aaee 100644
Binary files a/object_classifiers/train_obj_classifier.pyc and b/object_classifiers/train_obj_classifier.pyc differ