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dropout_dpp_old.py
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import torch
from alpaca.uncertainty_estimator.masks import build_mask
from .dppmask_ext import build_mask_ext
from .dropout_mc import DropoutMC
import numpy as np
import time
import datetime
import random
import logging
log = logging.getLogger(__name__)
import os
class DropoutDPP(DropoutMC):
dropout_id = -1
def __init__(
self,
p: float,
activate=False,
mask_name="dpp",
max_n=100,
max_frac=0.4,
coef=1.0,
):
super().__init__(p=p, activate=activate)
self.mask = (
build_mask_ext(mask_name)
if mask_name != "dpp"
else build_mask_ext(mask_name)["dpp"]
)
self.reset_mask = False
self.max_n = max_n
self.max_frac = max_frac
self.coef = coef
self.curr_dropout_id = DropoutDPP.update()
log.debug(f"Dropout id: {self.curr_dropout_id}")
# print(self.mask)
@classmethod
def update(cls):
cls.dropout_id += 1
return cls.dropout_id
def calc_mask(self, x: torch.Tensor):
return self.mask(x, dropout_rate=self.p, layer_num=self.curr_dropout_id).float()
def get_mask(self, x: torch.Tensor):
return self.mask(x, dropout_rate=self.p, layer_num=self.curr_dropout_id).float()
# if not self.reset_mask:
# if len(x.shape) == 2:
# return self.calc_mask(x)
# else:
# mask = self.calc_mask(x.reshape(-1, x.shape[-1]))
# while len(mask.shape) < len(x.shape):
# mask = mask.unsqueeze(dim=0)
# return mask
# else:
# self.mask.reset()
# self.calc_mask(x) # dry run
# for _ in range(random.randint(0, 100)):
# self.calc_mask(x)
# return self.calc_mask(x)
def calc_non_zero_neurons(self, sum_mask):
# print('Trash=========================')
# print(sum_mask)
frac_nonzero = (sum_mask != 0).sum(axis=-1).item() / sum_mask.shape[-1]
return frac_nonzero
def forward(self, x: torch.Tensor):
if self.training:
return torch.nn.functional.dropout(x, self.p, training=True)
else:
if not self.activate:
return x
sum_mask = self.get_mask(x)
# print('Mask')
# print(sum_mask)
norm = 1.0
i = 1
frac_nonzero = self.calc_non_zero_neurons(sum_mask)
# print('==========Non zero neurons:', frac_nonzero, 'iter:', i, 'id:', self.curr_dropout_id, '******************')
# while i < 30:
while i < self.max_n and frac_nonzero < self.max_frac:
# while frac_nonzero < self.max_frac:
mask = self.get_mask(x)
# sum_mask = self.coef * sum_mask + mask
sum_mask += mask
i += 1
# norm = self.coef * norm + 1
frac_nonzero = self.calc_non_zero_neurons(sum_mask)
# print('==========Non zero neurons:', frac_nonzero, 'iter:', i, '******************')
# res = x * sum_mask / norm
# print(sum_mask / i)
# print('Number of masks:', i)
res = x * sum_mask / i
return res
class DropoutDPP_v2(DropoutMC):
dropout_id = -1
def __init__(
self,
p: float,
activate=False,
mask_name="ht_dpp",
max_n=100,
max_frac=0.4,
coef=1.0,
is_reused_mask=False,
inference_step=0,
mask_name_for_mask="rbf",
):
super().__init__(p=p, activate=activate)
self.mask = (
build_mask_ext(mask_name)
if mask_name != "dpp"
else build_mask_ext(mask_name)["dpp"]
)
self.reset_mask = False
self.max_n = max_n
self.max_frac = max_frac
self.coef = coef
self.curr_dropout_id = DropoutDPP_v2.update()
self.is_reused_mask = is_reused_mask
self.change_mask = 1
if self.is_reused_mask:
self.saved_masks = []
self.dpp_masks = (
build_mask_ext(mask_name_for_mask)
if mask_name_for_mask != "dpp"
else build_mask_ext(mask_name_for_mask)["dpp"]
)
self.inference_step = inference_step
self.used_mask_id = 0
self.diverse_masks = None
log.debug(f"Dropout id: {self.curr_dropout_id}")
@classmethod
def update(cls):
cls.dropout_id += 1
return cls.dropout_id
def calc_mask(self, x: torch.Tensor):
return self.mask(x, dropout_rate=self.p, layer_num=self.curr_dropout_id).float()
def get_mask(self, x: torch.Tensor):
if x.dim() == 2:
return self.mask(
x, dropout_rate=self.p, layer_num=self.curr_dropout_id
).float()
return self.mask(
x.view(x.shape[0] * x.shape[1], -1),
dropout_rate=self.p,
layer_num=self.curr_dropout_id,
).float() # [None, None, :]
def calc_non_zero_neurons(self, sum_mask):
frac_nonzero = (sum_mask != 0).sum(axis=-1).item() / sum_mask.shape[-1]
return frac_nonzero
def dry_run(self, sampling=True):
self.saved_masks = torch.stack(self.saved_masks).T
self.saved_masks_clean = self.saved_masks.clone()
n = 7 # TODO:
if sampling:
mask_indices = torch.zeros(self.saved_masks.shape[1])
for i in range(n):
msk_idx = self.dpp_masks(
self.saved_masks,
dropout_rate=self.p,
layer_num=self.curr_dropout_id,
).float()
self.diverse_masks = self.saved_masks_clean[:, msk_idx > 0]
else:
self.diverse_masks = self.saved_masks_clean
max_n = 200 # TODO:
self.diverse_masks = self.diverse_masks[:, :max_n]
log.debug(f"\n\nself.diverse_masks: {self.diverse_masks.shape}")
self.count_diverse_masks = self.diverse_masks.shape[1] - 1
self.used_mask_id = 0
def forward(self, x: torch.Tensor):
if self.training:
return torch.nn.functional.dropout(x, self.p, training=True)
elif self.is_reused_mask and self.inference_step:
if not self.activate:
return x
if self.diverse_masks is None:
self.dry_run()
mask = self.diverse_masks[:, self.used_mask_id].to(device=x.device)
if self.change_mask:
self.used_mask_id += 1
if self.used_mask_id > self.diverse_masks.shape[1]:
self.used_mask_id = np.random.randint(
1, self.diverse_masks.shape[1]
)
self.change_mask = 1 - self.change_mask
return x * mask
else:
if not self.activate:
return x
sum_mask = self.get_mask(x)
norm = 1.0
i = 1
frac_nonzero = self.calc_non_zero_neurons(sum_mask)
while i < self.max_n and frac_nonzero < self.max_frac:
mask = self.get_mask(x)
# sum_mask = self.coef * sum_mask + mask
sum_mask += mask
i += 1
# norm = self.coef * norm + 1
frac_nonzero = self.calc_non_zero_neurons(sum_mask)
log.debug(
f"==========Non zero neurons: {frac_nonzero} iter: {i}*****************"
)
# res = x * sum_mask / norm
log.debug(f"Number of masks: {i}")
res = x * sum_mask / i
if self.is_reused_mask:
mask_i = sum_mask / i
mask_i = mask_i.to(device="cuda:0")
self.saved_masks.append(mask_i)
return res