megengine.module.Pad¶
- class Pad(pad_witdth, mode='constant', constant_val=0.0)[源代码]¶
Pad is python warpper for padding opr in megbrain, can padding in random one of the max 7 dimensions. Supported constant, edge(replicate) and reflect mode, constatnt is the default mode.
Methods
apply(fn)Applies function
fnto all the modules within this module, including itself.buffers([recursive])Returns an iterable for the buffers of the module.
children(**kwargs)Returns an iterable for all the submodules that are direct attributes of this module.
disable_quantize([value])Sets
module'squantize_disabledattribute and returnmodule.eval()Sets training mode of all the modules within this module (including itself) to
False.forward(src)load_state_dict(state_dict[, strict])Loads a given dictionary created by
state_dictinto this module.modules(**kwargs)Returns an iterable for all the modules within this module, including itself.
named_buffers([prefix, recursive])Returns an iterable for key buffer pairs of the module, where
keyis the dotted path from this module to the buffer.named_children(**kwargs)Returns an iterable of key-submodule pairs for all the submodules that are direct attributes of this module, where 'key' is the attribute name of submodules.
named_modules([prefix])Returns an iterable of key-module pairs for all the modules within this module, including itself, where 'key' is the dotted path from this module to the submodules.
named_parameters([prefix, recursive])Returns an iterable for key
Parameterpairs of the module, wherekeyis the dotted path from this module to theParameter.named_tensors([prefix, recursive])Returns an iterable for key tensor pairs of the module, where
keyis the dotted path from this module to the tensor.parameters([recursive])Returns an iterable for the
Parameterof the module.register_forward_hook(hook)Registers a hook to handle forward results.
Registers a hook to handle forward inputs.
replace_param(params, start_pos[, seen])Replaces module's parameters with
params, used byParamPacktostate_dict([rst, prefix, keep_var])tensors([recursive])Returns an iterable for the
Tensorof the module.train([mode, recursive])Sets training mode of all the modules within this module (including itself) to
mode.Sets all parameters' grads to zero