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import itertools
from omegaconf import OmegaConf
def bifpn_config(min_level, max_level, weight_method=None):
"""BiFPN config.
Adapted from https://github.com/google/automl/blob/56815c9986ffd4b508fe1d68508e268d129715c1/efficientdet/keras/fpn_configs.py
"""
p = OmegaConf.create()
weight_method = weight_method or 'fastattn'
num_levels = max_level - min_level + 1
node_ids = {min_level + i: [i] for i in range(num_levels)}
level_last_id = lambda level: node_ids[level][-1]
level_all_ids = lambda level: node_ids[level]
id_cnt = itertools.count(num_levels)
p.nodes = []
for i in range(max_level - 1, min_level - 1, -1):
# top-down path.
p.nodes.append({
'reduction': 1 << i,
'inputs_offsets': [level_last_id(i), level_last_id(i + 1)],
'weight_method': weight_method,
})
node_ids[i].append(next(id_cnt))
for i in range(min_level + 1, max_level + 1):
# bottom-up path.
p.nodes.append({
'reduction': 1 << i,
'inputs_offsets': level_all_ids(i) + [level_last_id(i - 1)],
'weight_method': weight_method,
})
node_ids[i].append(next(id_cnt))
return p
def panfpn_config(min_level, max_level, weight_method=None):
"""PAN FPN config.
This defines FPN layout from Path Aggregation Networks as an alternate to
BiFPN, it does not implement the full PAN spec.
Paper: https://arxiv.org/abs/1803.01534
"""
p = OmegaConf.create()
weight_method = weight_method or 'fastattn'
num_levels = max_level - min_level + 1
node_ids = {min_level + i: [i] for i in range(num_levels)}
level_last_id = lambda level: node_ids[level][-1]
id_cnt = itertools.count(num_levels)
p.nodes = []
for i in range(max_level, min_level - 1, -1):
# top-down path.
offsets = [level_last_id(i), level_last_id(i + 1)] if i != max_level else [level_last_id(i)]
p.nodes.append({
'reduction': 1 << i,
'inputs_offsets': offsets,
'weight_method': weight_method,
})
node_ids[i].append(next(id_cnt))
for i in range(min_level, max_level + 1):
# bottom-up path.
offsets = [level_last_id(i), level_last_id(i - 1)] if i != min_level else [level_last_id(i)]
p.nodes.append({
'reduction': 1 << i,
'inputs_offsets': offsets,
'weight_method': weight_method,
})
node_ids[i].append(next(id_cnt))
return p
def qufpn_config(min_level, max_level, weight_method=None):
"""A dynamic quad fpn config that can adapt to different min/max levels.
It extends the idea of BiFPN, and has four paths:
(up_down -> bottom_up) + (bottom_up -> up_down).
Paper: https://ieeexplore.ieee.org/document/9225379
Ref code: From contribution to TF EfficientDet
https://github.com/google/automl/blob/eb74c6739382e9444817d2ad97c4582dbe9a9020/efficientdet/keras/fpn_configs.py
"""
p = OmegaConf.create()
weight_method = weight_method or 'fastattn'
quad_method = 'fastattn'
num_levels = max_level - min_level + 1
node_ids = {min_level + i: [i] for i in range(num_levels)}
level_last_id = lambda level: node_ids[level][-1]
level_all_ids = lambda level: node_ids[level]
level_first_id = lambda level: node_ids[level][0]
id_cnt = itertools.count(num_levels)
p.nodes = []
for i in range(max_level - 1, min_level - 1, -1):
# top-down path 1.
p.nodes.append({
'reduction': 1 << i,
'inputs_offsets': [level_last_id(i), level_last_id(i + 1)],
'weight_method': weight_method
})
node_ids[i].append(next(id_cnt))
node_ids[max_level].append(node_ids[max_level][-1])
for i in range(min_level + 1, max_level):
# bottom-up path 2.
p.nodes.append({
'reduction': 1 << i,
'inputs_offsets': level_all_ids(i) + [level_last_id(i - 1)],
'weight_method': weight_method
})
node_ids[i].append(next(id_cnt))
i = max_level
p.nodes.append({
'reduction': 1 << i,
'inputs_offsets': [level_first_id(i)] + [level_last_id(i - 1)],
'weight_method': weight_method
})
node_ids[i].append(next(id_cnt))
node_ids[min_level].append(node_ids[min_level][-1])
for i in range(min_level + 1, max_level + 1, 1):
# bottom-up path 3.
p.nodes.append({
'reduction': 1 << i,
'inputs_offsets': [
level_first_id(i), level_last_id(i - 1) if i != min_level + 1 else level_first_id(i - 1)],
'weight_method': weight_method
})
node_ids[i].append(next(id_cnt))
node_ids[min_level].append(node_ids[min_level][-1])
for i in range(max_level - 1, min_level, -1):
# top-down path 4.
p.nodes.append({
'reduction': 1 << i,
'inputs_offsets': [node_ids[i][0]] + [node_ids[i][-1]] + [level_last_id(i + 1)],
'weight_method': weight_method
})
node_ids[i].append(next(id_cnt))
i = min_level
p.nodes.append({
'reduction': 1 << i,
'inputs_offsets': [node_ids[i][0]] + [level_last_id(i + 1)],
'weight_method': weight_method
})
node_ids[i].append(next(id_cnt))
node_ids[max_level].append(node_ids[max_level][-1])
# NOTE: the order of the quad path is reversed from the original, my code expects the output of
# each FPN repeat to be same as input from backbone, in order of increasing reductions
for i in range(min_level, max_level + 1):
# quad-add path.
p.nodes.append({
'reduction': 1 << i,
'inputs_offsets': [node_ids[i][2], node_ids[i][4]],
'weight_method': quad_method
})
node_ids[i].append(next(id_cnt))
return p
def get_fpn_config(fpn_name, min_level=3, max_level=7):
if not fpn_name:
fpn_name = 'bifpn_fa'
name_to_config = {
'bifpn_sum': bifpn_config(min_level=min_level, max_level=max_level, weight_method='sum'),
'bifpn_attn': bifpn_config(min_level=min_level, max_level=max_level, weight_method='attn'),
'bifpn_fa': bifpn_config(min_level=min_level, max_level=max_level, weight_method='fastattn'),
'pan_sum': panfpn_config(min_level=min_level, max_level=max_level, weight_method='sum'),
'pan_fa': panfpn_config(min_level=min_level, max_level=max_level, weight_method='fastattn'),
'qufpn_sum': qufpn_config(min_level=min_level, max_level=max_level, weight_method='sum'),
'qufpn_fa': qufpn_config(min_level=min_level, max_level=max_level, weight_method='fastattn'),
}
return name_to_config[fpn_name]
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