delete confusing files
This commit is contained in:
D-X-Y
@@ -1,7 +1,6 @@
|
||||
##################################################
|
||||
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
|
||||
##################################################
|
||||
from .model_search import Network
|
||||
from .CifarNet import NetworkCIFAR
|
||||
from .ImageNet import NetworkImageNet
|
||||
|
||||
|
||||
@@ -1,166 +0,0 @@
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from torch.nn.parameter import Parameter
|
||||
from .head_utils import CifarHEAD, ImageNetHEAD
|
||||
from .operations import OPS, FactorizedReduce, ReLUConvBN
|
||||
from .genotypes import PRIMITIVES, Genotype
|
||||
|
||||
|
||||
class MixedOp(nn.Module):
|
||||
|
||||
def __init__(self, C, stride):
|
||||
super(MixedOp, self).__init__()
|
||||
self._ops = nn.ModuleList()
|
||||
for primitive in PRIMITIVES:
|
||||
op = OPS[primitive](C, stride, False)
|
||||
self._ops.append(op)
|
||||
|
||||
def forward(self, x, weights):
|
||||
return sum(w * op(x) for w, op in zip(weights, self._ops))
|
||||
|
||||
|
||||
class Cell(nn.Module):
|
||||
|
||||
def __init__(self, steps, multiplier, C_prev_prev, C_prev, C, reduction, reduction_prev):
|
||||
super(Cell, self).__init__()
|
||||
self.reduction = reduction
|
||||
|
||||
if reduction_prev:
|
||||
self.preprocess0 = FactorizedReduce(C_prev_prev, C, affine=False)
|
||||
else:
|
||||
self.preprocess0 = ReLUConvBN(C_prev_prev, C, 1, 1, 0, affine=False)
|
||||
self.preprocess1 = ReLUConvBN(C_prev, C, 1, 1, 0, affine=False)
|
||||
self._steps = steps
|
||||
self._multiplier = multiplier
|
||||
|
||||
self._ops = nn.ModuleList()
|
||||
for i in range(self._steps):
|
||||
for j in range(2+i):
|
||||
stride = 2 if reduction and j < 2 else 1
|
||||
op = MixedOp(C, stride)
|
||||
self._ops.append(op)
|
||||
|
||||
def forward(self, s0, s1, weights):
|
||||
s0 = self.preprocess0(s0)
|
||||
s1 = self.preprocess1(s1)
|
||||
|
||||
states = [s0, s1]
|
||||
offset = 0
|
||||
for i in range(self._steps):
|
||||
clist = []
|
||||
for j, h in enumerate(states):
|
||||
x = self._ops[offset+j](h, weights[offset+j])
|
||||
clist.append( x )
|
||||
s = sum(clist)
|
||||
offset += len(states)
|
||||
states.append(s)
|
||||
|
||||
return torch.cat(states[-self._multiplier:], dim=1)
|
||||
|
||||
|
||||
class Network(nn.Module):
|
||||
|
||||
def __init__(self, C, num_classes, layers, steps=4, multiplier=4, stem_multiplier=3, head='cifar'):
|
||||
super(Network, self).__init__()
|
||||
self._C = C
|
||||
self._num_classes = num_classes
|
||||
self._layers = layers
|
||||
self._steps = steps
|
||||
self._multiplier = multiplier
|
||||
|
||||
C_curr = stem_multiplier*C
|
||||
if head == 'cifar':
|
||||
self.stem = nn.Sequential(
|
||||
nn.Conv2d(3, C_curr, 3, padding=1, bias=False),
|
||||
nn.BatchNorm2d(C_curr)
|
||||
)
|
||||
elif head == 'imagenet':
|
||||
self.stem = ImageNetHEAD(C_curr, stride=1)
|
||||
else:
|
||||
raise ValueError('Invalid head : {:}'.format(head))
|
||||
|
||||
C_prev_prev, C_prev, C_curr = C_curr, C_curr, C
|
||||
reduction_prev, cells = False, []
|
||||
for i in range(layers):
|
||||
if i in [layers//3, 2*layers//3]:
|
||||
C_curr *= 2
|
||||
reduction = True
|
||||
else:
|
||||
reduction = False
|
||||
cell = Cell(steps, multiplier, C_prev_prev, C_prev, C_curr, reduction, reduction_prev)
|
||||
reduction_prev = reduction
|
||||
cells.append( cell )
|
||||
C_prev_prev, C_prev = C_prev, multiplier*C_curr
|
||||
self.cells = nn.ModuleList(cells)
|
||||
|
||||
self.global_pooling = nn.AdaptiveAvgPool2d(1)
|
||||
self.classifier = nn.Linear(C_prev, num_classes)
|
||||
|
||||
# initialize architecture parameters
|
||||
k = sum(1 for i in range(self._steps) for n in range(2+i))
|
||||
num_ops = len(PRIMITIVES)
|
||||
|
||||
self.alphas_normal = Parameter(torch.Tensor(k, num_ops))
|
||||
self.alphas_reduce = Parameter(torch.Tensor(k, num_ops))
|
||||
nn.init.normal_(self.alphas_normal, 0, 0.001)
|
||||
nn.init.normal_(self.alphas_reduce, 0, 0.001)
|
||||
|
||||
def set_tau(self, tau):
|
||||
return -1
|
||||
|
||||
def get_tau(self):
|
||||
return -1
|
||||
|
||||
def arch_parameters(self):
|
||||
return [self.alphas_normal, self.alphas_reduce]
|
||||
|
||||
def base_parameters(self):
|
||||
lists = list(self.stem.parameters()) + list(self.cells.parameters())
|
||||
lists += list(self.global_pooling.parameters())
|
||||
lists += list(self.classifier.parameters())
|
||||
return lists
|
||||
|
||||
def forward(self, inputs):
|
||||
batch, C, H, W = inputs.size()
|
||||
s0 = s1 = self.stem(inputs)
|
||||
for i, cell in enumerate(self.cells):
|
||||
if cell.reduction:
|
||||
weights = F.softmax(self.alphas_reduce, dim=-1)
|
||||
else:
|
||||
weights = F.softmax(self.alphas_normal, dim=-1)
|
||||
s0, s1 = s1, cell(s0, s1, weights)
|
||||
out = self.global_pooling(s1)
|
||||
out = out.view(batch, -1)
|
||||
logits = self.classifier(out)
|
||||
return logits
|
||||
|
||||
def genotype(self):
|
||||
|
||||
def _parse(weights):
|
||||
gene, n, start = [], 2, 0
|
||||
for i in range(self._steps):
|
||||
end = start + n
|
||||
W = weights[start:end].copy()
|
||||
edges = sorted(range(i + 2), key=lambda x: -max(W[x][k] for k in range(len(W[x])) if k != PRIMITIVES.index('none')))[:2]
|
||||
for j in edges:
|
||||
k_best = None
|
||||
for k in range(len(W[j])):
|
||||
if k != PRIMITIVES.index('none'):
|
||||
if k_best is None or W[j][k] > W[j][k_best]:
|
||||
k_best = k
|
||||
gene.append((PRIMITIVES[k_best], j, float(W[j][k_best])))
|
||||
start = end
|
||||
n += 1
|
||||
return gene
|
||||
|
||||
with torch.no_grad():
|
||||
gene_normal = _parse(F.softmax(self.alphas_normal, dim=-1).cpu().numpy())
|
||||
gene_reduce = _parse(F.softmax(self.alphas_reduce, dim=-1).cpu().numpy())
|
||||
|
||||
concat = range(2+self._steps-self._multiplier, self._steps+2)
|
||||
genotype = Genotype(
|
||||
normal=gene_normal, normal_concat=concat,
|
||||
reduce=gene_reduce, reduce_concat=concat
|
||||
)
|
||||
return genotype
|
||||
Reference in New Issue
Block a user