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model_search.py
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model_search.py
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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from operations import *
from torch.autograd import Variable
from genotypes import PRIMITIVES
from genotypes import Genotype
from utils import gumbel_softmax
class MixedOp(nn.Module):
def __init__(self, C, stride, switch, p):
super(MixedOp, self).__init__()
self.m_ops = nn.ModuleList()
self.p = p
for i in range(len(switch)):
if switch[i]:
primitive = PRIMITIVES[i]
op = OPS[primitive](C, stride, False)
if 'pool' in primitive:
op = nn.Sequential(op, nn.BatchNorm2d(C, affine=False))
if isinstance(op, Identity) and p > 0:
op = nn.Sequential(op, nn.Dropout(self.p))
self.m_ops.append(op)
def update_p(self):
for op in self.m_ops:
if isinstance(op, nn.Sequential):
if isinstance(op[0], Identity):
op[1].p = self.p
def forward(self, x, weights):
return sum(w * op(x) for w, op in zip(weights, self.m_ops))
class Cell(nn.Module):
def __init__(self, steps, multiplier, C_prev_prev, C_prev, C, reduction, reduction_prev, switches, p):
super(Cell, self).__init__()
self.reduction = reduction
self.p = p
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.cell_ops = nn.ModuleList()
switch_count = 0
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, switch=switches[switch_count], p=self.p)
self.cell_ops.append(op)
switch_count = switch_count + 1
def update_p(self):
for op in self.cell_ops:
op.p = self.p
op.update_p()
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):
s = sum(self.cell_ops[offset + j](h, weights[offset + j]) for j, h in enumerate(states))
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, criterion, steps=4, multiplier=4, stem_multiplier=3, switches_normal=[],
switches_reduce=[], p=0.0):
super(Network, self).__init__()
self._C = C
self._num_classes = num_classes
self._layers = layers
self._criterion = criterion
self._steps = steps
self._multiplier = multiplier
self.p = p
self.switches_normal = switches_normal
switch_ons = []
for i in range(len(switches_normal)):
ons = 0
for j in range(len(switches_normal[i])):
if switches_normal[i][j]:
ons = ons + 1
switch_ons.append(ons)
ons = 0
self.switch_on = switch_ons[0]
C_curr = stem_multiplier * C
self.stem = nn.Sequential(
nn.Conv2d(3, C_curr, 3, padding=1, bias=False),
nn.BatchNorm2d(C_curr)
)
C_prev_prev, C_prev, C_curr = C_curr, C_curr, C
self.cells = nn.ModuleList()
reduction_prev = False
for i in range(layers):
if i in [layers // 3, 2 * layers // 3]:
C_curr *= 2
reduction = True
cell = Cell(steps, multiplier, C_prev_prev, C_prev, C_curr, reduction, reduction_prev, switches_reduce,
self.p)
else:
reduction = False
cell = Cell(steps, multiplier, C_prev_prev, C_prev, C_curr, reduction, reduction_prev, switches_normal,
self.p)
# cell = Cell(steps, multiplier, C_prev_prev, C_prev, C_curr, reduction, reduction_prev, switches)
reduction_prev = reduction
self.cells += [cell]
C_prev_prev, C_prev = C_prev, multiplier * C_curr
self.global_pooling = nn.AdaptiveAvgPool2d(1)
self.classifier = nn.Linear(C_prev, num_classes)
self._initialize_alphas()
def forward(self, input, temperature=5.0):
s0 = s1 = self.stem(input)
for i, cell in enumerate(self.cells):
if cell.reduction:
if self.alphas_reduce.size(1) == 1:
# weights = F.softmax(self.alphas_reduce, dim=0)
# weights = F.gumbel_softmax(self.alphas_reduce, temperature, dim=0)
weights = gumbel_softmax(F.log_softmax(self.alphas_reduce, dim=0), tau=temperature, hard=True,
dim=0)
else:
# weights = F.softmax(self.alphas_reduce, dim=-1)
# weights = F.gumbel_softmax(self.alphas_reduce, temperature, dim=-1)
weights = gumbel_softmax(F.log_softmax(self.alphas_reduce, dim=-1), tau=temperature, hard=True,
dim=-1)
else:
if self.alphas_normal.size(1) == 1:
# weights = F.softmax(self.alphas_normal, dim=0)
# weights = F.gumbel_softmax(self.alphas_normal, temperature, dim=0)
weights = gumbel_softmax(F.log_softmax(self.alphas_normal, dim=0), tau=temperature, hard=True,
dim=0)
else:
# weights = F.softmax(self.alphas_normal, dim=-1)
# weights = F.gumbel_softmax(self.alphas_normal, temperature, dim=-1)
weights = gumbel_softmax(F.log_softmax(self.alphas_normal, dim=-1), tau=temperature, hard=True,
dim=-1)
s0, s1 = s1, cell(s0, s1, weights)
out = self.global_pooling(s1)
logits = self.classifier(out.view(out.size(0), -1))
return logits
def update_p(self):
for cell in self.cells:
cell.p = self.p
cell.update_p()
def _loss(self, input, target):
logits = self(input)
return self._criterion(logits, target)
def _initialize_alphas(self):
k = sum(1 for i in range(self._steps) for n in range(2 + i))
num_ops = self.switch_on
self.alphas_normal = nn.Parameter(torch.FloatTensor(1e-3 * np.random.randn(k, num_ops)))
self.alphas_reduce = nn.Parameter(torch.FloatTensor(1e-3 * np.random.randn(k, num_ops)))
self._arch_parameters = [
self.alphas_normal,
self.alphas_reduce,
]
def arch_parameters(self):
return self._arch_parameters