Python mxnet.model.load_checkpoint() Examples
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code examples of mxnet.model.load_checkpoint().
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Example #1
| Source File: module.py From Faster_RCNN_for_DOTA with Apache License 2.0 | 5 votes |
|
def load(prefix, epoch, load_optimizer_states=False, **kwargs):
"""Create a model from previously saved checkpoint.
Parameters
----------
prefix : str
path prefix of saved model files. You should have
"prefix-symbol.json", "prefix-xxxx.params", and
optionally "prefix-xxxx.states", where xxxx is the
epoch number.
epoch : int
epoch to load.
load_optimizer_states : bool
whether to load optimizer states. Checkpoint needs
to have been made with save_optimizer_states=True.
data_names : list of str
Default is `('data')` for a typical model used in image classification.
label_names : list of str
Default is `('softmax_label')` for a typical model used in image
classification.
logger : Logger
Default is `logging`.
context : Context or list of Context
Default is `cpu()`.
work_load_list : list of number
Default `None`, indicating uniform workload.
fixed_param_names: list of str
Default `None`, indicating no network parameters are fixed.
"""
sym, args, auxs = load_checkpoint(prefix, epoch)
mod = Module(symbol=sym, **kwargs)
mod._arg_params = args
mod._aux_params = auxs
mod.params_initialized = True
if load_optimizer_states:
mod._preload_opt_states = '%s-%04d.states'%(prefix, epoch)
return mod
Example #2
| Source File: module.py From Deformable-ConvNets with MIT License | 5 votes |
|
def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True):
"""Assign parameter and aux state values.
Parameters
----------
arg_params : dict
Dictionary of name to value (`NDArray`) mapping.
aux_params : dict
Dictionary of name to value (`NDArray`) mapping.
allow_missing : bool
If true, params could contain missing values, and the initializer will be
called to fill those missing params.
force_init : bool
If true, will force re-initialize even if already initialized.
Examples
--------
An example of setting module parameters::
>>> sym, arg_params, aux_params = \
>>> mx.model.load_checkpoint(model_prefix, n_epoch_load)
>>> mod.set_params(arg_params=arg_params, aux_params=aux_params)
"""
if not allow_missing:
self.init_params(initializer=None, arg_params=arg_params, aux_params=aux_params,
allow_missing=allow_missing, force_init=force_init)
return
if self.params_initialized and not force_init:
warnings.warn("Parameters already initialized and force_init=False. "
"set_params call ignored.", stacklevel=2)
return
self._exec_group.set_params(arg_params, aux_params)
# because we didn't update self._arg_params, they are dirty now.
self._params_dirty = True
self.params_initialized = True
Example #3
| Source File: module.py From RoITransformer_DOTA with MIT License | 5 votes |
|
def load(prefix, epoch, load_optimizer_states=False, **kwargs):
"""Create a model from previously saved checkpoint.
Parameters
----------
prefix : str
path prefix of saved model files. You should have
"prefix-symbol.json", "prefix-xxxx.params", and
optionally "prefix-xxxx.states", where xxxx is the
epoch number.
epoch : int
epoch to load.
load_optimizer_states : bool
whether to load optimizer states. Checkpoint needs
to have been made with save_optimizer_states=True.
data_names : list of str
Default is `('data')` for a typical model used in image classification.
label_names : list of str
Default is `('softmax_label')` for a typical model used in image
classification.
logger : Logger
Default is `logging`.
context : Context or list of Context
Default is `cpu()`.
work_load_list : list of number
Default `None`, indicating uniform workload.
fixed_param_names: list of str
Default `None`, indicating no network parameters are fixed.
"""
sym, args, auxs = load_checkpoint(prefix, epoch)
mod = Module(symbol=sym, **kwargs)
mod._arg_params = args
mod._aux_params = auxs
mod.params_initialized = True
if load_optimizer_states:
mod._preload_opt_states = '%s-%04d.states'%(prefix, epoch)
return mod
Example #4
| Source File: module.py From RoITransformer_DOTA with MIT License | 5 votes |
|
def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True):
"""Assign parameter and aux state values.
Parameters
----------
arg_params : dict
Dictionary of name to value (`NDArray`) mapping.
aux_params : dict
Dictionary of name to value (`NDArray`) mapping.
allow_missing : bool
If true, params could contain missing values, and the initializer will be
called to fill those missing params.
force_init : bool
If true, will force re-initialize even if already initialized.
Examples
--------
An example of setting module parameters::
>>> sym, arg_params, aux_params = \
>>> mx.model.load_checkpoint(model_prefix, n_epoch_load)
>>> mod.set_params(arg_params=arg_params, aux_params=aux_params)
"""
if not allow_missing:
self.init_params(initializer=None, arg_params=arg_params, aux_params=aux_params,
allow_missing=allow_missing, force_init=force_init)
return
if self.params_initialized and not force_init:
warnings.warn("Parameters already initialized and force_init=False. "
"set_params call ignored.", stacklevel=2)
return
self._exec_group.set_params(arg_params, aux_params)
# because we didn't update self._arg_params, they are dirty now.
self._params_dirty = True
self.params_initialized = True
Example #5
| Source File: module.py From RoITransformer_DOTA with MIT License | 5 votes |
|
def load(prefix, epoch, load_optimizer_states=False, **kwargs):
"""Create a model from previously saved checkpoint.
Parameters
----------
prefix : str
path prefix of saved model files. You should have
"prefix-symbol.json", "prefix-xxxx.params", and
optionally "prefix-xxxx.states", where xxxx is the
epoch number.
epoch : int
epoch to load.
load_optimizer_states : bool
whether to load optimizer states. Checkpoint needs
to have been made with save_optimizer_states=True.
data_names : list of str
Default is `('data')` for a typical model used in image classification.
label_names : list of str
Default is `('softmax_label')` for a typical model used in image
classification.
logger : Logger
Default is `logging`.
context : Context or list of Context
Default is `cpu()`.
work_load_list : list of number
Default `None`, indicating uniform workload.
fixed_param_names: list of str
Default `None`, indicating no network parameters are fixed.
"""
sym, args, auxs = load_checkpoint(prefix, epoch)
mod = Module(symbol=sym, **kwargs)
mod._arg_params = args
mod._aux_params = auxs
mod.params_initialized = True
if load_optimizer_states:
mod._preload_opt_states = '%s-%04d.states'%(prefix, epoch)
return mod
Example #6
| Source File: module.py From RoITransformer_DOTA with MIT License | 5 votes |
|
def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True):
"""Assign parameter and aux state values.
Parameters
----------
arg_params : dict
Dictionary of name to value (`NDArray`) mapping.
aux_params : dict
Dictionary of name to value (`NDArray`) mapping.
allow_missing : bool
If true, params could contain missing values, and the initializer will be
called to fill those missing params.
force_init : bool
If true, will force re-initialize even if already initialized.
Examples
--------
An example of setting module parameters::
>>> sym, arg_params, aux_params = \
>>> mx.model.load_checkpoint(model_prefix, n_epoch_load)
>>> mod.set_params(arg_params=arg_params, aux_params=aux_params)
"""
if not allow_missing:
self.init_params(initializer=None, arg_params=arg_params, aux_params=aux_params,
allow_missing=allow_missing, force_init=force_init)
return
if self.params_initialized and not force_init:
warnings.warn("Parameters already initialized and force_init=False. "
"set_params call ignored.", stacklevel=2)
return
self._exec_group.set_params(arg_params, aux_params)
# because we didn't update self._arg_params, they are dirty now.
self._params_dirty = True
self.params_initialized = True
Example #7
| Source File: module.py From Relation-Networks-for-Object-Detection with MIT License | 5 votes |
|
def load(prefix, epoch, load_optimizer_states=False, **kwargs):
"""Create a model from previously saved checkpoint.
Parameters
----------
prefix : str
path prefix of saved model files. You should have
"prefix-symbol.json", "prefix-xxxx.params", and
optionally "prefix-xxxx.states", where xxxx is the
epoch number.
epoch : int
epoch to load.
load_optimizer_states : bool
whether to load optimizer states. Checkpoint needs
to have been made with save_optimizer_states=True.
data_names : list of str
Default is `('data')` for a typical model used in image classification.
label_names : list of str
Default is `('softmax_label')` for a typical model used in image
classification.
logger : Logger
Default is `logging`.
context : Context or list of Context
Default is `cpu()`.
work_load_list : list of number
Default `None`, indicating uniform workload.
fixed_param_names: list of str
Default `None`, indicating no network parameters are fixed.
"""
sym, args, auxs = load_checkpoint(prefix, epoch)
mod = Module(symbol=sym, **kwargs)
mod._arg_params = args
mod._aux_params = auxs
mod.params_initialized = True
if load_optimizer_states:
mod._preload_opt_states = '%s-%04d.states'%(prefix, epoch)
return mod
Example #8
| Source File: module.py From Relation-Networks-for-Object-Detection with MIT License | 5 votes |
|
def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True):
"""Assign parameter and aux state values.
Parameters
----------
arg_params : dict
Dictionary of name to value (`NDArray`) mapping.
aux_params : dict
Dictionary of name to value (`NDArray`) mapping.
allow_missing : bool
If true, params could contain missing values, and the initializer will be
called to fill those missing params.
force_init : bool
If true, will force re-initialize even if already initialized.
Examples
--------
An example of setting module parameters::
>>> sym, arg_params, aux_params = \
>>> mx.model.load_checkpoint(model_prefix, n_epoch_load)
>>> mod.set_params(arg_params=arg_params, aux_params=aux_params)
"""
if not allow_missing:
self.init_params(initializer=None, arg_params=arg_params, aux_params=aux_params,
allow_missing=allow_missing, force_init=force_init)
return
if self.params_initialized and not force_init:
warnings.warn("Parameters already initialized and force_init=False. "
"set_params call ignored.", stacklevel=2)
return
self._exec_group.set_params(arg_params, aux_params)
# because we didn't update self._arg_params, they are dirty now.
self._params_dirty = True
self.params_initialized = True
Example #9
| Source File: module.py From Accel with MIT License | 5 votes |
|
def load(prefix, epoch, load_optimizer_states=False, **kwargs):
"""Create a model from previously saved checkpoint.
Parameters
----------
prefix : str
path prefix of saved model files. You should have
"prefix-symbol.json", "prefix-xxxx.params", and
optionally "prefix-xxxx.states", where xxxx is the
epoch number.
epoch : int
epoch to load.
load_optimizer_states : bool
whether to load optimizer states. Checkpoint needs
to have been made with save_optimizer_states=True.
data_names : list of str
Default is `('data')` for a typical model used in image classification.
label_names : list of str
Default is `('softmax_label')` for a typical model used in image
classification.
logger : Logger
Default is `logging`.
context : Context or list of Context
Default is `cpu()`.
work_load_list : list of number
Default `None`, indicating uniform workload.
fixed_param_names: list of str
Default `None`, indicating no network parameters are fixed.
"""
sym, args, auxs = load_checkpoint(prefix, epoch)
mod = Module(symbol=sym, **kwargs)
mod._arg_params = args
mod._aux_params = auxs
mod.params_initialized = True
if load_optimizer_states:
mod._preload_opt_states = '%s-%04d.states'%(prefix, epoch)
return mod
Example #10
| Source File: module.py From Accel with MIT License | 5 votes |
|
def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True):
"""Assign parameter and aux state values.
Parameters
----------
arg_params : dict
Dictionary of name to value (`NDArray`) mapping.
aux_params : dict
Dictionary of name to value (`NDArray`) mapping.
allow_missing : bool
If true, params could contain missing values, and the initializer will be
called to fill those missing params.
force_init : bool
If true, will force re-initialize even if already initialized.
Examples
--------
An example of setting module parameters::
>>> sym, arg_params, aux_params = \
>>> mx.model.load_checkpoint(model_prefix, n_epoch_load)
>>> mod.set_params(arg_params=arg_params, aux_params=aux_params)
"""
if not allow_missing:
self.init_params(initializer=None, arg_params=arg_params, aux_params=aux_params,
allow_missing=allow_missing, force_init=force_init)
return
if self.params_initialized and not force_init:
warnings.warn("Parameters already initialized and force_init=False. "
"set_params call ignored.", stacklevel=2)
return
self._exec_group.set_params(arg_params, aux_params)
# because we didn't update self._arg_params, they are dirty now.
self._params_dirty = True
self.params_initialized = True
Example #11
| Source File: module.py From Accel with MIT License | 5 votes |
|
def load(prefix, epoch, load_optimizer_states=False, **kwargs):
"""Create a model from previously saved checkpoint.
Parameters
----------
prefix : str
path prefix of saved model files. You should have
"prefix-symbol.json", "prefix-xxxx.params", and
optionally "prefix-xxxx.states", where xxxx is the
epoch number.
epoch : int
epoch to load.
load_optimizer_states : bool
whether to load optimizer states. Checkpoint needs
to have been made with save_optimizer_states=True.
data_names : list of str
Default is `('data')` for a typical model used in image classification.
label_names : list of str
Default is `('softmax_label')` for a typical model used in image
classification.
logger : Logger
Default is `logging`.
context : Context or list of Context
Default is `cpu()`.
work_load_list : list of number
Default `None`, indicating uniform workload.
fixed_param_names: list of str
Default `None`, indicating no network parameters are fixed.
"""
sym, args, auxs = load_checkpoint(prefix, epoch)
mod = Module(symbol=sym, **kwargs)
mod._arg_params = args
mod._aux_params = auxs
mod.params_initialized = True
if load_optimizer_states:
mod._preload_opt_states = '%s-%04d.states'%(prefix, epoch)
return mod
Example #12
| Source File: module.py From Accel with MIT License | 5 votes |
|
def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True):
"""Assign parameter and aux state values.
Parameters
----------
arg_params : dict
Dictionary of name to value (`NDArray`) mapping.
aux_params : dict
Dictionary of name to value (`NDArray`) mapping.
allow_missing : bool
If true, params could contain missing values, and the initializer will be
called to fill those missing params.
force_init : bool
If true, will force re-initialize even if already initialized.
Examples
--------
An example of setting module parameters::
>>> sym, arg_params, aux_params = \
>>> mx.model.load_checkpoint(model_prefix, n_epoch_load)
>>> mod.set_params(arg_params=arg_params, aux_params=aux_params)
"""
if not allow_missing:
self.init_params(initializer=None, arg_params=arg_params, aux_params=aux_params,
allow_missing=allow_missing, force_init=force_init)
return
if self.params_initialized and not force_init:
warnings.warn("Parameters already initialized and force_init=False. "
"set_params call ignored.", stacklevel=2)
return
self._exec_group.set_params(arg_params, aux_params)
# because we didn't update self._arg_params, they are dirty now.
self._params_dirty = True
self.params_initialized = True
Example #13
| Source File: module.py From Accel with MIT License | 5 votes |
|
def load(prefix, epoch, load_optimizer_states=False, **kwargs):
"""Create a model from previously saved checkpoint.
Parameters
----------
prefix : str
path prefix of saved model files. You should have
"prefix-symbol.json", "prefix-xxxx.params", and
optionally "prefix-xxxx.states", where xxxx is the
epoch number.
epoch : int
epoch to load.
load_optimizer_states : bool
whether to load optimizer states. Checkpoint needs
to have been made with save_optimizer_states=True.
data_names : list of str
Default is `('data')` for a typical model used in image classification.
label_names : list of str
Default is `('softmax_label')` for a typical model used in image
classification.
logger : Logger
Default is `logging`.
context : Context or list of Context
Default is `cpu()`.
work_load_list : list of number
Default `None`, indicating uniform workload.
fixed_param_names: list of str
Default `None`, indicating no network parameters are fixed.
"""
sym, args, auxs = load_checkpoint(prefix, epoch)
mod = Module(symbol=sym, **kwargs)
mod._arg_params = args
mod._aux_params = auxs
mod.params_initialized = True
if load_optimizer_states:
mod._preload_opt_states = '%s-%04d.states'%(prefix, epoch)
return mod
Example #14
| Source File: module.py From Accel with MIT License | 5 votes |
|
def load(prefix, epoch, load_optimizer_states=False, **kwargs):
"""Create a model from previously saved checkpoint.
Parameters
----------
prefix : str
path prefix of saved model files. You should have
"prefix-symbol.json", "prefix-xxxx.params", and
optionally "prefix-xxxx.states", where xxxx is the
epoch number.
epoch : int
epoch to load.
load_optimizer_states : bool
whether to load optimizer states. Checkpoint needs
to have been made with save_optimizer_states=True.
data_names : list of str
Default is `('data')` for a typical model used in image classification.
label_names : list of str
Default is `('softmax_label')` for a typical model used in image
classification.
logger : Logger
Default is `logging`.
context : Context or list of Context
Default is `cpu()`.
work_load_list : list of number
Default `None`, indicating uniform workload.
fixed_param_names: list of str
Default `None`, indicating no network parameters are fixed.
"""
sym, args, auxs = load_checkpoint(prefix, epoch)
mod = Module(symbol=sym, **kwargs)
mod._arg_params = args
mod._aux_params = auxs
mod.params_initialized = True
if load_optimizer_states:
mod._preload_opt_states = '%s-%04d.states'%(prefix, epoch)
return mod
Example #15
| Source File: module.py From Accel with MIT License | 5 votes |
|
def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True):
"""Assign parameter and aux state values.
Parameters
----------
arg_params : dict
Dictionary of name to value (`NDArray`) mapping.
aux_params : dict
Dictionary of name to value (`NDArray`) mapping.
allow_missing : bool
If true, params could contain missing values, and the initializer will be
called to fill those missing params.
force_init : bool
If true, will force re-initialize even if already initialized.
Examples
--------
An example of setting module parameters::
>>> sym, arg_params, aux_params = \
>>> mx.model.load_checkpoint(model_prefix, n_epoch_load)
>>> mod.set_params(arg_params=arg_params, aux_params=aux_params)
"""
if not allow_missing:
self.init_params(initializer=None, arg_params=arg_params, aux_params=aux_params,
allow_missing=allow_missing, force_init=force_init)
return
if self.params_initialized and not force_init:
warnings.warn("Parameters already initialized and force_init=False. "
"set_params call ignored.", stacklevel=2)
return
self._exec_group.set_params(arg_params, aux_params)
# because we didn't update self._arg_params, they are dirty now.
self._params_dirty = True
self.params_initialized = True
Example #16
| Source File: module.py From Deformable-ConvNets with MIT License | 5 votes |
|
def load(prefix, epoch, load_optimizer_states=False, **kwargs):
"""Create a model from previously saved checkpoint.
Parameters
----------
prefix : str
path prefix of saved model files. You should have
"prefix-symbol.json", "prefix-xxxx.params", and
optionally "prefix-xxxx.states", where xxxx is the
epoch number.
epoch : int
epoch to load.
load_optimizer_states : bool
whether to load optimizer states. Checkpoint needs
to have been made with save_optimizer_states=True.
data_names : list of str
Default is `('data')` for a typical model used in image classification.
label_names : list of str
Default is `('softmax_label')` for a typical model used in image
classification.
logger : Logger
Default is `logging`.
context : Context or list of Context
Default is `cpu()`.
work_load_list : list of number
Default `None`, indicating uniform workload.
fixed_param_names: list of str
Default `None`, indicating no network parameters are fixed.
"""
sym, args, auxs = load_checkpoint(prefix, epoch)
mod = Module(symbol=sym, **kwargs)
mod._arg_params = args
mod._aux_params = auxs
mod.params_initialized = True
if load_optimizer_states:
mod._preload_opt_states = '%s-%04d.states'%(prefix, epoch)
return mod
Example #17
| Source File: module.py From Faster_RCNN_for_DOTA with Apache License 2.0 | 5 votes |
|
def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True, allow_extra=False):
"""Assign parameter and aux state values.
Parameters
----------
arg_params : dict
Dictionary of name to value (`NDArray`) mapping.
aux_params : dict
Dictionary of name to value (`NDArray`) mapping.
allow_missing : bool
If true, params could contain missing values, and the initializer will be
called to fill those missing params.
force_init : bool
If true, will force re-initialize even if already initialized.
Examples
--------
An example of setting module parameters::
>>> sym, arg_params, aux_params = \
>>> mx.model.load_checkpoint(model_prefix, n_epoch_load)
>>> mod.set_params(arg_params=arg_params, aux_params=aux_params)
"""
if not allow_missing:
self.init_params(initializer=None, arg_params=arg_params, aux_params=aux_params,
allow_missing=allow_missing, force_init=force_init, allow_extra=allow_extra)
return
if self.params_initialized and not force_init:
warnings.warn("Parameters already initialized and force_init=False. "
"set_params call ignored.", stacklevel=2)
return
self._exec_group.set_params(arg_params, aux_params, allow_extra=allow_extra)
# because we didn't update self._arg_params, they are dirty now.
self._params_dirty = True
self.params_initialized = True
Example #18
| Source File: module.py From Sequence-Level-Semantics-Aggregation with Apache License 2.0 | 5 votes |
|
def load(prefix, epoch, load_optimizer_states=False, **kwargs):
"""Create a model from previously saved checkpoint.
Parameters
----------
prefix : str
path prefix of saved model files. You should have
"prefix-symbol.json", "prefix-xxxx.params", and
optionally "prefix-xxxx.states", where xxxx is the
epoch number.
epoch : int
epoch to load.
load_optimizer_states : bool
whether to load optimizer states. Checkpoint needs
to have been made with save_optimizer_states=True.
data_names : list of str
Default is `('data')` for a typical model used in image classification.
label_names : list of str
Default is `('softmax_label')` for a typical model used in image
classification.
logger : Logger
Default is `logging`.
context : Context or list of Context
Default is `cpu()`.
work_load_list : list of number
Default `None`, indicating uniform workload.
fixed_param_names: list of str
Default `None`, indicating no network parameters are fixed.
"""
sym, args, auxs = load_checkpoint(prefix, epoch)
mod = Module(symbol=sym, **kwargs)
mod._arg_params = args
mod._aux_params = auxs
mod.params_initialized = True
if load_optimizer_states:
mod._preload_opt_states = '%s-%04d.states'%(prefix, epoch)
return mod
Example #19
| Source File: module.py From Sequence-Level-Semantics-Aggregation with Apache License 2.0 | 5 votes |
|
def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True, allow_extra=False):
"""Assign parameter and aux state values.
Parameters
----------
arg_params : dict
Dictionary of name to value (`NDArray`) mapping.
aux_params : dict
Dictionary of name to value (`NDArray`) mapping.
allow_missing : bool
If true, params could contain missing values, and the initializer will be
called to fill those missing params.
force_init : bool
If true, will force re-initialize even if already initialized.
Examples
--------
An example of setting module parameters::
>>> sym, arg_params, aux_params = \
>>> mx.model.load_checkpoint(model_prefix, n_epoch_load)
>>> mod.set_params(arg_params=arg_params, aux_params=aux_params)
"""
if not allow_missing:
self.init_params(initializer=None, arg_params=arg_params, aux_params=aux_params,
allow_missing=allow_missing, force_init=force_init, allow_extra=allow_extra)
return
if self.params_initialized and not force_init:
warnings.warn("Parameters already initialized and force_init=False. "
"set_params call ignored.", stacklevel=2)
return
self._exec_group.set_params(arg_params, aux_params)
# because we didn't update self._arg_params, they are dirty now.
self._params_dirty = True
self.params_initialized = True
Example #20
| Source File: detection_module.py From groupsoftmax-simpledet with Apache License 2.0 | 5 votes |
|
def load(prefix, epoch, load_optimizer_states=False, **kwargs):
"""Creates a model from previously saved checkpoint.
Parameters
----------
prefix : str
path prefix of saved model files. You should have
"prefix-symbol.json", "prefix-xxxx.params", and
optionally "prefix-xxxx.states", where xxxx is the
epoch number.
epoch : int
epoch to load.
load_optimizer_states : bool
whether to load optimizer states. Checkpoint needs
to have been made with save_optimizer_states=True.
data_names : list of str
Default is `('data')` for a typical model used in image classification.
label_names : list of str
Default is `('softmax_label')` for a typical model used in image
classification.
logger : Logger
Default is `logging`.
context : Context or list of Context
Default is ``cpu()``.
work_load_list : list of number
Default ``None``, indicating uniform workload.
fixed_param_names: list of str
Default ``None``, indicating no network parameters are fixed.
"""
sym, args, auxs = load_checkpoint(prefix, epoch)
mod = DetModule(symbol=sym, **kwargs)
mod._arg_params = args
mod._aux_params = auxs
mod.params_initialized = True
if load_optimizer_states:
mod._preload_opt_states = '%s-%04d.states'%(prefix, epoch)
return mod
Example #21
| Source File: detection_module.py From groupsoftmax-simpledet with Apache License 2.0 | 5 votes |
|
def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True,
allow_extra=False):
"""Assigns parameter and aux state values.
Parameters
----------
arg_params : dict
Dictionary of name to `NDArray`.
aux_params : dict
Dictionary of name to `NDArray`.
allow_missing : bool
If ``True``, params could contain missing values, and the initializer will be
called to fill those missing params.
force_init : bool
If ``True``, will force re-initialize even if already initialized.
allow_extra : boolean, optional
Whether allow extra parameters that are not needed by symbol.
If this is True, no error will be thrown when arg_params or aux_params
contain extra parameters that is not needed by the executor.
Examples
--------
>>> # An example of setting module parameters.
>>> sym, arg_params, aux_params = mx.model.load_checkpoint(model_prefix, n_epoch_load)
>>> mod.set_params(arg_params=arg_params, aux_params=aux_params)
"""
if not allow_missing:
self.init_params(initializer=None, arg_params=arg_params, aux_params=aux_params,
allow_missing=allow_missing, force_init=force_init,
allow_extra=allow_extra)
return
if self.params_initialized and not force_init:
warnings.warn("Parameters already initialized and force_init=False. "
"set_params call ignored.", stacklevel=2)
return
self._exec_group.set_params(arg_params, aux_params, allow_extra=allow_extra)
# because we didn't update self._arg_params, they are dirty now.
self._params_dirty = True
self.params_initialized = True
Example #22
| Source File: module.py From Decoupled-Classification-Refinement with MIT License | 5 votes |
|
def load(prefix, epoch, load_optimizer_states=False, **kwargs):
"""Create a model from previously saved checkpoint.
Parameters
----------
prefix : str
path prefix of saved model files. You should have
"prefix-symbol.json", "prefix-xxxx.params", and
optionally "prefix-xxxx.states", where xxxx is the
epoch number.
epoch : int
epoch to load.
load_optimizer_states : bool
whether to load optimizer states. Checkpoint needs
to have been made with save_optimizer_states=True.
data_names : list of str
Default is `('data')` for a typical model used in image classification.
label_names : list of str
Default is `('softmax_label')` for a typical model used in image
classification.
logger : Logger
Default is `logging`.
context : Context or list of Context
Default is `cpu()`.
work_load_list : list of number
Default `None`, indicating uniform workload.
fixed_param_names: list of str
Default `None`, indicating no network parameters are fixed.
"""
sym, args, auxs = load_checkpoint(prefix, epoch)
mod = Module(symbol=sym, **kwargs)
mod._arg_params = args
mod._aux_params = auxs
mod.params_initialized = True
if load_optimizer_states:
mod._preload_opt_states = '%s-%04d.states'%(prefix, epoch)
return mod
Example #23
| Source File: module.py From Decoupled-Classification-Refinement with MIT License | 5 votes |
|
def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True):
"""Assign parameter and aux state values.
Parameters
----------
arg_params : dict
Dictionary of name to value (`NDArray`) mapping.
aux_params : dict
Dictionary of name to value (`NDArray`) mapping.
allow_missing : bool
If true, params could contain missing values, and the initializer will be
called to fill those missing params.
force_init : bool
If true, will force re-initialize even if already initialized.
Examples
--------
An example of setting module parameters::
>>> sym, arg_params, aux_params = \
>>> mx.model.load_checkpoint(model_prefix, n_epoch_load)
>>> mod.set_params(arg_params=arg_params, aux_params=aux_params)
"""
if not allow_missing:
self.init_params(initializer=None, arg_params=arg_params, aux_params=aux_params,
allow_missing=allow_missing, force_init=force_init)
return
if self.params_initialized and not force_init:
warnings.warn("Parameters already initialized and force_init=False. "
"set_params call ignored.", stacklevel=2)
return
self._exec_group.set_params(arg_params, aux_params)
# because we didn't update self._arg_params, they are dirty now.
self._params_dirty = True
self.params_initialized = True
Example #24
| Source File: module.py From Decoupled-Classification-Refinement with MIT License | 5 votes |
|
def load(prefix, epoch, load_optimizer_states=False, **kwargs):
"""Create a model from previously saved checkpoint.
Parameters
----------
prefix : str
path prefix of saved model files. You should have
"prefix-symbol.json", "prefix-xxxx.params", and
optionally "prefix-xxxx.states", where xxxx is the
epoch number.
epoch : int
epoch to load.
load_optimizer_states : bool
whether to load optimizer states. Checkpoint needs
to have been made with save_optimizer_states=True.
data_names : list of str
Default is `('data')` for a typical model used in image classification.
label_names : list of str
Default is `('softmax_label')` for a typical model used in image
classification.
logger : Logger
Default is `logging`.
context : Context or list of Context
Default is `cpu()`.
work_load_list : list of number
Default `None`, indicating uniform workload.
fixed_param_names: list of str
Default `None`, indicating no network parameters are fixed.
"""
sym, args, auxs = load_checkpoint(prefix, epoch)
mod = Module(symbol=sym, **kwargs)
mod._arg_params = args
mod._aux_params = auxs
mod.params_initialized = True
if load_optimizer_states:
mod._preload_opt_states = '%s-%04d.states'%(prefix, epoch)
return mod
Example #25
| Source File: module.py From Decoupled-Classification-Refinement with MIT License | 5 votes |
|
def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True):
"""Assign parameter and aux state values.
Parameters
----------
arg_params : dict
Dictionary of name to value (`NDArray`) mapping.
aux_params : dict
Dictionary of name to value (`NDArray`) mapping.
allow_missing : bool
If true, params could contain missing values, and the initializer will be
called to fill those missing params.
force_init : bool
If true, will force re-initialize even if already initialized.
Examples
--------
An example of setting module parameters::
>>> sym, arg_params, aux_params = \
>>> mx.model.load_checkpoint(model_prefix, n_epoch_load)
>>> mod.set_params(arg_params=arg_params, aux_params=aux_params)
"""
if not allow_missing:
self.init_params(initializer=None, arg_params=arg_params, aux_params=aux_params,
allow_missing=allow_missing, force_init=force_init)
return
if self.params_initialized and not force_init:
warnings.warn("Parameters already initialized and force_init=False. "
"set_params call ignored.", stacklevel=2)
return
self._exec_group.set_params(arg_params, aux_params)
# because we didn't update self._arg_params, they are dirty now.
self._params_dirty = True
self.params_initialized = True
Example #26
| Source File: module.py From Decoupled-Classification-Refinement with MIT License | 5 votes |
|
def load(prefix, epoch, load_optimizer_states=False, **kwargs):
"""Create a model from previously saved checkpoint.
Parameters
----------
prefix : str
path prefix of saved model files. You should have
"prefix-symbol.json", "prefix-xxxx.params", and
optionally "prefix-xxxx.states", where xxxx is the
epoch number.
epoch : int
epoch to load.
load_optimizer_states : bool
whether to load optimizer states. Checkpoint needs
to have been made with save_optimizer_states=True.
data_names : list of str
Default is `('data')` for a typical model used in image classification.
label_names : list of str
Default is `('softmax_label')` for a typical model used in image
classification.
logger : Logger
Default is `logging`.
context : Context or list of Context
Default is `cpu()`.
work_load_list : list of number
Default `None`, indicating uniform workload.
fixed_param_names: list of str
Default `None`, indicating no network parameters are fixed.
"""
sym, args, auxs = load_checkpoint(prefix, epoch)
mod = Module(symbol=sym, **kwargs)
mod._arg_params = args
mod._aux_params = auxs
mod.params_initialized = True
if load_optimizer_states:
mod._preload_opt_states = '%s-%04d.states'%(prefix, epoch)
return mod
Example #27
| Source File: module.py From Decoupled-Classification-Refinement with MIT License | 5 votes |
|
def load(prefix, epoch, load_optimizer_states=False, **kwargs):
"""Create a model from previously saved checkpoint.
Parameters
----------
prefix : str
path prefix of saved model files. You should have
"prefix-symbol.json", "prefix-xxxx.params", and
optionally "prefix-xxxx.states", where xxxx is the
epoch number.
epoch : int
epoch to load.
load_optimizer_states : bool
whether to load optimizer states. Checkpoint needs
to have been made with save_optimizer_states=True.
data_names : list of str
Default is `('data')` for a typical model used in image classification.
label_names : list of str
Default is `('softmax_label')` for a typical model used in image
classification.
logger : Logger
Default is `logging`.
context : Context or list of Context
Default is `cpu()`.
work_load_list : list of number
Default `None`, indicating uniform workload.
fixed_param_names: list of str
Default `None`, indicating no network parameters are fixed.
"""
sym, args, auxs = load_checkpoint(prefix, epoch)
mod = Module(symbol=sym, **kwargs)
mod._arg_params = args
mod._aux_params = auxs
mod.params_initialized = True
if load_optimizer_states:
mod._preload_opt_states = '%s-%04d.states'%(prefix, epoch)
return mod
Example #28
| Source File: module.py From Decoupled-Classification-Refinement with MIT License | 5 votes |
|
def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True):
"""Assign parameter and aux state values.
Parameters
----------
arg_params : dict
Dictionary of name to value (`NDArray`) mapping.
aux_params : dict
Dictionary of name to value (`NDArray`) mapping.
allow_missing : bool
If true, params could contain missing values, and the initializer will be
called to fill those missing params.
force_init : bool
If true, will force re-initialize even if already initialized.
Examples
--------
An example of setting module parameters::
>>> sym, arg_params, aux_params = \
>>> mx.model.load_checkpoint(model_prefix, n_epoch_load)
>>> mod.set_params(arg_params=arg_params, aux_params=aux_params)
"""
if not allow_missing:
self.init_params(initializer=None, arg_params=arg_params, aux_params=aux_params,
allow_missing=allow_missing, force_init=force_init)
return
if self.params_initialized and not force_init:
warnings.warn("Parameters already initialized and force_init=False. "
"set_params call ignored.", stacklevel=2)
return
self._exec_group.set_params(arg_params, aux_params)
# because we didn't update self._arg_params, they are dirty now.
self._params_dirty = True
self.params_initialized = True
Example #29
| Source File: custom_module.py From SNIPER-mxnet with Apache License 2.0 | 5 votes |
|
def load(prefix, epoch, load_optimizer_states=False, **kwargs):
"""Creates a model from previously saved checkpoint.
Parameters
----------
prefix : str
path prefix of saved model files. You should have
"prefix-symbol.json", "prefix-xxxx.params", and
optionally "prefix-xxxx.states", where xxxx is the
epoch number.
epoch : int
epoch to load.
load_optimizer_states : bool
whether to load optimizer states. Checkpoint needs
to have been made with save_optimizer_states=True.
data_names : list of str
Default is `('data')` for a typical model used in image classification.
label_names : list of str
Default is `('softmax_label')` for a typical model used in image
classification.
logger : Logger
Default is `logging`.
context : Context or list of Context
Default is ``cpu()``.
work_load_list : list of number
Default ``None``, indicating uniform workload.
fixed_param_names: list of str
Default ``None``, indicating no network parameters are fixed.
"""
sym, args, auxs = load_checkpoint(prefix, epoch)
mod = CustomModule(symbol=sym, **kwargs)
mod._arg_params = args
mod._aux_params = auxs
mod.params_initialized = True
if load_optimizer_states:
mod._preload_opt_states = '%s-%04d.states'%(prefix, epoch)
return mod
Example #30
| Source File: module.py From kaggle-rsna18 with MIT License | 5 votes |
|
def set_params(self, arg_params, aux_params, allow_missing=False, force_init=True):
"""Assign parameter and aux state values.
Parameters
----------
arg_params : dict
Dictionary of name to value (`NDArray`) mapping.
aux_params : dict
Dictionary of name to value (`NDArray`) mapping.
allow_missing : bool
If true, params could contain missing values, and the initializer will be
called to fill those missing params.
force_init : bool
If true, will force re-initialize even if already initialized.
Examples
--------
An example of setting module parameters::
>>> sym, arg_params, aux_params = \
>>> mx.model.load_checkpoint(model_prefix, n_epoch_load)
>>> mod.set_params(arg_params=arg_params, aux_params=aux_params)
"""
if not allow_missing:
self.init_params(initializer=None, arg_params=arg_params, aux_params=aux_params,
allow_missing=allow_missing, force_init=force_init)
return
if self.params_initialized and not force_init:
warnings.warn("Parameters already initialized and force_init=False. "
"set_params call ignored.", stacklevel=2)
return
self._exec_group.set_params(arg_params, aux_params)
# because we didn't update self._arg_params, they are dirty now.
self._params_dirty = True
self.params_initialized = True
