def format_input_left_padding(inputs, **kwargs):
static_shape = inputs.get_shape()
if not static_shape or len(static_shape) != 4:
raise ValueError(
"Inputs to conv must have statically known rank 4. Shape: " + str(static_shape))
dilation = (1, 1)
assert kwargs['filter_size'] is not None
filter_size = kwargs['filter_size']
if isinstance(filter_size, int):
filter_size = [filter_size, filter_size]
if "dilation" in kwargs:
dilation_rate = kwargs["dilation"]
assert filter_size[0] % 2 == 1 and filter_size[1] % 2 == 1
height_padding = 2 * (filter_size[0] // 2) * dilation[0]
cond_padding = tf.cond(
tf.equal(tf.shape(inputs)[2], 1), lambda: tf.constant(0),
lambda: tf.constant(2 * (filter_size[1] // 2) * dilation[1]))
width_padding = 0 if static_shape[2] == 1 else cond_padding
padding = [[0, 0], [height_padding, 0], [width_padding, 0], [0, 0]]
inputs = tf.pad(inputs, padding)
# Set middle two dimensions to None to prevent convolution from complaining
inputs.set_shape([static_shape[0], None, None, static_shape[3]])
kwargs["padding"] = "VALID"
return inputs, kwargs
python类pad()的实例源码
threepart_aligner.py 文件源码
项目：almond-nnparser
作者: Stanford-Mobisocial-IoT-Lab
项目源码
文件源码
阅读 23
收藏 0
点赞 0
评论 0
def pad_up_to(vector, size, rank):
length_diff = tf.reshape(size - tf.shape(vector)[1], shape=(1,))
with tf.control_dependencies([tf.assert_non_negative(length_diff, data=(vector, size, tf.shape(vector)))]):
padding = tf.reshape(tf.concat([[0, 0, 0], length_diff, [0,0]*(rank-1)], axis=0), shape=((rank+1), 2))
return tf.pad(vector, padding, mode='constant')
seq2seq_aligner.py 文件源码
项目：almond-nnparser
作者: Stanford-Mobisocial-IoT-Lab
项目源码
文件源码
阅读 29
收藏 0
点赞 0
评论 0
def pad_up_to(vector, size):
rank = vector.get_shape().ndims - 1
length_diff = tf.reshape(size - tf.shape(vector)[1], shape=(1,))
with tf.control_dependencies([tf.assert_non_negative(length_diff, data=(vector, size, tf.shape(vector)))]):
padding = tf.reshape(tf.concat([[0, 0, 0], length_diff, [0,0]*(rank-1)], axis=0), shape=((rank+1), 2))
return tf.pad(vector, padding, mode='constant')
seq2seq_aligner.py 文件源码
项目：almond-nnparser
作者: Stanford-Mobisocial-IoT-Lab
项目源码
文件源码
阅读 30
收藏 0
点赞 0
评论 0
def add_loss_op(self, result):
logits = result.rnn_output
with tf.control_dependencies([tf.assert_positive(tf.shape(logits)[1], data=[tf.shape(logits)])]):
length_diff = tf.reshape(self.config.max_length - tf.shape(logits)[1], shape=(1,))
padding = tf.reshape(tf.concat([[0, 0, 0], length_diff, [0, 0]], axis=0), shape=(3, 2))
preds = tf.pad(logits, padding, mode='constant')
# add epsilon to avoid division by 0
preds = preds + 1e-5
mask = tf.sequence_mask(self.output_length_placeholder, self.config.max_length, dtype=tf.float32)
loss = tf.contrib.seq2seq.sequence_loss(preds, self.output_placeholder, mask)
with tf.control_dependencies([tf.assert_non_negative(loss, data=[preds, mask], summarize=256*60*300)]):
return tf.identity(loss)
def cnn(self,
model_input,
l2_penalty=1e-8,
num_filters = [1024, 1024, 1024],
filter_sizes = [1,2,3],
sub_scope="",
**unused_params):
max_frames = model_input.get_shape().as_list()[1]
num_features = model_input.get_shape().as_list()[2]
shift_inputs = []
for i in range(max(filter_sizes)):
if i == 0:
shift_inputs.append(model_input)
else:
shift_inputs.append(tf.pad(model_input, paddings=[[0,0],[i,0],[0,0]])[:,:max_frames,:])
cnn_outputs = []
for nf, fs in zip(num_filters, filter_sizes):
sub_input = tf.concat(shift_inputs[:fs], axis=2)
sub_filter = tf.get_variable(sub_scope+"cnn-filter-len%d"%fs,
shape=[num_features*fs, nf], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1),
regularizer=tf.contrib.layers.l2_regularizer(l2_penalty))
cnn_outputs.append(tf.einsum("ijk,kl->ijl", sub_input, sub_filter))
cnn_output = tf.concat(cnn_outputs, axis=2)
cnn_output = slim.batch_norm(
cnn_output,
center=True,
scale=True,
is_training=FLAGS.train,
scope=sub_scope+"cluster_bn")
return cnn_output, max_frames
def cnn(self,
model_input,
l2_penalty=1e-8,
num_filters = [1024, 1024, 1024],
filter_sizes = [1,2,3],
sub_scope="",
**unused_params):
max_frames = model_input.get_shape().as_list()[1]
num_features = model_input.get_shape().as_list()[2]
shift_inputs = []
for i in range(max(filter_sizes)):
if i == 0:
shift_inputs.append(model_input)
else:
shift_inputs.append(tf.pad(model_input, paddings=[[0,0],[i,0],[0,0]])[:,:max_frames,:])
cnn_outputs = []
for nf, fs in zip(num_filters, filter_sizes):
sub_input = tf.concat(shift_inputs[:fs], axis=2)
sub_filter = tf.get_variable(sub_scope+"cnn-filter-len%d"%fs,
shape=[num_features*fs, nf], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1),
regularizer=tf.contrib.layers.l2_regularizer(l2_penalty))
cnn_outputs.append(tf.einsum("ijk,kl->ijl", sub_input, sub_filter))
cnn_output = tf.concat(cnn_outputs, axis=2)
cnn_output = slim.batch_norm(
cnn_output,
center=True,
scale=True,
is_training=FLAGS.train,
scope=sub_scope+"cluster_bn")
return cnn_output, max_frames
def cnn(self,
model_input,
l2_penalty=1e-8,
num_filters=[1024,1024,1024],
filter_sizes=[1,2,3],
sub_scope="",
**unused_params):
max_frames = model_input.get_shape().as_list()[1]
num_features = model_input.get_shape().as_list()[2]
shift_inputs = []
for i in range(max(filter_sizes)):
if i == 0:
shift_inputs.append(model_input)
else:
shift_inputs.append(tf.pad(model_input, paddings=[[0,0],[i,0],[0,0]])[:,:max_frames,:])
cnn_outputs = []
for nf, fs in zip(num_filters, filter_sizes):
sub_input = tf.concat(shift_inputs[:fs], axis=2)
sub_filter = tf.get_variable(sub_scope+"cnn-filter-len%d"%fs,
shape=[num_features*fs, nf], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1),
regularizer=tf.contrib.layers.l2_regularizer(l2_penalty))
cnn_outputs.append(tf.einsum("ijk,kl->ijl", sub_input, sub_filter))
cnn_output = tf.concat(cnn_outputs, axis=2)
cnn_output = slim.batch_norm(
cnn_output,
center=True,
scale=True,
is_training=FLAGS.train,
scope=sub_scope+"cluster_bn")
return cnn_output, max_frames
def cnn(self,
model_input,
l2_penalty=1e-8,
num_filters=[1024,1024,1024],
filter_sizes=[1,2,3],
sub_scope="",
**unused_params):
max_frames = model_input.get_shape().as_list()[1]
num_features = model_input.get_shape().as_list()[2]
shift_inputs = []
for i in range(max(filter_sizes)):
if i == 0:
shift_inputs.append(model_input)
else:
shift_inputs.append(tf.pad(model_input, paddings=[[0,0],[i,0],[0,0]])[:,:max_frames,:])
cnn_outputs = []
for nf, fs in zip(num_filters, filter_sizes):
sub_input = tf.concat(shift_inputs[:fs], axis=2)
sub_filter = tf.get_variable(sub_scope+"cnn-filter-len%d"%fs,
shape=[num_features*fs, nf], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1),
regularizer=tf.contrib.layers.l2_regularizer(l2_penalty))
cnn_outputs.append(tf.einsum("ijk,kl->ijl", sub_input, sub_filter))
cnn_output = tf.concat(cnn_outputs, axis=2)
cnn_output = slim.batch_norm(
cnn_output,
center=True,
scale=True,
is_training=FLAGS.train,
scope=sub_scope+"cluster_bn")
return cnn_output, max_frames
def cnn(self,
model_input,
l2_penalty=1e-8,
num_filters = [1024, 1024, 1024],
filter_sizes = [1,2,3],
sub_scope="",
**unused_params):
max_frames = model_input.get_shape().as_list()[1]
num_features = model_input.get_shape().as_list()[2]
shift_inputs = []
for i in range(max(filter_sizes)):
if i == 0:
shift_inputs.append(model_input)
else:
shift_inputs.append(tf.pad(model_input, paddings=[[0,0],[i,0],[0,0]])[:,:max_frames,:])
cnn_outputs = []
for nf, fs in zip(num_filters, filter_sizes):
sub_input = tf.concat(shift_inputs[:fs], axis=2)
sub_filter = tf.get_variable(sub_scope+"cnn-filter-len%d"%fs,
shape=[num_features*fs, nf], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1),
regularizer=tf.contrib.layers.l2_regularizer(l2_penalty))
cnn_outputs.append(tf.einsum("ijk,kl->ijl", sub_input, sub_filter))
cnn_output = tf.concat(cnn_outputs, axis=2)
cnn_output = slim.batch_norm(
cnn_output,
center=True,
scale=True,
is_training=FLAGS.train,
scope=sub_scope+"cluster_bn")
return cnn_output, max_frames
def cnn(self,
model_input,
l2_penalty=1e-8,
num_filters = [1024, 1024, 1024],
filter_sizes = [1,2,3],
sub_scope="",
**unused_params):
max_frames = model_input.get_shape().as_list()[1]
num_features = model_input.get_shape().as_list()[2]
shift_inputs = []
for i in range(max(filter_sizes)):
if i == 0:
shift_inputs.append(model_input)
else:
shift_inputs.append(tf.pad(model_input, paddings=[[0,0],[i,0],[0,0]])[:,:max_frames,:])
cnn_outputs = []
for nf, fs in zip(num_filters, filter_sizes):
sub_input = tf.concat(shift_inputs[:fs], axis=2)
sub_filter = tf.get_variable(sub_scope+"cnn-filter-len%d"%fs,
shape=[num_features*fs, nf], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1),
regularizer=tf.contrib.layers.l2_regularizer(l2_penalty))
sub_bias = tf.get_variable(sub_scope+"cnn-bias-len%d"%fs,
shape=[nf], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1),
regularizer=tf.contrib.layers.l2_regularizer(l2_penalty))
cnn_outputs.append(tf.einsum("ijk,kl->ijl", sub_input, sub_filter) + sub_bias)
cnn_output = tf.concat(cnn_outputs, axis=2)
cnn_output = slim.batch_norm(
cnn_output,
center=True,
scale=True,
is_training=FLAGS.train,
scope=sub_scope+"cluster_bn")
return cnn_output, max_frames
def cnn(self,
model_input,
l2_penalty=1e-8,
num_filters=[1024, 1024, 1024],
filter_sizes=[1,2,3],
sub_scope="",
**unused_params):
max_frames = model_input.get_shape().as_list()[1]
num_features = model_input.get_shape().as_list()[2]
shift_inputs = []
for i in range(max(filter_sizes)):
if i == 0:
shift_inputs.append(model_input)
else:
shift_inputs.append(tf.pad(model_input, paddings=[[0,0],[i,0],[0,0]])[:,:max_frames,:])
cnn_outputs = []
for nf, fs in zip(num_filters, filter_sizes):
sub_input = tf.concat(shift_inputs[:fs], axis=2)
sub_filter = tf.get_variable(sub_scope+"cnn-filter-len%d"%fs,
shape=[num_features*fs, nf], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1),
regularizer=tf.contrib.layers.l2_regularizer(l2_penalty))
cnn_outputs.append(tf.einsum("ijk,kl->ijl", sub_input, sub_filter))
cnn_output = tf.concat(cnn_outputs, axis=2)
return cnn_output, max_frames
def cnn(self,
model_input,
l2_penalty=1e-8,
num_filters=[1024, 1024, 1024],
filter_sizes=[1,2,3],
sub_scope="",
**unused_params):
max_frames = model_input.get_shape().as_list()[1]
num_features = model_input.get_shape().as_list()[2]
shift_inputs = []
for i in range(max(filter_sizes)):
if i == 0:
shift_inputs.append(model_input)
else:
shift_inputs.append(tf.pad(model_input, paddings=[[0,0],[i,0],[0,0]])[:,:max_frames,:])
cnn_outputs = []
for nf, fs in zip(num_filters, filter_sizes):
sub_input = tf.concat(shift_inputs[:fs], axis=2)
sub_filter = tf.get_variable(sub_scope+"cnn-filter-len%d"%fs,
shape=[num_features*fs, nf], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1),
regularizer=tf.contrib.layers.l2_regularizer(l2_penalty))
cnn_outputs.append(tf.einsum("ijk,kl->ijl", sub_input, sub_filter))
cnn_output = tf.concat(cnn_outputs, axis=2)
return cnn_output, max_frames
def cnn(self,
model_input,
l2_penalty=1e-8,
num_filters = [1024, 1024, 1024],
filter_sizes = [1,2,3],
sub_scope="",
**unused_params):
max_frames = model_input.get_shape().as_list()[1]
num_features = model_input.get_shape().as_list()[2]
shift_inputs = []
for i in range(max(filter_sizes)):
if i == 0:
shift_inputs.append(model_input)
else:
shift_inputs.append(tf.pad(model_input, paddings=[[0,0],[i,0],[0,0]])[:,:max_frames,:])
cnn_outputs = []
for nf, fs in zip(num_filters, filter_sizes):
sub_input = tf.concat(shift_inputs[:fs], axis=2)
sub_filter = tf.get_variable(sub_scope+"cnn-filter-len%d"%fs,
shape=[num_features*fs, nf], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1),
regularizer=tf.contrib.layers.l2_regularizer(l2_penalty))
cnn_outputs.append(tf.einsum("ijk,kl->ijl", sub_input, sub_filter))
cnn_output = tf.concat(cnn_outputs, axis=2)
return cnn_output
def cnn(self,
model_input,
l2_penalty=1e-8,
num_filters = [128,128,256],
filter_sizes = [1,2,3],
sub_scope="",
**unused_params):
max_frames = model_input.get_shape().as_list()[1]
num_features = model_input.get_shape().as_list()[2]
shift_inputs = []
for i in xrange(max(filter_sizes)):
if i == 0:
shift_inputs.append(model_input)
else:
shift_inputs.append(tf.pad(model_input, paddings=[[0,0],[i,0],[0,0]])[:,:max_frames,:])
cnn_outputs = []
for nf, fs in zip(num_filters, filter_sizes):
sub_input = tf.concat(shift_inputs[:fs], axis=2)
sub_filter = tf.get_variable(sub_scope+"filter-len%d"%fs,
shape=[num_features*fs, nf], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1),
regularizer=tf.contrib.layers.l2_regularizer(l2_penalty))
cnn_outputs.append(tf.einsum("ijk,kl->ijl", sub_input, sub_filter))
cnn_output = tf.concat(cnn_outputs, axis=2)
return cnn_output
positional_cnn_deep_combine_chain_model.py 文件源码
项目：youtube-8m
作者: wangheda
项目源码
文件源码
阅读 24
收藏 0
点赞 0
评论 0
def cnn(self,
model_input,
l2_penalty=1e-8,
num_filters = [1024, 1024, 1024],
filter_sizes = [1,2,3],
sub_scope="",
**unused_params):
max_frames = model_input.get_shape().as_list()[1]
num_features = model_input.get_shape().as_list()[2]
shift_inputs = []
for i in xrange(max(filter_sizes)):
if i == 0:
shift_inputs.append(model_input)
else:
shift_inputs.append(tf.pad(model_input, paddings=[[0,0],[i,0],[0,0]])[:,:max_frames,:])
cnn_outputs = []
for nf, fs in zip(num_filters, filter_sizes):
sub_input = tf.concat(shift_inputs[:fs], axis=2)
sub_filter = tf.get_variable(sub_scope+"cnn-filter-len%d"%fs,
shape=[num_features*fs, nf], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1),
regularizer=tf.contrib.layers.l2_regularizer(l2_penalty))
cnn_outputs.append(tf.einsum("ijk,kl->ijl", sub_input, sub_filter))
cnn_output = tf.concat(cnn_outputs, axis=2)
return cnn_output
def cnn(self,
model_input,
l2_penalty=1e-8,
num_filters = [1024, 1024, 1024],
filter_sizes = [1,2,3],
sub_scope="",
**unused_params):
max_frames = model_input.get_shape().as_list()[1]
num_features = model_input.get_shape().as_list()[2]
shift_inputs = []
for i in xrange(max(filter_sizes)):
if i == 0:
shift_inputs.append(model_input)
else:
shift_inputs.append(tf.pad(model_input, paddings=[[0,0],[i,0],[0,0]])[:,:max_frames,:])
cnn_outputs = []
for nf, fs in zip(num_filters, filter_sizes):
sub_input = tf.concat(shift_inputs[:fs], axis=2)
sub_filter = tf.get_variable(sub_scope+"cnn-filter-len%d"%fs,
shape=[num_features*fs, nf], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1),
regularizer=tf.contrib.layers.l2_regularizer(l2_penalty))
cnn_outputs.append(tf.einsum("ijk,kl->ijl", sub_input, sub_filter))
cnn_output = tf.concat(cnn_outputs, axis=2)
cnn_output = slim.batch_norm(
cnn_output,
center=True,
scale=True,
is_training=FLAGS.is_training,
scope=sub_scope+"cluster_bn")
return cnn_output
def cnn(self,
model_input,
l2_penalty=1e-8,
num_filters = [1024, 1024, 1024],
filter_sizes = [1,2,3],
**unused_params):
max_frames = model_input.get_shape().as_list()[1]
num_features = model_input.get_shape().as_list()[2]
shift_inputs = []
for i in xrange(max(filter_sizes)):
if i == 0:
shift_inputs.append(model_input)
else:
shift_inputs.append(tf.pad(model_input, paddings=[[0,0],[i,0],[0,0]])[:,:max_frames,:])
cnn_outputs = []
for nf, fs in zip(num_filters, filter_sizes):
sub_input = tf.concat(shift_inputs[:fs], axis=2)
sub_filter = tf.get_variable("cnn-filter-len%d"%fs, shape=[num_features*fs, nf], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1),
regularizer=tf.contrib.layers.l2_regularizer(l2_penalty))
cnn_outputs.append(tf.einsum("ijk,kl->ijl", sub_input, sub_filter))
cnn_output = tf.concat(cnn_outputs, axis=2)
return cnn_output
distillchain_cnn_deep_combine_chain_model.py 文件源码
项目：youtube-8m
作者: wangheda
项目源码
文件源码
阅读 22
收藏 0
点赞 0
评论 0
def cnn(self,
model_input,
l2_penalty=1e-8,
num_filters = [1024, 1024, 1024],
filter_sizes = [1,2,3],
sub_scope="",
**unused_params):
max_frames = model_input.get_shape().as_list()[1]
num_features = model_input.get_shape().as_list()[2]
shift_inputs = []
for i in xrange(max(filter_sizes)):
if i == 0:
shift_inputs.append(model_input)
else:
shift_inputs.append(tf.pad(model_input, paddings=[[0,0],[i,0],[0,0]])[:,:max_frames,:])
cnn_outputs = []
for nf, fs in zip(num_filters, filter_sizes):
sub_input = tf.concat(shift_inputs[:fs], axis=2)
sub_filter = tf.get_variable(sub_scope+"cnn-filter-len%d"%fs,
shape=[num_features*fs, nf], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1),
regularizer=tf.contrib.layers.l2_regularizer(l2_penalty))
cnn_outputs.append(tf.einsum("ijk,kl->ijl", sub_input, sub_filter))
cnn_output = tf.concat(cnn_outputs, axis=2)
return cnn_output
def cnn(self,
model_input,
l2_penalty=1e-8,
num_filters = [1024, 1024, 1024],
filter_sizes = [1,2,3],
**unused_params):
max_frames = model_input.get_shape().as_list()[1]
num_features = model_input.get_shape().as_list()[2]
shift_inputs = []
for i in xrange(max(filter_sizes)):
if i == 0:
shift_inputs.append(model_input)
else:
shift_inputs.append(tf.pad(model_input, paddings=[[0,0],[i,0],[0,0]])[:,:max_frames,:])
cnn_outputs = []
for nf, fs in zip(num_filters, filter_sizes):
sub_input = tf.concat(shift_inputs[:fs], axis=2)
sub_filter = tf.get_variable("cnn-filter-len%d"%fs, shape=[num_features*fs, nf], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1),
regularizer=tf.contrib.layers.l2_regularizer(l2_penalty))
cnn_outputs.append(tf.einsum("ijk,kl->ijl", sub_input, sub_filter))
cnn_output = tf.concat(cnn_outputs, axis=2)
return cnn_output
def cnn(self,
model_input,
l2_penalty=1e-8,
num_filters = [1024, 1024, 1024],
filter_sizes = [1,2,3],
sub_scope="",
**unused_params):
max_frames = model_input.get_shape().as_list()[1]
num_features = model_input.get_shape().as_list()[2]
shift_inputs = []
for i in xrange(max(filter_sizes)):
if i == 0:
shift_inputs.append(model_input)
else:
shift_inputs.append(tf.pad(model_input, paddings=[[0,0],[i,0],[0,0]])[:,:max_frames,:])
cnn_outputs = []
for nf, fs in zip(num_filters, filter_sizes):
sub_input = tf.concat(shift_inputs[:fs], axis=2)
sub_filter = tf.get_variable(sub_scope+"cnn-filter-len%d"%fs,
shape=[num_features*fs, nf], dtype=tf.float32,
initializer=tf.truncated_normal_initializer(mean=0.0, stddev=0.1),
regularizer=tf.contrib.layers.l2_regularizer(l2_penalty))
cnn_outputs.append(tf.einsum("ijk,kl->ijl", sub_input, sub_filter))
cnn_output = tf.concat(cnn_outputs, axis=2)
return cnn_output
