new_key = old_key.replace("conv2.{}".format(param),
"conv2.conv.{}".format(param))
logger.info("pattern: {}, old_key: {}, new_key: {}".format(
pattern, old_key, new_key))
state_dict[new_key] = state_dict[old_key]
del state_dict[old_key]
return state_dict
_C2_STAGE_NAMES = {
"R-50": ["1.2", "2.3", "3.5", "4.2"],
"R-101": ["1.2", "2.3", "3.22", "4.2"],
"R-152": ["1.2", "2.7", "3.35", "4.2"],
}
C2_FORMAT_LOADER = Registry()
@C2_FORMAT_LOADER.register("R-50-C4")
@C2_FORMAT_LOADER.register("R-50-C5")
@C2_FORMAT_LOADER.register("R-101-C4")
@C2_FORMAT_LOADER.register("R-101-C5")
@C2_FORMAT_LOADER.register("R-50-FPN")
@C2_FORMAT_LOADER.register("R-50-FPN-RETINANET")
@C2_FORMAT_LOADER.register("R-101-FPN")
@C2_FORMAT_LOADER.register("R-101-FPN-RETINANET")
@C2_FORMAT_LOADER.register("R-152-FPN")
def load_resnet_c2_format(cfg, f):
state_dict = _load_c2_pickled_weights(f)
conv_body = cfg.MODEL.BACKBONE.CONV_BODY
arch = conv_body.replace("-C4", "").replace("-C5", "").replace("-FPN", "")
'block_per_stage': [1, 1, 4, 3]
}
VoVNet93FPNStagesTo5 = {
'config_stage_ch': [128, 160, 192, 224],
'config_concat_ch': [256, 512, 768, 1024],
'layer_per_block': 5,
'block_per_stage': [1, 3, 8, 3]
}
_STAGE_SPECS = Registry({
"V-27-FPN": VoVNet27FPNStagesTo5,
"V-39-FPN": VoVNet39FPNStagesTo5,
"V-57-FPN": VoVNet57FPNStagesTo5,
"V-93-FPN": VoVNet93FPNStagesTo5,
"V-27-FPN-RETINANET": VoVNet27FPNStagesTo5,
"V-39-FPN-RETINANET": VoVNet39FPNStagesTo5,
"V-57-FPN-RETINANET": VoVNet57FPNStagesTo5,
"V-93-FPN-RETINANET": VoVNet93FPNStagesTo5
})
def freeze_bn_params(m):
"""Freeze all the weights by setting requires_grad to False
"""
m.eval()
for p in m.parameters():
p.requires_grad = False
def conv3x3(in_channels, out_channels, module_name, postfix, stride=1, groups=1, kernel_size=3, padding=1):
"""3x3 convolution with padding"""
return [
self).__init__(cfg, norm_func=FrozenBatchNorm2d)
class StemWithBatchNorm(BaseStem):
def __init__(self, cfg):
super(StemWithBatchNorm, self).__init__(cfg, norm_func=nn.BatchNorm2d)
class StemWithGN(BaseStem):
def __init__(self, cfg):
super(StemWithGN, self).__init__(cfg, norm_func=group_norm)
_TRANSFORMATION_MODULES = Registry({
"BottleneckWithFixedBatchNorm": BottleneckWithFixedBatchNorm,
"BottleneckWithBatchNorm": BottleneckWithBatchNorm,
"BottleneckWithGN": BottleneckWithGN,
})
_STEM_MODULES = Registry({
"StemWithFixedBatchNorm": StemWithFixedBatchNorm,
"StemWithBatchNorm": StemWithBatchNorm,
"StemWithGN": StemWithGN,
})
_STAGE_SPECS = Registry({
"R-50-C4": ResNet50StagesTo4,
"R-50-C5": ResNet50StagesTo5,
"R-101-C4": ResNet101StagesTo4,
"R-101-C5": ResNet101StagesTo5,
"R-50-FPN": ResNet50FPNStagesTo5,
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved. from fcos_core.utils.registry import Registry BACKBONES = Registry() RPN_HEADS = Registry() ROI_BOX_FEATURE_EXTRACTORS = Registry() ROI_BOX_PREDICTOR = Registry() ROI_KEYPOINT_FEATURE_EXTRACTORS = Registry() ROI_KEYPOINT_PREDICTOR = Registry() ROI_MASK_FEATURE_EXTRACTORS = Registry() ROI_MASK_PREDICTOR = Registry()
