本文整理汇总了Python中tensorflow.contrib.slim.max_pool2d函数的典型用法代码示例。如果您正苦于以下问题：Python max_pool2d函数的具体用法？Python max_pool2d怎么用？Python max_pool2d使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了max_pool2d函数的20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，您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: network_det
def network_det(self,inputs,reuse=False):
if reuse:
tf.get_variable_scope().reuse_variables()
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn = tf.nn.relu,
weights_initializer = tf.truncated_normal_initializer(0.0, 0.01)):
conv1 = slim.conv2d(inputs, 96, [11,11], 4, padding= 'VALID', scope='conv1')
max1 = slim.max_pool2d(conv1, [3,3], 2, padding= 'VALID', scope='max1')
conv2 = slim.conv2d(max1, 256, [5,5], 1, scope='conv2')
max2 = slim.max_pool2d(conv2, [3,3], 2, padding= 'VALID', scope='max2')
conv3 = slim.conv2d(max2, 384, [3,3], 1, scope='conv3')
conv4 = slim.conv2d(conv3, 384, [3,3], 1, scope='conv4')
conv5 = slim.conv2d(conv4, 256, [3,3], 1, scope='conv5')
pool5 = slim.max_pool2d(conv5, [3,3], 2, padding= 'VALID', scope='pool5')
shape = int(np.prod(pool5.get_shape()[1:]))
fc6 = slim.fully_connected(tf.reshape(pool5, [-1, shape]), 4096, scope='fc6')
fc_detection = slim.fully_connected(fc6, 512, scope='fc_det1')
out_detection = slim.fully_connected(fc_detection, 2, scope='fc_det2', activation_fn = None)
return out_detection
开发者ID:dmehr，项目名称:HyperFace-TensorFlow-implementation，代码行数:27，代码来源:model_prediction.py
示例2: create_test_network_2
def create_test_network_2():
"""Aligned network for test.
The graph corresponds to a variation to the example from the second figure in
go/cnn-rf-computation#arbitrary-computation-graphs. Layers 2 and 3 are changed
to max-pooling operations. Since the functionality is the same as convolution,
the network is aligned and the receptive field size is the same as from the
network created using create_test_network_1().
Returns:
g: Tensorflow graph object (Graph proto).
"""
g = ops.Graph()
with g.as_default():
# An input test image with unknown spatial resolution.
x = array_ops.placeholder(
dtypes.float32, (None, None, None, 1), name='input_image')
# Left branch.
l1 = slim.conv2d(x, 1, [1, 1], stride=4, scope='L1', padding='VALID')
# Right branch.
l2_pad = array_ops.pad(x, [[0, 0], [1, 0], [1, 0], [0, 0]])
l2 = slim.max_pool2d(l2_pad, [3, 3], stride=2, scope='L2', padding='VALID')
l3 = slim.max_pool2d(l2, [1, 1], stride=2, scope='L3', padding='VALID')
# Addition.
nn.relu(l1 + l3, name='output')
return g
开发者ID:Ajaycs99，项目名称:tensorflow，代码行数:26，代码来源:receptive_field_test.py
示例3: localization_VGG16
def localization_VGG16(self,inputs):
with tf.variable_scope('localization_network'):
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn = tf.nn.relu,
weights_initializer = tf.constant_initializer(0.0)):
net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
net = slim.max_pool2d(net, [2, 2], scope='pool1')
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
net = slim.max_pool2d(net, [2, 2], scope='pool2')
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')
net = slim.max_pool2d(net, [2, 2], scope='pool3')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4')
net = slim.max_pool2d(net, [2, 2], scope='pool4')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5')
net = slim.max_pool2d(net, [2, 2], scope='pool5')
shape = int(np.prod(net.get_shape()[1:]))
net = slim.fully_connected(tf.reshape(net, [-1, shape]), 4096, scope='fc6')
net = slim.fully_connected(net, 1024, scope='fc7')
identity = np.array([[1., 0., 0.],
[0., 1., 0.]])
identity = identity.flatten()
net = slim.fully_connected(net, 6, biases_initializer = tf.constant_initializer(identity) , scope='fc8')
return net
开发者ID:dmehr，项目名称:HyperFace-TensorFlow-implementation，代码行数:27，代码来源:model.py
示例4: conv_net_kelz
def conv_net_kelz(inputs):
"""Builds the ConvNet from Kelz 2016."""
with slim.arg_scope(
[slim.conv2d, slim.fully_connected],
activation_fn=tf.nn.relu,
weights_initializer=tf.contrib.layers.variance_scaling_initializer(
factor=2.0, mode='FAN_AVG', uniform=True)):
net = slim.conv2d(
inputs, 32, [3, 3], scope='conv1', normalizer_fn=slim.batch_norm)
net = slim.conv2d(
net, 32, [3, 3], scope='conv2', normalizer_fn=slim.batch_norm)
net = slim.max_pool2d(net, [1, 2], stride=[1, 2], scope='pool2')
net = slim.dropout(net, 0.25, scope='dropout2')
net = slim.conv2d(
net, 64, [3, 3], scope='conv3', normalizer_fn=slim.batch_norm)
net = slim.max_pool2d(net, [1, 2], stride=[1, 2], scope='pool3')
net = slim.dropout(net, 0.25, scope='dropout3')
# Flatten while preserving batch and time dimensions.
dims = tf.shape(net)
net = tf.reshape(net, (dims[0], dims[1],
net.shape[2].value * net.shape[3].value), 'flatten4')
net = slim.fully_connected(net, 512, scope='fc5')
net = slim.dropout(net, 0.5, scope='dropout5')
return net
开发者ID:Alice-ren，项目名称:magenta，代码行数:29，代码来源:model.py
示例5: localization_squeezenet
def localization_squeezenet(self, inputs):
with tf.variable_scope('localization_network'):
with slim.arg_scope([slim.conv2d], activation_fn = tf.nn.relu,
padding = 'SAME',
weights_initializer = tf.constant_initializer(0.0)):
conv1 = slim.conv2d(inputs, 64, [3,3], 2, padding = 'VALID', scope='conv1')
pool1 = slim.max_pool2d(conv1, [2,2], 2, scope='pool1')
fire2 = self.fire_module(pool1, 16, 64, scope = 'fire2')
fire3 = self.fire_module(fire2, 16, 64, scope = 'fire3', res_connection=True)
fire4 = self.fire_module(fire3, 32, 128, scope = 'fire4')
pool4 = slim.max_pool2d(fire4, [2,2], 2, scope='pool4')
fire5 = self.fire_module(pool4, 32, 128, scope = 'fire5', res_connection=True)
fire6 = self.fire_module(fire5, 48, 192, scope = 'fire6')
fire7 = self.fire_module(fire6, 48, 192, scope = 'fire7', res_connection=True)
fire8 = self.fire_module(fire7, 64, 256, scope = 'fire8')
pool8 = slim.max_pool2d(fire8, [2,2], 2, scope='pool8')
fire9 = self.fire_module(pool8, 64, 256, scope = 'fire9', res_connection=True)
conv10 = slim.conv2d(fire9, 128, [1,1], 1, scope='conv10')
shape = int(np.prod(conv10.get_shape()[1:]))
identity = np.array([[1., 0., 0.],
[0., 1., 0.]])
identity = identity.flatten()
fc11 = slim.fully_connected(tf.reshape(conv10, [-1, shape]), 6, biases_initializer = tf.constant_initializer(identity), scope='fc11')
return fc11
开发者ID:dmehr，项目名称:HyperFace-TensorFlow-implementation，代码行数:26，代码来源:model.py
示例6: build_arch_baseline
def build_arch_baseline(input, is_train: bool, num_classes: int):
bias_initializer = tf.truncated_normal_initializer(
mean=0.0, stddev=0.01) # tf.constant_initializer(0.0)
# The paper didnot mention any regularization, a common l2 regularizer to weights is added here
weights_regularizer = tf.contrib.layers.l2_regularizer(5e-04)
tf.logging.info('input shape: {}'.format(input.get_shape()))
# weights_initializer=initializer,
with slim.arg_scope([slim.conv2d, slim.fully_connected], trainable=is_train, biases_initializer=bias_initializer, weights_regularizer=weights_regularizer):
with tf.variable_scope('relu_conv1') as scope:
output = slim.conv2d(input, num_outputs=32, kernel_size=[
5, 5], stride=1, padding='SAME', scope=scope, activation_fn=tf.nn.relu)
output = slim.max_pool2d(output, [2, 2], scope='max_2d_layer1')
tf.logging.info('output shape: {}'.format(output.get_shape()))
with tf.variable_scope('relu_conv2') as scope:
output = slim.conv2d(output, num_outputs=64, kernel_size=[
5, 5], stride=1, padding='SAME', scope=scope, activation_fn=tf.nn.relu)
output = slim.max_pool2d(output, [2, 2], scope='max_2d_layer2')
tf.logging.info('output shape: {}'.format(output.get_shape()))
output = slim.flatten(output)
output = slim.fully_connected(output, 1024, scope='relu_fc3', activation_fn=tf.nn.relu)
tf.logging.info('output shape: {}'.format(output.get_shape()))
output = slim.dropout(output, 0.5, scope='dp')
output = slim.fully_connected(output, num_classes, scope='final_layer', activation_fn=None)
tf.logging.info('output shape: {}'.format(output.get_shape()))
return output
开发者ID:lzqkean，项目名称:deep_learning，代码行数:32，代码来源:capsnet_em.py
示例7: make_tower
def make_tower(net):
net = slim.conv2d(net, 20, [5, 5], padding='VALID', scope='conv1')
net = slim.max_pool2d(net, [2, 2], padding='VALID', scope='pool1')
net = slim.conv2d(net, 50, [5, 5], padding='VALID', scope='conv2')
net = slim.max_pool2d(net, [2, 2], padding='VALID', scope='pool2')
net = slim.flatten(net)
net = slim.fully_connected(net, 500, scope='fc1')
net = slim.fully_connected(net, 2, activation_fn=None, scope='fc2')
return net
开发者ID:Dasona，项目名称:DIGITS，代码行数:9，代码来源:siamese-TF.py
示例8: build_graph
def build_graph(top_k):
keep_prob = tf.placeholder(dtype=tf.float32, shape=[], name='keep_prob')
images = tf.placeholder(dtype=tf.float32, shape=[None, 64, 64, 1], name='image_batch')
labels = tf.placeholder(dtype=tf.int64, shape=[None], name='label_batch')
is_training = tf.placeholder(dtype=tf.bool, shape=[], name='train_flag')
with tf.device('/gpu:0'):
with slim.arg_scope([slim.conv2d, slim.fully_connected],
normalizer_fn=slim.batch_norm,
normalizer_params={'is_training': is_training}):
conv3_1 = slim.conv2d(images, 64, [3, 3], 1, padding='SAME', scope='conv3_1')
max_pool_1 = slim.max_pool2d(conv3_1, [2, 2], [2, 2], padding='SAME', scope='pool1')
conv3_2 = slim.conv2d(max_pool_1, 128, [3, 3], padding='SAME', scope='conv3_2')
max_pool_2 = slim.max_pool2d(conv3_2, [2, 2], [2, 2], padding='SAME', scope='pool2')
conv3_3 = slim.conv2d(max_pool_2, 256, [3, 3], padding='SAME', scope='conv3_3')
max_pool_3 = slim.max_pool2d(conv3_3, [2, 2], [2, 2], padding='SAME', scope='pool3')
conv3_4 = slim.conv2d(max_pool_3, 512, [3, 3], padding='SAME', scope='conv3_4')
conv3_5 = slim.conv2d(conv3_4, 512, [3, 3], padding='SAME', scope='conv3_5')
max_pool_4 = slim.max_pool2d(conv3_5, [2, 2], [2, 2], padding='SAME', scope='pool4')
flatten = slim.flatten(max_pool_4)
fc1 = slim.fully_connected(slim.dropout(flatten, keep_prob), 1024,
activation_fn=tf.nn.relu, scope='fc1')
logits = slim.fully_connected(slim.dropout(fc1, keep_prob), FLAGS.charset_size, activation_fn=None,
scope='fc2')
loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels))
accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(logits, 1), labels), tf.float32))
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
if update_ops:
updates = tf.group(*update_ops)
loss = control_flow_ops.with_dependencies([updates], loss)
global_step = tf.get_variable("step", [], initializer=tf.constant_initializer(0.0), trainable=False)
optimizer = tf.train.AdamOptimizer(learning_rate=0.1)
train_op = slim.learning.create_train_op(loss, optimizer, global_step=global_step)
probabilities = tf.nn.softmax(logits)
tf.summary.scalar('loss', loss)
tf.summary.scalar('accuracy', accuracy)
merged_summary_op = tf.summary.merge_all()
predicted_val_top_k, predicted_index_top_k = tf.nn.top_k(probabilities, k=top_k)
accuracy_in_top_k = tf.reduce_mean(tf.cast(tf.nn.in_top_k(probabilities, labels, top_k), tf.float32))
return {'images': images,
'labels': labels,
'keep_prob': keep_prob,
'top_k': top_k,
'global_step': global_step,
'train_op': train_op,
'loss': loss,
'is_training': is_training,
'accuracy': accuracy,
'accuracy_top_k': accuracy_in_top_k,
'merged_summary_op': merged_summary_op,
'predicted_distribution': probabilities,
'predicted_index_top_k': predicted_index_top_k,
'predicted_val_top_k': predicted_val_top_k}
开发者ID:oraSC，项目名称:Chinese-Character-Recognition，代码行数:57，代码来源:chinese_character_recognition_bn.py
示例9: row_column_max_pooling
def row_column_max_pooling(bottom, prefix='', window=(7, 7)):
column_mx = slim.max_pool2d(bottom, [window[0], 1],
stride=[window[0], 1], scope=prefix + '_column_max')
row_mx = slim.max_pool2d(bottom, [1, window[1]],
stride=[1, window[1]], scope=prefix + '_row_max')
column_mean = slim.avg_pool2d(column_mx, [1, window[1]],
stride=[1, window[1]], scope=prefix + '_column_mean')
row_mean = slim.avg_pool2d(row_mx, [window[0], 1],
stride=[window[0], 1], scope=prefix + '_row_mean')
return row_mean + column_mean
开发者ID:Zumbalamambo，项目名称:light_head_rcnn，代码行数:12，代码来源:rfcn_plus_plus_opr.py
示例10: _build_network
def _build_network(self, sess, is_training=True):
with tf.variable_scope('vgg_16', 'vgg_16'):
# select initializers
if cfg.TRAIN.TRUNCATED:
initializer = tf.truncated_normal_initializer(mean=0.0, stddev=0.01)
initializer_bbox = tf.truncated_normal_initializer(mean=0.0, stddev=0.001)
else:
initializer = tf.random_normal_initializer(mean=0.0, stddev=0.01)
initializer_bbox = tf.random_normal_initializer(mean=0.0, stddev=0.001)
net = slim.repeat(self._image, 2, slim.conv2d, 64, [3, 3],
trainable=False, scope='conv1')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool1')
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3],
trainable=False, scope='conv2')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool2')
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3],
trainable=is_training, scope='conv3')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool3')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3],
trainable=is_training, scope='conv4')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool4')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3],
trainable=is_training, scope='conv5')
self._act_summaries.append(net)
self._layers['head'] = net
# build the anchors for the image
self._anchor_component()
# region proposal network
rois = self._region_proposal(net, is_training, initializer)
# region of interest pooling
if cfg.POOLING_MODE == 'crop':
pool5 = self._crop_pool_layer(net, rois, "pool5")
else:
raise NotImplementedError
pool5_flat = slim.flatten(pool5, scope='flatten')
fc6 = slim.fully_connected(pool5_flat, 4096, scope='fc6')
if is_training:
fc6 = slim.dropout(fc6, keep_prob=0.5, is_training=True, scope='dropout6')
fc7 = slim.fully_connected(fc6, 4096, scope='fc7')
if is_training:
fc7 = slim.dropout(fc7, keep_prob=0.5, is_training=True, scope='dropout7')
# region classification
cls_prob, bbox_pred = self._region_classification(fc7,
is_training,
initializer,
initializer_bbox)
self._score_summaries.update(self._predictions)
return rois, cls_prob, bbox_pred
开发者ID:deeplxx，项目名称:tf-faster-rcnn，代码行数:52，代码来源:vgg16.py
示例11: construct_net
def construct_net(self,is_trained = True):
with slim.arg_scope([slim.conv2d], padding='VALID',
weights_initializer=tf.truncated_normal_initializer(stddev=0.01),
weights_regularizer=slim.l2_regularizer(0.0005)):
net = slim.conv2d(self.input_images,6,[5,5],1,padding='SAME',scope='conv1')
net = slim.max_pool2d(net, [2, 2], scope='pool2')
net = slim.conv2d(net,16,[5,5],1,scope='conv3')
net = slim.max_pool2d(net, [2, 2], scope='pool4')
net = slim.conv2d(net,120,[5,5],1,scope='conv5')
net = slim.flatten(net, scope='flat6')
net = slim.fully_connected(net, 84, scope='fc7')
net = slim.dropout(net, self.dropout,is_training=is_trained, scope='dropout8')
digits = slim.fully_connected(net, 10, scope='fc9')
return digits
开发者ID:dcrmg，项目名称:LeNet，代码行数:14，代码来源:lenet.py
示例12: network
def network(inputs):
'''Define the network'''
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn=tf.nn.relu,
weights_initializer=tf.truncated_normal_initializer(0.0, 0.01),
weights_regularizer=slim.l2_regularizer(0.0005)):
net = tf.reshape(inputs,[-1,FLAGS.im_size ,FLAGS.im_size,3])
net = slim.conv2d(net, 32, [3,3], scope='conv1')
net = slim.max_pool2d(net, [4,4], scope = 'conv1')
net = slim.conv2d(net,128,[3,3], scope = 'conv2')
net = slim.max_pool2d(net,[4,4], scope = 'pool2')
net = slim.flatten(net)
net = slim.fully_connected(net,64, scope = 'fc')
net = slim.fully_connected(net, n_classes, activation_fn = None, scope = 'output')
return net
开发者ID:lun5，项目名称:tissue-component-classification，代码行数:15，代码来源:task.py
示例13: create_test_network
def create_test_network():
"""Convolutional neural network for test.
Returns:
g: Tensorflow graph object (Graph proto).
"""
g = ops.Graph()
with g.as_default():
# An input test image with unknown spatial resolution.
x = array_ops.placeholder(
dtypes.float32, (None, None, None, 1), name='input_image')
# Left branch before first addition.
l1 = slim.conv2d(x, 1, [1, 1], stride=4, scope='L1', padding='VALID')
# Right branch before first addition.
l2_pad = array_ops.pad(x, [[0, 0], [1, 0], [1, 0], [0, 0]], name='L2_pad')
l2 = slim.conv2d(l2_pad, 1, [3, 3], stride=2, scope='L2', padding='VALID')
l3 = slim.max_pool2d(l2, [3, 3], stride=2, scope='L3', padding='SAME')
# First addition.
l4 = nn.relu(l1 + l3, name='L4_relu')
# Left branch after first addition.
l5 = slim.conv2d(l4, 1, [1, 1], stride=2, scope='L5', padding='SAME')
# Right branch after first addition.
l6 = slim.conv2d(l4, 1, [3, 3], stride=2, scope='L6', padding='SAME')
# Final addition.
gen_math_ops.add(l5, l6, name='L7_add')
return g
开发者ID:Ajaycs99，项目名称:tensorflow，代码行数:27，代码来源:graph_compute_order_test.py
示例14: AddMaxPool
def AddMaxPool(self, prev_layer, index):
"""Add a maxpool layer.
Args:
prev_layer: Input tensor.
index: Position in model_str to start parsing
Returns:
Output tensor, end index in model_str.
"""
pattern = re.compile(R'(Mp)({\w+})?(\d+),(\d+)(?:,(\d+),(\d+))?')
m = pattern.match(self.model_str, index)
if m is None:
return None, None
name = self._GetLayerName(m.group(0), index, m.group(2))
height = int(m.group(3))
width = int(m.group(4))
y_stride = height if m.group(5) is None else m.group(5)
x_stride = width if m.group(6) is None else m.group(6)
self.reduction_factors[1] *= y_stride
self.reduction_factors[2] *= x_stride
return slim.max_pool2d(
prev_layer, [height, width], [y_stride, x_stride],
padding='SAME',
scope=name), m.end()
开发者ID:Peratham，项目名称:models，代码行数:25，代码来源:vgslspecs.py
示例15: inference
def inference(self):
x = tf.reshape(self.x, shape=[-1, self.input_shape[0], self.input_shape[1], self.input_shape[2]])
# scale (divide by MNIST std)
x = x * 0.0125
with slim.arg_scope([slim.conv2d, slim.fully_connected],
weights_initializer=tf.contrib.layers.xavier_initializer(),
weights_regularizer=slim.l2_regularizer(0.0005)):
model = slim.conv2d(x, 20, [5, 5], padding='VALID', scope='conv1')
model = slim.max_pool2d(model, [2, 2], padding='VALID', scope='pool1')
model = slim.conv2d(model, 50, [5, 5], padding='VALID', scope='conv2')
model = slim.max_pool2d(model, [2, 2], padding='VALID', scope='pool2')
model = slim.flatten(model)
model = slim.fully_connected(model, 500, scope='fc1')
model = slim.dropout(model, 0.5, is_training=self.is_training, scope='do1')
model = slim.fully_connected(model, self.nclasses, activation_fn=None, scope='fc2')
return model
开发者ID:Dasona，项目名称:DIGITS，代码行数:16，代码来源:lenet.py
示例16: _build_base
def _build_base(self):
with tf.variable_scope(self._scope, self._scope):
net = resnet_utils.conv2d_same(self._image, 64, 7, stride=2, scope='conv1')
net = tf.pad(net, [[0, 0], [1, 1], [1, 1], [0, 0]])
net = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', scope='pool1')
return net
开发者ID:StanislawAntol，项目名称:tf-faster-rcnn，代码行数:7，代码来源:resnet_v1.py
示例17: build_feature_pyramid
def build_feature_pyramid(self):
'''
reference: https://github.com/CharlesShang/FastMaskRCNN
build P2, P3, P4, P5, P6
:return: multi-scale feature map
'''
feature_pyramid = {}
with tf.variable_scope('feature_pyramid'):
with slim.arg_scope([slim.conv2d], weights_regularizer=slim.l2_regularizer(self.rpn_weight_decay)):
feature_pyramid['P5'] = slim.conv2d(self.feature_maps_dict['C5'],
num_outputs=256,
kernel_size=[1, 1],
stride=1,
scope='build_P5')
feature_pyramid['P6'] = slim.max_pool2d(feature_pyramid['P5'],
kernel_size=[2, 2], stride=2, scope='build_P6')
# P6 is down sample of P5
for layer in range(4, 1, -1):
p, c = feature_pyramid['P' + str(layer + 1)], self.feature_maps_dict['C' + str(layer)]
up_sample_shape = tf.shape(c)
up_sample = tf.image.resize_nearest_neighbor(p, [up_sample_shape[1], up_sample_shape[2]],
name='build_P%d/up_sample_nearest_neighbor' % layer)
c = slim.conv2d(c, num_outputs=256, kernel_size=[1, 1], stride=1,
scope='build_P%d/reduce_dimension' % layer)
p = up_sample + c
p = slim.conv2d(p, 256, kernel_size=[3, 3], stride=1,
padding='SAME', scope='build_P%d/avoid_aliasing' % layer)
feature_pyramid['P' + str(layer)] = p
return feature_pyramid
开发者ID:mbossX，项目名称:RRPN_FPN_Tensorflow，代码行数:35，代码来源:build_rpn.py
示例18: create_test_network
def create_test_network():
"""Convolutional neural network for test.
Returns:
name_to_node: Dict keyed by node name, each entry containing the node's
NodeDef.
"""
g = ops.Graph()
with g.as_default():
# An input test image with unknown spatial resolution.
x = array_ops.placeholder(
dtypes.float32, (None, None, None, 1), name='input_image')
# Left branch before first addition.
l1 = slim.conv2d(x, 1, [1, 1], stride=4, scope='L1', padding='VALID')
# Right branch before first addition.
l2_pad = array_ops.pad(x, [[0, 0], [1, 0], [1, 0], [0, 0]], name='L2_pad')
l2 = slim.conv2d(l2_pad, 1, [3, 3], stride=2, scope='L2', padding='VALID')
l3 = slim.max_pool2d(l2, [3, 3], stride=2, scope='L3', padding='SAME')
# First addition.
l4 = nn.relu(l1 + l3, name='L4_relu')
# Left branch after first addition.
l5 = slim.conv2d(l4, 1, [1, 1], stride=2, scope='L5', padding='SAME')
# Right branch after first addition.
l6 = slim.conv2d(l4, 1, [3, 3], stride=2, scope='L6', padding='SAME')
# Final addition.
gen_math_ops.add(l5, l6, name='L7_add')
name_to_node = graph_compute_order.parse_graph_nodes(g.as_graph_def())
return name_to_node
开发者ID:Ajaycs99，项目名称:tensorflow，代码行数:29，代码来源:parse_layer_parameters_test.py
示例19: build_backbones
def build_backbones(self):
inputs = self.inputs
with slim.arg_scope([slim.conv2d, slim.fully_connected],
padding='SAME', weights_initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.01),
weights_regularizer=slim.l2_regularizer(0.0005),
activation_fn=tf.nn.relu):
net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
net = slim.max_pool2d(net, [2, 2], scope='pool1')
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3], scope='conv2')
net = slim.max_pool2d(net, [2, 2], scope='pool2')
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3], scope='conv3')
net = slim.max_pool2d(net, [2, 2], scope='pool3')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv4')
net = slim.max_pool2d(net, [2, 2], scope='pool4')
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3], scope='conv5')
net = slim.max_pool2d(net, [2, 2], scope='pool5')
self.vgg_head = net
开发者ID:jacke121，项目名称:tf_rfcn，代码行数:17，代码来源:vgg16_rfcn.py
示例20: build_model
def build_model(self, input_image, center_map, batch_size):
self.batch_size = batch_size
self.input_image = input_image
self.center_map = center_map
with tf.variable_scope('pooled_center_map'):
# center map is a gaussion template which gather the respose
self.center_map = slim.avg_pool2d(self.center_map,
[9, 9], stride=8,
padding='SAME',
scope='center_map')
with slim.arg_scope([slim.conv2d],
padding='SAME',
activation_fn=tf.nn.relu,
weights_initializer=tf.contrib.layers.xavier_initializer()):
with tf.variable_scope('sub_stages'):
net = slim.conv2d(input_image, 64, [3, 3], scope='sub_conv1')
net = slim.conv2d(net, 64, [3, 3], scope='sub_conv2')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='sub_pool1')
net = slim.conv2d(net, 128, [3, 3], scope='sub_conv3')
net = slim.conv2d(net, 128, [3, 3], scope='sub_conv4')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='sub_pool2')
net = slim.conv2d(net, 256, [3, 3], scope='sub_conv5')
net = slim.conv2d(net, 256, [3, 3], scope='sub_conv6')
net = slim.conv2d(net, 256, [3, 3], scope='sub_conv7')
net = slim.conv2d(net, 256, [3, 3], scope='sub_conv8')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='sub_pool3')
net = slim.conv2d(net, 512, [3, 3], scope='sub_conv9')
net = slim.conv2d(net, 512, [3, 3], scope='sub_conv10')
net = slim.conv2d(net, 512, [3, 3], scope='sub_conv11')
net = slim.conv2d(net, 512, [3, 3], scope='sub_conv12')
net = slim.conv2d(net, 512, [3, 3], scope='sub_conv13')
net = slim.conv2d(net, 512, [3, 3], scope='sub_conv14')
self.sub_stage_img_feature = slim.conv2d(net, 128, [3, 3],
scope='sub_stage_img_feature')
with tf.variable_scope('stage_1'):
conv1 = slim.conv2d(self.sub_stage_img_feature, 512, [1, 1],
scope='conv1')
self.stage_heatmap.append(slim.conv2d(conv1, self.joints, [1, 1],
scope='stage_heatmap'))
for stage in range(2, self.stages + 1):
self._middle_conv(stage)
开发者ID:HumbleBee14，项目名称:convolutional-pose-machines-tensorflow，代码行数:45，代码来源:cpm_hand_slim.py
注：本文中的tensorflow.contrib.slim.max_pool2d函数示例由纯净天空整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。 |
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