def extract_features(self, inputs):
mean = tf.constant(
self.cfg['mean_pixel'], dtype=tf.float32, shape=[1, 1, 1, 3], name="img_mean"
)
im_centered = inputs - mean
if 'resnet' in self.cfg['net_type']:
# The next part of the code depends upon which tensorflow version you have.
vers = tf.__version__
vers = vers.split(
"."
) # Updated based on https://github.com/AlexEMG/DeepLabCut/issues/44
net_fun = net_funcs[self.cfg['net_type']]
if int(vers[0]) == 1 and int(vers[1]) < 4: # check if lower than version 1.4.
with slim.arg_scope(resnet_v1.resnet_arg_scope(False)):
net, end_points = net_fun(
im_centered, global_pool=False, output_stride=16
)
else:
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
net, end_points = net_fun(
im_centered, global_pool=False, output_stride=16, is_training=False
)
elif 'mobilenet' in self.cfg['net_type']:
net_fun = net_funcs[self.cfg['net_type']]
with slim.arg_scope(mobilenet_v2.training_scope()):
net, end_points = net_fun(im_centered)
elif 'efficientnet' in self.cfg['net_type']:
im_centered /= tf.constant(eff.STDDEV_RGB, shape=[1, 1, 3])
net, end_points = eff.build_model_base(im_centered,
self.cfg['net_type'],
use_batch_norm=self.cfg['use_batch_norm'],
drop_out=self.cfg['use_drop_out'])
return net, end_points
def extract_features(self, inputs):
net_fun = net_funcs[self.cfg.net_type]
mean = tf.constant(self.cfg.mean_pixel,
dtype=tf.float32,
shape=[1, 1, 1, 3],
name="img_mean")
im_centered = inputs - mean
# The next part of the code depends upon which tensorflow version you have.
vers = tf.__version__
vers = vers.split(
"."
) # Updated based on https://github.com/AlexEMG/DeepLabCut/issues/44
if int(vers[0]) == 1 and int(
vers[1]) < 4: # check if lower than version 1.4.
with slim.arg_scope(resnet_v1.resnet_arg_scope(False)):
net, end_points = net_fun(im_centered,
global_pool=False,
output_stride=16)
else:
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
net, end_points = net_fun(im_centered,
global_pool=False,
output_stride=16,
is_training=False)
return net, end_points
def eval(x, num_classes=110):
with slim.arg_scope(inception.inception_v1_arg_scope()):
logits_inc_v1, end_points_inc_v1 = inception.inception_v1(
x, num_classes=num_classes, is_training=False, scope='InceptionV1')
pred1 = tf.argmax(end_points_inc_v1['Predictions'], 1)
# rescale pixle range from [-1, 1] to [0, 255] for resnet_v1 and vgg's input
image = (((x + 1.0) * 0.5) * 255.0)
processed_imgs_res_v1_50 = preprocess_for_model(image, 'resnet_v1_50')
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
logits_res_v1_50, end_points_res_v1_50 = resnet_v1.resnet_v1_50(
processed_imgs_res_v1_50,
num_classes=num_classes,
is_training=False,
scope='resnet_v1_50')
end_points_res_v1_50['logits'] = tf.squeeze(
end_points_res_v1_50['resnet_v1_50/logits'], [1, 2])
end_points_res_v1_50['probs'] = tf.nn.softmax(
end_points_res_v1_50['logits'])
pred2 = tf.argmax(end_points_res_v1_50['probs'], 1)
# image = (((x + 1.0) * 0.5) * 255.0)#.astype(np.uint8)
processed_imgs_vgg_16 = preprocess_for_model(image, 'vgg_16')
with slim.arg_scope(vgg.vgg_arg_scope()):
logits_vgg_16, end_points_vgg_16 = vgg.vgg_16(processed_imgs_vgg_16,
num_classes=num_classes,
is_training=False,
scope='vgg_16')
end_points_vgg_16['logits'] = end_points_vgg_16['vgg_16/fc8']
end_points_vgg_16['probs'] = tf.nn.softmax(end_points_vgg_16['logits'])
pred3 = tf.argmax(end_points_vgg_16['probs'], 1)
return [pred1, pred2, pred3]
def _load_tf_model(checkpoint_file):
# Placeholder
input_tensor = tf.compat.v1.placeholder(tf.float32,
shape=(None, 224, 224, 3),
name='input_image')
# Make the Tensorflow warnings go away
# Load the model
sess = tf.compat.v1.Session()
arg_scope = resnet_v1.resnet_arg_scope()
with tf.contrib.slim.arg_scope(arg_scope):
logits, _ = resnet_v1.resnet_v1_152(input_tensor,
num_classes=1000,
is_training=False,
reuse=tf.AUTO_REUSE)
probabilities = tf.nn.softmax(logits)
saver = tf.train.Saver()
try:
saver.restore(sess, checkpoint_file)
except ValueError:
raise mlhub.utils.DataResourceNotFoundException(checkpoint_file)
return sess, logits, probabilities, input_tensor
def forward(self, input_tensor, is_training):
# inputs has shape [batch, 513, 513, 3]
input_tensor = tf.image.resize_images(input_tensor, [512, 512])
with slim.arg_scope(resnet_v1.resnet_arg_scope(is_training)):
net, end_points = resnet_v1.resnet_v1_101(input_tensor,
None,
global_pool=False,
output_stride=16)
print(net.get_shape())
h = L.convolution2d_transpose(net,
64, [5, 5], [4, 4],
activation_fn=None)
h = tf.nn.relu(h)
h = L.dropout(h, keep_prob=0.5, is_training=is_training)
h = L.convolution2d_transpose(h,
32, [5, 5], [2, 2],
activation_fn=None)
h = tf.nn.relu(h)
h = L.dropout(h, keep_prob=0.5, is_training=is_training)
print(h)
h = L.convolution2d(h,
len(self.classes) + 1, [1, 1], [1, 1],
activation_fn=None)
print(h)
return h
def build_net(self, x, is_training):
"""
Defines network architecture (ResNet-50 feature extractor + classifier)
"""
# network architecture
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
_, end_points = resnet_v1.resnet_v1_50(x,
num_classes=2,
is_training=is_training)
with slim.arg_scope([slim.conv2d], activation_fn=tf.nn.relu):
net = end_points[
'resnet_v1_50/block4'] # last bottleneck before logits
with tf.variable_scope('resnet_v1_50'):
z = slim.conv2d(net,
self.config.dim_z, [7, 7],
padding='VALID',
activation_fn=tf.nn.relu,
scope='bottleneck_layer')
logits = slim.conv2d(z,
2, [1, 1],
activation_fn=None,
scope='logit_layer')
return logits, z
def get_resnet(image_tensor, reuse):
with slim.arg_scope(resnet_v1.resnet_arg_scope(is_training=False)):
mean = tf.constant(cfg.pixel_mean,
dtype=tf.float32,
shape=[1, 1, 1, 3],
name='img_mean')
image = image_tensor - mean
assert cfg.resnet_type == '101'
net_fun = resnet_v1.resnet_v1_101
net, end_points = net_fun(image,
global_pool=False,
output_stride=16,
reuse=reuse)
layer_name = 'resnet_v1_{}/block3/unit_22/bottleneck_v1'.format(
cfg.resnet_type)
block2_out = end_points[layer_name]
stride = 16
layer_prefixes = ['resnet_v1_101/conv1']
layer_prefixes += [
'resnet_v1_101/block1/unit_{}'.format(i + 1) for i in range(3)
]
layer_prefixes += [
'resnet_v1_101/block2/unit_{}'.format(i + 1) for i in range(4)
]
layer_prefixes += [
'resnet_v1_101/block3/unit_{}'.format(i + 1) for i in range(23)
]
layer_prefixes += [
'resnet_v1_101/block4/unit_{}'.format(i + 1) for i in range(3)
]
ignore_prefixes = layer_prefixes[:cfg.gnet.freeze_n_imfeat_layers]
idx = layer_prefixes.index('resnet_v1_101/block3/unit_22')
ignore_prefixes += layer_prefixes[idx + 1:]
return block2_out, stride, ignore_prefixes, end_points
def test_base_fcn(net_name, img_fnames, weight_fname):
"Quick effort to test the base fcns and figure out what preprocessing is appropriate--isn't well documented."
inputs = tf.placeholder(tf.float32, [None, None, None, 3])
inputs = _vgg_preprocess(inputs)
if net_name == 'resnet_v1_152':
with slim.arg_scope(resnet_v1.resnet_arg_scope()) as scope:
base_net, _ = resnet_v1.resnet_v1_152(inputs,
is_training=False,
num_classes=1000)
else:
raise Exception('net_name not recognized.')
pred = tf.argmax(base_net, -1)
saver = tf.train.Saver()
sess = tf.InteractiveSession()
#tf.global_variables_initializer().run()
saver.restore(sess, weight_fname)
for img_fname in img_fnames:
img = cv2.imread(img_fname)
prediction = sess.run(pred,
feed_dict={inputs: img[np.newaxis, :, :, :]})
print('pred for %s:' % img_fname)
print(imagenet_names[int(prediction)])
print('=========')
def Eval(x_img_224, x_img_299, y):
input_image = x_img_224 - tf.reshape(tf.constant([123.68, 116.78, 103.94]),
[1, 1, 1, 3])
with slim.arg_scope(resnet_v1.resnet_arg_scope()) as scope:
logits_res_v1_50, end_points_res_v1_50 = resnet_v1.resnet_v1_50(
input_image,
num_classes=110,
is_training=False,
scope='resnet_v1_50',
reuse=tf.AUTO_REUSE)
end_points_res_v1_50['logits'] = tf.squeeze(
end_points_res_v1_50['resnet_v1_50/logits'], [1, 2])
end_points_res_v1_50['probs'] = tf.nn.softmax(
end_points_res_v1_50['logits'])
res_label = tf.argmax(end_points_res_v1_50['probs'][0], -1)
y_r = end_points_res_v1_50['probs'][0][y[0]]
with slim.arg_scope(vgg.vgg_arg_scope()) as scope:
logits_vgg_16, end_points_vgg_16 = vgg.vgg_16(input_image,
num_classes=110,
is_training=False,
scope='vgg_16',
reuse=tf.AUTO_REUSE)
end_points_vgg_16['logits'] = end_points_vgg_16['vgg_16/fc8']
end_points_vgg_16['probs'] = tf.nn.softmax(end_points_vgg_16['logits'])
vgg_label = tf.argmax(end_points_vgg_16['probs'][0], -1)
y_v = end_points_vgg_16['probs'][0][y[0]]
return res_label, vgg_label, y_r, y_v
def _build_model(self, visual_images):
"""
Builds a ResNet-50 network using slim.
"""
# visual_images = tf.placeholder(tf.float32, [None, self.height, self.width, self.channels], name='visual_images')
is_training = tf.placeholder(tf.bool, name='is_training')
keep_prob = tf.placeholder(tf.float32, name='keep_prob')
with slim.arg_scope(resnet.resnet_arg_scope(weight_decay=5e-4)):
output, network = resnet50.resnet_v1_50(
visual_images,
num_classes=self.num_classes,
is_training=is_training,
global_pool=False)
# output = tf.squeeze(output, [1, 2])
network.update({
'input': visual_images,
'is_training': is_training,
'keep_prob': keep_prob
})
self.output = output
self.network = network
self.train_vars2 = slim.get_trainable_variables(
self.scope + '/block') + slim.get_trainable_variables(self.scope +
'/conv1')
self.train_vars = slim.get_trainable_variables(
self.scope + '/logits') + slim.get_trainable_variables(self.scope +
'/conv_map')
def _load_tf_model(checkpoint_file):
# Placeholder
input_tensor = tf.placeholder(tf.float32,
shape=(None, 224, 224, 3),
name='input_image')
# Load the model
sess = tf.Session()
arg_scope = resnet_v1.resnet_arg_scope()
with tf.contrib.slim.arg_scope(arg_scope):
logits, _ = resnet_v1.resnet_v1_152(input_tensor,
num_classes=1000,
is_training=False,
reuse=tf.AUTO_REUSE)
probabilities = tf.nn.softmax(logits)
saver = tf.train.Saver()
saver.restore(sess, checkpoint_file)
def predict_for(image):
pred, pred_proba = sess.run([logits, probabilities],
feed_dict={input_tensor: image})
return pred_proba
return predict_for
def _build_model(self):
"""
Builds a ResNet-50 network using slim.
"""
visual_images = tf.placeholder(
tf.float32, [None, self.height, self.width, self.channels],
name='visual_images')
is_training = tf.placeholder(tf.bool, name='is_training')
keep_prob = tf.placeholder(tf.float32, name='keep_prob')
with slim.arg_scope(
resnet_v1.resnet_arg_scope(batch_norm_decay=0.997)):
output, network = resnet_v2.resnet_one_stream_main(
visual_images,
nr_frames=self.nr_frames,
num_classes=self.num_classes,
is_training=is_training,
scope=self.scope)
# predictions for each video are the avg of frames' predictions
# TRAIN ###############################
output = tf.reshape(output, [-1, self.nr_frames, self.num_classes])
output = tf.reduce_mean(output, axis=1)
network.update({
'input': visual_images,
'is_training': is_training,
'keep_prob': keep_prob
})
return output, network
def _build_model(self):
"""
Builds a ResNet-50 network using slim.
"""
visual_images = tf.placeholder(
tf.float32, [None, self.height, self.width, self.channels],
name='visual_images')
is_training = tf.placeholder(tf.bool, name='is_training')
keep_prob = tf.placeholder(tf.float32, name='keep_prob')
with slim.arg_scope(resnet_v1.resnet_arg_scope(weight_decay=5e-4)):
output, network = resnet_v1.resnet_v1_50(
visual_images,
num_classes=self.num_classes,
is_training=is_training)
output = tf.squeeze(output, [1, 2])
network.update({
'input': visual_images,
'is_training': is_training,
'keep_prob': keep_prob
})
return output, network
def localizationNet(input, is_train=False, reuse=False, scope='resnet_v1_50'):
lrelu = lambda x: tf.nn.leaky_relu(x, 0.2)
with tf.variable_scope(scope, reuse=reuse):
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
net, _ = resnet_v1.resnet_v1_50(scale_RGB(input),
global_pool=True,
is_training=is_train,
reuse=reuse)
net = tl.layers.InputLayer(net)
net = tl.layers.FlattenLayer(net, name='flatten')
net = tl.layers.DenseLayer(net,
n_units=2048,
act=tf.identity,
name='df/dense1')
net = tl.layers.DenseLayer(net,
n_units=1024,
act=tf.identity,
name='df/dense2')
net = tl.layers.DenseLayer(net,
n_units=512,
act=tf.identity,
name='df/dense3')
net = tl.layers.DenseLayer(net,
n_units=50,
act=tf.identity,
name='df/dense4')
thetas_affine = net.outputs
return thetas_affine
def single_stream(self, images, modality, is_training, reuse=False):
with tf.variable_scope(modality, reuse=reuse):
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
_, end_points = resnet_v1.resnet_v1_50(images,
self.no_classes,
is_training=is_training,
reuse=reuse)
# last bottleneck before logits
net = end_points[modality + '/resnet_v1_50/block4']
if 'autoencoder' in self.mode:
return net
with tf.variable_scope(modality + '/resnet_v1_50', reuse=reuse):
bottleneck = slim.conv2d(net,
self.hidden_repr_size, [7, 7],
padding='VALID',
activation_fn=tf.nn.relu,
scope='f_repr')
net = slim.conv2d(bottleneck,
self.no_classes, [1, 1],
activation_fn=None,
scope='_logits_')
if ('train_hallucination' in self.mode or 'test_disc' in self.mode
or 'train_eccv' in self.mode):
return net, bottleneck
return net
def inference (images, train=True, resnet_stride=8):
with slim.arg_scope(resnet_v1.resnet_arg_scope(train)):
net, end_points = resnet_v1_slim(images,
num_classes = None,
global_pool = False,
output_stride = resnet_stride)
# replace resnet_v1_slim above with resnet_v1.resnet_v1_50/101/...
# to use standard architectures.
# num_classes: Number of predicted classes for classification tasks. If None
# we return the features before the logit layer.
# global_pool: If True, we perform global average pooling before computing the
# logits. Set to True for image classification, False for dense prediction.
# output_stride: If None, then the output will be computed at the nominal
# network stride. If output_stride is not None, it specifies the requested
# ratio of input to output spatial resolution.
resnet_depth = utils.last_dimension(net.get_shape(), min_rank=4)
shape = tf.unpack(tf.shape(images))
print(shape.__class__)
shape.pop()
shape.append(tf.constant(FLAGS.out_channels, dtype=tf.int32))
print(len(shape))
filters = tf.Variable(
tf.truncated_normal(
[resnet_stride*2+1, resnet_stride*2+1, FLAGS.out_channels, resnet_depth],
dtype=tf.float32,
stddev=0.01),
name='filters')
logits = tf.nn.conv2d_transpose(net, filters, tf.pack(shape),
[1,resnet_stride,resnet_stride,1], padding='SAME', name='upscale')
return logits
def feature_extractor(patch):
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
net, end_points = resnet_v1.resnet_v1_50(
patch,
1000,
is_training=self.train_mode,
reuse=tf.AUTO_REUSE)
resnet_feature = end_points['resnet_v1_50/block4']
resnet_feature = tf.reduce_mean(resnet_feature, [1, 2],
keepdims=True)
resnet_feature = tf.squeeze(resnet_feature)
resnet_feature = tf.reshape(resnet_feature, [-1, 2048])
drop = tf.layers.dropout(resnet_feature,
rate=self.drop1,
training=self.train_mode)
glimpse_feature = tf.layers.dense(
inputs=drop,
units=512,
activation=tf.nn.relu,
kernel_initializer=tf.glorot_uniform_initializer(),
bias_initializer=tf.constant_initializer(0.1),
# kernel_regularizer=tf.nn.l2_loss,
name='glimpse_feature/fc',
reuse=tf.AUTO_REUSE)
return glimpse_feature
def build_graph(self, orig_image):
mean = tf.get_variable('resnet_v1_50/mean_rgb', shape=[3])
with guided_relu():
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
image = tf.expand_dims(orig_image - mean, 0)
logits, _ = resnet_v1.resnet_v1_50(image, 1000)
saliency_map(logits, orig_image, name="saliency")
def det_lesion_resnet(inputs, is_training_option=False, scope='det_lesion'):
"""Defines the network
Args:
inputs: Tensorflow placeholder that contains the input image
scope: Scope name for the network
Returns:
net: Output Tensor of the network
end_points: Dictionary with all Tensors of the network
"""
with tf.variable_scope(scope, 'det_lesion', [inputs]) as sc:
end_points_collection = sc.name + '_end_points'
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
net, end_points = resnet_v1.resnet_v1_50(
inputs, is_training=is_training_option)
net = slim.flatten(net, scope='flatten5')
net = slim.fully_connected(
net,
1,
activation_fn=tf.nn.sigmoid,
weights_initializer=initializers.xavier_initializer(),
scope='output')
utils.collect_named_outputs(end_points_collection,
'det_lesion/output', net)
end_points = slim.utils.convert_collection_to_dict(end_points_collection)
return net, end_points
def non_target_graph(x, y, i, x_max, x_min, grad):
eps = 2.0 * max_epsilon / 255.0
alpha = eps / num_iter
num_classes = 110
with slim.arg_scope(inception.inception_v1_arg_scope()):
logits_inc_v1, end_points_inc_v1 = inception.inception_v1(
x, num_classes=num_classes, is_training=False, scope='InceptionV1')
# rescale pixle range from [-1, 1] to [0, 255] for resnet_v1 and vgg's input
image = (((x + 1.0) * 0.5) * 255.0)
processed_imgs_res_v1_50 = preprocess_for_model(image, 'resnet_v1_50')
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
logits_res_v1_50, end_points_res_v1_50 = resnet_v1.resnet_v1_50(
processed_imgs_res_v1_50,
num_classes=num_classes,
is_training=False,
scope='resnet_v1_50')
end_points_res_v1_50['logits'] = tf.squeeze(
end_points_res_v1_50['resnet_v1_50/logits'], [1, 2])
end_points_res_v1_50['probs'] = tf.nn.softmax(
end_points_res_v1_50['logits'])
# image = (((x + 1.0) * 0.5) * 255.0)#.astype(np.uint8)
processed_imgs_vgg_16 = preprocess_for_model(image, 'vgg_16')
with slim.arg_scope(vgg.vgg_arg_scope()):
logits_vgg_16, end_points_vgg_16 = vgg.vgg_16(processed_imgs_vgg_16,
num_classes=num_classes,
is_training=False,
scope='vgg_16')
end_points_vgg_16['logits'] = end_points_vgg_16['vgg_16/fc8']
end_points_vgg_16['probs'] = tf.nn.softmax(end_points_vgg_16['logits'])
########################
# Using model predictions as ground truth to avoid label leaking
pred = tf.argmax(
end_points_inc_v1['Predictions'] + end_points_res_v1_50['probs'] +
end_points_vgg_16['probs'], 1)
first_round = tf.cast(tf.equal(i, 0), tf.int64)
y = first_round * pred + (1 - first_round) * y
one_hot = tf.one_hot(y, num_classes)
########################
logits = (end_points_inc_v1['Logits'] + end_points_res_v1_50['logits']
+ end_points_vgg_16['logits']) / 3.0
cross_entropy = tf.losses.softmax_cross_entropy(one_hot,
logits,
label_smoothing=0.0,
weights=1.0)
noise = tf.gradients(cross_entropy, x)[0]
noise = noise / tf.reduce_mean(tf.abs(noise), [1, 2, 3],
keep_dims=True)
noise = momentum * grad + noise
x = x + alpha * tf.sign(noise)
x = tf.clip_by_value(x, x_min, x_max)
i = tf.add(i, 1)
return x, y, i, x_max, x_min, noise
def _build_graph(self, inputs):
orig_image = inputs[0]
mean = tf.get_variable('resnet_v1_50/mean_rgb', shape=[3])
with guided_relu():
with slim.arg_scope(resnet_v1.resnet_arg_scope(is_training=False)):
image = tf.expand_dims(orig_image - mean, 0)
logits, _ = resnet_v1.resnet_v1_50(image, 1000)
tp.symbolic_functions.saliency_map(logits, orig_image, name="saliency")
def _build_graph(self, inputs):
orig_image = inputs[0]
mean = tf.get_variable('resnet_v1_50/mean_rgb', shape=[3])
with tp.symbolic_functions.guided_relu():
with slim.arg_scope(resnet_v1.resnet_arg_scope(is_training=False)):
image = tf.expand_dims(orig_image - mean, 0)
logits, _ = resnet_v1.resnet_v1_50(image, 1000)
tp.symbolic_functions.saliency_map(logits, orig_image, name="saliency")
def net_graph(inputs_X):
def encoder(tensor_name, layer_name):
with tf.variable_scope(layer_name):
encoder_tensor = tf.get_default_graph().get_tensor_by_name(
tensor_name)
encoder_tensor = layers_lib.conv2d(
encoder_tensor,
256, [1, 1],
stride=1,
padding='VALID',
scope="conv1",
activation_fn=tf.nn.relu,
normalizer_fn=layers.batch_norm,
weights_regularizer=layers_lib.l2_regularizer(1e-4))
encoder_tensor = layers_lib.conv2d(
encoder_tensor,
256, [3, 3],
stride=1,
padding='VALID',
scope="conv3",
activation_fn=tf.nn.relu,
normalizer_fn=layers.batch_norm,
weights_regularizer=layers_lib.l2_regularizer(1e-4))
out_tensor = math_ops.reduce_mean(encoder_tensor, [1, 2],
name='gap',
keepdims=False)
#old style
#out_tensor = tf.reduce_mean(encoder_tensor,axis=[1,2])
return out_tensor
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
net, end_points = resnet_v1.resnet_v1_50(inputs_X, is_training=True)
#orginal net
with tf.variable_scope("encoder"):
encoder1 = encoder("resnet_v1_50/block1/unit_3/bottleneck_v1/Relu:0",
"encoder1")
encoder2 = encoder("resnet_v1_50/block2/unit_4/bottleneck_v1/Relu:0",
"encoder2")
encoder3 = encoder("resnet_v1_50/block3/unit_6/bottleneck_v1/Relu:0",
"encoder3")
encoder4 = encoder("resnet_v1_50/block4/unit_3/bottleneck_v1/Relu:0",
"encoder4")
concat = tf.concat([encoder1, encoder2, encoder3, encoder4],
-1,
name='concat')
predictions = layers_lib.fully_connected(
concat,
1,
name="fintune_FC",
weights_regularizer=layers_lib.l2_regularizer(1e-4))
tf.add_to_collection("predictions", predictions)
current_epoch = tf.Variable(0, name="current_epoch")
return predictions, current_epoch
def target_graph(x, y, i, x_max, x_min, grad):
eps = 2.0 * max_epsilon / 255.0
alpha = eps / num_iter
num_classes = 110
#input image size[224,224,3]
images3 = tf.image.resize_bilinear(input_diversity(x), [224, 224], align_corners=False)
with slim.arg_scope(inception.inception_v1_arg_scope()):
logits_inc_v1, end_points_inc_v1 = inception.inception_v1(
images3, num_classes=num_classes, is_training=False, scope='InceptionV1')
# rescale pixle range from [-1, 1] to [0, 255] for resnet_v1 and vgg's input
image1 = (((input_diversity(x) + 1.0) * 0.5) * 255.0)
processed_imgs_res_v1_50 = preprocess_for_model(image1, 'resnet_v1_50')
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
logits_res_v1_50, end_points_res_v1_50 = resnet_v1.resnet_v1_50(
processed_imgs_res_v1_50, num_classes=num_classes, is_training=False, scope='resnet_v1_50')
end_points_res_v1_50['logits'] = tf.squeeze(end_points_res_v1_50['resnet_v1_50/logits'], [1, 2])
end_points_res_v1_50['probs'] = tf.nn.softmax(end_points_res_v1_50['logits'])
# image = (((x + 1.0) * 0.5) * 255.0)#.astype(np.uint8)
image2 = (((input_diversity(x) + 1.0) * 0.5) * 255.0)
processed_imgs_vgg_16 = preprocess_for_model(image2, 'vgg_16')
with slim.arg_scope(vgg.vgg_arg_scope()):
logits_vgg_16, end_points_vgg_16 = vgg.vgg_16(
processed_imgs_vgg_16, num_classes=num_classes, is_training=False, scope='vgg_16')
end_points_vgg_16['logits'] = end_points_vgg_16['vgg_16/fc8']
end_points_vgg_16['probs'] = tf.nn.softmax(end_points_vgg_16['logits'])
one_hot = tf.one_hot(y, num_classes)
logits = (end_points_inc_v1['Logits'] + end_points_res_v1_50['logits'] + end_points_vgg_16['logits']) / 3.0
cross_entropy = tf.losses.softmax_cross_entropy(one_hot,
logits,
label_smoothing=0.0,
weights=1.0)
noise = tf.gradients(cross_entropy, x)[0]
noise = tf.nn.depthwise_conv2d(noise, stack_kernel, strides=[1, 1, 1, 1], padding='SAME')
noise = noise / tf.reshape(tf.contrib.keras.backend.std(tf.reshape(noise, [batch_size, -1]), axis=1),
[batch_size, 1, 1, 1])
noise = momentum * grad + noise
noise = noise / tf.reshape(tf.contrib.keras.backend.std(tf.reshape(noise, [batch_size, -1]), axis=1),
[batch_size, 1, 1, 1])
noise1 = tf.image.resize_images(noise, [140, 140], method=tf.image.ResizeMethod.NEAREST_NEIGHBOR)
print("noise shape:", noise.shape)
noise1 = alpha * tf.clip_by_value(tf.round(noise1), -2, 2)
noise_paded = tf.pad(noise1,[[0, 0], [42, 42], [42, 42], [0, 0]], constant_values=0.)
x = x - noise_paded
x = tf.clip_by_value(x, x_min, x_max)
print("x.shape:", x.shape)
i = tf.add(i, 1)
return x, y, i, x_max, x_min, noise
def target_graph(x, y, i, x_max, x_min, grad):
eps = 2.0 * max_epsilon / 255.0
alpha = eps / num_iter
num_classes = 110
with slim.arg_scope(inception.inception_v1_arg_scope()):
logits_inc_v1, end_points_inc_v1 = inception.inception_v1(
x, num_classes=num_classes, is_training=False, scope='InceptionV1')
# rescale pixle range from [-1, 1] to [0, 255] for resnet_v1 and vgg's input
image = (((x + 1.0) * 0.5) * 255.0)
processed_imgs_res_v1_50 = preprocess_for_model(image, 'resnet_v1_50')
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
logits_res_v1_50, end_points_res_v1_50 = resnet_v1.resnet_v1_50(
processed_imgs_res_v1_50,
num_classes=num_classes,
is_training=False,
scope='resnet_v1_50')
end_points_res_v1_50['logits'] = tf.squeeze(
end_points_res_v1_50['resnet_v1_50/logits'], [1, 2])
end_points_res_v1_50['probs'] = tf.nn.softmax(
end_points_res_v1_50['logits'])
# image = (((x + 1.0) * 0.5) * 255.0)#.astype(np.uint8)
processed_imgs_vgg_16 = preprocess_for_model(image, 'vgg_16')
with slim.arg_scope(vgg.vgg_arg_scope()):
logits_vgg_16, end_points_vgg_16 = vgg.vgg_16(processed_imgs_vgg_16,
num_classes=num_classes,
is_training=False,
scope='vgg_16')
end_points_vgg_16['logits'] = end_points_vgg_16['vgg_16/fc8']
end_points_vgg_16['probs'] = tf.nn.softmax(end_points_vgg_16['logits'])
########################
one_hot = tf.one_hot(y, num_classes)
########################
logits = (end_points_inc_v1['Logits'] + end_points_res_v1_50['logits'] +
end_points_vgg_16['logits']) / 3.0
cross_entropy = tf.losses.softmax_cross_entropy(one_hot,
logits,
label_smoothing=0.0,
weights=1.0)
noise = tf.gradients(cross_entropy, x)[0]
noise = noise / tf.reshape(
tf.contrib.keras.backend.std(tf.reshape(noise, [batch_size, -1]),
axis=1), [batch_size, 1, 1, 1])
noise = momentum * grad + noise
noise = noise / tf.reshape(
tf.contrib.keras.backend.std(tf.reshape(noise, [batch_size, -1]),
axis=1), [batch_size, 1, 1, 1])
x = x - alpha * tf.clip_by_value(tf.round(noise), -2, 2)
x = tf.clip_by_value(x, x_min, x_max)
i = tf.add(i, 1)
return x, y, i, x_max, x_min, noise
def net_graph_debug(inputs_X):
def encoder(tensor_name, layer_name):
with tf.variable_scope(layer_name):
encoder_tensor = tf.get_default_graph().get_tensor_by_name(
tensor_name)
#tf.summary.histogram(layer_name+'resnet_out',encoder_tensor)
encoder_tensor = layers_lib.conv2d(encoder_tensor,
256, [1, 1],
stride=2,
padding='SAME',
scope="conv1",
activation_fn=tf.nn.relu,
normalizer_fn=layers.batch_norm,
trainable=True)
encoder_tensor = layers_lib.conv2d(encoder_tensor,
256, [3, 3],
stride=2,
padding='SAME',
scope="conv3",
activation_fn=tf.nn.relu,
normalizer_fn=layers.batch_norm,
trainable=True)
out_tensor = math_ops.reduce_mean(encoder_tensor, [1, 2],
name='gap',
keepdims=False)
#tf.summary.histogram(layer_name,out_tensor)
#old style
#out_tensor = tf.reduce_mean(encoder_tensor,axis=[1,2])
return out_tensor
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
net, end_points = resnet_v1.resnet_v1_50(inputs_X, is_training=True)
#current_epoch = tf.Variable(0, name="current_epoch")
with tf.variable_scope("encoder"):
encoder1 = encoder("resnet_v1_50/block1/unit_3/bottleneck_v1/Relu:0",
"encoder1")
encoder2 = encoder("resnet_v1_50/block2/unit_4/bottleneck_v1/Relu:0",
"encoder2")
encoder3 = encoder("resnet_v1_50/block3/unit_6/bottleneck_v1/Relu:0",
"encoder3")
encoder4 = encoder("resnet_v1_50/block4/unit_3/bottleneck_v1/Relu:0",
"encoder4")
concat = tf.concat([encoder1, encoder2, encoder3, encoder4],
-1,
name='concat')
#tf.summary.histogram('concat',concat)
predictions = layers_lib.fully_connected(concat,
1,
activation_fn=tf.nn.relu,
scope="fintune_FC")
current_epoch = tf.Variable(0, name="current_epoch")
return predictions, current_epoch
return end_points, current_epoch
def def_net(self):
if net == 'vgg_16':
with slim.arg_scope(vgg.vgg_arg_scope()):
_, end_points = vgg.vgg_16(self.images,
num_classes=FLAGS.num_classes,
dropout_keep_prob=1.0,
is_training=False)
elif net == 'resnet_v1_101':
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
_, end_points = resnet_v1.resnet_v1_101(
self.images, num_classes=FLAGS.num_classes)
elif net == 'resnet_v1_50':
with slim.arg_scope(resnet_v1.resnet_arg_scope(is_training=True)):
_, end_points = resnet_v1.resnet_v1_50(
self.images, num_classes=FLAGS.num_classes)
else:
raise Exception('No network matched with net %s' % net)
self.end_points = end_points
def build_single_resnet(train_tfdata,
is_train,
name_scope='resnet_v1_50',
variable_scope=''):
with slim.arg_scope(resnet_v1.resnet_arg_scope(is_training=is_train)):
identity, end_points = resnet_v1.resnet_v1_50(
train_tfdata, num_classes=FLAGS.num_class, global_pool=True)
feature = slim.flatten(tf.get_default_graph().get_tensor_by_name(
'%s%s/pool5:0' % (variable_scope, name_scope)))
return identity, feature
def extract_features(self, inputs):
net_fun = net_funcs[self.cfg.net_type]
mean = tf.constant(self.cfg.mean_pixel,
dtype=tf.float32, shape=[1, 1, 1, 3], name='img_mean')
im_centered = inputs - mean
# The next part of the code depends upon which tensorflow version you have.
vers = tf.__version__
if float(vers[0:3]) < 1.4:
with slim.arg_scope(resnet_v1.resnet_arg_scope(False)):
net, end_points = net_fun(im_centered,
global_pool=False, output_stride=16)
else:
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
net, end_points = net_fun(im_centered,
global_pool=False, output_stride=16,is_training=False)
return net,end_points
def resnet_v1_101_base(input_image):
#input_image = tf.expand_dims(tf_image_std, axis=0)
net = input_image
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
net, end_points = resnet_v1.resnet_v1_101(input_image,
num_classes=None,
global_pool=False,
output_stride=16,
is_training=True)
return net
def get_backbone(self,features):
if self.flags.model_variant.startswith('xception'):
assert False,'not implement'
elif self.flags.model_variant=='resnet_v2_50':
# inputs has shape [batch, 513, 513, 3]
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
net, end_points = resnet_v2.resnet_v2_50(features,
self.num_classes,
is_training=False,
global_pool=False,
output_stride=self.output_stride)
elif self.flags.model_variant=='resnet_v1_50':
# The key difference of the full preactivation 'v2' variant compared to the
# 'v1' variant in [1] is the use of batch normalization before every weight layer.
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
net, end_points = resnet_v1.resnet_v1_50(features,
self.num_classes,
is_training=False,
global_pool=False,
output_stride=self.output_stride)
elif self.flags.model_variant=='resnet_v2_101':
# inputs has shape [batch, 513, 513, 3]
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
net, end_points = resnet_v2.resnet_v2_101(features,
self.num_classes,
is_training=False,
global_pool=False,
output_stride=self.output_stride)
elif self.flags.model_variant=='resnet_v1_101':
# The key difference of the full preactivation 'v2' variant compared to the
# 'v1' variant in [1] is the use of batch normalization before every weight layer.
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
net, end_points = resnet_v1.resnet_v1_101(features,
self.num_classes,
is_training=False,
global_pool=False,
output_stride=self.output_stride)
else:
assert False,'not implement'
print(end_points.keys())
print(net)
def resnet_feature(self, images, scope_name, train_mode=True):
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
net, end_points = resnet_v1.resnet_v1_50(images,
1000,
is_training=train_mode,
reuse=tf.AUTO_REUSE)
resnet_block_4 = end_points[scope_name + '/resnet_v1_50/block4']
resnet_feature = tf.reduce_mean(resnet_block_4, [1, 2], keepdims=True)
resnet_feature = tf.squeeze(resnet_feature)
resnet_feature = tf.reshape(resnet_feature, [-1, 2048])
return resnet_block_4, resnet_feature
def _get_endpoints(model_name, img_tensor):
if model_name == "res50":
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
_, end_points = resnet_v1.resnet_v1_50(img_tensor,
1000,
is_training=False)
return end_points["predictions"]
elif model_name == "res152":
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
_, end_points = resnet_v1.resnet_v1_152(img_tensor,
1000,
is_training=False)
return end_points["predictions"]
elif model_name.startswith("mobilenet"):
with tf.contrib.slim.arg_scope(
mobilenet_v2.training_scope(is_training=False)):
_, endpoints = mobilenet_v2.mobilenet(img_tensor)
return endpoints["Predictions"]
def extract_features(self, inputs):
net_fun = net_funcs[self.cfg.net_type]
mean = tf.constant(self.cfg.mean_pixel,
dtype=tf.float32, shape=[1, 1, 1, 3], name='img_mean')
im_centered = inputs - mean
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
net, end_points = net_fun(im_centered, global_pool=False, output_stride=16, is_training=False)
return net, end_points
def setUp(self):
tf.reset_default_graph()
self.nbclasses = 1000
inputs = tf.placeholder(tf.float32, [1, 224, 224, 3])
with slim.arg_scope(resnet_v1.resnet_arg_scope()):
net, end_points = resnet_v1.resnet_v1_50(inputs, self.nbclasses, is_training=False)
saver = tf.train.Saver(tf.global_variables())
check_point = 'test/data/resnet_v1_50.ckpt'
sess = tf.InteractiveSession()
saver.restore(sess, check_point)
conv_name = 'resnet_v1_50/block4/unit_3/bottleneck_v1/Relu'
self.graph_origin = tf.get_default_graph().as_graph_def()
self.insp = darkon.Gradcam(inputs, self.nbclasses, conv_name)
self.sess = sess
def build_single_resnet(train_tfdata, is_train, name_scope = 'resnet_v1_50', variable_scope = ''):
with slim.arg_scope(resnet_v1.resnet_arg_scope(is_training=is_train)):
identity, end_points = resnet_v1.resnet_v1_50(train_tfdata, num_classes=FLAGS.num_class, global_pool = True)
feature = slim.flatten(tf.get_default_graph().get_tensor_by_name('%s%s/pool5:0' % (variable_scope, name_scope)))
return identity, feature