FM / modeling

/usr/local/lib64/python3.6/site-packages/caffe2/python/modeling/

/usr/local/lib64/python3.6/site-packages/caffe2/python/modeling

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
compute_histogram_for_blobs.py	3508	0644	edit dl rm
compute_histogram_for_blobs_test.py	4947	0644	edit dl rm
compute_norm_for_blobs.py	3642	0644	edit dl rm
compute_norm_for_blobs_test.py	7942	0644	edit dl rm
compute_statistics_for_blobs.py	1909	0644	edit dl rm
compute_statistics_for_blobs_test.py	2870	0644	edit dl rm
get_entry_from_blobs.py	3130	0644	edit dl rm
get_entry_from_blobs_test.py	3538	0644	edit dl rm
gradient_clipping.py	5766	0644	edit dl rm
gradient_clipping_test.py	10016	0644	edit dl rm
initializers.py	5378	0644	edit dl rm
initializers_test.py	2106	0644	edit dl rm
net_modifier.py	823	0644	edit dl rm
parameter_info.py	1438	0644	edit dl rm
parameter_sharing.py	3848	0644	edit dl rm
parameter_sharing_test.py	7223	0644	edit dl rm
__init__.py	0	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/modeling/parameter_info.py (1438B)

from caffe2.python import core

import numpy as np

class ParameterTags(object):
    BIAS = 'BIAS'
    WEIGHT = 'WEIGHT'
    COMPUTED_PARAM = 'COMPUTED_PARAM'

class ParameterInfo(object):

def __init__(
            self, param_id, param, key=None, shape=None, length=None,
            grad=None, blob_copy=None):
        assert isinstance(param, core.BlobReference)
        self.param_id = param_id
        self.name = str(param)
        self.blob = param
        self.key = key
        self.shape = shape
        self.size = None if shape is None else np.prod(shape)
        self.length = max(1, length if length is not None else 1)
        self.grad = grad
        self._cloned_init_net = None
        # Optionally store equivalent copies of the blob
        # in different precisions (i.e. half and float copies)
        # stored as a dict of TensorProto.DataType -> BlobReference
        self.blob_copy = blob_copy
        # each param_info can have its own optimizer. It can be set within
        # OptimizerContext (caffe2/python/optimizer.py)
        self._optimizer = None

@property
    def parameter(self):
        return self.blob

@property
    def optimizer(self):
        return self._optimizer

@optimizer.setter
    def optimizer(self, value):
        assert self._optimizer is None, "optimizer has already been set"
        self._optimizer = value

def __str__(self):
        return self.name

from caffe2.python import core import numpy as np class ParameterTags(object): BIAS = 'BIAS' WEIGHT = 'WEIGHT' COMPUTED_PARAM = 'COMPUTED_PARAM' class ParameterInfo(object): def __init__( self, param_id, param, key=None, shape=None, length=None, grad=None, blob_copy=None): assert isinstance(param, core.BlobReference) self.param_id = param_id self.name = str(param) self.blob = param self.key = key self.shape = shape self.size = None if shape is None else np.prod(shape) self.length = max(1, length if length is not None else 1) self.grad = grad self._cloned_init_net = None # Optionally store equivalent copies of the blob # in different precisions (i.e. half and float copies) # stored as a dict of TensorProto.DataType -> BlobReference self.blob_copy = blob_copy # each param_info can have its own optimizer. It can be set within # OptimizerContext (caffe2/python/optimizer.py) self._optimizer = None @property def parameter(self): return self.blob @property def optimizer(self): return self._optimizer @optimizer.setter def optimizer(self, value): assert self._optimizer is None, "optimizer has already been set" self._optimizer = value def __str__(self): return self.name