Commit 29b546d0 authored by zhangwenwei's avatar zhangwenwei
Browse files

Add numba requirements and doc

parent 6d71b439
...@@ -7,28 +7,22 @@ from .cocktail_optimizer import CocktailOptimizer ...@@ -7,28 +7,22 @@ from .cocktail_optimizer import CocktailOptimizer
@OPTIMIZER_BUILDERS.register_module @OPTIMIZER_BUILDERS.register_module
class CocktailOptimizerConstructor(object): class CocktailOptimizerConstructor(object):
"""Default constructor for optimizers. """Special constructor for cocktail optimizers.
This constructor constructs cocktail optimizer for multi-modality
detectors. It builds separate optimizers for separate branchs for
different modalities. More details can be found in the ECCV submission
(to be release).
Attributes: Attributes:
model (:obj:`nn.Module`): The model with parameters to be optimized. model (:obj:`nn.Module`): The model with parameters to be optimized.
optimizer_cfg (dict): The config dict of the optimizer. optimizer_cfg (dict): The config dict of the optimizer. The keys of
Positional fields are: the dict are used to search for the corresponding keys in the
- type: class name of the optimizer. model, and the value if a dict that really defines the optimizer.
- lr: base learning rate. See example below for the usage.
Optional fields are: paramwise_cfg (dict): The dict for paramwise options. This is not
- any arguments of the corresponding optimizer type, e.g., supported in the current version. But it should be supported in
weight_decay, momentum, etc. the future release.
paramwise_cfg (dict, optional): Parameter-wise options. Accepted fields
are:
- bias_lr_mult: It will be multiplied to the learning rate for
all bias parameters (except for those in normalization layers).
- bias_decay_mult: It will be multiplied to the weight decay for
all bias parameters (except for those in normalization layers and
depthwise conv layers).
- norm_decay_mult: will be multiplied to the weight decay
for all weight and bias parameters of normalization layers.
- dwconv_decay_mult: will be multiplied to the weight decay
for all weight and bias parameters of depthwise conv layers.
Example: Example:
>>> import torch >>> import torch
......
matplotlib matplotlib
mmcv>=0.2.15 mmcv>=0.2.15
numba>=0.45.1
numpy numpy
# need older pillow until torchvision is fixed # need older pillow until torchvision is fixed
Pillow<=6.2.2 Pillow<=6.2.2
......
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