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train | auto_reuse_variable_scope | A decorator which automatically reuses the current variable scope if the
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Example:
.. code-block:: python
@auto_reuse_variable_scope
def myfunc(x):
return tf.layers.conv2d(x, 128, 3)
myfunc(x1) # will inher... | tensorpack/tfutils/scope_utils.py | 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.co... | 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):
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train | under_name_scope | Args:
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Returns:
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Args:
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Returns:
A decorator which makes the function run under a name scope.
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Args:
name_scope(str): the default scope to use. If None, will use the name of the function.
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train | under_variable_scope | Returns:
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Example:
.. code-block:: python
@under_variable_scope()
def mid_level(x):
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"""
Returns:
A decorator which makes the function happen under a variable scope,
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Example:
.. code-block:: python
@under_variable_scope()
def mid_level(x):
with argscope(Conv2D, kernel_shape=... | def under_variable_scope():
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A decorator which makes the function happen under a variable scope,
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.. code-block:: python
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train | cached_name_scope | Return a context which either opens and caches a new name scope,
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Return a context which either opens and caches a new name scope,
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train | DataParallelBuilder._check_grad_list | Args:
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grad_list: list of list of tuples, shape is Ngpu x Nvar x 2
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train | DataParallelBuilder.call_for_each_tower | 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}'
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"""
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 ... | def call_for_each_tower(
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towers (list[int]): a list of GPU id.
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train | SyncMultiGPUParameterServerBuilder.build | Reduce the gradients, apply them with the optimizer,
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Args:
grad_list ([[(grad, var), ...], ...]): #GPU lists to be reduced. Each is the gradients computed on each GPU.
get_opt_fn (-> tf.train.Optimizer): ... | tensorpack/graph_builder/training.py | 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:
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Reduce the gradients, apply them with the optimizer,
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train | SyncMultiGPUReplicatedBuilder.call_for_each_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. | tensorpack/graph_builder/training.py | def call_for_each_tower(self, tower_fn):
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Call the function `tower_fn` under :class:`TowerContext` for each tower.
Returns:
a list, contains the return values of `tower_fn` on each tower.
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# if tower_fn returns [(grad, var), ...], this returns #GPU x #VAR x 2
... | def call_for_each_tower(self, tower_fn):
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Call the function `tower_fn` under :class:`TowerContext` for each tower.
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a list, contains the return values of `tower_fn` on each tower.
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train | SyncMultiGPUReplicatedBuilder.build | Reduce the gradients, apply them with the optimizer,
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Args:
grad_list ([[(grad, var), ...], ...]): #GPU lists to be reduced. Each is the gradients computed on each GPU.
get_... | tensorpack/graph_builder/training.py | def build(self, grad_list, get_opt_fn):
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Reduce the gradients, apply them with the optimizer,
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train | SyncMultiGPUReplicatedBuilder.get_post_init_ops | Copy values of variables on GPU 0 to other GPUs. | tensorpack/graph_builder/training.py | 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 a... | def get_post_init_ops():
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Copy values of variables on GPU 0 to other GPUs.
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all_vars = tf.global_variables() + tf.local_variables()
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train | AsyncMultiGPUBuilder.call_for_each_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. | tensorpack/graph_builder/training.py | 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_devic... | def call_for_each_tower(self, tower_fn):
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Call the function `tower_fn` under :class:`TowerContext` for each tower.
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a list, contains the return values of `tower_fn` on each tower.
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train | AsyncMultiGPUBuilder.build | 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:
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train | humanize_time_delta | 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(h... | tensorpack/utils/utils.py | 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)) ... | def humanize_time_delta(sec):
"""Humanize timedelta given in seconds
Args:
sec (float): time difference in seconds. Must be positive.
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str - time difference as a readable string
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.. code-block:: python
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name(str), val(str):
Returns:
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"""
Args:
name(str), val(str):
Returns:
a context where the environment variable ``name`` being set to
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"""
oldval = os.environ.get(name, None)
os.environ[name] = val
yield
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a context where the environment variable ``name`` being set to
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"""
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bool: whether this is the first time this function gets called from this line of code.
Example:
.. code-block:: python
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train | find_library_full_path | Similar to `from ctypes.util import find_library`, but try
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"""
Similar to `from ctypes.util import find_library`, but try
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"""
from ctypes.util import find_library
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train | LMDBSerializer.save | Args:
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path (str): output path. Either a directory or an lmdb file.
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df (DataFlow): the DataFlow to serialize.
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df (DataFlow): the DataFlow to serialize.
path (str): output tfrecord file.
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train | setup_keras_trainer | Args:
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train | KerasModel.fit | Args:
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train | get_dorefa | Return the three quantization functions fw, fa, fg, for weights, activations and gradients respectively | examples/DoReFa-Net/dorefa.py | def get_dorefa(bitW, bitA, bitG):
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train | ternarize | Implemented Trained Ternary Quantization:
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"""
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
"""
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train | interactive_imshow | Args:
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train | stack_patches | Stacked patches into grid, to produce visualizations like the following:
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Args:
patch_list(list[ndarray] or ndarray): NHW or NHWC images in [0,255].
nr_row(int), nr_col(int): rows and cols ... | tensorpack/utils/viz.py | def stack_patches(
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"""
Stacked patches into grid, to produce visualizations like the following:
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"""
Stacked patches into grid, to produce visualizations like the following:
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train | gen_stack_patches | Similar to :func:`stack_patches` but with a generator interface.
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For example, if ``patch_list`` contains 1000 images and ``nr_row==nr_col==10``,
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train | dump_dataflow_images | 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
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number (int): how many datapoint to take fro... | tensorpack/utils/viz.py | def dump_dataflow_images(df, index=0, batched=True,
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scale=1, resize=None, viz=None,
flipRGB=False):
"""
Dump or visualize images of a :class:`DataFlow`.
Args:
df (DataFlow): the DataFlow.
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Dump or visualize images of a :class:`DataFlow`.
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train | intensity_to_rgb | Convert a 1-channel matrix of intensities to an RGB image employing a colormap.
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Convert a 1-channel matrix of intensities to an RGB image employing a colormap.
This function requires matplotlib. See `matplotlib colormaps
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list of availab... | def intensity_to_rgb(intensity, cmap='cubehelix', normalize=False):
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Convert a 1-channel matrix of intensities to an RGB image employing a colormap.
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train | draw_text | Draw text on an image.
Args:
pos (tuple): x, y; the position of the text
text (str):
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color (tuple): a 3-tuple BGR color in [0, 255] | tensorpack/utils/viz.py | 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)
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Draw text on an image.
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pos (tuple): x, y; the position of the text
text (str):
font_scale (float):
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train | draw_boxes | 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)
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boxes (np.ndarray): a numpy array of shape Nx4 where each row is [x1, y1, x2, y2].
labels: (list[str] or None)
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"""
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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].
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train | ConcatWith | A wrapper around ``tf.concat`` to cooperate with :class:`LinearWrap`.
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x (tf.Tensor): input
tensor (list[tf.Tensor]): a tensor or list of tensors to concatenate with x.
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x (tf.Tensor): input
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A wrapper around ``tf.concat`` to cooperate with :class:`LinearWrap`.
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x (tf.Tensor): input
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train | point8_to_box | Args:
points: (nx4)x2
Returns:
nx4 boxes (x1y1x2y2) | examples/FasterRCNN/common.py | 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) | 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
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train | segmentation_to_mask | 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) | examples/FasterRCNN/common.py | 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 le... | 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)
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train | clip_boxes | Args:
boxes: (...)x4, float
shape: h, w | examples/FasterRCNN/common.py | 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(boxe... | 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)
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train | filter_boxes_inside_shape | Args:
boxes: (nx4), float
shape: (h, w)
Returns:
indices: (k, )
selection: (kx4) | examples/FasterRCNN/common.py | def filter_boxes_inside_shape(boxes, shape):
"""
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shape: (h, w)
Returns:
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selection: (kx4)
"""
assert boxes.ndim == 2, boxes.shape
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h, w = shape
indices = np.where(
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assert boxes.ndim == 2, boxes.shape
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train | MaxPooling | Same as `tf.layers.MaxPooling2D`. Default strides is equal to pool_size. | tensorpack/models/pool.py | def MaxPooling(
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"""
Same as `tf.layers.MaxPooling2D`. Default strides is equal to pool_size.
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if strides is None:
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pool_size,
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Same as `tf.layers.MaxPooling2D`. Default strides is equal to pool_size.
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strides = pool_size
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train | AvgPooling | Same as `tf.layers.AveragePooling2D`. Default strides is equal to pool_size. | tensorpack/models/pool.py | def AvgPooling(
inputs,
pool_size,
strides=None,
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"""
Same as `tf.layers.AveragePooling2D`. Default strides is equal to pool_size.
"""
if strides is None:
strides = pool_size
layer = tf.layers.AveragePoolin... | def AvgPooling(
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pool_size,
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padding='valid',
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Same as `tf.layers.AveragePooling2D`. Default strides is equal to pool_size.
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train | GlobalAvgPooling | 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``. | tensorpack/models/pool.py | 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
dat... | def GlobalAvgPooling(x, data_format='channels_last'):
"""
Global average pooling as in the paper `Network In Network
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Args:
x (tf.Tensor): a 4D tensor.
Returns:
tf.Tensor: a NC tensor named ``output``.
"""
assert x.shape.ndims == 4
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train | FixedUnPooling | 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.
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Returns:
... | tensorpack/models/pool.py | 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.
... | def FixedUnPooling(x, shape, unpool_mat=None, data_format='channels_last'):
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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.
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train | get_savename_from_varname | 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 | tensorpack/tfutils/varmanip.py | 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 al... | def get_savename_from_varname(
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savename_prefix=None):
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varname(str): a variable name in the graph
varname_prefix(str): an optional prefix that may need to be removed in varname
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train | dump_session_params | 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. | tensorpack/tfutils/varmanip.py | 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... | def dump_session_params(path):
"""
Dump value of all TRAINABLE + MODEL variables to a dict, and save as
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train | save_chkpt_vars | 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. | tensorpack/tfutils/varmanip.py | 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(pp... | 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()))
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train | get_checkpoint_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 | tensorpack/tfutils/varmanip.py | 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.... | 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.... | [
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train | load_chkpt_vars | Load all variables from a checkpoint to a dict.
Args:
model_path(str): path to a checkpoint.
Returns:
dict: a name:value dict | tensorpack/tfutils/varmanip.py | 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 ... | 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)
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train | is_training_name | **Guess** if this variable is only used in training.
Only used internally to avoid too many logging. Do not use it. | tensorpack/tfutils/varmanip.py | 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... | 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... | [
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train | SessionUpdate.relaxed_value_for_var | Returns a relaxed (possibly reshaped/upcast-ed) version of value,
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Args:
value (ndarray): an numpy array to be loaded to var
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ndarray: a possibly reshaped or casted version of value | tensorpack/tfutils/varmanip.py | def relaxed_value_for_var(value, var):
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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
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train | get_num_gpu | Returns:
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int: #available GPUs in CUDA_VISIBLE_DEVICES, or in the system.
"""
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train | Monitors.put_scalar | Put a scalar. | tensorpack/callbacks/monitor.py | def put_scalar(self, name, val):
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train | Monitors.put_image | Put an image.
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train | Monitors.put_event | Put an :class:`tf.Event`.
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train | JSONWriter.load_existing_json | Look for an existing json under :meth:`logger.get_logger_dir()` named "stats.json",
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train | JSONWriter._trigger | Add stats to json and dump to disk.
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"""
Add stats to json and dump to disk.
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train | sample | Args:
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coords: bxh2xw2x2. each coordinate is (y, x) integer.
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Return:
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Return:
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shape2... | def sample(img, coords):
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train | enable_call_trace | Enable trace for calls to any function. | tensorpack/utils/debug.py | def enable_call_trace():
""" Enable trace for calls to any function. """
def tracer(frame, event, arg):
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co = frame.f_code
func_name = co.co_name
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train | apply_default_prefetch | Apply a set of default rules to make a fast :class:`InputSource`.
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input_source_or_dataflow(InputSource | DataFlow):
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InputSource | tensorpack/train/interface.py | def apply_default_prefetch(input_source_or_dataflow, trainer):
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input_source_or_dataflow(InputSource | DataFlow):
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cb (Callback or [Callback]): a callback or a list of callbacks
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"""
Setup callbacks and monitors. Must be called after the main graph is built.
Args:
callbacks ([Callback]):
monitors ([MonitorBase]):
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It must be called after callbacks are setup.
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session_creator (tf.train.SessionCreator):
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steps_per_epoch, starting_epoch, max_epoch (int):
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Run the main training loop.
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train | Trainer.train_with_defaults | Same as :meth:`train()`, except:
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`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`. | tensorpack/train/base.py | def train_with_defaults(
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train | get_default_sess_config | Return a tf.ConfigProto to use as default session config.
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Args:
mem_fraction(float): see the `per_process_gpu_memory_fraction` option
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https://github.com/tensorflow/tensorflow/blob/master/t... | tensorpack/tfutils/common.py | def get_default_sess_config(mem_fraction=0.99):
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You can modify the returned config to fit your needs.
Args:
mem_fraction(float): see the `per_process_gpu_memory_fraction` option
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train | get_global_step_var | Returns:
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Returns:
tf.Tensor: the global_step variable in the current graph. Create if doesn't exist.
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"""
Returns:
tf.Tensor: the global_step variable in the current graph. Create if doesn't exist.
"""
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train | get_tensors_by_names | Get a list of tensors in the default graph by a list of names.
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names (list): | tensorpack/tfutils/common.py | def get_tensors_by_names(names):
"""
Get a list of tensors in the default graph by a list of names.
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names (list):
"""
ret = []
G = tfv1.get_default_graph()
for n in names:
opn, varn = get_op_tensor_name(n)
ret.append(G.get_tensor_by_name(varn))
return ret | def get_tensors_by_names(names):
"""
Get a list of tensors in the default graph by a list of names.
Args:
names (list):
"""
ret = []
G = tfv1.get_default_graph()
for n in names:
opn, varn = get_op_tensor_name(n)
ret.append(G.get_tensor_by_name(varn))
return ret | [
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train | get_op_or_tensor_by_name | Get either tf.Operation of tf.Tensor from names.
Args:
name (list[str] or str): names of operations or tensors.
Raises:
KeyError, if the name doesn't exist | tensorpack/tfutils/common.py | def get_op_or_tensor_by_name(name):
"""
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Args:
name (list[str] or str): names of operations or tensors.
Raises:
KeyError, if the name doesn't exist
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train | collect_env_info | Returns:
str - a table contains important information about the environment | tensorpack/tfutils/common.py | def collect_env_info():
"""
Returns:
str - a table contains important information about the environment
"""
data = []
data.append(("sys.platform", sys.platform))
data.append(("Python", sys.version.replace("\n", "")))
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data.append(("Nump... | def collect_env_info():
"""
Returns:
str - a table contains important information about the environment
"""
data = []
data.append(("sys.platform", sys.platform))
data.append(("Python", sys.version.replace("\n", "")))
data.append(("Tensorpack", __git_version__))
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train | DistributedBuilderBase._add_sync_queues_and_barrier | Adds ops to enqueue on all worker queues.
Args:
name: prefixed for the shared_name of ops.
dependencies: control dependency from ops.
Returns:
an op that should be used as control dependency before starting next step. | tensorpack/graph_builder/distributed.py | def _add_sync_queues_and_barrier(self, name, dependencies):
"""Adds ops to enqueue on all worker queues.
Args:
name: prefixed for the shared_name of ops.
dependencies: control dependency from ops.
Returns:
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name: prefixed for the shared_name of ops.
dependencies: control dependency from ops.
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train | DistributedReplicatedBuilder._apply_shadow_vars | Create shadow variables on PS, and replace variables in avg_grads
by these shadow variables.
Args:
avg_grads: list of (grad, var) tuples | tensorpack/graph_builder/distributed.py | def _apply_shadow_vars(avg_grads):
"""
Create shadow variables on PS, and replace variables in avg_grads
by these shadow variables.
Args:
avg_grads: list of (grad, var) tuples
"""
ps_var_grads = []
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"""
Create shadow variables on PS, and replace variables in avg_grads
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Args:
avg_grads: list of (grad, var) tuples
"""
ps_var_grads = []
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train | DistributedReplicatedBuilder._shadow_model_variables | Create shadow vars for model_variables as well, and add to the list of ``shadow_vars``.
Returns:
list of (shadow_model_var, local_model_var) used for syncing. | tensorpack/graph_builder/distributed.py | def _shadow_model_variables(shadow_vars):
"""
Create shadow vars for model_variables as well, and add to the list of ``shadow_vars``.
Returns:
list of (shadow_model_var, local_model_var) used for syncing.
"""
G = tf.get_default_graph()
curr_shadow_vars = set(... | def _shadow_model_variables(shadow_vars):
"""
Create shadow vars for model_variables as well, and add to the list of ``shadow_vars``.
Returns:
list of (shadow_model_var, local_model_var) used for syncing.
"""
G = tf.get_default_graph()
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train | DistributedReplicatedBuilder.build | Args:
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get_opt_fn (-> tf.train.Optimizer): callable which returns an optimizer
Returns:
(tf.Operation, tf.Operation, tf.Operation):
1. the training op.
2. the op which sync all the local variables from PS.
... | tensorpack/graph_builder/distributed.py | def build(self, get_grad_fn, get_opt_fn):
"""
Args:
get_grad_fn (-> [(grad, var)]):
get_opt_fn (-> tf.train.Optimizer): callable which returns an optimizer
Returns:
(tf.Operation, tf.Operation, tf.Operation):
1. the training op.
2. t... | def build(self, get_grad_fn, get_opt_fn):
"""
Args:
get_grad_fn (-> [(grad, var)]):
get_opt_fn (-> tf.train.Optimizer): callable which returns an optimizer
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train | DistributedReplicatedBuilder._apply_gradients_and_copy | Apply averaged gradients to ps vars, and then copy the updated
variables back to each tower.
Args:
raw_grad_list: Ngpu x Nvar x 2 gradient list from all towers
ps_var_grads: Nvar x 2 (grad, ps_var)
Returns:
list of copy ops | tensorpack/graph_builder/distributed.py | def _apply_gradients_and_copy(self, opt, raw_grad_list, ps_var_grads):
"""
Apply averaged gradients to ps vars, and then copy the updated
variables back to each tower.
Args:
raw_grad_list: Ngpu x Nvar x 2 gradient list from all towers
ps_var_grads: Nvar x 2 (grad... | def _apply_gradients_and_copy(self, opt, raw_grad_list, ps_var_grads):
"""
Apply averaged gradients to ps vars, and then copy the updated
variables back to each tower.
Args:
raw_grad_list: Ngpu x Nvar x 2 gradient list from all towers
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train | DistributedReplicatedBuilder._get_initial_sync_op | Get the op to copy-initialized all local variables from PS. | tensorpack/graph_builder/distributed.py | def _get_initial_sync_op(self):
"""
Get the op to copy-initialized all local variables from PS.
"""
def strip_port(s):
if s.endswith(':0'):
return s[:-2]
return s
local_vars = tf.local_variables()
local_var_by_name = dict([(strip_po... | def _get_initial_sync_op(self):
"""
Get the op to copy-initialized all local variables from PS.
"""
def strip_port(s):
if s.endswith(':0'):
return s[:-2]
return s
local_vars = tf.local_variables()
local_var_by_name = dict([(strip_po... | [
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train | DistributedReplicatedBuilder._get_sync_model_vars_op | Get the op to sync local model_variables to PS. | tensorpack/graph_builder/distributed.py | def _get_sync_model_vars_op(self):
"""
Get the op to sync local model_variables to PS.
"""
ops = []
for (shadow_v, local_v) in self._shadow_model_vars:
ops.append(shadow_v.assign(local_v.read_value()))
assert len(ops)
return tf.group(*ops, name='sync_{... | def _get_sync_model_vars_op(self):
"""
Get the op to sync local model_variables to PS.
"""
ops = []
for (shadow_v, local_v) in self._shadow_model_vars:
ops.append(shadow_v.assign(local_v.read_value()))
assert len(ops)
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train | get_tensors_inputs | Args:
placeholders (list[Tensor]):
tensors (list[Tensor]): list of tf.Tensor
names (list[str]): names matching the given tensors
Returns:
list[Tensor]: inputs to used for the tower function,
with the corresponding placeholders replaced by tensors. | tensorpack/input_source/input_source_base.py | def get_tensors_inputs(placeholders, tensors, names):
"""
Args:
placeholders (list[Tensor]):
tensors (list[Tensor]): list of tf.Tensor
names (list[str]): names matching the given tensors
Returns:
list[Tensor]: inputs to used for the tower function,
with the corre... | def get_tensors_inputs(placeholders, tensors, names):
"""
Args:
placeholders (list[Tensor]):
tensors (list[Tensor]): list of tf.Tensor
names (list[str]): names matching the given tensors
Returns:
list[Tensor]: inputs to used for the tower function,
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train | get_sublist_by_names | Args:
lst (list): list of objects with "name" property.
Returns:
list: a sublist of objects, matching names | tensorpack/input_source/input_source_base.py | def get_sublist_by_names(lst, names):
"""
Args:
lst (list): list of objects with "name" property.
Returns:
list: a sublist of objects, matching names
"""
orig_names = [p.name for p in lst]
ret = []
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try:
idx = orig_names.index(name)
... | def get_sublist_by_names(lst, names):
"""
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lst (list): list of objects with "name" property.
Returns:
list: a sublist of objects, matching names
"""
orig_names = [p.name for p in lst]
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train | remap_input_source | When you have some :class:`InputSource` which doesn't match the inputs of
your tower function, use `RemapInputSource`.
It produces placeholders for all the inputs in your model,
except that the corresponding ones are replaced with the tensor produced
by the given :class:`InputSource`.
Args:
... | tensorpack/input_source/input_source_base.py | def remap_input_source(input, names):
"""
When you have some :class:`InputSource` which doesn't match the inputs of
your tower function, use `RemapInputSource`.
It produces placeholders for all the inputs in your model,
except that the corresponding ones are replaced with the tensor produced
by ... | def remap_input_source(input, names):
"""
When you have some :class:`InputSource` which doesn't match the inputs of
your tower function, use `RemapInputSource`.
It produces placeholders for all the inputs in your model,
except that the corresponding ones are replaced with the tensor produced
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train | rpn_head | Returns:
label_logits: fHxfWxNA
box_logits: fHxfWxNAx4 | examples/FasterRCNN/model_rpn.py | def rpn_head(featuremap, channel, num_anchors):
"""
Returns:
label_logits: fHxfWxNA
box_logits: fHxfWxNAx4
"""
with argscope(Conv2D, data_format='channels_first',
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hidden = Conv2D('conv0', featuremap,... | def rpn_head(featuremap, channel, num_anchors):
"""
Returns:
label_logits: fHxfWxNA
box_logits: fHxfWxNAx4
"""
with argscope(Conv2D, data_format='channels_first',
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hidden = Conv2D('conv0', featuremap,... | [
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train | rpn_losses | Args:
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label_loss, box_loss | examples/FasterRCNN/model_rpn.py | def rpn_losses(anchor_labels, anchor_boxes, label_logits, box_logits):
"""
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anchor_boxes: fHxfWxNAx4, encoded
label_logits: fHxfWxNA
box_logits: fHxfWxNAx4
Returns:
label_loss, box_loss
"""
with tf.device('/cpu:0'):
valid... | def rpn_losses(anchor_labels, anchor_boxes, label_logits, box_logits):
"""
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anchor_boxes: fHxfWxNAx4, encoded
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train | generate_rpn_proposals | Sample RPN proposals by the following steps:
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boxes: nx4 float dtype, the proposal boxes. Decoded to floatbox already
scores: n float, the logits
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"""
Sample RPN proposals by the following steps:
1. Pick top k1 by scores
2. NMS them
3. Pick top k2 by scores. Default k2 == k1, i.e. does not filter the NMS output.
Args:
... | def generate_rpn_proposals(boxes, scores, img_shape,
pre_nms_topk, post_nms_topk=None):
"""
Sample RPN proposals by the following steps:
1. Pick top k1 by scores
2. NMS them
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train | MergeAllSummaries | This callback is enabled by default.
Evaluate all summaries by ``tf.summary.merge_all``, and write them to logs.
Args:
period (int): by default the callback summarizes once every epoch.
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run_alone (... | tensorpack/callbacks/summary.py | def MergeAllSummaries(period=0, run_alone=False, key=None):
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This callback is enabled by default.
Evaluate all summaries by ``tf.summary.merge_all``, and write them to logs.
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period (int): by default the callback summarizes once every epoch.
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Evaluate all summaries by ``tf.summary.merge_all``, and write them to logs.
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train | ReplayMemory.append | Args:
exp (Experience): | examples/DeepQNetwork/expreplay.py | def append(self, exp):
"""
Args:
exp (Experience):
"""
if self._curr_size < self.max_size:
self._assign(self._curr_pos, exp)
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exp (Experience):
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train | ReplayMemory.sample | return a tuple of (s,r,a,o),
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k = self.history_len + 1
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train | EnvRunner.step | Run the environment for one step.
If the episode ends, store the entire episode to the replay memory. | examples/DeepQNetwork/expreplay.py | def step(self, exploration):
"""
Run the environment for one step.
If the episode ends, store the entire episode to the replay memory.
"""
old_s = self._current_ob
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Run the environment for one step.
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"""
old_s = self._current_ob
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train | EnvRunner.recent_state | Get the recent state (with stacked history) of the environment.
Returns:
a list of ``hist_len-1`` elements, each of shape ``self.state_shape`` | examples/DeepQNetwork/expreplay.py | def recent_state(self):
"""
Get the recent state (with stacked history) of the environment.
Returns:
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"""
expected_len = self.history_len - 1
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... | def recent_state(self):
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Get the recent state (with stacked history) of the environment.
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a list of ``hist_len-1`` elements, each of shape ``self.state_shape``
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expected_len = self.history_len - 1
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train | EnvRunnerManager.step | Execute one step in any of the runners. | examples/DeepQNetwork/expreplay.py | def step(self, exploration):
"""
Execute one step in any of the runners.
"""
if len(self._runners) > 1:
self._populate_job_queue.put(exploration)
else:
self._runners[0].step(exploration) | def step(self, exploration):
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Execute one step in any of the runners.
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"... | d7a13cb74c9066bc791d7aafc3b744b60ee79a9f |
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