def testRaiseValueErrorWithInvalidDepthMultiplier(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
with self.assertRaises(ValueError):
_ = mobilenet_v1.mobilenet_v1(
inputs, num_classes, depth_multiplier=-0.1)
with self.assertRaises(ValueError):
_ = mobilenet_v1.mobilenet_v1(
inputs, num_classes, depth_multiplier=0.0)
def testRaiseValueErrorWithInvalidDepthMultiplier(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
with self.assertRaises(ValueError):
_ = mobilenet_v1.mobilenet_v1(
inputs, num_classes, depth_multiplier=-0.1)
with self.assertRaises(ValueError):
_ = mobilenet_v1.mobilenet_v1(
inputs, num_classes, depth_multiplier=0.0)
def build(self, cost, model, train):
if model == "MobilenetV1":
with slim.arg_scope(mobilenet_v1.mobilenet_v1_arg_scope()):
self.logits, self.end_points = mobilenet_v1.mobilenet_v1(
self.input,
num_classes=self.numb_logits,
dropout_keep_prob=self.kp,
is_training=train)
elif model == "vgg_16":
with slim.arg_scope(vgg.vgg_arg_scope()):
self.logits, self.end_points = vgg.vgg_16(
self.input,
num_classes=self.numb_logits,
dropout_keep_prob=self.kp,
is_training=True)
self.prob = tf.nn.softmax(self.logits, name="prob")
self.loss = tf.reduce_mean(
tf.reduce_sum(tf.pow(self.prob - self.target, 2), axis=1))
tf.summary.scalar('loss', self.loss)
if cost == "mse":
self.cost = self.loss
else:
self.xtarget = self.target * (1 - 1e-11) + 1e-12
assert self.xtarget.get_shape().as_list()[1] == self.numb_logits
self.xprob = self.prob * (1 - 1e-11) + 1e-12
assert self.xprob.get_shape().as_list()[1] == self.numb_logits
self.cost = tf.reduce_mean(
tf.reduce_sum(self.xtarget * tf.log(self.xtarget / self.prob),
axis=1))
tf.summary.scalar('cost_kl', self.cost)
def build_model():
"""Build the mobilenet_v1 model for evaluation.
Returns:
g: graph with rewrites after insertion of quantization ops and batch norm
folding.
eval_ops: eval ops for inference.
variables_to_restore: List of variables to restore from checkpoint.
"""
g = tf.Graph()
with g.as_default():
inputs, labels = imagenet_input(is_training=False)
scope = mobilenet_v1.mobilenet_v1_arg_scope(
is_training=False, weight_decay=0.0)
with slim.arg_scope(scope):
logits, _ = mobilenet_v1.mobilenet_v1(
inputs,
is_training=False,
depth_multiplier=FLAGS.depth_multiplier,
num_classes=FLAGS.num_classes)
if FLAGS.quantize:
tf.contrib.quantize.create_eval_graph()
eval_ops = metrics(logits, labels)
return g, eval_ops
def generator(images, n_filter=4,train=True, reuse=False):
"""define generator model
Args:
images: input images for generator
n_filter: number of filter to learn from each image
train: boolean value to specify if the network is in training mode or inference mode
reuse: whether to reuse network variables or not return:
output_2: enhanced version of input value
"""
with tf.variable_scope("generator",reuse=reuse):
# first generator network
with slim.arg_scope(mobilenet_v1.mobilenet_v1_arg_scope()):
logits, end_points = mobilenet_v1.mobilenet_v1(images, activation_fn=leakyRely, dropout_keep_prob=1, is_training=train)
net = end_points['Conv2d_13_pointwise']
filters_1 = slim.conv2d(net,256, [3, 3],stride=2, activation_fn=tf.nn.relu, padding='VALID',
normalizer_fn=None, scope='filters_1')
filters_2_1 = slim.conv2d(filters_1,n_filter, [1, 1],stride=1, activation_fn=None, padding='SAME',
normalizer_fn=None, scope='filters_2_1')
filters_2_2 = tf.expand_dims(filters_2_1,axis=4, name='filters_2_2')
output_1 = adapt_filter(images[:,:,:,1:2], filters_2_2[:,:,:,0:4,:], name="sat_adapt", train=train)
print("output_1", output_1.get_shape().as_list())
output_2 = tf.concat([images[:,:,:,0:1], output_1,images[:,:,:,2:3] ], axis=3)
print("output_2", output_2.get_shape().as_list())
return output_2
def freeze_graph(checkpoint_path, output_node_name, outfile):
input_layer = tf.placeholder(tf.uint8, shape=[None, None, 3], name='input')
with tf.variable_scope('input_scaling'):
image = tf.expand_dims(input_layer, axis=0)
image = tf.image.resize_bilinear(image, [224, 224])
image = tf.cast(image, tf.float32)
image = image / 127.5
image = image - 1
logits, _ = mobilenet_v1.mobilenet_v1(image,
num_classes=2,
is_training=False)
preds = tf.squeeze(tf.nn.softmax(logits), name='preds')
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(checkpoint_path)
saver = tf.train.Saver()
saver.restore(sess, ckpt.model_checkpoint_path)
output_graph_def = tf.graph_util.convert_variables_to_constants(
sess,
tf.get_default_graph().as_graph_def(), [output_node_name])
with tf.gfile.GFile(outfile, 'wb') as f:
f.write(output_graph_def.SerializeToString())
# print a list of ops
for op in output_graph_def.node:
print(op.name)
print('Saved frozen model to {}'.format(outfile))
print('{:d} ops in the final graph.'.format(len(
output_graph_def.node)))
def create(self, images, num_classes, is_training):
"""See baseclass."""
with slim.arg_scope(mobilenet_v1.mobilenet_v1_arg_scope()):
_, endpoints = mobilenet_v1.mobilenet_v1(inputs=images,
num_classes=num_classes,
is_training=is_training)
return endpoints
def build_model():
"""Build the mobilenet_v1 model for evaluation.
Returns:
g: graph with rewrites after insertion of quantization ops and batch norm
folding.
eval_ops: eval ops for inference.
variables_to_restore: List of variables to restore from checkpoint.
"""
g = tf.Graph()
with g.as_default():
inputs, labels = imagenet_input(is_training=False)
scope = mobilenet_v1.mobilenet_v1_arg_scope(is_training=False,
weight_decay=0.0)
with slim.arg_scope(scope):
logits, _ = mobilenet_v1.mobilenet_v1(
inputs,
is_training=False,
depth_multiplier=FLAGS.depth_multiplier,
num_classes=FLAGS.num_classes,
final_endpoint=FLAGS.final_endpoint)
if FLAGS.quantize:
contrib_quantize.create_eval_graph()
eval_ops = metrics(logits, labels)
return g, eval_ops
def create_mnist_mobilenet_model(input, label_count, is_training):
dropout_prob = tf.placeholder(tf.float32, name='dropout_prob')
X_img = tf.reshape(input, [-1, 28, 28, 1])
resizing_img = tf.image.resize_bilinear(X_img, [224, 224])
logits, end_points = mobilenet_v1.mobilenet_v1(resizing_img, label_count)
return logits, dropout_prob
def testTrainEvalWithReuse(self):
train_batch_size = 5
eval_batch_size = 2
height, width = 150, 150
num_classes = 1000
train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
mobilenet_v1.mobilenet_v1(train_inputs, num_classes)
eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
logits, _ = mobilenet_v1.mobilenet_v1(eval_inputs, num_classes,
reuse=True)
predictions = tf.argmax(logits, 1)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(predictions)
self.assertEquals(output.shape, (eval_batch_size,))
def testTrainEvalWithReuse(self):
train_batch_size = 5
eval_batch_size = 2
height, width = 150, 150
num_classes = 1000
train_inputs = tf.random_uniform((train_batch_size, height, width, 3))
mobilenet_v1.mobilenet_v1(train_inputs, num_classes)
eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3))
logits, _ = mobilenet_v1.mobilenet_v1(eval_inputs, num_classes,
reuse=True)
predictions = tf.argmax(logits, 1)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(predictions)
self.assertEquals(output.shape, (eval_batch_size,))
def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
_, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
endpoint_keys = [key for key in end_points.keys() if key.startswith('Conv')]
_, end_points_with_multiplier = mobilenet_v1.mobilenet_v1(
inputs, num_classes, scope='depth_multiplied_net',
depth_multiplier=0.5)
for key in endpoint_keys:
original_depth = end_points[key].get_shape().as_list()[3]
new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
self.assertEqual(0.5 * original_depth, new_depth)
def testBuildEndPointsWithDepthMultiplierLessThanOne(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
_, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
endpoint_keys = [key for key in end_points.keys() if key.startswith('Conv')]
_, end_points_with_multiplier = mobilenet_v1.mobilenet_v1(
inputs, num_classes, scope='depth_multiplied_net',
depth_multiplier=0.5)
for key in endpoint_keys:
original_depth = end_points[key].get_shape().as_list()[3]
new_depth = end_points_with_multiplier[key].get_shape().as_list()[3]
self.assertEqual(0.5 * original_depth, new_depth)
def testLogitsNotSqueezed(self):
num_classes = 25
images = tf.random_uniform([1, 224, 224, 3])
logits, _ = mobilenet_v1.mobilenet_v1(images,
num_classes=num_classes,
spatial_squeeze=False)
with self.test_session() as sess:
tf.global_variables_initializer().run()
logits_out = sess.run(logits)
self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes])
def testLogitsNotSqueezed(self):
num_classes = 25
images = tf.random_uniform([1, 224, 224, 3])
logits, _ = mobilenet_v1.mobilenet_v1(images,
num_classes=num_classes,
spatial_squeeze=False)
with self.test_session() as sess:
tf.global_variables_initializer().run()
logits_out = sess.run(logits)
self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes])
def testBuildPreLogitsNetwork(self):
batch_size = 5
height, width = 224, 224
num_classes = None
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
self.assertTrue(net.op.name.startswith('MobilenetV1/Logits/AvgPool'))
self.assertListEqual(net.get_shape().as_list(), [batch_size, 1, 1, 1024])
self.assertFalse('Logits' in end_points)
self.assertFalse('Predictions' in end_points)
def testBuildPreLogitsNetwork(self):
batch_size = 5
height, width = 224, 224
num_classes = None
inputs = tf.random_uniform((batch_size, height, width, 3))
net, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
self.assertTrue(net.op.name.startswith('MobilenetV1/Logits/AvgPool'))
self.assertListEqual(net.get_shape().as_list(), [batch_size, 1, 1, 1024])
self.assertFalse('Logits' in end_points)
self.assertFalse('Predictions' in end_points)
def build_model():
"""Builds graph for model to train with rewrites for quantization.
Returns:
g: Graph with fake quantization ops and batch norm folding suitable for
training quantized weights.
train_tensor: Train op for execution during training.
"""
g = tf.Graph()
with g.as_default(), tf.device(
tf.train.replica_device_setter(FLAGS.ps_tasks)):
#Reads in data and/or performs pre-processing on the images.
inputs, labels, _ = tf_input.input(is_random=True, is_training=True)
labels = labels - 1
labels = slim.one_hot_encoding(labels, FLAGS.num_classes)
with slim.arg_scope(mobilenet_v1.mobilenet_v1_arg_scope(is_training=True)):
logits, _ = mobilenet_v1.mobilenet_v1(
inputs,
is_training=True,
depth_multiplier=FLAGS.depth_multiplier,
num_classes=FLAGS.num_classes)
tf.losses.softmax_cross_entropy(labels, logits)
# Call rewriter to produce graph with fake quant ops and folded batch norms
# quant_delay delays start of quantization till quant_delay steps, allowing
# for better model accuracy.
if FLAGS.quantize:
tf.contrib.quantize.create_training_graph(quant_delay=get_quant_delay())
total_loss = tf.losses.get_total_loss(name='total_loss')
# Configure the learning rate using an exponential decay.
num_epochs_per_decay = 2.5
data_size = tf_input.TRAINING_SET_SIZE
decay_steps = int(data_size / FLAGS.batch_size * num_epochs_per_decay)
learning_rate = tf.train.exponential_decay(
get_learning_rate(),
tf.train.get_or_create_global_step(),
decay_steps,
_LEARNING_RATE_DECAY_FACTOR,
staircase=True)
opt = tf.train.GradientDescentOptimizer(learning_rate)
train_tensor = slim.learning.create_train_op(
total_loss,
optimizer=opt)
slim.summaries.add_scalar_summary(total_loss, 'total_loss', 'losses')
slim.summaries.add_scalar_summary(learning_rate, 'learning_rate', 'training')
return g, train_tensor
def fcn_mobv1(images, num_classes, is_training=True):
with slim.arg_scope(mobilenet_v1.mobilenet_v1_arg_scope()):
_, end_points = mobilenet_v1.mobilenet_v1(images,
num_classes,
is_training=False,
spatial_squeeze=False)
# for v,k in end_points.items():
# print('{v}:{k}'.format(v = v, k = k))
# pool4=end_points['resnet_v1_101/pool4']
#
# dconv1_out=pool4.get_shape().as_list()
#
#
# deconv1=slim.conv2d_transpose(net,dconv1_out[3],[4,4], stride=2,scope='deconv1')
#
# fu1=tf.add(deconv1,pool4)
#
#
# pool3=end_points['resnet_v1_101/pool3']
# dconv2_out=pool3.get_shape().as_list()
# deconv2=slim.conv2d_transpose(fu1,dconv2_out[3],[4,4], stride=2,scope='deconv2')
#
# fu2=tf.add(deconv2,pool3)
net_7 = end_points['Conv2d_13_pointwise']
net_14 = end_points['Conv2d_11_pointwise']
net_28 = end_points['Conv2d_5_pointwise']
up1 = slim.conv2d_transpose(net_7,
512, [4, 4],
stride=2,
scope='deconv32')
fu1 = tf.add(up1, net_14, name='fu1')
up2 = slim.conv2d_transpose(fu1,
256, [4, 4],
stride=2,
scope='deconv16')
fu2 = tf.add(up2, net_28, name='fu2')
logit = slim.conv2d_transpose(fu2,
num_classes, [16, 16],
stride=8,
scope='deconv8')
prediction = tf.argmax(logit, dimension=3) #, name="prediction")
print('logit', logit)
return logit, tf.expand_dims(prediction, axis=3)
def testBuildClassificationNetwork(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
self.assertTrue('Predictions' in end_points)
self.assertListEqual(end_points['Predictions'].get_shape().as_list(),
[batch_size, num_classes])
def testHalfSizeImages(self):
batch_size = 5
height, width = 112, 112
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
pre_pool = end_points['Conv2d_13_pointwise']
self.assertListEqual(pre_pool.get_shape().as_list(),
[batch_size, 4, 4, 1024])
def testBuildClassificationNetwork(self):
batch_size = 5
height, width = 224, 224
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
self.assertTrue('Predictions' in end_points)
self.assertListEqual(end_points['Predictions'].get_shape().as_list(),
[batch_size, num_classes])
def _build_mobilenet_model(is_training, images, params):
with slim.arg_scope(
mobilenet_v1.mobilenet_v1_arg_scope(is_training=is_training)):
out, _ = mobilenet_v1.mobilenet_v1(
images,
is_training=is_training,
depth_multiplier=params.depth_multiplier,
num_classes=None)
tf.logging.info("mobilenet preembedding shape{}".format(
out.get_shape().as_list()))
out = tf.reshape(out, [-1, 256])
out = tf.layers.dense(out, params.embedding_size, name="embeddings")
return out
def testHalfSizeImages(self):
batch_size = 5
height, width = 112, 112
num_classes = 1000
inputs = tf.random_uniform((batch_size, height, width, 3))
logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
pre_pool = end_points['Conv2d_13_pointwise']
self.assertListEqual(pre_pool.get_shape().as_list(),
[batch_size, 4, 4, 1024])
def endpoints(image, is_training):
if image.get_shape().ndims != 4:
raise ValueError('Input must be of size [batch, height, width, 3]')
image = tf.divide(image, 255.0)
with tf.contrib.slim.arg_scope(mobilenet_v1_arg_scope(batch_norm_decay=0.9, weight_decay=0.0)):
_, endpoints = mobilenet_v1(image, num_classes=1001, is_training=is_training)
endpoints['model_output'] = endpoints['global_pool'] = tf.reduce_mean(
endpoints['Conv2d_13_pointwise'], [1, 2], name='global_pool', keep_dims=False)
return endpoints, 'MobilenetV1'
def perceptual_params(images, reuse=False):
"""get semntics params of images
Args:
images: input images for generator
reuse: whether to reuse network variables or not
return:
sementics params of images
"""
with tf.variable_scope("semantic",reuse=reuse):
# first generator network
with slim.arg_scope(mobilenet_v1.mobilenet_v1_arg_scope()):
logits, end_points = mobilenet_v1.mobilenet_v1(images, num_classes=1001, dropout_keep_prob=1, is_training=False)
return tf.squeeze(end_points['AvgPool_1a'],[1,2])
def testEvaluation(self):
batch_size = 2
height, width = 224, 224
num_classes = 1000
eval_inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = mobilenet_v1.mobilenet_v1(eval_inputs, num_classes,
is_training=False)
predictions = tf.argmax(logits, 1)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(predictions)
self.assertEquals(output.shape, (batch_size,))
def testEvaluation(self):
batch_size = 2
height, width = 224, 224
num_classes = 1000
eval_inputs = tf.random_uniform((batch_size, height, width, 3))
logits, _ = mobilenet_v1.mobilenet_v1(eval_inputs, num_classes,
is_training=False)
predictions = tf.argmax(logits, 1)
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(predictions)
self.assertEquals(output.shape, (batch_size,))
def testUnknowBatchSize(self):
batch_size = 1
height, width = 224, 224
num_classes = 1000
inputs = tf.placeholder(tf.float32, (None, height, width, 3))
logits, _ = mobilenet_v1.mobilenet_v1(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
self.assertListEqual(logits.get_shape().as_list(), [None, num_classes])
images = tf.random_uniform((batch_size, height, width, 3))
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(logits, {inputs: images.eval()})
self.assertEquals(output.shape, (batch_size, num_classes))
def build_model():
"""Builds graph for model to train with rewrites for quantization.
Returns:
g: Graph with fake quantization ops and batch norm folding suitable for
training quantized weights.
train_tensor: Train op for execution during training.
"""
g = tf.Graph()
with g.as_default(), tf.device(
tf.train.replica_device_setter(FLAGS.ps_tasks)):
inputs, labels = imagenet_input(is_training=True)
with slim.arg_scope(mobilenet_v1.mobilenet_v1_arg_scope(is_training=True)):
logits, _ = mobilenet_v1.mobilenet_v1(
inputs,
is_training=True,
depth_multiplier=FLAGS.depth_multiplier,
num_classes=FLAGS.num_classes)
tf.losses.softmax_cross_entropy(labels, logits)
# Call rewriter to produce graph with fake quant ops and folded batch norms
# quant_delay delays start of quantization till quant_delay steps, allowing
# for better model accuracy.
if FLAGS.quantize:
tf.contrib.quantize.create_training_graph(quant_delay=get_quant_delay())
total_loss = tf.losses.get_total_loss(name='total_loss')
# Configure the learning rate using an exponential decay.
num_epochs_per_decay = 2.5
imagenet_size = 1271167
decay_steps = int(imagenet_size / FLAGS.batch_size * num_epochs_per_decay)
learning_rate = tf.train.exponential_decay(
get_learning_rate(),
tf.train.get_or_create_global_step(),
decay_steps,
_LEARNING_RATE_DECAY_FACTOR,
staircase=True)
opt = tf.train.GradientDescentOptimizer(learning_rate)
train_tensor = slim.learning.create_train_op(
total_loss,
optimizer=opt)
slim.summaries.add_scalar_summary(total_loss, 'total_loss', 'losses')
slim.summaries.add_scalar_summary(learning_rate, 'learning_rate', 'training')
return g, train_tensor
def testUnknowBatchSize(self):
batch_size = 1
height, width = 224, 224
num_classes = 1000
inputs = tf.placeholder(tf.float32, (None, height, width, 3))
logits, _ = mobilenet_v1.mobilenet_v1(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[None, num_classes])
images = tf.random_uniform((batch_size, height, width, 3))
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
output = sess.run(logits, {inputs: images.eval()})
self.assertEquals(output.shape, (batch_size, num_classes))
def create_model(self, inputs, num_classes, is_training):
with slim.arg_scope(
mobilenet_v1_arg_scope(
is_training,
FLAGS.weight_decay,
regularize_depthwise=FLAGS.regularize_depthwise)):
self.logits, self.end_points = mobilenet_v1(
inputs,
num_classes,
FLAGS.dropout_keep_prob,
is_training,
depth_multiplier=FLAGS.depth_multiplier)
for var in tf.model_variables():
if 'weights' in var.op.name:
tf.add_to_collection(tf.GraphKeys.WEIGHTS, var)
def load_model(model='Mobile'):
global x, y, sess
# 占位符
x = tf.placeholder(tf.float32, [None, IMG_SIZE, IMG_SIZE, 1])
# 模型保存路径，前向传播
if model == 'Alex':
log_path = 'weight/Alex'
y, _ = alexnet.alexnet_v2(
x,
num_classes=CLASSES, # 分类的类别
is_training=False, # 是否在训练
dropout_keep_prob=1.0, # 保留比率
spatial_squeeze=True, # 压缩掉1维的维度
global_pool=GLOBAL_POOL) # 输入不是规定的尺寸时，需要global_pool
elif model == 'Mobile':
log_path = 'weight/Mobile'
y, _ = mobilenet_v1.mobilenet_v1(x,
num_classes=CLASSES,
dropout_keep_prob=1.0,
is_training=False,
min_depth=8,
depth_multiplier=1.0,
conv_defs=None,
prediction_fn=None,
spatial_squeeze=True,
reuse=None,
scope='MobilenetV1',
global_pool=GLOBAL_POOL)
else:
print('Error: model name not exist')
return
y = tf.nn.softmax(y)
saver = tf.train.Saver()
sess = tf.Session()
# 恢复模型权重
# print('Reading checkpoints from: ', model)
ckpt = tf.train.get_checkpoint_state(log_path)
if ckpt and ckpt.model_checkpoint_path:
global_step = ckpt.model_checkpoint_path.split('/')[-1].split('-')[-1]
saver.restore(sess, ckpt.model_checkpoint_path)
# print('Loading success, global_step is %s' % global_step)
else:
print('Error: no checkpoint file found')
return -1
def testUnknownImageShape(self):
tf.reset_default_graph()
batch_size = 2
height, width = 224, 224
num_classes = 1000
input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
with self.test_session() as sess:
inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
pre_pool = end_points['Conv2d_13_pointwise']
feed_dict = {inputs: input_np}
tf.global_variables_initializer().run()
pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024])
def testUnknownImageShape(self):
tf.reset_default_graph()
batch_size = 2
height, width = 224, 224
num_classes = 1000
input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
with self.test_session() as sess:
inputs = tf.placeholder(tf.float32, shape=(batch_size, None, None, 3))
logits, end_points = mobilenet_v1.mobilenet_v1(inputs, num_classes)
self.assertTrue(logits.op.name.startswith('MobilenetV1/Logits'))
self.assertListEqual(logits.get_shape().as_list(),
[batch_size, num_classes])
pre_pool = end_points['Conv2d_13_pointwise']
feed_dict = {inputs: input_np}
tf.global_variables_initializer().run()
pre_pool_out = sess.run(pre_pool, feed_dict=feed_dict)
self.assertListEqual(list(pre_pool_out.shape), [batch_size, 7, 7, 1024])
def freeze_mobilenet(meta_file, img_size=224, factor=1.0, num_classes=1001):
tf.reset_default_graph()
inp = tf.placeholder(tf.float32,
shape=(None, img_size, img_size, 3),
name="input")
is_training = False
weight_decay = 0.0
arg_scope = mobilenet_v1.mobilenet_v1_arg_scope(weight_decay=weight_decay)
with slim.arg_scope(arg_scope):
logits, _ = mobilenet_v1.mobilenet_v1(inp,
num_classes=num_classes,
is_training=is_training,
depth_multiplier=factor)
predictions = tf.contrib.layers.softmax(logits)
output = tf.identity(predictions, name='output')
ckpt_file = meta_file.replace('.meta', '')
output_graph_fn = ckpt_file.replace('.ckpt', '.pb')
output_node_names = "output"
rest_var = slim.get_variables_to_restore()
with tf.Session() as sess:
graph = tf.get_default_graph()
input_graph_def = graph.as_graph_def()
saver = tf.train.Saver(rest_var)
saver.restore(sess, ckpt_file)
# We use a built-in TF helper to export variables to constant
output_graph_def = graph_util.convert_variables_to_constants(
sess, # The session is used to retrieve the weights
input_graph_def, # The graph_def is used to retrieve the nodes
output_node_names.split(
","
) # The output node names are used to select the usefull nodes
)
# Finally we serialize and dump the output graph to the filesystem
with tf.gfile.GFile(output_graph_fn, "wb") as f:
f.write(output_graph_def.SerializeToString())
print("{} ops in the final graph.".format(len(output_graph_def.node)))
def freeze_mobilenet(meta_file, img_size=224, factor=1.0, num_classes=1001):
tf.reset_default_graph()
inp = tf.placeholder(tf.float32,
shape=(None, img_size, img_size, 3),
name="input")
is_training=False
weight_decay = 0.0
arg_scope = mobilenet_v1.mobilenet_v1_arg_scope(weight_decay=weight_decay)
with slim.arg_scope(arg_scope):
logits, _ = mobilenet_v1.mobilenet_v1(inp,
num_classes=num_classes,
is_training=is_training,
depth_multiplier=factor)
predictions = tf.contrib.layers.softmax(logits)
output = tf.identity(predictions, name='output')
ckpt_file = meta_file.replace('.meta', '')
output_graph_fn = ckpt_file.replace('.ckpt', '.pb')
output_node_names = "output"
rest_var = slim.get_variables_to_restore()
with tf.Session() as sess:
graph = tf.get_default_graph()
input_graph_def = graph.as_graph_def()
saver = tf.train.Saver(rest_var)
saver.restore(sess, ckpt_file)
# We use a built-in TF helper to export variables to constant
output_graph_def = graph_util.convert_variables_to_constants(
sess, # The session is used to retrieve the weights
input_graph_def, # The graph_def is used to retrieve the nodes
# The output node names are used to select the useful nodes
output_node_names.split(",")
)
# Finally we serialize and dump the output graph to the filesystem
with tf.gfile.GFile(output_graph_fn, "wb") as f:
f.write(output_graph_def.SerializeToString())
print("{} ops in the final graph.".format(len(output_graph_def.node)))
def mobilenet_v1_100(inputs, is_training, opts):
with slim.arg_scope(mobilenet_v1.mobilenet_v1_arg_scope(
is_training=is_training,
weight_decay=opts.weight_decay,
stddev=0.09,
regularize_depthwise=False,
batch_norm_decay=opts.batch_norm_decay,
batch_norm_epsilon=opts.batch_norm_epsilon)):
return mobilenet_v1.mobilenet_v1(
inputs,
num_classes=opts.num_classes,
dropout_keep_prob=opts.dropout_keep_prob,
is_training=is_training,
min_depth=8,
depth_multiplier=1.0,
global_pool=opts.global_pool,
spatial_squeeze=opts.spatial_squeeze,
reuse=None)
def discriminator(images, kp, n_output=10, reuse=False, train=True):
"""define discriminator model
Args:
images: input images for discriminator
kp: keeping probality for droping layer of discriminator
n_output: discriminator output size
reuse: whether reuse variable or not
train: training mode or inference mode
return:
preds: discriminator output containing image aesthetic rating and other variables
"""
with tf.variable_scope("discriminator", reuse=reuse):
with slim.arg_scope(mobilenet_v1.mobilenet_v1_arg_scope()):
logits, end_points = mobilenet_v1.mobilenet_v1(images, num_classes=n_output,
dropout_keep_prob=kp, is_training=train)
preds = tf.nn.softmax(logits)
print("preds: ",preds.get_shape().as_list())
return preds
def export_eval_pbtxt():
"""Export eval.pbtxt."""
g = tf.Graph()
with g.as_default():
inputs = tf.placeholder(dtype=tf.float32,
shape=[None, IMAGE_SIZE, IMAGE_SIZE, CHANNEL])
scope = mobilenet_v1.mobilenet_v1_arg_scope(is_training=False,
weight_decay=0.0)
with slim.arg_scope(scope):
_, _ = mobilenet_v1.mobilenet_v1(
inputs,
is_training=False,
depth_multiplier=FLAGS.depth_multiplier,
num_classes=FLAGS.num_classes)
if FLAGS.quantize:
tf.contrib.quantize.create_eval_graph()
with tf.Session() as sess:
with open(FLAGS.eval_graph_file, 'w') as f:
f.write(str(g.as_graph_def()))
def __call__(self, x_input, batch_size=None, is_training=False):
"""Constructs model and return probabilities for given input."""
reuse = True if self.built else None
preproc = tf.map_fn(
lambda img: inception_preprocess(img,
mobilenet_v1.mobilenet_v1.default_image_size,
mobilenet_v1.mobilenet_v1.default_image_size), x_input)
with slim.arg_scope(mobilenet_v1.mobilenet_v1_arg_scope()):
with tf.variable_scope(self.ckpt):
logits, end_points = mobilenet_v1.mobilenet_v1(
preproc, num_classes=self.num_classes, is_training=is_training,
reuse=reuse)
preds = tf.argmax(logits, axis=1)
self.built = True
self.logits = logits
self.preds = preds
return logits
def model_fn(features,labels,mode,params):
global_step = tf.train.get_global_step()
inputs = features['MRI']
logits, end_points= mobilenet_v1.mobilenet_v1(inputs=inputs,
num_classes=30)
# predict mode
predicted_classes = tf.argmax(logits, 1)
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
'class':predicted_classes,
'prob':tf.nn.softmax(logits)
}
return tf.estimator.EstimatorSpec(mode,predictions=predictions)
with tf.name_scope('accuracy'):
accuracy = tf.metrics.accuracy(labels=labels,predictions=predicted_classes)
my_acc = tf.reduce_mean(tf.cast(tf.equal(labels, predicted_classes), tf.float32))
tf.summary.scalar('accuracy',my_acc)
# compute loss
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels,logits=logits)
# hook
train_hook_list = []
train_tensors_log = {'accuracy':accuracy[1],
'my_acc': my_acc,
'loss':loss,
'global_step':global_step}
train_hook_list.append(tf.train.LoggingTensorHook(tensors=train_tensors_log,every_n_iter=200))
# training op
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = tf.train.MomentumOptimizer(learning_rate=0.001,momentum=0.9)
train_op = optimizer.minimize(loss,global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(mode=mode,loss=loss,train_op=train_op,training_hooks=train_hook_list)
# compute evaluation metrics
eval_metric_ops = {
'accuracy':tf.metrics.accuracy(labels=labels,predictions=predicted_classes)
}
return tf.estimator.EstimatorSpec(mode=mode,loss=loss,eval_metric_ops=eval_metric_ops)
def build_model():
"""Build the mobilenet_v1 model for evaluation.
Returns:
g: graph with rewrites after insertion of quantization ops and batch norm
folding.
eval_ops: eval ops for inference.
variables_to_restore: List of variables to restore from checkpoint.
"""
g = tf.Graph()
with g.as_default():
inputs, labels = dataset_input(is_training=False)
# inputs, labels = merge_dataset(is_training=False)
scope = mobilenet_v1.mobilenet_v1_arg_scope(is_training=False,
weight_decay=0.0)
with slim.arg_scope(scope):
dataset = dataset_factory.get_dataset(FLAGS.dataset_name,
FLAGS.dataset_split_name,
FLAGS.dataset_dir)
logits, _ = mobilenet_v1.mobilenet_v1(
inputs,
is_training=False,
depth_multiplier=FLAGS.depth_multiplier,
num_classes=dataset.num_classes,
model_scope=FLAGS.model_scope,
logits_scope=FLAGS.logits_scope,
conv2d_0_scope=FLAGS.conv2d_0_scope,
depthwise_scope=FLAGS.depthwise_scope,
pointwise_scope=FLAGS.pointwise_scope,
pointwise_merged_mask=FLAGS.pointwise_merged_mask)
if FLAGS.quantize:
tf.contrib.quantize.create_eval_graph()
eval_ops = metrics(logits, labels)
tf.logging.info('Evaluating %s' % FLAGS.checkpoint_path)
return g, eval_ops
def create(self, images, num_classes, is_training):
"""See baseclass."""
with slim.arg_scope(mobilenet_v1.mobilenet_v1_arg_scope()):
_, endpoints = mobilenet_v1.mobilenet_v1(
inputs=images, num_classes=num_classes, is_training=is_training)
return endpoints
