INSTRUCTION
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Returns the model dump as a list of strings.
Parameters
----------
fmap : string, optional
Name of the file containing feature map names.
with_stats : bool, optional
Controls whether the split statistics are output.
dump_format : string, optional
Format of model dump. Can be 'text' or 'json'. | def get_dump(self, fmap='', with_stats=False, dump_format="text"):
"""
Returns the model dump as a list of strings.
Parameters
----------
fmap : string, optional
Name of the file containing feature map names.
with_stats : bool, optional
Controls whether the split statistics are output.
dump_format : string, optional
Format of model dump. Can be 'text' or 'json'.
"""
length = c_bst_ulong()
sarr = ctypes.POINTER(ctypes.c_char_p)()
if self.feature_names is not None and fmap == '':
flen = len(self.feature_names)
fname = from_pystr_to_cstr(self.feature_names)
if self.feature_types is None:
# use quantitative as default
# {'q': quantitative, 'i': indicator}
ftype = from_pystr_to_cstr(['q'] * flen)
else:
ftype = from_pystr_to_cstr(self.feature_types)
_check_call(_LIB.XGBoosterDumpModelExWithFeatures(
self.handle,
ctypes.c_int(flen),
fname,
ftype,
ctypes.c_int(with_stats),
c_str(dump_format),
ctypes.byref(length),
ctypes.byref(sarr)))
else:
if fmap != '' and not os.path.exists(fmap):
raise ValueError("No such file: {0}".format(fmap))
_check_call(_LIB.XGBoosterDumpModelEx(self.handle,
c_str(fmap),
ctypes.c_int(with_stats),
c_str(dump_format),
ctypes.byref(length),
ctypes.byref(sarr)))
res = from_cstr_to_pystr(sarr, length)
return res |
Get feature importance of each feature.
Importance type can be defined as:
* 'weight': the number of times a feature is used to split the data across all trees.
* 'gain': the average gain across all splits the feature is used in.
* 'cover': the average coverage across all splits the feature is used in.
* 'total_gain': the total gain across all splits the feature is used in.
* 'total_cover': the total coverage across all splits the feature is used in.
.. note:: Feature importance is defined only for tree boosters
Feature importance is only defined when the decision tree model is chosen as base
learner (`booster=gbtree`). It is not defined for other base learner types, such
as linear learners (`booster=gblinear`).
Parameters
----------
fmap: str (optional)
The name of feature map file.
importance_type: str, default 'weight'
One of the importance types defined above. | def get_score(self, fmap='', importance_type='weight'):
"""Get feature importance of each feature.
Importance type can be defined as:
* 'weight': the number of times a feature is used to split the data across all trees.
* 'gain': the average gain across all splits the feature is used in.
* 'cover': the average coverage across all splits the feature is used in.
* 'total_gain': the total gain across all splits the feature is used in.
* 'total_cover': the total coverage across all splits the feature is used in.
.. note:: Feature importance is defined only for tree boosters
Feature importance is only defined when the decision tree model is chosen as base
learner (`booster=gbtree`). It is not defined for other base learner types, such
as linear learners (`booster=gblinear`).
Parameters
----------
fmap: str (optional)
The name of feature map file.
importance_type: str, default 'weight'
One of the importance types defined above.
"""
if getattr(self, 'booster', None) is not None and self.booster not in {'gbtree', 'dart'}:
raise ValueError('Feature importance is not defined for Booster type {}'
.format(self.booster))
allowed_importance_types = ['weight', 'gain', 'cover', 'total_gain', 'total_cover']
if importance_type not in allowed_importance_types:
msg = ("importance_type mismatch, got '{}', expected one of " +
repr(allowed_importance_types))
raise ValueError(msg.format(importance_type))
# if it's weight, then omap stores the number of missing values
if importance_type == 'weight':
# do a simpler tree dump to save time
trees = self.get_dump(fmap, with_stats=False)
fmap = {}
for tree in trees:
for line in tree.split('\n'):
# look for the opening square bracket
arr = line.split('[')
# if no opening bracket (leaf node), ignore this line
if len(arr) == 1:
continue
# extract feature name from string between []
fid = arr[1].split(']')[0].split('<')[0]
if fid not in fmap:
# if the feature hasn't been seen yet
fmap[fid] = 1
else:
fmap[fid] += 1
return fmap
average_over_splits = True
if importance_type == 'total_gain':
importance_type = 'gain'
average_over_splits = False
elif importance_type == 'total_cover':
importance_type = 'cover'
average_over_splits = False
trees = self.get_dump(fmap, with_stats=True)
importance_type += '='
fmap = {}
gmap = {}
for tree in trees:
for line in tree.split('\n'):
# look for the opening square bracket
arr = line.split('[')
# if no opening bracket (leaf node), ignore this line
if len(arr) == 1:
continue
# look for the closing bracket, extract only info within that bracket
fid = arr[1].split(']')
# extract gain or cover from string after closing bracket
g = float(fid[1].split(importance_type)[1].split(',')[0])
# extract feature name from string before closing bracket
fid = fid[0].split('<')[0]
if fid not in fmap:
# if the feature hasn't been seen yet
fmap[fid] = 1
gmap[fid] = g
else:
fmap[fid] += 1
gmap[fid] += g
# calculate average value (gain/cover) for each feature
if average_over_splits:
for fid in gmap:
gmap[fid] = gmap[fid] / fmap[fid]
return gmap |
Parse a boosted tree model text dump into a pandas DataFrame structure.
This feature is only defined when the decision tree model is chosen as base
learner (`booster in {gbtree, dart}`). It is not defined for other base learner
types, such as linear learners (`booster=gblinear`).
Parameters
----------
fmap: str (optional)
The name of feature map file. | def trees_to_dataframe(self, fmap=''):
"""Parse a boosted tree model text dump into a pandas DataFrame structure.
This feature is only defined when the decision tree model is chosen as base
learner (`booster in {gbtree, dart}`). It is not defined for other base learner
types, such as linear learners (`booster=gblinear`).
Parameters
----------
fmap: str (optional)
The name of feature map file.
"""
# pylint: disable=too-many-locals
if not PANDAS_INSTALLED:
raise Exception(('pandas must be available to use this method.'
'Install pandas before calling again.'))
if getattr(self, 'booster', None) is not None and self.booster not in {'gbtree', 'dart'}:
raise ValueError('This method is not defined for Booster type {}'
.format(self.booster))
tree_ids = []
node_ids = []
fids = []
splits = []
y_directs = []
n_directs = []
missings = []
gains = []
covers = []
trees = self.get_dump(fmap, with_stats=True)
for i, tree in enumerate(trees):
for line in tree.split('\n'):
arr = line.split('[')
# Leaf node
if len(arr) == 1:
# Last element of line.split is an empy string
if arr == ['']:
continue
# parse string
parse = arr[0].split(':')
stats = re.split('=|,', parse[1])
# append to lists
tree_ids.append(i)
node_ids.append(int(re.findall(r'\b\d+\b', parse[0])[0]))
fids.append('Leaf')
splits.append(float('NAN'))
y_directs.append(float('NAN'))
n_directs.append(float('NAN'))
missings.append(float('NAN'))
gains.append(float(stats[1]))
covers.append(float(stats[3]))
# Not a Leaf Node
else:
# parse string
fid = arr[1].split(']')
parse = fid[0].split('<')
stats = re.split('=|,', fid[1])
# append to lists
tree_ids.append(i)
node_ids.append(int(re.findall(r'\b\d+\b', arr[0])[0]))
fids.append(parse[0])
splits.append(float(parse[1]))
str_i = str(i)
y_directs.append(str_i + '-' + stats[1])
n_directs.append(str_i + '-' + stats[3])
missings.append(str_i + '-' + stats[5])
gains.append(float(stats[7]))
covers.append(float(stats[9]))
ids = [str(t_id) + '-' + str(n_id) for t_id, n_id in zip(tree_ids, node_ids)]
df = DataFrame({'Tree': tree_ids, 'Node': node_ids, 'ID': ids,
'Feature': fids, 'Split': splits, 'Yes': y_directs,
'No': n_directs, 'Missing': missings, 'Gain': gains,
'Cover': covers})
if callable(getattr(df, 'sort_values', None)):
# pylint: disable=no-member
return df.sort_values(['Tree', 'Node']).reset_index(drop=True)
# pylint: disable=no-member
return df.sort(['Tree', 'Node']).reset_index(drop=True) |
Validate Booster and data's feature_names are identical.
Set feature_names and feature_types from DMatrix | def _validate_features(self, data):
"""
Validate Booster and data's feature_names are identical.
Set feature_names and feature_types from DMatrix
"""
if self.feature_names is None:
self.feature_names = data.feature_names
self.feature_types = data.feature_types
else:
# Booster can't accept data with different feature names
if self.feature_names != data.feature_names:
dat_missing = set(self.feature_names) - set(data.feature_names)
my_missing = set(data.feature_names) - set(self.feature_names)
msg = 'feature_names mismatch: {0} {1}'
if dat_missing:
msg += ('\nexpected ' + ', '.join(str(s) for s in dat_missing) +
' in input data')
if my_missing:
msg += ('\ntraining data did not have the following fields: ' +
', '.join(str(s) for s in my_missing))
raise ValueError(msg.format(self.feature_names,
data.feature_names)) |
Get split value histogram of a feature
Parameters
----------
feature: str
The name of the feature.
fmap: str (optional)
The name of feature map file.
bin: int, default None
The maximum number of bins.
Number of bins equals number of unique split values n_unique,
if bins == None or bins > n_unique.
as_pandas: bool, default True
Return pd.DataFrame when pandas is installed.
If False or pandas is not installed, return numpy ndarray.
Returns
-------
a histogram of used splitting values for the specified feature
either as numpy array or pandas DataFrame. | def get_split_value_histogram(self, feature, fmap='', bins=None, as_pandas=True):
"""Get split value histogram of a feature
Parameters
----------
feature: str
The name of the feature.
fmap: str (optional)
The name of feature map file.
bin: int, default None
The maximum number of bins.
Number of bins equals number of unique split values n_unique,
if bins == None or bins > n_unique.
as_pandas: bool, default True
Return pd.DataFrame when pandas is installed.
If False or pandas is not installed, return numpy ndarray.
Returns
-------
a histogram of used splitting values for the specified feature
either as numpy array or pandas DataFrame.
"""
xgdump = self.get_dump(fmap=fmap)
values = []
regexp = re.compile(r"\[{0}<([\d.Ee+-]+)\]".format(feature))
for i, _ in enumerate(xgdump):
m = re.findall(regexp, xgdump[i])
values.extend([float(x) for x in m])
n_unique = len(np.unique(values))
bins = max(min(n_unique, bins) if bins is not None else n_unique, 1)
nph = np.histogram(values, bins=bins)
nph = np.column_stack((nph[1][1:], nph[0]))
nph = nph[nph[:, 1] > 0]
if as_pandas and PANDAS_INSTALLED:
return DataFrame(nph, columns=['SplitValue', 'Count'])
if as_pandas and not PANDAS_INSTALLED:
sys.stderr.write(
"Returning histogram as ndarray (as_pandas == True, but pandas is not installed).")
return nph |
Plot importance based on fitted trees.
Parameters
----------
booster : Booster, XGBModel or dict
Booster or XGBModel instance, or dict taken by Booster.get_fscore()
ax : matplotlib Axes, default None
Target axes instance. If None, new figure and axes will be created.
grid : bool, Turn the axes grids on or off. Default is True (On).
importance_type : str, default "weight"
How the importance is calculated: either "weight", "gain", or "cover"
* "weight" is the number of times a feature appears in a tree
* "gain" is the average gain of splits which use the feature
* "cover" is the average coverage of splits which use the feature
where coverage is defined as the number of samples affected by the split
max_num_features : int, default None
Maximum number of top features displayed on plot. If None, all features will be displayed.
height : float, default 0.2
Bar height, passed to ax.barh()
xlim : tuple, default None
Tuple passed to axes.xlim()
ylim : tuple, default None
Tuple passed to axes.ylim()
title : str, default "Feature importance"
Axes title. To disable, pass None.
xlabel : str, default "F score"
X axis title label. To disable, pass None.
ylabel : str, default "Features"
Y axis title label. To disable, pass None.
show_values : bool, default True
Show values on plot. To disable, pass False.
kwargs :
Other keywords passed to ax.barh()
Returns
-------
ax : matplotlib Axes | def plot_importance(booster, ax=None, height=0.2,
xlim=None, ylim=None, title='Feature importance',
xlabel='F score', ylabel='Features',
importance_type='weight', max_num_features=None,
grid=True, show_values=True, **kwargs):
"""Plot importance based on fitted trees.
Parameters
----------
booster : Booster, XGBModel or dict
Booster or XGBModel instance, or dict taken by Booster.get_fscore()
ax : matplotlib Axes, default None
Target axes instance. If None, new figure and axes will be created.
grid : bool, Turn the axes grids on or off. Default is True (On).
importance_type : str, default "weight"
How the importance is calculated: either "weight", "gain", or "cover"
* "weight" is the number of times a feature appears in a tree
* "gain" is the average gain of splits which use the feature
* "cover" is the average coverage of splits which use the feature
where coverage is defined as the number of samples affected by the split
max_num_features : int, default None
Maximum number of top features displayed on plot. If None, all features will be displayed.
height : float, default 0.2
Bar height, passed to ax.barh()
xlim : tuple, default None
Tuple passed to axes.xlim()
ylim : tuple, default None
Tuple passed to axes.ylim()
title : str, default "Feature importance"
Axes title. To disable, pass None.
xlabel : str, default "F score"
X axis title label. To disable, pass None.
ylabel : str, default "Features"
Y axis title label. To disable, pass None.
show_values : bool, default True
Show values on plot. To disable, pass False.
kwargs :
Other keywords passed to ax.barh()
Returns
-------
ax : matplotlib Axes
"""
try:
import matplotlib.pyplot as plt
except ImportError:
raise ImportError('You must install matplotlib to plot importance')
if isinstance(booster, XGBModel):
importance = booster.get_booster().get_score(importance_type=importance_type)
elif isinstance(booster, Booster):
importance = booster.get_score(importance_type=importance_type)
elif isinstance(booster, dict):
importance = booster
else:
raise ValueError('tree must be Booster, XGBModel or dict instance')
if not importance:
raise ValueError('Booster.get_score() results in empty')
tuples = [(k, importance[k]) for k in importance]
if max_num_features is not None:
# pylint: disable=invalid-unary-operand-type
tuples = sorted(tuples, key=lambda x: x[1])[-max_num_features:]
else:
tuples = sorted(tuples, key=lambda x: x[1])
labels, values = zip(*tuples)
if ax is None:
_, ax = plt.subplots(1, 1)
ylocs = np.arange(len(values))
ax.barh(ylocs, values, align='center', height=height, **kwargs)
if show_values is True:
for x, y in zip(values, ylocs):
ax.text(x + 1, y, x, va='center')
ax.set_yticks(ylocs)
ax.set_yticklabels(labels)
if xlim is not None:
if not isinstance(xlim, tuple) or len(xlim) != 2:
raise ValueError('xlim must be a tuple of 2 elements')
else:
xlim = (0, max(values) * 1.1)
ax.set_xlim(xlim)
if ylim is not None:
if not isinstance(ylim, tuple) or len(ylim) != 2:
raise ValueError('ylim must be a tuple of 2 elements')
else:
ylim = (-1, len(values))
ax.set_ylim(ylim)
if title is not None:
ax.set_title(title)
if xlabel is not None:
ax.set_xlabel(xlabel)
if ylabel is not None:
ax.set_ylabel(ylabel)
ax.grid(grid)
return ax |
parse dumped node | def _parse_node(graph, text, condition_node_params, leaf_node_params):
"""parse dumped node"""
match = _NODEPAT.match(text)
if match is not None:
node = match.group(1)
graph.node(node, label=match.group(2), **condition_node_params)
return node
match = _LEAFPAT.match(text)
if match is not None:
node = match.group(1)
graph.node(node, label=match.group(2), **leaf_node_params)
return node
raise ValueError('Unable to parse node: {0}'.format(text)) |
parse dumped edge | def _parse_edge(graph, node, text, yes_color='#0000FF', no_color='#FF0000'):
"""parse dumped edge"""
try:
match = _EDGEPAT.match(text)
if match is not None:
yes, no, missing = match.groups()
if yes == missing:
graph.edge(node, yes, label='yes, missing', color=yes_color)
graph.edge(node, no, label='no', color=no_color)
else:
graph.edge(node, yes, label='yes', color=yes_color)
graph.edge(node, no, label='no, missing', color=no_color)
return
except ValueError:
pass
match = _EDGEPAT2.match(text)
if match is not None:
yes, no = match.groups()
graph.edge(node, yes, label='yes', color=yes_color)
graph.edge(node, no, label='no', color=no_color)
return
raise ValueError('Unable to parse edge: {0}'.format(text)) |
Convert specified tree to graphviz instance. IPython can automatically plot the
returned graphiz instance. Otherwise, you should call .render() method
of the returned graphiz instance.
Parameters
----------
booster : Booster, XGBModel
Booster or XGBModel instance
fmap: str (optional)
The name of feature map file
num_trees : int, default 0
Specify the ordinal number of target tree
rankdir : str, default "UT"
Passed to graphiz via graph_attr
yes_color : str, default '#0000FF'
Edge color when meets the node condition.
no_color : str, default '#FF0000'
Edge color when doesn't meet the node condition.
condition_node_params : dict (optional)
condition node configuration,
{'shape':'box',
'style':'filled,rounded',
'fillcolor':'#78bceb'
}
leaf_node_params : dict (optional)
leaf node configuration
{'shape':'box',
'style':'filled',
'fillcolor':'#e48038'
}
kwargs :
Other keywords passed to graphviz graph_attr
Returns
-------
ax : matplotlib Axes | def to_graphviz(booster, fmap='', num_trees=0, rankdir='UT',
yes_color='#0000FF', no_color='#FF0000',
condition_node_params=None, leaf_node_params=None, **kwargs):
"""Convert specified tree to graphviz instance. IPython can automatically plot the
returned graphiz instance. Otherwise, you should call .render() method
of the returned graphiz instance.
Parameters
----------
booster : Booster, XGBModel
Booster or XGBModel instance
fmap: str (optional)
The name of feature map file
num_trees : int, default 0
Specify the ordinal number of target tree
rankdir : str, default "UT"
Passed to graphiz via graph_attr
yes_color : str, default '#0000FF'
Edge color when meets the node condition.
no_color : str, default '#FF0000'
Edge color when doesn't meet the node condition.
condition_node_params : dict (optional)
condition node configuration,
{'shape':'box',
'style':'filled,rounded',
'fillcolor':'#78bceb'
}
leaf_node_params : dict (optional)
leaf node configuration
{'shape':'box',
'style':'filled',
'fillcolor':'#e48038'
}
kwargs :
Other keywords passed to graphviz graph_attr
Returns
-------
ax : matplotlib Axes
"""
if condition_node_params is None:
condition_node_params = {}
if leaf_node_params is None:
leaf_node_params = {}
try:
from graphviz import Digraph
except ImportError:
raise ImportError('You must install graphviz to plot tree')
if not isinstance(booster, (Booster, XGBModel)):
raise ValueError('booster must be Booster or XGBModel instance')
if isinstance(booster, XGBModel):
booster = booster.get_booster()
tree = booster.get_dump(fmap=fmap)[num_trees]
tree = tree.split()
kwargs = kwargs.copy()
kwargs.update({'rankdir': rankdir})
graph = Digraph(graph_attr=kwargs)
for i, text in enumerate(tree):
if text[0].isdigit():
node = _parse_node(
graph, text, condition_node_params=condition_node_params,
leaf_node_params=leaf_node_params)
else:
if i == 0:
# 1st string must be node
raise ValueError('Unable to parse given string as tree')
_parse_edge(graph, node, text, yes_color=yes_color,
no_color=no_color)
return graph |
Create a new action and assign callbacks, shortcuts, etc. | def newAction(parent, text, slot=None, shortcut=None, icon=None,
tip=None, checkable=False, enabled=True):
"""Create a new action and assign callbacks, shortcuts, etc."""
a = QAction(text, parent)
if icon is not None:
a.setIcon(newIcon(icon))
if shortcut is not None:
if isinstance(shortcut, (list, tuple)):
a.setShortcuts(shortcut)
else:
a.setShortcut(shortcut)
if tip is not None:
a.setToolTip(tip)
a.setStatusTip(tip)
if slot is not None:
a.triggered.connect(slot)
if checkable:
a.setCheckable(True)
a.setEnabled(enabled)
return a |
Sort the list into natural alphanumeric order. | def natural_sort(list, key=lambda s:s):
"""
Sort the list into natural alphanumeric order.
"""
def get_alphanum_key_func(key):
convert = lambda text: int(text) if text.isdigit() else text
return lambda s: [convert(c) for c in re.split('([0-9]+)', key(s))]
sort_key = get_alphanum_key_func(key)
list.sort(key=sort_key) |
Select the first shape created which contains this point. | def selectShapePoint(self, point):
"""Select the first shape created which contains this point."""
self.deSelectShape()
if self.selectedVertex(): # A vertex is marked for selection.
index, shape = self.hVertex, self.hShape
shape.highlightVertex(index, shape.MOVE_VERTEX)
self.selectShape(shape)
return
for shape in reversed(self.shapes):
if self.isVisible(shape) and shape.containsPoint(point):
self.selectShape(shape)
self.calculateOffsets(shape, point)
return |
Update line with last point and current coordinates. | def mouseMoveEvent(self, ev):
"""Update line with last point and current coordinates."""
pos = self.transformPos(ev.pos())
# Update coordinates in status bar if image is opened
window = self.parent().window()
if window.filePath is not None:
self.parent().window().labelCoordinates.setText(
'X: %d; Y: %d' % (pos.x(), pos.y()))
# Polygon drawing.
if self.drawing():
self.overrideCursor(CURSOR_DRAW)
if self.current:
color = self.drawingLineColor
if self.outOfPixmap(pos):
# Don't allow the user to draw outside the pixmap.
# Project the point to the pixmap's edges.
pos = self.intersectionPoint(self.current[-1], pos)
elif len(self.current) > 1 and self.closeEnough(pos, self.current[0]):
# Attract line to starting point and colorise to alert the
# user:
pos = self.current[0]
color = self.current.line_color
self.overrideCursor(CURSOR_POINT)
self.current.highlightVertex(0, Shape.NEAR_VERTEX)
if self.drawSquare:
initPos = self.current[0]
minX = initPos.x()
minY = initPos.y()
min_size = min(abs(pos.x() - minX), abs(pos.y() - minY))
directionX = -1 if pos.x() - minX < 0 else 1
directionY = -1 if pos.y() - minY < 0 else 1
self.line[1] = QPointF(minX + directionX * min_size, minY + directionY * min_size)
else:
self.line[1] = pos
self.line.line_color = color
self.prevPoint = QPointF()
self.current.highlightClear()
else:
self.prevPoint = pos
self.repaint()
return
# Polygon copy moving.
if Qt.RightButton & ev.buttons():
if self.selectedShapeCopy and self.prevPoint:
self.overrideCursor(CURSOR_MOVE)
self.boundedMoveShape(self.selectedShapeCopy, pos)
self.repaint()
elif self.selectedShape:
self.selectedShapeCopy = self.selectedShape.copy()
self.repaint()
return
# Polygon/Vertex moving.
if Qt.LeftButton & ev.buttons():
if self.selectedVertex():
self.boundedMoveVertex(pos)
self.shapeMoved.emit()
self.repaint()
elif self.selectedShape and self.prevPoint:
self.overrideCursor(CURSOR_MOVE)
self.boundedMoveShape(self.selectedShape, pos)
self.shapeMoved.emit()
self.repaint()
return
# Just hovering over the canvas, 2 posibilities:
# - Highlight shapes
# - Highlight vertex
# Update shape/vertex fill and tooltip value accordingly.
self.setToolTip("Image")
for shape in reversed([s for s in self.shapes if self.isVisible(s)]):
# Look for a nearby vertex to highlight. If that fails,
# check if we happen to be inside a shape.
index = shape.nearestVertex(pos, self.epsilon)
if index is not None:
if self.selectedVertex():
self.hShape.highlightClear()
self.hVertex, self.hShape = index, shape
shape.highlightVertex(index, shape.MOVE_VERTEX)
self.overrideCursor(CURSOR_POINT)
self.setToolTip("Click & drag to move point")
self.setStatusTip(self.toolTip())
self.update()
break
elif shape.containsPoint(pos):
if self.selectedVertex():
self.hShape.highlightClear()
self.hVertex, self.hShape = None, shape
self.setToolTip(
"Click & drag to move shape '%s'" % shape.label)
self.setStatusTip(self.toolTip())
self.overrideCursor(CURSOR_GRAB)
self.update()
break
else: # Nothing found, clear highlights, reset state.
if self.hShape:
self.hShape.highlightClear()
self.update()
self.hVertex, self.hShape = None, None
self.overrideCursor(CURSOR_DEFAULT) |
Moves a point x,y to within the boundaries of the canvas.
:return: (x,y,snapped) where snapped is True if x or y were changed, False if not. | def snapPointToCanvas(self, x, y):
"""
Moves a point x,y to within the boundaries of the canvas.
:return: (x,y,snapped) where snapped is True if x or y were changed, False if not.
"""
if x < 0 or x > self.pixmap.width() or y < 0 or y > self.pixmap.height():
x = max(x, 0)
y = max(y, 0)
x = min(x, self.pixmap.width())
y = min(y, self.pixmap.height())
return x, y, True
return x, y, False |
For each edge formed by `points', yield the intersection
with the line segment `(x1,y1) - (x2,y2)`, if it exists.
Also return the distance of `(x2,y2)' to the middle of the
edge along with its index, so that the one closest can be chosen. | def intersectingEdges(self, x1y1, x2y2, points):
"""For each edge formed by `points', yield the intersection
with the line segment `(x1,y1) - (x2,y2)`, if it exists.
Also return the distance of `(x2,y2)' to the middle of the
edge along with its index, so that the one closest can be chosen."""
x1, y1 = x1y1
x2, y2 = x2y2
for i in range(4):
x3, y3 = points[i]
x4, y4 = points[(i + 1) % 4]
denom = (y4 - y3) * (x2 - x1) - (x4 - x3) * (y2 - y1)
nua = (x4 - x3) * (y1 - y3) - (y4 - y3) * (x1 - x3)
nub = (x2 - x1) * (y1 - y3) - (y2 - y1) * (x1 - x3)
if denom == 0:
# This covers two cases:
# nua == nub == 0: Coincident
# otherwise: Parallel
continue
ua, ub = nua / denom, nub / denom
if 0 <= ua <= 1 and 0 <= ub <= 1:
x = x1 + ua * (x2 - x1)
y = y1 + ua * (y2 - y1)
m = QPointF((x3 + x4) / 2, (y3 + y4) / 2)
d = distance(m - QPointF(x2, y2))
yield d, i, (x, y) |
Standard boilerplate Qt application code.
Do everything but app.exec_() -- so that we can test the application in one thread | def get_main_app(argv=[]):
"""
Standard boilerplate Qt application code.
Do everything but app.exec_() -- so that we can test the application in one thread
"""
app = QApplication(argv)
app.setApplicationName(__appname__)
app.setWindowIcon(newIcon("app"))
# Tzutalin 201705+: Accept extra agruments to change predefined class file
# Usage : labelImg.py image predefClassFile saveDir
win = MainWindow(argv[1] if len(argv) >= 2 else None,
argv[2] if len(argv) >= 3 else os.path.join(
os.path.dirname(sys.argv[0]),
'data', 'predefined_classes.txt'),
argv[3] if len(argv) >= 4 else None)
win.show()
return app, win |
Enable/Disable widgets which depend on an opened image. | def toggleActions(self, value=True):
"""Enable/Disable widgets which depend on an opened image."""
for z in self.actions.zoomActions:
z.setEnabled(value)
for action in self.actions.onLoadActive:
action.setEnabled(value) |
In the middle of drawing, toggling between modes should be disabled. | def toggleDrawingSensitive(self, drawing=True):
"""In the middle of drawing, toggling between modes should be disabled."""
self.actions.editMode.setEnabled(not drawing)
if not drawing and self.beginner():
# Cancel creation.
print('Cancel creation.')
self.canvas.setEditing(True)
self.canvas.restoreCursor()
self.actions.create.setEnabled(True) |
Function to handle difficult examples
Update on each object | def btnstate(self, item= None):
""" Function to handle difficult examples
Update on each object """
if not self.canvas.editing():
return
item = self.currentItem()
if not item: # If not selected Item, take the first one
item = self.labelList.item(self.labelList.count()-1)
difficult = self.diffcButton.isChecked()
try:
shape = self.itemsToShapes[item]
except:
pass
# Checked and Update
try:
if difficult != shape.difficult:
shape.difficult = difficult
self.setDirty()
else: # User probably changed item visibility
self.canvas.setShapeVisible(shape, item.checkState() == Qt.Checked)
except:
pass |
Pop-up and give focus to the label editor.
position MUST be in global coordinates. | def newShape(self):
"""Pop-up and give focus to the label editor.
position MUST be in global coordinates.
"""
if not self.useDefaultLabelCheckbox.isChecked() or not self.defaultLabelTextLine.text():
if len(self.labelHist) > 0:
self.labelDialog = LabelDialog(
parent=self, listItem=self.labelHist)
# Sync single class mode from PR#106
if self.singleClassMode.isChecked() and self.lastLabel:
text = self.lastLabel
else:
text = self.labelDialog.popUp(text=self.prevLabelText)
self.lastLabel = text
else:
text = self.defaultLabelTextLine.text()
# Add Chris
self.diffcButton.setChecked(False)
if text is not None:
self.prevLabelText = text
generate_color = generateColorByText(text)
shape = self.canvas.setLastLabel(text, generate_color, generate_color)
self.addLabel(shape)
if self.beginner(): # Switch to edit mode.
self.canvas.setEditing(True)
self.actions.create.setEnabled(True)
else:
self.actions.editMode.setEnabled(True)
self.setDirty()
if text not in self.labelHist:
self.labelHist.append(text)
else:
# self.canvas.undoLastLine()
self.canvas.resetAllLines() |
Load the specified file, or the last opened file if None. | def loadFile(self, filePath=None):
"""Load the specified file, or the last opened file if None."""
self.resetState()
self.canvas.setEnabled(False)
if filePath is None:
filePath = self.settings.get(SETTING_FILENAME)
# Make sure that filePath is a regular python string, rather than QString
filePath = ustr(filePath)
unicodeFilePath = ustr(filePath)
# Tzutalin 20160906 : Add file list and dock to move faster
# Highlight the file item
if unicodeFilePath and self.fileListWidget.count() > 0:
index = self.mImgList.index(unicodeFilePath)
fileWidgetItem = self.fileListWidget.item(index)
fileWidgetItem.setSelected(True)
if unicodeFilePath and os.path.exists(unicodeFilePath):
if LabelFile.isLabelFile(unicodeFilePath):
try:
self.labelFile = LabelFile(unicodeFilePath)
except LabelFileError as e:
self.errorMessage(u'Error opening file',
(u"<p><b>%s</b></p>"
u"<p>Make sure <i>%s</i> is a valid label file.")
% (e, unicodeFilePath))
self.status("Error reading %s" % unicodeFilePath)
return False
self.imageData = self.labelFile.imageData
self.lineColor = QColor(*self.labelFile.lineColor)
self.fillColor = QColor(*self.labelFile.fillColor)
self.canvas.verified = self.labelFile.verified
else:
# Load image:
# read data first and store for saving into label file.
self.imageData = read(unicodeFilePath, None)
self.labelFile = None
self.canvas.verified = False
image = QImage.fromData(self.imageData)
if image.isNull():
self.errorMessage(u'Error opening file',
u"<p>Make sure <i>%s</i> is a valid image file." % unicodeFilePath)
self.status("Error reading %s" % unicodeFilePath)
return False
self.status("Loaded %s" % os.path.basename(unicodeFilePath))
self.image = image
self.filePath = unicodeFilePath
self.canvas.loadPixmap(QPixmap.fromImage(image))
if self.labelFile:
self.loadLabels(self.labelFile.shapes)
self.setClean()
self.canvas.setEnabled(True)
self.adjustScale(initial=True)
self.paintCanvas()
self.addRecentFile(self.filePath)
self.toggleActions(True)
# Label xml file and show bound box according to its filename
# if self.usingPascalVocFormat is True:
if self.defaultSaveDir is not None:
basename = os.path.basename(
os.path.splitext(self.filePath)[0])
xmlPath = os.path.join(self.defaultSaveDir, basename + XML_EXT)
txtPath = os.path.join(self.defaultSaveDir, basename + TXT_EXT)
"""Annotation file priority:
PascalXML > YOLO
"""
if os.path.isfile(xmlPath):
self.loadPascalXMLByFilename(xmlPath)
elif os.path.isfile(txtPath):
self.loadYOLOTXTByFilename(txtPath)
else:
xmlPath = os.path.splitext(filePath)[0] + XML_EXT
txtPath = os.path.splitext(filePath)[0] + TXT_EXT
if os.path.isfile(xmlPath):
self.loadPascalXMLByFilename(xmlPath)
elif os.path.isfile(txtPath):
self.loadYOLOTXTByFilename(txtPath)
self.setWindowTitle(__appname__ + ' ' + filePath)
# Default : select last item if there is at least one item
if self.labelList.count():
self.labelList.setCurrentItem(self.labelList.item(self.labelList.count()-1))
self.labelList.item(self.labelList.count()-1).setSelected(True)
self.canvas.setFocus(True)
return True
return False |
py2/py3 unicode helper | def ustr(x):
'''py2/py3 unicode helper'''
if sys.version_info < (3, 0, 0):
from PyQt4.QtCore import QString
if type(x) == str:
return x.decode(DEFAULT_ENCODING)
if type(x) == QString:
#https://blog.csdn.net/friendan/article/details/51088476
#https://blog.csdn.net/xxm524/article/details/74937308
return unicode(x.toUtf8(), DEFAULT_ENCODING, 'ignore')
return x
else:
return x |
Figure out the size of the pixmap in order to fit the main widget. | def scaleFitWindow(self):
"""Figure out the size of the pixmap in order to fit the main widget."""
e = 2.0 # So that no scrollbars are generated.
w1 = self.centralWidget().width() - e
h1 = self.centralWidget().height() - e
a1 = w1 / h1
# Calculate a new scale value based on the pixmap's aspect ratio.
w2 = self.canvas.pixmap.width() - 0.0
h2 = self.canvas.pixmap.height() - 0.0
a2 = w2 / h2
return w1 / w2 if a2 >= a1 else h1 / h2 |
Return a pretty-printed XML string for the Element. | def prettify(self, elem):
"""
Return a pretty-printed XML string for the Element.
"""
rough_string = ElementTree.tostring(elem, 'utf8')
root = etree.fromstring(rough_string)
return etree.tostring(root, pretty_print=True, encoding=ENCODE_METHOD).replace(" ".encode(), "\t".encode())
# minidom does not support UTF-8
'''reparsed = minidom.parseString(rough_string)
return reparsed.toprettyxml(indent="\t", encoding=ENCODE_METHOD)''' |
Return XML root | def genXML(self):
"""
Return XML root
"""
# Check conditions
if self.filename is None or \
self.foldername is None or \
self.imgSize is None:
return None
top = Element('annotation')
if self.verified:
top.set('verified', 'yes')
folder = SubElement(top, 'folder')
folder.text = self.foldername
filename = SubElement(top, 'filename')
filename.text = self.filename
if self.localImgPath is not None:
localImgPath = SubElement(top, 'path')
localImgPath.text = self.localImgPath
source = SubElement(top, 'source')
database = SubElement(source, 'database')
database.text = self.databaseSrc
size_part = SubElement(top, 'size')
width = SubElement(size_part, 'width')
height = SubElement(size_part, 'height')
depth = SubElement(size_part, 'depth')
width.text = str(self.imgSize[1])
height.text = str(self.imgSize[0])
if len(self.imgSize) == 3:
depth.text = str(self.imgSize[2])
else:
depth.text = '1'
segmented = SubElement(top, 'segmented')
segmented.text = '0'
return top |
Perform a HTTP request and return decoded JSON data | async def fetch(self, url, method='GET', headers=None, body=None):
"""Perform a HTTP request and return decoded JSON data"""
request_headers = self.prepare_request_headers(headers)
url = self.proxy + url
if self.verbose:
print("\nRequest:", method, url, headers, body)
self.logger.debug("%s %s, Request: %s %s", method, url, headers, body)
encoded_body = body.encode() if body else None
session_method = getattr(self.session, method.lower())
response = None
http_response = None
json_response = None
try:
async with session_method(yarl.URL(url, encoded=True),
data=encoded_body,
headers=request_headers,
timeout=(self.timeout / 1000),
proxy=self.aiohttp_proxy) as response:
http_response = await response.text()
json_response = self.parse_json(http_response) if self.is_json_encoded_object(http_response) else None
headers = response.headers
if self.enableLastHttpResponse:
self.last_http_response = http_response
if self.enableLastResponseHeaders:
self.last_response_headers = headers
if self.enableLastJsonResponse:
self.last_json_response = json_response
if self.verbose:
print("\nResponse:", method, url, response.status, headers, http_response)
self.logger.debug("%s %s, Response: %s %s %s", method, url, response.status, headers, http_response)
except socket.gaierror as e:
self.raise_error(ExchangeNotAvailable, url, method, e, None)
except concurrent.futures._base.TimeoutError as e:
self.raise_error(RequestTimeout, method, url, e, None)
except aiohttp.client_exceptions.ClientConnectionError as e:
self.raise_error(ExchangeNotAvailable, url, method, e, None)
except aiohttp.client_exceptions.ClientError as e: # base exception class
self.raise_error(ExchangeError, url, method, e, None)
self.handle_errors(response.status, response.reason, url, method, headers, http_response, json_response)
self.handle_rest_errors(None, response.status, http_response, url, method)
self.handle_rest_response(http_response, json_response, url, method, headers, body)
if json_response is not None:
return json_response
return http_response |
A better wrapper over request for deferred signing | def fetch2(self, path, api='public', method='GET', params={}, headers=None, body=None):
"""A better wrapper over request for deferred signing"""
if self.enableRateLimit:
self.throttle()
self.lastRestRequestTimestamp = self.milliseconds()
request = self.sign(path, api, method, params, headers, body)
return self.fetch(request['url'], request['method'], request['headers'], request['body']) |
Exchange.request is the entry point for all generated methods | def request(self, path, api='public', method='GET', params={}, headers=None, body=None):
"""Exchange.request is the entry point for all generated methods"""
return self.fetch2(path, api, method, params, headers, body) |
A helper method for matching error strings exactly vs broadly | def find_broadly_matched_key(self, broad, string):
"""A helper method for matching error strings exactly vs broadly"""
keys = list(broad.keys())
for i in range(0, len(keys)):
key = keys[i]
if string.find(key) >= 0:
return key
return None |
Perform a HTTP request and return decoded JSON data | def fetch(self, url, method='GET', headers=None, body=None):
"""Perform a HTTP request and return decoded JSON data"""
request_headers = self.prepare_request_headers(headers)
url = self.proxy + url
if self.verbose:
print("\nRequest:", method, url, request_headers, body)
self.logger.debug("%s %s, Request: %s %s", method, url, request_headers, body)
if body:
body = body.encode()
self.session.cookies.clear()
response = None
http_response = None
json_response = None
try:
response = self.session.request(
method,
url,
data=body,
headers=request_headers,
timeout=int(self.timeout / 1000),
proxies=self.proxies
)
http_response = response.text
json_response = self.parse_json(http_response) if self.is_json_encoded_object(http_response) else None
headers = response.headers
# FIXME remove last_x_responses from subclasses
if self.enableLastHttpResponse:
self.last_http_response = http_response
if self.enableLastJsonResponse:
self.last_json_response = json_response
if self.enableLastResponseHeaders:
self.last_response_headers = headers
if self.verbose:
print("\nResponse:", method, url, response.status_code, headers, http_response)
self.logger.debug("%s %s, Response: %s %s %s", method, url, response.status_code, headers, http_response)
response.raise_for_status()
except Timeout as e:
self.raise_error(RequestTimeout, method, url, e)
except TooManyRedirects as e:
self.raise_error(ExchangeError, url, method, e)
except SSLError as e:
self.raise_error(ExchangeError, url, method, e)
except HTTPError as e:
self.handle_errors(response.status_code, response.reason, url, method, headers, http_response, json_response)
self.handle_rest_errors(e, response.status_code, http_response, url, method)
self.raise_error(ExchangeError, url, method, e, http_response)
except RequestException as e: # base exception class
error_string = str(e)
if ('ECONNRESET' in error_string) or ('Connection aborted.' in error_string):
self.raise_error(NetworkError, url, method, e)
else:
self.raise_error(ExchangeError, url, method, e)
self.handle_errors(response.status_code, response.reason, url, method, headers, http_response, json_response)
self.handle_rest_response(http_response, json_response, url, method, headers, body)
if json_response is not None:
return json_response
return http_response |
A helper-wrapper for the safe_value_2() family. | def safe_either(method, dictionary, key1, key2, default_value=None):
"""A helper-wrapper for the safe_value_2() family."""
value = method(dictionary, key1)
return value if value is not None else method(dictionary, key2, default_value) |
Deprecated, use decimal_to_precision instead | def truncate(num, precision=0):
"""Deprecated, use decimal_to_precision instead"""
if precision > 0:
decimal_precision = math.pow(10, precision)
return math.trunc(num * decimal_precision) / decimal_precision
return int(Exchange.truncate_to_string(num, precision)) |
Deprecated, todo: remove references from subclasses | def truncate_to_string(num, precision=0):
"""Deprecated, todo: remove references from subclasses"""
if precision > 0:
parts = ('{0:.%df}' % precision).format(Decimal(num)).split('.')
decimal_digits = parts[1][:precision].rstrip('0')
decimal_digits = decimal_digits if len(decimal_digits) else '0'
return parts[0] + '.' + decimal_digits
return ('%d' % num) |
Checks an address is not the same character repeated or an empty sequence | def check_address(self, address):
"""Checks an address is not the same character repeated or an empty sequence"""
if address is None:
self.raise_error(InvalidAddress, details='address is None')
if all(letter == address[0] for letter in address) or len(address) < self.minFundingAddressLength or ' ' in address:
self.raise_error(InvalidAddress, details='address is invalid or has less than ' + str(self.minFundingAddressLength) + ' characters: "' + str(address) + '"')
return address |
r'''
Expects something like /tmp/tmpAjry4Gdsbench/test.weights.e5.XXX.YYY.pb
Where XXX is a variation on the model size for example
And where YYY is a const related to the training dataset | def reduce_filename(f):
r'''
Expects something like /tmp/tmpAjry4Gdsbench/test.weights.e5.XXX.YYY.pb
Where XXX is a variation on the model size for example
And where YYY is a const related to the training dataset
'''
f = os.path.basename(f).split('.')
return keep_only_digits(f[-3]) |
r'''
local helper to just keep digits | def keep_only_digits(s):
r'''
local helper to just keep digits
'''
fs = ''
for c in s:
if c.isdigit():
fs += c
return int(fs) |
r"""
Parses an STM file at ``stm_file`` into a list of :class:`STMSegment`. | def parse_stm_file(stm_file):
r"""
Parses an STM file at ``stm_file`` into a list of :class:`STMSegment`.
"""
stm_segments = []
with codecs.open(stm_file, encoding="utf-8") as stm_lines:
for stm_line in stm_lines:
stmSegment = STMSegment(stm_line)
if not "ignore_time_segment_in_scoring" == stmSegment.transcript:
stm_segments.append(stmSegment)
return stm_segments |
Reads a .wav file.
Takes the path, and returns (PCM audio data, sample rate). | def read_wave(path):
"""Reads a .wav file.
Takes the path, and returns (PCM audio data, sample rate).
"""
with contextlib.closing(wave.open(path, 'rb')) as wf:
num_channels = wf.getnchannels()
assert num_channels == 1
sample_width = wf.getsampwidth()
assert sample_width == 2
sample_rate = wf.getframerate()
assert sample_rate in (8000, 16000, 32000)
frames = wf.getnframes()
pcm_data = wf.readframes(frames)
duration = frames / sample_rate
return pcm_data, sample_rate, duration |
Writes a .wav file.
Takes path, PCM audio data, and sample rate. | def write_wave(path, audio, sample_rate):
"""Writes a .wav file.
Takes path, PCM audio data, and sample rate.
"""
with contextlib.closing(wave.open(path, 'wb')) as wf:
wf.setnchannels(1)
wf.setsampwidth(2)
wf.setframerate(sample_rate)
wf.writeframes(audio) |
Generates audio frames from PCM audio data.
Takes the desired frame duration in milliseconds, the PCM data, and
the sample rate.
Yields Frames of the requested duration. | def frame_generator(frame_duration_ms, audio, sample_rate):
"""Generates audio frames from PCM audio data.
Takes the desired frame duration in milliseconds, the PCM data, and
the sample rate.
Yields Frames of the requested duration.
"""
n = int(sample_rate * (frame_duration_ms / 1000.0) * 2)
offset = 0
timestamp = 0.0
duration = (float(n) / sample_rate) / 2.0
while offset + n < len(audio):
yield Frame(audio[offset:offset + n], timestamp, duration)
timestamp += duration
offset += n |
Initialise the runner function with the passed args, kwargs | def run(self):
'''
Initialise the runner function with the passed args, kwargs
'''
# Retrieve args/kwargs here; and fire up the processing using them
try:
transcript = self.fn(*self.args, **self.kwargs)
except:
traceback.print_exc()
exctype, value = sys.exc_info()[:2]
self.signals.error.emit((exctype, value, traceback.format_exc()))
else:
# Return the result of the processing
self.signals.result.emit(transcript)
finally:
# Done
self.signals.finished.emit() |
r'''
Helper to exec locally (subprocess) or remotely (paramiko) | def exec_command(command, cwd=None):
r'''
Helper to exec locally (subprocess) or remotely (paramiko)
'''
rc = None
stdout = stderr = None
if ssh_conn is None:
ld_library_path = {'LD_LIBRARY_PATH': '.:%s' % os.environ.get('LD_LIBRARY_PATH', '')}
p = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=ld_library_path, cwd=cwd)
stdout, stderr = p.communicate()
rc = p.returncode
else:
# environment= requires paramiko >= 2.1 (fails with 2.0.2)
final_command = command if cwd is None else 'cd %s && %s %s' % (cwd, 'LD_LIBRARY_PATH=.:$LD_LIBRARY_PATH', command)
ssh_stdin, ssh_stdout, ssh_stderr = ssh_conn.exec_command(final_command)
stdout = ''.join(ssh_stdout.readlines())
stderr = ''.join(ssh_stderr.readlines())
rc = ssh_stdout.channel.recv_exit_status()
return rc, stdout, stderr |
r'''
Check local or remote system arch, to produce TaskCluster proper link. | def get_arch_string():
r'''
Check local or remote system arch, to produce TaskCluster proper link.
'''
rc, stdout, stderr = exec_command('uname -sm')
if rc > 0:
raise AssertionError('Error checking OS')
stdout = stdout.lower().strip()
if not 'linux' in stdout:
raise AssertionError('Unsupported OS')
if 'armv7l' in stdout:
return 'arm'
if 'x86_64' in stdout:
nv_rc, nv_stdout, nv_stderr = exec_command('nvidia-smi')
nv_stdout = nv_stdout.lower().strip()
if 'NVIDIA-SMI' in nv_stdout:
return 'gpu'
else:
return 'cpu'
raise AssertionError('Unsupported arch:', stdout) |
r'''
Download a native_client.tar.xz file from TaskCluster and extract it to dir. | def extract_native_client_tarball(dir):
r'''
Download a native_client.tar.xz file from TaskCluster and extract it to dir.
'''
assert_valid_dir(dir)
target_tarball = os.path.join(dir, 'native_client.tar.xz')
if os.path.isfile(target_tarball) and os.stat(target_tarball).st_size == 0:
return
subprocess.check_call(['pixz', '-d', 'native_client.tar.xz'], cwd=dir)
subprocess.check_call(['tar', 'xf', 'native_client.tar'], cwd=dir)
os.unlink(os.path.join(dir, 'native_client.tar'))
open(target_tarball, 'w').close() |
r'''
Ensure that a path is a zip file by:
- checking length is 1
- checking extension is '.zip' | def is_zip_file(models):
r'''
Ensure that a path is a zip file by:
- checking length is 1
- checking extension is '.zip'
'''
ext = os.path.splitext(models[0])[1]
return (len(models) == 1) and (ext == '.zip') |
r'''
Detect if models is a list of protocolbuffer files or a ZIP file.
If the latter, then unzip it and return the list of protocolbuffer files
that were inside. | def maybe_inspect_zip(models):
r'''
Detect if models is a list of protocolbuffer files or a ZIP file.
If the latter, then unzip it and return the list of protocolbuffer files
that were inside.
'''
if not(is_zip_file(models)):
return models
if len(models) > 1:
return models
if len(models) < 1:
raise AssertionError('No models at all')
return zipfile.ZipFile(models[0]).namelist() |
r'''
Copy models, libs and binary to a directory (new one if dir is None) | def setup_tempdir(dir, models, wav, alphabet, lm_binary, trie, binaries):
r'''
Copy models, libs and binary to a directory (new one if dir is None)
'''
if dir is None:
dir = tempfile.mkdtemp(suffix='dsbench')
sorted_models = all_files(models=models)
if binaries is None:
maybe_download_binaries(dir)
else:
print('Using local binaries: %s' % (binaries))
shutil.copy2(binaries, dir)
extract_native_client_tarball(dir)
filenames = map(lambda x: os.path.join(dir, os.path.basename(x)), sorted_models)
missing_models = filter(lambda x: not os.path.isfile(x), filenames)
if len(missing_models) > 0:
# If we have a ZIP file, directly extract it to the proper path
if is_zip_file(models):
print('Extracting %s to %s' % (models[0], dir))
zipfile.ZipFile(models[0]).extractall(path=dir)
print('Extracted %s.' % models[0])
else:
# If one model is missing, let's copy everything again. Be safe.
for f in sorted_models:
print('Copying %s to %s' % (f, dir))
shutil.copy2(f, dir)
for extra_file in [ wav, alphabet, lm_binary, trie ]:
if extra_file and not os.path.isfile(os.path.join(dir, os.path.basename(extra_file))):
print('Copying %s to %s' % (extra_file, dir))
shutil.copy2(extra_file, dir)
if ssh_conn:
copy_tree(dir)
return dir, sorted_models |
r'''
Return a list of full path of files matching 'models', sorted in human
numerical order (i.e., 0 1 2 ..., 10 11 12, ..., 100, ..., 1000).
Files are supposed to be named identically except one variable component
e.g. the list,
test.weights.e5.lstm1200.ldc93s1.pb
test.weights.e5.lstm1000.ldc93s1.pb
test.weights.e5.lstm800.ldc93s1.pb
gets sorted:
test.weights.e5.lstm800.ldc93s1.pb
test.weights.e5.lstm1000.ldc93s1.pb
test.weights.e5.lstm1200.ldc93s1.pb | def all_files(models=[]):
r'''
Return a list of full path of files matching 'models', sorted in human
numerical order (i.e., 0 1 2 ..., 10 11 12, ..., 100, ..., 1000).
Files are supposed to be named identically except one variable component
e.g. the list,
test.weights.e5.lstm1200.ldc93s1.pb
test.weights.e5.lstm1000.ldc93s1.pb
test.weights.e5.lstm800.ldc93s1.pb
gets sorted:
test.weights.e5.lstm800.ldc93s1.pb
test.weights.e5.lstm1000.ldc93s1.pb
test.weights.e5.lstm1200.ldc93s1.pb
'''
def nsort(a, b):
fa = os.path.basename(a).split('.')
fb = os.path.basename(b).split('.')
elements_to_remove = []
assert len(fa) == len(fb)
for i in range(0, len(fa)):
if fa[i] == fb[i]:
elements_to_remove.append(fa[i])
for e in elements_to_remove:
fa.remove(e)
fb.remove(e)
assert len(fa) == len(fb)
assert len(fa) == 1
fa = keep_only_digits(fa[0])
fb = keep_only_digits(fb[0])
if fa < fb:
return -1
if fa == fb:
return 0
if fa > fb:
return 1
base = list(map(lambda x: os.path.abspath(x), maybe_inspect_zip(models)))
base.sort(cmp=nsort)
return base |
r'''
Cleanup temporary directory. | def teardown_tempdir(dir):
r'''
Cleanup temporary directory.
'''
if ssh_conn:
delete_tree(dir)
assert_valid_dir(dir)
shutil.rmtree(dir) |
r'''
Read user's SSH configuration file | def get_sshconfig():
r'''
Read user's SSH configuration file
'''
with open(os.path.expanduser('~/.ssh/config')) as f:
cfg = paramiko.SSHConfig()
cfg.parse(f)
ret_dict = {}
for d in cfg._config:
_copy = dict(d)
# Avoid buggy behavior with strange host definitions, we need
# Hostname and not Host.
del _copy['host']
for host in d['host']:
ret_dict[host] = _copy['config']
return ret_dict |
r'''
Establish a SSH connection to a remote host. It should be able to use
SSH's config file Host name declarations. By default, will not automatically
add trust for hosts, will use SSH agent and will try to load keys. | def establish_ssh(target=None, auto_trust=False, allow_agent=True, look_keys=True):
r'''
Establish a SSH connection to a remote host. It should be able to use
SSH's config file Host name declarations. By default, will not automatically
add trust for hosts, will use SSH agent and will try to load keys.
'''
def password_prompt(username, hostname):
r'''
If the Host is relying on password authentication, lets ask it.
Relying on SSH itself to take care of that would not work when the
remote authentication is password behind a SSH-key+2FA jumphost.
'''
return getpass.getpass('No SSH key for %s@%s, please provide password: ' % (username, hostname))
ssh_conn = None
if target is not None:
ssh_conf = get_sshconfig()
cfg = {
'hostname': None,
'port': 22,
'allow_agent': allow_agent,
'look_for_keys': look_keys
}
if ssh_conf.has_key(target):
user_config = ssh_conf.get(target)
# If ssh_config file's Host defined 'User' instead of 'Username'
if user_config.has_key('user') and not user_config.has_key('username'):
user_config['username'] = user_config['user']
del user_config['user']
for k in ('username', 'hostname', 'port'):
if k in user_config:
cfg[k] = user_config[k]
# Assume Password auth. If we don't do that, then when connecting
# through a jumphost we will run into issues and the user will
# not be able to input his password to the SSH prompt.
if 'identityfile' in user_config:
cfg['key_filename'] = user_config['identityfile']
else:
cfg['password'] = password_prompt(cfg['username'], cfg['hostname'] or target)
# Should be the last one, since ProxyCommand will issue connection to remote host
if 'proxycommand' in user_config:
cfg['sock'] = paramiko.ProxyCommand(user_config['proxycommand'])
else:
cfg['username'] = target.split('@')[0]
cfg['hostname'] = target.split('@')[1].split(':')[0]
cfg['password'] = password_prompt(cfg['username'], cfg['hostname'])
try:
cfg['port'] = int(target.split('@')[1].split(':')[1])
except IndexError:
# IndexError will happen if no :PORT is there.
# Default value 22 is defined above in 'cfg'.
pass
ssh_conn = paramiko.SSHClient()
if auto_trust:
ssh_conn.set_missing_host_key_policy(paramiko.AutoAddPolicy())
ssh_conn.connect(**cfg)
return ssh_conn |
r'''
Core of the running of the benchmarks. We will run on all of models, against
the WAV file provided as wav, and the provided alphabet. | def run_benchmarks(dir, models, wav, alphabet, lm_binary=None, trie=None, iters=-1):
r'''
Core of the running of the benchmarks. We will run on all of models, against
the WAV file provided as wav, and the provided alphabet.
'''
assert_valid_dir(dir)
inference_times = [ ]
for model in models:
model_filename = model
current_model = {
'name': model,
'iters': [ ],
'mean': numpy.infty,
'stddev': numpy.infty
}
if lm_binary and trie:
cmdline = './deepspeech --model "%s" --alphabet "%s" --lm "%s" --trie "%s" --audio "%s" -t' % (model_filename, alphabet, lm_binary, trie, wav)
else:
cmdline = './deepspeech --model "%s" --alphabet "%s" --audio "%s" -t' % (model_filename, alphabet, wav)
for it in range(iters):
sys.stdout.write('\rRunning %s: %d/%d' % (os.path.basename(model), (it+1), iters))
sys.stdout.flush()
rc, stdout, stderr = exec_command(cmdline, cwd=dir)
if rc == 0:
inference_time = float(stdout.split('\n')[1].split('=')[-1])
# print("[%d] model=%s inference=%f" % (it, model, inference_time))
current_model['iters'].append(inference_time)
else:
print('exec_command("%s") failed with rc=%d' % (cmdline, rc))
print('stdout: %s' % stdout)
print('stderr: %s' % stderr)
raise AssertionError('Execution failure: rc=%d' % (rc))
sys.stdout.write('\n')
sys.stdout.flush()
current_model['mean'] = numpy.mean(current_model['iters'])
current_model['stddev'] = numpy.std(current_model['iters'])
inference_times.append(current_model)
return inference_times |
r'''
Take an input dictionnary and write it to the object-file output. | def produce_csv(input, output):
r'''
Take an input dictionnary and write it to the object-file output.
'''
output.write('"model","mean","std"\n')
for model_data in input:
output.write('"%s",%f,%f\n' % (model_data['name'], model_data['mean'], model_data['stddev']))
output.flush()
output.close()
print("Wrote as %s" % output.name) |
r"""Creates a sparse representention of ``sequence``.
Returns a tuple with (indices, values, shape) | def to_sparse_tuple(sequence):
r"""Creates a sparse representention of ``sequence``.
Returns a tuple with (indices, values, shape)
"""
indices = np.asarray(list(zip([0]*len(sequence), range(len(sequence)))), dtype=np.int64)
shape = np.asarray([1, len(sequence)], dtype=np.int64)
return indices, sequence, shape |
Generate a function to download a file based on given parameters
This works by currying the above given arguments into a closure
in the form of the following function.
:param voxforge_url: the base voxforge URL
:param archive_dir: the location to store the downloaded file
:param total: the total number of files to download
:param counter: an atomic counter to keep track of # of downloaded files
:return: a function that actually downloads a file given these params | def _parallel_downloader(voxforge_url, archive_dir, total, counter):
"""Generate a function to download a file based on given parameters
This works by currying the above given arguments into a closure
in the form of the following function.
:param voxforge_url: the base voxforge URL
:param archive_dir: the location to store the downloaded file
:param total: the total number of files to download
:param counter: an atomic counter to keep track of # of downloaded files
:return: a function that actually downloads a file given these params
"""
def download(d):
"""Binds voxforge_url, archive_dir, total, and counter into this scope
Downloads the given file
:param d: a tuple consisting of (index, file) where index is the index
of the file to download and file is the name of the file to download
"""
(i, file) = d
download_url = voxforge_url + '/' + file
c = counter.increment()
print('Downloading file {} ({}/{})...'.format(i+1, c, total))
maybe_download(filename_of(download_url), archive_dir, download_url)
return download |
Generate a function to extract a tar file based on given parameters
This works by currying the above given arguments into a closure
in the form of the following function.
:param data_dir: the target directory to extract into
:param number_of_test: the number of files to keep as the test set
:param number_of_dev: the number of files to keep as the dev set
:param total: the total number of files to extract
:param counter: an atomic counter to keep track of # of extracted files
:return: a function that actually extracts a tar file given these params | def _parallel_extracter(data_dir, number_of_test, number_of_dev, total, counter):
"""Generate a function to extract a tar file based on given parameters
This works by currying the above given arguments into a closure
in the form of the following function.
:param data_dir: the target directory to extract into
:param number_of_test: the number of files to keep as the test set
:param number_of_dev: the number of files to keep as the dev set
:param total: the total number of files to extract
:param counter: an atomic counter to keep track of # of extracted files
:return: a function that actually extracts a tar file given these params
"""
def extract(d):
"""Binds data_dir, number_of_test, number_of_dev, total, and counter into this scope
Extracts the given file
:param d: a tuple consisting of (index, file) where index is the index
of the file to extract and file is the name of the file to extract
"""
(i, archive) = d
if i < number_of_test:
dataset_dir = path.join(data_dir, "test")
elif i<number_of_test+number_of_dev:
dataset_dir = path.join(data_dir, "dev")
else:
dataset_dir = path.join(data_dir, "train")
if not gfile.Exists(path.join(dataset_dir, '.'.join(filename_of(archive).split(".")[:-1]))):
c = counter.increment()
print('Extracting file {} ({}/{})...'.format(i+1, c, total))
tar = tarfile.open(archive)
tar.extractall(dataset_dir)
tar.close()
return extract |
Increments the counter by the given amount
:param amount: the amount to increment by (default 1)
:return: the incremented value of the counter | def increment(self, amount=1):
"""Increments the counter by the given amount
:param amount: the amount to increment by (default 1)
:return: the incremented value of the counter
"""
self.__lock.acquire()
self.__count += amount
v = self.value()
self.__lock.release()
return v |
r"""
Given a :class:`tf.SparseTensor` ``value``, return an array of Python strings
representing its values, converting tokens to strings using ``alphabet``. | def sparse_tensor_value_to_texts(value, alphabet):
r"""
Given a :class:`tf.SparseTensor` ``value``, return an array of Python strings
representing its values, converting tokens to strings using ``alphabet``.
"""
return sparse_tuple_to_texts((value.indices, value.values, value.dense_shape), alphabet) |
Parse command line parameters
Args:
args ([str]): Command line parameters as list of strings
Returns:
:obj:`argparse.Namespace`: command line parameters namespace | def parse_args(args):
"""Parse command line parameters
Args:
args ([str]): Command line parameters as list of strings
Returns:
:obj:`argparse.Namespace`: command line parameters namespace
"""
parser = argparse.ArgumentParser(
description="Imports GramVaani data for Deep Speech"
)
parser.add_argument(
"--version",
action="version",
version="GramVaaniImporter {ver}".format(ver=__version__),
)
parser.add_argument(
"-v",
"--verbose",
action="store_const",
required=False,
help="set loglevel to INFO",
dest="loglevel",
const=logging.INFO,
)
parser.add_argument(
"-vv",
"--very-verbose",
action="store_const",
required=False,
help="set loglevel to DEBUG",
dest="loglevel",
const=logging.DEBUG,
)
parser.add_argument(
"-c",
"--csv_filename",
required=True,
help="Path to the GramVaani csv",
dest="csv_filename",
)
parser.add_argument(
"-t",
"--target_dir",
required=True,
help="Directory in which to save the importer GramVaani data",
dest="target_dir",
)
return parser.parse_args(args) |
Setup basic logging
Args:
level (int): minimum log level for emitting messages | def setup_logging(level):
"""Setup basic logging
Args:
level (int): minimum log level for emitting messages
"""
format = "[%(asctime)s] %(levelname)s:%(name)s:%(message)s"
logging.basicConfig(
level=level, stream=sys.stdout, format=format, datefmt="%Y-%m-%d %H:%M:%S"
) |
Main entry point allowing external calls
Args:
args ([str]): command line parameter list | def main(args):
"""Main entry point allowing external calls
Args:
args ([str]): command line parameter list
"""
args = parse_args(args)
setup_logging(args.loglevel)
_logger.info("Starting GramVaani importer...")
_logger.info("Starting loading GramVaani csv...")
csv = GramVaaniCSV(args.csv_filename)
_logger.info("Starting downloading GramVaani mp3's...")
downloader = GramVaaniDownloader(csv, args.target_dir)
mp3_directory = downloader.download()
_logger.info("Starting converting GramVaani mp3's to wav's...")
converter = GramVaaniConverter(args.target_dir, mp3_directory)
wav_directory = converter.convert()
datasets = GramVaaniDataSets(args.target_dir, wav_directory, csv)
datasets.create()
datasets.save()
_logger.info("Finished GramVaani importer...") |
Downloads the data associated with this instance
Return:
mp3_directory (os.path): The directory into which the associated mp3's were downloaded | def download(self):
"""Downloads the data associated with this instance
Return:
mp3_directory (os.path): The directory into which the associated mp3's were downloaded
"""
mp3_directory = self._pre_download()
self.data.swifter.apply(func=lambda arg: self._download(*arg, mp3_directory), axis=1, raw=True)
return mp3_directory |
Converts the mp3's associated with this instance to wav's
Return:
wav_directory (os.path): The directory into which the associated wav's were downloaded | def convert(self):
"""Converts the mp3's associated with this instance to wav's
Return:
wav_directory (os.path): The directory into which the associated wav's were downloaded
"""
wav_directory = self._pre_convert()
for mp3_filename in self.mp3_directory.glob('**/*.mp3'):
wav_filename = path.join(wav_directory, os.path.splitext(os.path.basename(mp3_filename))[0] + ".wav")
if not path.exists(wav_filename):
_logger.debug("Converting mp3 file %s to wav file %s" % (mp3_filename, wav_filename))
transformer = Transformer()
transformer.convert(samplerate=SAMPLE_RATE, n_channels=N_CHANNELS, bitdepth=BITDEPTH)
transformer.build(str(mp3_filename), str(wav_filename))
else:
_logger.debug("Already converted mp3 file %s to wav file %s" % (mp3_filename, wav_filename))
return wav_directory |
r"""
Given a Python string ``original``, remove unsupported characters, map characters
to integers and return a numpy array representing the processed string. | def text_to_char_array(original, alphabet):
r"""
Given a Python string ``original``, remove unsupported characters, map characters
to integers and return a numpy array representing the processed string.
"""
return np.asarray([alphabet.label_from_string(c) for c in original]) |
r"""
The WER is defined as the editing/Levenshtein distance on word level
divided by the amount of words in the original text.
In case of the original having more words (N) than the result and both
being totally different (all N words resulting in 1 edit operation each),
the WER will always be 1 (N / N = 1). | def wer_cer_batch(originals, results):
r"""
The WER is defined as the editing/Levenshtein distance on word level
divided by the amount of words in the original text.
In case of the original having more words (N) than the result and both
being totally different (all N words resulting in 1 edit operation each),
the WER will always be 1 (N / N = 1).
"""
# The WER is calculated on word (and NOT on character) level.
# Therefore we split the strings into words first
assert len(originals) == len(results)
total_cer = 0.0
total_char_length = 0.0
total_wer = 0.0
total_word_length = 0.0
for original, result in zip(originals, results):
total_cer += levenshtein(original, result)
total_char_length += len(original)
total_wer += levenshtein(original.split(), result.split())
total_word_length += len(original.split())
return total_wer / total_word_length, total_cer / total_char_length |
r"""
Next we concern ourselves with graph creation.
However, before we do so we must introduce a utility function ``variable_on_cpu()``
used to create a variable in CPU memory. | def variable_on_cpu(name, shape, initializer):
r"""
Next we concern ourselves with graph creation.
However, before we do so we must introduce a utility function ``variable_on_cpu()``
used to create a variable in CPU memory.
"""
# Use the /cpu:0 device for scoped operations
with tf.device(Config.cpu_device):
# Create or get apropos variable
var = tf.get_variable(name=name, shape=shape, initializer=initializer)
return var |
r'''
This routine beam search decodes a mini-batch and calculates the loss and mean edit distance.
Next to total and average loss it returns the mean edit distance,
the decoded result and the batch's original Y. | def calculate_mean_edit_distance_and_loss(iterator, dropout, reuse):
r'''
This routine beam search decodes a mini-batch and calculates the loss and mean edit distance.
Next to total and average loss it returns the mean edit distance,
the decoded result and the batch's original Y.
'''
# Obtain the next batch of data
(batch_x, batch_seq_len), batch_y = iterator.get_next()
# Calculate the logits of the batch
logits, _ = create_model(batch_x, batch_seq_len, dropout, reuse=reuse)
# Compute the CTC loss using TensorFlow's `ctc_loss`
total_loss = tf.nn.ctc_loss(labels=batch_y, inputs=logits, sequence_length=batch_seq_len)
# Calculate the average loss across the batch
avg_loss = tf.reduce_mean(total_loss)
# Finally we return the average loss
return avg_loss |
r'''
With this preliminary step out of the way, we can for each GPU introduce a
tower for which's batch we calculate and return the optimization gradients
and the average loss across towers. | def get_tower_results(iterator, optimizer, dropout_rates):
r'''
With this preliminary step out of the way, we can for each GPU introduce a
tower for which's batch we calculate and return the optimization gradients
and the average loss across towers.
'''
# To calculate the mean of the losses
tower_avg_losses = []
# Tower gradients to return
tower_gradients = []
with tf.variable_scope(tf.get_variable_scope()):
# Loop over available_devices
for i in range(len(Config.available_devices)):
# Execute operations of tower i on device i
device = Config.available_devices[i]
with tf.device(device):
# Create a scope for all operations of tower i
with tf.name_scope('tower_%d' % i):
# Calculate the avg_loss and mean_edit_distance and retrieve the decoded
# batch along with the original batch's labels (Y) of this tower
avg_loss = calculate_mean_edit_distance_and_loss(iterator, dropout_rates, reuse=i > 0)
# Allow for variables to be re-used by the next tower
tf.get_variable_scope().reuse_variables()
# Retain tower's avg losses
tower_avg_losses.append(avg_loss)
# Compute gradients for model parameters using tower's mini-batch
gradients = optimizer.compute_gradients(avg_loss)
# Retain tower's gradients
tower_gradients.append(gradients)
avg_loss_across_towers = tf.reduce_mean(tower_avg_losses, 0)
tf.summary.scalar(name='step_loss', tensor=avg_loss_across_towers, collections=['step_summaries'])
# Return gradients and the average loss
return tower_gradients, avg_loss_across_towers |
r'''
A routine for computing each variable's average of the gradients obtained from the GPUs.
Note also that this code acts as a synchronization point as it requires all
GPUs to be finished with their mini-batch before it can run to completion. | def average_gradients(tower_gradients):
r'''
A routine for computing each variable's average of the gradients obtained from the GPUs.
Note also that this code acts as a synchronization point as it requires all
GPUs to be finished with their mini-batch before it can run to completion.
'''
# List of average gradients to return to the caller
average_grads = []
# Run this on cpu_device to conserve GPU memory
with tf.device(Config.cpu_device):
# Loop over gradient/variable pairs from all towers
for grad_and_vars in zip(*tower_gradients):
# Introduce grads to store the gradients for the current variable
grads = []
# Loop over the gradients for the current variable
for g, _ in grad_and_vars:
# Add 0 dimension to the gradients to represent the tower.
expanded_g = tf.expand_dims(g, 0)
# Append on a 'tower' dimension which we will average over below.
grads.append(expanded_g)
# Average over the 'tower' dimension
grad = tf.concat(grads, 0)
grad = tf.reduce_mean(grad, 0)
# Create a gradient/variable tuple for the current variable with its average gradient
grad_and_var = (grad, grad_and_vars[0][1])
# Add the current tuple to average_grads
average_grads.append(grad_and_var)
# Return result to caller
return average_grads |
r'''
We introduce a function for logging a tensor variable's current state.
It logs scalar values for the mean, standard deviation, minimum and maximum.
Furthermore it logs a histogram of its state and (if given) of an optimization gradient. | def log_variable(variable, gradient=None):
r'''
We introduce a function for logging a tensor variable's current state.
It logs scalar values for the mean, standard deviation, minimum and maximum.
Furthermore it logs a histogram of its state and (if given) of an optimization gradient.
'''
name = variable.name.replace(':', '_')
mean = tf.reduce_mean(variable)
tf.summary.scalar(name='%s/mean' % name, tensor=mean)
tf.summary.scalar(name='%s/sttdev' % name, tensor=tf.sqrt(tf.reduce_mean(tf.square(variable - mean))))
tf.summary.scalar(name='%s/max' % name, tensor=tf.reduce_max(variable))
tf.summary.scalar(name='%s/min' % name, tensor=tf.reduce_min(variable))
tf.summary.histogram(name=name, values=variable)
if gradient is not None:
if isinstance(gradient, tf.IndexedSlices):
grad_values = gradient.values
else:
grad_values = gradient
if grad_values is not None:
tf.summary.histogram(name='%s/gradients' % name, values=grad_values) |
r'''
Restores the trained variables into a simpler graph that will be exported for serving. | def export():
r'''
Restores the trained variables into a simpler graph that will be exported for serving.
'''
log_info('Exporting the model...')
from tensorflow.python.framework.ops import Tensor, Operation
inputs, outputs, _ = create_inference_graph(batch_size=FLAGS.export_batch_size, n_steps=FLAGS.n_steps, tflite=FLAGS.export_tflite)
output_names_tensors = [tensor.op.name for tensor in outputs.values() if isinstance(tensor, Tensor)]
output_names_ops = [op.name for op in outputs.values() if isinstance(op, Operation)]
output_names = ",".join(output_names_tensors + output_names_ops)
if not FLAGS.export_tflite:
mapping = {v.op.name: v for v in tf.global_variables() if not v.op.name.startswith('previous_state_')}
else:
# Create a saver using variables from the above newly created graph
def fixup(name):
if name.startswith('rnn/lstm_cell/'):
return name.replace('rnn/lstm_cell/', 'lstm_fused_cell/')
return name
mapping = {fixup(v.op.name): v for v in tf.global_variables()}
saver = tf.train.Saver(mapping)
# Restore variables from training checkpoint
checkpoint = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
checkpoint_path = checkpoint.model_checkpoint_path
output_filename = 'output_graph.pb'
if FLAGS.remove_export:
if os.path.isdir(FLAGS.export_dir):
log_info('Removing old export')
shutil.rmtree(FLAGS.export_dir)
try:
output_graph_path = os.path.join(FLAGS.export_dir, output_filename)
if not os.path.isdir(FLAGS.export_dir):
os.makedirs(FLAGS.export_dir)
def do_graph_freeze(output_file=None, output_node_names=None, variables_blacklist=None):
return freeze_graph.freeze_graph_with_def_protos(
input_graph_def=tf.get_default_graph().as_graph_def(),
input_saver_def=saver.as_saver_def(),
input_checkpoint=checkpoint_path,
output_node_names=output_node_names,
restore_op_name=None,
filename_tensor_name=None,
output_graph=output_file,
clear_devices=False,
variable_names_blacklist=variables_blacklist,
initializer_nodes='')
if not FLAGS.export_tflite:
frozen_graph = do_graph_freeze(output_node_names=output_names, variables_blacklist='previous_state_c,previous_state_h')
frozen_graph.version = int(file_relative_read('GRAPH_VERSION').strip())
# Add a no-op node to the graph with metadata information to be loaded by the native client
metadata = frozen_graph.node.add()
metadata.name = 'model_metadata'
metadata.op = 'NoOp'
metadata.attr['sample_rate'].i = FLAGS.audio_sample_rate
metadata.attr['feature_win_len'].i = FLAGS.feature_win_len
metadata.attr['feature_win_step'].i = FLAGS.feature_win_step
if FLAGS.export_language:
metadata.attr['language'].s = FLAGS.export_language.encode('ascii')
with open(output_graph_path, 'wb') as fout:
fout.write(frozen_graph.SerializeToString())
else:
frozen_graph = do_graph_freeze(output_node_names=output_names, variables_blacklist='')
output_tflite_path = os.path.join(FLAGS.export_dir, output_filename.replace('.pb', '.tflite'))
converter = tf.lite.TFLiteConverter(frozen_graph, input_tensors=inputs.values(), output_tensors=outputs.values())
converter.post_training_quantize = True
# AudioSpectrogram and Mfcc ops are custom but have built-in kernels in TFLite
converter.allow_custom_ops = True
tflite_model = converter.convert()
with open(output_tflite_path, 'wb') as fout:
fout.write(tflite_model)
log_info('Exported model for TF Lite engine as {}'.format(os.path.basename(output_tflite_path)))
log_info('Models exported at %s' % (FLAGS.export_dir))
except RuntimeError as e:
log_error(str(e)) |
Wrapper for the CTC Beam Search Decoder.
:param probs_seq: 2-D list of probability distributions over each time
step, with each element being a list of normalized
probabilities over alphabet and blank.
:type probs_seq: 2-D list
:param alphabet: alphabet list.
:alphabet: Alphabet
:param beam_size: Width for beam search.
:type beam_size: int
:param cutoff_prob: Cutoff probability in pruning,
default 1.0, no pruning.
:type cutoff_prob: float
:param cutoff_top_n: Cutoff number in pruning, only top cutoff_top_n
characters with highest probs in alphabet will be
used in beam search, default 40.
:type cutoff_top_n: int
:param scorer: External scorer for partially decoded sentence, e.g. word
count or language model.
:type scorer: Scorer
:return: List of tuples of log probability and sentence as decoding
results, in descending order of the probability.
:rtype: list | def ctc_beam_search_decoder(probs_seq,
alphabet,
beam_size,
cutoff_prob=1.0,
cutoff_top_n=40,
scorer=None):
"""Wrapper for the CTC Beam Search Decoder.
:param probs_seq: 2-D list of probability distributions over each time
step, with each element being a list of normalized
probabilities over alphabet and blank.
:type probs_seq: 2-D list
:param alphabet: alphabet list.
:alphabet: Alphabet
:param beam_size: Width for beam search.
:type beam_size: int
:param cutoff_prob: Cutoff probability in pruning,
default 1.0, no pruning.
:type cutoff_prob: float
:param cutoff_top_n: Cutoff number in pruning, only top cutoff_top_n
characters with highest probs in alphabet will be
used in beam search, default 40.
:type cutoff_top_n: int
:param scorer: External scorer for partially decoded sentence, e.g. word
count or language model.
:type scorer: Scorer
:return: List of tuples of log probability and sentence as decoding
results, in descending order of the probability.
:rtype: list
"""
beam_results = swigwrapper.ctc_beam_search_decoder(
probs_seq, alphabet.config_file(), beam_size, cutoff_prob, cutoff_top_n,
scorer)
beam_results = [(res.probability, alphabet.decode(res.tokens)) for res in beam_results]
return beam_results |
Wrapper for the batched CTC beam search decoder.
:param probs_seq: 3-D list with each element as an instance of 2-D list
of probabilities used by ctc_beam_search_decoder().
:type probs_seq: 3-D list
:param alphabet: alphabet list.
:alphabet: Alphabet
:param beam_size: Width for beam search.
:type beam_size: int
:param num_processes: Number of parallel processes.
:type num_processes: int
:param cutoff_prob: Cutoff probability in alphabet pruning,
default 1.0, no pruning.
:type cutoff_prob: float
:param cutoff_top_n: Cutoff number in pruning, only top cutoff_top_n
characters with highest probs in alphabet will be
used in beam search, default 40.
:type cutoff_top_n: int
:param num_processes: Number of parallel processes.
:type num_processes: int
:param scorer: External scorer for partially decoded sentence, e.g. word
count or language model.
:type scorer: Scorer
:return: List of tuples of log probability and sentence as decoding
results, in descending order of the probability.
:rtype: list | def ctc_beam_search_decoder_batch(probs_seq,
seq_lengths,
alphabet,
beam_size,
num_processes,
cutoff_prob=1.0,
cutoff_top_n=40,
scorer=None):
"""Wrapper for the batched CTC beam search decoder.
:param probs_seq: 3-D list with each element as an instance of 2-D list
of probabilities used by ctc_beam_search_decoder().
:type probs_seq: 3-D list
:param alphabet: alphabet list.
:alphabet: Alphabet
:param beam_size: Width for beam search.
:type beam_size: int
:param num_processes: Number of parallel processes.
:type num_processes: int
:param cutoff_prob: Cutoff probability in alphabet pruning,
default 1.0, no pruning.
:type cutoff_prob: float
:param cutoff_top_n: Cutoff number in pruning, only top cutoff_top_n
characters with highest probs in alphabet will be
used in beam search, default 40.
:type cutoff_top_n: int
:param num_processes: Number of parallel processes.
:type num_processes: int
:param scorer: External scorer for partially decoded sentence, e.g. word
count or language model.
:type scorer: Scorer
:return: List of tuples of log probability and sentence as decoding
results, in descending order of the probability.
:rtype: list
"""
batch_beam_results = swigwrapper.ctc_beam_search_decoder_batch(
probs_seq, seq_lengths, alphabet.config_file(), beam_size, num_processes,
cutoff_prob, cutoff_top_n, scorer)
batch_beam_results = [
[(res.probability, alphabet.decode(res.tokens)) for res in beam_results]
for beam_results in batch_beam_results
]
return batch_beam_results |
Microphone may not support our native processing sampling rate, so
resample from input_rate to RATE_PROCESS here for webrtcvad and
deepspeech
Args:
data (binary): Input audio stream
input_rate (int): Input audio rate to resample from | def resample(self, data, input_rate):
"""
Microphone may not support our native processing sampling rate, so
resample from input_rate to RATE_PROCESS here for webrtcvad and
deepspeech
Args:
data (binary): Input audio stream
input_rate (int): Input audio rate to resample from
"""
data16 = np.fromstring(string=data, dtype=np.int16)
resample_size = int(len(data16) / self.input_rate * self.RATE_PROCESS)
resample = signal.resample(data16, resample_size)
resample16 = np.array(resample, dtype=np.int16)
return resample16.tostring() |
Return a block of audio data resampled to 16000hz, blocking if necessary. | def read_resampled(self):
"""Return a block of audio data resampled to 16000hz, blocking if necessary."""
return self.resample(data=self.buffer_queue.get(),
input_rate=self.input_rate) |
Generator that yields all audio frames from microphone. | def frame_generator(self):
"""Generator that yields all audio frames from microphone."""
if self.input_rate == self.RATE_PROCESS:
while True:
yield self.read()
else:
while True:
yield self.read_resampled() |
Generator that yields series of consecutive audio frames comprising each utterence, separated by yielding a single None.
Determines voice activity by ratio of frames in padding_ms. Uses a buffer to include padding_ms prior to being triggered.
Example: (frame, ..., frame, None, frame, ..., frame, None, ...)
|---utterence---| |---utterence---| | def vad_collector(self, padding_ms=300, ratio=0.75, frames=None):
"""Generator that yields series of consecutive audio frames comprising each utterence, separated by yielding a single None.
Determines voice activity by ratio of frames in padding_ms. Uses a buffer to include padding_ms prior to being triggered.
Example: (frame, ..., frame, None, frame, ..., frame, None, ...)
|---utterence---| |---utterence---|
"""
if frames is None: frames = self.frame_generator()
num_padding_frames = padding_ms // self.frame_duration_ms
ring_buffer = collections.deque(maxlen=num_padding_frames)
triggered = False
for frame in frames:
is_speech = self.vad.is_speech(frame, self.sample_rate)
if not triggered:
ring_buffer.append((frame, is_speech))
num_voiced = len([f for f, speech in ring_buffer if speech])
if num_voiced > ratio * ring_buffer.maxlen:
triggered = True
for f, s in ring_buffer:
yield f
ring_buffer.clear()
else:
yield frame
ring_buffer.append((frame, is_speech))
num_unvoiced = len([f for f, speech in ring_buffer if not speech])
if num_unvoiced > ratio * ring_buffer.maxlen:
triggered = False
yield None
ring_buffer.clear() |
Global `cut` function that supports parallel processing.
Note that this only works using dt, custom POSTokenizer
instances are not supported. | def cut(sentence, HMM=True):
"""
Global `cut` function that supports parallel processing.
Note that this only works using dt, custom POSTokenizer
instances are not supported.
"""
global dt
if jieba.pool is None:
for w in dt.cut(sentence, HMM=HMM):
yield w
else:
parts = strdecode(sentence).splitlines(True)
if HMM:
result = jieba.pool.map(_lcut_internal, parts)
else:
result = jieba.pool.map(_lcut_internal_no_hmm, parts)
for r in result:
for w in r:
yield w |
Change the module's `cut` and `cut_for_search` functions to the
parallel version.
Note that this only works using dt, custom Tokenizer
instances are not supported. | def enable_parallel(processnum=None):
"""
Change the module's `cut` and `cut_for_search` functions to the
parallel version.
Note that this only works using dt, custom Tokenizer
instances are not supported.
"""
global pool, dt, cut, cut_for_search
from multiprocessing import cpu_count
if os.name == 'nt':
raise NotImplementedError(
"jieba: parallel mode only supports posix system")
else:
from multiprocessing import Pool
dt.check_initialized()
if processnum is None:
processnum = cpu_count()
pool = Pool(processnum)
cut = _pcut
cut_for_search = _pcut_for_search |
The main function that segments an entire sentence that contains
Chinese characters into separated words.
Parameter:
- sentence: The str(unicode) to be segmented.
- cut_all: Model type. True for full pattern, False for accurate pattern.
- HMM: Whether to use the Hidden Markov Model. | def cut(self, sentence, cut_all=False, HMM=True):
'''
The main function that segments an entire sentence that contains
Chinese characters into separated words.
Parameter:
- sentence: The str(unicode) to be segmented.
- cut_all: Model type. True for full pattern, False for accurate pattern.
- HMM: Whether to use the Hidden Markov Model.
'''
sentence = strdecode(sentence)
if cut_all:
re_han = re_han_cut_all
re_skip = re_skip_cut_all
else:
re_han = re_han_default
re_skip = re_skip_default
if cut_all:
cut_block = self.__cut_all
elif HMM:
cut_block = self.__cut_DAG
else:
cut_block = self.__cut_DAG_NO_HMM
blocks = re_han.split(sentence)
for blk in blocks:
if not blk:
continue
if re_han.match(blk):
for word in cut_block(blk):
yield word
else:
tmp = re_skip.split(blk)
for x in tmp:
if re_skip.match(x):
yield x
elif not cut_all:
for xx in x:
yield xx
else:
yield x |
Finer segmentation for search engines. | def cut_for_search(self, sentence, HMM=True):
"""
Finer segmentation for search engines.
"""
words = self.cut(sentence, HMM=HMM)
for w in words:
if len(w) > 2:
for i in xrange(len(w) - 1):
gram2 = w[i:i + 2]
if self.FREQ.get(gram2):
yield gram2
if len(w) > 3:
for i in xrange(len(w) - 2):
gram3 = w[i:i + 3]
if self.FREQ.get(gram3):
yield gram3
yield w |
Load personalized dict to improve detect rate.
Parameter:
- f : A plain text file contains words and their ocurrences.
Can be a file-like object, or the path of the dictionary file,
whose encoding must be utf-8.
Structure of dict file:
word1 freq1 word_type1
word2 freq2 word_type2
...
Word type may be ignored | def load_userdict(self, f):
'''
Load personalized dict to improve detect rate.
Parameter:
- f : A plain text file contains words and their ocurrences.
Can be a file-like object, or the path of the dictionary file,
whose encoding must be utf-8.
Structure of dict file:
word1 freq1 word_type1
word2 freq2 word_type2
...
Word type may be ignored
'''
self.check_initialized()
if isinstance(f, string_types):
f_name = f
f = open(f, 'rb')
else:
f_name = resolve_filename(f)
for lineno, ln in enumerate(f, 1):
line = ln.strip()
if not isinstance(line, text_type):
try:
line = line.decode('utf-8').lstrip('\ufeff')
except UnicodeDecodeError:
raise ValueError('dictionary file %s must be utf-8' % f_name)
if not line:
continue
# match won't be None because there's at least one character
word, freq, tag = re_userdict.match(line).groups()
if freq is not None:
freq = freq.strip()
if tag is not None:
tag = tag.strip()
self.add_word(word, freq, tag) |
Add a word to dictionary.
freq and tag can be omitted, freq defaults to be a calculated value
that ensures the word can be cut out. | def add_word(self, word, freq=None, tag=None):
"""
Add a word to dictionary.
freq and tag can be omitted, freq defaults to be a calculated value
that ensures the word can be cut out.
"""
self.check_initialized()
word = strdecode(word)
freq = int(freq) if freq is not None else self.suggest_freq(word, False)
self.FREQ[word] = freq
self.total += freq
if tag:
self.user_word_tag_tab[word] = tag
for ch in xrange(len(word)):
wfrag = word[:ch + 1]
if wfrag not in self.FREQ:
self.FREQ[wfrag] = 0
if freq == 0:
finalseg.add_force_split(word) |
Suggest word frequency to force the characters in a word to be
joined or splitted.
Parameter:
- segment : The segments that the word is expected to be cut into,
If the word should be treated as a whole, use a str.
- tune : If True, tune the word frequency.
Note that HMM may affect the final result. If the result doesn't change,
set HMM=False. | def suggest_freq(self, segment, tune=False):
"""
Suggest word frequency to force the characters in a word to be
joined or splitted.
Parameter:
- segment : The segments that the word is expected to be cut into,
If the word should be treated as a whole, use a str.
- tune : If True, tune the word frequency.
Note that HMM may affect the final result. If the result doesn't change,
set HMM=False.
"""
self.check_initialized()
ftotal = float(self.total)
freq = 1
if isinstance(segment, string_types):
word = segment
for seg in self.cut(word, HMM=False):
freq *= self.FREQ.get(seg, 1) / ftotal
freq = max(int(freq * self.total) + 1, self.FREQ.get(word, 1))
else:
segment = tuple(map(strdecode, segment))
word = ''.join(segment)
for seg in segment:
freq *= self.FREQ.get(seg, 1) / ftotal
freq = min(int(freq * self.total), self.FREQ.get(word, 0))
if tune:
add_word(word, freq)
return freq |
Tokenize a sentence and yields tuples of (word, start, end)
Parameter:
- sentence: the str(unicode) to be segmented.
- mode: "default" or "search", "search" is for finer segmentation.
- HMM: whether to use the Hidden Markov Model. | def tokenize(self, unicode_sentence, mode="default", HMM=True):
"""
Tokenize a sentence and yields tuples of (word, start, end)
Parameter:
- sentence: the str(unicode) to be segmented.
- mode: "default" or "search", "search" is for finer segmentation.
- HMM: whether to use the Hidden Markov Model.
"""
if not isinstance(unicode_sentence, text_type):
raise ValueError("jieba: the input parameter should be unicode.")
start = 0
if mode == 'default':
for w in self.cut(unicode_sentence, HMM=HMM):
width = len(w)
yield (w, start, start + width)
start += width
else:
for w in self.cut(unicode_sentence, HMM=HMM):
width = len(w)
if len(w) > 2:
for i in xrange(len(w) - 1):
gram2 = w[i:i + 2]
if self.FREQ.get(gram2):
yield (gram2, start + i, start + i + 2)
if len(w) > 3:
for i in xrange(len(w) - 2):
gram3 = w[i:i + 3]
if self.FREQ.get(gram3):
yield (gram3, start + i, start + i + 3)
yield (w, start, start + width)
start += width |
Extract keywords from sentence using TextRank algorithm.
Parameter:
- topK: return how many top keywords. `None` for all possible words.
- withWeight: if True, return a list of (word, weight);
if False, return a list of words.
- allowPOS: the allowed POS list eg. ['ns', 'n', 'vn', 'v'].
if the POS of w is not in this list, it will be filtered.
- withFlag: if True, return a list of pair(word, weight) like posseg.cut
if False, return a list of words | def textrank(self, sentence, topK=20, withWeight=False, allowPOS=('ns', 'n', 'vn', 'v'), withFlag=False):
"""
Extract keywords from sentence using TextRank algorithm.
Parameter:
- topK: return how many top keywords. `None` for all possible words.
- withWeight: if True, return a list of (word, weight);
if False, return a list of words.
- allowPOS: the allowed POS list eg. ['ns', 'n', 'vn', 'v'].
if the POS of w is not in this list, it will be filtered.
- withFlag: if True, return a list of pair(word, weight) like posseg.cut
if False, return a list of words
"""
self.pos_filt = frozenset(allowPOS)
g = UndirectWeightedGraph()
cm = defaultdict(int)
words = tuple(self.tokenizer.cut(sentence))
for i, wp in enumerate(words):
if self.pairfilter(wp):
for j in xrange(i + 1, i + self.span):
if j >= len(words):
break
if not self.pairfilter(words[j]):
continue
if allowPOS and withFlag:
cm[(wp, words[j])] += 1
else:
cm[(wp.word, words[j].word)] += 1
for terms, w in cm.items():
g.addEdge(terms[0], terms[1], w)
nodes_rank = g.rank()
if withWeight:
tags = sorted(nodes_rank.items(), key=itemgetter(1), reverse=True)
else:
tags = sorted(nodes_rank, key=nodes_rank.__getitem__, reverse=True)
if topK:
return tags[:topK]
else:
return tags |
Extract keywords from sentence using TF-IDF algorithm.
Parameter:
- topK: return how many top keywords. `None` for all possible words.
- withWeight: if True, return a list of (word, weight);
if False, return a list of words.
- allowPOS: the allowed POS list eg. ['ns', 'n', 'vn', 'v','nr'].
if the POS of w is not in this list,it will be filtered.
- withFlag: only work with allowPOS is not empty.
if True, return a list of pair(word, weight) like posseg.cut
if False, return a list of words | def extract_tags(self, sentence, topK=20, withWeight=False, allowPOS=(), withFlag=False):
"""
Extract keywords from sentence using TF-IDF algorithm.
Parameter:
- topK: return how many top keywords. `None` for all possible words.
- withWeight: if True, return a list of (word, weight);
if False, return a list of words.
- allowPOS: the allowed POS list eg. ['ns', 'n', 'vn', 'v','nr'].
if the POS of w is not in this list,it will be filtered.
- withFlag: only work with allowPOS is not empty.
if True, return a list of pair(word, weight) like posseg.cut
if False, return a list of words
"""
if allowPOS:
allowPOS = frozenset(allowPOS)
words = self.postokenizer.cut(sentence)
else:
words = self.tokenizer.cut(sentence)
freq = {}
for w in words:
if allowPOS:
if w.flag not in allowPOS:
continue
elif not withFlag:
w = w.word
wc = w.word if allowPOS and withFlag else w
if len(wc.strip()) < 2 or wc.lower() in self.stop_words:
continue
freq[w] = freq.get(w, 0.0) + 1.0
total = sum(freq.values())
for k in freq:
kw = k.word if allowPOS and withFlag else k
freq[k] *= self.idf_freq.get(kw, self.median_idf) / total
if withWeight:
tags = sorted(freq.items(), key=itemgetter(1), reverse=True)
else:
tags = sorted(freq, key=freq.__getitem__, reverse=True)
if topK:
return tags[:topK]
else:
return tags |
Generates raw (English, other) pairs from a ParaCrawl V3.0 data file.
Args:
paracrawl_file: A ParaCrawl V3.0 en-.. data file.
Yields:
Pairs of (sentence_en, sentence_xx), as Unicode strings.
Raises:
StopIteration: If the file ends while this method is in the middle of
creating a translation pair. | def paracrawl_v3_pairs(paracrawl_file):
"""Generates raw (English, other) pairs from a ParaCrawl V3.0 data file.
Args:
paracrawl_file: A ParaCrawl V3.0 en-.. data file.
Yields:
Pairs of (sentence_en, sentence_xx), as Unicode strings.
Raises:
StopIteration: If the file ends while this method is in the middle of
creating a translation pair.
"""
raw_sentences = _raw_sentences(paracrawl_file)
for s_en in raw_sentences:
try:
s_xx = next(raw_sentences)
if s_en and s_xx: # Prevent empty string examples.
yield s_en, s_xx
except StopIteration:
tf.logging.error(
'Unmatched final sentence while reading in sentence pairs: [%s]',
s_en) |
Generates Unicode strings, one for each <seg> in a ParaCrawl data file.
Also decodes some of the most common HTML entities found in ParaCrawl data.
Args:
paracrawl_file: A ParaCrawl V3.0 en-.. data file.
Yields:
One Unicode string for each <seg> element in the ParaCrawl data file. | def _raw_sentences(paracrawl_file):
"""Generates Unicode strings, one for each <seg> in a ParaCrawl data file.
Also decodes some of the most common HTML entities found in ParaCrawl data.
Args:
paracrawl_file: A ParaCrawl V3.0 en-.. data file.
Yields:
One Unicode string for each <seg> element in the ParaCrawl data file.
"""
for line_utf8 in paracrawl_file:
line_uni = line_utf8.decode('UTF-8')
text_match = re.match(r' +<seg>(.*)</seg>$', line_uni)
if text_match:
txt = text_match.group(1)
txt = re.sub(r'&', r'&', txt)
txt = re.sub(r'& ?amp;', r'&', txt)
txt = re.sub(r'& ?apos;', r"'", txt)
txt = re.sub(r'& ?quot;', r'"', txt)
txt = re.sub(r'& ?lt;', r'<', txt)
txt = re.sub(r'& ?gt;', r'>', txt)
yield txt |
Generates a cleaned-up stream of (English, other) translation pairs.
Cleaning includes both filtering and simplistic sentence splitting, with
minimal assumptions on the non-English pair member: (1) All filtering is
done based on the English member of the pair, and (2) sentence splitting
assumes only that sentences can end with one of '.!?' and begin with an
ASCII uppercase letter. Input pairs that would get split into different
numbers of sentences (e.g., three English sentences vs. two German ones) are
discarded.
Args:
en_xx_pairs: A stream (iterable) of Unicode string pairs. Each item in the
stream should be a (sentence_en, sentence_xx) pair.
Yields:
Cleaned-up (sentence_en, sentence_xx) pairs. | def clean_en_xx_pairs(en_xx_pairs):
"""Generates a cleaned-up stream of (English, other) translation pairs.
Cleaning includes both filtering and simplistic sentence splitting, with
minimal assumptions on the non-English pair member: (1) All filtering is
done based on the English member of the pair, and (2) sentence splitting
assumes only that sentences can end with one of '.!?' and begin with an
ASCII uppercase letter. Input pairs that would get split into different
numbers of sentences (e.g., three English sentences vs. two German ones) are
discarded.
Args:
en_xx_pairs: A stream (iterable) of Unicode string pairs. Each item in the
stream should be a (sentence_en, sentence_xx) pair.
Yields:
Cleaned-up (sentence_en, sentence_xx) pairs.
"""
for s1, s2 in en_xx_pairs:
if _regex_filter(s1):
continue
s1_list, s2_list = _split_sentences(s1, s2)
if len(s1_list) != len(s2_list):
continue # discard this pair
elif len(s1_list) == 1:
yield s1, s2
else:
for s1_subsentence, s2_subsentence in itertools.izip(s1_list, s2_list):
if _regex_filter(s1_subsentence):
continue
yield s1_subsentence, s2_subsentence |
Download a set of images from api.brain-map.org to `target_dir`.
Args:
image_ids: list, a list of image ids.
target_dir: str, a directory to which to download the images. | def maybe_download_image_dataset(image_ids, target_dir):
"""Download a set of images from api.brain-map.org to `target_dir`.
Args:
image_ids: list, a list of image ids.
target_dir: str, a directory to which to download the images.
"""
tf.gfile.MakeDirs(target_dir)
num_images = len(image_ids)
for i, image_id in enumerate(image_ids):
destination = os.path.join(target_dir, "%s.jpg" % i)
tmp_destination = "%s.temp" % destination
source_url = ("http://api.brain-map.org/api/v2/"
"section_image_download/%s" % image_id)
if tf.gfile.Exists(destination):
tf.logging.info("Image with ID already present, "
"skipping download (%s of %s)." % (
i+1, num_images
))
continue
tf.logging.info("Downloading image with id %s (%s of %s)" % (
image_id, i+1, num_images
))
response = requests.get(source_url, stream=True)
response.raise_for_status()
with tf.gfile.Open(tmp_destination, "w") as f:
for block in response.iter_content(1024):
f.write(block)
tf.gfile.Rename(tmp_destination, destination) |
Obtain a list of image paths corresponding to training or eval case.
Args:
tmp_dir: str, the root path to which raw images were written, at the
top level having meta/ and raw/ subdirs.
case: bool, whether obtaining file paths for training (true) or eval
(false).
training_fraction: float, the fraction of the sub-image path list to
consider as the basis for training examples.
Returns:
list: A list of file paths.
Raises:
ValueError: if images not found in tmp_dir, or if training_fraction would
leave no examples for eval. | def _get_case_file_paths(tmp_dir, case, training_fraction=0.95):
"""Obtain a list of image paths corresponding to training or eval case.
Args:
tmp_dir: str, the root path to which raw images were written, at the
top level having meta/ and raw/ subdirs.
case: bool, whether obtaining file paths for training (true) or eval
(false).
training_fraction: float, the fraction of the sub-image path list to
consider as the basis for training examples.
Returns:
list: A list of file paths.
Raises:
ValueError: if images not found in tmp_dir, or if training_fraction would
leave no examples for eval.
"""
paths = tf.gfile.Glob("%s/*.jpg" % tmp_dir)
if not paths:
raise ValueError("Search of tmp_dir (%s) " % tmp_dir,
"for subimage paths yielded an empty list, ",
"can't proceed with returning training/eval split.")
split_index = int(math.floor(len(paths)*training_fraction))
if split_index >= len(paths):
raise ValueError("For a path list of size %s "
"and a training_fraction of %s "
"the resulting split_index of the paths list, "
"%s, would leave no elements for the eval "
"condition." % (len(paths),
training_fraction,
split_index))
if case:
return paths[:split_index]
else:
return paths[split_index:] |
Create a numpy array with specified shape and masked fraction.
Args:
shape: tuple, shape of the mask to create.
fraction: float, fraction of the mask area to populate with `mask_scalar`.
Returns:
numpy.array: A numpy array storing the mask. | def random_square_mask(shape, fraction):
"""Create a numpy array with specified shape and masked fraction.
Args:
shape: tuple, shape of the mask to create.
fraction: float, fraction of the mask area to populate with `mask_scalar`.
Returns:
numpy.array: A numpy array storing the mask.
"""
mask = np.ones(shape)
patch_area = shape[0]*shape[1]*fraction
patch_dim = np.int(math.floor(math.sqrt(patch_area)))
if patch_area == 0 or patch_dim == 0:
return mask
x = np.random.randint(shape[0] - patch_dim)
y = np.random.randint(shape[1] - patch_dim)
mask[x:(x + patch_dim), y:(y + patch_dim), :] = 0
return mask |
Base problem example generator for Allen Brain Atlas problems.
Args:
tmp_dir: str, a directory where raw example input data has been stored.
training: bool, whether the mode of operation is training (or,
alternatively, evaluation), determining whether examples in tmp_dir
prefixed with train or dev will be used.
size: int, the image size to add to the example annotation.
training_fraction: float, the fraction of the sub-image path list to
consider as the basis for training examples.
Yields:
A dictionary representing the images with the following fields:
* image/encoded: The string encoding the image as JPEG.
* image/format: The string "jpeg" indicating the image format.
* image/height: The integer indicating the image height.
* image/width: The integer indicating the image height. | def _generator(tmp_dir, training, size=_BASE_EXAMPLE_IMAGE_SIZE,
training_fraction=0.95):
"""Base problem example generator for Allen Brain Atlas problems.
Args:
tmp_dir: str, a directory where raw example input data has been stored.
training: bool, whether the mode of operation is training (or,
alternatively, evaluation), determining whether examples in tmp_dir
prefixed with train or dev will be used.
size: int, the image size to add to the example annotation.
training_fraction: float, the fraction of the sub-image path list to
consider as the basis for training examples.
Yields:
A dictionary representing the images with the following fields:
* image/encoded: The string encoding the image as JPEG.
* image/format: The string "jpeg" indicating the image format.
* image/height: The integer indicating the image height.
* image/width: The integer indicating the image height.
"""
maybe_download_image_dataset(_IMAGE_IDS, tmp_dir)
image_files = _get_case_file_paths(tmp_dir=tmp_dir,
case=training,
training_fraction=training_fraction)
image_obj = PIL_Image()
tf.logging.info("Loaded case file paths (n=%s)" % len(image_files))
height = size
width = size
for input_path in image_files:
img = image_obj.open(input_path)
img = np.float32(img)
shape = np.shape(img)
for h_index in range(0, int(math.floor(shape[0]/size))):
h_offset = h_index * size
h_end = h_offset + size - 1
for v_index in range(0, int(math.floor(shape[1]/size))):
v_offset = v_index * size
v_end = v_offset + size - 1
# Extract a sub-image tile.
subimage = np.uint8(img[h_offset:h_end, v_offset:v_end]) # pylint: disable=invalid-sequence-index
# Filter images that are likely background (not tissue).
if np.amax(subimage) < 230:
continue
subimage = image_obj.fromarray(subimage)
buff = BytesIO()
subimage.save(buff, format="JPEG")
subimage_encoded = buff.getvalue()
yield {
"image/encoded": [subimage_encoded],
"image/format": ["jpeg"],
"image/height": [height],
"image/width": [width]
} |
Set of hyperparameters. | def transformer_moe_base():
"""Set of hyperparameters."""
hparams = common_hparams.basic_params1()
hparams.norm_type = "layer"
hparams.hidden_size = 512
hparams.batch_size = 4096
hparams.max_length = 2001
hparams.max_input_seq_length = 2000
hparams.max_target_seq_length = 2000
hparams.dropout = 0.0
hparams.clip_grad_norm = 0. # i.e. no gradient clipping
hparams.optimizer_adam_epsilon = 1e-9
hparams.learning_rate_decay_scheme = "noam"
hparams.learning_rate = 0.1
hparams.learning_rate_warmup_steps = 2000
hparams.initializer_gain = 1.0
hparams.num_hidden_layers = 5
hparams.initializer = "uniform_unit_scaling"
hparams.weight_decay = 0.0
hparams.optimizer_adam_beta1 = 0.9
hparams.optimizer_adam_beta2 = 0.98
hparams.num_sampled_classes = 0
hparams.label_smoothing = 0.0
hparams.shared_embedding_and_softmax_weights = True
# According to noam, ("n", "da") seems better for harder-to-learn models
hparams.layer_preprocess_sequence = "n"
hparams.layer_postprocess_sequence = "da"
# Hparams used by transformer_prepare_decoder() function
hparams.add_hparam("pos", "timing") # timing, none
hparams.add_hparam("proximity_bias", False)
hparams.add_hparam("causal_decoder_self_attention", True)
hparams = common_attention.add_standard_attention_hparams(hparams)
# Decoder layers type. If set, num_decoder_layers parameter will be ignored
# and the number of decoder layer will be deduced from the string
# See top file comment for example of usage
hparams.add_hparam("layer_types", "")
# Default attention type (ex: a, loc, red,...) and feed-forward type (ex: fc,
# sep, moe,...)
hparams.add_hparam("default_att", "a")
hparams.add_hparam("default_ff", "fc")
return hparams |
Hyper parameters specifics for long sequence generation. | def transformer_moe_8k():
"""Hyper parameters specifics for long sequence generation."""
hparams = transformer_moe_base()
hparams.batch_size = 8192
hparams.max_length = 0 # max_length == batch_size
hparams.eval_drop_long_sequences = True
hparams.min_length_bucket = 256 # Avoid cyclic problems for big batches
hparams.default_ff = "sep"
hparams.hidden_size = 1024
return hparams |
Base transformers model with moe.
Will have the following architecture:
* No encoder.
* Layer 0: a - sep (self-attention - unmasked separable convolutions)
* Layer 1: a - sep
* Layer 2: a - sep
* Layer 3: a - sep
* Layer 4: a - sep
* Decoder architecture:
* Layer 0: a - a - sepm (self-attention - enco/deco-attention - masked sep)
* Layer 1: a - a - sepm
* Layer 2: a - a - moe (mixture of expert layers in the middle)
* Layer 3: a - a - sepm
* Layer 4: a - a - sepm
Returns:
hparams | def transformer_moe_2k():
"""Base transformers model with moe.
Will have the following architecture:
* No encoder.
* Layer 0: a - sep (self-attention - unmasked separable convolutions)
* Layer 1: a - sep
* Layer 2: a - sep
* Layer 3: a - sep
* Layer 4: a - sep
* Decoder architecture:
* Layer 0: a - a - sepm (self-attention - enco/deco-attention - masked sep)
* Layer 1: a - a - sepm
* Layer 2: a - a - moe (mixture of expert layers in the middle)
* Layer 3: a - a - sepm
* Layer 4: a - a - sepm
Returns:
hparams
"""
hparams = transformer_moe_8k()
hparams.batch_size = 2048
hparams.default_ff = "sep"
# hparams.layer_types contains the network architecture:
encoder_archi = "a/a/a/a/a"
decoder_archi = "a-sepm/a-sepm/a-moe/a-sepm/a-sepm"
hparams.layer_types = "{}#{}".format(encoder_archi, decoder_archi)
return hparams |
Model which formulate a seq2seq problem as language modeling. | def transformer_moe_prepend_8k():
"""Model which formulate a seq2seq problem as language modeling."""
hparams = transformer_moe_8k()
hparams.prepend_mode = "prepend_inputs_masked_attention"
hparams.eval_drop_long_sequences = False
hparams.max_input_seq_length = 7500
hparams.default_ff = "sepm"
hparams.layer_types = "locm/redm/locm-moe/redm/locm"
hparams.moe_num_experts = 256
return hparams |
Applies residual function for RevNet.
Args:
x: input tensor
depth1: Number of output channels for the first and second conv layers.
depth2: Number of output channels for the third conv layer.
dim: '2d' if 2-dimensional, '3d' if 3-dimensional.
first_batch_norm: Whether to keep the first batch norm layer or not.
Typically used in the first RevNet block.
stride: Stride for the first conv filter. Note that this particular
RevNet architecture only varies the stride for the first conv
filter. The stride for the second conv filter is always set to 1.
training: True for train phase, False for eval phase.
bottleneck: If true, apply bottleneck 1x1 down/up sampling.
padding: Padding for each conv layer.
Returns:
Output tensor after applying residual function for RevNet. | def f(x, depth1, depth2, dim='2d', first_batch_norm=True, stride=1,
training=True, bottleneck=True, padding='SAME'):
"""Applies residual function for RevNet.
Args:
x: input tensor
depth1: Number of output channels for the first and second conv layers.
depth2: Number of output channels for the third conv layer.
dim: '2d' if 2-dimensional, '3d' if 3-dimensional.
first_batch_norm: Whether to keep the first batch norm layer or not.
Typically used in the first RevNet block.
stride: Stride for the first conv filter. Note that this particular
RevNet architecture only varies the stride for the first conv
filter. The stride for the second conv filter is always set to 1.
training: True for train phase, False for eval phase.
bottleneck: If true, apply bottleneck 1x1 down/up sampling.
padding: Padding for each conv layer.
Returns:
Output tensor after applying residual function for RevNet.
"""
conv = CONFIG[dim]['conv']
with tf.variable_scope('f', reuse=tf.AUTO_REUSE):
if first_batch_norm:
net = tf.layers.batch_normalization(x, training=training)
net = tf.nn.relu(net)
else:
net = x
if bottleneck:
net = conv(net, depth1, 1, strides=stride,
padding=padding, activation=None)
net = tf.layers.batch_normalization(net, training=training)
net = tf.nn.relu(net)
net = conv(net, depth1, 3, strides=1,
padding=padding, activation=None)
net = tf.layers.batch_normalization(net, training=training)
net = tf.nn.relu(net)
net = conv(net, depth2, 1, strides=1,
padding=padding, activation=None)
else:
net = conv(net, depth2, 3, strides=stride,
padding=padding, activation=None)
net = tf.layers.batch_normalization(x, training=training)
net = tf.nn.relu(net)
net = conv(net, depth2, 3, strides=stride,
padding=padding, activation=None)
return net |
Downsamples 'x' by `stride` using a 1x1 convolution filter.
Args:
x: input tensor of size [N, H, W, C]
output_channels: Desired number of output channels.
dim: '2d' if 2-dimensional, '3d' if 3-dimensional.
stride: What stride to use. Usually 1 or 2.
scope: Optional variable scope.
Returns:
A downsampled tensor of size [N, H/2, W/2, output_channels] if stride
is 2, else returns a tensor of size [N, H, W, output_channels] if
stride is 1. | def downsample_bottleneck(x, output_channels, dim='2d', stride=1, scope='h'):
"""Downsamples 'x' by `stride` using a 1x1 convolution filter.
Args:
x: input tensor of size [N, H, W, C]
output_channels: Desired number of output channels.
dim: '2d' if 2-dimensional, '3d' if 3-dimensional.
stride: What stride to use. Usually 1 or 2.
scope: Optional variable scope.
Returns:
A downsampled tensor of size [N, H/2, W/2, output_channels] if stride
is 2, else returns a tensor of size [N, H, W, output_channels] if
stride is 1.
"""
conv = CONFIG[dim]['conv']
with tf.variable_scope(scope):
x = conv(x, output_channels, 1, strides=stride, padding='SAME',
activation=None)
return x |
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