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def zero_state(self, batch_size, dtype=LayersConfig.tf_dtype):
"""Return zero-filled state tensor(s).
Args:
batch_size: int, float, or unit Tensor representing the batch size.
Returns:
tensor of shape '[batch_size x shape[0] x shape[1] x num_features]
filled with zeros
"""
shape = self.shape
num_features = self.num_features
# TODO : TypeError: 'NoneType' object is not subscriptable
zeros = tf.zeros([batch_size, shape[0], shape[1], num_features * 2], dtype=dtype)
return zeros |
def state_size(self):
"""State size of the LSTMStateTuple."""
return (LSTMStateTuple(self._num_units, self._num_units) if self._state_is_tuple else 2 * self._num_units) |
def _to_bc_h_w(self, x, x_shape):
"""(b, h, w, c) -> (b*c, h, w)"""
x = tf.transpose(x, [0, 3, 1, 2])
x = tf.reshape(x, (-1, x_shape[1], x_shape[2]))
return x |
def _to_b_h_w_n_c(self, x, x_shape):
"""(b*c, h, w, n) -> (b, h, w, n, c)"""
x = tf.reshape(x, (-1, x_shape[4], x_shape[1], x_shape[2], x_shape[3]))
x = tf.transpose(x, [0, 2, 3, 4, 1])
return x |
def _tf_repeat(self, a, repeats):
"""Tensorflow version of np.repeat for 1D"""
# https://github.com/tensorflow/tensorflow/issues/8521
if len(a.get_shape()) != 1:
raise AssertionError("This is not a 1D Tensor")
a = tf.expand_dims(a, -1)
a = tf.tile(a, [1, repeats])
a = self.tf_flatten(a)
return a |
def _tf_batch_map_coordinates(self, inputs, coords):
"""Batch version of tf_map_coordinates
Only supports 2D feature maps
Parameters
----------
inputs : ``tf.Tensor``
shape = (b*c, h, w)
coords : ``tf.Tensor``
shape = (b*c, h, w, n, 2)
Returns
-------
``tf.Tensor``
A Tensor with the shape as (b*c, h, w, n)
"""
input_shape = inputs.get_shape()
coords_shape = coords.get_shape()
batch_channel = tf.shape(inputs)[0]
input_h = int(input_shape[1])
input_w = int(input_shape[2])
kernel_n = int(coords_shape[3])
n_coords = input_h * input_w * kernel_n
coords_lt = tf.cast(tf.floor(coords), 'int32')
coords_rb = tf.cast(tf.ceil(coords), 'int32')
coords_lb = tf.stack([coords_lt[:, :, :, :, 0], coords_rb[:, :, :, :, 1]], axis=-1)
coords_rt = tf.stack([coords_rb[:, :, :, :, 0], coords_lt[:, :, :, :, 1]], axis=-1)
idx = self._tf_repeat(tf.range(batch_channel), n_coords)
vals_lt = self._get_vals_by_coords(inputs, coords_lt, idx, (batch_channel, input_h, input_w, kernel_n))
vals_rb = self._get_vals_by_coords(inputs, coords_rb, idx, (batch_channel, input_h, input_w, kernel_n))
vals_lb = self._get_vals_by_coords(inputs, coords_lb, idx, (batch_channel, input_h, input_w, kernel_n))
vals_rt = self._get_vals_by_coords(inputs, coords_rt, idx, (batch_channel, input_h, input_w, kernel_n))
coords_offset_lt = coords - tf.cast(coords_lt, 'float32')
vals_t = vals_lt + (vals_rt - vals_lt) * coords_offset_lt[:, :, :, :, 0]
vals_b = vals_lb + (vals_rb - vals_lb) * coords_offset_lt[:, :, :, :, 0]
mapped_vals = vals_t + (vals_b - vals_t) * coords_offset_lt[:, :, :, :, 1]
return mapped_vals |
def _tf_batch_map_offsets(self, inputs, offsets, grid_offset):
"""Batch map offsets into input
Parameters
------------
inputs : ``tf.Tensor``
shape = (b, h, w, c)
offsets: ``tf.Tensor``
shape = (b, h, w, 2*n)
grid_offset: `tf.Tensor``
Offset grids shape = (h, w, n, 2)
Returns
-------
``tf.Tensor``
A Tensor with the shape as (b, h, w, c)
"""
input_shape = inputs.get_shape()
batch_size = tf.shape(inputs)[0]
kernel_n = int(int(offsets.get_shape()[3]) / 2)
input_h = input_shape[1]
input_w = input_shape[2]
channel = input_shape[3]
# inputs (b, h, w, c) --> (b*c, h, w)
inputs = self._to_bc_h_w(inputs, input_shape)
# offsets (b, h, w, 2*n) --> (b, h, w, n, 2)
offsets = tf.reshape(offsets, (batch_size, input_h, input_w, kernel_n, 2))
# offsets (b, h, w, n, 2) --> (b*c, h, w, n, 2)
# offsets = tf.tile(offsets, [channel, 1, 1, 1, 1])
coords = tf.expand_dims(grid_offset, 0) # grid_offset --> (1, h, w, n, 2)
coords = tf.tile(coords, [batch_size, 1, 1, 1, 1]) + offsets # grid_offset --> (b, h, w, n, 2)
# clip out of bound
coords = tf.stack(
[
tf.clip_by_value(coords[:, :, :, :, 0], 0.0, tf.cast(input_h - 1, 'float32')),
tf.clip_by_value(coords[:, :, :, :, 1], 0.0, tf.cast(input_w - 1, 'float32'))
], axis=-1
)
coords = tf.tile(coords, [channel, 1, 1, 1, 1])
mapped_vals = self._tf_batch_map_coordinates(inputs, coords)
# (b*c, h, w, n) --> (b, h, w, n, c)
mapped_vals = self._to_b_h_w_n_c(mapped_vals, [batch_size, input_h, input_w, kernel_n, channel])
return mapped_vals |
def minibatches(inputs=None, targets=None, batch_size=None, allow_dynamic_batch_size=False, shuffle=False):
"""Generate a generator that input a group of example in numpy.array and
their labels, return the examples and labels by the given batch size.
Parameters
----------
inputs : numpy.array
The input features, every row is a example.
targets : numpy.array
The labels of inputs, every row is a example.
batch_size : int
The batch size.
allow_dynamic_batch_size: boolean
Allow the use of the last data batch in case the number of examples is not a multiple of batch_size, this may result in unexpected behaviour if other functions expect a fixed-sized batch-size.
shuffle : boolean
Indicating whether to use a shuffling queue, shuffle the dataset before return.
Examples
--------
>>> X = np.asarray([['a','a'], ['b','b'], ['c','c'], ['d','d'], ['e','e'], ['f','f']])
>>> y = np.asarray([0,1,2,3,4,5])
>>> for batch in tl.iterate.minibatches(inputs=X, targets=y, batch_size=2, shuffle=False):
>>> print(batch)
(array([['a', 'a'], ['b', 'b']], dtype='<U1'), array([0, 1]))
(array([['c', 'c'], ['d', 'd']], dtype='<U1'), array([2, 3]))
(array([['e', 'e'], ['f', 'f']], dtype='<U1'), array([4, 5]))
Notes
-----
If you have two inputs and one label and want to shuffle them together, e.g. X1 (1000, 100), X2 (1000, 80) and Y (1000, 1), you can stack them together (`np.hstack((X1, X2))`)
into (1000, 180) and feed to ``inputs``. After getting a batch, you can split it back into X1 and X2.
"""
if len(inputs) != len(targets):
raise AssertionError("The length of inputs and targets should be equal")
if shuffle:
indices = np.arange(len(inputs))
np.random.shuffle(indices)
# for start_idx in range(0, len(inputs) - batch_size + 1, batch_size):
# chulei: handling the case where the number of samples is not a multiple of batch_size, avoiding wasting samples
for start_idx in range(0, len(inputs), batch_size):
end_idx = start_idx + batch_size
if end_idx > len(inputs):
if allow_dynamic_batch_size:
end_idx = len(inputs)
else:
break
if shuffle:
excerpt = indices[start_idx:end_idx]
else:
excerpt = slice(start_idx, end_idx)
if (isinstance(inputs, list) or isinstance(targets, list)) and (shuffle ==True):
# zsdonghao: for list indexing when shuffle==True
yield [inputs[i] for i in excerpt], [targets[i] for i in excerpt]
else:
yield inputs[excerpt], targets[excerpt] |
def seq_minibatches(inputs, targets, batch_size, seq_length, stride=1):
"""Generate a generator that return a batch of sequence inputs and targets.
If `batch_size=100` and `seq_length=5`, one return will have 500 rows (examples).
Parameters
----------
inputs : numpy.array
The input features, every row is a example.
targets : numpy.array
The labels of inputs, every element is a example.
batch_size : int
The batch size.
seq_length : int
The sequence length.
stride : int
The stride step, default is 1.
Examples
--------
Synced sequence input and output.
>>> X = np.asarray([['a','a'], ['b','b'], ['c','c'], ['d','d'], ['e','e'], ['f','f']])
>>> y = np.asarray([0, 1, 2, 3, 4, 5])
>>> for batch in tl.iterate.seq_minibatches(inputs=X, targets=y, batch_size=2, seq_length=2, stride=1):
>>> print(batch)
(array([['a', 'a'], ['b', 'b'], ['b', 'b'], ['c', 'c']], dtype='<U1'), array([0, 1, 1, 2]))
(array([['c', 'c'], ['d', 'd'], ['d', 'd'], ['e', 'e']], dtype='<U1'), array([2, 3, 3, 4]))
Many to One
>>> return_last = True
>>> num_steps = 2
>>> X = np.asarray([['a','a'], ['b','b'], ['c','c'], ['d','d'], ['e','e'], ['f','f']])
>>> Y = np.asarray([0,1,2,3,4,5])
>>> for batch in tl.iterate.seq_minibatches(inputs=X, targets=Y, batch_size=2, seq_length=num_steps, stride=1):
>>> x, y = batch
>>> if return_last:
>>> tmp_y = y.reshape((-1, num_steps) + y.shape[1:])
>>> y = tmp_y[:, -1]
>>> print(x, y)
[['a' 'a']
['b' 'b']
['b' 'b']
['c' 'c']] [1 2]
[['c' 'c']
['d' 'd']
['d' 'd']
['e' 'e']] [3 4]
"""
if len(inputs) != len(targets):
raise AssertionError("The length of inputs and targets should be equal")
n_loads = (batch_size * stride) + (seq_length - stride)
for start_idx in range(0, len(inputs) - n_loads + 1, (batch_size * stride)):
seq_inputs = np.zeros((batch_size, seq_length) + inputs.shape[1:], dtype=inputs.dtype)
seq_targets = np.zeros((batch_size, seq_length) + targets.shape[1:], dtype=targets.dtype)
for b_idx in xrange(batch_size):
start_seq_idx = start_idx + (b_idx * stride)
end_seq_idx = start_seq_idx + seq_length
seq_inputs[b_idx] = inputs[start_seq_idx:end_seq_idx]
seq_targets[b_idx] = targets[start_seq_idx:end_seq_idx]
flatten_inputs = seq_inputs.reshape((-1, ) + inputs.shape[1:])
flatten_targets = seq_targets.reshape((-1, ) + targets.shape[1:])
yield flatten_inputs, flatten_targets |
def seq_minibatches2(inputs, targets, batch_size, num_steps):
"""Generate a generator that iterates on two list of words. Yields (Returns) the source contexts and
the target context by the given batch_size and num_steps (sequence_length).
In TensorFlow's tutorial, this generates the `batch_size` pointers into the raw PTB data, and allows minibatch iteration along these pointers.
Parameters
----------
inputs : list of data
The context in list format; note that context usually be represented by splitting by space, and then convert to unique word IDs.
targets : list of data
The context in list format; note that context usually be represented by splitting by space, and then convert to unique word IDs.
batch_size : int
The batch size.
num_steps : int
The number of unrolls. i.e. sequence length
Yields
------
Pairs of the batched data, each a matrix of shape [batch_size, num_steps].
Raises
------
ValueError : if batch_size or num_steps are too high.
Examples
--------
>>> X = [i for i in range(20)]
>>> Y = [i for i in range(20,40)]
>>> for batch in tl.iterate.seq_minibatches2(X, Y, batch_size=2, num_steps=3):
... x, y = batch
... print(x, y)
[[ 0. 1. 2.]
[ 10. 11. 12.]]
[[ 20. 21. 22.]
[ 30. 31. 32.]]
[[ 3. 4. 5.]
[ 13. 14. 15.]]
[[ 23. 24. 25.]
[ 33. 34. 35.]]
[[ 6. 7. 8.]
[ 16. 17. 18.]]
[[ 26. 27. 28.]
[ 36. 37. 38.]]
Notes
-----
- Hint, if the input data are images, you can modify the source code `data = np.zeros([batch_size, batch_len)` to `data = np.zeros([batch_size, batch_len, inputs.shape[1], inputs.shape[2], inputs.shape[3]])`.
"""
if len(inputs) != len(targets):
raise AssertionError("The length of inputs and targets should be equal")
data_len = len(inputs)
batch_len = data_len // batch_size
# data = np.zeros([batch_size, batch_len])
data = np.zeros((batch_size, batch_len) + inputs.shape[1:], dtype=inputs.dtype)
data2 = np.zeros([batch_size, batch_len])
for i in range(batch_size):
data[i] = inputs[batch_len * i:batch_len * (i + 1)]
data2[i] = targets[batch_len * i:batch_len * (i + 1)]
epoch_size = (batch_len - 1) // num_steps
if epoch_size == 0:
raise ValueError("epoch_size == 0, decrease batch_size or num_steps")
for i in range(epoch_size):
x = data[:, i * num_steps:(i + 1) * num_steps]
x2 = data2[:, i * num_steps:(i + 1) * num_steps]
yield (x, x2) |
def ptb_iterator(raw_data, batch_size, num_steps):
"""Generate a generator that iterates on a list of words, see `PTB example <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_ptb_lstm_state_is_tuple.py>`__.
Yields the source contexts and the target context by the given batch_size and num_steps (sequence_length).
In TensorFlow's tutorial, this generates `batch_size` pointers into the raw
PTB data, and allows minibatch iteration along these pointers.
Parameters
----------
raw_data : a list
the context in list format; note that context usually be
represented by splitting by space, and then convert to unique
word IDs.
batch_size : int
the batch size.
num_steps : int
the number of unrolls. i.e. sequence_length
Yields
------
Pairs of the batched data, each a matrix of shape [batch_size, num_steps].
The second element of the tuple is the same data time-shifted to the
right by one.
Raises
------
ValueError : if batch_size or num_steps are too high.
Examples
--------
>>> train_data = [i for i in range(20)]
>>> for batch in tl.iterate.ptb_iterator(train_data, batch_size=2, num_steps=3):
>>> x, y = batch
>>> print(x, y)
[[ 0 1 2] <---x 1st subset/ iteration
[10 11 12]]
[[ 1 2 3] <---y
[11 12 13]]
[[ 3 4 5] <--- 1st batch input 2nd subset/ iteration
[13 14 15]] <--- 2nd batch input
[[ 4 5 6] <--- 1st batch target
[14 15 16]] <--- 2nd batch target
[[ 6 7 8] 3rd subset/ iteration
[16 17 18]]
[[ 7 8 9]
[17 18 19]]
"""
raw_data = np.array(raw_data, dtype=np.int32)
data_len = len(raw_data)
batch_len = data_len // batch_size
data = np.zeros([batch_size, batch_len], dtype=np.int32)
for i in range(batch_size):
data[i] = raw_data[batch_len * i:batch_len * (i + 1)]
epoch_size = (batch_len - 1) // num_steps
if epoch_size == 0:
raise ValueError("epoch_size == 0, decrease batch_size or num_steps")
for i in range(epoch_size):
x = data[:, i * num_steps:(i + 1) * num_steps]
y = data[:, i * num_steps + 1:(i + 1) * num_steps + 1]
yield (x, y) |
def deconv2d_bilinear_upsampling_initializer(shape):
"""Returns the initializer that can be passed to DeConv2dLayer for initializing the
weights in correspondence to channel-wise bilinear up-sampling.
Used in segmentation approaches such as [FCN](https://arxiv.org/abs/1605.06211)
Parameters
----------
shape : tuple of int
The shape of the filters, [height, width, output_channels, in_channels].
It must match the shape passed to DeConv2dLayer.
Returns
-------
``tf.constant_initializer``
A constant initializer with weights set to correspond to per channel bilinear upsampling
when passed as W_int in DeConv2dLayer
Examples
--------
- Upsampling by a factor of 2, ie e.g 100->200
>>> import tensorflow as tf
>>> import tensorlayer as tl
>>> rescale_factor = 2
>>> imsize = 128
>>> num_channels = 3
>>> filter_shape = (5, 5)
>>> filter_size = (2 * rescale_factor - rescale_factor % 2) #Corresponding bilinear filter size
>>> num_in_channels = 3
>>> num_out_channels = 3
>>> deconv_filter_shape = (filter_size, filter_size, num_out_channels, num_in_channels)
>>> x = tf.placeholder(tf.float32, (1, imsize, imsize, num_channels))
>>> net = tl.layers.InputLayer(x, name='input_layer')
>>> bilinear_init = deconv2d_bilinear_upsampling_initializer(shape=filter_shape)
>>> net = tl.layers.DeConv2dLayer(net,
... shape=filter_shape,
... output_shape=(1, imsize*rescale_factor, imsize*rescale_factor, num_out_channels),
... strides=(1, rescale_factor, rescale_factor, 1),
... W_init=bilinear_init,
... padding='SAME',
... act=None, name='g/h1/decon2d')
"""
if shape[0] != shape[1]:
raise Exception('deconv2d_bilinear_upsampling_initializer only supports symmetrical filter sizes')
if shape[3] < shape[2]:
raise Exception(
'deconv2d_bilinear_upsampling_initializer behaviour is not defined for num_in_channels < num_out_channels '
)
filter_size = shape[0]
num_out_channels = shape[2]
num_in_channels = shape[3]
# Create bilinear filter kernel as numpy array
bilinear_kernel = np.zeros([filter_size, filter_size], dtype=np.float32)
scale_factor = (filter_size + 1) // 2
if filter_size % 2 == 1:
center = scale_factor - 1
else:
center = scale_factor - 0.5
for x in range(filter_size):
for y in range(filter_size):
bilinear_kernel[x, y] = (1 - abs(x - center) / scale_factor) * (1 - abs(y - center) / scale_factor)
weights = np.zeros((filter_size, filter_size, num_out_channels, num_in_channels))
for i in range(num_out_channels):
weights[:, :, i, i] = bilinear_kernel
# assign numpy array to constant_initalizer and pass to get_variable
return tf.constant_initializer(value=weights, dtype=LayersConfig.tf_dtype) |
def save_model(self, network=None, model_name='model', **kwargs):
"""Save model architecture and parameters into database, timestamp will be added automatically.
Parameters
----------
network : TensorLayer layer
TensorLayer layer instance.
model_name : str
The name/key of model.
kwargs : other events
Other events, such as name, accuracy, loss, step number and etc (optinal).
Examples
---------
Save model architecture and parameters into database.
>>> db.save_model(net, accuracy=0.8, loss=2.3, name='second_model')
Load one model with parameters from database (run this in other script)
>>> net = db.find_top_model(sess=sess, accuracy=0.8, loss=2.3)
Find and load the latest model.
>>> net = db.find_top_model(sess=sess, sort=[("time", pymongo.DESCENDING)])
>>> net = db.find_top_model(sess=sess, sort=[("time", -1)])
Find and load the oldest model.
>>> net = db.find_top_model(sess=sess, sort=[("time", pymongo.ASCENDING)])
>>> net = db.find_top_model(sess=sess, sort=[("time", 1)])
Get model information
>>> net._accuracy
... 0.8
Returns
---------
boolean : True for success, False for fail.
"""
kwargs.update({'model_name': model_name})
self._fill_project_info(kwargs) # put project_name into kwargs
params = network.get_all_params()
s = time.time()
kwargs.update({'architecture': network.all_graphs, 'time': datetime.utcnow()})
try:
params_id = self.model_fs.put(self._serialization(params))
kwargs.update({'params_id': params_id, 'time': datetime.utcnow()})
self.db.Model.insert_one(kwargs)
print("[Database] Save model: SUCCESS, took: {}s".format(round(time.time() - s, 2)))
return True
except Exception as e:
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
logging.info("{} {} {} {} {}".format(exc_type, exc_obj, fname, exc_tb.tb_lineno, e))
print("[Database] Save model: FAIL")
return False |
def find_top_model(self, sess, sort=None, model_name='model', **kwargs):
"""Finds and returns a model architecture and its parameters from the database which matches the requirement.
Parameters
----------
sess : Session
TensorFlow session.
sort : List of tuple
PyMongo sort comment, search "PyMongo find one sorting" and `collection level operations <http://api.mongodb.com/python/current/api/pymongo/collection.html>`__ for more details.
model_name : str or None
The name/key of model.
kwargs : other events
Other events, such as name, accuracy, loss, step number and etc (optinal).
Examples
---------
- see ``save_model``.
Returns
---------
network : TensorLayer layer
Note that, the returned network contains all information of the document (record), e.g. if you saved accuracy in the document, you can get the accuracy by using ``net._accuracy``.
"""
# print(kwargs) # {}
kwargs.update({'model_name': model_name})
self._fill_project_info(kwargs)
s = time.time()
d = self.db.Model.find_one(filter=kwargs, sort=sort)
_temp_file_name = '_find_one_model_ztemp_file'
if d is not None:
params_id = d['params_id']
graphs = d['architecture']
_datetime = d['time']
exists_or_mkdir(_temp_file_name, False)
with open(os.path.join(_temp_file_name, 'graph.pkl'), 'wb') as file:
pickle.dump(graphs, file, protocol=pickle.HIGHEST_PROTOCOL)
else:
print("[Database] FAIL! Cannot find model: {}".format(kwargs))
return False
try:
params = self._deserialization(self.model_fs.get(params_id).read())
np.savez(os.path.join(_temp_file_name, 'params.npz'), params=params)
network = load_graph_and_params(name=_temp_file_name, sess=sess)
del_folder(_temp_file_name)
pc = self.db.Model.find(kwargs)
print(
"[Database] Find one model SUCCESS. kwargs:{} sort:{} save time:{} took: {}s".
format(kwargs, sort, _datetime, round(time.time() - s, 2))
)
# put all informations of model into the TL layer
for key in d:
network.__dict__.update({"_%s" % key: d[key]})
# check whether more parameters match the requirement
params_id_list = pc.distinct('params_id')
n_params = len(params_id_list)
if n_params != 1:
print(" Note that there are {} models match the kwargs".format(n_params))
return network
except Exception as e:
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
logging.info("{} {} {} {} {}".format(exc_type, exc_obj, fname, exc_tb.tb_lineno, e))
return False |
def delete_model(self, **kwargs):
"""Delete model.
Parameters
-----------
kwargs : logging information
Find items to delete, leave it empty to delete all log.
"""
self._fill_project_info(kwargs)
self.db.Model.delete_many(kwargs)
logging.info("[Database] Delete Model SUCCESS") |
def save_dataset(self, dataset=None, dataset_name=None, **kwargs):
"""Saves one dataset into database, timestamp will be added automatically.
Parameters
----------
dataset : any type
The dataset you want to store.
dataset_name : str
The name of dataset.
kwargs : other events
Other events, such as description, author and etc (optinal).
Examples
----------
Save dataset
>>> db.save_dataset([X_train, y_train, X_test, y_test], 'mnist', description='this is a tutorial')
Get dataset
>>> dataset = db.find_top_dataset('mnist')
Returns
---------
boolean : Return True if save success, otherwise, return False.
"""
self._fill_project_info(kwargs)
if dataset_name is None:
raise Exception("dataset_name is None, please give a dataset name")
kwargs.update({'dataset_name': dataset_name})
s = time.time()
try:
dataset_id = self.dataset_fs.put(self._serialization(dataset))
kwargs.update({'dataset_id': dataset_id, 'time': datetime.utcnow()})
self.db.Dataset.insert_one(kwargs)
# print("[Database] Save params: {} SUCCESS, took: {}s".format(file_name, round(time.time()-s, 2)))
print("[Database] Save dataset: SUCCESS, took: {}s".format(round(time.time() - s, 2)))
return True
except Exception as e:
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
logging.info("{} {} {} {} {}".format(exc_type, exc_obj, fname, exc_tb.tb_lineno, e))
print("[Database] Save dataset: FAIL")
return False |
def find_top_dataset(self, dataset_name=None, sort=None, **kwargs):
"""Finds and returns a dataset from the database which matches the requirement.
Parameters
----------
dataset_name : str
The name of dataset.
sort : List of tuple
PyMongo sort comment, search "PyMongo find one sorting" and `collection level operations <http://api.mongodb.com/python/current/api/pymongo/collection.html>`__ for more details.
kwargs : other events
Other events, such as description, author and etc (optinal).
Examples
---------
Save dataset
>>> db.save_dataset([X_train, y_train, X_test, y_test], 'mnist', description='this is a tutorial')
Get dataset
>>> dataset = db.find_top_dataset('mnist')
>>> datasets = db.find_datasets('mnist')
Returns
--------
dataset : the dataset or False
Return False if nothing found.
"""
self._fill_project_info(kwargs)
if dataset_name is None:
raise Exception("dataset_name is None, please give a dataset name")
kwargs.update({'dataset_name': dataset_name})
s = time.time()
d = self.db.Dataset.find_one(filter=kwargs, sort=sort)
if d is not None:
dataset_id = d['dataset_id']
else:
print("[Database] FAIL! Cannot find dataset: {}".format(kwargs))
return False
try:
dataset = self._deserialization(self.dataset_fs.get(dataset_id).read())
pc = self.db.Dataset.find(kwargs)
print("[Database] Find one dataset SUCCESS, {} took: {}s".format(kwargs, round(time.time() - s, 2)))
# check whether more datasets match the requirement
dataset_id_list = pc.distinct('dataset_id')
n_dataset = len(dataset_id_list)
if n_dataset != 1:
print(" Note that there are {} datasets match the requirement".format(n_dataset))
return dataset
except Exception as e:
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
logging.info("{} {} {} {} {}".format(exc_type, exc_obj, fname, exc_tb.tb_lineno, e))
return False |
def find_datasets(self, dataset_name=None, **kwargs):
"""Finds and returns all datasets from the database which matches the requirement.
In some case, the data in a dataset can be stored separately for better management.
Parameters
----------
dataset_name : str
The name/key of dataset.
kwargs : other events
Other events, such as description, author and etc (optional).
Returns
--------
params : the parameters, return False if nothing found.
"""
self._fill_project_info(kwargs)
if dataset_name is None:
raise Exception("dataset_name is None, please give a dataset name")
kwargs.update({'dataset_name': dataset_name})
s = time.time()
pc = self.db.Dataset.find(kwargs)
if pc is not None:
dataset_id_list = pc.distinct('dataset_id')
dataset_list = []
for dataset_id in dataset_id_list: # you may have multiple Buckets files
tmp = self.dataset_fs.get(dataset_id).read()
dataset_list.append(self._deserialization(tmp))
else:
print("[Database] FAIL! Cannot find any dataset: {}".format(kwargs))
return False
print("[Database] Find {} datasets SUCCESS, took: {}s".format(len(dataset_list), round(time.time() - s, 2)))
return dataset_list |
def delete_datasets(self, **kwargs):
"""Delete datasets.
Parameters
-----------
kwargs : logging information
Find items to delete, leave it empty to delete all log.
"""
self._fill_project_info(kwargs)
self.db.Dataset.delete_many(kwargs)
logging.info("[Database] Delete Dataset SUCCESS") |
def save_training_log(self, **kwargs):
"""Saves the training log, timestamp will be added automatically.
Parameters
-----------
kwargs : logging information
Events, such as accuracy, loss, step number and etc.
Examples
---------
>>> db.save_training_log(accuracy=0.33, loss=0.98)
"""
self._fill_project_info(kwargs)
kwargs.update({'time': datetime.utcnow()})
_result = self.db.TrainLog.insert_one(kwargs)
_log = self._print_dict(kwargs)
logging.info("[Database] train log: " + _log) |
def save_validation_log(self, **kwargs):
"""Saves the validation log, timestamp will be added automatically.
Parameters
-----------
kwargs : logging information
Events, such as accuracy, loss, step number and etc.
Examples
---------
>>> db.save_validation_log(accuracy=0.33, loss=0.98)
"""
self._fill_project_info(kwargs)
kwargs.update({'time': datetime.utcnow()})
_result = self.db.ValidLog.insert_one(kwargs)
_log = self._print_dict(kwargs)
logging.info("[Database] valid log: " + _log) |
def delete_training_log(self, **kwargs):
"""Deletes training log.
Parameters
-----------
kwargs : logging information
Find items to delete, leave it empty to delete all log.
Examples
---------
Save training log
>>> db.save_training_log(accuracy=0.33)
>>> db.save_training_log(accuracy=0.44)
Delete logs that match the requirement
>>> db.delete_training_log(accuracy=0.33)
Delete all logs
>>> db.delete_training_log()
"""
self._fill_project_info(kwargs)
self.db.TrainLog.delete_many(kwargs)
logging.info("[Database] Delete TrainLog SUCCESS") |
def delete_validation_log(self, **kwargs):
"""Deletes validation log.
Parameters
-----------
kwargs : logging information
Find items to delete, leave it empty to delete all log.
Examples
---------
- see ``save_training_log``.
"""
self._fill_project_info(kwargs)
self.db.ValidLog.delete_many(kwargs)
logging.info("[Database] Delete ValidLog SUCCESS") |
def create_task(self, task_name=None, script=None, hyper_parameters=None, saved_result_keys=None, **kwargs):
"""Uploads a task to the database, timestamp will be added automatically.
Parameters
-----------
task_name : str
The task name.
script : str
File name of the python script.
hyper_parameters : dictionary
The hyper parameters pass into the script.
saved_result_keys : list of str
The keys of the task results to keep in the database when the task finishes.
kwargs : other parameters
Users customized parameters such as description, version number.
Examples
-----------
Uploads a task
>>> db.create_task(task_name='mnist', script='example/tutorial_mnist_simple.py', description='simple tutorial')
Finds and runs the latest task
>>> db.run_top_task(sess=sess, sort=[("time", pymongo.DESCENDING)])
>>> db.run_top_task(sess=sess, sort=[("time", -1)])
Finds and runs the oldest task
>>> db.run_top_task(sess=sess, sort=[("time", pymongo.ASCENDING)])
>>> db.run_top_task(sess=sess, sort=[("time", 1)])
"""
if not isinstance(task_name, str): # is None:
raise Exception("task_name should be string")
if not isinstance(script, str): # is None:
raise Exception("script should be string")
if hyper_parameters is None:
hyper_parameters = {}
if saved_result_keys is None:
saved_result_keys = []
self._fill_project_info(kwargs)
kwargs.update({'time': datetime.utcnow()})
kwargs.update({'hyper_parameters': hyper_parameters})
kwargs.update({'saved_result_keys': saved_result_keys})
_script = open(script, 'rb').read()
kwargs.update({'status': 'pending', 'script': _script, 'result': {}})
self.db.Task.insert_one(kwargs)
logging.info("[Database] Saved Task - task_name: {} script: {}".format(task_name, script)) |
def run_top_task(self, task_name=None, sort=None, **kwargs):
"""Finds and runs a pending task that in the first of the sorting list.
Parameters
-----------
task_name : str
The task name.
sort : List of tuple
PyMongo sort comment, search "PyMongo find one sorting" and `collection level operations <http://api.mongodb.com/python/current/api/pymongo/collection.html>`__ for more details.
kwargs : other parameters
Users customized parameters such as description, version number.
Examples
---------
Monitors the database and pull tasks to run
>>> while True:
>>> print("waiting task from distributor")
>>> db.run_top_task(task_name='mnist', sort=[("time", -1)])
>>> time.sleep(1)
Returns
--------
boolean : True for success, False for fail.
"""
if not isinstance(task_name, str): # is None:
raise Exception("task_name should be string")
self._fill_project_info(kwargs)
kwargs.update({'status': 'pending'})
# find task and set status to running
task = self.db.Task.find_one_and_update(kwargs, {'$set': {'status': 'running'}}, sort=sort)
try:
# get task info e.g. hyper parameters, python script
if task is None:
logging.info("[Database] Find Task FAIL: key: {} sort: {}".format(task_name, sort))
return False
else:
logging.info("[Database] Find Task SUCCESS: key: {} sort: {}".format(task_name, sort))
_datetime = task['time']
_script = task['script']
_id = task['_id']
_hyper_parameters = task['hyper_parameters']
_saved_result_keys = task['saved_result_keys']
logging.info(" hyper parameters:")
for key in _hyper_parameters:
globals()[key] = _hyper_parameters[key]
logging.info(" {}: {}".format(key, _hyper_parameters[key]))
# run task
s = time.time()
logging.info("[Database] Start Task: key: {} sort: {} push time: {}".format(task_name, sort, _datetime))
_script = _script.decode('utf-8')
with tf.Graph().as_default(): # as graph: # clear all TF graphs
exec(_script, globals())
# set status to finished
_ = self.db.Task.find_one_and_update({'_id': _id}, {'$set': {'status': 'finished'}})
# return results
__result = {}
for _key in _saved_result_keys:
logging.info(" result: {}={} {}".format(_key, globals()[_key], type(globals()[_key])))
__result.update({"%s" % _key: globals()[_key]})
_ = self.db.Task.find_one_and_update(
{
'_id': _id
}, {'$set': {
'result': __result
}}, return_document=pymongo.ReturnDocument.AFTER
)
logging.info(
"[Database] Finished Task: task_name - {} sort: {} push time: {} took: {}s".
format(task_name, sort, _datetime,
time.time() - s)
)
return True
except Exception as e:
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
logging.info("{} {} {} {} {}".format(exc_type, exc_obj, fname, exc_tb.tb_lineno, e))
logging.info("[Database] Fail to run task")
# if fail, set status back to pending
_ = self.db.Task.find_one_and_update({'_id': _id}, {'$set': {'status': 'pending'}})
return False |
def delete_tasks(self, **kwargs):
"""Delete tasks.
Parameters
-----------
kwargs : logging information
Find items to delete, leave it empty to delete all log.
Examples
---------
>>> db.delete_tasks()
"""
self._fill_project_info(kwargs)
self.db.Task.delete_many(kwargs)
logging.info("[Database] Delete Task SUCCESS") |
def check_unfinished_task(self, task_name=None, **kwargs):
"""Finds and runs a pending task.
Parameters
-----------
task_name : str
The task name.
kwargs : other parameters
Users customized parameters such as description, version number.
Examples
---------
Wait until all tasks finish in user's local console
>>> while not db.check_unfinished_task():
>>> time.sleep(1)
>>> print("all tasks finished")
>>> sess = tf.InteractiveSession()
>>> net = db.find_top_model(sess=sess, sort=[("test_accuracy", -1)])
>>> print("the best accuracy {} is from model {}".format(net._test_accuracy, net._name))
Returns
--------
boolean : True for success, False for fail.
"""
if not isinstance(task_name, str): # is None:
raise Exception("task_name should be string")
self._fill_project_info(kwargs)
kwargs.update({'$or': [{'status': 'pending'}, {'status': 'running'}]})
# ## find task
# task = self.db.Task.find_one(kwargs)
task = self.db.Task.find(kwargs)
task_id_list = task.distinct('_id')
n_task = len(task_id_list)
if n_task == 0:
logging.info("[Database] No unfinished task - task_name: {}".format(task_name))
return False
else:
logging.info("[Database] Find {} unfinished task - task_name: {}".format(n_task, task_name))
return True |
def augment_with_ngrams(unigrams, unigram_vocab_size, n_buckets, n=2):
"""Augment unigram features with hashed n-gram features."""
def get_ngrams(n):
return list(zip(*[unigrams[i:] for i in range(n)]))
def hash_ngram(ngram):
bytes_ = array.array('L', ngram).tobytes()
hash_ = int(hashlib.sha256(bytes_).hexdigest(), 16)
return unigram_vocab_size + hash_ % n_buckets
return unigrams + [hash_ngram(ngram) for i in range(2, n + 1) for ngram in get_ngrams(i)] |
def load_and_preprocess_imdb_data(n_gram=None):
"""Load IMDb data and augment with hashed n-gram features."""
X_train, y_train, X_test, y_test = tl.files.load_imdb_dataset(nb_words=VOCAB_SIZE)
if n_gram is not None:
X_train = np.array([augment_with_ngrams(x, VOCAB_SIZE, N_BUCKETS, n=n_gram) for x in X_train])
X_test = np.array([augment_with_ngrams(x, VOCAB_SIZE, N_BUCKETS, n=n_gram) for x in X_test])
return X_train, y_train, X_test, y_test |
def read_image(image, path=''):
"""Read one image.
Parameters
-----------
image : str
The image file name.
path : str
The image folder path.
Returns
-------
numpy.array
The image.
"""
return imageio.imread(os.path.join(path, image)) |
def read_images(img_list, path='', n_threads=10, printable=True):
"""Returns all images in list by given path and name of each image file.
Parameters
-------------
img_list : list of str
The image file names.
path : str
The image folder path.
n_threads : int
The number of threads to read image.
printable : boolean
Whether to print information when reading images.
Returns
-------
list of numpy.array
The images.
"""
imgs = []
for idx in range(0, len(img_list), n_threads):
b_imgs_list = img_list[idx:idx + n_threads]
b_imgs = tl.prepro.threading_data(b_imgs_list, fn=read_image, path=path)
# tl.logging.info(b_imgs.shape)
imgs.extend(b_imgs)
if printable:
tl.logging.info('read %d from %s' % (len(imgs), path))
return imgs |
def save_image(image, image_path='_temp.png'):
"""Save a image.
Parameters
-----------
image : numpy array
[w, h, c]
image_path : str
path
"""
try: # RGB
imageio.imwrite(image_path, image)
except Exception: # Greyscale
imageio.imwrite(image_path, image[:, :, 0]) |
def save_images(images, size, image_path='_temp.png'):
"""Save multiple images into one single image.
Parameters
-----------
images : numpy array
(batch, w, h, c)
size : list of 2 ints
row and column number.
number of images should be equal or less than size[0] * size[1]
image_path : str
save path
Examples
---------
>>> import numpy as np
>>> import tensorlayer as tl
>>> images = np.random.rand(64, 100, 100, 3)
>>> tl.visualize.save_images(images, [8, 8], 'temp.png')
"""
if len(images.shape) == 3: # Greyscale [batch, h, w] --> [batch, h, w, 1]
images = images[:, :, :, np.newaxis]
def merge(images, size):
h, w = images.shape[1], images.shape[2]
img = np.zeros((h * size[0], w * size[1], 3), dtype=images.dtype)
for idx, image in enumerate(images):
i = idx % size[1]
j = idx // size[1]
img[j * h:j * h + h, i * w:i * w + w, :] = image
return img
def imsave(images, size, path):
if np.max(images) <= 1 and (-1 <= np.min(images) < 0):
images = ((images + 1) * 127.5).astype(np.uint8)
elif np.max(images) <= 1 and np.min(images) >= 0:
images = (images * 255).astype(np.uint8)
return imageio.imwrite(path, merge(images, size))
if len(images) > size[0] * size[1]:
raise AssertionError("number of images should be equal or less than size[0] * size[1] {}".format(len(images)))
return imsave(images, size, image_path) |
def draw_boxes_and_labels_to_image(
image, classes, coords, scores, classes_list, is_center=True, is_rescale=True, save_name=None
):
"""Draw bboxes and class labels on image. Return or save the image with bboxes, example in the docs of ``tl.prepro``.
Parameters
-----------
image : numpy.array
The RGB image [height, width, channel].
classes : list of int
A list of class ID (int).
coords : list of int
A list of list for coordinates.
- Should be [x, y, x2, y2] (up-left and botton-right format)
- If [x_center, y_center, w, h] (set is_center to True).
scores : list of float
A list of score (float). (Optional)
classes_list : list of str
for converting ID to string on image.
is_center : boolean
Whether the coordinates is [x_center, y_center, w, h]
- If coordinates are [x_center, y_center, w, h], set it to True for converting it to [x, y, x2, y2] (up-left and botton-right) internally.
- If coordinates are [x1, x2, y1, y2], set it to False.
is_rescale : boolean
Whether to rescale the coordinates from pixel-unit format to ratio format.
- If True, the input coordinates are the portion of width and high, this API will scale the coordinates to pixel unit internally.
- If False, feed the coordinates with pixel unit format.
save_name : None or str
The name of image file (i.e. image.png), if None, not to save image.
Returns
-------
numpy.array
The saved image.
References
-----------
- OpenCV rectangle and putText.
- `scikit-image <http://scikit-image.org/docs/dev/api/skimage.draw.html#skimage.draw.rectangle>`__.
"""
if len(coords) != len(classes):
raise AssertionError("number of coordinates and classes are equal")
if len(scores) > 0 and len(scores) != len(classes):
raise AssertionError("number of scores and classes are equal")
# don't change the original image, and avoid error https://stackoverflow.com/questions/30249053/python-opencv-drawing-errors-after-manipulating-array-with-numpy
image = image.copy()
imh, imw = image.shape[0:2]
thick = int((imh + imw) // 430)
for i, _v in enumerate(coords):
if is_center:
x, y, x2, y2 = tl.prepro.obj_box_coord_centroid_to_upleft_butright(coords[i])
else:
x, y, x2, y2 = coords[i]
if is_rescale: # scale back to pixel unit if the coords are the portion of width and high
x, y, x2, y2 = tl.prepro.obj_box_coord_scale_to_pixelunit([x, y, x2, y2], (imh, imw))
cv2.rectangle(
image,
(int(x), int(y)),
(int(x2), int(y2)), # up-left and botton-right
[0, 255, 0],
thick
)
cv2.putText(
image,
classes_list[classes[i]] + ((" %.2f" % (scores[i])) if (len(scores) != 0) else " "),
(int(x), int(y)), # button left
0,
1.5e-3 * imh, # bigger = larger font
[0, 0, 256], # self.meta['colors'][max_indx],
int(thick / 2) + 1
) # bold
if save_name is not None:
# cv2.imwrite('_my.png', image)
save_image(image, save_name)
# if len(coords) == 0:
# tl.logging.info("draw_boxes_and_labels_to_image: no bboxes exist, cannot draw !")
return image |
def draw_mpii_pose_to_image(image, poses, save_name='image.png'):
"""Draw people(s) into image using MPII dataset format as input, return or save the result image.
This is an experimental API, can be changed in the future.
Parameters
-----------
image : numpy.array
The RGB image [height, width, channel].
poses : list of dict
The people(s) annotation in MPII format, see ``tl.files.load_mpii_pose_dataset``.
save_name : None or str
The name of image file (i.e. image.png), if None, not to save image.
Returns
--------
numpy.array
The saved image.
Examples
--------
>>> import pprint
>>> import tensorlayer as tl
>>> img_train_list, ann_train_list, img_test_list, ann_test_list = tl.files.load_mpii_pose_dataset()
>>> image = tl.vis.read_image(img_train_list[0])
>>> tl.vis.draw_mpii_pose_to_image(image, ann_train_list[0], 'image.png')
>>> pprint.pprint(ann_train_list[0])
References
-----------
- `MPII Keyponts and ID <http://human-pose.mpi-inf.mpg.de/#download>`__
"""
# import skimage
# don't change the original image, and avoid error https://stackoverflow.com/questions/30249053/python-opencv-drawing-errors-after-manipulating-array-with-numpy
image = image.copy()
imh, imw = image.shape[0:2]
thick = int((imh + imw) // 430)
# radius = int(image.shape[1] / 500) + 1
radius = int(thick * 1.5)
if image.max() < 1:
image = image * 255
for people in poses:
# Pose Keyponts
joint_pos = people['joint_pos']
# draw sketch
# joint id (0 - r ankle, 1 - r knee, 2 - r hip, 3 - l hip, 4 - l knee,
# 5 - l ankle, 6 - pelvis, 7 - thorax, 8 - upper neck,
# 9 - head top, 10 - r wrist, 11 - r elbow, 12 - r shoulder,
# 13 - l shoulder, 14 - l elbow, 15 - l wrist)
#
# 9
# 8
# 12 ** 7 ** 13
# * * *
# 11 * 14
# * * *
# 10 2 * 6 * 3 15
# * *
# 1 4
# * *
# 0 5
lines = [
[(0, 1), [100, 255, 100]],
[(1, 2), [50, 255, 50]],
[(2, 6), [0, 255, 0]], # right leg
[(3, 4), [100, 100, 255]],
[(4, 5), [50, 50, 255]],
[(6, 3), [0, 0, 255]], # left leg
[(6, 7), [255, 255, 100]],
[(7, 8), [255, 150, 50]], # body
[(8, 9), [255, 200, 100]], # head
[(10, 11), [255, 100, 255]],
[(11, 12), [255, 50, 255]],
[(12, 8), [255, 0, 255]], # right hand
[(8, 13), [0, 255, 255]],
[(13, 14), [100, 255, 255]],
[(14, 15), [200, 255, 255]] # left hand
]
for line in lines:
start, end = line[0]
if (start in joint_pos) and (end in joint_pos):
cv2.line(
image,
(int(joint_pos[start][0]), int(joint_pos[start][1])),
(int(joint_pos[end][0]), int(joint_pos[end][1])), # up-left and botton-right
line[1],
thick
)
# rr, cc, val = skimage.draw.line_aa(int(joint_pos[start][1]), int(joint_pos[start][0]), int(joint_pos[end][1]), int(joint_pos[end][0]))
# image[rr, cc] = line[1]
# draw circles
for pos in joint_pos.items():
_, pos_loc = pos # pos_id, pos_loc
pos_loc = (int(pos_loc[0]), int(pos_loc[1]))
cv2.circle(image, center=pos_loc, radius=radius, color=(200, 200, 200), thickness=-1)
# rr, cc = skimage.draw.circle(int(pos_loc[1]), int(pos_loc[0]), radius)
# image[rr, cc] = [0, 255, 0]
# Head
head_rect = people['head_rect']
if head_rect: # if head exists
cv2.rectangle(
image,
(int(head_rect[0]), int(head_rect[1])),
(int(head_rect[2]), int(head_rect[3])), # up-left and botton-right
[0, 180, 0],
thick
)
if save_name is not None:
# cv2.imwrite(save_name, image)
save_image(image, save_name)
return image |
def frame(I=None, second=5, saveable=True, name='frame', cmap=None, fig_idx=12836):
"""Display a frame. Make sure OpenAI Gym render() is disable before using it.
Parameters
----------
I : numpy.array
The image.
second : int
The display second(s) for the image(s), if saveable is False.
saveable : boolean
Save or plot the figure.
name : str
A name to save the image, if saveable is True.
cmap : None or str
'gray' for greyscale, None for default, etc.
fig_idx : int
matplotlib figure index.
Examples
--------
>>> env = gym.make("Pong-v0")
>>> observation = env.reset()
>>> tl.visualize.frame(observation)
"""
import matplotlib.pyplot as plt
if saveable is False:
plt.ion()
plt.figure(fig_idx) # show all feature images
if len(I.shape) and I.shape[-1] == 1: # (10,10,1) --> (10,10)
I = I[:, :, 0]
plt.imshow(I, cmap)
plt.title(name)
# plt.gca().xaxis.set_major_locator(plt.NullLocator()) # distable tick
# plt.gca().yaxis.set_major_locator(plt.NullLocator())
if saveable:
plt.savefig(name + '.pdf', format='pdf')
else:
plt.draw()
plt.pause(second) |
def CNN2d(CNN=None, second=10, saveable=True, name='cnn', fig_idx=3119362):
"""Display a group of RGB or Greyscale CNN masks.
Parameters
----------
CNN : numpy.array
The image. e.g: 64 5x5 RGB images can be (5, 5, 3, 64).
second : int
The display second(s) for the image(s), if saveable is False.
saveable : boolean
Save or plot the figure.
name : str
A name to save the image, if saveable is True.
fig_idx : int
The matplotlib figure index.
Examples
--------
>>> tl.visualize.CNN2d(network.all_params[0].eval(), second=10, saveable=True, name='cnn1_mnist', fig_idx=2012)
"""
import matplotlib.pyplot as plt
# tl.logging.info(CNN.shape) # (5, 5, 3, 64)
# exit()
n_mask = CNN.shape[3]
n_row = CNN.shape[0]
n_col = CNN.shape[1]
n_color = CNN.shape[2]
row = int(np.sqrt(n_mask))
col = int(np.ceil(n_mask / row))
plt.ion() # active mode
fig = plt.figure(fig_idx)
count = 1
for _ir in range(1, row + 1):
for _ic in range(1, col + 1):
if count > n_mask:
break
fig.add_subplot(col, row, count)
# tl.logging.info(CNN[:,:,:,count-1].shape, n_row, n_col) # (5, 1, 32) 5 5
# exit()
# plt.imshow(
# np.reshape(CNN[count-1,:,:,:], (n_row, n_col)),
# cmap='gray', interpolation="nearest") # theano
if n_color == 1:
plt.imshow(np.reshape(CNN[:, :, :, count - 1], (n_row, n_col)), cmap='gray', interpolation="nearest")
elif n_color == 3:
plt.imshow(
np.reshape(CNN[:, :, :, count - 1], (n_row, n_col, n_color)), cmap='gray', interpolation="nearest"
)
else:
raise Exception("Unknown n_color")
plt.gca().xaxis.set_major_locator(plt.NullLocator()) # distable tick
plt.gca().yaxis.set_major_locator(plt.NullLocator())
count = count + 1
if saveable:
plt.savefig(name + '.pdf', format='pdf')
else:
plt.draw()
plt.pause(second) |
def tsne_embedding(embeddings, reverse_dictionary, plot_only=500, second=5, saveable=False, name='tsne', fig_idx=9862):
"""Visualize the embeddings by using t-SNE.
Parameters
----------
embeddings : numpy.array
The embedding matrix.
reverse_dictionary : dictionary
id_to_word, mapping id to unique word.
plot_only : int
The number of examples to plot, choice the most common words.
second : int
The display second(s) for the image(s), if saveable is False.
saveable : boolean
Save or plot the figure.
name : str
A name to save the image, if saveable is True.
fig_idx : int
matplotlib figure index.
Examples
--------
>>> see 'tutorial_word2vec_basic.py'
>>> final_embeddings = normalized_embeddings.eval()
>>> tl.visualize.tsne_embedding(final_embeddings, labels, reverse_dictionary,
... plot_only=500, second=5, saveable=False, name='tsne')
"""
import matplotlib.pyplot as plt
def plot_with_labels(low_dim_embs, labels, figsize=(18, 18), second=5, saveable=True, name='tsne', fig_idx=9862):
if low_dim_embs.shape[0] < len(labels):
raise AssertionError("More labels than embeddings")
if saveable is False:
plt.ion()
plt.figure(fig_idx)
plt.figure(figsize=figsize) # in inches
for i, label in enumerate(labels):
x, y = low_dim_embs[i, :]
plt.scatter(x, y)
plt.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points', ha='right', va='bottom')
if saveable:
plt.savefig(name + '.pdf', format='pdf')
else:
plt.draw()
plt.pause(second)
try:
from sklearn.manifold import TSNE
from six.moves import xrange
tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
# plot_only = 500
low_dim_embs = tsne.fit_transform(embeddings[:plot_only, :])
labels = [reverse_dictionary[i] for i in xrange(plot_only)]
plot_with_labels(low_dim_embs, labels, second=second, saveable=saveable, name=name, fig_idx=fig_idx)
except ImportError:
_err = "Please install sklearn and matplotlib to visualize embeddings."
tl.logging.error(_err)
raise ImportError(_err) |
def draw_weights(W=None, second=10, saveable=True, shape=None, name='mnist', fig_idx=2396512):
"""Visualize every columns of the weight matrix to a group of Greyscale img.
Parameters
----------
W : numpy.array
The weight matrix
second : int
The display second(s) for the image(s), if saveable is False.
saveable : boolean
Save or plot the figure.
shape : a list with 2 int or None
The shape of feature image, MNIST is [28, 80].
name : a string
A name to save the image, if saveable is True.
fig_idx : int
matplotlib figure index.
Examples
--------
>>> tl.visualize.draw_weights(network.all_params[0].eval(), second=10, saveable=True, name='weight_of_1st_layer', fig_idx=2012)
"""
if shape is None:
shape = [28, 28]
import matplotlib.pyplot as plt
if saveable is False:
plt.ion()
fig = plt.figure(fig_idx) # show all feature images
n_units = W.shape[1]
num_r = int(np.sqrt(n_units)) # 每行显示的个数 若25个hidden unit -> 每行显示5个
num_c = int(np.ceil(n_units / num_r))
count = int(1)
for _row in range(1, num_r + 1):
for _col in range(1, num_c + 1):
if count > n_units:
break
fig.add_subplot(num_r, num_c, count)
# ------------------------------------------------------------
# plt.imshow(np.reshape(W[:,count-1],(28,28)), cmap='gray')
# ------------------------------------------------------------
feature = W[:, count - 1] / np.sqrt((W[:, count - 1]**2).sum())
# feature[feature<0.0001] = 0 # value threshold
# if count == 1 or count == 2:
# print(np.mean(feature))
# if np.std(feature) < 0.03: # condition threshold
# feature = np.zeros_like(feature)
# if np.mean(feature) < -0.015: # condition threshold
# feature = np.zeros_like(feature)
plt.imshow(
np.reshape(feature, (shape[0], shape[1])), cmap='gray', interpolation="nearest"
) # , vmin=np.min(feature), vmax=np.max(feature))
# plt.title(name)
# ------------------------------------------------------------
# plt.imshow(np.reshape(W[:,count-1] ,(np.sqrt(size),np.sqrt(size))), cmap='gray', interpolation="nearest")
plt.gca().xaxis.set_major_locator(plt.NullLocator()) # distable tick
plt.gca().yaxis.set_major_locator(plt.NullLocator())
count = count + 1
if saveable:
plt.savefig(name + '.pdf', format='pdf')
else:
plt.draw()
plt.pause(second) |
def data_to_tfrecord(images, labels, filename):
"""Save data into TFRecord."""
if os.path.isfile(filename):
print("%s exists" % filename)
return
print("Converting data into %s ..." % filename)
# cwd = os.getcwd()
writer = tf.python_io.TFRecordWriter(filename)
for index, img in enumerate(images):
img_raw = img.tobytes()
# Visualize a image
# tl.visualize.frame(np.asarray(img, dtype=np.uint8), second=1, saveable=False, name='frame', fig_idx=1236)
label = int(labels[index])
# print(label)
# Convert the bytes back to image as follow:
# image = Image.frombytes('RGB', (32, 32), img_raw)
# image = np.fromstring(img_raw, np.float32)
# image = image.reshape([32, 32, 3])
# tl.visualize.frame(np.asarray(image, dtype=np.uint8), second=1, saveable=False, name='frame', fig_idx=1236)
example = tf.train.Example(
features=tf.train.Features(
feature={
"label": tf.train.Feature(int64_list=tf.train.Int64List(value=[label])),
'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw])),
}
)
)
writer.write(example.SerializeToString()) # Serialize To String
writer.close() |
def read_and_decode(filename, is_train=None):
"""Return tensor to read from TFRecord."""
filename_queue = tf.train.string_input_producer([filename])
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example, features={
'label': tf.FixedLenFeature([], tf.int64),
'img_raw': tf.FixedLenFeature([], tf.string),
}
)
# You can do more image distortion here for training data
img = tf.decode_raw(features['img_raw'], tf.float32)
img = tf.reshape(img, [32, 32, 3])
# img = tf.cast(img, tf.float32) #* (1. / 255) - 0.5
if is_train ==True:
# 1. Randomly crop a [height, width] section of the image.
img = tf.random_crop(img, [24, 24, 3])
# 2. Randomly flip the image horizontally.
img = tf.image.random_flip_left_right(img)
# 3. Randomly change brightness.
img = tf.image.random_brightness(img, max_delta=63)
# 4. Randomly change contrast.
img = tf.image.random_contrast(img, lower=0.2, upper=1.8)
# 5. Subtract off the mean and divide by the variance of the pixels.
img = tf.image.per_image_standardization(img)
elif is_train == False:
# 1. Crop the central [height, width] of the image.
img = tf.image.resize_image_with_crop_or_pad(img, 24, 24)
# 2. Subtract off the mean and divide by the variance of the pixels.
img = tf.image.per_image_standardization(img)
elif is_train == None:
img = img
label = tf.cast(features['label'], tf.int32)
return img, label |
def print_params(self, details=True, session=None):
"""Print all info of parameters in the network"""
for i, p in enumerate(self.all_params):
if details:
try:
val = p.eval(session=session)
logging.info(
" param {:3}: {:20} {:15} {} (mean: {:<18}, median: {:<18}, std: {:<18}) ".
format(i, p.name, str(val.shape), p.dtype.name, val.mean(), np.median(val), val.std())
)
except Exception as e:
logging.info(str(e))
raise Exception(
"Hint: print params details after tl.layers.initialize_global_variables(sess) "
"or use network.print_params(False)."
)
else:
logging.info(" param {:3}: {:20} {:15} {}".format(i, p.name, str(p.get_shape()), p.dtype.name))
logging.info(" num of params: %d" % self.count_params()) |
def print_layers(self):
"""Print all info of layers in the network."""
for i, layer in enumerate(self.all_layers):
# logging.info(" layer %d: %s" % (i, str(layer)))
logging.info(
" layer {:3}: {:20} {:15} {}".format(i, layer.name, str(layer.get_shape()), layer.dtype.name)
) |
def count_params(self):
"""Returns the number of parameters in the network."""
n_params = 0
for _i, p in enumerate(self.all_params):
n = 1
# for s in p.eval().shape:
for s in p.get_shape():
try:
s = int(s)
except Exception:
s = 1
if s:
n = n * s
n_params = n_params + n
return n_params |
def get_all_params(self, session=None):
"""Return the parameters in a list of array."""
_params = []
for p in self.all_params:
if session is None:
_params.append(p.eval())
else:
_params.append(session.run(p))
return _params |
def _get_init_args(self, skip=4):
"""Get all arguments of current layer for saving the graph."""
stack = inspect.stack()
if len(stack) < skip + 1:
raise ValueError("The length of the inspection stack is shorter than the requested start position.")
args, _, _, values = inspect.getargvalues(stack[skip][0])
params = {}
for arg in args:
# some args dont need to be saved into the graph. e.g. the input placeholder
if values[arg] is not None and arg not in ['self', 'prev_layer', 'inputs']:
val = values[arg]
# change function (e.g. act) into dictionary of module path and function name
if inspect.isfunction(val):
params[arg] = {"module_path": val.__module__, "func_name": val.__name__}
# ignore more args e.g. TF class
elif arg.endswith('init'):
continue
# for other data type, save them directly
else:
params[arg] = val
return params |
def roi_pooling(input, rois, pool_height, pool_width):
"""
returns a tensorflow operation for computing the Region of Interest Pooling
@arg input: feature maps on which to perform the pooling operation
@arg rois: list of regions of interest in the format (feature map index, upper left, bottom right)
@arg pool_width: size of the pooling sections
"""
# TODO(maciek): ops scope
out = roi_pooling_module.roi_pooling(input, rois, pool_height=pool_height, pool_width=pool_width)
output, argmax_output = out[0], out[1]
return output |
def _int64_feature(value):
"""Wrapper for inserting an int64 Feature into a SequenceExample proto,
e.g, An integer label.
"""
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value])) |
def _bytes_feature(value):
"""Wrapper for inserting a bytes Feature into a SequenceExample proto,
e.g, an image in byte
"""
# return tf.train.Feature(bytes_list=tf.train.BytesList(value=[str(value)]))
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value])) |
def _int64_feature_list(values):
"""Wrapper for inserting an int64 FeatureList into a SequenceExample proto,
e.g, sentence in list of ints
"""
return tf.train.FeatureList(feature=[_int64_feature(v) for v in values]) |
def _bytes_feature_list(values):
"""Wrapper for inserting a bytes FeatureList into a SequenceExample proto,
e.g, sentence in list of bytes
"""
return tf.train.FeatureList(feature=[_bytes_feature(v) for v in values]) |
def distort_image(image, thread_id):
"""Perform random distortions on an image.
Args:
image: A float32 Tensor of shape [height, width, 3] with values in [0, 1).
thread_id: Preprocessing thread id used to select the ordering of color
distortions. There should be a multiple of 2 preprocessing threads.
Returns:````
distorted_image: A float32 Tensor of shape [height, width, 3] with values in
[0, 1].
"""
# Randomly flip horizontally.
with tf.name_scope("flip_horizontal"): # , values=[image]): # DH MOdify
# with tf.name_scope("flip_horizontal", values=[image]):
image = tf.image.random_flip_left_right(image)
# Randomly distort the colors based on thread id.
color_ordering = thread_id % 2
with tf.name_scope("distort_color"): # , values=[image]): # DH MOdify
# with tf.name_scope("distort_color", values=[image]): # DH MOdify
if color_ordering == 0:
image = tf.image.random_brightness(image, max_delta=32. / 255.)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.032)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
elif color_ordering == 1:
image = tf.image.random_brightness(image, max_delta=32. / 255.)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.032)
# The random_* ops do not necessarily clamp.
image = tf.clip_by_value(image, 0.0, 1.0)
return image |
def prefetch_input_data(
reader, file_pattern, is_training, batch_size, values_per_shard, input_queue_capacity_factor=16,
num_reader_threads=1, shard_queue_name="filename_queue", value_queue_name="input_queue"
):
"""Prefetches string values from disk into an input queue.
In training the capacity of the queue is important because a larger queue
means better mixing of training examples between shards. The minimum number of
values kept in the queue is values_per_shard * input_queue_capacity_factor,
where input_queue_memory factor should be chosen to trade-off better mixing
with memory usage.
Args:
reader: Instance of tf.ReaderBase.
file_pattern: Comma-separated list of file patterns (e.g.
/tmp/train_data-?????-of-00100).
is_training: Boolean; whether prefetching for training or eval.
batch_size: Model batch size used to determine queue capacity.
values_per_shard: Approximate number of values per shard.
input_queue_capacity_factor: Minimum number of values to keep in the queue
in multiples of values_per_shard. See comments above.
num_reader_threads: Number of reader threads to fill the queue.
shard_queue_name: Name for the shards filename queue.
value_queue_name: Name for the values input queue.
Returns:
A Queue containing prefetched string values.
"""
data_files = []
for pattern in file_pattern.split(","):
data_files.extend(tf.gfile.Glob(pattern))
if not data_files:
tl.logging.fatal("Found no input files matching %s", file_pattern)
else:
tl.logging.info("Prefetching values from %d files matching %s", len(data_files), file_pattern)
if is_training:
print(" is_training == True : RandomShuffleQueue")
filename_queue = tf.train.string_input_producer(data_files, shuffle=True, capacity=16, name=shard_queue_name)
min_queue_examples = values_per_shard * input_queue_capacity_factor
capacity = min_queue_examples + 100 * batch_size
values_queue = tf.RandomShuffleQueue(
capacity=capacity, min_after_dequeue=min_queue_examples, dtypes=[tf.string],
name="random_" + value_queue_name
)
else:
print(" is_training == False : FIFOQueue")
filename_queue = tf.train.string_input_producer(data_files, shuffle=False, capacity=1, name=shard_queue_name)
capacity = values_per_shard + 3 * batch_size
values_queue = tf.FIFOQueue(capacity=capacity, dtypes=[tf.string], name="fifo_" + value_queue_name)
enqueue_ops = []
for _ in range(num_reader_threads):
_, value = reader.read(filename_queue)
enqueue_ops.append(values_queue.enqueue([value]))
tf.train.queue_runner.add_queue_runner(tf.train.queue_runner.QueueRunner(values_queue, enqueue_ops))
tf.summary.scalar(
"queue/%s/fraction_of_%d_full" % (values_queue.name, capacity),
tf.cast(values_queue.size(), tf.float32) * (1. / capacity)
)
return values_queue |
def batch_with_dynamic_pad(images_and_captions, batch_size, queue_capacity, add_summaries=True):
"""Batches input images and captions.
This function splits the caption into an input sequence and a target sequence,
where the target sequence is the input sequence right-shifted by 1. Input and
target sequences are batched and padded up to the maximum length of sequences
in the batch. A mask is created to distinguish real words from padding words.
Example:
Actual captions in the batch ('-' denotes padded character):
[
[ 1 2 5 4 5 ],
[ 1 2 3 4 - ],
[ 1 2 3 - - ],
]
input_seqs:
[
[ 1 2 3 4 ],
[ 1 2 3 - ],
[ 1 2 - - ],
]
target_seqs:
[
[ 2 3 4 5 ],
[ 2 3 4 - ],
[ 2 3 - - ],
]
mask:
[
[ 1 1 1 1 ],
[ 1 1 1 0 ],
[ 1 1 0 0 ],
]
Args:
images_and_captions: A list of pairs [image, caption], where image is a
Tensor of shape [height, width, channels] and caption is a 1-D Tensor of
any length. Each pair will be processed and added to the queue in a
separate thread.
batch_size: Batch size.
queue_capacity: Queue capacity.
add_summaries: If true, add caption length summaries.
Returns:
images: A Tensor of shape [batch_size, height, width, channels].
input_seqs: An int32 Tensor of shape [batch_size, padded_length].
target_seqs: An int32 Tensor of shape [batch_size, padded_length].
mask: An int32 0/1 Tensor of shape [batch_size, padded_length].
"""
enqueue_list = []
for image, caption in images_and_captions:
caption_length = tf.shape(caption)[0]
input_length = tf.expand_dims(tf.subtract(caption_length, 1), 0)
input_seq = tf.slice(caption, [0], input_length)
target_seq = tf.slice(caption, [1], input_length)
indicator = tf.ones(input_length, dtype=tf.int32)
enqueue_list.append([image, input_seq, target_seq, indicator])
images, input_seqs, target_seqs, mask = tf.train.batch_join(
enqueue_list, batch_size=batch_size, capacity=queue_capacity, dynamic_pad=True, name="batch_and_pad"
)
if add_summaries:
lengths = tf.add(tf.reduce_sum(mask, 1), 1)
tf.summary.scalar("caption_length/batch_min", tf.reduce_min(lengths))
tf.summary.scalar("caption_length/batch_max", tf.reduce_max(lengths))
tf.summary.scalar("caption_length/batch_mean", tf.reduce_mean(lengths))
return images, input_seqs, target_seqs, mask |
def _to_channel_first_bias(b):
"""Reshape [c] to [c, 1, 1]."""
channel_size = int(b.shape[0])
new_shape = (channel_size, 1, 1)
# new_shape = [-1, 1, 1] # doesn't work with tensorRT
return tf.reshape(b, new_shape) |
def _bias_scale(x, b, data_format):
"""The multiplication counter part of tf.nn.bias_add."""
if data_format == 'NHWC':
return x * b
elif data_format == 'NCHW':
return x * _to_channel_first_bias(b)
else:
raise ValueError('invalid data_format: %s' % data_format) |
def _bias_add(x, b, data_format):
"""Alternative implementation of tf.nn.bias_add which is compatiable with tensorRT."""
if data_format == 'NHWC':
return tf.add(x, b)
elif data_format == 'NCHW':
return tf.add(x, _to_channel_first_bias(b))
else:
raise ValueError('invalid data_format: %s' % data_format) |
def batch_normalization(x, mean, variance, offset, scale, variance_epsilon, data_format, name=None):
"""Data Format aware version of tf.nn.batch_normalization."""
with ops.name_scope(name, 'batchnorm', [x, mean, variance, scale, offset]):
inv = math_ops.rsqrt(variance + variance_epsilon)
if scale is not None:
inv *= scale
a = math_ops.cast(inv, x.dtype)
b = math_ops.cast(offset - mean * inv if offset is not None else -mean * inv, x.dtype)
# Return a * x + b with customized data_format.
# Currently TF doesn't have bias_scale, and tensorRT has bug in converting tf.nn.bias_add
# So we reimplemted them to allow make the model work with tensorRT.
# See https://github.com/tensorlayer/openpose-plus/issues/75 for more details.
df = {'channels_first': 'NCHW', 'channels_last': 'NHWC'}
return _bias_add(_bias_scale(x, a, df[data_format]), b, df[data_format]) |
def compute_alpha(x):
"""Computing the scale parameter."""
threshold = _compute_threshold(x)
alpha1_temp1 = tf.where(tf.greater(x, threshold), x, tf.zeros_like(x, tf.float32))
alpha1_temp2 = tf.where(tf.less(x, -threshold), x, tf.zeros_like(x, tf.float32))
alpha_array = tf.add(alpha1_temp1, alpha1_temp2, name=None)
alpha_array_abs = tf.abs(alpha_array)
alpha_array_abs1 = tf.where(
tf.greater(alpha_array_abs, 0), tf.ones_like(alpha_array_abs, tf.float32),
tf.zeros_like(alpha_array_abs, tf.float32)
)
alpha_sum = tf.reduce_sum(alpha_array_abs)
n = tf.reduce_sum(alpha_array_abs1)
alpha = tf.div(alpha_sum, n)
return alpha |
def flatten_reshape(variable, name='flatten'):
"""Reshapes a high-dimension vector input.
[batch_size, mask_row, mask_col, n_mask] ---> [batch_size, mask_row x mask_col x n_mask]
Parameters
----------
variable : TensorFlow variable or tensor
The variable or tensor to be flatten.
name : str
A unique layer name.
Returns
-------
Tensor
Flatten Tensor
Examples
--------
>>> import tensorflow as tf
>>> import tensorlayer as tl
>>> x = tf.placeholder(tf.float32, [None, 128, 128, 3])
>>> # Convolution Layer with 32 filters and a kernel size of 5
>>> network = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu)
>>> # Max Pooling (down-sampling) with strides of 2 and kernel size of 2
>>> network = tf.layers.max_pooling2d(network, 2, 2)
>>> print(network.get_shape()[:].as_list())
>>> [None, 62, 62, 32]
>>> network = tl.layers.flatten_reshape(network)
>>> print(network.get_shape()[:].as_list()[1:])
>>> [None, 123008]
"""
dim = 1
for d in variable.get_shape()[1:].as_list():
dim *= d
return tf.reshape(variable, shape=[-1, dim], name=name) |
def get_layers_with_name(net, name="", verbose=False):
"""Get a list of layers' output in a network by a given name scope.
Parameters
-----------
net : :class:`Layer`
The last layer of the network.
name : str
Get the layers' output that contain this name.
verbose : boolean
If True, print information of all the layers' output
Returns
--------
list of Tensor
A list of layers' output (TensorFlow tensor)
Examples
---------
>>> import tensorlayer as tl
>>> layers = tl.layers.get_layers_with_name(net, "CNN", True)
"""
logging.info(" [*] geting layers with %s" % name)
layers = []
i = 0
for layer in net.all_layers:
# logging.info(type(layer.name))
if name in layer.name:
layers.append(layer)
if verbose:
logging.info(" got {:3}: {:15} {}".format(i, layer.name, str(layer.get_shape())))
i = i + 1
return layers |
def get_variables_with_name(name=None, train_only=True, verbose=False):
"""Get a list of TensorFlow variables by a given name scope.
Parameters
----------
name : str
Get the variables that contain this name.
train_only : boolean
If Ture, only get the trainable variables.
verbose : boolean
If True, print the information of all variables.
Returns
-------
list of Tensor
A list of TensorFlow variables
Examples
--------
>>> import tensorlayer as tl
>>> dense_vars = tl.layers.get_variables_with_name('dense', True, True)
"""
if name is None:
raise Exception("please input a name")
logging.info(" [*] geting variables with %s" % name)
# tvar = tf.trainable_variables() if train_only else tf.all_variables()
if train_only:
t_vars = tf.trainable_variables()
else:
t_vars = tf.global_variables()
d_vars = [var for var in t_vars if name in var.name]
if verbose:
for idx, v in enumerate(d_vars):
logging.info(" got {:3}: {:15} {}".format(idx, v.name, str(v.get_shape())))
return d_vars |
def initialize_rnn_state(state, feed_dict=None):
"""Returns the initialized RNN state.
The inputs are `LSTMStateTuple` or `State` of `RNNCells`, and an optional `feed_dict`.
Parameters
----------
state : RNN state.
The TensorFlow's RNN state.
feed_dict : dictionary
Initial RNN state; if None, returns zero state.
Returns
-------
RNN state
The TensorFlow's RNN state.
"""
if isinstance(state, LSTMStateTuple):
c = state.c.eval(feed_dict=feed_dict)
h = state.h.eval(feed_dict=feed_dict)
return c, h
else:
new_state = state.eval(feed_dict=feed_dict)
return new_state |
def list_remove_repeat(x):
"""Remove the repeated items in a list, and return the processed list.
You may need it to create merged layer like Concat, Elementwise and etc.
Parameters
----------
x : list
Input
Returns
-------
list
A list that after removing it's repeated items
Examples
-------
>>> l = [2, 3, 4, 2, 3]
>>> l = list_remove_repeat(l)
[2, 3, 4]
"""
y = []
for i in x:
if i not in y:
y.append(i)
return y |
def merge_networks(layers=None):
"""Merge all parameters, layers and dropout probabilities to a :class:`Layer`.
The output of return network is the first network in the list.
Parameters
----------
layers : list of :class:`Layer`
Merge all parameters, layers and dropout probabilities to the first layer in the list.
Returns
--------
:class:`Layer`
The network after merging all parameters, layers and dropout probabilities to the first network in the list.
Examples
---------
>>> import tensorlayer as tl
>>> n1 = ...
>>> n2 = ...
>>> n1 = tl.layers.merge_networks([n1, n2])
"""
if layers is None:
raise Exception("layers should be a list of TensorLayer's Layers.")
layer = layers[0]
all_params = []
all_layers = []
all_drop = {}
for l in layers:
all_params.extend(l.all_params)
all_layers.extend(l.all_layers)
all_drop.update(l.all_drop)
layer.all_params = list(all_params)
layer.all_layers = list(all_layers)
layer.all_drop = dict(all_drop)
layer.all_layers = list_remove_repeat(layer.all_layers)
layer.all_params = list_remove_repeat(layer.all_params)
return layer |
def print_all_variables(train_only=False):
"""Print information of trainable or all variables,
without ``tl.layers.initialize_global_variables(sess)``.
Parameters
----------
train_only : boolean
Whether print trainable variables only.
- If True, print the trainable variables.
- If False, print all variables.
"""
# tvar = tf.trainable_variables() if train_only else tf.all_variables()
if train_only:
t_vars = tf.trainable_variables()
logging.info(" [*] printing trainable variables")
else:
t_vars = tf.global_variables()
logging.info(" [*] printing global variables")
for idx, v in enumerate(t_vars):
logging.info(" var {:3}: {:15} {}".format(idx, str(v.get_shape()), v.name)) |
def ternary_operation(x):
"""Ternary operation use threshold computed with weights."""
g = tf.get_default_graph()
with g.gradient_override_map({"Sign": "Identity"}):
threshold = _compute_threshold(x)
x = tf.sign(tf.add(tf.sign(tf.add(x, threshold)), tf.sign(tf.add(x, -threshold))))
return x |
def _compute_threshold(x):
"""
ref: https://github.com/XJTUWYD/TWN
Computing the threshold.
"""
x_sum = tf.reduce_sum(tf.abs(x), reduction_indices=None, keepdims=False, name=None)
threshold = tf.div(x_sum, tf.cast(tf.size(x), tf.float32), name=None)
threshold = tf.multiply(0.7, threshold, name=None)
return threshold |
def freeze_graph(graph_path, checkpoint_path, output_path, end_node_names, is_binary_graph):
"""Reimplementation of the TensorFlow official freeze_graph function to freeze the graph and checkpoint together:
Parameters
-----------
graph_path : string
the path where your graph file save.
checkpoint_output_path : string
the path where your checkpoint save.
output_path : string
the path where you want to save the output proto buff
end_node_names : string
the name of the end node in your graph you want to get in your proto buff
is_binary_graph : boolean
declare your file whether is a binary graph
References
----------
- `onnx-tf exporting tutorial <https://github.com/onnx/tutorials/blob/master/tutorials/OnnxTensorflowExport.ipynb>`__
- `tensorflow freeze_graph <https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py>`
"""
_freeze_graph(
input_graph=graph_path, input_saver='', input_binary=is_binary_graph, input_checkpoint=checkpoint_path,
output_graph=output_path, output_node_names=end_node_names, restore_op_name='save/restore_all',
filename_tensor_name='save/Const:0', clear_devices=True, initializer_nodes=None
) |
def convert_model_to_onnx(frozen_graph_path, end_node_names, onnx_output_path):
"""Reimplementation of the TensorFlow-onnx official tutorial convert the proto buff to onnx file:
Parameters
-----------
frozen_graph_path : string
the path where your frozen graph file save.
end_node_names : string
the name of the end node in your graph you want to get in your proto buff
onnx_output_path : string
the path where you want to save the onnx file.
References
-----------
- `onnx-tf exporting tutorial <https://github.com/onnx/tutorials/blob/master/tutorials/OnnxTensorflowExport.ipynb>`
"""
with tf.gfile.GFile(frozen_graph_path, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
onnx_model = tensorflow_graph_to_onnx_model(graph_def, end_node_names, opset=6)
file = open(onnx_output_path, "wb")
file.write(onnx_model.SerializeToString())
file.close() |
def convert_onnx_to_model(onnx_input_path):
"""Reimplementation of the TensorFlow-onnx official tutorial convert the onnx file to specific: model
Parameters
-----------
onnx_input_path : string
the path where you save the onnx file.
References
-----------
- `onnx-tf exporting tutorial <https://github.com/onnx/tutorials/blob/master/tutorials/OnnxTensorflowExport.ipynb>`__
"""
model = onnx.load(onnx_input_path)
tf_rep = prepare(model)
# Image Path
img = np.load("./assets/image.npz")
output = tf_rep.run(img.reshape([1, 784]))
print("The digit is classified as ", np.argmax(output)) |
def _add_deprecated_function_notice_to_docstring(doc, date, instructions):
"""Adds a deprecation notice to a docstring for deprecated functions."""
if instructions:
deprecation_message = """
.. warning::
**THIS FUNCTION IS DEPRECATED:** It will be removed after %s.
*Instructions for updating:* %s.
""" % (('in a future version' if date is None else ('after %s' % date)), instructions)
else:
deprecation_message = """
.. warning::
**THIS FUNCTION IS DEPRECATED:** It will be removed after %s.
""" % (('in a future version' if date is None else ('after %s' % date)))
main_text = [deprecation_message]
return _add_notice_to_docstring(doc, 'DEPRECATED FUNCTION', main_text) |
def _add_notice_to_docstring(doc, no_doc_str, notice):
"""Adds a deprecation notice to a docstring."""
if not doc:
lines = [no_doc_str]
else:
lines = _normalize_docstring(doc).splitlines()
notice = [''] + notice
if len(lines) > 1:
# Make sure that we keep our distance from the main body
if lines[1].strip():
notice.append('')
lines[1:1] = notice
else:
lines += notice
return '\n'.join(lines) |
def alphas(shape, alpha_value, name=None):
"""Creates a tensor with all elements set to `alpha_value`.
This operation returns a tensor of type `dtype` with shape `shape` and all
elements set to alpha.
Parameters
----------
shape: A list of integers, a tuple of integers, or a 1-D `Tensor` of type `int32`.
The shape of the desired tensor
alpha_value: `float32`, `float64`, `int8`, `uint8`, `int16`, `uint16`, int32`, `int64`
The value used to fill the resulting `Tensor`.
name: str
A name for the operation (optional).
Returns
-------
A `Tensor` with all elements set to alpha.
Examples
--------
>>> tl.alphas([2, 3], tf.int32) # [[alpha, alpha, alpha], [alpha, alpha, alpha]]
"""
with ops.name_scope(name, "alphas", [shape]) as name:
alpha_tensor = convert_to_tensor(alpha_value)
alpha_dtype = dtypes.as_dtype(alpha_tensor.dtype).base_dtype
if not isinstance(shape, ops.Tensor):
try:
shape = constant_op._tensor_shape_tensor_conversion_function(tensor_shape.TensorShape(shape))
except (TypeError, ValueError):
shape = ops.convert_to_tensor(shape, dtype=dtypes.int32)
if not shape._shape_tuple():
shape = reshape(shape, [-1]) # Ensure it's a vector
try:
output = constant(alpha_value, shape=shape, dtype=alpha_dtype, name=name)
except (TypeError, ValueError):
output = fill(shape, constant(alpha_value, dtype=alpha_dtype), name=name)
if output.dtype.base_dtype != alpha_dtype:
raise AssertionError("Dtypes do not corresponds: %s and %s" % (output.dtype.base_dtype, alpha_dtype))
return output |
def alphas_like(tensor, alpha_value, name=None, optimize=True):
"""Creates a tensor with all elements set to `alpha_value`.
Given a single tensor (`tensor`), this operation returns a tensor of the same
type and shape as `tensor` with all elements set to `alpha_value`.
Parameters
----------
tensor: tf.Tensor
The Tensorflow Tensor that will be used as a template.
alpha_value: `float32`, `float64`, `int8`, `uint8`, `int16`, `uint16`, int32`, `int64`
The value used to fill the resulting `Tensor`.
name: str
A name for the operation (optional).
optimize: bool
if true, attempt to statically determine the shape of 'tensor' and encode it as a constant.
Returns
-------
A `Tensor` with all elements set to `alpha_value`.
Examples
--------
>>> tensor = tf.constant([[1, 2, 3], [4, 5, 6]])
>>> tl.alphas_like(tensor, 0.5) # [[0.5, 0.5, 0.5], [0.5, 0.5, 0.5]]
"""
with ops.name_scope(name, "alphas_like", [tensor]) as name:
tensor = ops.convert_to_tensor(tensor, name="tensor")
if context.in_eager_mode(): # and dtype is not None and dtype != tensor.dtype:
ret = alphas(shape_internal(tensor, optimize=optimize), alpha_value=alpha_value, name=name)
else: # if context.in_graph_mode():
# For now, variant types must be created via zeros_like; as we need to
# pass the input variant object to the proper zeros callback.
if (optimize and tensor.shape.is_fully_defined()):
# We can produce a zeros tensor independent of the value of 'tensor',
# since the shape is known statically.
ret = alphas(tensor.shape, alpha_value=alpha_value, name=name)
# elif dtype is not None and dtype != tensor.dtype and dtype != dtypes.variant:
else:
ret = alphas(shape_internal(tensor, optimize=optimize), alpha_value=alpha_value, name=name)
ret.set_shape(tensor.get_shape())
return ret |
def example1():
""" Example 1: Applying transformation one-by-one is very SLOW ! """
st = time.time()
for _ in range(100): # Try 100 times and compute the averaged speed
xx = tl.prepro.rotation(image, rg=-20, is_random=False)
xx = tl.prepro.flip_axis(xx, axis=1, is_random=False)
xx = tl.prepro.shear2(xx, shear=(0., -0.2), is_random=False)
xx = tl.prepro.zoom(xx, zoom_range=1 / 0.8)
xx = tl.prepro.shift(xx, wrg=-0.1, hrg=0, is_random=False)
print("apply transforms one-by-one took %fs for each image" % ((time.time() - st) / 100))
tl.vis.save_image(xx, '_result_slow.png') |
def example2():
""" Example 2: Applying all transforms in one is very FAST ! """
st = time.time()
for _ in range(100): # Repeat 100 times and compute the averaged speed
transform_matrix = create_transformation_matrix()
result = tl.prepro.affine_transform_cv2(image, transform_matrix) # Transform the image using a single operation
print("apply all transforms once took %fs for each image" % ((time.time() - st) / 100)) # usually 50x faster
tl.vis.save_image(result, '_result_fast.png') |
def example3():
""" Example 3: Using TF dataset API to load and process image for training """
n_data = 100
imgs_file_list = ['tiger.jpeg'] * n_data
train_targets = [np.ones(1)] * n_data
def generator():
if len(imgs_file_list) != len(train_targets):
raise RuntimeError('len(imgs_file_list) != len(train_targets)')
for _input, _target in zip(imgs_file_list, train_targets):
yield _input, _target
def _data_aug_fn(image):
transform_matrix = create_transformation_matrix()
result = tl.prepro.affine_transform_cv2(image, transform_matrix) # Transform the image using a single operation
return result
def _map_fn(image_path, target):
image = tf.read_file(image_path)
image = tf.image.decode_jpeg(image, channels=3) # Get RGB with 0~1
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
image = tf.py_func(_data_aug_fn, [image], [tf.float32])
# image = tf.reshape(image, (h, w, 3))
target = tf.reshape(target, ())
return image, target
n_epoch = 10
batch_size = 5
dataset = tf.data.Dataset().from_generator(generator, output_types=(tf.string, tf.int64))
dataset = dataset.shuffle(buffer_size=4096) # shuffle before loading images
dataset = dataset.repeat(n_epoch)
dataset = dataset.map(_map_fn, num_parallel_calls=multiprocessing.cpu_count())
dataset = dataset.batch(batch_size) # TODO: consider using tf.contrib.map_and_batch
dataset = dataset.prefetch(1) # prefetch 1 batch
iterator = dataset.make_one_shot_iterator()
one_element = iterator.get_next()
sess = tf.Session()
# feed `one_element` into a network, for demo, we simply get the data as follows
n_step = round(n_epoch * n_data / batch_size)
st = time.time()
for _ in range(n_step):
_images, _targets = sess.run(one_element)
print("dataset APIs took %fs for each image" % ((time.time() - st) / batch_size / n_step)) |
def example4():
""" Example 4: Transforming coordinates using affine matrix. """
transform_matrix = create_transformation_matrix()
result = tl.prepro.affine_transform_cv2(image, transform_matrix) # 76 times faster
# Transform keypoint coordinates
coords = [[(50, 100), (100, 100), (100, 50), (200, 200)], [(250, 50), (200, 50), (200, 100)]]
coords_result = tl.prepro.affine_transform_keypoints(coords, transform_matrix)
def imwrite(image, coords_list, name):
coords_list_ = []
for coords in coords_list:
coords = np.array(coords, np.int32)
coords = coords.reshape((-1, 1, 2))
coords_list_.append(coords)
image = cv2.polylines(image, coords_list_, True, (0, 255, 255), 3)
cv2.imwrite(name, image[..., ::-1])
imwrite(image, coords, '_with_keypoints_origin.png')
imwrite(result, coords_result, '_with_keypoints_result.png') |
def distort_fn(x, is_train=False):
"""
The images are processed as follows:
.. They are cropped to 24 x 24 pixels, centrally for evaluation or randomly for training.
.. They are approximately whitened to make the model insensitive to dynamic range.
For training, we additionally apply a series of random distortions to
artificially increase the data set size:
.. Randomly flip the image from left to right.
.. Randomly distort the image brightness.
"""
# print('begin',x.shape, np.min(x), np.max(x))
x = tl.prepro.crop(x, 24, 24, is_random=is_train)
# print('after crop',x.shape, np.min(x), np.max(x))
if is_train:
# x = tl.prepro.zoom(x, zoom_range=(0.9, 1.0), is_random=True)
# print('after zoom', x.shape, np.min(x), np.max(x))
x = tl.prepro.flip_axis(x, axis=1, is_random=True)
# print('after flip',x.shape, np.min(x), np.max(x))
x = tl.prepro.brightness(x, gamma=0.1, gain=1, is_random=True)
# print('after brightness',x.shape, np.min(x), np.max(x))
# tmp = np.max(x)
# x += np.random.uniform(-20, 20)
# x /= tmp
# normalize the image
x = (x - np.mean(x)) / max(np.std(x), 1e-5) # avoid values divided by 0
# print('after norm', x.shape, np.min(x), np.max(x), np.mean(x))
return x |
def fit(
sess, network, train_op, cost, X_train, y_train, x, y_, acc=None, batch_size=100, n_epoch=100, print_freq=5,
X_val=None, y_val=None, eval_train=True, tensorboard_dir=None, tensorboard_epoch_freq=5,
tensorboard_weight_histograms=True, tensorboard_graph_vis=True
):
"""Training a given non time-series network by the given cost function, training data, batch_size, n_epoch etc.
- MNIST example click `here <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_mnist_simple.py>`_.
- In order to control the training details, the authors HIGHLY recommend ``tl.iterate`` see two MNIST examples `1 <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_mlp_dropout1.py>`_, `2 <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_mlp_dropout1.py>`_.
Parameters
----------
sess : Session
TensorFlow Session.
network : TensorLayer layer
the network to be trained.
train_op : TensorFlow optimizer
The optimizer for training e.g. tf.train.AdamOptimizer.
X_train : numpy.array
The input of training data
y_train : numpy.array
The target of training data
x : placeholder
For inputs.
y_ : placeholder
For targets.
acc : TensorFlow expression or None
Metric for accuracy or others. If None, would not print the information.
batch_size : int
The batch size for training and evaluating.
n_epoch : int
The number of training epochs.
print_freq : int
Print the training information every ``print_freq`` epochs.
X_val : numpy.array or None
The input of validation data. If None, would not perform validation.
y_val : numpy.array or None
The target of validation data. If None, would not perform validation.
eval_train : boolean
Whether to evaluate the model during training.
If X_val and y_val are not None, it reflects whether to evaluate the model on training data.
tensorboard_dir : string
path to log dir, if set, summary data will be stored to the tensorboard_dir/ directory for visualization with tensorboard. (default None)
Also runs `tl.layers.initialize_global_variables(sess)` internally in fit() to setup the summary nodes.
tensorboard_epoch_freq : int
How many epochs between storing tensorboard checkpoint for visualization to log/ directory (default 5).
tensorboard_weight_histograms : boolean
If True updates tensorboard data in the logs/ directory for visualization
of the weight histograms every tensorboard_epoch_freq epoch (default True).
tensorboard_graph_vis : boolean
If True stores the graph in the tensorboard summaries saved to log/ (default True).
Examples
--------
See `tutorial_mnist_simple.py <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_mnist_simple.py>`_
>>> tl.utils.fit(sess, network, train_op, cost, X_train, y_train, x, y_,
... acc=acc, batch_size=500, n_epoch=200, print_freq=5,
... X_val=X_val, y_val=y_val, eval_train=False)
>>> tl.utils.fit(sess, network, train_op, cost, X_train, y_train, x, y_,
... acc=acc, batch_size=500, n_epoch=200, print_freq=5,
... X_val=X_val, y_val=y_val, eval_train=False,
... tensorboard=True, tensorboard_weight_histograms=True, tensorboard_graph_vis=True)
Notes
--------
If tensorboard_dir not None, the `global_variables_initializer` will be run inside the fit function
in order to initialize the automatically generated summary nodes used for tensorboard visualization,
thus `tf.global_variables_initializer().run()` before the `fit()` call will be undefined.
"""
if X_train.shape[0] < batch_size:
raise AssertionError("Number of training examples should be bigger than the batch size")
if tensorboard_dir is not None:
tl.logging.info("Setting up tensorboard ...")
#Set up tensorboard summaries and saver
tl.files.exists_or_mkdir(tensorboard_dir)
#Only write summaries for more recent TensorFlow versions
if hasattr(tf, 'summary') and hasattr(tf.summary, 'FileWriter'):
if tensorboard_graph_vis:
train_writer = tf.summary.FileWriter(tensorboard_dir + '/train', sess.graph)
val_writer = tf.summary.FileWriter(tensorboard_dir + '/validation', sess.graph)
else:
train_writer = tf.summary.FileWriter(tensorboard_dir + '/train')
val_writer = tf.summary.FileWriter(tensorboard_dir + '/validation')
#Set up summary nodes
if (tensorboard_weight_histograms):
for param in network.all_params:
if hasattr(tf, 'summary') and hasattr(tf.summary, 'histogram'):
tl.logging.info('Param name %s' % param.name)
tf.summary.histogram(param.name, param)
if hasattr(tf, 'summary') and hasattr(tf.summary, 'histogram'):
tf.summary.scalar('cost', cost)
merged = tf.summary.merge_all()
#Initalize all variables and summaries
tl.layers.initialize_global_variables(sess)
tl.logging.info("Finished! use `tensorboard --logdir=%s/` to start tensorboard" % tensorboard_dir)
tl.logging.info("Start training the network ...")
start_time_begin = time.time()
tensorboard_train_index, tensorboard_val_index = 0, 0
for epoch in range(n_epoch):
start_time = time.time()
loss_ep = 0
n_step = 0
for X_train_a, y_train_a in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True):
feed_dict = {x: X_train_a, y_: y_train_a}
feed_dict.update(network.all_drop) # enable noise layers
loss, _ = sess.run([cost, train_op], feed_dict=feed_dict)
loss_ep += loss
n_step += 1
loss_ep = loss_ep / n_step
if tensorboard_dir is not None and hasattr(tf, 'summary'):
if epoch + 1 == 1 or (epoch + 1) % tensorboard_epoch_freq == 0:
for X_train_a, y_train_a in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True):
dp_dict = dict_to_one(network.all_drop) # disable noise layers
feed_dict = {x: X_train_a, y_: y_train_a}
feed_dict.update(dp_dict)
result = sess.run(merged, feed_dict=feed_dict)
train_writer.add_summary(result, tensorboard_train_index)
tensorboard_train_index += 1
if (X_val is not None) and (y_val is not None):
for X_val_a, y_val_a in tl.iterate.minibatches(X_val, y_val, batch_size, shuffle=True):
dp_dict = dict_to_one(network.all_drop) # disable noise layers
feed_dict = {x: X_val_a, y_: y_val_a}
feed_dict.update(dp_dict)
result = sess.run(merged, feed_dict=feed_dict)
val_writer.add_summary(result, tensorboard_val_index)
tensorboard_val_index += 1
if epoch + 1 == 1 or (epoch + 1) % print_freq == 0:
if (X_val is not None) and (y_val is not None):
tl.logging.info("Epoch %d of %d took %fs" % (epoch + 1, n_epoch, time.time() - start_time))
if eval_train is True:
train_loss, train_acc, n_batch = 0, 0, 0
for X_train_a, y_train_a in tl.iterate.minibatches(X_train, y_train, batch_size, shuffle=True):
dp_dict = dict_to_one(network.all_drop) # disable noise layers
feed_dict = {x: X_train_a, y_: y_train_a}
feed_dict.update(dp_dict)
if acc is not None:
err, ac = sess.run([cost, acc], feed_dict=feed_dict)
train_acc += ac
else:
err = sess.run(cost, feed_dict=feed_dict)
train_loss += err
n_batch += 1
tl.logging.info(" train loss: %f" % (train_loss / n_batch))
if acc is not None:
tl.logging.info(" train acc: %f" % (train_acc / n_batch))
val_loss, val_acc, n_batch = 0, 0, 0
for X_val_a, y_val_a in tl.iterate.minibatches(X_val, y_val, batch_size, shuffle=True):
dp_dict = dict_to_one(network.all_drop) # disable noise layers
feed_dict = {x: X_val_a, y_: y_val_a}
feed_dict.update(dp_dict)
if acc is not None:
err, ac = sess.run([cost, acc], feed_dict=feed_dict)
val_acc += ac
else:
err = sess.run(cost, feed_dict=feed_dict)
val_loss += err
n_batch += 1
tl.logging.info(" val loss: %f" % (val_loss / n_batch))
if acc is not None:
tl.logging.info(" val acc: %f" % (val_acc / n_batch))
else:
tl.logging.info(
"Epoch %d of %d took %fs, loss %f" % (epoch + 1, n_epoch, time.time() - start_time, loss_ep)
)
tl.logging.info("Total training time: %fs" % (time.time() - start_time_begin)) |
def predict(sess, network, X, x, y_op, batch_size=None):
"""
Return the predict results of given non time-series network.
Parameters
----------
sess : Session
TensorFlow Session.
network : TensorLayer layer
The network.
X : numpy.array
The inputs.
x : placeholder
For inputs.
y_op : placeholder
The argmax expression of softmax outputs.
batch_size : int or None
The batch size for prediction, when dataset is large, we should use minibatche for prediction;
if dataset is small, we can set it to None.
Examples
--------
See `tutorial_mnist_simple.py <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_mnist_simple.py>`_
>>> y = network.outputs
>>> y_op = tf.argmax(tf.nn.softmax(y), 1)
>>> print(tl.utils.predict(sess, network, X_test, x, y_op))
"""
if batch_size is None:
dp_dict = dict_to_one(network.all_drop) # disable noise layers
feed_dict = {
x: X,
}
feed_dict.update(dp_dict)
return sess.run(y_op, feed_dict=feed_dict)
else:
result = None
for X_a, _ in tl.iterate.minibatches(X, X, batch_size, shuffle=False):
dp_dict = dict_to_one(network.all_drop)
feed_dict = {
x: X_a,
}
feed_dict.update(dp_dict)
result_a = sess.run(y_op, feed_dict=feed_dict)
if result is None:
result = result_a
else:
result = np.concatenate((result, result_a))
if result is None:
if len(X) % batch_size != 0:
dp_dict = dict_to_one(network.all_drop)
feed_dict = {
x: X[-(len(X) % batch_size):, :],
}
feed_dict.update(dp_dict)
result_a = sess.run(y_op, feed_dict=feed_dict)
result = result_a
else:
if len(X) != len(result) and len(X) % batch_size != 0:
dp_dict = dict_to_one(network.all_drop)
feed_dict = {
x: X[-(len(X) % batch_size):, :],
}
feed_dict.update(dp_dict)
result_a = sess.run(y_op, feed_dict=feed_dict)
result = np.concatenate((result, result_a))
return result |
def evaluation(y_test=None, y_predict=None, n_classes=None):
"""
Input the predicted results, targets results and
the number of class, return the confusion matrix, F1-score of each class,
accuracy and macro F1-score.
Parameters
----------
y_test : list
The target results
y_predict : list
The predicted results
n_classes : int
The number of classes
Examples
--------
>>> c_mat, f1, acc, f1_macro = tl.utils.evaluation(y_test, y_predict, n_classes)
"""
c_mat = confusion_matrix(y_test, y_predict, labels=[x for x in range(n_classes)])
f1 = f1_score(y_test, y_predict, average=None, labels=[x for x in range(n_classes)])
f1_macro = f1_score(y_test, y_predict, average='macro')
acc = accuracy_score(y_test, y_predict)
tl.logging.info('confusion matrix: \n%s' % c_mat)
tl.logging.info('f1-score : %s' % f1)
tl.logging.info('f1-score(macro) : %f' % f1_macro) # same output with > f1_score(y_true, y_pred, average='macro')
tl.logging.info('accuracy-score : %f' % acc)
return c_mat, f1, acc, f1_macro |
def class_balancing_oversample(X_train=None, y_train=None, printable=True):
"""Input the features and labels, return the features and labels after oversampling.
Parameters
----------
X_train : numpy.array
The inputs.
y_train : numpy.array
The targets.
Examples
--------
One X
>>> X_train, y_train = class_balancing_oversample(X_train, y_train, printable=True)
Two X
>>> X, y = tl.utils.class_balancing_oversample(X_train=np.hstack((X1, X2)), y_train=y, printable=False)
>>> X1 = X[:, 0:5]
>>> X2 = X[:, 5:]
"""
# ======== Classes balancing
if printable:
tl.logging.info("Classes balancing for training examples...")
c = Counter(y_train)
if printable:
tl.logging.info('the occurrence number of each stage: %s' % c.most_common())
tl.logging.info('the least stage is Label %s have %s instances' % c.most_common()[-1])
tl.logging.info('the most stage is Label %s have %s instances' % c.most_common(1)[0])
most_num = c.most_common(1)[0][1]
if printable:
tl.logging.info('most num is %d, all classes tend to be this num' % most_num)
locations = {}
number = {}
for lab, num in c.most_common(): # find the index from y_train
number[lab] = num
locations[lab] = np.where(np.array(y_train) == lab)[0]
if printable:
tl.logging.info('convert list(np.array) to dict format')
X = {} # convert list to dict
for lab, num in number.items():
X[lab] = X_train[locations[lab]]
# oversampling
if printable:
tl.logging.info('start oversampling')
for key in X:
temp = X[key]
while True:
if len(X[key]) >= most_num:
break
X[key] = np.vstack((X[key], temp))
if printable:
tl.logging.info('first features of label 0 > %d' % len(X[0][0]))
tl.logging.info('the occurrence num of each stage after oversampling')
for key in X:
tl.logging.info("%s %d" % (key, len(X[key])))
if printable:
tl.logging.info('make each stage have same num of instances')
for key in X:
X[key] = X[key][0:most_num, :]
tl.logging.info("%s %d" % (key, len(X[key])))
# convert dict to list
if printable:
tl.logging.info('convert from dict to list format')
y_train = []
X_train = np.empty(shape=(0, len(X[0][0])))
for key in X:
X_train = np.vstack((X_train, X[key]))
y_train.extend([key for i in range(len(X[key]))])
# tl.logging.info(len(X_train), len(y_train))
c = Counter(y_train)
if printable:
tl.logging.info('the occurrence number of each stage after oversampling: %s' % c.most_common())
# ================ End of Classes balancing
return X_train, y_train |
def get_random_int(min_v=0, max_v=10, number=5, seed=None):
"""Return a list of random integer by the given range and quantity.
Parameters
-----------
min_v : number
The minimum value.
max_v : number
The maximum value.
number : int
Number of value.
seed : int or None
The seed for random.
Examples
---------
>>> r = get_random_int(min_v=0, max_v=10, number=5)
[10, 2, 3, 3, 7]
"""
rnd = random.Random()
if seed:
rnd = random.Random(seed)
# return [random.randint(min,max) for p in range(0, number)]
return [rnd.randint(min_v, max_v) for p in range(0, number)] |
def list_string_to_dict(string):
"""Inputs ``['a', 'b', 'c']``, returns ``{'a': 0, 'b': 1, 'c': 2}``."""
dictionary = {}
for idx, c in enumerate(string):
dictionary.update({c: idx})
return dictionary |
def exit_tensorflow(sess=None, port=6006):
"""Close TensorFlow session, TensorBoard and Nvidia-process if available.
Parameters
----------
sess : Session
TensorFlow Session.
tb_port : int
TensorBoard port you want to close, `6006` as default.
"""
text = "[TL] Close tensorboard and nvidia-process if available"
text2 = "[TL] Close tensorboard and nvidia-process not yet supported by this function (tl.ops.exit_tf) on "
if sess is not None:
sess.close()
if _platform == "linux" or _platform == "linux2":
tl.logging.info('linux: %s' % text)
os.system('nvidia-smi')
os.system('fuser ' + port + '/tcp -k') # kill tensorboard 6006
os.system("nvidia-smi | grep python |awk '{print $3}'|xargs kill") # kill all nvidia-smi python process
_exit()
elif _platform == "darwin":
tl.logging.info('OS X: %s' % text)
subprocess.Popen(
"lsof -i tcp:" + str(port) + " | grep -v PID | awk '{print $2}' | xargs kill", shell=True
) # kill tensorboard
elif _platform == "win32":
raise NotImplementedError("this function is not supported on the Windows platform")
else:
tl.logging.info(text2 + _platform) |
def open_tensorboard(log_dir='/tmp/tensorflow', port=6006):
"""Open Tensorboard.
Parameters
----------
log_dir : str
Directory where your tensorboard logs are saved
port : int
TensorBoard port you want to open, 6006 is tensorboard default
"""
text = "[TL] Open tensorboard, go to localhost:" + str(port) + " to access"
text2 = " not yet supported by this function (tl.ops.open_tb)"
if not tl.files.exists_or_mkdir(log_dir, verbose=False):
tl.logging.info("[TL] Log reportory was created at %s" % log_dir)
if _platform == "linux" or _platform == "linux2":
raise NotImplementedError()
elif _platform == "darwin":
tl.logging.info('OS X: %s' % text)
subprocess.Popen(
sys.prefix + " | python -m tensorflow.tensorboard --logdir=" + log_dir + " --port=" + str(port), shell=True
) # open tensorboard in localhost:6006/ or whatever port you chose
elif _platform == "win32":
raise NotImplementedError("this function is not supported on the Windows platform")
else:
tl.logging.info(_platform + text2) |
def clear_all_placeholder_variables(printable=True):
"""Clears all the placeholder variables of keep prob,
including keeping probabilities of all dropout, denoising, dropconnect etc.
Parameters
----------
printable : boolean
If True, print all deleted variables.
"""
tl.logging.info('clear all .....................................')
gl = globals().copy()
for var in gl:
if var[0] == '_': continue
if 'func' in str(globals()[var]): continue
if 'module' in str(globals()[var]): continue
if 'class' in str(globals()[var]): continue
if printable:
tl.logging.info(" clear_all ------- %s" % str(globals()[var]))
del globals()[var] |
def set_gpu_fraction(gpu_fraction=0.3):
"""Set the GPU memory fraction for the application.
Parameters
----------
gpu_fraction : float
Fraction of GPU memory, (0 ~ 1]
References
----------
- `TensorFlow using GPU <https://www.tensorflow.org/versions/r0.9/how_tos/using_gpu/index.html>`__
"""
tl.logging.info("[TL]: GPU MEM Fraction %f" % gpu_fraction)
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=gpu_fraction)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
return sess |
def generate_skip_gram_batch(data, batch_size, num_skips, skip_window, data_index=0):
"""Generate a training batch for the Skip-Gram model.
See `Word2Vec example <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_word2vec_basic.py>`__.
Parameters
----------
data : list of data
To present context, usually a list of integers.
batch_size : int
Batch size to return.
num_skips : int
How many times to reuse an input to generate a label.
skip_window : int
How many words to consider left and right.
data_index : int
Index of the context location. This code use `data_index` to instead of yield like ``tl.iterate``.
Returns
-------
batch : list of data
Inputs.
labels : list of data
Labels
data_index : int
Index of the context location.
Examples
--------
Setting num_skips=2, skip_window=1, use the right and left words.
In the same way, num_skips=4, skip_window=2 means use the nearby 4 words.
>>> data = [1,2,3,4,5,6,7,8,9,10,11]
>>> batch, labels, data_index = tl.nlp.generate_skip_gram_batch(data=data, batch_size=8, num_skips=2, skip_window=1, data_index=0)
>>> print(batch)
[2 2 3 3 4 4 5 5]
>>> print(labels)
[[3]
[1]
[4]
[2]
[5]
[3]
[4]
[6]]
"""
# global data_index # you can put data_index outside the function, then
# modify the global data_index in the function without return it.
# note: without using yield, this code use data_index to instead.
if batch_size % num_skips != 0:
raise Exception("batch_size should be able to be divided by num_skips.")
if num_skips > 2 * skip_window:
raise Exception("num_skips <= 2 * skip_window")
batch = np.ndarray(shape=(batch_size), dtype=np.int32)
labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)
span = 2 * skip_window + 1 # [ skip_window target skip_window ]
buffer = collections.deque(maxlen=span)
for _ in range(span):
buffer.append(data[data_index])
data_index = (data_index + 1) % len(data)
for i in range(batch_size // num_skips):
target = skip_window # target label at the center of the buffer
targets_to_avoid = [skip_window]
for j in range(num_skips):
while target in targets_to_avoid:
target = random.randint(0, span - 1)
targets_to_avoid.append(target)
batch[i * num_skips + j] = buffer[skip_window]
labels[i * num_skips + j, 0] = buffer[target]
buffer.append(data[data_index])
data_index = (data_index + 1) % len(data)
return batch, labels, data_index |
def sample(a=None, temperature=1.0):
"""Sample an index from a probability array.
Parameters
----------
a : list of float
List of probabilities.
temperature : float or None
The higher the more uniform. When a = [0.1, 0.2, 0.7],
- temperature = 0.7, the distribution will be sharpen [0.05048273, 0.13588945, 0.81362782]
- temperature = 1.0, the distribution will be the same [0.1, 0.2, 0.7]
- temperature = 1.5, the distribution will be filtered [0.16008435, 0.25411807, 0.58579758]
- If None, it will be ``np.argmax(a)``
Notes
------
- No matter what is the temperature and input list, the sum of all probabilities will be one. Even if input list = [1, 100, 200], the sum of all probabilities will still be one.
- For large vocabulary size, choice a higher temperature or ``tl.nlp.sample_top`` to avoid error.
"""
if a is None:
raise Exception("a : list of float")
b = np.copy(a)
try:
if temperature == 1:
return np.argmax(np.random.multinomial(1, a, 1))
if temperature is None:
return np.argmax(a)
else:
a = np.log(a) / temperature
a = np.exp(a) / np.sum(np.exp(a))
return np.argmax(np.random.multinomial(1, a, 1))
except Exception:
# np.set_printoptions(threshold=np.nan)
# tl.logging.info(a)
# tl.logging.info(np.sum(a))
# tl.logging.info(np.max(a))
# tl.logging.info(np.min(a))
# exit()
message = "For large vocabulary_size, choice a higher temperature\
to avoid log error. Hint : use ``sample_top``. "
warnings.warn(message, Warning)
# tl.logging.info(a)
# tl.logging.info(b)
return np.argmax(np.random.multinomial(1, b, 1)) |
def sample_top(a=None, top_k=10):
"""Sample from ``top_k`` probabilities.
Parameters
----------
a : list of float
List of probabilities.
top_k : int
Number of candidates to be considered.
"""
if a is None:
a = []
idx = np.argpartition(a, -top_k)[-top_k:]
probs = a[idx]
# tl.logging.info("new %f" % probs)
probs = probs / np.sum(probs)
choice = np.random.choice(idx, p=probs)
return choice |
def process_sentence(sentence, start_word="<S>", end_word="</S>"):
"""Seperate a sentence string into a list of string words, add start_word and end_word,
see ``create_vocab()`` and ``tutorial_tfrecord3.py``.
Parameters
----------
sentence : str
A sentence.
start_word : str or None
The start word. If None, no start word will be appended.
end_word : str or None
The end word. If None, no end word will be appended.
Returns
---------
list of str
A list of strings that separated into words.
Examples
-----------
>>> c = "how are you?"
>>> c = tl.nlp.process_sentence(c)
>>> print(c)
['<S>', 'how', 'are', 'you', '?', '</S>']
Notes
-------
- You have to install the following package.
- `Installing NLTK <http://www.nltk.org/install.html>`__
- `Installing NLTK data <http://www.nltk.org/data.html>`__
"""
if start_word is not None:
process_sentence = [start_word]
else:
process_sentence = []
process_sentence.extend(nltk.tokenize.word_tokenize(sentence.lower()))
if end_word is not None:
process_sentence.append(end_word)
return process_sentence |
def create_vocab(sentences, word_counts_output_file, min_word_count=1):
"""Creates the vocabulary of word to word_id.
See ``tutorial_tfrecord3.py``.
The vocabulary is saved to disk in a text file of word counts. The id of each
word in the file is its corresponding 0-based line number.
Parameters
------------
sentences : list of list of str
All sentences for creating the vocabulary.
word_counts_output_file : str
The file name.
min_word_count : int
Minimum number of occurrences for a word.
Returns
--------
:class:`SimpleVocabulary`
The simple vocabulary object, see :class:`Vocabulary` for more.
Examples
--------
Pre-process sentences
>>> captions = ["one two , three", "four five five"]
>>> processed_capts = []
>>> for c in captions:
>>> c = tl.nlp.process_sentence(c, start_word="<S>", end_word="</S>")
>>> processed_capts.append(c)
>>> print(processed_capts)
...[['<S>', 'one', 'two', ',', 'three', '</S>'], ['<S>', 'four', 'five', 'five', '</S>']]
Create vocabulary
>>> tl.nlp.create_vocab(processed_capts, word_counts_output_file='vocab.txt', min_word_count=1)
Creating vocabulary.
Total words: 8
Words in vocabulary: 8
Wrote vocabulary file: vocab.txt
Get vocabulary object
>>> vocab = tl.nlp.Vocabulary('vocab.txt', start_word="<S>", end_word="</S>", unk_word="<UNK>")
INFO:tensorflow:Initializing vocabulary from file: vocab.txt
[TL] Vocabulary from vocab.txt : <S> </S> <UNK>
vocabulary with 10 words (includes start_word, end_word, unk_word)
start_id: 2
end_id: 3
unk_id: 9
pad_id: 0
"""
tl.logging.info("Creating vocabulary.")
counter = Counter()
for c in sentences:
counter.update(c)
# tl.logging.info('c',c)
tl.logging.info(" Total words: %d" % len(counter))
# Filter uncommon words and sort by descending count.
word_counts = [x for x in counter.items() if x[1] >= min_word_count]
word_counts.sort(key=lambda x: x[1], reverse=True)
word_counts = [("<PAD>", 0)] + word_counts # 1st id should be reserved for padding
# tl.logging.info(word_counts)
tl.logging.info(" Words in vocabulary: %d" % len(word_counts))
# Write out the word counts file.
with tf.gfile.FastGFile(word_counts_output_file, "w") as f:
f.write("\n".join(["%s %d" % (w, c) for w, c in word_counts]))
tl.logging.info(" Wrote vocabulary file: %s" % word_counts_output_file)
# Create the vocabulary dictionary.
reverse_vocab = [x[0] for x in word_counts]
unk_id = len(reverse_vocab)
vocab_dict = dict([(x, y) for (y, x) in enumerate(reverse_vocab)])
vocab = SimpleVocabulary(vocab_dict, unk_id)
return vocab |
def read_words(filename="nietzsche.txt", replace=None):
"""Read list format context from a file.
For customized read_words method, see ``tutorial_generate_text.py``.
Parameters
----------
filename : str
a file path.
replace : list of str
replace original string by target string.
Returns
-------
list of str
The context in a list (split using space).
"""
if replace is None:
replace = ['\n', '<eos>']
with tf.gfile.GFile(filename, "r") as f:
try: # python 3.4 or older
context_list = f.read().replace(*replace).split()
except Exception: # python 3.5
f.seek(0)
replace = [x.encode('utf-8') for x in replace]
context_list = f.read().replace(*replace).split()
return context_list |
def read_analogies_file(eval_file='questions-words.txt', word2id=None):
"""Reads through an analogy question file, return its id format.
Parameters
----------
eval_file : str
The file name.
word2id : dictionary
a dictionary that maps word to ID.
Returns
--------
numpy.array
A ``[n_examples, 4]`` numpy array containing the analogy question's word IDs.
Examples
---------
The file should be in this format
>>> : capital-common-countries
>>> Athens Greece Baghdad Iraq
>>> Athens Greece Bangkok Thailand
>>> Athens Greece Beijing China
>>> Athens Greece Berlin Germany
>>> Athens Greece Bern Switzerland
>>> Athens Greece Cairo Egypt
>>> Athens Greece Canberra Australia
>>> Athens Greece Hanoi Vietnam
>>> Athens Greece Havana Cuba
Get the tokenized analogy question data
>>> words = tl.files.load_matt_mahoney_text8_dataset()
>>> data, count, dictionary, reverse_dictionary = tl.nlp.build_words_dataset(words, vocabulary_size, True)
>>> analogy_questions = tl.nlp.read_analogies_file(eval_file='questions-words.txt', word2id=dictionary)
>>> print(analogy_questions)
[[ 3068 1248 7161 1581]
[ 3068 1248 28683 5642]
[ 3068 1248 3878 486]
...,
[ 1216 4309 19982 25506]
[ 1216 4309 3194 8650]
[ 1216 4309 140 312]]
"""
if word2id is None:
word2id = {}
questions = []
questions_skipped = 0
with open(eval_file, "rb") as analogy_f:
for line in analogy_f:
if line.startswith(b":"): # Skip comments.
continue
words = line.strip().lower().split(b" ") # lowercase
ids = [word2id.get(w.strip()) for w in words]
if None in ids or len(ids) != 4:
questions_skipped += 1
else:
questions.append(np.array(ids))
tl.logging.info("Eval analogy file: %s" % eval_file)
tl.logging.info("Questions: %d", len(questions))
tl.logging.info("Skipped: %d", questions_skipped)
analogy_questions = np.array(questions, dtype=np.int32)
return analogy_questions |
def build_reverse_dictionary(word_to_id):
"""Given a dictionary that maps word to integer id.
Returns a reverse dictionary that maps a id to word.
Parameters
----------
word_to_id : dictionary
that maps word to ID.
Returns
--------
dictionary
A dictionary that maps IDs to words.
"""
reverse_dictionary = dict(zip(word_to_id.values(), word_to_id.keys()))
return reverse_dictionary |
def build_words_dataset(words=None, vocabulary_size=50000, printable=True, unk_key='UNK'):
"""Build the words dictionary and replace rare words with 'UNK' token.
The most common word has the smallest integer id.
Parameters
----------
words : list of str or byte
The context in list format. You may need to do preprocessing on the words, such as lower case, remove marks etc.
vocabulary_size : int
The maximum vocabulary size, limiting the vocabulary size. Then the script replaces rare words with 'UNK' token.
printable : boolean
Whether to print the read vocabulary size of the given words.
unk_key : str
Represent the unknown words.
Returns
--------
data : list of int
The context in a list of ID.
count : list of tuple and list
Pair words and IDs.
- count[0] is a list : the number of rare words
- count[1:] are tuples : the number of occurrence of each word
- e.g. [['UNK', 418391], (b'the', 1061396), (b'of', 593677), (b'and', 416629), (b'one', 411764)]
dictionary : dictionary
It is `word_to_id` that maps word to ID.
reverse_dictionary : a dictionary
It is `id_to_word` that maps ID to word.
Examples
--------
>>> words = tl.files.load_matt_mahoney_text8_dataset()
>>> vocabulary_size = 50000
>>> data, count, dictionary, reverse_dictionary = tl.nlp.build_words_dataset(words, vocabulary_size)
References
-----------------
- `tensorflow/examples/tutorials/word2vec/word2vec_basic.py <https://github.com/tensorflow/tensorflow/blob/r0.7/tensorflow/examples/tutorials/word2vec/word2vec_basic.py>`__
"""
if words is None:
raise Exception("words : list of str or byte")
count = [[unk_key, -1]]
count.extend(collections.Counter(words).most_common(vocabulary_size - 1))
dictionary = dict()
for word, _ in count:
dictionary[word] = len(dictionary)
data = list()
unk_count = 0
for word in words:
if word in dictionary:
index = dictionary[word]
else:
index = 0 # dictionary['UNK']
unk_count += 1
data.append(index)
count[0][1] = unk_count
reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys()))
if printable:
tl.logging.info('Real vocabulary size %d' % len(collections.Counter(words).keys()))
tl.logging.info('Limited vocabulary size {}'.format(vocabulary_size))
if len(collections.Counter(words).keys()) < vocabulary_size:
raise Exception(
"len(collections.Counter(words).keys()) >= vocabulary_size , the limited vocabulary_size must be less than or equal to the read vocabulary_size"
)
return data, count, dictionary, reverse_dictionary |
def words_to_word_ids(data=None, word_to_id=None, unk_key='UNK'):
"""Convert a list of string (words) to IDs.
Parameters
----------
data : list of string or byte
The context in list format
word_to_id : a dictionary
that maps word to ID.
unk_key : str
Represent the unknown words.
Returns
--------
list of int
A list of IDs to represent the context.
Examples
--------
>>> words = tl.files.load_matt_mahoney_text8_dataset()
>>> vocabulary_size = 50000
>>> data, count, dictionary, reverse_dictionary = tl.nlp.build_words_dataset(words, vocabulary_size, True)
>>> context = [b'hello', b'how', b'are', b'you']
>>> ids = tl.nlp.words_to_word_ids(words, dictionary)
>>> context = tl.nlp.word_ids_to_words(ids, reverse_dictionary)
>>> print(ids)
[6434, 311, 26, 207]
>>> print(context)
[b'hello', b'how', b'are', b'you']
References
---------------
- `tensorflow.models.rnn.ptb.reader <https://github.com/tensorflow/tensorflow/tree/master/tensorflow/models/rnn/ptb>`__
"""
if data is None:
raise Exception("data : list of string or byte")
if word_to_id is None:
raise Exception("word_to_id : a dictionary")
# if isinstance(data[0], six.string_types):
# tl.logging.info(type(data[0]))
# # exit()
# tl.logging.info(data[0])
# tl.logging.info(word_to_id)
# return [word_to_id[str(word)] for word in data]
# else:
word_ids = []
for word in data:
if word_to_id.get(word) is not None:
word_ids.append(word_to_id[word])
else:
word_ids.append(word_to_id[unk_key])
return word_ids |
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