def classify(image, top_k, k_patches, ckpt_path, imagenet_path):
wnids, words = tu.load_imagenet_meta(
os.path.join(imagenet_path, 'data/meta.mat'))
image_patches = tu.read_k_patches(image, k_patches)
x = tf.placeholder(tf.float32, [None, 224, 224, 3])
_, pred = model.classifier(x, dropout=1.0)
avg_prediction = tf.div(tf.reduce_sum(pred, 0), k_patches)
scores, indexes = tf.nn.top_k(avg_prediction, k=top_k)
saver = tf.train.Saver()
with tf.Session(config=tf.ConfigProto()) as sess:
saver.restore(sess, os.path.join(ckpt_path, 'cnn.ckpt'))
s, i = sess.run([scores, indexes], feed_dict={x: image_patches})
s, i = np.squeeze(s), np.squeeze(i)
print('AlexNet saw:')
for idx in range(top_k):
print('{} - score: {}'.format(words[i[idx]], s[idx]))
def test(
top_k,
k_patches,
display_step,
imagenet_path,
ckpt_path):
test_images = sorted(os.listdir(os.path.join(imagenet_path, 'ILSVRC2012_img_val')))
test_labels = tu.read_test_labels(os.path.join(imagenet_path, 'data/ILSVRC2012_validation_ground_truth.txt'))
test_examples = len(test_images)
x = tf.placeholder(tf.float32, [None, 224, 224, 3])
y = tf.placeholder(tf.float32, [None, 1000])
_, pred = model.classifier(x, 1.0)
avg_prediction = tf.div(tf.reduce_sum(pred, 0), k_patches)
top1_correct = tf.equal(tf.argmax(avg_prediction, 0), tf.argmax(y, 1))
top1_accuracy = tf.reduce_mean(tf.cast(top1_correct, tf.float32))
topk_correct = tf.nn.in_top_k(tf.stack([avg_prediction]), tf.argmax(y, 1), k=top_k)
topk_accuracy = tf.reduce_mean(tf.cast(topk_correct, tf.float32))
saver = tf.train.Saver()
with tf.Session(config=tf.ConfigProto()) as sess:
saver.restore(sess, os.path.join(ckpt_path, 'cnn.ckpt'))
total_top1_accuracy = 0.
total_topk_accuracy = 0.
for i in range(test_examples):
image_patches = tu.read_k_patches(os.path.join(imagenet_path, 'ILSVRC2012_img_val', test_images[i]), k_patches)
label = test_labels[i]
top1_a, topk_a = sess.run([top1_accuracy, topk_accuracy], feed_dict={x: image_patches, y: [label]})
total_top1_accuracy += top1_a
total_topk_accuracy += topk_a
if i % display_step == 0:
print ('Examples done: {:5d}/{} ---- Top-1: {:.4f} -- Top-{}: {:.4f}'.format(i + 1, test_examples, total_top1_accuracy / (i + 1), top_k, total_topk_accuracy / (i + 1)))
print ('---- Final accuracy ----')
print ('Top-1: {:.4f} -- Top-{}: {:.4f}'.format(total_top1_accuracy / test_examples, top_k, total_topk_accuracy / test_examples))
print ('Top-1 error rate: {:.4f} -- Top-{} error rate: {:.4f}'.format(1 - (total_top1_accuracy / test_examples), top_k, 1 - (total_topk_accuracy / test_examples)))
def classify(
image,
top_k,
k_patches,
ckpt_path,
imagenet_path):
""" Procedure to classify the image given through the command line
Args:
image: path to the image to classify
top_k: integer representing the number of predictions with highest probability
to retrieve
k_patches: number of crops taken from an image and to input to the model
ckpt_path: path to model's tensorflow checkpoint
imagenet_path: path to ILSRVC12 ImageNet folder containing train images,
validation images, annotations and metadata file
"""
wnids, words = tu.load_imagenet_meta(os.path.join(imagenet_path, 'data/meta.mat'))
# taking a few crops from an image
image_patches = tu.read_k_patches(image, k_patches)
x = tf.placeholder(tf.float32, [None, 224, 224, 3])
_, pred = model.classifier(x, dropout=1.0)
# calculate the average precision through the crops
avg_prediction = tf.div(tf.reduce_sum(pred, 0), k_patches)
# retrieve top 5 scores
scores, indexes = tf.nn.top_k(avg_prediction, k=top_k)
saver = tf.train.Saver()
with tf.Session(config=tf.ConfigProto()) as sess:
saver.restore(sess, os.path.join(ckpt_path, 'cnn.ckpt'))
s, i = sess.run([scores, indexes], feed_dict={x: image_patches})
s, i = np.squeeze(s), np.squeeze(i)
print('AlexNet saw:')
for idx in range(top_k):
print ('{} - score: {}'.format(words[i[idx]], s[idx]))
def test(top_k, k_patches, display_step, imagenet_path, ckpt_path):
"""
Procedure to evaluate top-1 and top-k accuracy (and error-rate) on the
ILSVRC2012 validation (test) set.
Args:
top_k: integer representing the number of predictions with highest probability
to retrieve
k_patches: number of crops taken from an image and to input to the model
display_step: number representing how often printing the current testing accuracy
imagenet_path: path to ILSRVC12 ImageNet folder containing train images,
validation images, annotations and metadata file
ckpt_path: path to model's tensorflow checkpoint
"""
test_images = sorted(
os.listdir(os.path.join(imagenet_path, 'ILSVRC2012_img_val')))
test_labels = tu.read_test_labels(
os.path.join(imagenet_path,
'data/ILSVRC2012_validation_ground_truth.txt'))
test_examples = len(test_images)
x = tf.placeholder(tf.float32, [None, 224, 224, 3])
y = tf.placeholder(tf.float32, [None, 1000])
_, pred = alexnet.classifier(x, 1.0)
# calculate the average precision of the crops of the image
avg_prediction = tf.div(tf.reduce_sum(pred, 0), k_patches)
# accuracy
top1_correct = tf.equal(tf.argmax(avg_prediction, 0), tf.argmax(y, 1))
top1_accuracy = tf.reduce_mean(tf.cast(top1_correct, tf.float32))
topk_correct = tf.nn.in_top_k(tf.stack([avg_prediction]),
tf.argmax(y, 1),
k=top_k)
topk_accuracy = tf.reduce_mean(tf.cast(topk_correct, tf.float32))
saver = tf.train.Saver()
with tf.Session(config=tf.ConfigProto()) as sess:
saver.restore(sess, os.path.join(ckpt_path, 'alexnet-cnn.ckpt'))
total_top1_accuracy = 0.
total_topk_accuracy = 0.
for i in range(test_examples):
# taking a few patches from an image
image_patches = tu.read_k_patches(
os.path.join(imagenet_path, 'ILSVRC2012_img_val',
test_images[i]), k_patches)
label = test_labels[i]
top1_a, topk_a = sess.run([top1_accuracy, topk_accuracy],
feed_dict={
x: image_patches,
y: [label]
})
total_top1_accuracy += top1_a
total_topk_accuracy += topk_a
if i % display_step == 0:
print(
'Examples done: {:5d}/{} ---- Top-1: {:.4f} -- Top-{}: {:.4f}'
.format(i + 1, test_examples,
total_top1_accuracy / (i + 1), top_k,
total_topk_accuracy / (i + 1)))
print('---- Final accuracy ----')
print('Top-1: {:.4f} -- Top-{}: {:.4f}'.format(
total_top1_accuracy / test_examples, top_k,
total_topk_accuracy / test_examples))
print('Top-1 error rate: {:.4f} -- Top-{} error rate: {:.4f}'.format(
1 - (total_top1_accuracy / test_examples), top_k,
1 - (total_topk_accuracy / test_examples)))
