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update GDAS and SETN

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This commit is contained in:
D-X-Y
2019-11-05 23:35:28 +11:00
parent 7f13385f28
commit a84f483882
46 changed files with 2065 additions and 135 deletions
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29
lib/models/cell_searchs/search_model_gdas.py
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@@ -6,9 +6,9 @@
import torch
import torch.nn as nn
from copy import deepcopy
from .infer_cells import ResNetBasicblock
from .search_cells import SearchCell
from .genotypes import Structure
from ..cell_operations import ResNetBasicblock
from .search_cells import SearchCell
from .genotypes import Structure
class TinyNetworkGDAS(nn.Module):
@@ -44,7 +44,6 @@ class TinyNetworkGDAS(nn.Module):
self.classifier = nn.Linear(C_prev, num_classes)
self.arch_parameters = nn.Parameter( 1e-3*torch.randn(num_edge, len(search_space)) )
self.tau = 10
self.nan_count = 0
def get_weights(self):
xlist = list( self.stem.parameters() ) + list( self.cells.parameters() )
@@ -52,9 +51,8 @@ class TinyNetworkGDAS(nn.Module):
xlist+= list( self.classifier.parameters() )
return xlist
def set_tau(self, tau, _nan_count=0):
def set_tau(self, tau):
self.tau = tau
self.nan_count = _nan_count
def get_tau(self):
return self.tau
@@ -85,27 +83,10 @@ class TinyNetworkGDAS(nn.Module):
return Structure( genotypes )
def forward(self, inputs):
def gumbel_softmax(_logits, _tau):
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)
index = probs.max(-1, keepdim=True)[1]
if index[0].item() == self.op_names.index('none') and index[3].item() == self.op_names.index('none') and index[5].item() == self.op_names.index('none'): continue
if index[1].item() == self.op_names.index('none') and index[2].item() == self.op_names.index('none') and index[3].item() == self.op_names.index('none') and index[4].item() == self.op_names.index('none'): continue
if index[3].item() == self.op_names.index('none') and index[4].item() == self.op_names.index('none') and index[5].item() == self.op_names.index('none'): continue
if index[3].item() == self.op_names.index('none') and index[0].item() == self.op_names.index('none') and index[1].item() == self.op_names.index('none'): continue
one_h = torch.zeros_like(_logits).scatter_(-1, index, 1.0)
xres = one_h - probs.detach() + probs
if (not torch.isinf(gumbels).any()) and (not torch.isinf(probs).any()) and (not torch.isnan(probs).any()): break
self.nan_count += 1
return xres, index
feature = self.stem(inputs)
for i, cell in enumerate(self.cells):
if isinstance(cell, SearchCell):
alphas, IDX = gumbel_softmax(self.arch_parameters, self.tau)
feature = cell.forward_gdas(feature, alphas, IDX.cpu())
feature = cell.forward_gdas(feature, self.arch_parameters, self.tau)
else:
feature = cell(feature)