def testModelVariables(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
vgg.vgg_19(inputs, num_classes)
expected_names = [
'vgg_19/conv1/conv1_1/weights',
'vgg_19/conv1/conv1_1/biases',
'vgg_19/conv1/conv1_2/weights',
'vgg_19/conv1/conv1_2/biases',
'vgg_19/conv2/conv2_1/weights',
'vgg_19/conv2/conv2_1/biases',
'vgg_19/conv2/conv2_2/weights',
'vgg_19/conv2/conv2_2/biases',
'vgg_19/conv3/conv3_1/weights',
'vgg_19/conv3/conv3_1/biases',
'vgg_19/conv3/conv3_2/weights',
'vgg_19/conv3/conv3_2/biases',
'vgg_19/conv3/conv3_3/weights',
'vgg_19/conv3/conv3_3/biases',
'vgg_19/conv3/conv3_4/weights',
'vgg_19/conv3/conv3_4/biases',
'vgg_19/conv4/conv4_1/weights',
'vgg_19/conv4/conv4_1/biases',
'vgg_19/conv4/conv4_2/weights',
'vgg_19/conv4/conv4_2/biases',
'vgg_19/conv4/conv4_3/weights',
'vgg_19/conv4/conv4_3/biases',
'vgg_19/conv4/conv4_4/weights',
'vgg_19/conv4/conv4_4/biases',
'vgg_19/conv5/conv5_1/weights',
'vgg_19/conv5/conv5_1/biases',
'vgg_19/conv5/conv5_2/weights',
'vgg_19/conv5/conv5_2/biases',
'vgg_19/conv5/conv5_3/weights',
'vgg_19/conv5/conv5_3/biases',
'vgg_19/conv5/conv5_4/weights',
'vgg_19/conv5/conv5_4/biases',
'vgg_19/fc6/weights',
'vgg_19/fc6/biases',
'vgg_19/fc7/weights',
'vgg_19/fc7/biases',
'vgg_19/fc8/weights',
'vgg_19/fc8/biases',
]
model_variables = [v.op.name for v in slim.get_model_variables()]
self.assertSetEqual(set(model_variables), set(expected_names))
def testModelVariables(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
vgg.vgg_19(inputs, num_classes)
expected_names = [
'vgg_19/conv1/conv1_1/weights',
'vgg_19/conv1/conv1_1/biases',
'vgg_19/conv1/conv1_2/weights',
'vgg_19/conv1/conv1_2/biases',
'vgg_19/conv2/conv2_1/weights',
'vgg_19/conv2/conv2_1/biases',
'vgg_19/conv2/conv2_2/weights',
'vgg_19/conv2/conv2_2/biases',
'vgg_19/conv3/conv3_1/weights',
'vgg_19/conv3/conv3_1/biases',
'vgg_19/conv3/conv3_2/weights',
'vgg_19/conv3/conv3_2/biases',
'vgg_19/conv3/conv3_3/weights',
'vgg_19/conv3/conv3_3/biases',
'vgg_19/conv3/conv3_4/weights',
'vgg_19/conv3/conv3_4/biases',
'vgg_19/conv4/conv4_1/weights',
'vgg_19/conv4/conv4_1/biases',
'vgg_19/conv4/conv4_2/weights',
'vgg_19/conv4/conv4_2/biases',
'vgg_19/conv4/conv4_3/weights',
'vgg_19/conv4/conv4_3/biases',
'vgg_19/conv4/conv4_4/weights',
'vgg_19/conv4/conv4_4/biases',
'vgg_19/conv5/conv5_1/weights',
'vgg_19/conv5/conv5_1/biases',
'vgg_19/conv5/conv5_2/weights',
'vgg_19/conv5/conv5_2/biases',
'vgg_19/conv5/conv5_3/weights',
'vgg_19/conv5/conv5_3/biases',
'vgg_19/conv5/conv5_4/weights',
'vgg_19/conv5/conv5_4/biases',
'vgg_19/fc6/weights',
'vgg_19/fc6/biases',
'vgg_19/fc7/weights',
'vgg_19/fc7/biases',
'vgg_19/fc8/weights',
'vgg_19/fc8/biases',
]
model_variables = [v.op.name for v in slim.get_model_variables()]
self.assertSetEqual(set(model_variables), set(expected_names))
def testEndPoints(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
_, end_points = vgg.vgg_19(inputs, num_classes)
expected_names = [
'vgg_19/conv1/conv1_1',
'vgg_19/conv1/conv1_2',
'vgg_19/pool1',
'vgg_19/conv2/conv2_1',
'vgg_19/conv2/conv2_2',
'vgg_19/pool2',
'vgg_19/conv3/conv3_1',
'vgg_19/conv3/conv3_2',
'vgg_19/conv3/conv3_3',
'vgg_19/conv3/conv3_4',
'vgg_19/pool3',
'vgg_19/conv4/conv4_1',
'vgg_19/conv4/conv4_2',
'vgg_19/conv4/conv4_3',
'vgg_19/conv4/conv4_4',
'vgg_19/pool4',
'vgg_19/conv5/conv5_1',
'vgg_19/conv5/conv5_2',
'vgg_19/conv5/conv5_3',
'vgg_19/conv5/conv5_4',
'vgg_19/pool5',
'vgg_19/fc6',
'vgg_19/fc7',
'vgg_19/fc8'
]
self.assertSetEqual(set(end_points.keys()), set(expected_names))
def build_cnn(self):
with tf.contrib.slim.arg_scope(vgg.vgg_arg_scope()):
_, end_points = vgg.vgg_19(inputs=self.images)
net = end_points['vgg_19/fc7'] # shape = [batch size, 1, 1, 4096]
with tf.variable_scope('mlc'):
net = tf.contrib.slim.dropout(net,
self.dropout_keep_prob,
is_training=self.is_training,
scope='dropout7')
net = tf.contrib.slim.conv2d(
net,
1024, [1, 1],
activation_fn=tf.nn.relu,
normalizer_fn=None,
scope='fc8') # shape = [batch size, 1, 1, 1024]
net = tf.contrib.slim.dropout(net,
self.dropout_keep_prob,
is_training=self.is_training,
scope='dropout8')
net = tf.contrib.slim.conv2d(
net,
self.label_num, [1, 1],
activation_fn=None,
normalizer_fn=None,
scope='fc9') # shape = [batch size, 1, 1, 15]
logits = tf.squeeze(net, [1, 2]) # shape = [batch size, 15]
self.logits = logits
self.predictions = tf.nn.sigmoid(logits)
self.conv5_3_feats = end_points['vgg_19/conv5/conv5_3']
print('cnn built.')
def testEndPoints(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
_, end_points = vgg.vgg_19(inputs, num_classes)
expected_names = [
'vgg_19/conv1/conv1_1',
'vgg_19/conv1/conv1_2',
'vgg_19/pool1',
'vgg_19/conv2/conv2_1',
'vgg_19/conv2/conv2_2',
'vgg_19/pool2',
'vgg_19/conv3/conv3_1',
'vgg_19/conv3/conv3_2',
'vgg_19/conv3/conv3_3',
'vgg_19/conv3/conv3_4',
'vgg_19/pool3',
'vgg_19/conv4/conv4_1',
'vgg_19/conv4/conv4_2',
'vgg_19/conv4/conv4_3',
'vgg_19/conv4/conv4_4',
'vgg_19/pool4',
'vgg_19/conv5/conv5_1',
'vgg_19/conv5/conv5_2',
'vgg_19/conv5/conv5_3',
'vgg_19/conv5/conv5_4',
'vgg_19/pool5',
'vgg_19/fc6',
'vgg_19/fc7',
'vgg_19/fc8'
]
self.assertSetEqual(set(end_points.keys()), set(expected_names))
def testNoClasses(self):
batch_size = 5
height, width = 224, 224
num_classes = None
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = vgg.vgg_19(inputs, num_classes)
expected_names = [
'vgg_19/conv1/conv1_1',
'vgg_19/conv1/conv1_2',
'vgg_19/pool1',
'vgg_19/conv2/conv2_1',
'vgg_19/conv2/conv2_2',
'vgg_19/pool2',
'vgg_19/conv3/conv3_1',
'vgg_19/conv3/conv3_2',
'vgg_19/conv3/conv3_3',
'vgg_19/conv3/conv3_4',
'vgg_19/pool3',
'vgg_19/conv4/conv4_1',
'vgg_19/conv4/conv4_2',
'vgg_19/conv4/conv4_3',
'vgg_19/conv4/conv4_4',
'vgg_19/pool4',
'vgg_19/conv5/conv5_1',
'vgg_19/conv5/conv5_2',
'vgg_19/conv5/conv5_3',
'vgg_19/conv5/conv5_4',
'vgg_19/pool5',
'vgg_19/fc6',
'vgg_19/fc7',
]
self.assertSetEqual(set(end_points.keys()), set(expected_names))
self.assertTrue(net.op.name.startswith('vgg_19/fc7'))
def test_vgg_19(img_dir):
"""
Test VGG-19 with a single image.
:param img_dir: Path of the image to be classified
:return: classification result and probability of a single image
"""
img = cv2.imread(img_dir)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (224, 224))
img = img.reshape((1, 224, 224, 3))
tf.reset_default_graph()
inputs = tf.placeholder(name='input_images',
shape=[None, 224, 224, 3],
dtype=tf.float32)
with slim.arg_scope(vgg_arg_scope()):
_, _ = vgg_19(inputs, is_training=False)
with tf.Session() as sess:
tf.train.Saver().restore(sess, './models/vgg_19.ckpt')
inputs = sess.graph.get_tensor_by_name('input_images:0')
outputs = sess.graph.get_tensor_by_name('vgg_19/fc8/squeezed:0')
pred = tf.argmax(tf.nn.softmax(outputs), axis=1)[0]
prob = tf.reduce_max(tf.nn.softmax(outputs), axis=1)[0]
pred, prob = sess.run([pred, prob], feed_dict={inputs: img})
name = label_dict[pred + 1]
print('Result of VGG-19:', name, prob)
return name, prob
def testNoClasses(self):
batch_size = 5
height, width = 224, 224
num_classes = None
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = vgg.vgg_19(inputs, num_classes)
expected_names = [
'vgg_19/conv1/conv1_1',
'vgg_19/conv1/conv1_2',
'vgg_19/pool1',
'vgg_19/conv2/conv2_1',
'vgg_19/conv2/conv2_2',
'vgg_19/pool2',
'vgg_19/conv3/conv3_1',
'vgg_19/conv3/conv3_2',
'vgg_19/conv3/conv3_3',
'vgg_19/conv3/conv3_4',
'vgg_19/pool3',
'vgg_19/conv4/conv4_1',
'vgg_19/conv4/conv4_2',
'vgg_19/conv4/conv4_3',
'vgg_19/conv4/conv4_4',
'vgg_19/pool4',
'vgg_19/conv5/conv5_1',
'vgg_19/conv5/conv5_2',
'vgg_19/conv5/conv5_3',
'vgg_19/conv5/conv5_4',
'vgg_19/pool5',
'vgg_19/fc6',
'vgg_19/fc7',
]
self.assertSetEqual(set(end_points.keys()), set(expected_names))
self.assertTrue(net.op.name.startswith('vgg_19/fc7'))
def build_train_op(image_tensor, label_tensor, is_training):
vgg_argscope = vgg_arg_scope(weight_decay=FLAGS.weight_decay)
global_step = tf.get_variable(name="global_step",
shape=[],
dtype=tf.int32,
trainable=False)
with slim.arg_scope(vgg_argscope):
logits, end_points = vgg_19(image_tensor,
is_training=is_training,
num_classes=10)
loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=label_tensor))
accuracy = tf.reduce_sum(
tf.cast(
tf.equal(tf.cast(tf.argmax(logits, 1), tf.int32), label_tensor),
tf.int32))
end_points['loss'], end_points['accuracy'] = loss, accuracy
if is_training:
optimizer = tf.train.AdadeltaOptimizer(
learning_rate=FLAGS.learning_rate)
train_op = optimizer.minimize(loss, global_step=global_step)
return train_op, end_points
else:
return None, end_points
def testForward(self):
batch_size = 1
height, width = 224, 224
with self.test_session() as sess:
inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = vgg.vgg_19(inputs)
sess.run(tf.global_variables_initializer())
output = sess.run(logits)
self.assertTrue(output.any())
def extract_features(input_file_path):
image_names = os.listdir(input_file_path)
print 'len image list', len(image_names)
image_list = list()
num_images = len(image_names)
for i in range(num_images):
img = image_names[i]
image_list.append(os.path.join(input_file_path, img))
print image_list[:10]
images = image_list
slim = tf.contrib.slim
# Get the image size that vgg_19 accepts
image_size = vgg.vgg_19.default_image_size
preprocessed_images = list()
out_features_list = []
total_count = 0
batch = 0
while total_count < len(images):
batch += 1
print 'batch number', batch
preprocessed_images = list()
with tf.Graph().as_default():
# This allows for default parameters
with slim.arg_scope(vgg.vgg_arg_scope()):
for c in range(10):
if total_count >= len(images):
break ##
print total_count
print images[total_count]
image = tf.read_file(image_list[total_count])
decoded_image = tf.image.decode_jpeg(image, channels=3)
preprocessed_images.append(preproc.preprocess_image(decoded_image, image_size, image_size, is_training=True))
total_count += 1
stacked_images = tf.stack(preprocessed_images)
print 'stacked images', stacked_images
_, end_points = vgg.vgg_19(stacked_images, is_training=False)
with tf.Session() as sess:
print 'inside tf sess'
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
out_features = sess.run(stacked_images)
out_features_list.extend(out_features)
print 'accumulated features array'
print np.array(out_features_list).shape
def testForward(self):
batch_size = 1
height, width = 224, 224
with self.test_session() as sess:
inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = vgg.vgg_19(inputs)
sess.run(tf.global_variables_initializer())
output = sess.run(logits)
self.assertTrue(output.any())
def vgg_19(inputs):
with slim.arg_scope(vgg.vgg_arg_scope()):
logits, end_points = vgg.vgg_19(
inputs,
num_classes=None,
is_training=False,
fc_conv_padding='VALID',
global_pool=True)
return logits, end_points, vgg_19_ckpt_path
def testFullyConvolutional(self):
batch_size = 1
height, width = 256, 256
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = vgg.vgg_19(inputs, num_classes, spatial_squeeze=False)
self.assertEquals(logits.op.name, 'vgg_19/fc8/BiasAdd')
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, 2, 2, num_classes])
def testBuild(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = vgg.vgg_19(inputs, num_classes)
self.assertEquals(logits.op.name, 'vgg_19/fc8/squeezed')
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
def testBuild(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = vgg.vgg_19(inputs, num_classes)
self.assertEquals(logits.op.name, 'vgg_19/fc8/squeezed')
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
def testFullyConvolutional(self):
batch_size = 1
height, width = 256, 256
num_classes = 1000
with self.test_session():
inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = vgg.vgg_19(inputs, num_classes, spatial_squeeze=False)
self.assertEquals(logits.op.name, 'vgg_19/fc8/BiasAdd')
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, 2, 2, num_classes])
def vgg_19(inputs, is_training, opts):
with slim.arg_scope(vgg.vgg_arg_scope(weight_decay=opts.weight_decay)):
return vgg.vgg_19(
inputs,
num_classes=opts.num_classes,
dropout_keep_prob=opts.dropout_keep_prob,
spatial_squeeze=opts.spatial_squeeze,
is_training=is_training,
fc_conv_padding='VALID',
global_pool=opts.global_pool)
def create_network(self):
with tf.contrib.slim.arg_scope(vgg_arg_scope()):
logits, end_points = vgg_19(self.img,
num_classes=self.nb_class,
is_training=self.is_training,
fc_conv_padding='SAME',
global_pool=True)
self.logits = logits
self.probabilities = tf.nn.sigmoid(self.logits)
self.predictions = tf.cast(
self.probabilities >= self.prediction_threshold, tf.float32)
def testEvaluation(self):
batch_size = 2
height, width = 224, 224
num_classes = 1000
with self.test_session():
eval_inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = vgg.vgg_19(eval_inputs, is_training=False)
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
predictions = tf.argmax(logits, 1)
self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
def eval(params):
batch_size = params['batch_size']
num_examples = len(params['test_files'][0])
with tf.Graph().as_default():
batch = dut.distorted_inputs(params,is_training=is_training)
with slim.arg_scope(vgg.vgg_arg_scope()):
logits, end_points = vgg.vgg_19(batch[0], num_classes=params['n_output'], is_training=is_training)
init_fn=ut.get_init_fn(slim,params)
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = params['per_process_gpu_memory_fraction']
with tf.Session(config=config) as sess:
# sess.run(tf.initialize_all_variables())
sess.run(tf.initialize_local_variables())
coord = tf.train.Coordinator()
threads = []
for qr in tf.get_collection(tf.GraphKeys.QUEUE_RUNNERS):
threads.extend(qr.create_threads(sess, coord=coord, daemon=True, start=True))
init_fn(sess)
num_iter = int(math.ceil(num_examples / batch_size))
print('%s: Testing started.' % (datetime.now()))
step = 0
loss_lst=[]
run_lst=[]
run_lst.append(logits)
[run_lst.append(lst) for lst in batch[1:len(batch)]]
while step < num_iter and not coord.should_stop():
try:
batch_res= sess.run(run_lst)
except tf.errors.OutOfRangeError:
print ('Testing finished....%d'%step)
break
if(params['write_est']==True):
ut.write_est(params,batch_res)
est=batch_res[0]
gt=batch_res[1]
loss= ut.get_loss(params,gt,est)
loss_lst.append(loss)
s ='VAL --> batch %i/%i | error %f'%(step,num_iter,loss)
ut.log_write(s,params)
# joint_list=['/'.join(p1.split('/')[0:-1]).replace('joints','img').replace('.cdf','')+'/frame_'+(p1.split('/')[-1].replace('.txt','')).zfill(5)+'.png' for p1 in image_names]
# print ('List equality check:')
# print len(label_names) == len(set(label_names))
# print sum(joint_list==label_names)==(len(est))
# print(len(label_names))
step += 1
coord.request_stop()
coord.join(threads)
return np.mean(loss_lst)
def testEvaluation(self):
batch_size = 2
height, width = 224, 224
num_classes = 1000
with self.test_session():
eval_inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = vgg.vgg_19(eval_inputs, is_training=False)
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
predictions = tf.argmax(logits, 1)
self.assertListEqual(predictions.get_shape().as_list(), [batch_size])
def build_cnn(self):
_, end_points = vgg.vgg_19(self.images,
num_classes=1000,
is_training=self.is_training)
visual_feats = end_points[
'vgg_19/conv5/conv5_4'] # [batch_size, 14, 14, 512]
self.visual_feats = tf.reshape(visual_feats,
[self.batch_size, 196, 512])
print('cnn built.')
def testTrainEvalWithReuse(self):
train_batch_size = 2
eval_batch_size = 1
train_height, train_width = 224, 224
eval_height, eval_width = 256, 256
num_classes = 1000
with self.test_session():
train_inputs = tf.random_uniform(
(train_batch_size, train_height, train_width, 3))
logits, _ = vgg.vgg_19(train_inputs)
self.assertListEqual(logits.get_shape().as_list(),
[train_batch_size, num_classes])
tf.get_variable_scope().reuse_variables()
eval_inputs = tf.random_uniform(
(eval_batch_size, eval_height, eval_width, 3))
logits, _ = vgg.vgg_19(eval_inputs, is_training=False,
spatial_squeeze=False)
self.assertListEqual(logits.get_shape().as_list(),
[eval_batch_size, 2, 2, num_classes])
logits = tf.reduce_mean(logits, [1, 2])
predictions = tf.argmax(logits, 1)
self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
def testTrainEvalWithReuse(self):
train_batch_size = 2
eval_batch_size = 1
train_height, train_width = 224, 224
eval_height, eval_width = 256, 256
num_classes = 1000
with self.test_session():
train_inputs = tf.random_uniform(
(train_batch_size, train_height, train_width, 3))
logits, _ = vgg.vgg_19(train_inputs)
self.assertListEqual(logits.get_shape().as_list(),
[train_batch_size, num_classes])
tf.get_variable_scope().reuse_variables()
eval_inputs = tf.random_uniform(
(eval_batch_size, eval_height, eval_width, 3))
logits, _ = vgg.vgg_19(eval_inputs, is_training=False,
spatial_squeeze=False)
self.assertListEqual(logits.get_shape().as_list(),
[eval_batch_size, 2, 2, num_classes])
logits = tf.reduce_mean(logits, [1, 2])
predictions = tf.argmax(logits, 1)
self.assertEquals(predictions.get_shape().as_list(), [eval_batch_size])
def run_steps(params, epoch_counter):
with tf.Graph().as_default():
num_examples = len(params['training_files'][0])
number_of_steps = int(math.ceil(
num_examples / params['batch_size'])) - 1
number_of_steps = number_of_steps * (epoch_counter + 1)
tf.logging.set_verbosity(tf.logging.INFO)
batch = dut.distorted_inputs(params, is_training=is_training)
with slim.arg_scope(vgg.vgg_arg_scope()):
logits, endpoint = vgg.vgg_19(batch[0],
num_classes=params['n_output'],
is_training=is_training)
# # Create the model:
# with slim.arg_scope(inception.inception_v2_arg_scope()):
# logits, _ = inception.inception_v2(batch[0], num_classes=params['n_output'], is_training=is_training)
err = tf.sub(logits, batch[1])
losses = tf.reduce_mean(tf.reduce_sum(tf.square(err), 1))
reg_loss = slim.losses.get_total_loss()
total_loss = losses + reg_loss
tf.scalar_summary('losses/total_loss', total_loss)
tf.scalar_summary('losses/losses', losses)
tf.scalar_summary('losses/reg_loss', reg_loss)
summary_writer = tf.train.SummaryWriter(params["sm"])
# Specify the optimizer and create the train op:
optimizer = tf.train.AdamOptimizer(learning_rate=params['lr'])
train_op = slim.learning.create_train_op(total_loss,
optimizer,
summarize_gradients=True)
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = params[
'per_process_gpu_memory_fraction']
# Run the training:
final_loss = learn.train(
loss=losses,
logits=logits,
batch=batch,
endpoint=endpoint,
train_op=train_op,
logdir=params["cp_file"],
init_fn=ut.get_init_fn(slim, params),
number_of_steps=number_of_steps,
summary_writer=summary_writer,
session_config=config,
)
return final_loss
def main():
"""
You can also run these commands manually to generate the pb file
1. git clone https://github.com/tensorflow/models.git
2. export PYTHONPATH=Path_to_your_model_folder
3. python alexnet.py
"""
height, width = 224, 224
inputs = tf.Variable(tf.random_uniform((1, height, width, 3)), name='input')
net, end_points = vgg.vgg_19(inputs, is_training = False)
print("nodes in the graph")
for n in end_points:
print(n + " => " + str(end_points[n]))
net_outputs = map(lambda x: tf.get_default_graph().get_tensor_by_name(x), argv[2].split())
run_model(net_outputs, argv[1])
def get_vgg_model(x, is_training):
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn=tf.nn.relu,
weights_regularizer=slim.l2_regularizer(
FLAGS.WEIGHT_DECAY),
biases_initializer=tf.zeros_initializer()):
with slim.arg_scope([slim.conv2d], padding='SAME'):
logits, _ = vgg.vgg_19(x,
num_classes=FLAGS.NUM_CLASSES,
is_training=is_training,
dropout_keep_prob=FLAGS.KEEP_PROB,
spatial_squeeze=True,
scope='vgg_19',
fc_conv_padding='VALID',
global_pool=True)
return logits
def main():
"""
You can also run these commands manually to generate the pb file
1. git clone https://github.com/tensorflow/models.git
2. export PYTHONPATH=Path_to_your_model_folder
3. python alexnet.py
"""
height, width = 224, 224
inputs = tf.Variable(tf.random_uniform((1, height, width, 3)), name='input')
inputs = tf.identity(inputs, "input_node")
net, end_points = vgg.vgg_19(inputs, is_training = False)
print("nodes in the graph")
for n in end_points:
print(n + " => " + str(end_points[n]))
net_outputs = map(lambda x: tf.get_default_graph().get_tensor_by_name(x), argv[2].split(','))
run_model(net_outputs, argv[1], 'vgg_19', argv[3] == 'True')
def __call__(self, x_input):
"""Constructs model and return probabilities for given input."""
reuse = True if self.built else None
x_input = image_normalize(x_input, normalization_method[10])
x_input = tf.image.resize_images(x_input, [224, 224])
with slim.arg_scope(vgg.vgg_arg_scope()):
_, end_points = vgg.vgg_19(x_input,
num_classes=1000,
is_training=False)
end_points['predictions'] = tf.nn.softmax(end_points['vgg_19/fc8'])
end_points['predictions'] = \
tf.concat([tf.zeros([tf.shape(x_input)[0], 1]),
tf.reshape(end_points['predictions'], [-1, 1000])],
axis=1)
self.built = True
output = end_points['predictions']
return output
def extract_feature_vgg_19(checkpoints_dir, input_image, layer):
image_size = vgg.vgg_19.default_image_size
processed_image = vgg_preprocessing.preprocess_image(input_image,
image_size,
image_size,
is_training=False)
processed_images = tf.expand_dims(processed_image, 0)
with slim.arg_scope(vgg.vgg_arg_scope()):
_, model_var = vgg.vgg_19(processed_images,
num_classes=1000,
is_training=False)
init_fn = slim.assign_from_checkpoint_fn(os.path.join(checkpoints_dir, 'vgg_16.ckpt'),
slim.get_model_variables('vgg_16'))
with tf.Session() as sess:
init_fn(sess)
features = sess.run(model_var)
fea=features['vgg_19/'+layer]
return fea
def main(_):
batch_shape = [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, 3]
num_classes = 1001
ensemble_type = FLAGS.ensemble_type
tf.logging.set_verbosity(tf.logging.INFO)
checkpoint_path_list = [
FLAGS.checkpoint_path_inception_v1, FLAGS.checkpoint_path_inception_v2,
FLAGS.checkpoint_path_inception_v3, FLAGS.checkpoint_path_inception_v4,
FLAGS.checkpoint_path_inception_resnet_v2,
FLAGS.checkpoint_path_resnet_v1_101,
FLAGS.checkpoint_path_resnet_v1_152,
FLAGS.checkpoint_path_resnet_v2_101,
FLAGS.checkpoint_path_resnet_v2_152, FLAGS.checkpoint_path_vgg_16,
FLAGS.checkpoint_path_vgg_19
]
normalization_method = [
'default', 'default', 'default', 'default', 'global', 'caffe_rgb',
'caffe_rgb', 'default', 'default', 'caffe_rgb', 'caffe_rgb'
]
pred_list = []
for idx, checkpoint_path in enumerate(checkpoint_path_list, 1):
with tf.Graph().as_default():
if int(FLAGS.test_idx) == 20 and idx in [3]:
continue
if int(FLAGS.test_idx) in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11
] and int(FLAGS.test_idx) != idx:
continue
# Prepare graph
if idx in [1, 2, 6, 7, 10, 11]:
_x_input = tf.placeholder(tf.float32, shape=batch_shape)
x_input = tf.image.resize_images(_x_input, [224, 224])
else:
_x_input = tf.placeholder(tf.float32, shape=batch_shape)
x_input = _x_input
x_input = image_normalize(x_input, normalization_method[idx - 1])
if idx == 1:
with slim.arg_scope(inception.inception_v1_arg_scope()):
_, end_points = inception.inception_v1(
x_input, num_classes=num_classes, is_training=False)
elif idx == 2:
with slim.arg_scope(inception.inception_v2_arg_scope()):
_, end_points = inception.inception_v2(
x_input, num_classes=num_classes, is_training=False)
elif idx == 3:
with slim.arg_scope(inception.inception_v3_arg_scope()):
_, end_points = inception.inception_v3(
x_input, num_classes=num_classes, is_training=False)
elif idx == 4:
with slim.arg_scope(inception.inception_v4_arg_scope()):
_, end_points = inception.inception_v4(
x_input, num_classes=num_classes, is_training=False)
elif idx == 5:
with slim.arg_scope(inception.inception_resnet_v2_arg_scope()):
_, end_points = inception.inception_resnet_v2(
x_input, num_classes=num_classes, is_training=False)
elif idx == 6:
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
_, end_points = resnet_v1.resnet_v1_101(x_input,
num_classes=1000,
is_training=False)
elif idx == 7:
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
_, end_points = resnet_v1.resnet_v1_152(x_input,
num_classes=1000,
is_training=False)
elif idx == 8:
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
_, end_points = resnet_v2.resnet_v2_101(
x_input, num_classes=num_classes, is_training=False)
elif idx == 9:
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
_, end_points = resnet_v2.resnet_v2_152(
x_input, num_classes=num_classes, is_training=False)
elif idx == 10:
with slim.arg_scope(vgg.vgg_arg_scope()):
_, end_points = vgg.vgg_16(x_input,
num_classes=1000,
is_training=False)
end_points['predictions'] = tf.nn.softmax(
end_points['vgg_16/fc8'])
elif idx == 11:
with slim.arg_scope(vgg.vgg_arg_scope()):
_, end_points = vgg.vgg_19(x_input,
num_classes=1000,
is_training=False)
end_points['predictions'] = tf.nn.softmax(
end_points['vgg_19/fc8'])
#end_points = tf.reduce_mean([end_points1['Predictions'], end_points2['Predictions'], end_points3['Predictions'], end_points4['Predictions']], axis=0)
#predicted_labels = tf.argmax(end_points, 1)
# Run computation
saver = tf.train.Saver(slim.get_model_variables())
session_creator = tf.train.ChiefSessionCreator(
scaffold=tf.train.Scaffold(saver=saver),
checkpoint_filename_with_path=checkpoint_path,
master=FLAGS.master)
pred_in = []
filenames_list = []
with tf.train.MonitoredSession(
session_creator=session_creator) as sess:
for filenames, images in load_images(FLAGS.input_dir,
batch_shape):
#if idx in [1,2,6,7,10,11]:
# # 16x299x299x3
# images = zoom(images, (1, 0.7491638795986622, 0.7491638795986622, 1), order=2)
filenames_list.extend(filenames)
end_points_dict = sess.run(end_points,
feed_dict={_x_input: images})
if idx in [6, 7, 10, 11]:
end_points_dict['predictions'] = \
np.concatenate([np.zeros([FLAGS.batch_size, 1]),
np.array(end_points_dict['predictions'].reshape(-1, 1000))],
axis=1)
try:
pred_in.extend(end_points_dict['Predictions'].reshape(
-1, num_classes))
except KeyError:
pred_in.extend(end_points_dict['predictions'].reshape(
-1, num_classes))
pred_list.append(pred_in)
if ensemble_type == 'mean':
pred = np.mean(pred_list, axis=0)
labels = np.argmax(
pred, axis=1
) # model_num X batch X class_num ==(np.mean)==> batch X class_num ==(np.argmax)==> batch
elif ensemble_type == 'vote':
pred = np.argmax(
pred_list, axis=2
) # model_num X batch X class_num ==(np.mean)==> batch X class_num ==(np.argmax)==> batch
labels = np.median(pred, axis=0)
with tf.gfile.Open(FLAGS.output_file, 'w') as out_file:
for filename, label in zip(filenames_list, labels):
out_file.write('{0},{1}\n'.format(filename, label))
input_imgs = tf.placeholder("float", [None,None,3])
# 每个像素减去像素的均值
processed_image = _mean_image_subtraction(input_imgs,
[_R_MEAN, _G_MEAN, _B_MEAN])
input_image = tf.expand_dims(processed_image, 0)
#print(input_image.shape)
with slim.arg_scope(vgg.vgg_arg_scope()):# spatial_squeeze选项指定是否压缩结果的空间维度将不必要的空间维度删除
logits, _ = vgg.vgg_19(input_image,
num_classes=1000,
is_training=False,
spatial_squeeze=False)
pred = tf.argmax(logits, dimension=3)
init_fn = slim.assign_from_checkpoint_fn(
os.path.join(checkpoints_dir, 'vgg_19.ckpt'),
slim.get_model_variables('vgg_19'))
with tf.Session() as sess:
init_fn(sess)
for image in sample_images:
reimg = Image.open(image)
plt.suptitle("原始图片", fontsize=14, fontweight='bold')
plt.imshow(reimg) # 显示图片
def __init__(self, num_classes, train_layers=None, weights_path='DEFAULT'):
"""Create the graph of the vgg19 model.
"""
# Parse input arguments into class variables
if weights_path == 'DEFAULT':
self.WEIGHTS_PATH = "./pre_trained_models/vgg_19.ckpt"
else:
self.WEIGHTS_PATH = weights_path
self.train_layers = train_layers
with tf.variable_scope("input"):
self.image_size = vgg.vgg_19.default_image_size
self.x_input = tf.placeholder(
tf.float32, [None, self.image_size, self.image_size, 3],
name="x_input")
self.y_input = tf.placeholder(tf.float32, [None, num_classes],
name="y_input")
self.learning_rate = tf.placeholder(tf.float32,
name="learning_rate")
self.keep_prob = tf.placeholder(tf.float32, name="keep_prob")
# train
with arg_scope(vgg.vgg_arg_scope()):
self.logits, _ = vgg.vgg_19(self.x_input,
num_classes=num_classes,
is_training=True,
reuse=tf.AUTO_REUSE,
dropout_keep_prob=self.keep_prob)
# validation
with arg_scope(vgg.vgg_arg_scope()):
self.logits_val, _ = vgg.vgg_19(self.x_input,
num_classes=num_classes,
is_training=False,
reuse=tf.AUTO_REUSE,
dropout_keep_prob=self.keep_prob)
with tf.name_scope("loss"):
self.loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits_v2(
logits=self.logits, labels=self.y_input))
self.loss_val = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits_v2(
logits=self.logits_val, labels=self.y_input))
with tf.name_scope("train"):
self.global_step = tf.Variable(0,
name="global_step",
trainable=False)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
var_list = [
v for v in tf.trainable_variables()
if v.name.split('/')[-2] in train_layers
or v.name.split('/')[-3] in train_layers
]
gradients = tf.gradients(self.loss, var_list)
self.grads_and_vars = list(zip(gradients, var_list))
optimizer = tf.train.GradientDescentOptimizer(self.learning_rate)
with tf.control_dependencies(update_ops):
self.train_op = optimizer.apply_gradients(
grads_and_vars=self.grads_and_vars,
global_step=self.global_step)
with tf.name_scope("probability"):
self.probability = tf.nn.softmax(self.logits_val,
name="probability")
with tf.name_scope("prediction"):
self.prediction = tf.argmax(self.logits_val, 1, name="prediction")
with tf.name_scope("accuracy"):
correct_prediction = tf.equal(self.prediction,
tf.argmax(self.y_input, 1))
self.accuracy = tf.reduce_mean(tf.cast(correct_prediction,
"float"),
name="accuracy")
def main(_):
batch_shape = [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, 3]
num_classes = 1001
# max_epsilon over checking
# get original images
origin_img_list=np.sort(glob.glob(FLAGS.origin_img_dir+"*.png"));
origin_imgs=np.zeros((len(origin_img_list),FLAGS.image_height,FLAGS.image_width,3),dtype=float);
for i in range(len(origin_img_list)):
origin_imgs[i]=imread(origin_img_list[i],mode='RGB').astype(np.float);
# get adv images
adv_img_list=np.sort(glob.glob(FLAGS.input_dir+"*.png"));
adv_imgs=np.zeros((len(adv_img_list),FLAGS.image_height,FLAGS.image_width,3),dtype=float);
for i in range(len(adv_img_list)):
adv_imgs[i]=imread(adv_img_list[i],mode='RGB').astype(np.float);
epsilon_list=np.linalg.norm(np.reshape(abs(origin_imgs-adv_imgs),[-1,FLAGS.image_height*FLAGS.image_width*3]),ord=np.inf,axis=1);
#print(epsilon_list);exit(1);
over_epsilon_list=np.zeros((len(origin_img_list),2),dtype=object);
cnt=0;
for i in range(len(origin_img_list)):
file_name=origin_img_list[i].split("/")[-1];
file_name=file_name.split(".")[0];
over_epsilon_list[i,0]=file_name;
if(epsilon_list[i]>FLAGS.max_epsilon):
over_epsilon_list[i,1]="1";
cnt+=1;
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default():
# Prepare graph
x_input = tf.placeholder(tf.float32, shape=batch_shape)
if(FLAGS.checkpoint_file_name=="inception_v3.ckpt"):
with slim.arg_scope(inception.inception_v3_arg_scope()):
_, end_points = inception.inception_v3(
x_input, num_classes=num_classes, is_training=False)
predicted_labels = tf.argmax(end_points['Predictions'], 1)
elif(FLAGS.checkpoint_file_name=="inception_v4.ckpt"):
with slim.arg_scope(inception.inception_v4_arg_scope()):
_, end_points = inception.inception_v4(
x_input, num_classes=num_classes, is_training=False)
predicted_labels = tf.argmax(end_points['Predictions'], 1)
elif(FLAGS.checkpoint_file_name=="inception_resnet_v2_2016_08_30.ckpt"):
with slim.arg_scope(inception.inception_resnet_v2_arg_scope()):
_, end_points = inception.inception_resnet_v2(
x_input, num_classes=num_classes, is_training=False)
predicted_labels = tf.argmax(end_points['Predictions'], 1)
elif(FLAGS.checkpoint_file_name=="resnet_v2_101.ckpt"):
x_input2 = tf.image.resize_bilinear(x_input,[224,224],align_corners=False);
with slim.arg_scope(resnet_utils.resnet_arg_scope()):
_, end_points = resnet_v2.resnet_v2_101(
x_input2, num_classes=num_classes, is_training=False)
predicted_labels = tf.argmax(end_points['predictions'], 1)
elif(FLAGS.checkpoint_file_name=="resnet_v2_50.ckpt"):
x_input2 = tf.image.resize_bilinear(x_input,[224,224],align_corners=False);
with slim.arg_scope(resnet_utils.resnet_arg_scope()):
_, end_points = resnet_v2.resnet_v2_50(
x_input2, num_classes=num_classes, is_training=False)
predicted_labels = tf.argmax(end_points['predictions'], 1)
elif(FLAGS.checkpoint_file_name=="resnet_v2_152.ckpt"):
x_input2 = tf.image.resize_bilinear(x_input,[224,224],align_corners=False);
with slim.arg_scope(resnet_utils.resnet_arg_scope()):
_, end_points = resnet_v2.resnet_v2_152(
x_input2, num_classes=num_classes, is_training=False)
predicted_labels = tf.argmax(end_points['predictions'], 1)
elif(FLAGS.checkpoint_file_name=="inception_v1.ckpt"):
x_input2 = tf.image.resize_bilinear(x_input,[224,224],align_corners=False);
with slim.arg_scope(inception.inception_v1_arg_scope()):
_, end_points = inception.inception_v1(
x_input2, num_classes=num_classes, is_training=False)
predicted_labels = tf.argmax(end_points['Predictions'], 1)
elif(FLAGS.checkpoint_file_name=="inception_v2.ckpt"):
x_input2 = tf.image.resize_bilinear(x_input,[224,224],align_corners=False);
with slim.arg_scope(inception.inception_v2_arg_scope()):
_, end_points = inception.inception_v2(
x_input2, num_classes=num_classes, is_training=False)
predicted_labels = tf.argmax(end_points['Predictions'], 1)
# Resnet v1 and vgg are not working now
elif(FLAGS.checkpoint_file_name=="vgg_16.ckpt"):
x_input_list=tf.unstack(x_input,FLAGS.batch_size,0);
for i in range(FLAGS.batch_size):
x_input_list[i]=vgg_preprocessing.preprocess_image(x_input_list[i],224,224);
x_input2=tf.stack(x_input_list,0);
with slim.arg_scope(vgg.vgg_arg_scope()):
_, end_points = vgg.vgg_16(
x_input2, num_classes=num_classes-1, is_training=False)
predicted_labels = tf.argmax(end_points['vgg_16/fc8'], 1)+1
elif(FLAGS.checkpoint_file_name=="vgg_19.ckpt"):
x_input_list=tf.unstack(x_input,FLAGS.batch_size,0);
for i in range(FLAGS.batch_size):
x_input_list[i]=vgg_preprocessing.preprocess_image(x_input_list[i],224,224);
x_input2=tf.stack(x_input_list,0);
with slim.arg_scope(vgg.vgg_arg_scope()):
_, end_points = vgg.vgg_19(
x_input2, num_classes=num_classes-1, is_training=False)
predicted_labels = tf.argmax(end_points['vgg_19/fc8'], 1)+1
elif(FLAGS.checkpoint_file_name=="resnet_v1_50.ckpt"):
x_input_list=tf.unstack(x_input,FLAGS.batch_size,0);
for i in range(FLAGS.batch_size):
x_input_list[i]=vgg_preprocessing.preprocess_image(x_input_list[i],224,224);
x_input2=tf.stack(x_input_list,0);
with slim.arg_scope(resnet_utils.resnet_arg_scope()):
_, end_points = resnet_v1.resnet_v1_50(
x_input, num_classes=num_classes-1, is_training=False)
predicted_labels = tf.argmax(end_points['predictions'], 1)+1
elif(FLAGS.checkpoint_file_name=="resnet_v1_101.ckpt"):
x_input_list=tf.unstack(x_input,FLAGS.batch_size,0);
for i in range(FLAGS.batch_size):
x_input_list[i]=vgg_preprocessing.preprocess_image(x_input_list[i],224,224);
x_input2=tf.stack(x_input_list,0);
with slim.arg_scope(resnet_utils.resnet_arg_scope()):
_, end_points = resnet_v1.resnet_v1_101(
x_input2, num_classes=num_classes-1, is_training=False)
predicted_labels = tf.argmax(end_points['predictions'], 1)+1
elif(FLAGS.checkpoint_file_name=="resnet_v1_152.ckpt"):
x_input_list=tf.unstack(x_input,FLAGS.batch_size,0);
for i in range(FLAGS.batch_size):
x_input_list[i]=vgg_preprocessing.preprocess_image(x_input_list[i],224,224);
x_input2=tf.stack(x_input_list,0);
with slim.arg_scope(resnet_utils.resnet_arg_scope()):
_, end_points = resnet_v1.resnet_v1_152(
x_input2, num_classes=num_classes-1, is_training=False)
predicted_labels = tf.argmax(end_points['predictions'], 1)+1
# Run computation
saver = tf.train.Saver(slim.get_model_variables())
session_creator = tf.train.ChiefSessionCreator(
scaffold=tf.train.Scaffold(saver=saver),
checkpoint_filename_with_path=FLAGS.checkpoint_path+FLAGS.checkpoint_file_name,
master=FLAGS.master)
f=open(FLAGS.true_label,"r");
t_label_list=np.array([i[:-1].split(",") for i in f.readlines()]);
score=0;
with tf.train.MonitoredSession(session_creator=session_creator) as sess:
with tf.gfile.Open(FLAGS.output_file, 'w') as out_file:
for filenames, images in load_images(FLAGS.input_dir, batch_shape):
labels = sess.run(predicted_labels, feed_dict={x_input: images})
for filename, label in zip(filenames, labels):
f_name=filename.split(".")[0];
t_label=int(t_label_list[t_label_list[:,0]==f_name,1][0]);
if(t_label!=label):
if(over_epsilon_list[over_epsilon_list[:,0]==f_name,1]!="1"):
score+=1;
#out_file.write('{0},{1}\n'.format(filename, label))
print("Over max epsilon#: "+str(cnt));
print(str(FLAGS.max_epsilon)+" max epsilon Score: "+str(score));
return rgbmean
resnetimg = gen(x_smalls2)
result = (resnetimg + 1) * 127.5
gen_var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
y_pred = tf.maximum(result, 0)
y_pred = tf.minimum(y_pred, 255)
dbatch = tf.concat([images, result], 0)
rgbmean = rgbmeanfun(dbatch)
#vgg 特征值
_, end_points = vgg.vgg_19(rgbmean,
num_classes=1000,
is_training=False,
spatial_squeeze=False)
conv54 = end_points['vgg_19/conv5/conv5_4']
print("vgg.conv5_4", conv54.shape)
fmap = tf.split(conv54, 2)
content_loss = tf.losses.mean_squared_error(fmap[0], fmap[1])
######################################
def Discriminator(dbatch, name="Discriminator"):
with tf.variable_scope(name):
net = slim.conv2d(dbatch, 64, 1, activation_fn=leaky_relu)
ochannels = [64, 128, 128, 256, 256, 512, 512]
stride = [2, 1]
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.InteractiveSession(config=config)
g = tf.get_default_graph()
#%%
with g.as_default():
if is_save:
opts = tf.python_io.TFRecordOptions(
tf.python_io.TFRecordCompressionType.ZLIB)
feature_writer = tf.python_io.TFRecordWriter(feature_tfrecord_filename,
options=opts)
img_input_ph = tf.placeholder(dtype=tf.float32,
shape=[None, height, width, 3])
with slim.arg_scope(vgg.vgg_arg_scope()):
_, _ = vgg.vgg_19(img_input_ph, num_classes=1000, is_training=False)
init_fn = slim.assign_from_checkpoint_fn(checkpoints_dir,
slim.get_model_variables())
features = g.get_tensor_by_name('vgg_19/conv5/conv5_4/Relu:0')
idx = 0
init_fn(sess)
while True: #idx < 3125:
try:
processed_images_v, img_ids_v, labels_v = sess.run(
[processed_images, img_ids, labels])
features_v = sess.run(features, {img_input_ph: processed_images_v})
print('batch no. {}'.format(idx))
for idx_s in range(features_v.shape[0]):
feature = features_v[idx_s, :, :, :]
feature = np.reshape(feature, [196, 512])
def compute_feature_of_batch_ts_with_cnn(file_path_of_ts, file_path_of_feature,
cnn_model_name,
file_path_of_pretrained_model):
r'''
compute feature of somme time series with pretrained CNN
:param file_path_of_ts: file path of time series
:param file_path_of_feature: file path of saving feature
:param cnn_model_name: name of CNN model
:param file_path_of_pretrained_model: file path of pretrained CNN
:return: ''
'''
#tf.reset_default_graph()
#read data
data = pd.read_csv(file_path_of_ts)
#data=data.sample(20)
#change dataframe to list
id_list = data.iloc[:, 0].tolist()
data_list = change_dataframe_to_dict_(data)
model = cnn_model_name
checkpoint_file = file_path_of_pretrained_model
# I only have these because I thought some take in size of (299,299), but maybe not
if 'inception' in model: height, width, channels = 224, 224, 3
if 'resnet' in model: height, width, channels = 224, 224, 3
if 'vgg' in model: height, width, channels = 224, 224, 3
if model == 'inception_resnet_v2': height, width, channels = 299, 299, 3
x = tf.placeholder(tf.float32, shape=(1, height, width, channels))
# load up model specific stuff
if model == 'inception_v1':
#from inception_v1 import *
from nets import inception_v1
arg_scope = inception_v1.inception_v1_arg_scope()
with slim.arg_scope(arg_scope):
logits, end_points = inception_v1.inception_v1(x,
is_training=False,
num_classes=None)
features = end_points['AvgPool_0a_7x7']
# print('logits')
# print(logits.shape)
# print('features')
# print(features.shape)
elif model == 'inception_v2':
#from inception_v2 import *
from nets import inception_v2
arg_scope = inception_v2.inception_v2_arg_scope()
with slim.arg_scope(arg_scope):
logits, end_points = inception_v2(x,
is_training=False,
num_classes=None)
features = end_points['AvgPool_1a']
elif model == 'inception_v3':
#from inception_v3 import *
from nets import inception_v3
arg_scope = inception_v3.inception_v3_arg_scope()
with slim.arg_scope(arg_scope):
logits, end_points = inception_v3(x,
is_training=False,
num_classes=None)
features = end_points['AvgPool_1a']
elif model == 'inception_resnet_v2':
#from inception_resnet_v2 import *
from nets import inception_resnet_v2
arg_scope = inception_resnet_v2.inception_resnet_v2_arg_scope()
with slim.arg_scope(arg_scope):
logits, end_points = inception_resnet_v2(x,
is_training=False,
num_classes=1001)
features = end_points['PreLogitsFlatten']
elif model == 'resnet_v1_50':
#from resnet_v1 import *
from nets import resnet_v1
arg_scope = resnet_v1.resnet_arg_scope()
with slim.arg_scope(arg_scope):
logits, end_points = resnet_v1.resnet_v1_50(x,
is_training=False,
num_classes=1000)
features = end_points['global_pool']
elif model == 'resnet_v1_101':
#from resnet_v1 import *
from nets import resnet_v1
arg_scope = resnet_v1.resnet_arg_scope()
with slim.arg_scope(arg_scope):
logits, end_points = resnet_v1.resnet_v1_101(x,
is_training=False,
num_classes=1000)
features = end_points['global_pool']
elif model == 'vgg_16':
#from vgg import *
from nets import vgg
arg_scope = vgg.vgg_arg_scope()
with slim.arg_scope(arg_scope):
logits, end_points = vgg.vgg_16(x, is_training=False)
features = end_points['vgg_16/fc8']
elif model == 'vgg_19':
#from vgg import *
from nets import vgg
arg_scope = vgg.vgg_arg_scope()
with slim.arg_scope(arg_scope):
logits, end_points = vgg.vgg_19(x, is_training=False)
features = end_points['vgg_19/fc8']
#cpu_config = tf.ConfigProto(intra_op_parallelism_threads = 8, inter_op_parallelism_threads = 8, device_count = {'CPU': 3})
#sess = tf.Session(config = cpu_config)
sess = tf.Session()
saver = tf.train.Saver()
saver.restore(sess, checkpoint_file)
feature_list = []
count_temp = 0
for i in range(len(data_list)):
count_temp = count_temp + 1
#imaging ts
ts_dict = data_list[i]
ts = ts_dict['ts']
id = ts_dict['id']
new_ts = min_max_transform(ts)
normalized = np.array(new_ts)
fig, ax = plt.subplots()
#plt.imshow(recurrence_plot.rec_plot(normalized), cmap=plt.cm.gray)
plt.imshow(recurrence_plot.rec_plot(normalized))
ax.set_xticks([])
ax.set_yticks([])
#print(id)
path = "inception-v1/" + id + ".jpg"
plt.savefig(path)
plt.close(fig)
#compute feature
# #begin to compute features
image = misc.imread(path)
#from matplotlib.pyplot import imread
#image=imread(path)
# print('image')
# print(image.size)
image = misc.imresize(image, (height, width))
image = np.expand_dims(image, 0)
feature = np.squeeze(sess.run(features, feed_dict={x: image}))
feature_list.append(feature)
# print('feature-test')
# print(feature)
os.remove(path)
if count_temp % 100 == 0:
print(count_temp)
#begin to process parellel result and write_to_csv
feature_array = np.array(feature_list)
feature_df = pd.DataFrame(feature_array)
# print(feature_df.shape)
# print(len(id_list))
#add id
feature_df.insert(loc=0, column='id', value=id_list)
# print(feature_final_df.shape)
# print(feature_final_df.head())
feature_df.to_csv(file_path_of_feature, index=False)
gc.collect()
lr = 0.0001
return {images: imgs, labels: lbls,learning_rate: lr, keep_prob: keep_prob_per, keep_prob_data: keep_prob_data_per}
tf.reset_default_graph()
images = tf.placeholder(tf.float32,shape=[None,height_image,width_image,3])
labels = tf.placeholder(tf.float32,shape=[None,3])
learning_rate = tf.placeholder(tf.float32,shape=[])
keep_prob = tf.placeholder(tf.float32,shape=[])
keep_prob_data = tf.placeholder(tf.float32,shape=[])
with slim.arg_scope(vgg.vgg_arg_scope()):
logits_drop_1 = tf.contrib.layers.dropout(images, keep_prob_data)
vgg_logits, end_points = vgg.vgg_19(logits_drop_1, num_classes=3, dropout_keep_prob = 0.95, global_pool=True, is_training=True)
checkpoint_exclude_scopes=["vgg_19/fc8"]
#checkpoint_exclude_scopes=[]
exclusions = checkpoint_exclude_scopes
#review code!
variables_to_restore = []
for var in slim.get_model_variables():
excluded = False
for exclusion in exclusions:
if var.op.name.startswith(exclusion):
excluded = True
break
if not excluded:
variables_to_restore.append(var)
#variables_to_restore = slim.get_variables_to_restore(exclude = checkpoint_exclude_scopes)
