本文整理汇总了Python中nets.vgg.vgg_arg_scope方法的典型用法代码示例。如果您正苦于以下问题:Python vgg.vgg_arg_scope方法的具体用法?Python vgg.vgg_arg_scope怎么用?Python vgg.vgg_arg_scope使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类nets.vgg
的用法示例。
在下文中一共展示了vgg.vgg_arg_scope方法的5个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: model
# 需要导入模块: from nets import vgg [as 别名]
# 或者: from nets.vgg import vgg_arg_scope [as 别名]
def model(image):
image = mean_image_subtraction(image)
with slim.arg_scope(vgg.vgg_arg_scope()):
conv5_3 = vgg.vgg_16(image)
rpn_conv = slim.conv2d(conv5_3, 512, 3)
lstm_output = Bilstm(rpn_conv, 512, 128, 512, scope_name='BiLSTM')
bbox_pred = lstm_fc(lstm_output, 512, 10 * 4, scope_name="bbox_pred")
cls_pred = lstm_fc(lstm_output, 512, 10 * 2, scope_name="cls_pred")
# transpose: (1, H, W, A x d) -> (1, H, WxA, d)
cls_pred_shape = tf.shape(cls_pred)
cls_pred_reshape = tf.reshape(cls_pred, [cls_pred_shape[0], cls_pred_shape[1], -1, 2])
cls_pred_reshape_shape = tf.shape(cls_pred_reshape)
cls_prob = tf.reshape(tf.nn.softmax(tf.reshape(cls_pred_reshape, [-1, cls_pred_reshape_shape[3]])),
[-1, cls_pred_reshape_shape[1], cls_pred_reshape_shape[2], cls_pred_reshape_shape[3]],
name="cls_prob")
return bbox_pred, cls_pred, cls_prob
示例2: _extract_proposal_features
# 需要导入模块: from nets import vgg [as 别名]
# 或者: from nets.vgg import vgg_arg_scope [as 别名]
def _extract_proposal_features(self, preprocessed_inputs, scope):
if len(preprocessed_inputs.get_shape().as_list()) != 4:
raise ValueError('`preprocessed_inputs` must be 4 dimensional, got a '
'tensor of shape %s' % preprocessed_inputs.get_shape())
shape_assert = tf.Assert(
tf.logical_and(
tf.greater_equal(tf.shape(preprocessed_inputs)[1], 33),
tf.greater_equal(tf.shape(preprocessed_inputs)[2], 33)),
['image size must at least be 33 in both height and width.'])
with tf.control_dependencies([shape_assert]):
with slim.arg_scope(
vgg.vgg_arg_scope(weight_decay=self._weight_decay)):
with tf.variable_scope(
self._architecture, reuse=self._reuse_weights) as var_scope:
_, endpoints = self._vgg_model(
preprocessed_inputs,
final_endpoint='conv5',
trainable=self._is_training,
freeze_layer=self._freeze_layer,
scope=var_scope)
handle = self._base_features
return endpoints[handle]
示例3: graph
# 需要导入模块: from nets import vgg [as 别名]
# 或者: from nets.vgg import vgg_arg_scope [as 别名]
def graph(x, y, i, x_max, x_min, grad):
eps = FLAGS.max_epsilon
num_iter = FLAGS.num_iter
alpha = eps / num_iter
momentum = FLAGS.momentum
num_classes = 1000
with slim.arg_scope(vgg.vgg_arg_scope()):
logits, end_points = vgg.vgg_16(
x, num_classes=num_classes, is_training=False)
pred = tf.argmax(logits, 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)
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.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
示例4: use_vgg16
# 需要导入模块: from nets import vgg [as 别名]
# 或者: from nets.vgg import vgg_arg_scope [as 别名]
def use_vgg16(self):
with tf.Graph().as_default():
image_size = vgg.vgg_16.default_image_size
img_path = "../../data/misec_images/First_Student_IC_school_bus_202076.jpg"
checkpoint_path = "../../data/trained_models/vgg16/vgg_16.ckpt"
image_string = tf.read_file(img_path)
image = tf.image.decode_jpeg(image_string, channels=3)
processed_image = vgg_preprocessing.preprocess_image(image, image_size, image_size, is_training=False)
processed_images = tf.expand_dims(processed_image, 0)
# Create the model, use the default arg scope to configure the batch norm parameters.
with slim.arg_scope(vgg.vgg_arg_scope()):
# 1000 classes instead of 1001.
logits, _ = vgg.vgg_16(processed_images, num_classes=1000, is_training=False)
probabilities = tf.nn.softmax(logits)
init_fn = slim.assign_from_checkpoint_fn(
checkpoint_path,
slim.get_model_variables('vgg_16'))
with tf.Session() as sess:
init_fn(sess)
np_image, probabilities = sess.run([image, probabilities])
probabilities = probabilities[0, 0:]
sorted_inds = [i[0] for i in sorted(enumerate(-probabilities), key=lambda x:x[1])]
self.disp_names(sorted_inds,probabilities,include_background=False)
plt.figure()
plt.imshow(np_image.astype(np.uint8))
plt.axis('off')
plt.title(img_path)
plt.show()
return
示例5: _extract_box_classifier_features
# 需要导入模块: from nets import vgg [as 别名]
# 或者: from nets.vgg import vgg_arg_scope [as 别名]
def _extract_box_classifier_features(self, proposal_feature_maps, scope):
with tf.variable_scope(self._architecture, reuse=self._reuse_weights):
with slim.arg_scope(
vgg.vgg_arg_scope(weight_decay=self._weight_decay)):
proposal_classifier_features = tf.identity(proposal_feature_maps)
return proposal_classifier_features