112 lines
3.8 KiB
Python
112 lines
3.8 KiB
Python
##################################################
|
|
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
|
|
##################################################
|
|
import math, torch
|
|
import torch.nn as nn
|
|
|
|
|
|
def select2withP(logits, tau, just_prob=False, num=2, eps=1e-7):
|
|
if tau <= 0:
|
|
new_logits = logits
|
|
probs = nn.functional.softmax(new_logits, dim=1)
|
|
else :
|
|
while True: # a trick to avoid the gumbels bug
|
|
gumbels = -torch.empty_like(logits).exponential_().log()
|
|
new_logits = (logits.log_softmax(dim=1) + gumbels) / tau
|
|
probs = nn.functional.softmax(new_logits, dim=1)
|
|
if (not torch.isinf(gumbels).any()) and (not torch.isinf(probs).any()) and (not torch.isnan(probs).any()): break
|
|
|
|
if just_prob: return probs
|
|
|
|
#with torch.no_grad(): # add eps for unexpected torch error
|
|
# probs = nn.functional.softmax(new_logits, dim=1)
|
|
# selected_index = torch.multinomial(probs + eps, 2, False)
|
|
with torch.no_grad(): # add eps for unexpected torch error
|
|
probs = probs.cpu()
|
|
selected_index = torch.multinomial(probs + eps, num, False).to(logits.device)
|
|
selected_logit = torch.gather(new_logits, 1, selected_index)
|
|
selcted_probs = nn.functional.softmax(selected_logit, dim=1)
|
|
return selected_index, selcted_probs
|
|
|
|
|
|
def ChannelWiseInter(inputs, oC, mode='v2'):
|
|
if mode == 'v1':
|
|
return ChannelWiseInterV1(inputs, oC)
|
|
elif mode == 'v2':
|
|
return ChannelWiseInterV2(inputs, oC)
|
|
else:
|
|
raise ValueError('invalid mode : {:}'.format(mode))
|
|
|
|
|
|
def ChannelWiseInterV1(inputs, oC):
|
|
assert inputs.dim() == 4, 'invalid dimension : {:}'.format(inputs.size())
|
|
def start_index(a, b, c):
|
|
return int( math.floor(float(a * c) / b) )
|
|
def end_index(a, b, c):
|
|
return int( math.ceil(float((a + 1) * c) / b) )
|
|
batch, iC, H, W = inputs.size()
|
|
outputs = torch.zeros((batch, oC, H, W), dtype=inputs.dtype, device=inputs.device)
|
|
if iC == oC: return inputs
|
|
for ot in range(oC):
|
|
istartT, iendT = start_index(ot, oC, iC), end_index(ot, oC, iC)
|
|
values = inputs[:, istartT:iendT].mean(dim=1)
|
|
outputs[:, ot, :, :] = values
|
|
return outputs
|
|
|
|
|
|
def ChannelWiseInterV2(inputs, oC):
|
|
assert inputs.dim() == 4, 'invalid dimension : {:}'.format(inputs.size())
|
|
batch, C, H, W = inputs.size()
|
|
if C == oC: return inputs
|
|
else : return nn.functional.adaptive_avg_pool3d(inputs, (oC,H,W))
|
|
#inputs_5D = inputs.view(batch, 1, C, H, W)
|
|
#otputs_5D = nn.functional.interpolate(inputs_5D, (oC,H,W), None, 'area', None)
|
|
#otputs = otputs_5D.view(batch, oC, H, W)
|
|
#otputs_5D = nn.functional.interpolate(inputs_5D, (oC,H,W), None, 'trilinear', False)
|
|
#return otputs
|
|
|
|
|
|
def linear_forward(inputs, linear):
|
|
if linear is None: return inputs
|
|
iC = inputs.size(1)
|
|
weight = linear.weight[:, :iC]
|
|
if linear.bias is None: bias = None
|
|
else : bias = linear.bias
|
|
return nn.functional.linear(inputs, weight, bias)
|
|
|
|
|
|
def get_width_choices(nOut):
|
|
xsrange = [0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]
|
|
if nOut is None:
|
|
return len(xsrange)
|
|
else:
|
|
Xs = [int(nOut * i) for i in xsrange]
|
|
#xs = [ int(nOut * i // 10) for i in range(2, 11)]
|
|
#Xs = [x for i, x in enumerate(xs) if i+1 == len(xs) or xs[i+1] > x+1]
|
|
Xs = sorted( list( set(Xs) ) )
|
|
return tuple(Xs)
|
|
|
|
|
|
def get_depth_choices(nDepth):
|
|
if nDepth is None:
|
|
return 3
|
|
else:
|
|
assert nDepth >= 3, 'nDepth should be greater than 2 vs {:}'.format(nDepth)
|
|
if nDepth == 1 : return (1, 1, 1)
|
|
elif nDepth == 2: return (1, 1, 2)
|
|
elif nDepth >= 3:
|
|
return (nDepth//3, nDepth*2//3, nDepth)
|
|
else:
|
|
raise ValueError('invalid Depth : {:}'.format(nDepth))
|
|
|
|
|
|
def drop_path(x, drop_prob):
|
|
if drop_prob > 0.:
|
|
keep_prob = 1. - drop_prob
|
|
mask = x.new_zeros(x.size(0), 1, 1, 1)
|
|
mask = mask.bernoulli_(keep_prob)
|
|
x = x * (mask / keep_prob)
|
|
#x.div_(keep_prob)
|
|
#x.mul_(mask)
|
|
return x
|