PulseFocusPlatform/ppdet/modeling/backbones/name_adapter.py

70 lines
2.3 KiB
Python

class NameAdapter(object):
"""Fix the backbones variable names for pretrained weight"""
def __init__(self, model):
super(NameAdapter, self).__init__()
self.model = model
@property
def model_type(self):
return getattr(self.model, '_model_type', '')
@property
def variant(self):
return getattr(self.model, 'variant', '')
def fix_conv_norm_name(self, name):
if name == "conv1":
bn_name = "bn_" + name
else:
bn_name = "bn" + name[3:]
# the naming rule is same as pretrained weight
if self.model_type == 'SEResNeXt':
bn_name = name + "_bn"
return bn_name
def fix_shortcut_name(self, name):
if self.model_type == 'SEResNeXt':
name = 'conv' + name + '_prj'
return name
def fix_bottleneck_name(self, name):
if self.model_type == 'SEResNeXt':
conv_name1 = 'conv' + name + '_x1'
conv_name2 = 'conv' + name + '_x2'
conv_name3 = 'conv' + name + '_x3'
shortcut_name = name
else:
conv_name1 = name + "_branch2a"
conv_name2 = name + "_branch2b"
conv_name3 = name + "_branch2c"
shortcut_name = name + "_branch1"
return conv_name1, conv_name2, conv_name3, shortcut_name
def fix_basicblock_name(self, name):
if self.model_type == 'SEResNeXt':
conv_name1 = 'conv' + name + '_x1'
conv_name2 = 'conv' + name + '_x2'
shortcut_name = name
else:
conv_name1 = name + "_branch2a"
conv_name2 = name + "_branch2b"
shortcut_name = name + "_branch1"
return conv_name1, conv_name2, shortcut_name
def fix_layer_warp_name(self, stage_num, count, i):
name = 'res' + str(stage_num)
if count > 10 and stage_num == 4:
if i == 0:
conv_name = name + "a"
else:
conv_name = name + "b" + str(i)
else:
conv_name = name + chr(ord("a") + i)
if self.model_type == 'SEResNeXt':
conv_name = str(stage_num + 2) + '_' + str(i + 1)
return conv_name
def fix_c1_stage_name(self):
return "res_conv1" if self.model_type == 'ResNeXt' else "conv1"