diff --git a/classifiers/attribute_classifiers/#eval_atr_classifier.py# b/classifiers/attribute_classifiers/#eval_atr_classifier.py#
deleted file mode 100644
index 00debb0f950577dc0f57f85397dcb32a8a92aeb3..0000000000000000000000000000000000000000
--- a/classifiers/attribute_classifiers/#eval_atr_classifier.py#
+++ /dev/null
@@ -1,90 +0,0 @@
-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()
diff --git a/classifiers/attribute_classifiers/.#eval_atr_classifier.py b/classifiers/attribute_classifiers/.#eval_atr_classifier.py
deleted file mode 120000
index a7436f7a65f3e6f88d5ffc73b0dbd29c105d536d..0000000000000000000000000000000000000000
--- a/classifiers/attribute_classifiers/.#eval_atr_classifier.py
+++ /dev/null
@@ -1 +0,0 @@
-tanmay@crunchy.29101:1450461082
\ No newline at end of file
diff --git a/classifiers/attribute_classifiers/eval_atr_classifier.py b/classifiers/attribute_classifiers/eval_atr_classifier.py
index da1b343663ac50a438fcd0493029a1973f4c75ca..00debb0f950577dc0f57f85397dcb32a8a92aeb3 100644
--- a/classifiers/attribute_classifiers/eval_atr_classifier.py
+++ b/classifiers/attribute_classifiers/eval_atr_classifier.py
@@ -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'}
diff --git a/color_classifiers/__init__.py b/color_classifiers/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/color_classifiers/atr_data_io_helper.py b/color_classifiers/atr_data_io_helper.py
deleted file mode 100644
index a68f75e533f9009d3129a5d5a2d9233591856f88..0000000000000000000000000000000000000000
--- a/color_classifiers/atr_data_io_helper.py
+++ /dev/null
@@ -1,90 +0,0 @@
-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()
-
diff --git a/color_classifiers/atr_data_io_helper.pyc b/color_classifiers/atr_data_io_helper.pyc
deleted file mode 100644
index f1e7f0bf9e4b06fc1016b65356fdae76ff343f8e..0000000000000000000000000000000000000000
Binary files a/color_classifiers/atr_data_io_helper.pyc and /dev/null differ
diff --git a/color_classifiers/eval_atr_classifier.py b/color_classifiers/eval_atr_classifier.py
deleted file mode 100644
index c6a14dd52a072f22e3c85a1b815501b79ce6b675..0000000000000000000000000000000000000000
--- a/color_classifiers/eval_atr_classifier.py
+++ /dev/null
@@ -1,69 +0,0 @@
-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))
diff --git a/color_classifiers/train_atr_classifier.py b/color_classifiers/train_atr_classifier.py
deleted file mode 100644
index 1bc622a5cebe9cc8df1927d22039b06a3f571656..0000000000000000000000000000000000000000
--- a/color_classifiers/train_atr_classifier.py
+++ /dev/null
@@ -1,196 +0,0 @@
-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()
-
diff --git a/color_classifiers/train_atr_classifier.pyc b/color_classifiers/train_atr_classifier.pyc
deleted file mode 100644
index 513dd9fab63523be1e847885b6c07fe3093f399c..0000000000000000000000000000000000000000
Binary files a/color_classifiers/train_atr_classifier.pyc and /dev/null differ
diff --git a/object_classifiers/#eval_obj_classifier.py# b/object_classifiers/#eval_obj_classifier.py#
deleted file mode 100644
index 28fa50eefc4a383fe4c3ff5c65c47c6f69069e74..0000000000000000000000000000000000000000
--- a/object_classifiers/#eval_obj_classifier.py#
+++ /dev/null
@@ -1,45 +0,0 @@
-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))
diff --git a/object_classifiers/__init__.py b/object_classifiers/__init__.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/object_classifiers/eval_obj_classifier.py b/object_classifiers/eval_obj_classifier.py
deleted file mode 100644
index 9853116cdccde23b3e043b83a53044c64d7ae585..0000000000000000000000000000000000000000
--- a/object_classifiers/eval_obj_classifier.py
+++ /dev/null
@@ -1,69 +0,0 @@
-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))
diff --git a/object_classifiers/obj_data_io_helper.py b/object_classifiers/obj_data_io_helper.py
deleted file mode 100644
index d0427abb6d1f3d56ec3d99e5a3b913adc7177959..0000000000000000000000000000000000000000
--- a/object_classifiers/obj_data_io_helper.py
+++ /dev/null
@@ -1,90 +0,0 @@
-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()
-
diff --git a/object_classifiers/obj_data_io_helper.pyc b/object_classifiers/obj_data_io_helper.pyc
deleted file mode 100644
index 48169a55a825c93a57adb1cf4cc9cfeb258f4b15..0000000000000000000000000000000000000000
Binary files a/object_classifiers/obj_data_io_helper.pyc and /dev/null differ
diff --git a/object_classifiers/obj_data_io_helper.py~ b/object_classifiers/obj_data_io_helper.py~
deleted file mode 100644
index c550af7fd9353169527b007eabe08e1badd2137b..0000000000000000000000000000000000000000
--- a/object_classifiers/obj_data_io_helper.py~
+++ /dev/null
@@ -1,48 +0,0 @@
-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)
-
diff --git a/object_classifiers/train_obj_classifier.py b/object_classifiers/train_obj_classifier.py
deleted file mode 100644
index 19b48a25fa3dcf668d8c2350a0e20538d1eb934e..0000000000000000000000000000000000000000
--- a/object_classifiers/train_obj_classifier.py
+++ /dev/null
@@ -1,197 +0,0 @@
-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()
-
diff --git a/object_classifiers/train_obj_classifier.pyc b/object_classifiers/train_obj_classifier.pyc
deleted file mode 100644
index 550d73d4e5c289d02061f24457f8bc52c638aaee..0000000000000000000000000000000000000000
Binary files a/object_classifiers/train_obj_classifier.pyc and /dev/null differ