File size: 4,196 Bytes
f3507ef | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | # Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Configurations for loading checkpoints."""
import dataclasses
from typing import Dict, Optional
import numpy as np
from official.projects.centernet.utils.checkpoints import config_classes
Conv2DBNCFG = config_classes.Conv2DBNCFG
HeadConvCFG = config_classes.HeadConvCFG
ResidualBlockCFG = config_classes.ResidualBlockCFG
HourglassCFG = config_classes.HourglassCFG
@dataclasses.dataclass
class BackboneConfigData:
"""Backbone Config."""
weights_dict: Optional[Dict[str, np.ndarray]] = dataclasses.field(
repr=False, default=None)
def get_cfg_list(self, name):
"""Get list of block configs for the module."""
if name == 'hourglass104_512':
return [
# Downsampling Layers
Conv2DBNCFG(
weights_dict=self.weights_dict['downsample_input']['conv_block']),
ResidualBlockCFG(
weights_dict=self.weights_dict['downsample_input'][
'residual_block']),
# Hourglass
HourglassCFG(
weights_dict=self.weights_dict['hourglass_network']['0']),
Conv2DBNCFG(
weights_dict=self.weights_dict['output_conv']['0']),
# Intermediate
Conv2DBNCFG(
weights_dict=self.weights_dict['intermediate_conv1']['0']),
Conv2DBNCFG(
weights_dict=self.weights_dict['intermediate_conv2']['0']),
ResidualBlockCFG(
weights_dict=self.weights_dict['intermediate_residual']['0']),
# Hourglass
HourglassCFG(
weights_dict=self.weights_dict['hourglass_network']['1']),
Conv2DBNCFG(
weights_dict=self.weights_dict['output_conv']['1']),
]
elif name == 'extremenet':
return [
# Downsampling Layers
Conv2DBNCFG(
weights_dict=self.weights_dict['downsample_input']['conv_block']),
ResidualBlockCFG(
weights_dict=self.weights_dict['downsample_input'][
'residual_block']),
# Hourglass
HourglassCFG(
weights_dict=self.weights_dict['hourglass_network']['0']),
Conv2DBNCFG(
weights_dict=self.weights_dict['output_conv']['0']),
# Intermediate
Conv2DBNCFG(
weights_dict=self.weights_dict['intermediate_conv1']['0']),
Conv2DBNCFG(
weights_dict=self.weights_dict['intermediate_conv2']['0']),
ResidualBlockCFG(
weights_dict=self.weights_dict['intermediate_residual']['0']),
# Hourglass
HourglassCFG(
weights_dict=self.weights_dict['hourglass_network']['1']),
Conv2DBNCFG(
weights_dict=self.weights_dict['output_conv']['1']),
]
@dataclasses.dataclass
class HeadConfigData:
"""Head Config."""
weights_dict: Optional[Dict[str, np.ndarray]] = dataclasses.field(
repr=False, default=None)
def get_cfg_list(self, name):
if name == 'detection_2d':
return [
HeadConvCFG(weights_dict=self.weights_dict['object_center']['0']),
HeadConvCFG(weights_dict=self.weights_dict['object_center']['1']),
HeadConvCFG(weights_dict=self.weights_dict['box.Soffset']['0']),
HeadConvCFG(weights_dict=self.weights_dict['box.Soffset']['1']),
HeadConvCFG(weights_dict=self.weights_dict['box.Sscale']['0']),
HeadConvCFG(weights_dict=self.weights_dict['box.Sscale']['1'])
]
|