414 lines
17 KiB
Python
414 lines
17 KiB
Python
######################################################################################
|
|
# Copyright (c) muhanzhang, D-VAE, NeurIPS 2019 [GitHub D-VAE]
|
|
# Modified by Hayeon Lee, Eunyoung Hyung, MetaD2A, ICLR2021, 2021. 03 [GitHub MetaD2A]
|
|
######################################################################################
|
|
# import math
|
|
# import random
|
|
import torch
|
|
import json
|
|
from torch import nn
|
|
import os
|
|
from torch.nn import functional as F
|
|
import datetime
|
|
|
|
|
|
## Our packages
|
|
import gpytorch
|
|
import logging
|
|
|
|
from transfer_nag_lib.DeepKernelGPHelpers import Metric
|
|
from transfer_nag_lib.DeepKernelGPModules import StandardDeepGP, ExactGPLayer
|
|
from transfer_nag_lib.MetaD2A_mobilenetV3.set_encoder.setenc_models import SetPool
|
|
|
|
|
|
class EncoderFSBO(nn.Module):
|
|
def __init__(self, args, graph_config, dgp_arch):
|
|
super(EncoderFSBO, self).__init__()
|
|
|
|
## GP parameters
|
|
space="OFA_MBV3"
|
|
c, D = 4230, 64
|
|
dim = args.nz * 2
|
|
rootdir = os.path.dirname(os.path.realpath(__file__))
|
|
backbone_params = json.load(open(os.path.join(rootdir, "Setconfig90.json"), "rb"))
|
|
backbone_params.update({"dim": dim})
|
|
backbone_params.update({"fixed_context_size": dim})
|
|
backbone_params.update({"minibatch_size": 256})
|
|
backbone_params.update({"n_inner_steps": 1})
|
|
backbone_params.update({"output_size_A": dgp_arch})
|
|
|
|
checkpoint_path = os.path.join(rootdir, "checkpoints", "FSBO-metalearn", f"{space}",
|
|
datetime.datetime.now().strftime('meta-%Y-%m-%d-%H-%M-%S-%f'))
|
|
backbone_params.update({"checkpoint_path": checkpoint_path})
|
|
self.fixed_context_size = backbone_params["fixed_context_size"]
|
|
self.minibatch_size = backbone_params["minibatch_size"]
|
|
self.n_inner_steps = backbone_params["n_inner_steps"]
|
|
self.checkpoint_path = backbone_params["checkpoint_path"]
|
|
os.makedirs(self.checkpoint_path, exist_ok=False)
|
|
json.dump(backbone_params, open(os.path.join(self.checkpoint_path, "configuration.json"), "w"))
|
|
# self.device = torch.device("cpu") # "cuda:0" if torch.cuda.is_available() else "cpu")
|
|
self.device = torch.device("cuda:0") if torch.cuda.is_available() else torch.device("cpu")
|
|
logging.basicConfig(filename=os.path.join(self.checkpoint_path, "log.txt"), level=logging.DEBUG)
|
|
self.config = backbone_params
|
|
self.likelihood = gpytorch.likelihoods.GaussianLikelihood()
|
|
self.gp = ExactGPLayer(train_x=None, train_y=None, likelihood=self.likelihood, config=self.config,
|
|
dims=self.fixed_context_size)
|
|
self.mll = gpytorch.mlls.ExactMarginalLogLikelihood(self.likelihood, self.gp).to(self.device)
|
|
self.gp.double()
|
|
self.likelihood.double()
|
|
self.mll.double()
|
|
self.mse = nn.MSELoss()
|
|
# self.mtrloader = get_meta_train_loader(
|
|
# args.batch_size, args.data_path, args.num_sample)
|
|
# self.get_tasks()
|
|
self.setup_writers()
|
|
|
|
self.train_metrics = Metric()
|
|
self.valid_metrics = Metric(prefix="valid: ")
|
|
|
|
self.max_n = graph_config['max_n'] # maximum number of vertices
|
|
self.nvt = graph_config['num_vertex_type'] if args.search_space == 'ofa' else args.nvt # number of vertex types
|
|
self.START_TYPE = graph_config['START_TYPE']
|
|
self.END_TYPE = graph_config['END_TYPE']
|
|
self.hs = args.hs # hidden state size of each vertex
|
|
self.nz = args.nz # size of latent representation z
|
|
self.gs = args.hs # size of graph state
|
|
self.bidir = True # whether to use bidirectional encoding
|
|
self.vid = True
|
|
self.input_type = 'DG'
|
|
self.num_sample = args.num_sample
|
|
|
|
if self.vid:
|
|
self.vs = self.hs + self.max_n # vertex state size = hidden state + vid
|
|
else:
|
|
self.vs = self.hs
|
|
|
|
# 0. encoding-related
|
|
self.grue_forward = nn.GRUCell(self.nvt, self.hs) # encoder GRU
|
|
self.grue_backward = nn.GRUCell(self.nvt, self.hs) # backward encoder GRU
|
|
self.fc1 = nn.Linear(self.gs, self.nz) # latent mean
|
|
self.fc2 = nn.Linear(self.gs, self.nz) # latent logvar
|
|
|
|
# 2. gate-related
|
|
self.gate_forward = nn.Sequential(
|
|
nn.Linear(self.vs, self.hs),
|
|
nn.Sigmoid()
|
|
)
|
|
self.gate_backward = nn.Sequential(
|
|
nn.Linear(self.vs, self.hs),
|
|
nn.Sigmoid()
|
|
)
|
|
self.mapper_forward = nn.Sequential(
|
|
nn.Linear(self.vs, self.hs, bias=False),
|
|
) # disable bias to ensure padded zeros also mapped to zeros
|
|
self.mapper_backward = nn.Sequential(
|
|
nn.Linear(self.vs, self.hs, bias=False),
|
|
)
|
|
|
|
# 3. bidir-related, to unify sizes
|
|
if self.bidir:
|
|
self.hv_unify = nn.Sequential(
|
|
nn.Linear(self.hs * 2, self.hs),
|
|
)
|
|
self.hg_unify = nn.Sequential(
|
|
nn.Linear(self.gs * 2, self.gs),
|
|
)
|
|
|
|
# 4. other
|
|
self.relu = nn.ReLU()
|
|
self.sigmoid = nn.Sigmoid()
|
|
self.tanh = nn.Tanh()
|
|
self.logsoftmax1 = nn.LogSoftmax(1)
|
|
|
|
# 6. predictor
|
|
np = self.gs
|
|
self.intra_setpool = SetPool(dim_input=512,
|
|
num_outputs=1,
|
|
dim_output=self.nz,
|
|
dim_hidden=self.nz,
|
|
mode='sabPF').to(self.device)
|
|
self.inter_setpool = SetPool(dim_input=self.nz,
|
|
num_outputs=1,
|
|
dim_output=self.nz,
|
|
dim_hidden=self.nz,
|
|
mode='sabPF').to(self.device)
|
|
self.set_fc = nn.Sequential(
|
|
nn.Linear(512, self.nz),
|
|
nn.ReLU()).to(self.device)
|
|
|
|
input_dim = 0
|
|
if 'D' in self.input_type:
|
|
input_dim += self.nz
|
|
if 'G' in self.input_type:
|
|
input_dim += self.nz
|
|
|
|
self.pred_fc = StandardDeepGP(backbone_params)
|
|
self.mseloss = nn.MSELoss(reduction='sum')
|
|
# self.nasbench201 = torch.load(
|
|
# os.path.join(args.data_path, 'nasbench201.pt'))
|
|
self.data_path = args.data_path
|
|
self.pred_fc.to(self.device)
|
|
self.inter_setpool.to(self.device)
|
|
self.intra_setpool.to(self.device)
|
|
self.grue_backward.to(self.device)
|
|
self.grue_forward.to(self.device)
|
|
self.gate_backward.to(self.device)
|
|
self.gate_forward.to(self.device)
|
|
self.mapper_backward.to(self.device)
|
|
self.mapper_forward.to(self.device)
|
|
self.hg_unify.to(self.device)
|
|
self.hv_unify.to(self.device)
|
|
self.fc1.to(self.device)
|
|
self.fc2.to(self.device)
|
|
|
|
# def get_topk_idx(self, topk=1):
|
|
# self.mtrloader.dataset.set_mode('train')
|
|
# if self.nasbench201 is None:
|
|
# self.nasbench201 = torch.load(
|
|
# os.path.join(self.data_path, 'nasbench201.pt'))
|
|
# z_repr = []
|
|
# g_repr = []
|
|
# acc_repr = []
|
|
# for x, g, acc in tqdm(self.mtrloader):
|
|
# str = decode_igraph_to_NAS_BENCH_201_string(g[0])
|
|
# arch_idx = -1
|
|
# for idx, arch_str in enumerate(self.nasbench201['arch']['str']):
|
|
# if arch_str == str:
|
|
# arch_idx = idx
|
|
# break
|
|
# g_repr.append(arch_idx)
|
|
# acc_repr.append(acc.detach().cpu().numpy()[0])
|
|
# best = np.argsort(-1 * np.array(acc_repr))[:topk]
|
|
# self.nasbench201 = None
|
|
# return np.array(g_repr)[best], np.array(acc_repr)[best]
|
|
|
|
def randomly_init_deepgp(self, ):
|
|
self.pred_fc = StandardDeepGP(self.config)
|
|
self.likelihood = gpytorch.likelihoods.GaussianLikelihood()
|
|
self.gp = ExactGPLayer(train_x=None, train_y=None, likelihood=self.likelihood, config=self.config,
|
|
dims=self.fixed_context_size)
|
|
self.mll = gpytorch.mlls.ExactMarginalLogLikelihood(self.likelihood, self.gp).to(self.device)
|
|
|
|
|
|
def setup_writers(self, ):
|
|
train_log_dir = os.path.join(self.checkpoint_path, "train")
|
|
os.makedirs(train_log_dir, exist_ok=True)
|
|
# self.train_summary_writer = SummaryWriter(train_log_dir)
|
|
|
|
valid_log_dir = os.path.join(self.checkpoint_path, "valid")
|
|
os.makedirs(valid_log_dir, exist_ok=True)
|
|
# self.valid_summary_writer = SummaryWriter(valid_log_dir)
|
|
|
|
def get_mu_and_std(self, x_support, y_support, x_query, y_query):
|
|
if x_support is not None:
|
|
x_support.to(self.device)
|
|
y_support.to(self.device)
|
|
|
|
self.gp.set_train_data(inputs=x_support, targets=y_support, strict=False)
|
|
self.gp.to(self.device)
|
|
self.gp.eval()
|
|
self.likelihood.eval()
|
|
pred = self.likelihood(self.gp(x_query.to(self.device)))
|
|
mu = pred.mean.detach().to("cpu").numpy().reshape(-1, )
|
|
stddev = pred.stddev.detach().to("cpu").numpy().reshape(-1, )
|
|
return mu, stddev
|
|
|
|
def predict_finetune(self, z, labels=None, train=False):
|
|
if len(labels) > 1:
|
|
z = torch.squeeze(z)
|
|
if train:
|
|
self.gp.set_train_data(inputs=z, targets=labels, strict=False)
|
|
y_dist = self.gp(z)
|
|
predictions = self.likelihood(y_dist)
|
|
return predictions.mean, y_dist
|
|
|
|
def predict(self, D_mu, G_mu, labels=None, train=False):
|
|
input_vec = []
|
|
if 'D' in self.input_type:
|
|
input_vec.append(D_mu)
|
|
if 'G' in self.input_type:
|
|
input_vec.append(G_mu)
|
|
print(input_vec)
|
|
input_vec = torch.cat(input_vec, dim=1)
|
|
z = self.pred_fc(input_vec).double()
|
|
if train:
|
|
self.gp.set_train_data(inputs=z, targets=labels, strict=False)
|
|
y_dist = self.gp(z.type(torch.DoubleTensor))
|
|
predictions = self.likelihood(y_dist)
|
|
return predictions.mean, y_dist
|
|
|
|
def get_data_and_graph_repr(self, x, g, matrix=False):
|
|
input_vec = []
|
|
# self.pred_fc.to(self.device)
|
|
self.pred_fc.eval()
|
|
# self.inter_setpool.to(self.device)
|
|
self.inter_setpool.eval()
|
|
# self.intra_setpool.to(self.device)
|
|
self.intra_setpool.eval()
|
|
# self.grue_backward.to(self.device)
|
|
self.grue_backward.eval()
|
|
# self.grue_forward.to(self.device)
|
|
self.grue_forward.eval()
|
|
# self.gate_backward.to(self.device)
|
|
self.gate_backward.eval()
|
|
# self.gate_forward.to(self.device)
|
|
self.gate_forward.eval()
|
|
# self.mapper_backward.to(self.device)
|
|
self.mapper_backward.eval()
|
|
# self.mapper_forward.to(self.device)
|
|
self.mapper_forward.eval()
|
|
# self.hg_unify.to(self.device)
|
|
self.hg_unify.eval()
|
|
# self.hv_unify.to(self.device)
|
|
self.hv_unify.eval()
|
|
# self.fc1.to(self.device)
|
|
self.fc1.eval()
|
|
# self.fc2.to(self.device)
|
|
self.fc2.eval()
|
|
if 'D' in self.input_type:
|
|
input_vec.append(self.set_encode([x for i in range(len(g))]).to(self.device))
|
|
if 'G' in self.input_type:
|
|
input_vec.append(self.graph_encode(g, matrix=matrix).squeeze())
|
|
# print(input_vec)
|
|
if len(g) == 1:
|
|
input_vec = torch.cat(input_vec, dim=0)
|
|
print(input_vec)
|
|
else:
|
|
input_vec = torch.cat(input_vec, dim=1)
|
|
z = self.pred_fc(input_vec)
|
|
return z.detach().cpu().numpy().tolist()
|
|
|
|
def get_device(self):
|
|
if self.device is None:
|
|
self.device = next(self.parameters()).device
|
|
return self.device
|
|
|
|
def _get_zeros(self, n, length):
|
|
return torch.zeros(n, length).to(self.get_device()) # get a zero hidden state
|
|
|
|
def _get_zero_hidden(self, n=1):
|
|
return self._get_zeros(n, self.hs) # get a zero hidden state
|
|
|
|
def _one_hot(self, idx, length):
|
|
if type(idx) in [list, range]:
|
|
if idx == []:
|
|
return None
|
|
idx = torch.LongTensor(idx).unsqueeze(0).t()
|
|
x = torch.zeros((len(idx), length)).scatter_(1, idx, 1).to(self.get_device())
|
|
else:
|
|
idx = torch.LongTensor([idx]).unsqueeze(0)
|
|
x = torch.zeros((1, length)).scatter_(1, idx, 1).to(self.get_device())
|
|
return x
|
|
|
|
def _gated(self, h, gate, mapper):
|
|
return gate(h) * mapper(h)
|
|
|
|
def _collate_fn(self, G):
|
|
return [g.copy() for g in G]
|
|
|
|
def _propagate_to(self, G, v, propagator, H=None, reverse=False, gate=None, mapper=None):
|
|
# propagate messages to vertex index v for all graphs in G
|
|
# return the new messages (states) at v
|
|
G = [g for g in G if g.vcount() > v]
|
|
if len(G) == 0:
|
|
return
|
|
if H is not None:
|
|
idx = [i for i, g in enumerate(G) if g.vcount() > v]
|
|
H = H[idx]
|
|
v_types = [g.vs[v]['type'] for g in G]
|
|
X = self._one_hot(v_types, self.nvt)
|
|
if reverse:
|
|
H_name = 'H_backward' # name of the hidden states attribute
|
|
H_pred = [[g.vs[x][H_name] for x in g.successors(v)] for g in G]
|
|
if self.vid:
|
|
vids = [self._one_hot(g.successors(v), self.max_n) for g in G]
|
|
gate, mapper = self.gate_backward, self.mapper_backward
|
|
else:
|
|
H_name = 'H_forward' # name of the hidden states attribute
|
|
H_pred = [[g.vs[x][H_name] for x in g.predecessors(v)] for g in G]
|
|
if self.vid:
|
|
vids = [self._one_hot(g.predecessors(v), self.max_n) for g in G]
|
|
if gate is None:
|
|
gate, mapper = self.gate_forward, self.mapper_forward
|
|
if self.vid:
|
|
H_pred = [[torch.cat([x[i], y[i:i + 1]], 1) for i in range(len(x))] for x, y in zip(H_pred, vids)]
|
|
# if h is not provided, use gated sum of v's predecessors' states as the input hidden state
|
|
if H is None:
|
|
max_n_pred = max([len(x) for x in H_pred]) # maximum number of predecessors
|
|
if max_n_pred == 0:
|
|
H = self._get_zero_hidden(len(G))
|
|
else:
|
|
H_pred = [torch.cat(h_pred +
|
|
[self._get_zeros(max_n_pred - len(h_pred), self.vs)], 0).unsqueeze(0)
|
|
for h_pred in H_pred] # pad all to same length
|
|
H_pred = torch.cat(H_pred, 0) # batch * max_n_pred * vs
|
|
H = self._gated(H_pred, gate, mapper).sum(1) # batch * hs
|
|
Hv = propagator(X, H)
|
|
for i, g in enumerate(G):
|
|
g.vs[v][H_name] = Hv[i:i + 1]
|
|
return Hv
|
|
|
|
def _propagate_from(self, G, v, propagator, H0=None, reverse=False):
|
|
# perform a series of propagation_to steps starting from v following a topo order
|
|
# assume the original vertex indices are in a topological order
|
|
if reverse:
|
|
prop_order = range(v, -1, -1)
|
|
else:
|
|
prop_order = range(v, self.max_n)
|
|
Hv = self._propagate_to(G, v, propagator, H0, reverse=reverse) # the initial vertex
|
|
for v_ in prop_order[1:]:
|
|
self._propagate_to(G, v_, propagator, reverse=reverse)
|
|
return Hv
|
|
|
|
def _get_graph_state(self, G, decode=False):
|
|
# get the graph states
|
|
# when decoding, use the last generated vertex's state as the graph state
|
|
# when encoding, use the ending vertex state or unify the starting and ending vertex states
|
|
Hg = []
|
|
for g in G:
|
|
hg = g.vs[g.vcount() - 1]['H_forward']
|
|
if self.bidir and not decode: # decoding never uses backward propagation
|
|
hg_b = g.vs[0]['H_backward']
|
|
hg = torch.cat([hg, hg_b], 1)
|
|
Hg.append(hg)
|
|
Hg = torch.cat(Hg, 0)
|
|
if self.bidir and not decode:
|
|
Hg = self.hg_unify(Hg)
|
|
return Hg
|
|
|
|
def set_encode(self, X):
|
|
proto_batch = []
|
|
for x in X:
|
|
cls_protos = self.intra_setpool(
|
|
x.view(-1, self.num_sample, 512)).squeeze(1)
|
|
proto_batch.append(
|
|
self.inter_setpool(cls_protos.unsqueeze(0)))
|
|
v = torch.stack(proto_batch).squeeze()
|
|
return v
|
|
|
|
def graph_encode(self, G, matrix=False):
|
|
# encode graphs G into latent vectors
|
|
if matrix:
|
|
mu = torch.Tensor([decode_igraph_to_NAS201_matrix(g).flatten() for g in G]).to(self.device)
|
|
else:
|
|
if type(G) != list:
|
|
G = [G]
|
|
self._propagate_from(G, 0, self.grue_forward, H0=self._get_zero_hidden(len(G)),
|
|
reverse=False)
|
|
if self.bidir:
|
|
self._propagate_from(G, self.max_n - 1, self.grue_backward,
|
|
H0=self._get_zero_hidden(len(G)), reverse=True)
|
|
Hg = self._get_graph_state(G)
|
|
mu = self.fc1(Hg)
|
|
# logvar = self.fc2(Hg)
|
|
return mu # , logvar
|
|
|
|
def reparameterize(self, mu, logvar, eps_scale=0.01):
|
|
# return z ~ N(mu, std)
|
|
if self.training:
|
|
std = logvar.mul(0.5).exp_()
|
|
eps = torch.randn_like(std) * eps_scale
|
|
return eps.mul(std).add_(mu)
|
|
else:
|
|
return mu
|