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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2020.07 #
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import torch, random
import torch.nn as nn
from copy import deepcopy
from typing import Text
from torch.distributions.categorical import Categorical
from ..cell_operations import ResNetBasicblock, drop_path
from .search_cells import NAS201SearchCell as SearchCell
from .genotypes import Structure
class Controller(nn.Module):
# we refer to https://github.com/TDeVries/enas_pytorch/blob/master/models/controller.py
def __init__(
self,
edge2index,
op_names,
max_nodes,
lstm_size=32,
lstm_num_layers=2,
tanh_constant=2.5,
temperature=5.0,
):
super(Controller, self).__init__()
# assign the attributes
self.max_nodes = max_nodes
self.num_edge = len(edge2index)
self.edge2index = edge2index
self.num_ops = len(op_names)
self.op_names = op_names
self.lstm_size = lstm_size
self.lstm_N = lstm_num_layers
self.tanh_constant = tanh_constant
self.temperature = temperature
# create parameters
self.register_parameter(
"input_vars", nn.Parameter(torch.Tensor(1, 1, lstm_size))
)
self.w_lstm = nn.LSTM(
input_size=self.lstm_size,
hidden_size=self.lstm_size,
num_layers=self.lstm_N,
)
self.w_embd = nn.Embedding(self.num_ops, self.lstm_size)
self.w_pred = nn.Linear(self.lstm_size, self.num_ops)
nn.init.uniform_(self.input_vars, -0.1, 0.1)
nn.init.uniform_(self.w_lstm.weight_hh_l0, -0.1, 0.1)
nn.init.uniform_(self.w_lstm.weight_ih_l0, -0.1, 0.1)
nn.init.uniform_(self.w_embd.weight, -0.1, 0.1)
nn.init.uniform_(self.w_pred.weight, -0.1, 0.1)
def convert_structure(self, _arch):
genotypes = []
for i in range(1, self.max_nodes):
xlist = []
for j in range(i):
node_str = "{:}<-{:}".format(i, j)
op_index = _arch[self.edge2index[node_str]]
op_name = self.op_names[op_index]
xlist.append((op_name, j))
genotypes.append(tuple(xlist))
return Structure(genotypes)
def forward(self):
inputs, h0 = self.input_vars, None
log_probs, entropys, sampled_arch = [], [], []
for iedge in range(self.num_edge):
outputs, h0 = self.w_lstm(inputs, h0)
logits = self.w_pred(outputs)
logits = logits / self.temperature
logits = self.tanh_constant * torch.tanh(logits)
# distribution
op_distribution = Categorical(logits=logits)
op_index = op_distribution.sample()
sampled_arch.append(op_index.item())
op_log_prob = op_distribution.log_prob(op_index)
log_probs.append(op_log_prob.view(-1))
op_entropy = op_distribution.entropy()
entropys.append(op_entropy.view(-1))
# obtain the input embedding for the next step
inputs = self.w_embd(op_index)
return (
torch.sum(torch.cat(log_probs)),
torch.sum(torch.cat(entropys)),
self.convert_structure(sampled_arch),
)
class GenericNAS201Model(nn.Module):
def __init__(
self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats
):
super(GenericNAS201Model, self).__init__()
self._C = C
self._layerN = N
self._max_nodes = max_nodes
self._stem = nn.Sequential(
nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(C)
)
layer_channels = [C] * N + [C * 2] + [C * 2] * N + [C * 4] + [C * 4] * N
layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N
C_prev, num_edge, edge2index = C, None, None
self._cells = nn.ModuleList()
for index, (C_curr, reduction) in enumerate(
zip(layer_channels, layer_reductions)
):
if reduction:
cell = ResNetBasicblock(C_prev, C_curr, 2)
else:
cell = SearchCell(
C_prev,
C_curr,
1,
max_nodes,
search_space,
affine,
track_running_stats,
)
if num_edge is None:
num_edge, edge2index = cell.num_edges, cell.edge2index
else:
assert (
num_edge == cell.num_edges and edge2index == cell.edge2index
), "invalid {:} vs. {:}.".format(num_edge, cell.num_edges)
self._cells.append(cell)
C_prev = cell.out_dim
self._op_names = deepcopy(search_space)
self._Layer = len(self._cells)
self.edge2index = edge2index
self.lastact = nn.Sequential(
nn.BatchNorm2d(
C_prev, affine=affine, track_running_stats=track_running_stats
),
nn.ReLU(inplace=True),
)
self.global_pooling = nn.AdaptiveAvgPool2d(1)
self.classifier = nn.Linear(C_prev, num_classes)
self._num_edge = num_edge
# algorithm related
self.arch_parameters = nn.Parameter(
1e-3 * torch.randn(num_edge, len(search_space))
)
self._mode = None
self.dynamic_cell = None
self._tau = None
self._algo = None
self._drop_path = None
self.verbose = False
def set_algo(self, algo: Text):
# used for searching
assert self._algo is None, "This functioin can only be called once."
self._algo = algo
if algo == "enas":
self.controller = Controller(
self.edge2index, self._op_names, self._max_nodes
)
else:
self.arch_parameters = nn.Parameter(
1e-3 * torch.randn(self._num_edge, len(self._op_names))
)
if algo == "gdas":
self._tau = 10
def set_cal_mode(self, mode, dynamic_cell=None):
assert mode in ["gdas", "enas", "urs", "joint", "select", "dynamic"]
self._mode = mode
if mode == "dynamic":
self.dynamic_cell = deepcopy(dynamic_cell)
else:
self.dynamic_cell = None
def set_drop_path(self, progress, drop_path_rate):
if drop_path_rate is None:
self._drop_path = None
elif progress is None:
self._drop_path = drop_path_rate
else:
self._drop_path = progress * drop_path_rate
@property
def mode(self):
return self._mode
@property
def drop_path(self):
return self._drop_path
@property
def weights(self):
xlist = list(self._stem.parameters())
xlist += list(self._cells.parameters())
xlist += list(self.lastact.parameters())
xlist += list(self.global_pooling.parameters())
xlist += list(self.classifier.parameters())
return xlist
def set_tau(self, tau):
self._tau = tau
@property
def tau(self):
return self._tau
@property
def alphas(self):
if self._algo == "enas":
return list(self.controller.parameters())
else:
return [self.arch_parameters]
@property
def message(self):
string = self.extra_repr()
for i, cell in enumerate(self._cells):
string += "\n {:02d}/{:02d} :: {:}".format(
i, len(self._cells), cell.extra_repr()
)
return string
def show_alphas(self):
with torch.no_grad():
if self._algo == "enas":
return "w_pred :\n{:}".format(self.controller.w_pred.weight)
else:
return "arch-parameters :\n{:}".format(
nn.functional.softmax(self.arch_parameters, dim=-1).cpu()
)
def extra_repr(self):
return "{name}(C={_C}, Max-Nodes={_max_nodes}, N={_layerN}, L={_Layer}, alg={_algo})".format(
name=self.__class__.__name__, **self.__dict__
)
@property
def genotype(self):
genotypes = []
for i in range(1, self._max_nodes):
xlist = []
for j in range(i):
node_str = "{:}<-{:}".format(i, j)
with torch.no_grad():
weights = self.arch_parameters[self.edge2index[node_str]]
op_name = self._op_names[weights.argmax().item()]
xlist.append((op_name, j))
genotypes.append(tuple(xlist))
return Structure(genotypes)
def dync_genotype(self, use_random=False):
genotypes = []
with torch.no_grad():
alphas_cpu = nn.functional.softmax(self.arch_parameters, dim=-1)
for i in range(1, self._max_nodes):
xlist = []
for j in range(i):
node_str = "{:}<-{:}".format(i, j)
if use_random:
op_name = random.choice(self._op_names)
else:
weights = alphas_cpu[self.edge2index[node_str]]
op_index = torch.multinomial(weights, 1).item()
op_name = self._op_names[op_index]
xlist.append((op_name, j))
genotypes.append(tuple(xlist))
return Structure(genotypes)
def get_log_prob(self, arch):
with torch.no_grad():
logits = nn.functional.log_softmax(self.arch_parameters, dim=-1)
select_logits = []
for i, node_info in enumerate(arch.nodes):
for op, xin in node_info:
node_str = "{:}<-{:}".format(i + 1, xin)
op_index = self._op_names.index(op)
select_logits.append(logits[self.edge2index[node_str], op_index])
return sum(select_logits).item()
def return_topK(self, K, use_random=False):
archs = Structure.gen_all(self._op_names, self._max_nodes, False)
pairs = [(self.get_log_prob(arch), arch) for arch in archs]
if K < 0 or K >= len(archs):
K = len(archs)
if use_random:
return random.sample(archs, K)
else:
sorted_pairs = sorted(pairs, key=lambda x: -x[0])
return_pairs = [sorted_pairs[_][1] for _ in range(K)]
return return_pairs
def normalize_archp(self):
if self.mode == "gdas":
while True:
gumbels = -torch.empty_like(self.arch_parameters).exponential_().log()
logits = (self.arch_parameters.log_softmax(dim=1) + gumbels) / self.tau
probs = nn.functional.softmax(logits, dim=1)
index = probs.max(-1, keepdim=True)[1]
one_h = torch.zeros_like(logits).scatter_(-1, index, 1.0)
hardwts = one_h - probs.detach() + probs
if (
(torch.isinf(gumbels).any())
or (torch.isinf(probs).any())
or (torch.isnan(probs).any())
):
continue
else:
break
with torch.no_grad():
hardwts_cpu = hardwts.detach().cpu()
return hardwts, hardwts_cpu, index, "GUMBEL"
else:
alphas = nn.functional.softmax(self.arch_parameters, dim=-1)
index = alphas.max(-1, keepdim=True)[1]
with torch.no_grad():
alphas_cpu = alphas.detach().cpu()
return alphas, alphas_cpu, index, "SOFTMAX"
def forward(self, inputs):
alphas, alphas_cpu, index, verbose_str = self.normalize_archp()
feature = self._stem(inputs)
for i, cell in enumerate(self._cells):
if isinstance(cell, SearchCell):
if self.mode == "urs":
feature = cell.forward_urs(feature)
if self.verbose:
verbose_str += "-forward_urs"
elif self.mode == "select":
feature = cell.forward_select(feature, alphas_cpu)
if self.verbose:
verbose_str += "-forward_select"
elif self.mode == "joint":
feature = cell.forward_joint(feature, alphas)
if self.verbose:
verbose_str += "-forward_joint"
elif self.mode == "dynamic":
feature = cell.forward_dynamic(feature, self.dynamic_cell)
if self.verbose:
verbose_str += "-forward_dynamic"
elif self.mode == "gdas":
feature = cell.forward_gdas(feature, alphas, index)
if self.verbose:
verbose_str += "-forward_gdas"
else:
raise ValueError("invalid mode={:}".format(self.mode))
else:
feature = cell(feature)
if self.drop_path is not None:
feature = drop_path(feature, self.drop_path)
if self.verbose and random.random() < 0.001:
print(verbose_str)
out = self.lastact(feature)
out = self.global_pooling(out)
out = out.view(out.size(0), -1)
logits = self.classifier(out)
return out, logits