diff --git a/classifiers/attribute_classifiers/atr_data_io_helper.py b/classifiers/attribute_classifiers/atr_data_io_helper.py
index 572704d7e7ab247f1e921f2f665172efa8a122a7..36b810bd07db5f7f83b5d7b8c29bf15c00a93990 100644
--- a/classifiers/attribute_classifiers/atr_data_io_helper.py
+++ b/classifiers/attribute_classifiers/atr_data_io_helper.py
@@ -19,6 +19,7 @@ def atr_mini_batch_loader(json_data, image_dir, mean_image, start_index, batch_s
#image = np.zeros(shape=[img_height, img_width, channels])
crop_shape = np.array([image.shape[0], image.shape[1]])/3
grid_config = json_data[i]
+
counter = 0;
for grid_row in xrange(0,3):
for grid_col in xrange(0,3):
diff --git a/classifiers/attribute_classifiers/atr_data_io_helper.pyc b/classifiers/attribute_classifiers/atr_data_io_helper.pyc
index 457ee8ea56d594f13e29953a5ac035b031255cd4..187b0e4658b6c36ac6b9bbd41bd3757353a845e4 100644
Binary files a/classifiers/attribute_classifiers/atr_data_io_helper.pyc and b/classifiers/attribute_classifiers/atr_data_io_helper.pyc differ
diff --git a/classifiers/attribute_classifiers/train_atr_classifier.py b/classifiers/attribute_classifiers/train_atr_classifier.py
index 0c59564150a6cfd0ee4f0f6ccee80674ba743ac6..00fc144ad3bf313dab9bbf6bc12c83ee0874a4c8 100644
--- a/classifiers/attribute_classifiers/train_atr_classifier.py
+++ b/classifiers/attribute_classifiers/train_atr_classifier.py
@@ -83,20 +83,15 @@ def train(train_params):
train_acc_array_epoch = np.zeros([max_epoch])
for epoch in range(max_epoch):
for i in range(max_iter):
- print(i)
- # start = time.time()
+ if i%100==0:
+ print('Iter: ' + str(i))
train_batch = atr_data_loader.atr_mini_batch_loader(train_json_data, image_dir, mean_image, 1+i*batch_size, batch_size, img_height, img_width)
- # end = time.time()
- # print(end-start)
feed_dict_train={x: train_batch[0], y: train_batch[1], keep_prob: 0.5}
_, current_train_batch_acc = sess.run([train_step, accuracy], feed_dict=feed_dict_train)
train_acc_array_epoch[epoch] = train_acc_array_epoch[epoch] + current_train_batch_acc
- # val_acc_array_iter[i+epoch*max_iter] = accuracy.eval(feed_dict_val)
- # plotter.plot_accuracy(np.arange(0,i+1+epoch*max_iter)+1, val_acc_array_iter[0:i+1+epoch*max_iter], xlim=[1, max_epoch*max_iter], ylim=[0, 1.], savePath=os.path.join(outdir,'valAcc_vs_iter.pdf'))
- # print('Step: {} Val Accuracy: {}'.format(i+1+epoch*max_iter, val_acc_array_iter[i+epoch*max_iter]))
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]
+ val_acc_array_epoch[epoch] = accuracy.eval(feed_dict_val)
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'))
_ = atr_saver.save(sess, os.path.join(outdir,'atr_classifier'), global_step=epoch)
_ = obj_atr_saver.save(sess, os.path.join(outdir,'obj_atr_classifier'), global_step=epoch)
diff --git a/classifiers/attribute_classifiers/train_atr_classifier.pyc b/classifiers/attribute_classifiers/train_atr_classifier.pyc
index 500169dccf17b7b3efcb303cb4ea2258248b13b3..a80445734eae7f1bbe09a014ed07d0acab095d64 100644
Binary files a/classifiers/attribute_classifiers/train_atr_classifier.pyc and b/classifiers/attribute_classifiers/train_atr_classifier.pyc differ
diff --git a/classifiers/object_classifiers/eval_obj_classifier.py b/classifiers/object_classifiers/eval_obj_classifier.py
index e9320c43652b62c54349d950f69415ed4ac93cfa..6d209dfb2b9500fb7453d464bc86fef11290fb06 100644
--- a/classifiers/object_classifiers/eval_obj_classifier.py
+++ b/classifiers/object_classifiers/eval_obj_classifier.py
@@ -1,4 +1,5 @@
import sys
+import json
import os
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
@@ -20,6 +21,13 @@ def eval(eval_params):
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):
@@ -39,9 +47,7 @@ def eval(eval_params):
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)
- #print(test_batch[0].dtype)
-# print([np.amax(test_batch[0][0,:, :, :]), np.amax(mean_image)])
+ test_batch = shape_data_loader.obj_mini_batch_loader(test_json_data, image_dir, mean_image, 10000 + i * batch_size, batch_size, 75, 75)
feed_dict_test = {x: test_batch[0], y: test_batch[1], keep_prob: 1.0}
result = sess.run([accuracy, y_pred], feed_dict=feed_dict_test)
correct = correct + result[0] * batch_size
diff --git a/classifiers/object_classifiers/eval_obj_classifier.pyc b/classifiers/object_classifiers/eval_obj_classifier.pyc
index 48898ca0a27747f6356b9311b4ebe8a2e4f3f745..8b6be912f6e9110be8d3644516b9293701cce808 100644
Binary files a/classifiers/object_classifiers/eval_obj_classifier.pyc and b/classifiers/object_classifiers/eval_obj_classifier.pyc differ
diff --git a/classifiers/object_classifiers/obj_data_io_helper.py b/classifiers/object_classifiers/obj_data_io_helper.py
index 1e9b435f2b9f058837044ca08a182622af0b750e..16280b4790d82098c662dc78b320d45bc80ac411 100644
--- a/classifiers/object_classifiers/obj_data_io_helper.py
+++ b/classifiers/object_classifiers/obj_data_io_helper.py
@@ -7,10 +7,7 @@ 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)
+def obj_mini_batch_loader(json_data, image_dir, mean_image, start_index, batch_size, img_height = 100, img_width = 100, channels = 3):
obj_images = np.empty(shape=[9 * batch_size, img_height / 3, img_width / 3, channels])
obj_labels = np.zeros(shape=[9 * batch_size, 4])
@@ -19,8 +16,7 @@ def obj_mini_batch_loader(json_filename, image_dir, mean_image, start_index, bat
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']
+ grid_config = json_data[i]
counter = 0
for grid_row in range(0, 3):
@@ -28,27 +24,29 @@ def obj_mini_batch_loader(json_filename, image_dir, mean_image, start_index, bat
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:
obj_images[9 * (i - start_index) + counter, :, :, :] = cropped_image / 254.0
else:
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
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)
+def mean_image_batch(json_data, image_dir, start_index, batch_size, img_height = 100, img_width = 100, channels = 3):
+ batch = obj_mini_batch_loader(json_data, image_dir, np.empty([]), start_index, batch_size, img_height, img_width, channels)
mean_image = np.mean(batch[0], 0)
return mean_image
-def mean_image(json_filename, image_dir, num_images, batch_size, img_height = 100, img_width = 100, channels = 3):
+def mean_image(json_data, image_dir, num_images, batch_size, img_height = 100, img_width = 100, channels = 3):
max_iter = np.floor(num_images / batch_size)
mean_image = np.zeros([img_height / 3, img_width / 3, channels])
for i in range(max_iter.astype(np.int16)):
- mean_image = mean_image + mean_image_batch(json_filename, image_dir, 1 + i * batch_size, batch_size, img_height, img_width, channels)
+ mean_image = mean_image + mean_image_batch(json_data, image_dir, 1 + i * batch_size, batch_size, img_height, img_width, channels)
mean_image = mean_image / max_iter
return mean_image
diff --git a/classifiers/object_classifiers/obj_data_io_helper.pyc b/classifiers/object_classifiers/obj_data_io_helper.pyc
index 9bdb6c7015cc3a0da7906325045969549a720070..ae5847b62758c1ea8aec0706d785fa461c734d1f 100644
Binary files a/classifiers/object_classifiers/obj_data_io_helper.pyc and b/classifiers/object_classifiers/obj_data_io_helper.pyc differ
diff --git a/classifiers/object_classifiers/train_obj_classifier.py b/classifiers/object_classifiers/train_obj_classifier.py
index debc76d22ea4565b002b57b77273898abe6e6751..b3711271bc3b6a25ccfab0145724800d104eebe6 100644
--- a/classifiers/object_classifiers/train_obj_classifier.py
+++ b/classifiers/object_classifiers/train_obj_classifier.py
@@ -1,5 +1,6 @@
import sys
import os
+import json
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
@@ -25,17 +26,25 @@ def train(train_params):
img_width = 75
img_height = 75
train_json_filename = train_params['train_json']
+ with open(train_json_filename, 'r') as json_file:
+ raw_json_data = json.load(json_file)
+ train_json_data = dict()
+ for entry in raw_json_data:
+ if entry['image_id'] not in train_json_data:
+ train_json_data[entry['image_id']]=entry['config']
+
+
image_dir = train_params['image_dir']
if train_params['mean_image']=='':
print('Computing mean image')
- mean_image = atr_data_loader.mean_image(train_json_filename, image_dir, 1000, 100, img_height, img_width)
+ mean_image = shape_data_loader.mean_image(train_json_data, image_dir, 1000, 100, img_height, img_width)
else:
print('Loading mean image')
mean_image = np.load(train_params['mean_image'])
np.save(os.path.join(outdir, 'mean_image.npy'), mean_image)
# Val Data
- val_batch = shape_data_loader.obj_mini_batch_loader(train_json_filename, image_dir, mean_image, 9501, 499, img_height, img_width)
+ val_batch = shape_data_loader.obj_mini_batch_loader(train_json_data, image_dir, mean_image, 9501, 499, img_height, img_width)
feed_dict_val = {x: val_batch[0], y: val_batch[1], keep_prob: 1.0}
# Session Saver
@@ -51,16 +60,17 @@ def train(train_params):
train_acc_array_epoch = np.zeros([max_epoch])
for epoch in range(max_epoch):
for i in range(max_iter):
- train_batch = shape_data_loader.obj_mini_batch_loader(train_json_filename, image_dir, mean_image, 1 + i * batch_size, batch_size, img_height, img_width)
+ if i%100==0:
+ print('Iter: ' + str(i))
+ print('Val Acc: ' + str(accuracy.eval(feed_dict_val)))
+
+ train_batch = shape_data_loader.obj_mini_batch_loader(train_json_data, 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, 1.0], savePath=os.path.join(outdir, 'valAcc_vs_iter.pdf'))
- print 'Step: {} Val Accuracy: {}'.format(i + 1 + epoch * max_iter, val_acc_array_iter[i + epoch * max_iter])
train_acc_array_epoch[epoch] = train_acc_array_epoch[epoch] / max_iter
- val_acc_array_epoch[epoch] = val_acc_array_iter[i + epoch * max_iter]
+ val_acc_array_epoch[epoch] = accuracy.eval(feed_dict_val)
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, 1.0], savePath=os.path.join(outdir, 'acc_vs_epoch.pdf'))
save_path = saver.save(sess, os.path.join(outdir, 'obj_classifier'), global_step=epoch)
diff --git a/classifiers/object_classifiers/train_obj_classifier.pyc b/classifiers/object_classifiers/train_obj_classifier.pyc
index b0dd5d511a461b073a33139526028efddda13b3c..ade9078d1c8faa943b94a78edd7d8a3f03589eaf 100644
Binary files a/classifiers/object_classifiers/train_obj_classifier.pyc and b/classifiers/object_classifiers/train_obj_classifier.pyc differ
diff --git a/classifiers/train_classifiers.py b/classifiers/train_classifiers.py
index e7d0ba922063e68a0e3e8f6c54e853356a283410..dc0d5aa5317ecb257eed174c990f96607600dc20 100644
--- a/classifiers/train_classifiers.py
+++ b/classifiers/train_classifiers.py
@@ -1,4 +1,5 @@
import sys
+import json
import os
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
@@ -10,9 +11,9 @@ 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': False,
+ 'train_obj': True,
+ 'eval_obj': True,
+ 'train_atr': True,
'eval_atr': True,
}
@@ -21,8 +22,8 @@ obj_classifier_train_params = {
'adam_lr': 0.001,
'train_json': '/home/tanmay/Code/GenVQA/GenVQA/shapes_dataset/train_anno.json',
'image_dir': '/home/tanmay/Code/GenVQA/GenVQA/shapes_dataset/images',
- 'mean_image': '/home/tanmay/Code/GenVQA/Exp_Results/Obj_Classifier/mean_image.npy',
-# 'mean_image': '',
+# 'mean_image': '/home/tanmay/Code/GenVQA/Exp_Results/Obj_Classifier/mean_image.npy',
+ 'mean_image': '',
}
obj_classifier_eval_params = {