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ClassPrioritySampler.py
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ClassPrioritySampler.py
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"""Copyright (c) Facebook, Inc. and its affiliates.
All rights reserved.
This source code is licensed under the license found in the
LICENSE file in the root directory of this source tree.
"""
import random
import numpy as np
from torch.utils.data.sampler import Sampler
class RandomCycleIter:
def __init__ (self, data, test_mode=False):
self.data_list = list(data)
self.length = len(self.data_list)
self.i = self.length - 1
self.test_mode = test_mode
def __iter__ (self):
return self
def __next__ (self):
self.i += 1
if self.i == self.length:
self.i = 0
if not self.test_mode:
random.shuffle(self.data_list)
return self.data_list[self.i]
class PriorityTree(object):
def __init__(self, capacity, init_weights, fixed_weights=None, fixed_scale=1.0,
alpha=1.0):
"""
fixed_weights: weights that wont be updated by self.update()
"""
assert fixed_weights is None or len(fixed_weights) == capacity
assert len(init_weights) == capacity
self.alpha = alpha
self._capacity = capacity
self._tree_size = 2 * capacity - 1
self.fixed_scale = fixed_scale
self.fixed_weights = np.zeros(self._capacity) if fixed_weights is None \
else fixed_weights
self.tree = np.zeros(self._tree_size)
self._initialized = False
self.initialize(init_weights)
def initialize(self, init_weights):
"""Initialize the tree."""
# Rescale the fixed_weights if it is not zero
self.fixed_scale_init = self.fixed_scale
if self.fixed_weights.sum() > 0 and init_weights.sum() > 0:
self.fixed_scale_init *= init_weights.sum() / self.fixed_weights.sum()
self.fixed_weights *= self.fixed_scale * init_weights.sum() \
/ self.fixed_weights.sum()
print('FixedWeights: {}'.format(self.fixed_weights.sum()))
self.update_whole(init_weights + self.fixed_weights)
self._initialized = True
def reset_adaptive_weights(self, adaptive_weights):
self.update_whole(self.fixed_weights + adaptive_weights)
def reset_fixed_weights(self, fixed_weights, rescale=False):
""" Reset the manually designed weights and
update the whole tree accordingly.
@rescale: rescale the fixed_weights such that
fixed_weights.sum() = self.fixed_scale * adaptive_weights.sum()
"""
adaptive_weights = self.get_adaptive_weights()
fixed_sum = fixed_weights.sum()
if rescale and fixed_sum > 0:
# Rescale fixedweight based on adaptive weights
scale = self.fixed_scale * adaptive_weights.sum() / fixed_sum
else:
# Rescale fixedweight based on previous fixedweight
scale = self.fixed_weights.sum() / fixed_sum
self.fixed_weights = fixed_weights * scale
self.update_whole(self.fixed_weights + adaptive_weights)
def update_whole(self, total_weights):
""" Update the whole tree based on per-example sampling weights """
if self.alpha != 1:
total_weights = np.power(total_weights, self.alpha)
lefti = self.pointer_to_treeidx(0)
righti = self.pointer_to_treeidx(self.capacity-1)
self.tree[lefti:righti+1] = total_weights
# Iteratively find a parent layer
while lefti != 0 and righti != 0:
lefti = (lefti - 1) // 2 if lefti != 0 else 0
righti = (righti - 1) // 2 if righti != 0 else 0
# Assign paraent weights from right to left
for i in range(righti, lefti-1, -1):
self.tree[i] = self.tree[2*i+1] + self.tree[2*i+2]
def get_adaptive_weights(self):
""" Get the instance-aware weights, that are not mannually designed"""
if self.alpha == 1:
return self.get_total_weights() - self.fixed_weights
else:
return self.get_raw_total_weights() - self.fixed_weights
def get_total_weights(self):
""" Get the per-example sampling weights
return shape: [capacity]
"""
lefti = self.pointer_to_treeidx(0)
righti = self.pointer_to_treeidx(self.capacity-1)
return self.tree[lefti:righti+1]
def get_raw_total_weights(self):
""" Get the per-example sampling weights
return shape: [capacity]
"""
lefti = self.pointer_to_treeidx(0)
righti = self.pointer_to_treeidx(self.capacity-1)
return np.power(self.tree[lefti:righti+1], 1/self.alpha)
@property
def size(self):
return self._tree_size
@property
def capacity(self):
return self._capacity
def __len__(self):
return self.capacity
def pointer_to_treeidx(self, pointer):
assert pointer < self.capacity
return int(pointer + self.capacity - 1)
def update(self, pointer, priority):
assert pointer < self.capacity
tree_idx = self.pointer_to_treeidx(pointer)
priority += self.fixed_weights[pointer]
if self.alpha != 1:
priority = np.power(priority, self.alpha)
delta = priority - self.tree[tree_idx]
self.tree[tree_idx] = priority
while tree_idx != 0:
tree_idx = (tree_idx - 1) // 2
self.tree[tree_idx] += delta
def update_delta(self, pointer, delta):
assert pointer < self.capacity
tree_idx = self.pointer_to_treeidx(pointer)
ratio = 1- self.fixed_weights[pointer] / self.tree[tree_idx]
# delta *= ratio
if self.alpha != 1:
# Update delta
if self.tree[tree_idx] < 0 or \
np.power(self.tree[tree_idx], 1/self.alpha) + delta < 0:
import pdb; pdb.set_trace()
delta = np.power(np.power(self.tree[tree_idx], 1/self.alpha) + delta,
self.alpha) \
- self.tree[tree_idx]
self.tree[tree_idx] += delta
while tree_idx != 0:
tree_idx = (tree_idx - 1) // 2
self.tree[tree_idx] += delta
def get_leaf(self, value):
assert self._initialized, 'PriorityTree not initialized!!!!'
assert self.total > 0, 'No priority weights setted!!'
parent = 0
while True:
left_child = 2 * parent + 1
right_child = 2 * parent + 2
if left_child >= len(self.tree):
tgt_leaf = parent
break
if value < self.tree[left_child]:
parent = left_child
else:
value -= self.tree[left_child]
parent = right_child
data_idx = tgt_leaf - self.capacity + 1
return data_idx, self.tree[tgt_leaf] # data idx, priority
@property
def total(self):
assert self._initialized, 'PriorityTree not initialized!!!!'
return self.tree[0]
@property
def max(self):
return np.max(self.tree[-self.capacity:])
@property
def min(self):
assert self._initialized, 'PriorityTree not initialized!!!!'
return np.min(self.tree[-self.capacity:])
def get_weights(self):
wdict = {'fixed_weights': self.fixed_weights,
'total_weights': self.get_total_weights()}
if self.alpha != 1:
wdict.update({'raw_total_weights': self.get_raw_total_weights(),
'alpha': self.alpha})
return wdict
class ClassPrioritySampler(Sampler):
"""
A sampler combining manually designed sampling strategy and prioritized
sampling strategy.
Manually disigned strategy contains two parts:
$$ manual_weights = lam * balanced_weights + (1-lam) uniform_weights
Here we use a generalized version of balanced weights as follows,
when n limits to infinity, balanced_weights = real_balanced_weights
$$ balanced_weights = uniform_weights ^ (1/n)
Then the balanced weights are scaled such that
$$ balanced_weights.sum() = balance_scale * uniform_weights.sum()
Note: above weights are per-class weights
Overall sampling weights are given as
$$ sampling_weights = manual_weights * fixed_scale + priority_weights
Arguments:
@dataset: A dataset
@balance_scale: The scale of balanced_weights
@lam: A weight to combine balanced weights and uniform weights
- None for shifting sampling
- 0 for uniform sampling
- 1 for balanced sampling
@fixed_scale: The scale of manually designed weights
- fixed_scale < 0 means, the manually designed distribution will
be used as the backend distribution of priorities.
@cycle: shifting strategy
- 0 for linear shifting: 3 -> 2 - > 1
- 1 for periodic shifting:
3 -> 2 - > 1 -> 3 -> 2 - > 1 -> 3 -> 2 - > 1
- 2 for cosine-like periodic shifting:
3 -> 2 - > 1 -> 1 -> 2 - > 3 -> 3 -> 2 - > 1
@nroot:
- None for truly balanced weights
- >= 2 for pseudo-balanced weights
@rescale: whether to rebalance the manual weights and priority weights
every epoch
@root_decay:
- 'exp': for exponential decay
- 'linear': for linear decay
"""
def __init__(self, dataset, balance_scale=1.0, fixed_scale=1.0,
lam=None, epochs=90, cycle=0, nroot=None, manual_only=False,
rescale=False, root_decay=None, decay_gap=30, ptype='score',
pri_mode='train', momentum=0., alpha=1.0):
"""
"""
self.dataset = dataset
self.balance_scale = balance_scale
self.fixed_scale = fixed_scale
self.epochs = epochs
self.lam = lam
self.cycle = cycle
self.nroot = nroot
self.rescale = rescale
self.manual_only = manual_only
self.root_decay = root_decay
self.decay_gap = decay_gap
self.ptype = ptype
self.pri_mode = pri_mode
self.num_samples = len(dataset)
self.manual_as_backend = False
self.momentum = momentum
self.alpha = alpha
assert 0. <= self.momentum <= 1.0
assert 0. <= self.alpha
# Change the backend distribution of priority if needed
if self.fixed_scale < 0:
self.fixed_scale = 0
self.manual_as_backend = True
# If using root_decay, reset relevent parameters
if self.root_decay in ['exp', 'linear', 'autoexp']:
self.lam = 1
self.manual_only = True
self.nroot = 1
if self.root_decay == 'autoexp':
self.decay_gap = 1
self.decay_factor = np.power(nroot, 1/(self.epochs-1))
else:
assert self.root_decay is None
assert self.nroot is None or self.nroot > 1
print("====> Decay GAP: {}".format(self.decay_gap))
# Take care of lambdas
self.freeze = True
if self.lam is None:
self.freeze = False
if cycle == 0:
self.lams = np.linspace(0, 1, epochs)
elif cycle == 1:
self.lams = np.concatenate([np.linspace(0,1,epochs//3)] * 3)
elif cycle == 2:
self.lams = np.concatenate([np.linspace(0,1,epochs//3),
np.linspace(0,1,epochs//3)[::-1],
np.linspace(0,1,epochs//3)])
else:
raise NotImplementedError(
'cycle = {} not implemented'.format(cycle))
else:
self.lams = [self.lam]
# Get num of samples per class
self.cls_cnts = []
self.labels = labels = np.array(self.dataset.labels)
for l in np.unique(labels):
self.cls_cnts.append(np.sum(labels==l))
self.num_classes = len(self.cls_cnts)
self.cnts = np.array(self.cls_cnts).astype(float)
# Get per-class image indexes
self.cls_idxs = [[] for _ in range(self.num_classes)]
for i, label in enumerate(self.dataset.labels):
self.cls_idxs[label].append(i)
self.data_iter_list = [RandomCycleIter(x) for x in self.cls_idxs]
for ci in range(self.num_classes):
self.cls_idxs[ci] = np.array(self.cls_idxs[ci])
# Build balanced weights based on class counts
self.balanced_weights = self.get_balanced_weights(self.nroot)
self.uniform_weights = self.get_uniform_weights()
self.manual_weights = self.get_manual_weights(self.lams[0])
# back_weights = self.get_balanced_weights(1.5)
back_weights = self.uniform_weights
# Calculate priority ratios that reshape priority into target distribution
self.per_cls_ratios = self.get_cls_ratios(
self.manual_weights if self.manual_as_backend else back_weights)
self.per_example_ratios = self.broadcast(self.per_cls_ratios)
# Setup priority tree
if self.ptype == 'score':
self.init_weight = 1.
elif self.ptype in ['CE', 'entropy']:
self.init_weight = 6.9
else:
raise NotImplementedError('ptype {} not implemented'.format(self.ptype))
if self.manual_only:
self.init_weight = 0.
self.per_example_uni_weights = np.ones(self.num_samples) * self.init_weight
self.per_example_velocities = np.zeros(self.num_samples)
# init_priorities = np.power(self.init_weight, self.alpha) \
# * self.uniform_weights * self.per_cls_ratios
init_priorities = self.init_weight * self.uniform_weights * self.per_cls_ratios
self.ptree = PriorityTree(self.num_classes, init_priorities,
self.manual_weights.copy(), fixed_scale=self.fixed_scale,
alpha=self.alpha)
def get_cls_ratios(self, tgt_weights):
if tgt_weights is self.uniform_weights:
return np.ones_like(self.uniform_weights)
per_cls_ratios = tgt_weights / self.uniform_weights
per_cls_ratios *= self.uniform_weights.sum() / tgt_weights.sum()
return per_cls_ratios
def get_cls_weights(self):
ratioed_ws = self.per_example_uni_weights * self.per_example_ratios
return self.debroadcast_sum(ratioed_ws)
def broadcast(self, per_cls_info):
per_exmaple_info = np.zeros(self.num_samples)
# Braodcast per-cls info to each example
for ci in range(self.num_classes):
per_exmaple_info[self.cls_idxs[ci]] = per_cls_info[ci]
return per_exmaple_info
def debroadcast_sum(self, per_example_info):
per_cls_info = np.zeros(self.num_classes)
# DeBraodcast per-example info to each cls by summation
for ci in range(self.num_classes):
per_cls_info[ci] = per_example_info[self.cls_idxs[ci]].sum()
return per_cls_info
def get_manual_weights(self, lam):
# Merge balanced weights and uniform weights
if lam == 1:
manual_weights = self.balanced_weights.copy()
elif lam == 0:
manual_weights = self.uniform_weights.copy()
else:
manual_weights = self.balanced_weights * lam + (1-lam) * self.uniform_weights
return manual_weights
def get_uniform_weights(self):
return self.cnts.copy()
def get_balanced_weights(self, nroot):
""" Calculate normalized generalized balanced weights """
cnts = self.cnts
if nroot is None:
# Real balanced sampling weights, each class has the same weights
# Un-normalized !!!
cls_ws = np.ones(len(cnts))
elif nroot >= 1:
# Generalized balanced weights
# Un-normalized !!!
cls_ws = cnts / cnts.sum()
cls_ws = np.power(cls_ws, 1./nroot) * cnts.sum()
cls_ws = cls_ws
else:
raise NotImplementedError('root:{} not implemented'.format(nroot))
# Get un-normalized weights
balanced_weights = cls_ws
# Normalization and rescale
balanced_weights *= self.num_samples / balanced_weights.sum() * \
self.balance_scale
return balanced_weights
def __iter__(self):
for _ in range(self.num_samples):
w = random.random() * self.ptree.total
ci, pri = self.ptree.get_leaf(w)
yield next(self.data_iter_list[ci])
def __len__(self):
return self.num_samples
def reset_weights(self, epoch):
# If it is linear shifting
if not self.freeze:
e = np.clip(epoch, 0, self.epochs-1)
self.manual_weights = self.get_manual_weights(self.lams[e])
# make sure 'self.fixed_scale > 0' and 'self.manual_as_backend = True' are
# mutually exclusive
if self.fixed_scale > 0:
self.ptree.reset_fixed_weights(self.manual_weights, self.rescale)
if self.manual_as_backend:
self.update_backend_distribution(self.manual_weights)
# If it is root decay
if self.root_decay in ['exp', 'linear', 'autoexp'] and epoch % self.decay_gap == 0:
if self.root_decay == 'exp':
self.nroot *= 2
elif self.root_decay == 'linear':
self.nroot += 1
elif self.root_decay == 'autoexp':
# self.nroot *= self.decay_factor
self.nroot = np.power(self.decay_factor, epoch)
bw = self.get_balanced_weights(self.nroot)
if self.manual_as_backend:
self.update_backend_distribution(bw)
else:
self.ptree.reset_fixed_weights(bw)
def update_backend_distribution(self, tgt_weights):
# Recalculate the cls ratios based on the given target distribution
self.per_cls_ratios = self.get_cls_ratios(tgt_weights)
self.per_example_ratios = self.broadcast(self.per_cls_ratios)
# Recalculate the new per-class weights based on the new ratios
# new_backend_weights = self.init_weight * self.uniform_weights * self.per_cls_ratios
new_cls_weights = self.get_cls_weights()
self.ptree.reset_adaptive_weights(new_cls_weights)
def update_weights(self, inds, weights, labels):
""" Update priority weights """
if not self.manual_only and self.pri_mode == 'train':
weights = np.clip(weights, 0, self.init_weight)
# Iterate over all classes in the batch
for l in np.unique(labels):
# Calculate per-class delta weights
example_inds = inds[labels==l]
last_weights = self.per_example_uni_weights[example_inds]
# delta = np.power(weights[labels==l], self.alpha) - \
# np.power(last_weights, self.alpha)
delta = weights[labels==l] - last_weights
delta = self.momentum * self.per_example_velocities[example_inds] + \
(1-self.momentum) * delta
# Update velocities
self.per_example_velocities[example_inds] = delta
# Update per-example weights
# self.per_example_uni_weights[example_inds] = weights[labels==l]
self.per_example_uni_weights[example_inds] += delta
# Sacle the delta
# (ie, the per-example weights both before and after update)
delta *= self.per_example_ratios[example_inds]
# Update tree
if self.alpha == 1:
self.ptree.update_delta(l, delta.sum())
else:
self.ptree.update(l, self.per_example_uni_weights[self.cls_idxs[l]].sum())
def reset_priority(self, weights, labels):
if self.pri_mode == 'valid':
assert len(np.unique(labels)) == self.num_classes
weights = np.clip(weights, 0, self.init_weight)
cls_weights = np.zeros(self.num_classes)
for c in np.unique(labels):
cls_weights[c] = weights[labels==c].mean()
cls_weights *= self.cnts
cls_weights *= self.per_cls_ratios
self.ptree.reset_adaptive_weights(cls_weights)
def get_weights(self):
return self.ptree.get_weights()
def get_sampler():
return ClassPrioritySampler