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Draw top3 proposals for each gt.
Args:
proposals: NPx4
proposal_scores: NP
gt_boxes: NG
def draw_proposal_recall(img, proposals, proposal_scores, gt_boxes):
"""
Draw top3 proposals for each gt.
Args:
proposals: NPx4
proposal_scores: NP
gt_boxes: NG
"""
box_ious = np_iou(gt_boxes, proposals) # ng x np
box_ious_argsort = np.argsort(-box_ious, axis=1)
good_proposals_ind = box_ious_argsort[:, :3] # for each gt, find 3 best proposals
good_proposals_ind = np.unique(good_proposals_ind.ravel())
proposals = proposals[good_proposals_ind, :]
tags = list(map(str, proposal_scores[good_proposals_ind]))
img = viz.draw_boxes(img, proposals, tags)
return img, good_proposals_ind |
Args:
boxes: kx4
scores: kxC
def draw_predictions(img, boxes, scores):
"""
Args:
boxes: kx4
scores: kxC
"""
if len(boxes) == 0:
return img
labels = scores.argmax(axis=1)
scores = scores.max(axis=1)
tags = ["{},{:.2f}".format(cfg.DATA.CLASS_NAMES[lb], score) for lb, score in zip(labels, scores)]
return viz.draw_boxes(img, boxes, tags) |
Args:
results: [DetectionResult]
def draw_final_outputs(img, results):
"""
Args:
results: [DetectionResult]
"""
if len(results) == 0:
return img
# Display in largest to smallest order to reduce occlusion
boxes = np.asarray([r.box for r in results])
areas = np_area(boxes)
sorted_inds = np.argsort(-areas)
ret = img
tags = []
for result_id in sorted_inds:
r = results[result_id]
if r.mask is not None:
ret = draw_mask(ret, r.mask)
for r in results:
tags.append(
"{},{:.2f}".format(cfg.DATA.CLASS_NAMES[r.class_id], r.score))
ret = viz.draw_boxes(ret, boxes, tags)
return ret |
Overlay a mask on top of the image.
Args:
im: a 3-channel uint8 image in BGR
mask: a binary 1-channel image of the same size
color: if None, will choose automatically
def draw_mask(im, mask, alpha=0.5, color=None):
"""
Overlay a mask on top of the image.
Args:
im: a 3-channel uint8 image in BGR
mask: a binary 1-channel image of the same size
color: if None, will choose automatically
"""
if color is None:
color = PALETTE_RGB[np.random.choice(len(PALETTE_RGB))][::-1]
im = np.where(np.repeat((mask > 0)[:, :, None], 3, axis=2),
im * (1 - alpha) + color * alpha, im)
im = im.astype('uint8')
return im |
Run DataFlow and send data to a ZMQ socket addr.
It will serialize and send each datapoint to this address with a PUSH socket.
This function never returns.
Args:
df (DataFlow): Will infinitely loop over the DataFlow.
addr: a ZMQ socket endpoint.
hwm (int): ZMQ high-water mark (buffer size)
format (str): The serialization format.
Default format uses :mod:`tensorpack.utils.serialize`.
This format works with :class:`dataflow.RemoteDataZMQ`.
An alternate format is 'zmq_ops', used by https://github.com/tensorpack/zmq_ops
and :class:`input_source.ZMQInput`.
bind (bool): whether to bind or connect to the endpoint address.
def send_dataflow_zmq(df, addr, hwm=50, format=None, bind=False):
"""
Run DataFlow and send data to a ZMQ socket addr.
It will serialize and send each datapoint to this address with a PUSH socket.
This function never returns.
Args:
df (DataFlow): Will infinitely loop over the DataFlow.
addr: a ZMQ socket endpoint.
hwm (int): ZMQ high-water mark (buffer size)
format (str): The serialization format.
Default format uses :mod:`tensorpack.utils.serialize`.
This format works with :class:`dataflow.RemoteDataZMQ`.
An alternate format is 'zmq_ops', used by https://github.com/tensorpack/zmq_ops
and :class:`input_source.ZMQInput`.
bind (bool): whether to bind or connect to the endpoint address.
"""
assert format in [None, 'zmq_op', 'zmq_ops']
if format is None:
dump_fn = dumps
else:
from zmq_ops import dump_arrays
dump_fn = dump_arrays
ctx = zmq.Context()
socket = ctx.socket(zmq.PUSH)
socket.set_hwm(hwm)
if bind:
socket.bind(addr)
else:
socket.connect(addr)
try:
df.reset_state()
logger.info("Serving data to {} with {} format ...".format(
addr, 'default' if format is None else 'zmq_ops'))
INTERVAL = 200
q = deque(maxlen=INTERVAL)
try:
total = len(df)
except NotImplementedError:
total = 0
tqdm_args = get_tqdm_kwargs(leave=True, smoothing=0.8)
tqdm_args['bar_format'] = tqdm_args['bar_format'] + "{postfix}"
while True:
with tqdm.trange(total, **tqdm_args) as pbar:
for dp in df:
start = time.time()
socket.send(dump_fn(dp), copy=False)
q.append(time.time() - start)
pbar.update(1)
if pbar.n % INTERVAL == 0:
avg = "{:.3f}".format(sum(q) / len(q))
pbar.set_postfix({'AvgSendLat': avg})
finally:
logger.info("Exiting send_dataflow_zmq ...")
socket.setsockopt(zmq.LINGER, 0)
socket.close()
if not ctx.closed:
ctx.destroy(0) |
Convert a DataFlow to a :class:`multiprocessing.Queue`.
The DataFlow will only be reset in the spawned process.
Args:
df (DataFlow): the DataFlow to dump.
size (int): size of the queue
nr_consumer (int): number of consumer of the queue.
The producer will add this many of ``DIE`` sentinel to the end of the queue.
Returns:
tuple(queue, process):
The process will take data from ``df`` and fill
the queue, once you start it. Each element in the queue is (idx,
dp). idx can be the ``DIE`` sentinel when ``df`` is exhausted.
def dump_dataflow_to_process_queue(df, size, nr_consumer):
"""
Convert a DataFlow to a :class:`multiprocessing.Queue`.
The DataFlow will only be reset in the spawned process.
Args:
df (DataFlow): the DataFlow to dump.
size (int): size of the queue
nr_consumer (int): number of consumer of the queue.
The producer will add this many of ``DIE`` sentinel to the end of the queue.
Returns:
tuple(queue, process):
The process will take data from ``df`` and fill
the queue, once you start it. Each element in the queue is (idx,
dp). idx can be the ``DIE`` sentinel when ``df`` is exhausted.
"""
q = mp.Queue(size)
class EnqueProc(mp.Process):
def __init__(self, df, q, nr_consumer):
super(EnqueProc, self).__init__()
self.df = df
self.q = q
def run(self):
self.df.reset_state()
try:
for idx, dp in enumerate(self.df):
self.q.put((idx, dp))
finally:
for _ in range(nr_consumer):
self.q.put((DIE, None))
proc = EnqueProc(df, q, nr_consumer)
return q, proc |
:returns: the current 3-channel image
def _grab_raw_image(self):
"""
:returns: the current 3-channel image
"""
m = self.ale.getScreenRGB()
return m.reshape((self.height, self.width, 3)) |
:returns: a gray-scale (h, w) uint8 image
def _current_state(self):
"""
:returns: a gray-scale (h, w) uint8 image
"""
ret = self._grab_raw_image()
# max-pooled over the last screen
ret = np.maximum(ret, self.last_raw_screen)
if self.viz:
if isinstance(self.viz, float):
cv2.imshow(self.windowname, ret)
cv2.waitKey(int(self.viz * 1000))
ret = ret.astype('float32')
# 0.299,0.587.0.114. same as rgb2y in torch/image
ret = cv2.cvtColor(ret, cv2.COLOR_RGB2GRAY)[:, :]
return ret.astype('uint8') |
Args:
boxes: nx4, xyxy
window: [h, w]
def clip_boxes(boxes, window, name=None):
"""
Args:
boxes: nx4, xyxy
window: [h, w]
"""
boxes = tf.maximum(boxes, 0.0)
m = tf.tile(tf.reverse(window, [0]), [2]) # (4,)
boxes = tf.minimum(boxes, tf.cast(m, tf.float32), name=name)
return boxes |
Args:
box_predictions: (..., 4), logits
anchors: (..., 4), floatbox. Must have the same shape
Returns:
box_decoded: (..., 4), float32. With the same shape.
def decode_bbox_target(box_predictions, anchors):
"""
Args:
box_predictions: (..., 4), logits
anchors: (..., 4), floatbox. Must have the same shape
Returns:
box_decoded: (..., 4), float32. With the same shape.
"""
orig_shape = tf.shape(anchors)
box_pred_txtytwth = tf.reshape(box_predictions, (-1, 2, 2))
box_pred_txty, box_pred_twth = tf.split(box_pred_txtytwth, 2, axis=1)
# each is (...)x1x2
anchors_x1y1x2y2 = tf.reshape(anchors, (-1, 2, 2))
anchors_x1y1, anchors_x2y2 = tf.split(anchors_x1y1x2y2, 2, axis=1)
waha = anchors_x2y2 - anchors_x1y1
xaya = (anchors_x2y2 + anchors_x1y1) * 0.5
clip = np.log(config.PREPROC.MAX_SIZE / 16.)
wbhb = tf.exp(tf.minimum(box_pred_twth, clip)) * waha
xbyb = box_pred_txty * waha + xaya
x1y1 = xbyb - wbhb * 0.5
x2y2 = xbyb + wbhb * 0.5 # (...)x1x2
out = tf.concat([x1y1, x2y2], axis=-2)
return tf.reshape(out, orig_shape) |
Args:
boxes: (..., 4), float32
anchors: (..., 4), float32
Returns:
box_encoded: (..., 4), float32 with the same shape.
def encode_bbox_target(boxes, anchors):
"""
Args:
boxes: (..., 4), float32
anchors: (..., 4), float32
Returns:
box_encoded: (..., 4), float32 with the same shape.
"""
anchors_x1y1x2y2 = tf.reshape(anchors, (-1, 2, 2))
anchors_x1y1, anchors_x2y2 = tf.split(anchors_x1y1x2y2, 2, axis=1)
waha = anchors_x2y2 - anchors_x1y1
xaya = (anchors_x2y2 + anchors_x1y1) * 0.5
boxes_x1y1x2y2 = tf.reshape(boxes, (-1, 2, 2))
boxes_x1y1, boxes_x2y2 = tf.split(boxes_x1y1x2y2, 2, axis=1)
wbhb = boxes_x2y2 - boxes_x1y1
xbyb = (boxes_x2y2 + boxes_x1y1) * 0.5
# Note that here not all boxes are valid. Some may be zero
txty = (xbyb - xaya) / waha
twth = tf.log(wbhb / waha) # may contain -inf for invalid boxes
encoded = tf.concat([txty, twth], axis=1) # (-1x2x2)
return tf.reshape(encoded, tf.shape(boxes)) |
Aligned version of tf.image.crop_and_resize, following our definition of floating point boxes.
Args:
image: NCHW
boxes: nx4, x1y1x2y2
box_ind: (n,)
crop_size (int):
Returns:
n,C,size,size
def crop_and_resize(image, boxes, box_ind, crop_size, pad_border=True):
"""
Aligned version of tf.image.crop_and_resize, following our definition of floating point boxes.
Args:
image: NCHW
boxes: nx4, x1y1x2y2
box_ind: (n,)
crop_size (int):
Returns:
n,C,size,size
"""
assert isinstance(crop_size, int), crop_size
boxes = tf.stop_gradient(boxes)
# TF's crop_and_resize produces zeros on border
if pad_border:
# this can be quite slow
image = tf.pad(image, [[0, 0], [0, 0], [1, 1], [1, 1]], mode='SYMMETRIC')
boxes = boxes + 1
@under_name_scope()
def transform_fpcoor_for_tf(boxes, image_shape, crop_shape):
"""
The way tf.image.crop_and_resize works (with normalized box):
Initial point (the value of output[0]): x0_box * (W_img - 1)
Spacing: w_box * (W_img - 1) / (W_crop - 1)
Use the above grid to bilinear sample.
However, what we want is (with fpcoor box):
Spacing: w_box / W_crop
Initial point: x0_box + spacing/2 - 0.5
(-0.5 because bilinear sample (in my definition) assumes floating point coordinate
(0.0, 0.0) is the same as pixel value (0, 0))
This function transform fpcoor boxes to a format to be used by tf.image.crop_and_resize
Returns:
y1x1y2x2
"""
x0, y0, x1, y1 = tf.split(boxes, 4, axis=1)
spacing_w = (x1 - x0) / tf.cast(crop_shape[1], tf.float32)
spacing_h = (y1 - y0) / tf.cast(crop_shape[0], tf.float32)
imshape = [tf.cast(image_shape[0] - 1, tf.float32), tf.cast(image_shape[1] - 1, tf.float32)]
nx0 = (x0 + spacing_w / 2 - 0.5) / imshape[1]
ny0 = (y0 + spacing_h / 2 - 0.5) / imshape[0]
nw = spacing_w * tf.cast(crop_shape[1] - 1, tf.float32) / imshape[1]
nh = spacing_h * tf.cast(crop_shape[0] - 1, tf.float32) / imshape[0]
return tf.concat([ny0, nx0, ny0 + nh, nx0 + nw], axis=1)
# Expand bbox to a minium size of 1
# boxes_x1y1, boxes_x2y2 = tf.split(boxes, 2, axis=1)
# boxes_wh = boxes_x2y2 - boxes_x1y1
# boxes_center = tf.reshape((boxes_x2y2 + boxes_x1y1) * 0.5, [-1, 2])
# boxes_newwh = tf.maximum(boxes_wh, 1.)
# boxes_x1y1new = boxes_center - boxes_newwh * 0.5
# boxes_x2y2new = boxes_center + boxes_newwh * 0.5
# boxes = tf.concat([boxes_x1y1new, boxes_x2y2new], axis=1)
image_shape = tf.shape(image)[2:]
boxes = transform_fpcoor_for_tf(boxes, image_shape, [crop_size, crop_size])
image = tf.transpose(image, [0, 2, 3, 1]) # nhwc
ret = tf.image.crop_and_resize(
image, boxes, tf.cast(box_ind, tf.int32),
crop_size=[crop_size, crop_size])
ret = tf.transpose(ret, [0, 3, 1, 2]) # ncss
return ret |
Args:
featuremap: 1xCxHxW
boxes: Nx4 floatbox
resolution: output spatial resolution
Returns:
NxCx res x res
def roi_align(featuremap, boxes, resolution):
"""
Args:
featuremap: 1xCxHxW
boxes: Nx4 floatbox
resolution: output spatial resolution
Returns:
NxCx res x res
"""
# sample 4 locations per roi bin
ret = crop_and_resize(
featuremap, boxes,
tf.zeros([tf.shape(boxes)[0]], dtype=tf.int32),
resolution * 2)
ret = tf.nn.avg_pool(ret, [1, 1, 2, 2], [1, 1, 2, 2], padding='SAME', data_format='NCHW')
return ret |
Slice anchors to the spatial size of this featuremap.
def narrow_to(self, featuremap):
"""
Slice anchors to the spatial size of this featuremap.
"""
shape2d = tf.shape(featuremap)[2:] # h,w
slice3d = tf.concat([shape2d, [-1]], axis=0)
slice4d = tf.concat([shape2d, [-1, -1]], axis=0)
boxes = tf.slice(self.boxes, [0, 0, 0, 0], slice4d)
gt_labels = tf.slice(self.gt_labels, [0, 0, 0], slice3d)
gt_boxes = tf.slice(self.gt_boxes, [0, 0, 0, 0], slice4d)
return RPNAnchors(boxes, gt_labels, gt_boxes) |
img: bgr, [0,255]
heatmap: [0,1]
def colorize(img, heatmap):
""" img: bgr, [0,255]
heatmap: [0,1]
"""
heatmap = viz.intensity_to_rgb(heatmap, cmap='jet')[:, :, ::-1]
return img * 0.5 + heatmap * 0.5 |
The correct center is shape*0.5-0.5. This can be verified by:
SHAPE = 7
arr = np.random.rand(SHAPE, SHAPE)
orig = arr
c = SHAPE * 0.5 - 0.5
c = (c, c)
for k in range(4):
mat = cv2.getRotationMatrix2D(c, 90, 1)
arr = cv2.warpAffine(arr, mat, arr.shape)
assert np.all(arr == orig)
def _get_augment_params(self, img):
center = img.shape[1::-1] * self._rand_range(
self.center_range[0], self.center_range[1], (2,))
deg = self._rand_range(-self.max_deg, self.max_deg)
if self.step_deg:
deg = deg // self.step_deg * self.step_deg
"""
The correct center is shape*0.5-0.5. This can be verified by:
SHAPE = 7
arr = np.random.rand(SHAPE, SHAPE)
orig = arr
c = SHAPE * 0.5 - 0.5
c = (c, c)
for k in range(4):
mat = cv2.getRotationMatrix2D(c, 90, 1)
arr = cv2.warpAffine(arr, mat, arr.shape)
assert np.all(arr == orig)
"""
mat = cv2.getRotationMatrix2D(tuple(center - 0.5), deg, 1)
return WarpAffineTransform(
mat, img.shape[1::-1], interp=self.interp,
borderMode=self.border, borderValue=self.border_value) |
Get largest rectangle after rotation.
http://stackoverflow.com/questions/16702966/rotate-image-and-crop-out-black-borders
def largest_rotated_rect(w, h, angle):
"""
Get largest rectangle after rotation.
http://stackoverflow.com/questions/16702966/rotate-image-and-crop-out-black-borders
"""
angle = angle / 180.0 * math.pi
if w <= 0 or h <= 0:
return 0, 0
width_is_longer = w >= h
side_long, side_short = (w, h) if width_is_longer else (h, w)
# since the solutions for angle, -angle and 180-angle are all the same,
# if suffices to look at the first quadrant and the absolute values of sin,cos:
sin_a, cos_a = abs(math.sin(angle)), abs(math.cos(angle))
if side_short <= 2. * sin_a * cos_a * side_long:
# half constrained case: two crop corners touch the longer side,
# the other two corners are on the mid-line parallel to the longer line
x = 0.5 * side_short
wr, hr = (x / sin_a, x / cos_a) if width_is_longer else (x / cos_a, x / sin_a)
else:
# fully constrained case: crop touches all 4 sides
cos_2a = cos_a * cos_a - sin_a * sin_a
wr, hr = (w * cos_a - h * sin_a) / cos_2a, (h * cos_a - w * sin_a) / cos_2a
return int(np.round(wr)), int(np.round(hr)) |
Apply a mapping on certain argument before calling the original function.
Args:
maps (dict): {argument_name: map_func}
def map_arg(**maps):
"""
Apply a mapping on certain argument before calling the original function.
Args:
maps (dict): {argument_name: map_func}
"""
def deco(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
if six.PY2:
argmap = inspect.getcallargs(func, *args, **kwargs)
else:
# getcallargs was deprecated since 3.5
sig = inspect.signature(func)
argmap = sig.bind_partial(*args, **kwargs).arguments
for k, map_func in six.iteritems(maps):
if k in argmap:
argmap[k] = map_func(argmap[k])
return func(**argmap)
return wrapper
return deco |
Like memoized, but keep one cache per default graph.
def graph_memoized(func):
"""
Like memoized, but keep one cache per default graph.
"""
# TODO it keeps the graph alive
from ..compat import tfv1
GRAPH_ARG_NAME = '__IMPOSSIBLE_NAME_FOR_YOU__'
@memoized
def func_with_graph_arg(*args, **kwargs):
kwargs.pop(GRAPH_ARG_NAME)
return func(*args, **kwargs)
@functools.wraps(func)
def wrapper(*args, **kwargs):
assert GRAPH_ARG_NAME not in kwargs, "No Way!!"
graph = tfv1.get_default_graph()
kwargs[GRAPH_ARG_NAME] = graph
return func_with_graph_arg(*args, **kwargs)
return wrapper |
A decorator. It performs memoization ignoring the arguments used to call
the function.
def memoized_ignoreargs(func):
"""
A decorator. It performs memoization ignoring the arguments used to call
the function.
"""
def wrapper(*args, **kwargs):
if func not in _MEMOIZED_NOARGS:
res = func(*args, **kwargs)
_MEMOIZED_NOARGS[func] = res
return res
return _MEMOIZED_NOARGS[func]
return wrapper |
Ensure a 2D shape.
Args:
a: a int or tuple/list of length 2
Returns:
list: of length 2. if ``a`` is a int, return ``[a, a]``.
def shape2d(a):
"""
Ensure a 2D shape.
Args:
a: a int or tuple/list of length 2
Returns:
list: of length 2. if ``a`` is a int, return ``[a, a]``.
"""
if type(a) == int:
return [a, a]
if isinstance(a, (list, tuple)):
assert len(a) == 2
return list(a)
raise RuntimeError("Illegal shape: {}".format(a)) |
Ensuer a 4D shape, to use with 4D symbolic functions.
Args:
a: a int or tuple/list of length 2
Returns:
list: of length 4. if ``a`` is a int, return ``[1, a, a, 1]``
or ``[1, 1, a, a]`` depending on data_format.
def shape4d(a, data_format='NHWC'):
"""
Ensuer a 4D shape, to use with 4D symbolic functions.
Args:
a: a int or tuple/list of length 2
Returns:
list: of length 4. if ``a`` is a int, return ``[1, a, a, 1]``
or ``[1, 1, a, a]`` depending on data_format.
"""
s2d = shape2d(a)
if get_data_format(data_format, False) == 'NHWC':
return [1] + s2d + [1]
else:
return [1, 1] + s2d |
Decorate a method or property of a class, so that this method can only
be called once for every instance.
Calling it more than once will result in exception.
def call_only_once(func):
"""
Decorate a method or property of a class, so that this method can only
be called once for every instance.
Calling it more than once will result in exception.
"""
@functools.wraps(func)
def wrapper(*args, **kwargs):
self = args[0]
# cannot use hasattr here, because hasattr tries to getattr, which
# fails if func is a property
assert func.__name__ in dir(self), "call_only_once can only be used on method or property!"
if not hasattr(self, '_CALL_ONLY_ONCE_CACHE'):
cache = self._CALL_ONLY_ONCE_CACHE = set()
else:
cache = self._CALL_ONLY_ONCE_CACHE
cls = type(self)
# cannot use ismethod(), because decorated method becomes a function
is_method = inspect.isfunction(getattr(cls, func.__name__))
assert func not in cache, \
"{} {}.{} can only be called once per object!".format(
'Method' if is_method else 'Property',
cls.__name__, func.__name__)
cache.add(func)
return func(*args, **kwargs)
return wrapper |
A decorator that performs memoization on methods. It stores the cache on the object instance itself.
def memoized_method(func):
"""
A decorator that performs memoization on methods. It stores the cache on the object instance itself.
"""
@functools.wraps(func)
def wrapper(*args, **kwargs):
self = args[0]
assert func.__name__ in dir(self), "memoized_method can only be used on method!"
if not hasattr(self, '_MEMOIZED_CACHE'):
cache = self._MEMOIZED_CACHE = {}
else:
cache = self._MEMOIZED_CACHE
key = (func, ) + args[1:] + tuple(kwargs)
ret = cache.get(key, None)
if ret is not None:
return ret
value = func(*args, **kwargs)
cache[key] = value
return value
return wrapper |
A decorator which automatically reuses the current variable scope if the
function has been called with the same variable scope before.
Example:
.. code-block:: python
@auto_reuse_variable_scope
def myfunc(x):
return tf.layers.conv2d(x, 128, 3)
myfunc(x1) # will inherit parent scope reuse
myfunc(x2) # will reuse
with tf.variable_scope('newscope'):
myfunc(x3) # will inherit parent scope reuse
myfunc(x4) # will reuse
def auto_reuse_variable_scope(func):
"""
A decorator which automatically reuses the current variable scope if the
function has been called with the same variable scope before.
Example:
.. code-block:: python
@auto_reuse_variable_scope
def myfunc(x):
return tf.layers.conv2d(x, 128, 3)
myfunc(x1) # will inherit parent scope reuse
myfunc(x2) # will reuse
with tf.variable_scope('newscope'):
myfunc(x3) # will inherit parent scope reuse
myfunc(x4) # will reuse
"""
used_scope = set()
@functools.wraps(func)
def wrapper(*args, **kwargs):
scope = tf.get_variable_scope()
h = hash((tf.get_default_graph(), scope.name))
# print("Entering " + scope.name + " reuse: " + str(h in used_scope))
if h in used_scope:
if get_tf_version_tuple() >= (1, 5):
with tf.variable_scope(scope, reuse=True, auxiliary_name_scope=False):
return func(*args, **kwargs)
else:
ns = tf.get_default_graph().get_name_scope()
with tf.variable_scope(scope, reuse=True), \
tf.name_scope(ns + '/' if ns else ''):
return func(*args, **kwargs)
else:
used_scope.add(h)
return func(*args, **kwargs)
return wrapper |
Args:
name_scope(str): the default scope to use. If None, will use the name of the function.
Returns:
A decorator which makes the function run under a name scope.
The name scope is obtained by the following:
1. The 'name_scope' keyword argument when the decorated function is called.
2. The 'name_scope' argument of the decorator.
3. (default) The name of the decorated function itself.
Example:
.. code-block:: python
@under_name_scope()
def rms(x):
return tf.sqrt(
tf.reduce_mean(tf.square(x)))
rms(tensor) # will be called under name scope 'rms'
rms(tensor, name_scope='scope') # will be called under name scope 'scope'
Todo:
Add a reuse option.
def under_name_scope(name_scope=None):
"""
Args:
name_scope(str): the default scope to use. If None, will use the name of the function.
Returns:
A decorator which makes the function run under a name scope.
The name scope is obtained by the following:
1. The 'name_scope' keyword argument when the decorated function is called.
2. The 'name_scope' argument of the decorator.
3. (default) The name of the decorated function itself.
Example:
.. code-block:: python
@under_name_scope()
def rms(x):
return tf.sqrt(
tf.reduce_mean(tf.square(x)))
rms(tensor) # will be called under name scope 'rms'
rms(tensor, name_scope='scope') # will be called under name scope 'scope'
Todo:
Add a reuse option.
"""
def _impl(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
scopename = kwargs.pop('name_scope', name_scope)
if scopename is None:
scopename = func.__name__
with tf.name_scope(scopename):
return func(*args, **kwargs)
return wrapper
return _impl |
Returns:
A decorator which makes the function happen under a variable scope,
which is named by the function itself.
Example:
.. code-block:: python
@under_variable_scope()
def mid_level(x):
with argscope(Conv2D, kernel_shape=3, nl=BNReLU):
x = Conv2D('conv1', x, 512, stride=1)
x = Conv2D('conv2', x, 256, stride=1)
return x
def under_variable_scope():
"""
Returns:
A decorator which makes the function happen under a variable scope,
which is named by the function itself.
Example:
.. code-block:: python
@under_variable_scope()
def mid_level(x):
with argscope(Conv2D, kernel_shape=3, nl=BNReLU):
x = Conv2D('conv1', x, 512, stride=1)
x = Conv2D('conv2', x, 256, stride=1)
return x
"""
def _impl(func):
@functools.wraps(func)
def wrapper(*args, **kwargs):
name = func.__name__
with tf.variable_scope(name):
return func(*args, **kwargs)
return wrapper
return _impl |
Return a context which either opens and caches a new name scope,
or reenter an existing one.
Args:
top_level(bool): if True, the name scope will always be top-level.
It will not be nested under any existing name scope of the caller.
def cached_name_scope(name, top_level=True):
"""
Return a context which either opens and caches a new name scope,
or reenter an existing one.
Args:
top_level(bool): if True, the name scope will always be top-level.
It will not be nested under any existing name scope of the caller.
"""
if not top_level:
current_ns = tf.get_default_graph().get_name_scope()
if current_ns:
name = current_ns + '/' + name
ns = _get_cached_ns(name)
with tf.name_scope(ns):
yield ns |
Args:
grad_list: list of list of tuples, shape is Ngpu x Nvar x 2
def _check_grad_list(grad_list):
"""
Args:
grad_list: list of list of tuples, shape is Ngpu x Nvar x 2
"""
nvars = [len(k) for k in grad_list]
def basename(x):
return re.sub('tower[0-9]+/', '', x.op.name)
if len(set(nvars)) != 1:
names_per_gpu = [set([basename(k[1]) for k in grad_and_vars]) for grad_and_vars in grad_list]
inters = copy.copy(names_per_gpu[0])
for s in names_per_gpu:
inters &= s
for s in names_per_gpu:
s -= inters
logger.error("Unique trainable variables on towers: " + pprint.pformat(names_per_gpu))
raise ValueError("Number of gradients from each tower is different! " + str(nvars)) |
Run `func` on all GPUs (towers) and return the results.
Args:
towers (list[int]): a list of GPU id.
func: a lambda to be called inside each tower
devices: a list of devices to be used. By default will use '/gpu:{tower}'
use_vs (list[bool]): list of use_vs to passed to TowerContext
Returns:
List of outputs of ``func``, evaluated on each tower.
def call_for_each_tower(
towers, func, devices=None, use_vs=None):
"""
Run `func` on all GPUs (towers) and return the results.
Args:
towers (list[int]): a list of GPU id.
func: a lambda to be called inside each tower
devices: a list of devices to be used. By default will use '/gpu:{tower}'
use_vs (list[bool]): list of use_vs to passed to TowerContext
Returns:
List of outputs of ``func``, evaluated on each tower.
"""
ret = []
if devices is not None:
assert len(devices) == len(towers)
if use_vs is not None:
assert len(use_vs) == len(towers)
tower_names = ['tower{}'.format(idx) for idx in range(len(towers))]
for idx, t in enumerate(towers):
device = devices[idx] if devices is not None else '/gpu:{}'.format(t)
usevs = use_vs[idx] if use_vs is not None else False
reuse = not usevs and idx > 0
with tfv1.device(device), _maybe_reuse_vs(reuse), TrainTowerContext(
tower_names[idx],
vs_name=tower_names[idx] if usevs else '',
index=idx, total=len(towers)):
if len(str(device)) < 10: # a device function doesn't have good string description
logger.info("Building graph for training tower {} on device {} ...".format(idx, device))
else:
logger.info("Building graph for training tower {} ...".format(idx))
# When use_vs is True, use LOCAL_VARIABLES,
# so these duplicated variables won't be saved by default.
with override_to_local_variable(enable=usevs):
ret.append(func())
return ret |
Reduce the gradients, apply them with the optimizer,
and set self.grads to a list of (g, v), containing the averaged gradients.
Args:
grad_list ([[(grad, var), ...], ...]): #GPU lists to be reduced. Each is the gradients computed on each GPU.
get_opt_fn (-> tf.train.Optimizer): callable which returns an optimizer
Returns:
tf.Operation: the training op
def build(self, grad_list, get_opt_fn):
"""
Reduce the gradients, apply them with the optimizer,
and set self.grads to a list of (g, v), containing the averaged gradients.
Args:
grad_list ([[(grad, var), ...], ...]): #GPU lists to be reduced. Each is the gradients computed on each GPU.
get_opt_fn (-> tf.train.Optimizer): callable which returns an optimizer
Returns:
tf.Operation: the training op
"""
assert len(grad_list) == len(self.towers)
DataParallelBuilder._check_grad_list(grad_list)
# debug tower performance (without update):
# ops = [k[0] for k in grad_list[1]] + [k[0] for k in grad_list[0]]
# self.train_op = tf.group(*ops)
# return
self.grads = aggregate_grads(grad_list, colocation=True)
# grads = grad_list[0]
opt = get_opt_fn()
if self.ps_device == 'cpu':
with tf.device('/cpu:0'):
train_op = opt.apply_gradients(self.grads, name='train_op')
else:
train_op = opt.apply_gradients(self.grads, name='train_op')
return train_op |
Call the function `tower_fn` under :class:`TowerContext` for each tower.
Returns:
a list, contains the return values of `tower_fn` on each tower.
def call_for_each_tower(self, tower_fn):
"""
Call the function `tower_fn` under :class:`TowerContext` for each tower.
Returns:
a list, contains the return values of `tower_fn` on each tower.
"""
# if tower_fn returns [(grad, var), ...], this returns #GPU x #VAR x 2
return DataParallelBuilder.build_on_towers(
self.towers,
tower_fn,
# use no variable scope for the first tower
use_vs=[False] + [True] * (len(self.towers) - 1)) |
Reduce the gradients, apply them with the optimizer,
and set self.grads to #GPU number of lists of (g, v), containing the all-reduced gradients on each device.
Args:
grad_list ([[(grad, var), ...], ...]): #GPU lists to be reduced. Each is the gradients computed on each GPU.
get_opt_fn (-> tf.train.Optimizer): callable which returns an optimizer
Returns:
(tf.Operation, tf.Operation)
1. the training op.
2. the op which sync variables from GPU 0 to other GPUs.
It has to be run before the training has started.
And you can optionally run it later to sync non-trainable variables.
def build(self, grad_list, get_opt_fn):
"""
Reduce the gradients, apply them with the optimizer,
and set self.grads to #GPU number of lists of (g, v), containing the all-reduced gradients on each device.
Args:
grad_list ([[(grad, var), ...], ...]): #GPU lists to be reduced. Each is the gradients computed on each GPU.
get_opt_fn (-> tf.train.Optimizer): callable which returns an optimizer
Returns:
(tf.Operation, tf.Operation)
1. the training op.
2. the op which sync variables from GPU 0 to other GPUs.
It has to be run before the training has started.
And you can optionally run it later to sync non-trainable variables.
"""
assert len(grad_list) == len(self.towers)
raw_devices = ['/gpu:{}'.format(k) for k in self.towers]
DataParallelBuilder._check_grad_list(grad_list)
dtypes = set([x[0].dtype.base_dtype for x in grad_list[0]])
dtypes_nccl_supported = [tf.float32, tf.float64]
if get_tf_version_tuple() >= (1, 8):
dtypes_nccl_supported.append(tf.float16)
valid_for_nccl = all([k in dtypes_nccl_supported for k in dtypes])
if self._mode == 'nccl' and not valid_for_nccl:
logger.warn("Cannot use mode='nccl' because some gradients have unsupported types. Fallback to mode='cpu'")
self._mode = 'cpu'
if self._mode in ['nccl', 'hierarchical']:
all_grads, all_vars = split_grad_list(grad_list)
# use allreduce from tf-benchmarks
# from .batch_allreduce import AllReduceSpecAlgorithm
# algo = AllReduceSpecAlgorithm('nccl', list(range(8)), 0, 10)
# all_grads, warmup_ops = algo.batch_all_reduce(all_grads, 1, True, False)
# print("WARMUP OPS", warmup_ops)
if self._mode == 'nccl':
all_grads = allreduce_grads(all_grads, average=self._average) # #gpu x #param
else:
packer = GradientPacker(len(raw_devices))
succ = packer.compute_strategy(all_grads[0])
if succ:
packed_grads = packer.pack_all(all_grads, raw_devices)
packed_grads_aggr = allreduce_grads_hierarchical(
packed_grads, raw_devices, average=self._average)
all_grads = packer.unpack_all(packed_grads_aggr, raw_devices)
else:
all_grads = allreduce_grads_hierarchical(all_grads, raw_devices, average=self._average)
self.grads = merge_grad_list(all_grads, all_vars)
elif self._mode == 'cpu':
agg_grad_and_vars = aggregate_grads(
grad_list, colocation=False,
devices=['/cpu:0'], average=self._average) # #param x 2
self.grads = [] # #gpu x #param x 2
for grad_and_vars in grad_list: # grad_and_vars: #paramx2
# take v from each tower, and g from average.
self.grads.append(
[(g, v) for (_, v), (g, _) in zip(grad_and_vars, agg_grad_and_vars)])
train_ops = []
opt = get_opt_fn()
with tf.name_scope('apply_gradients'):
for idx, grad_and_vars in enumerate(self.grads):
with tf.device(raw_devices[idx]):
# apply_gradients may create variables. Make them LOCAL_VARIABLES
with override_to_local_variable(enable=idx > 0):
train_ops.append(opt.apply_gradients(
grad_and_vars, name='apply_grad_{}'.format(idx)))
train_op = tf.group(*train_ops, name='train_op')
with tf.name_scope('sync_variables'):
post_init_op = SyncMultiGPUReplicatedBuilder.get_post_init_ops()
return train_op, post_init_op |
Copy values of variables on GPU 0 to other GPUs.
def get_post_init_ops():
"""
Copy values of variables on GPU 0 to other GPUs.
"""
# literally all variables, because it's better to sync optimizer-internal variables as well
all_vars = tf.global_variables() + tf.local_variables()
var_by_name = dict([(v.name, v) for v in all_vars])
trainable_names = set([x.name for x in tf.trainable_variables()])
post_init_ops = []
def log_failure(name, reason):
logger.warn("[ReplicatedTrainer] Do not know how to sync variable '{}' across GPUs. "
"Reason: {} ".format(name, reason))
assert name not in trainable_names, \
"The aforementioned variable is trainable, so this is probably a fatal error."
logger.warn(
"[ReplicatedTrainer] This variable is non-trainable. "
"Ignore this warning if you know it's OK to leave it out-of-sync.")
for v in all_vars:
if not v.name.startswith('tower'):
continue
if v.name.startswith('tower0'):
# in this trainer, the master name doesn't have the towerx/ prefix
log_failure(v.name, "Name should not have prefix 'tower0' in this trainer!")
continue # TODO some vars (EMA) may still startswith tower0
split_name = v.name.split('/')
prefix = split_name[0]
realname = '/'.join(split_name[1:])
if prefix in realname:
log_failure(v.name, "Prefix {} appears multiple times in its name!".format(prefix))
continue
copy_from = var_by_name.get(realname)
if copy_from is not None:
post_init_ops.append(v.assign(copy_from.read_value()))
else:
log_failure(v.name, "Cannot find {} in the graph!".format(realname))
logger.info(
"'sync_variables_from_main_tower' includes {} operations.".format(len(post_init_ops)))
return tf.group(*post_init_ops, name='sync_variables_from_main_tower') |
Call the function `tower_fn` under :class:`TowerContext` for each tower.
Returns:
a list, contains the return values of `tower_fn` on each tower.
def call_for_each_tower(self, tower_fn):
"""
Call the function `tower_fn` under :class:`TowerContext` for each tower.
Returns:
a list, contains the return values of `tower_fn` on each tower.
"""
ps_device = 'cpu' if len(self.towers) >= 4 else 'gpu'
raw_devices = ['/gpu:{}'.format(k) for k in self.towers]
if ps_device == 'gpu':
devices = [LeastLoadedDeviceSetter(d, raw_devices) for d in raw_devices]
else:
devices = [tf.train.replica_device_setter(
worker_device=d, ps_device='/cpu:0', ps_tasks=1) for d in raw_devices]
return DataParallelBuilder.build_on_towers(self.towers, tower_fn, devices) |
Args:
grad_list ([[(grad, var), ...], ...]): #GPU lists to be reduced. Each is the gradients computed on each GPU.
get_opt_fn (-> tf.train.Optimizer): callable which returns an optimizer
Returns:
tf.Operation: the training op
def build(self, grad_list, get_opt_fn):
"""
Args:
grad_list ([[(grad, var), ...], ...]): #GPU lists to be reduced. Each is the gradients computed on each GPU.
get_opt_fn (-> tf.train.Optimizer): callable which returns an optimizer
Returns:
tf.Operation: the training op
"""
assert len(grad_list) == len(self.towers)
DataParallelBuilder._check_grad_list(grad_list)
if self._scale_gradient and len(self.towers) > 1:
# pretend to average the grads, in order to make async and
# sync have consistent effective learning rate
gradproc = ScaleGradient(('.*', 1.0 / len(self.towers)), verbose=False)
grad_list = [gradproc.process(gv) for gv in grad_list]
# Ngpu x Nvar x 2
train_ops = []
opt = get_opt_fn()
with tf.name_scope('async_apply_gradients'):
for i, grad_and_vars in enumerate(zip(*grad_list)):
# Ngpu x 2
v = grad_and_vars[0][1]
with tf.device(v.device):
# will call apply_gradients (therefore gradproc) multiple times
train_ops.append(opt.apply_gradients(
grad_and_vars, name='apply_grad_{}'.format(i)))
return tf.group(*train_ops, name='train_op') |
Humanize timedelta given in seconds
Args:
sec (float): time difference in seconds. Must be positive.
Returns:
str - time difference as a readable string
Example:
.. code-block:: python
print(humanize_time_delta(1)) # 1 second
print(humanize_time_delta(60 + 1)) # 1 minute 1 second
print(humanize_time_delta(87.6)) # 1 minute 27 seconds
print(humanize_time_delta(0.01)) # 0.01 seconds
print(humanize_time_delta(60 * 60 + 1)) # 1 hour 1 second
print(humanize_time_delta(60 * 60 * 24 + 1)) # 1 day 1 second
print(humanize_time_delta(60 * 60 * 24 + 60 * 2 + 60*60*9 + 3)) # 1 day 9 hours 2 minutes 3 seconds
def humanize_time_delta(sec):
"""Humanize timedelta given in seconds
Args:
sec (float): time difference in seconds. Must be positive.
Returns:
str - time difference as a readable string
Example:
.. code-block:: python
print(humanize_time_delta(1)) # 1 second
print(humanize_time_delta(60 + 1)) # 1 minute 1 second
print(humanize_time_delta(87.6)) # 1 minute 27 seconds
print(humanize_time_delta(0.01)) # 0.01 seconds
print(humanize_time_delta(60 * 60 + 1)) # 1 hour 1 second
print(humanize_time_delta(60 * 60 * 24 + 1)) # 1 day 1 second
print(humanize_time_delta(60 * 60 * 24 + 60 * 2 + 60*60*9 + 3)) # 1 day 9 hours 2 minutes 3 seconds
"""
if sec < 0:
logger.warn("humanize_time_delta() obtains negative seconds!")
return "{:.3g} seconds".format(sec)
if sec == 0:
return "0 second"
time = datetime(2000, 1, 1) + timedelta(seconds=int(sec))
units = ['day', 'hour', 'minute', 'second']
vals = [int(sec // 86400), time.hour, time.minute, time.second]
if sec < 60:
vals[-1] = sec
def _format(v, u):
return "{:.3g} {}{}".format(v, u, "s" if v > 1 else "")
ans = []
for v, u in zip(vals, units):
if v > 0:
ans.append(_format(v, u))
return " ".join(ans) |
Args:
name(str), val(str):
Returns:
a context where the environment variable ``name`` being set to
``val``. It will be set back after the context exits.
def change_env(name, val):
"""
Args:
name(str), val(str):
Returns:
a context where the environment variable ``name`` being set to
``val``. It will be set back after the context exits.
"""
oldval = os.environ.get(name, None)
os.environ[name] = val
yield
if oldval is None:
del os.environ[name]
else:
os.environ[name] = oldval |
Get a good RNG seeded with time, pid and the object.
Args:
obj: some object to use to generate random seed.
Returns:
np.random.RandomState: the RNG.
def get_rng(obj=None):
"""
Get a good RNG seeded with time, pid and the object.
Args:
obj: some object to use to generate random seed.
Returns:
np.random.RandomState: the RNG.
"""
seed = (id(obj) + os.getpid() +
int(datetime.now().strftime("%Y%m%d%H%M%S%f"))) % 4294967295
if _RNG_SEED is not None:
seed = _RNG_SEED
return np.random.RandomState(seed) |
Each called in the code to this function is guaranteed to return True the
first time and False afterwards.
Returns:
bool: whether this is the first time this function gets called from this line of code.
Example:
.. code-block:: python
if execute_only_once():
# do something only once
def execute_only_once():
"""
Each called in the code to this function is guaranteed to return True the
first time and False afterwards.
Returns:
bool: whether this is the first time this function gets called from this line of code.
Example:
.. code-block:: python
if execute_only_once():
# do something only once
"""
f = inspect.currentframe().f_back
ident = (f.f_code.co_filename, f.f_lineno)
if ident in _EXECUTE_HISTORY:
return False
_EXECUTE_HISTORY.add(ident)
return True |
Return default arguments to be used with tqdm.
Args:
kwargs: extra arguments to be used.
Returns:
dict:
def get_tqdm_kwargs(**kwargs):
"""
Return default arguments to be used with tqdm.
Args:
kwargs: extra arguments to be used.
Returns:
dict:
"""
default = dict(
smoothing=0.5,
dynamic_ncols=True,
ascii=True,
bar_format='{l_bar}{bar}|{n_fmt}/{total_fmt}[{elapsed}<{remaining},{rate_noinv_fmt}]'
)
try:
# Use this env var to override the refresh interval setting
interval = float(os.environ['TENSORPACK_PROGRESS_REFRESH'])
except KeyError:
interval = _pick_tqdm_interval(kwargs.get('file', sys.stderr))
default['mininterval'] = interval
default.update(kwargs)
return default |
Similar to `from ctypes.util import find_library`, but try
to return full path if possible.
def find_library_full_path(name):
"""
Similar to `from ctypes.util import find_library`, but try
to return full path if possible.
"""
from ctypes.util import find_library
if os.name == "posix" and sys.platform == "darwin":
# on Mac, ctypes already returns full path
return find_library(name)
def _use_proc_maps(name):
"""
Find so from /proc/pid/maps
Only works with libraries that has already been loaded.
But this is the most accurate method -- it finds the exact library that's being used.
"""
procmap = os.path.join('/proc', str(os.getpid()), 'maps')
if not os.path.isfile(procmap):
return None
with open(procmap, 'r') as f:
for line in f:
line = line.strip().split(' ')
sofile = line[-1]
basename = os.path.basename(sofile)
if 'lib' + name + '.so' in basename:
if os.path.isfile(sofile):
return os.path.realpath(sofile)
# The following two methods come from https://github.com/python/cpython/blob/master/Lib/ctypes/util.py
def _use_ld(name):
"""
Find so with `ld -lname -Lpath`.
It will search for files in LD_LIBRARY_PATH, but not in ldconfig.
"""
cmd = "ld -t -l{} -o {}".format(name, os.devnull)
ld_lib_path = os.environ.get('LD_LIBRARY_PATH', '')
for d in ld_lib_path.split(':'):
cmd = cmd + " -L " + d
result, ret = subproc_call(cmd + '|| true')
expr = r'[^\(\)\s]*lib%s\.[^\(\)\s]*' % re.escape(name)
res = re.search(expr, result.decode('utf-8'))
if res:
res = res.group(0)
if not os.path.isfile(res):
return None
return os.path.realpath(res)
def _use_ldconfig(name):
"""
Find so in `ldconfig -p`.
It does not handle LD_LIBRARY_PATH.
"""
with change_env('LC_ALL', 'C'), change_env('LANG', 'C'):
ldconfig, ret = subproc_call("ldconfig -p")
ldconfig = ldconfig.decode('utf-8')
if ret != 0:
return None
expr = r'\s+(lib%s\.[^\s]+)\s+\(.*=>\s+(.*)' % (re.escape(name))
res = re.search(expr, ldconfig)
if not res:
return None
else:
ret = res.group(2)
return os.path.realpath(ret)
if sys.platform.startswith('linux'):
return _use_proc_maps(name) or _use_ld(name) or _use_ldconfig(name) or find_library(name)
return find_library(name) |
Args:
df (DataFlow): the DataFlow to serialize.
path (str): output path. Either a directory or an lmdb file.
write_frequency (int): the frequency to write back data to disk.
def save(df, path, write_frequency=5000):
"""
Args:
df (DataFlow): the DataFlow to serialize.
path (str): output path. Either a directory or an lmdb file.
write_frequency (int): the frequency to write back data to disk.
"""
assert isinstance(df, DataFlow), type(df)
isdir = os.path.isdir(path)
if isdir:
assert not os.path.isfile(os.path.join(path, 'data.mdb')), "LMDB file exists!"
else:
assert not os.path.isfile(path), "LMDB file {} exists!".format(path)
db = lmdb.open(path, subdir=isdir,
map_size=1099511627776 * 2, readonly=False,
meminit=False, map_async=True) # need sync() at the end
size = _reset_df_and_get_size(df)
with get_tqdm(total=size) as pbar:
idx = -1
# LMDB transaction is not exception-safe!
# although it has a context manager interface
txn = db.begin(write=True)
for idx, dp in enumerate(df):
txn.put(u'{:08}'.format(idx).encode('ascii'), dumps(dp))
pbar.update()
if (idx + 1) % write_frequency == 0:
txn.commit()
txn = db.begin(write=True)
txn.commit()
keys = [u'{:08}'.format(k).encode('ascii') for k in range(idx + 1)]
with db.begin(write=True) as txn:
txn.put(b'__keys__', dumps(keys))
logger.info("Flushing database ...")
db.sync()
db.close() |
Note:
If you found deserialization being the bottleneck, you can use :class:`LMDBData` as the reader
and run deserialization as a mapper in parallel.
def load(path, shuffle=True):
"""
Note:
If you found deserialization being the bottleneck, you can use :class:`LMDBData` as the reader
and run deserialization as a mapper in parallel.
"""
df = LMDBData(path, shuffle=shuffle)
return MapData(df, lambda dp: loads(dp[1])) |
Args:
df (DataFlow): the DataFlow to serialize.
path (str): output npz file.
def save(df, path):
"""
Args:
df (DataFlow): the DataFlow to serialize.
path (str): output npz file.
"""
buffer = []
size = _reset_df_and_get_size(df)
with get_tqdm(total=size) as pbar:
for dp in df:
buffer.append(dp)
pbar.update()
np.savez_compressed(path, buffer=np.asarray(buffer, dtype=np.object)) |
Args:
df (DataFlow): the DataFlow to serialize.
path (str): output tfrecord file.
def save(df, path):
"""
Args:
df (DataFlow): the DataFlow to serialize.
path (str): output tfrecord file.
"""
if os.environ.get('TENSORPACK_COMPATIBLE_SERIALIZE', 'msgpack') == 'msgpack':
def _dumps(dp):
return dumps(dp)
else:
def _dumps(dp):
return dumps(dp).to_pybytes()
size = _reset_df_and_get_size(df)
with tf.python_io.TFRecordWriter(path) as writer, get_tqdm(total=size) as pbar:
for dp in df:
writer.write(_dumps(dp))
pbar.update() |
Args:
size (int): total number of records. If not provided, the returned dataflow will have no `__len__()`.
It's needed because this metadata is not stored in the TFRecord file.
def load(path, size=None):
"""
Args:
size (int): total number of records. If not provided, the returned dataflow will have no `__len__()`.
It's needed because this metadata is not stored in the TFRecord file.
"""
gen = tf.python_io.tf_record_iterator(path)
ds = DataFromGenerator(gen)
ds = MapData(ds, loads)
if size is not None:
ds = FixedSizeData(ds, size)
return ds |
Args:
df (DataFlow): the DataFlow to serialize.
path (str): output hdf5 file.
data_paths (list[str]): list of h5 paths. It should have the same
length as each datapoint, and each path should correspond to one
component of the datapoint.
def save(df, path, data_paths):
"""
Args:
df (DataFlow): the DataFlow to serialize.
path (str): output hdf5 file.
data_paths (list[str]): list of h5 paths. It should have the same
length as each datapoint, and each path should correspond to one
component of the datapoint.
"""
size = _reset_df_and_get_size(df)
buffer = defaultdict(list)
with get_tqdm(total=size) as pbar:
for dp in df:
assert len(dp) == len(data_paths), "Datapoint has {} components!".format(len(dp))
for k, el in zip(data_paths, dp):
buffer[k].append(el)
pbar.update()
with h5py.File(path, 'w') as hf, get_tqdm(total=len(data_paths)) as pbar:
for data_path in data_paths:
hf.create_dataset(data_path, data=buffer[data_path])
pbar.update() |
Args:
trainer (SingleCostTrainer):
get_model (input1, input2, ... -> tf.keras.Model):
A function which takes tensors, builds and returns a Keras model.
It will be part of the tower function.
input (InputSource):
optimizer (tf.train.Optimizer):
loss, metrics: list of strings
def setup_keras_trainer(
trainer, get_model,
input_signature, target_signature,
input, optimizer, loss, metrics):
"""
Args:
trainer (SingleCostTrainer):
get_model (input1, input2, ... -> tf.keras.Model):
A function which takes tensors, builds and returns a Keras model.
It will be part of the tower function.
input (InputSource):
optimizer (tf.train.Optimizer):
loss, metrics: list of strings
"""
assert isinstance(optimizer, tf.train.Optimizer), optimizer
assert isinstance(loss, list), loss
assert len(loss) >= 1, "No loss was given!"
assert isinstance(metrics, list), metrics
model_caller = KerasModelCaller(get_model)
nr_inputs = len(input_signature)
def get_cost(*inputs):
ctx = get_current_tower_context()
input_tensors = list(inputs[:nr_inputs])
target_tensors = list(inputs[nr_inputs:])
# TODO mapping between target tensors & output tensors
outputs = model_caller(input_tensors)
if isinstance(outputs, tf.Tensor):
outputs = [outputs]
assert len(outputs) == len(target_tensors), \
"len({}) != len({})".format(str(outputs), str(target_tensors))
assert len(outputs) == len(loss), \
"len({}) != len({})".format(str(outputs), str(loss))
loss_tensors = []
for idx, loss_name in enumerate(loss):
with cached_name_scope('keras_loss', top_level=False):
loss_fn = keras.losses.get(loss_name)
curr_loss = loss_fn(target_tensors[idx], outputs[idx])
curr_loss = tf.reduce_mean(curr_loss, name=loss_name)
_check_name(curr_loss, loss_name)
loss_tensors.append(curr_loss)
loss_reg = regularize_cost_from_collection()
if loss_reg is not None:
total_loss = tf.add_n(loss_tensors + [loss_reg], name=TOTAL_LOSS_NAME)
add_moving_summary(loss_reg, total_loss, *loss_tensors)
else:
total_loss = tf.add_n(loss_tensors, name=TOTAL_LOSS_NAME)
add_moving_summary(total_loss, *loss_tensors)
if metrics and (ctx.is_main_training_tower or not ctx.is_training):
# for list: one metric for each output
metric_tensors = []
for oid, metric_name in enumerate(metrics):
output_tensor = outputs[oid]
target_tensor = target_tensors[oid] # TODO may not have the same mapping?
with cached_name_scope('keras_metric', top_level=False):
metric_fn = keras.metrics.get(metric_name)
metric_tensor = metric_fn(target_tensor, output_tensor)
metric_tensor = tf.reduce_mean(metric_tensor, name=metric_name)
_check_name(metric_tensor, metric_name)
# check name conflict here
metric_tensors.append(metric_tensor)
add_moving_summary(*metric_tensors)
return total_loss
trainer.setup_graph(
input_signature + target_signature,
input,
get_cost,
lambda: optimizer)
if len(keras.backend.learning_phase().consumers()) > 0:
# check if learning_phase is used in this model
trainer.register_callback(KerasPhaseCallback(True)) |
Args:
optimizer (tf.train.Optimizer):
loss, metrics: string or list of strings
def compile(self, optimizer, loss, metrics=None):
"""
Args:
optimizer (tf.train.Optimizer):
loss, metrics: string or list of strings
"""
if isinstance(loss, six.string_types):
loss = [loss]
if metrics is None:
metrics = []
if isinstance(metrics, six.string_types):
metrics = [metrics]
self._stats_to_inference = loss + metrics + [TOTAL_LOSS_NAME]
setup_keras_trainer(
self.trainer, get_model=self.get_model,
input_signature=self.input_signature,
target_signature=self.target_signature,
input=self.input,
optimizer=optimizer,
loss=loss,
metrics=metrics) |
Args:
validation_data (DataFlow or InputSource): to be used for inference.
The inference callback is added as the first in the callback list.
If you need to use it in a different order, please write it in the callback list manually.
kwargs: same arguments as :meth:`Trainer.train_with_defaults`.
def fit(self, validation_data=None, **kwargs):
"""
Args:
validation_data (DataFlow or InputSource): to be used for inference.
The inference callback is added as the first in the callback list.
If you need to use it in a different order, please write it in the callback list manually.
kwargs: same arguments as :meth:`Trainer.train_with_defaults`.
"""
callbacks = kwargs.pop('callbacks', [])
if validation_data is not None:
# There is no way to guess where users want this callback. So we have to choose one.
# MinSaver may need results from this callback,
# so we put this callback at first.
callbacks.insert(0, InferenceRunner(
validation_data, ScalarStats(self._stats_to_inference)))
self.trainer.train_with_defaults(callbacks=callbacks, **kwargs) |
Return the three quantization functions fw, fa, fg, for weights, activations and gradients respectively
def get_dorefa(bitW, bitA, bitG):
"""
Return the three quantization functions fw, fa, fg, for weights, activations and gradients respectively
"""
def quantize(x, k):
n = float(2 ** k - 1)
@tf.custom_gradient
def _quantize(x):
return tf.round(x * n) / n, lambda dy: dy
return _quantize(x)
def fw(x):
if bitW == 32:
return x
if bitW == 1: # BWN
E = tf.stop_gradient(tf.reduce_mean(tf.abs(x)))
@tf.custom_gradient
def _sign(x):
return tf.where(tf.equal(x, 0), tf.ones_like(x), tf.sign(x / E)) * E, lambda dy: dy
return _sign(x)
x = tf.tanh(x)
x = x / tf.reduce_max(tf.abs(x)) * 0.5 + 0.5
return 2 * quantize(x, bitW) - 1
def fa(x):
if bitA == 32:
return x
return quantize(x, bitA)
def fg(x):
if bitG == 32:
return x
@tf.custom_gradient
def _identity(input):
def grad_fg(x):
rank = x.get_shape().ndims
assert rank is not None
maxx = tf.reduce_max(tf.abs(x), list(range(1, rank)), keep_dims=True)
x = x / maxx
n = float(2**bitG - 1)
x = x * 0.5 + 0.5 + tf.random_uniform(
tf.shape(x), minval=-0.5 / n, maxval=0.5 / n)
x = tf.clip_by_value(x, 0.0, 1.0)
x = quantize(x, bitG) - 0.5
return x * maxx * 2
return input, grad_fg
return _identity(x)
return fw, fa, fg |
Implemented Trained Ternary Quantization:
https://arxiv.org/abs/1612.01064
Code modified from the authors' at:
https://github.com/czhu95/ternarynet/blob/master/examples/Ternary-Net/ternary.py
def ternarize(x, thresh=0.05):
"""
Implemented Trained Ternary Quantization:
https://arxiv.org/abs/1612.01064
Code modified from the authors' at:
https://github.com/czhu95/ternarynet/blob/master/examples/Ternary-Net/ternary.py
"""
shape = x.get_shape()
thre_x = tf.stop_gradient(tf.reduce_max(tf.abs(x)) * thresh)
w_p = tf.get_variable('Wp', initializer=1.0, dtype=tf.float32)
w_n = tf.get_variable('Wn', initializer=1.0, dtype=tf.float32)
tf.summary.scalar(w_p.op.name + '-summary', w_p)
tf.summary.scalar(w_n.op.name + '-summary', w_n)
mask = tf.ones(shape)
mask_p = tf.where(x > thre_x, tf.ones(shape) * w_p, mask)
mask_np = tf.where(x < -thre_x, tf.ones(shape) * w_n, mask_p)
mask_z = tf.where((x < thre_x) & (x > - thre_x), tf.zeros(shape), mask)
@tf.custom_gradient
def _sign_mask(x):
return tf.sign(x) * mask_z, lambda dy: dy
w = _sign_mask(x)
w = w * mask_np
tf.summary.histogram(w.name, w)
return w |
Args:
img (np.ndarray): an image (expect BGR) to show.
lclick_cb, rclick_cb: a callback ``func(img, x, y)`` for left/right click event.
kwargs: can be {key_cb_a: callback_img, key_cb_b: callback_img}, to
specify a callback ``func(img)`` for keypress.
Some existing keypress event handler:
* q: destroy the current window
* x: execute ``sys.exit()``
* s: save image to "out.png"
def interactive_imshow(img, lclick_cb=None, rclick_cb=None, **kwargs):
"""
Args:
img (np.ndarray): an image (expect BGR) to show.
lclick_cb, rclick_cb: a callback ``func(img, x, y)`` for left/right click event.
kwargs: can be {key_cb_a: callback_img, key_cb_b: callback_img}, to
specify a callback ``func(img)`` for keypress.
Some existing keypress event handler:
* q: destroy the current window
* x: execute ``sys.exit()``
* s: save image to "out.png"
"""
name = 'tensorpack_viz_window'
cv2.imshow(name, img)
def mouse_cb(event, x, y, *args):
if event == cv2.EVENT_LBUTTONUP and lclick_cb is not None:
lclick_cb(img, x, y)
elif event == cv2.EVENT_RBUTTONUP and rclick_cb is not None:
rclick_cb(img, x, y)
cv2.setMouseCallback(name, mouse_cb)
key = cv2.waitKey(-1)
while key >= 128:
key = cv2.waitKey(-1)
key = chr(key & 0xff)
cb_name = 'key_cb_' + key
if cb_name in kwargs:
kwargs[cb_name](img)
elif key == 'q':
cv2.destroyWindow(name)
elif key == 'x':
sys.exit()
elif key == 's':
cv2.imwrite('out.png', img)
elif key in ['+', '=']:
img = cv2.resize(img, None, fx=1.3, fy=1.3, interpolation=cv2.INTER_CUBIC)
interactive_imshow(img, lclick_cb, rclick_cb, **kwargs)
elif key == '-':
img = cv2.resize(img, None, fx=0.7, fy=0.7, interpolation=cv2.INTER_CUBIC)
interactive_imshow(img, lclick_cb, rclick_cb, **kwargs) |
Stacked patches into grid, to produce visualizations like the following:
.. image:: https://github.com/tensorpack/tensorpack/raw/master/examples/GAN/demo/BEGAN-CelebA-samples.jpg
Args:
patch_list(list[ndarray] or ndarray): NHW or NHWC images in [0,255].
nr_row(int), nr_col(int): rows and cols of the grid.
``nr_col * nr_row`` must be no less than ``len(patch_list)``.
border(int): border length between images.
Defaults to ``0.05 * min(patch_width, patch_height)``.
pad (boolean): when `patch_list` is a list, pad all patches to the maximum height and width.
This option allows stacking patches of different shapes together.
bgcolor(int or 3-tuple): background color in [0, 255]. Either an int
or a BGR tuple.
viz(bool): whether to use :func:`interactive_imshow` to visualize the results.
lclick_cb: A callback function ``f(patch, patch index in patch_list)``
to get called when a patch get clicked in imshow.
Returns:
np.ndarray: the stacked image.
def stack_patches(
patch_list, nr_row, nr_col, border=None,
pad=False, bgcolor=255, viz=False, lclick_cb=None):
"""
Stacked patches into grid, to produce visualizations like the following:
.. image:: https://github.com/tensorpack/tensorpack/raw/master/examples/GAN/demo/BEGAN-CelebA-samples.jpg
Args:
patch_list(list[ndarray] or ndarray): NHW or NHWC images in [0,255].
nr_row(int), nr_col(int): rows and cols of the grid.
``nr_col * nr_row`` must be no less than ``len(patch_list)``.
border(int): border length between images.
Defaults to ``0.05 * min(patch_width, patch_height)``.
pad (boolean): when `patch_list` is a list, pad all patches to the maximum height and width.
This option allows stacking patches of different shapes together.
bgcolor(int or 3-tuple): background color in [0, 255]. Either an int
or a BGR tuple.
viz(bool): whether to use :func:`interactive_imshow` to visualize the results.
lclick_cb: A callback function ``f(patch, patch index in patch_list)``
to get called when a patch get clicked in imshow.
Returns:
np.ndarray: the stacked image.
"""
if pad:
patch_list = _pad_patch_list(patch_list, bgcolor)
patch_list = _preprocess_patch_list(patch_list)
if lclick_cb is not None:
viz = True
ph, pw = patch_list.shape[1:3]
canvas = Canvas(ph, pw, nr_row, nr_col,
patch_list.shape[-1], border, bgcolor)
if lclick_cb is not None:
def lclick_callback(img, x, y):
idx = canvas.get_patchid_from_coord(x, y)
lclick_cb(patch_list[idx], idx)
else:
lclick_callback = None
canvas.draw_patches(patch_list)
if viz:
interactive_imshow(canvas.canvas, lclick_cb=lclick_callback)
return canvas.canvas |
Similar to :func:`stack_patches` but with a generator interface.
It takes a much-longer list and yields stacked results one by one.
For example, if ``patch_list`` contains 1000 images and ``nr_row==nr_col==10``,
this generator yields 10 stacked images.
Args:
nr_row(int), nr_col(int): rows and cols of each result.
max_width(int), max_height(int): Maximum allowed size of the
stacked image. If ``nr_row/nr_col`` are None, this number
will be used to infer the rows and cols. Otherwise the option is
ignored.
patch_list, border, viz, lclick_cb: same as in :func:`stack_patches`.
Yields:
np.ndarray: the stacked image.
def gen_stack_patches(patch_list,
nr_row=None, nr_col=None, border=None,
max_width=1000, max_height=1000,
bgcolor=255, viz=False, lclick_cb=None):
"""
Similar to :func:`stack_patches` but with a generator interface.
It takes a much-longer list and yields stacked results one by one.
For example, if ``patch_list`` contains 1000 images and ``nr_row==nr_col==10``,
this generator yields 10 stacked images.
Args:
nr_row(int), nr_col(int): rows and cols of each result.
max_width(int), max_height(int): Maximum allowed size of the
stacked image. If ``nr_row/nr_col`` are None, this number
will be used to infer the rows and cols. Otherwise the option is
ignored.
patch_list, border, viz, lclick_cb: same as in :func:`stack_patches`.
Yields:
np.ndarray: the stacked image.
"""
# setup parameters
patch_list = _preprocess_patch_list(patch_list)
if lclick_cb is not None:
viz = True
ph, pw = patch_list.shape[1:3]
if border is None:
border = int(0.05 * min(ph, pw))
if nr_row is None:
nr_row = int(max_height / (ph + border))
if nr_col is None:
nr_col = int(max_width / (pw + border))
canvas = Canvas(ph, pw, nr_row, nr_col, patch_list.shape[-1], border, bgcolor)
nr_patch = nr_row * nr_col
start = 0
if lclick_cb is not None:
def lclick_callback(img, x, y):
idx = canvas.get_patchid_from_coord(x, y)
idx = idx + start
if idx < end:
lclick_cb(patch_list[idx], idx)
else:
lclick_callback = None
while True:
end = start + nr_patch
cur_list = patch_list[start:end]
if not len(cur_list):
return
canvas.draw_patches(cur_list)
if viz:
interactive_imshow(canvas.canvas, lclick_cb=lclick_callback)
yield canvas.canvas
start = end |
Dump or visualize images of a :class:`DataFlow`.
Args:
df (DataFlow): the DataFlow.
index (int): the index of the image component.
batched (bool): whether the component contains batched images (NHW or
NHWC) or not (HW or HWC).
number (int): how many datapoint to take from the DataFlow.
output_dir (str): output directory to save images, default to not save.
scale (float): scale the value, usually either 1 or 255.
resize (tuple or None): tuple of (h, w) to resize the images to.
viz (tuple or None): tuple of (h, w) determining the grid size to use
with :func:`gen_stack_patches` for visualization. No visualization will happen by
default.
flipRGB (bool): apply a RGB<->BGR conversion or not.
def dump_dataflow_images(df, index=0, batched=True,
number=1000, output_dir=None,
scale=1, resize=None, viz=None,
flipRGB=False):
"""
Dump or visualize images of a :class:`DataFlow`.
Args:
df (DataFlow): the DataFlow.
index (int): the index of the image component.
batched (bool): whether the component contains batched images (NHW or
NHWC) or not (HW or HWC).
number (int): how many datapoint to take from the DataFlow.
output_dir (str): output directory to save images, default to not save.
scale (float): scale the value, usually either 1 or 255.
resize (tuple or None): tuple of (h, w) to resize the images to.
viz (tuple or None): tuple of (h, w) determining the grid size to use
with :func:`gen_stack_patches` for visualization. No visualization will happen by
default.
flipRGB (bool): apply a RGB<->BGR conversion or not.
"""
if output_dir:
mkdir_p(output_dir)
if viz is not None:
viz = shape2d(viz)
vizsize = viz[0] * viz[1]
if resize is not None:
resize = tuple(shape2d(resize))
vizlist = []
df.reset_state()
cnt = 0
while True:
for dp in df:
if not batched:
imgbatch = [dp[index]]
else:
imgbatch = dp[index]
for img in imgbatch:
cnt += 1
if cnt == number:
return
if scale != 1:
img = img * scale
if resize is not None:
img = cv2.resize(img, resize)
if flipRGB:
img = img[:, :, ::-1]
if output_dir:
fname = os.path.join(output_dir, '{:03d}.jpg'.format(cnt))
cv2.imwrite(fname, img)
if viz is not None:
vizlist.append(img)
if viz is not None and len(vizlist) >= vizsize:
stack_patches(
vizlist[:vizsize],
nr_row=viz[0], nr_col=viz[1], viz=True)
vizlist = vizlist[vizsize:] |
Convert a 1-channel matrix of intensities to an RGB image employing a colormap.
This function requires matplotlib. See `matplotlib colormaps
<http://matplotlib.org/examples/color/colormaps_reference.html>`_ for a
list of available colormap.
Args:
intensity (np.ndarray): array of intensities such as saliency.
cmap (str): name of the colormap to use.
normalize (bool): if True, will normalize the intensity so that it has
minimum 0 and maximum 1.
Returns:
np.ndarray: an RGB float32 image in range [0, 255], a colored heatmap.
def intensity_to_rgb(intensity, cmap='cubehelix', normalize=False):
"""
Convert a 1-channel matrix of intensities to an RGB image employing a colormap.
This function requires matplotlib. See `matplotlib colormaps
<http://matplotlib.org/examples/color/colormaps_reference.html>`_ for a
list of available colormap.
Args:
intensity (np.ndarray): array of intensities such as saliency.
cmap (str): name of the colormap to use.
normalize (bool): if True, will normalize the intensity so that it has
minimum 0 and maximum 1.
Returns:
np.ndarray: an RGB float32 image in range [0, 255], a colored heatmap.
"""
assert intensity.ndim == 2, intensity.shape
intensity = intensity.astype("float")
if normalize:
intensity -= intensity.min()
intensity /= intensity.max()
cmap = plt.get_cmap(cmap)
intensity = cmap(intensity)[..., :3]
return intensity.astype('float32') * 255.0 |
Draw text on an image.
Args:
pos (tuple): x, y; the position of the text
text (str):
font_scale (float):
color (tuple): a 3-tuple BGR color in [0, 255]
def draw_text(img, pos, text, color, font_scale=0.4):
"""
Draw text on an image.
Args:
pos (tuple): x, y; the position of the text
text (str):
font_scale (float):
color (tuple): a 3-tuple BGR color in [0, 255]
"""
img = img.astype(np.uint8)
x0, y0 = int(pos[0]), int(pos[1])
# Compute text size.
font = cv2.FONT_HERSHEY_SIMPLEX
((text_w, text_h), _) = cv2.getTextSize(text, font, font_scale, 1)
# Place text background.
if x0 + text_w > img.shape[1]:
x0 = img.shape[1] - text_w
if y0 - int(1.15 * text_h) < 0:
y0 = int(1.15 * text_h)
back_topleft = x0, y0 - int(1.3 * text_h)
back_bottomright = x0 + text_w, y0
cv2.rectangle(img, back_topleft, back_bottomright, color, -1)
# Show text.
text_bottomleft = x0, y0 - int(0.25 * text_h)
cv2.putText(img, text, text_bottomleft, font, font_scale, (222, 222, 222), lineType=cv2.LINE_AA)
return img |
Args:
im (np.ndarray): a BGR image in range [0,255]. It will not be modified.
boxes (np.ndarray): a numpy array of shape Nx4 where each row is [x1, y1, x2, y2].
labels: (list[str] or None)
color: a 3-tuple BGR color (in range [0, 255])
Returns:
np.ndarray: a new image.
def draw_boxes(im, boxes, labels=None, color=None):
"""
Args:
im (np.ndarray): a BGR image in range [0,255]. It will not be modified.
boxes (np.ndarray): a numpy array of shape Nx4 where each row is [x1, y1, x2, y2].
labels: (list[str] or None)
color: a 3-tuple BGR color (in range [0, 255])
Returns:
np.ndarray: a new image.
"""
boxes = np.asarray(boxes, dtype='int32')
if labels is not None:
assert len(labels) == len(boxes), "{} != {}".format(len(labels), len(boxes))
areas = (boxes[:, 2] - boxes[:, 0] + 1) * (boxes[:, 3] - boxes[:, 1] + 1)
sorted_inds = np.argsort(-areas) # draw large ones first
assert areas.min() > 0, areas.min()
# allow equal, because we are not very strict about rounding error here
assert boxes[:, 0].min() >= 0 and boxes[:, 1].min() >= 0 \
and boxes[:, 2].max() <= im.shape[1] and boxes[:, 3].max() <= im.shape[0], \
"Image shape: {}\n Boxes:\n{}".format(str(im.shape), str(boxes))
im = im.copy()
if color is None:
color = (15, 128, 15)
if im.ndim == 2 or (im.ndim == 3 and im.shape[2] == 1):
im = cv2.cvtColor(im, cv2.COLOR_GRAY2BGR)
for i in sorted_inds:
box = boxes[i, :]
if labels is not None:
im = draw_text(im, (box[0], box[1]), labels[i], color=color)
cv2.rectangle(im, (box[0], box[1]), (box[2], box[3]),
color=color, thickness=1)
return im |
A wrapper around ``tf.concat`` to cooperate with :class:`LinearWrap`.
Args:
x (tf.Tensor): input
tensor (list[tf.Tensor]): a tensor or list of tensors to concatenate with x.
x will be at the beginning
dim (int): the dimension along which to concatenate
Returns:
tf.Tensor: ``tf.concat([x] + tensor, dim)``
def ConcatWith(x, tensor, dim):
"""
A wrapper around ``tf.concat`` to cooperate with :class:`LinearWrap`.
Args:
x (tf.Tensor): input
tensor (list[tf.Tensor]): a tensor or list of tensors to concatenate with x.
x will be at the beginning
dim (int): the dimension along which to concatenate
Returns:
tf.Tensor: ``tf.concat([x] + tensor, dim)``
"""
if type(tensor) != list:
tensor = [tensor]
return tf.concat([x] + tensor, dim) |
Args:
points: (nx4)x2
Returns:
nx4 boxes (x1y1x2y2)
def point8_to_box(points):
"""
Args:
points: (nx4)x2
Returns:
nx4 boxes (x1y1x2y2)
"""
p = points.reshape((-1, 4, 2))
minxy = p.min(axis=1) # nx2
maxxy = p.max(axis=1) # nx2
return np.concatenate((minxy, maxxy), axis=1) |
Convert polygons to binary masks.
Args:
polys: a list of nx2 float array. Each array contains many (x, y) coordinates.
Returns:
a binary matrix of (height, width)
def segmentation_to_mask(polys, height, width):
"""
Convert polygons to binary masks.
Args:
polys: a list of nx2 float array. Each array contains many (x, y) coordinates.
Returns:
a binary matrix of (height, width)
"""
polys = [p.flatten().tolist() for p in polys]
assert len(polys) > 0, "Polygons are empty!"
import pycocotools.mask as cocomask
rles = cocomask.frPyObjects(polys, height, width)
rle = cocomask.merge(rles)
return cocomask.decode(rle) |
Args:
boxes: (...)x4, float
shape: h, w
def clip_boxes(boxes, shape):
"""
Args:
boxes: (...)x4, float
shape: h, w
"""
orig_shape = boxes.shape
boxes = boxes.reshape([-1, 4])
h, w = shape
boxes[:, [0, 1]] = np.maximum(boxes[:, [0, 1]], 0)
boxes[:, 2] = np.minimum(boxes[:, 2], w)
boxes[:, 3] = np.minimum(boxes[:, 3], h)
return boxes.reshape(orig_shape) |
Args:
boxes: (nx4), float
shape: (h, w)
Returns:
indices: (k, )
selection: (kx4)
def filter_boxes_inside_shape(boxes, shape):
"""
Args:
boxes: (nx4), float
shape: (h, w)
Returns:
indices: (k, )
selection: (kx4)
"""
assert boxes.ndim == 2, boxes.shape
assert len(shape) == 2, shape
h, w = shape
indices = np.where(
(boxes[:, 0] >= 0) &
(boxes[:, 1] >= 0) &
(boxes[:, 2] <= w) &
(boxes[:, 3] <= h))[0]
return indices, boxes[indices, :] |
Same as `tf.layers.MaxPooling2D`. Default strides is equal to pool_size.
def MaxPooling(
inputs,
pool_size,
strides=None,
padding='valid',
data_format='channels_last'):
"""
Same as `tf.layers.MaxPooling2D`. Default strides is equal to pool_size.
"""
if strides is None:
strides = pool_size
layer = tf.layers.MaxPooling2D(pool_size, strides, padding=padding, data_format=data_format)
ret = layer.apply(inputs, scope=tf.get_variable_scope())
return tf.identity(ret, name='output') |
Same as `tf.layers.AveragePooling2D`. Default strides is equal to pool_size.
def AvgPooling(
inputs,
pool_size,
strides=None,
padding='valid',
data_format='channels_last'):
"""
Same as `tf.layers.AveragePooling2D`. Default strides is equal to pool_size.
"""
if strides is None:
strides = pool_size
layer = tf.layers.AveragePooling2D(pool_size, strides, padding=padding, data_format=data_format)
ret = layer.apply(inputs, scope=tf.get_variable_scope())
return tf.identity(ret, name='output') |
Global average pooling as in the paper `Network In Network
<http://arxiv.org/abs/1312.4400>`_.
Args:
x (tf.Tensor): a 4D tensor.
Returns:
tf.Tensor: a NC tensor named ``output``.
def GlobalAvgPooling(x, data_format='channels_last'):
"""
Global average pooling as in the paper `Network In Network
<http://arxiv.org/abs/1312.4400>`_.
Args:
x (tf.Tensor): a 4D tensor.
Returns:
tf.Tensor: a NC tensor named ``output``.
"""
assert x.shape.ndims == 4
data_format = get_data_format(data_format)
axis = [1, 2] if data_format == 'channels_last' else [2, 3]
return tf.reduce_mean(x, axis, name='output') |
Unpool the input with a fixed matrix to perform kronecker product with.
Args:
x (tf.Tensor): a 4D image tensor
shape: int or (h, w) tuple
unpool_mat: a tf.Tensor or np.ndarray 2D matrix with size=shape.
If is None, will use a matrix with 1 at top-left corner.
Returns:
tf.Tensor: a 4D image tensor.
def FixedUnPooling(x, shape, unpool_mat=None, data_format='channels_last'):
"""
Unpool the input with a fixed matrix to perform kronecker product with.
Args:
x (tf.Tensor): a 4D image tensor
shape: int or (h, w) tuple
unpool_mat: a tf.Tensor or np.ndarray 2D matrix with size=shape.
If is None, will use a matrix with 1 at top-left corner.
Returns:
tf.Tensor: a 4D image tensor.
"""
data_format = get_data_format(data_format, keras_mode=False)
shape = shape2d(shape)
output_shape = StaticDynamicShape(x)
output_shape.apply(1 if data_format == 'NHWC' else 2, lambda x: x * shape[0])
output_shape.apply(2 if data_format == 'NHWC' else 3, lambda x: x * shape[1])
# a faster implementation for this special case
if shape[0] == 2 and shape[1] == 2 and unpool_mat is None and data_format == 'NHWC':
ret = UnPooling2x2ZeroFilled(x)
else:
# check unpool_mat
if unpool_mat is None:
mat = np.zeros(shape, dtype='float32')
mat[0][0] = 1
unpool_mat = tf.constant(mat, name='unpool_mat')
elif isinstance(unpool_mat, np.ndarray):
unpool_mat = tf.constant(unpool_mat, name='unpool_mat')
assert unpool_mat.shape.as_list() == list(shape)
if data_format == 'NHWC':
x = tf.transpose(x, [0, 3, 1, 2])
# perform a tensor-matrix kronecker product
x = tf.expand_dims(x, -1) # bchwx1
mat = tf.expand_dims(unpool_mat, 0) # 1xshxsw
ret = tf.tensordot(x, mat, axes=1) # bxcxhxwxshxsw
if data_format == 'NHWC':
ret = tf.transpose(ret, [0, 2, 4, 3, 5, 1])
else:
ret = tf.transpose(ret, [0, 1, 2, 4, 3, 5])
shape3_dyn = [output_shape.get_dynamic(k) for k in range(1, 4)]
ret = tf.reshape(ret, tf.stack([-1] + shape3_dyn))
ret.set_shape(tf.TensorShape(output_shape.get_static()))
return ret |
Args:
varname(str): a variable name in the graph
varname_prefix(str): an optional prefix that may need to be removed in varname
savename_prefix(str): an optional prefix to append to all savename
Returns:
str: the name used to save the variable
def get_savename_from_varname(
varname, varname_prefix=None,
savename_prefix=None):
"""
Args:
varname(str): a variable name in the graph
varname_prefix(str): an optional prefix that may need to be removed in varname
savename_prefix(str): an optional prefix to append to all savename
Returns:
str: the name used to save the variable
"""
name = varname
if varname_prefix is not None \
and name.startswith(varname_prefix):
name = name[len(varname_prefix) + 1:]
if savename_prefix is not None:
name = savename_prefix + '/' + name
return name |
Dump value of all TRAINABLE + MODEL variables to a dict, and save as
npz format (loadable by :func:`sessinit.get_model_loader`).
Args:
path(str): the file name to save the parameters. Must ends with npz.
def dump_session_params(path):
"""
Dump value of all TRAINABLE + MODEL variables to a dict, and save as
npz format (loadable by :func:`sessinit.get_model_loader`).
Args:
path(str): the file name to save the parameters. Must ends with npz.
"""
# save variables that are GLOBAL, and either TRAINABLE or MODEL
var = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
var.extend(tf.get_collection(tf.GraphKeys.MODEL_VARIABLES))
# TODO dedup
assert len(set(var)) == len(var), "TRAINABLE and MODEL variables have duplication!"
gvars = set([k.name for k in tf.global_variables()])
var = [v for v in var if v.name in gvars]
result = {}
for v in var:
result[v.name] = v.eval()
save_chkpt_vars(result, path) |
Save variables in dic to path.
Args:
dic: {name: value}
path: save as npz if the name ends with '.npz', otherwise save as a checkpoint.
def save_chkpt_vars(dic, path):
"""
Save variables in dic to path.
Args:
dic: {name: value}
path: save as npz if the name ends with '.npz', otherwise save as a checkpoint.
"""
logger.info("Variables to save to {}:".format(path))
keys = sorted(list(dic.keys()))
logger.info(pprint.pformat(keys))
assert not path.endswith('.npy')
if path.endswith('.npz'):
np.savez_compressed(path, **dic)
else:
with tf.Graph().as_default(), \
tf.Session() as sess:
for k, v in six.iteritems(dic):
k = get_op_tensor_name(k)[0]
_ = tf.Variable(name=k, initial_value=v) # noqa
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.save(sess, path, write_meta_graph=False) |
Work around TF problems in checkpoint path handling.
Args:
model_path: a user-input path
Returns:
str: the argument that can be passed to NewCheckpointReader
def get_checkpoint_path(model_path):
"""
Work around TF problems in checkpoint path handling.
Args:
model_path: a user-input path
Returns:
str: the argument that can be passed to NewCheckpointReader
"""
if os.path.basename(model_path) == model_path:
model_path = os.path.join('.', model_path) # avoid #4921 and #6142
if os.path.basename(model_path) == 'checkpoint':
assert tfv1.gfile.Exists(model_path), model_path
model_path = tf.train.latest_checkpoint(os.path.dirname(model_path))
# to be consistent with either v1 or v2
# fix paths if provided a wrong one
new_path = model_path
if '00000-of-00001' in model_path:
new_path = model_path.split('.data')[0]
elif model_path.endswith('.index'):
new_path = model_path.split('.index')[0]
if new_path != model_path:
logger.info(
"Checkpoint path {} is auto-corrected to {}.".format(model_path, new_path))
model_path = new_path
assert tfv1.gfile.Exists(model_path) or tfv1.gfile.Exists(model_path + '.index'), model_path
return model_path |
Load all variables from a checkpoint to a dict.
Args:
model_path(str): path to a checkpoint.
Returns:
dict: a name:value dict
def load_chkpt_vars(model_path):
""" Load all variables from a checkpoint to a dict.
Args:
model_path(str): path to a checkpoint.
Returns:
dict: a name:value dict
"""
model_path = get_checkpoint_path(model_path)
reader = tfv1.train.NewCheckpointReader(model_path)
var_names = reader.get_variable_to_shape_map().keys()
result = {}
for n in var_names:
result[n] = reader.get_tensor(n)
return result |
**Guess** if this variable is only used in training.
Only used internally to avoid too many logging. Do not use it.
def is_training_name(name):
"""
**Guess** if this variable is only used in training.
Only used internally to avoid too many logging. Do not use it.
"""
# TODO: maybe simply check against TRAINABLE_VARIABLES and MODEL_VARIABLES?
# TODO or use get_slot_names()
name = get_op_tensor_name(name)[0]
if name.endswith('/Adam') or name.endswith('/Adam_1'):
return True
if name.endswith('/Momentum'):
return True
if name.endswith('/Adadelta') or name.endswith('/Adadelta_1'):
return True
if name.endswith('/RMSProp') or name.endswith('/RMSProp_1'):
return True
if name.endswith('/Adagrad'):
return True
if name.startswith('EMA/') or '/EMA/' in name: # all the moving average summaries
return True
if name.startswith('AccumGrad') or name.endswith('/AccumGrad'):
return True
if name.startswith('apply_gradients'):
return True
return False |
Returns a relaxed (possibly reshaped/upcast-ed) version of value,
to be loaded to the given variable.
Args:
value (ndarray): an numpy array to be loaded to var
var (tf.Variable):
Returns:
ndarray: a possibly reshaped or casted version of value
def relaxed_value_for_var(value, var):
"""
Returns a relaxed (possibly reshaped/upcast-ed) version of value,
to be loaded to the given variable.
Args:
value (ndarray): an numpy array to be loaded to var
var (tf.Variable):
Returns:
ndarray: a possibly reshaped or casted version of value
"""
assert isinstance(var, tf.Variable)
name = var.op.name
# check incompatible shape
varshape = tuple(var.get_shape().as_list())
if varshape != value.shape:
# TODO only allow reshape when shape different by empty axis
if np.prod(varshape) != np.prod(value.shape):
raise ValueError(
"Trying to load a tensor of shape {} into the variable '{}' whose shape is {}.".format(
value.shape, name, varshape))
logger.warn("The tensor is reshaped from {} to {} when assigned to '{}'".format(
value.shape, varshape, name))
value = value.reshape(varshape)
# fix some common type incompatibility problems, but not all
def upcast(vartype, valtype):
# vartype: a tf dtype
# valtype: a numpy dtype
# allow up-casting
if vartype == tf.float64 and valtype == np.float32:
return np.float64
if vartype in [tf.int64, tf.int32] and valtype in [np.int32, np.int16, np.int8]:
return np.int64 if vartype == tf.int64 else np.int32
return None
if hasattr(value, 'dtype'):
vartype = var.dtype.as_numpy_dtype
if vartype != value.dtype:
msg = "Variable {} has dtype {} but was given a value of dtype {}.".format(name, vartype, value.dtype)
newtype = upcast(var.dtype.base_dtype, value.dtype)
if newtype is not None:
value = newtype(value)
logger.warn(msg + " Load it after casting!")
else:
assert vartype == value.dtype, msg
return value |
Args:
prms(dict): dict of {variable name: value}
Any name in prms must be in the graph and in vars_to_update.
def update(self, prms):
"""
Args:
prms(dict): dict of {variable name: value}
Any name in prms must be in the graph and in vars_to_update.
"""
with self.sess.as_default():
fetches = []
feeds = {}
for name, value in six.iteritems(prms):
assert name in self.name_map
var = self.name_map[name]
fetches.append(var.initializer)
# This is the implementation of `var.load`
feeds[var.initializer.inputs[1]] = SessionUpdate.relaxed_value_for_var(value, var)
self.sess.run(fetches, feed_dict=feeds) |
Args:
server (tf.train.Server):
Returns:
tf.train.SessionCreator
def get_distributed_session_creator(server):
"""
Args:
server (tf.train.Server):
Returns:
tf.train.SessionCreator
"""
server_def = server.server_def
is_chief = (server_def.job_name == 'worker') and (server_def.task_index == 0)
init_op = tf.global_variables_initializer()
local_init_op = tf.local_variables_initializer()
ready_op = tf.report_uninitialized_variables()
ready_for_local_init_op = tf.report_uninitialized_variables(tf.global_variables())
sm = tf.train.SessionManager(
local_init_op=local_init_op,
ready_op=ready_op,
ready_for_local_init_op=ready_for_local_init_op,
graph=tf.get_default_graph())
# to debug wrong variable collection
# from pprint import pprint
# print("GLOBAL:")
# pprint([(k.name, k.device) for k in tf.global_variables()])
# print("LOCAL:")
# pprint([(k.name, k.device) for k in tf.local_variables()])
class _Creator(tf.train.SessionCreator):
def create_session(self):
if is_chief:
return sm.prepare_session(master=server.target, init_op=init_op)
else:
tf.logging.set_verbosity(tf.logging.INFO) # print message about uninitialized vars
ret = sm.wait_for_session(master=server.target)
tf.logging.set_verbosity(tf.logging.WARN)
return ret
return _Creator() |
Returns:
int: #available GPUs in CUDA_VISIBLE_DEVICES, or in the system.
def get_num_gpu():
"""
Returns:
int: #available GPUs in CUDA_VISIBLE_DEVICES, or in the system.
"""
def warn_return(ret, message):
try:
import tensorflow as tf
except ImportError:
return ret
built_with_cuda = tf.test.is_built_with_cuda()
if not built_with_cuda and ret > 0:
logger.warn(message + "But TensorFlow was not built with CUDA support and could not use GPUs!")
return ret
env = os.environ.get('CUDA_VISIBLE_DEVICES', None)
if env:
return warn_return(len(env.split(',')), "Found non-empty CUDA_VISIBLE_DEVICES. ")
output, code = subproc_call("nvidia-smi -L", timeout=5)
if code == 0:
output = output.decode('utf-8')
return warn_return(len(output.strip().split('\n')), "Found nvidia-smi. ")
try:
# Use NVML to query device properties
with NVMLContext() as ctx:
return warn_return(ctx.num_devices(), "NVML found nvidia devices. ")
except Exception:
# Fallback
logger.info("Loading local devices by TensorFlow ...")
try:
import tensorflow as tf
# available since TF 1.14
gpu_devices = tf.config.experimental.list_physical_devices('GPU')
except AttributeError:
from tensorflow.python.client import device_lib
local_device_protos = device_lib.list_local_devices()
# Note this will initialize all GPUs and therefore has side effect
# https://github.com/tensorflow/tensorflow/issues/8136
gpu_devices = [x.name for x in local_device_protos if x.device_type == 'GPU']
return len(gpu_devices) |
Put a `tf.Summary`.
def put_summary(self, summary):
"""
Put a `tf.Summary`.
"""
if isinstance(summary, six.binary_type):
summary = tf.Summary.FromString(summary)
assert isinstance(summary, tf.Summary), type(summary)
# TODO other types
for val in summary.value:
if val.WhichOneof('value') == 'simple_value':
val.tag = re.sub('tower[0-9]+/', '', val.tag) # TODO move to subclasses
# TODO This hack is still needed, seem to disappear only when
# compiled from source.
suffix = '-summary' # tensorflow#6150, tensorboard#59
if val.tag.endswith(suffix):
val.tag = val.tag[:-len(suffix)]
self._dispatch(lambda m: m.process_scalar(val.tag, val.simple_value))
self._dispatch(lambda m: m.process_summary(summary)) |
Put a scalar.
def put_scalar(self, name, val):
"""
Put a scalar.
"""
if isinstance(val, np.floating):
val = float(val)
if isinstance(val, np.integer):
val = int(val)
self._dispatch(lambda m: m.process_scalar(name, val))
s = create_scalar_summary(name, val)
self._dispatch(lambda m: m.process_summary(s)) |
Put an image.
Args:
name (str):
val (np.ndarray): 2D, 3D (HWC) or 4D (NHWC) numpy array of images
in range [0,255]. If channel is 3, assumed to be RGB.
def put_image(self, name, val):
"""
Put an image.
Args:
name (str):
val (np.ndarray): 2D, 3D (HWC) or 4D (NHWC) numpy array of images
in range [0,255]. If channel is 3, assumed to be RGB.
"""
assert isinstance(val, np.ndarray)
arr = image_to_nhwc(val)
self._dispatch(lambda m: m.process_image(name, arr))
s = create_image_summary(name, arr)
self._dispatch(lambda m: m.process_summary(s)) |
Put an :class:`tf.Event`.
`step` and `wall_time` fields of :class:`tf.Event` will be filled automatically.
Args:
evt (tf.Event):
def put_event(self, evt):
"""
Put an :class:`tf.Event`.
`step` and `wall_time` fields of :class:`tf.Event` will be filled automatically.
Args:
evt (tf.Event):
"""
evt.step = self.global_step
evt.wall_time = time.time()
self._dispatch(lambda m: m.process_event(evt)) |
Look for an existing json under :meth:`logger.get_logger_dir()` named "stats.json",
and return the loaded list of statistics if found. Returns None otherwise.
def load_existing_json():
"""
Look for an existing json under :meth:`logger.get_logger_dir()` named "stats.json",
and return the loaded list of statistics if found. Returns None otherwise.
"""
dir = logger.get_logger_dir()
fname = os.path.join(dir, JSONWriter.FILENAME)
if tf.gfile.Exists(fname):
with open(fname) as f:
stats = json.load(f)
assert isinstance(stats, list), type(stats)
return stats
return None |
Add stats to json and dump to disk.
Note that this method is idempotent.
def _trigger(self):
"""
Add stats to json and dump to disk.
Note that this method is idempotent.
"""
if len(self._stat_now):
self._stat_now['epoch_num'] = self.epoch_num
self._stat_now['global_step'] = self.global_step
self._stats.append(self._stat_now)
self._stat_now = {}
self._write_stat() |
Args:
img: bxhxwxc
coords: bxh2xw2x2. each coordinate is (y, x) integer.
Out of boundary coordinates will be clipped.
Return:
bxh2xw2xc image
def sample(img, coords):
"""
Args:
img: bxhxwxc
coords: bxh2xw2x2. each coordinate is (y, x) integer.
Out of boundary coordinates will be clipped.
Return:
bxh2xw2xc image
"""
shape = img.get_shape().as_list()[1:] # h, w, c
batch = tf.shape(img)[0]
shape2 = coords.get_shape().as_list()[1:3] # h2, w2
assert None not in shape2, coords.get_shape()
max_coor = tf.constant([shape[0] - 1, shape[1] - 1], dtype=tf.float32)
coords = tf.clip_by_value(coords, 0., max_coor) # borderMode==repeat
coords = tf.cast(coords, tf.int32)
batch_index = tf.range(batch, dtype=tf.int32)
batch_index = tf.reshape(batch_index, [-1, 1, 1, 1])
batch_index = tf.tile(batch_index, [1, shape2[0], shape2[1], 1]) # bxh2xw2x1
indices = tf.concat([batch_index, coords], axis=3) # bxh2xw2x3
sampled = tf.gather_nd(img, indices)
return sampled |
Sample the images using the given coordinates, by bilinear interpolation.
This was described in the paper:
`Spatial Transformer Networks <http://arxiv.org/abs/1506.02025>`_.
This is equivalent to `torch.nn.functional.grid_sample`,
up to some non-trivial coordinate transformation.
This implementation returns pixel value at pixel (1, 1) for a floating point coordinate (1.0, 1.0).
Note that this may not be what you need.
Args:
inputs (list): [images, coords]. images has shape NHWC.
coords has shape (N, H', W', 2), where each pair of the last dimension is a (y, x) real-value
coordinate.
borderMode: either "repeat" or "constant" (zero-filled)
Returns:
tf.Tensor: a tensor named ``output`` of shape (N, H', W', C).
def GridSample(inputs, borderMode='repeat'):
"""
Sample the images using the given coordinates, by bilinear interpolation.
This was described in the paper:
`Spatial Transformer Networks <http://arxiv.org/abs/1506.02025>`_.
This is equivalent to `torch.nn.functional.grid_sample`,
up to some non-trivial coordinate transformation.
This implementation returns pixel value at pixel (1, 1) for a floating point coordinate (1.0, 1.0).
Note that this may not be what you need.
Args:
inputs (list): [images, coords]. images has shape NHWC.
coords has shape (N, H', W', 2), where each pair of the last dimension is a (y, x) real-value
coordinate.
borderMode: either "repeat" or "constant" (zero-filled)
Returns:
tf.Tensor: a tensor named ``output`` of shape (N, H', W', C).
"""
image, mapping = inputs
assert image.get_shape().ndims == 4 and mapping.get_shape().ndims == 4
input_shape = image.get_shape().as_list()[1:]
assert None not in input_shape, \
"Images in GridSample layer must have fully-defined shape"
assert borderMode in ['repeat', 'constant']
orig_mapping = mapping
mapping = tf.maximum(mapping, 0.0)
lcoor = tf.floor(mapping)
ucoor = lcoor + 1
diff = mapping - lcoor
neg_diff = 1.0 - diff # bxh2xw2x2
lcoory, lcoorx = tf.split(lcoor, 2, 3)
ucoory, ucoorx = tf.split(ucoor, 2, 3)
lyux = tf.concat([lcoory, ucoorx], 3)
uylx = tf.concat([ucoory, lcoorx], 3)
diffy, diffx = tf.split(diff, 2, 3)
neg_diffy, neg_diffx = tf.split(neg_diff, 2, 3)
ret = tf.add_n([sample(image, lcoor) * neg_diffx * neg_diffy,
sample(image, ucoor) * diffx * diffy,
sample(image, lyux) * neg_diffy * diffx,
sample(image, uylx) * diffy * neg_diffx], name='sampled')
if borderMode == 'constant':
max_coor = tf.constant([input_shape[0] - 1, input_shape[1] - 1], dtype=tf.float32)
mask = tf.greater_equal(orig_mapping, 0.0)
mask2 = tf.less_equal(orig_mapping, max_coor)
mask = tf.logical_and(mask, mask2) # bxh2xw2x2
mask = tf.reduce_all(mask, [3]) # bxh2xw2 boolean
mask = tf.expand_dims(mask, 3)
ret = ret * tf.cast(mask, tf.float32)
return tf.identity(ret, name='output') |
Enable trace for calls to any function.
def enable_call_trace():
""" Enable trace for calls to any function. """
def tracer(frame, event, arg):
if event == 'call':
co = frame.f_code
func_name = co.co_name
if func_name == 'write' or func_name == 'print':
# ignore write() calls from print statements
return
func_line_no = frame.f_lineno
func_filename = co.co_filename
caller = frame.f_back
if caller:
caller_line_no = caller.f_lineno
caller_filename = caller.f_code.co_filename
print('Call to `%s` on line %s:%s from %s:%s' %
(func_name, func_filename, func_line_no,
caller_filename, caller_line_no))
return
sys.settrace(tracer) |
Apply a set of default rules to make a fast :class:`InputSource`.
Args:
input_source_or_dataflow(InputSource | DataFlow):
trainer (Trainer):
Returns:
InputSource
def apply_default_prefetch(input_source_or_dataflow, trainer):
"""
Apply a set of default rules to make a fast :class:`InputSource`.
Args:
input_source_or_dataflow(InputSource | DataFlow):
trainer (Trainer):
Returns:
InputSource
"""
if not isinstance(input_source_or_dataflow, InputSource):
# to mimic same behavior of the old trainer interface
if type(trainer) == SimpleTrainer:
input = FeedInput(input_source_or_dataflow)
else:
logger.info("Automatically applying QueueInput on the DataFlow.")
input = QueueInput(input_source_or_dataflow)
else:
input = input_source_or_dataflow
if hasattr(trainer, 'devices'):
towers = trainer.devices
if len(towers) > 1:
# seem to only improve on >1 GPUs
assert not isinstance(trainer, SimpleTrainer)
if isinstance(input, FeedfreeInput) and \
not isinstance(input, (StagingInput, DummyConstantInput)):
logger.info("Automatically applying StagingInput on the DataFlow.")
input = StagingInput(input)
return input |
Train with a :class:`TrainConfig` and a :class:`Trainer`, to
present the simple and old training interface. It basically does the following
3 things (and you can easily do them by yourself if you need more control):
1. Setup the input with automatic prefetching heuristics,
from `config.data` or `config.dataflow`.
2. Call `trainer.setup_graph` with the input as well as `config.model`.
3. Call `trainer.train` with rest of the attributes of config.
See the `related tutorial
<https://tensorpack.readthedocs.io/tutorial/training-interface.html#with-modeldesc-and-trainconfig>`_
to learn more.
Args:
config (TrainConfig):
trainer (Trainer): an instance of :class:`SingleCostTrainer`.
Example:
.. code-block:: python
launch_train_with_config(
config, SyncMultiGPUTrainerParameterServer(8, ps_device='gpu'))
def launch_train_with_config(config, trainer):
"""
Train with a :class:`TrainConfig` and a :class:`Trainer`, to
present the simple and old training interface. It basically does the following
3 things (and you can easily do them by yourself if you need more control):
1. Setup the input with automatic prefetching heuristics,
from `config.data` or `config.dataflow`.
2. Call `trainer.setup_graph` with the input as well as `config.model`.
3. Call `trainer.train` with rest of the attributes of config.
See the `related tutorial
<https://tensorpack.readthedocs.io/tutorial/training-interface.html#with-modeldesc-and-trainconfig>`_
to learn more.
Args:
config (TrainConfig):
trainer (Trainer): an instance of :class:`SingleCostTrainer`.
Example:
.. code-block:: python
launch_train_with_config(
config, SyncMultiGPUTrainerParameterServer(8, ps_device='gpu'))
"""
if is_tfv2():
tfv1.disable_eager_execution()
assert isinstance(trainer, SingleCostTrainer), trainer
assert isinstance(config, TrainConfig), config
assert config.model is not None
assert config.dataflow is not None or config.data is not None
model = config.model
input = config.data or config.dataflow
input = apply_default_prefetch(input, trainer)
# This is the only place where the `ModelDesc` abstraction is useful.
# We should gradually stay away from this unuseful abstraction.
# TowerFuncWrapper is a better abstraction (similar to tf.defun in the future)
trainer.setup_graph(
model.get_input_signature(), input,
model.build_graph, model.get_optimizer)
_check_unused_regularization()
trainer.train_with_defaults(
callbacks=config.callbacks,
monitors=config.monitors,
session_creator=config.session_creator,
session_init=config.session_init,
steps_per_epoch=config.steps_per_epoch,
starting_epoch=config.starting_epoch,
max_epoch=config.max_epoch,
extra_callbacks=config.extra_callbacks) |
Delegate property to self.loop
def _get_property(name):
"""
Delegate property to self.loop
"""
ret = property(
lambda self: getattr(self.loop, name))
if six.PY3: # __doc__ is readonly in Py2
try:
ret.__doc__ = getattr(TrainLoop, name).__doc__
except AttributeError:
pass
return ret |
Configure the loop given the settings.
def config(self, steps_per_epoch, starting_epoch, max_epoch):
"""
Configure the loop given the settings.
"""
self.starting_epoch = int(starting_epoch)
self.max_epoch = int(max_epoch)
self.steps_per_epoch = int(steps_per_epoch)
# Allow empty epoch (no steps), if we want to run the callbacks only.
assert self.steps_per_epoch >= 0 and self.max_epoch >= 0
self._epoch_num = starting_epoch - 1 |
Register callbacks to the trainer.
It can only be called before :meth:`Trainer.train()`.
Args:
cb (Callback or [Callback]): a callback or a list of callbacks
Returns:
succeed or not
def _register_callback(self, cb):
"""
Register callbacks to the trainer.
It can only be called before :meth:`Trainer.train()`.
Args:
cb (Callback or [Callback]): a callback or a list of callbacks
Returns:
succeed or not
"""
if isinstance(cb, (list, tuple)):
for x in cb:
self._register_callback(x)
return
assert isinstance(cb, Callback), cb
assert not isinstance(self._callbacks, Callbacks), \
"Cannot register more callbacks after trainer was setup!"
if not self.is_chief and cb.chief_only:
logger.warn("Callback {} is chief-only, skipped.".format(str(cb)))
return False
else:
self._callbacks.append(cb)
return True |
Defines what to do in one iteration. The default is:
``self.hooked_sess.run(self.train_op)``.
The behavior of each iteration can be changed by either setting ``trainer.train_op``,
or overriding this method.
def run_step(self):
"""
Defines what to do in one iteration. The default is:
``self.hooked_sess.run(self.train_op)``.
The behavior of each iteration can be changed by either setting ``trainer.train_op``,
or overriding this method.
"""
if not hasattr(self, 'train_op'):
raise NotImplementedError(
"Please either set `Trainer.train_op` or provide an implementation "
"of Trainer.run_step()!")
self.hooked_sess.run(self.train_op) |
Setup callbacks and monitors. Must be called after the main graph is built.
Args:
callbacks ([Callback]):
monitors ([MonitorBase]):
def setup_callbacks(self, callbacks, monitors):
"""
Setup callbacks and monitors. Must be called after the main graph is built.
Args:
callbacks ([Callback]):
monitors ([MonitorBase]):
"""
assert isinstance(callbacks, list), callbacks
assert isinstance(monitors, list), monitors
describe_trainable_vars() # TODO weird
self.register_callback(MaintainStepCounter())
for cb in callbacks:
self.register_callback(cb)
for cb in self._callbacks:
assert not isinstance(cb, MonitorBase), "Monitor cannot be pre-registered for now!"
registered_monitors = []
for m in monitors:
if self.register_callback(m):
registered_monitors.append(m)
self.monitors = Monitors(registered_monitors)
self.register_callback(self.monitors) # monitors is also a callback
# some final operations that might modify the graph
logger.info("Setup callbacks graph ...")
self._callbacks = Callbacks(self._callbacks)
self._callbacks.setup_graph(weakref.proxy(self)) |
Create the session and set `self.sess`.
Call `self.initiailize_hooks()`
Finalize the graph.
It must be called after callbacks are setup.
Args:
session_creator (tf.train.SessionCreator):
session_init (sessinit.SessionInit):
def initialize(self, session_creator, session_init):
"""
Create the session and set `self.sess`.
Call `self.initiailize_hooks()`
Finalize the graph.
It must be called after callbacks are setup.
Args:
session_creator (tf.train.SessionCreator):
session_init (sessinit.SessionInit):
"""
assert isinstance(session_creator, tfv1.train.SessionCreator), session_creator
assert isinstance(session_init, SessionInit), session_init
session_init._setup_graph()
logger.info("Creating the session ...")
self.sess = session_creator.create_session()
self.initialize_hooks()
if self.is_chief:
logger.info("Initializing the session ...")
session_init._run_init(self.sess)
else:
if not isinstance(session_init, JustCurrentSession):
logger.warn("This is not a chief worker, 'session_init' was ignored!")
self.sess.graph.finalize()
logger.info("Graph Finalized.") |
Create SessionRunHooks for all callbacks, and hook it onto `self.sess` to create `self.hooked_sess`.
A new trainer may override this method to create multiple groups of hooks,
which can be useful when the training is not done by a single `train_op`.
def initialize_hooks(self):
"""
Create SessionRunHooks for all callbacks, and hook it onto `self.sess` to create `self.hooked_sess`.
A new trainer may override this method to create multiple groups of hooks,
which can be useful when the training is not done by a single `train_op`.
"""
hooks = self._callbacks.get_hooks()
self.hooked_sess = tfv1.train.MonitoredSession(
session_creator=ReuseSessionCreator(self.sess), hooks=hooks) |
Run the main training loop.
Args:
steps_per_epoch, starting_epoch, max_epoch (int):
def main_loop(self, steps_per_epoch, starting_epoch, max_epoch):
"""
Run the main training loop.
Args:
steps_per_epoch, starting_epoch, max_epoch (int):
"""
with self.sess.as_default():
self.loop.config(steps_per_epoch, starting_epoch, max_epoch)
self.loop.update_global_step()
try:
self._callbacks.before_train()
# refresh global step (might have changed by callbacks) TODO ugly
# what if gs is changed later?
self.loop.update_global_step()
for self.loop._epoch_num in range(
self.loop.starting_epoch, self.loop.max_epoch + 1):
logger.info("Start Epoch {} ...".format(self.loop.epoch_num))
self._callbacks.before_epoch()
start_time = time.time()
for self.loop._local_step in range(self.loop.steps_per_epoch):
if self.hooked_sess.should_stop():
return
self.run_step() # implemented by subclass
self._callbacks.trigger_step()
self._callbacks.after_epoch()
logger.info("Epoch {} (global_step {}) finished, time:{}.".format(
self.loop.epoch_num, self.loop.global_step, humanize_time_delta(time.time() - start_time)))
# trigger epoch outside the timing region.
self._callbacks.trigger_epoch()
logger.info("Training has finished!")
except (StopTraining, tf.errors.OutOfRangeError) as e:
logger.info("Training was stopped by exception {}.".format(str(e)))
except KeyboardInterrupt:
logger.info("Detected Ctrl-C and exiting main loop.")
raise
finally:
self._callbacks.after_train()
self.hooked_sess.close() |
Implemented by three lines:
.. code-block:: python
self.setup_callbacks(callbacks, monitors)
self.initialize(session_creator, session_init)
self.main_loop(steps_per_epoch, starting_epoch, max_epoch)
You can call those methods by yourself to have better control on details if needed.
def train(self,
callbacks, monitors,
session_creator, session_init,
steps_per_epoch, starting_epoch=1, max_epoch=9999999):
"""
Implemented by three lines:
.. code-block:: python
self.setup_callbacks(callbacks, monitors)
self.initialize(session_creator, session_init)
self.main_loop(steps_per_epoch, starting_epoch, max_epoch)
You can call those methods by yourself to have better control on details if needed.
"""
self.setup_callbacks(callbacks, monitors)
self.initialize(session_creator, session_init)
self.main_loop(steps_per_epoch, starting_epoch, max_epoch) |
Same as :meth:`train()`, except:
1. Add `extra_callbacks` to callbacks. The default value for
`extra_callbacks` is :meth:`DEFAULT_CALLBACKS()`.
2. Default value for `monitors` is :meth:`DEFAULT_MONITORS()`.
3. Provide default values for every option except `steps_per_epoch`.
def train_with_defaults(
self, _sentinel=None,
callbacks=None, monitors=None,
session_creator=None, session_init=None,
steps_per_epoch=None, starting_epoch=1, max_epoch=9999999,
extra_callbacks=None):
"""
Same as :meth:`train()`, except:
1. Add `extra_callbacks` to callbacks. The default value for
`extra_callbacks` is :meth:`DEFAULT_CALLBACKS()`.
2. Default value for `monitors` is :meth:`DEFAULT_MONITORS()`.
3. Provide default values for every option except `steps_per_epoch`.
"""
assert _sentinel is None, "Please call `train_with_defaults` with keyword arguments only!"
callbacks = copy.copy(callbacks or [])
monitors = DEFAULT_MONITORS() if monitors is None else monitors
extra_callbacks = DEFAULT_CALLBACKS() if extra_callbacks is None else extra_callbacks
callbacks.extend(extra_callbacks)
assert steps_per_epoch is not None
session_creator = session_creator or NewSessionCreator()
session_init = session_init or JustCurrentSession()
self.train(callbacks, monitors,
session_creator, session_init,
steps_per_epoch, starting_epoch, max_epoch) |
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