def main(_):
# Placeholders
x = tf.placeholder(tf.float32, [FLAGS.batch_size, 224, 224, 3])
y = tf.placeholder(tf.float32, [None, FLAGS.num_classes])
dropout_keep_prob = tf.placeholder(tf.float32)
# Model
model = VggNetModel(num_classes=FLAGS.num_classes,
dropout_keep_prob=dropout_keep_prob)
model.inference(x)
# Accuracy of the model
correct_pred = tf.equal(tf.argmax(model.score, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
saver = tf.train.Saver()
test_preprocessor = BatchPreprocessor(dataset_file_path=FLAGS.test_file,
num_classes=FLAGS.num_classes,
output_size=[224, 224])
test_batches_per_epoch = np.floor(
len(test_preprocessor.labels) / FLAGS.batch_size).astype(np.int16)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# Directly restore (your model should be exactly the same with checkpoint)
saver.restore(sess, FLAGS.ckpt)
test_acc = 0.
test_count = 0
for _ in range(test_batches_per_epoch):
batch_tx, batch_ty = test_preprocessor.next_batch(FLAGS.batch_size)
acc = sess.run(accuracy,
feed_dict={
x: batch_tx,
y: batch_ty,
dropout_keep_prob: 1.
})
test_acc += acc
test_count += 1
test_acc /= test_count
print("{} Test Accuracy = {:.4f}".format(datetime.datetime.now(),
test_acc))
def main(_):
# Placeholders
x = tf.placeholder(tf.float32, [1, 224, 224, 3])
dropout_keep_prob = tf.placeholder(tf.float32)
# Model
model = VggNetModel(num_classes=FLAGS.num_classes,
dropout_keep_prob=dropout_keep_prob)
model.inference(x)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# Directly restore (your model should be exactly the same with checkpoint)
saver.restore(sess, FLAGS.ckpt)
batch_x = np.ndarray([1, 224, 224, 3])
# Read image and resize it
img = cv2.imread(FLAGS.input_image)
img = cv2.resize(img, (224, 224))
img = img.astype(np.float32)
# Subtract mean color
img -= np.array([132.2766, 139.6506, 146.9702])
batch_x[0] = img
scores = sess.run(model.score,
feed_dict={
x: batch_x,
dropout_keep_prob: 1.
})
print(scores)
def predict(path, modelpath):
with tf.Graph().as_default():
# Placeholders
x = tf.placeholder(tf.float32, [1, 224, 224, 3])
dropout_keep_prob = tf.placeholder(tf.float32)
imgs = []
# path='/home/ugrad/Shang/animal/1_.jpg'
# image = cv2.imread(path,0)
# cv2.imwrite(path,img)
img = cv2.imread(path)
img = cv2.resize(img, (224, 224))
img = img.astype(np.float32)
imgs.append(img)
# img=Image.open(path)
# img = np.array(img)
# img = tf.cast(img, tf.float32)
# img = tf.reshape(img, [1, 227, 227, 3])
# Model
model = VggNetModel(num_classes=FLAGS.num_classes,
dropout_keep_prob=dropout_keep_prob)
logits = model.inference(x)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
# Directly restore (your model should be exactly the same with checkpoint)
# Load the pretrained weights
saver = tf.train.Saver(tf.global_variables())
saver.restore(sess, modelpath)
prediction = sess.run(logits,
feed_dict={
x: imgs,
dropout_keep_prob: 1.
})
# print(prediction)
max_index = np.argmax(prediction)
print(max_index)
return max_index