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RNNResNet8.py
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RNNResNet8.py
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import logging
import networkx as nx
import matplotlib.pyplot as plt
from naslib.defaults.trainer import Trainer
from naslib.optimizers import DARTSOptimizer
from naslib.utils import utils, setup_logger
from naslib.search_spaces.core.graph import Graph, EdgeData
from naslib.search_spaces.core import primitives as ops
from torch import nn
from IPython.display import clear_output
import torch
from naslib.search_spaces.core.primitives import AbstractPrimitive
from activation_sub_func.binary_func import Maximum, Minimum, Sub, Add, Mul, Div, SigMul, ExpBetaSub2, ExpBetaSubAbs, \
BetaMix, Stack
from activation_sub_func.unary_func import Power, Sin, Cos, Abs_op, Sign, Beta, Beta_mul, Beta_add, Log, Exp, \
Sinh, Cosh, \
Tanh, Asinh, Atan, Maximum0, Minimum0, Sigmoid, LogExp, Exp2, Erf, Sinc, Sqrt
import argparse
import time
"""Naslib search space for ResNet8 with simple and complex activation cells"""
class ActivationFuncResNet8SearchSpace(Graph):
"""
https://www.researchgate.net/figure/ResNet-20-architecture_fig3_351046093
"""
OPTIMIZER_SCOPE = [
f"activation_{i}" for i in range(1, 8)
]
QUERYABLE = False
def __init__(self, size="small"):
super().__init__()
# cell definition
activation_cell = Graph()
activation_cell.name = 'activation_cell'
activation_cell.add_node(1) # input node
activation_cell.add_node(2) # unary node / intermediate node
activation_cell.add_node(3) # unary node / intermediate node
activation_cell.add_node(4) # binary node / output node
activation_cell.add_edges_from([(1, 2, EdgeData())]) # mutable intermediate edge
activation_cell.add_edges_from([(1, 3, EdgeData())]) # mutable intermediate edge
activation_cell.add_edges_from([(2, 4, EdgeData().finalize())]) # mutable intermediate edge
activation_cell.add_edges_from([(3, 4, EdgeData().finalize())]) # mutable intermediate edge
activation_cell.nodes[4]['comb_op'] = Stack()
activation_cell.add_node(5) # binary node / output node
activation_cell.add_edges_from([(4, 5, EdgeData())]) # mutable intermediate edge
if size == "huge":
activation_cell.add_node(6)
activation_cell.add_edges_from([(5, 6, EdgeData().finalize())]) # unary node / intermediate node
activation_cell.add_node(7)
activation_cell.add_edges_from([(6, 7, EdgeData())]) # mutable intermediate edge
activation_cell.add_node(8)
activation_cell.add_edges_from([(1, 8, EdgeData())]) # mutable intermediate edge
activation_cell.add_node(9)
activation_cell.add_edges_from([(8, 9, EdgeData().finalize())]) # mutable intermediate edge
activation_cell.add_edges_from([(7, 9, EdgeData().finalize())]) # mutable intermediate edge
activation_cell.nodes[9]['comb_op'] = Stack()
activation_cell.add_node(10)
activation_cell.add_edges_from([(9, 10, EdgeData())]) # mutable intermediate edge
activation_cell.add_node(11)
activation_cell.add_edges_from([(10, 11, EdgeData().finalize())]) # mutable intermediate edge
else:
activation_cell.add_node(6)
activation_cell.add_edges_from([(5, 6, EdgeData().finalize())]) # mutable intermediate edge
# macroarchitecture definition
self.name = 'makrograph'
self.add_node(1) # input
self.add_node(2) # intermediate
self.add_node(3,
subgraph=activation_cell.copy().set_scope("activation_1").set_input([2])) # activation cell 3
# self.nodes[3]['subgraph'].name = "activation_1"
self.update_edges(
update_func=lambda edge: self._set_ops(edge, 16),
scope=f"activation_{1}",
private_edge_data=True, )
self.add_node(4)
self.add_node(5,
subgraph=activation_cell.copy().set_scope("activation_2").set_input([4])) # activation cell 3
# self.nodes[5]['subgraph'].name = "activation_2"
self.update_edges(
update_func=lambda edge: self._set_ops(edge, 16),
scope=f"activation_{2}",
private_edge_data=True, )
self.add_node(6)
self.add_node(7,
subgraph=activation_cell.copy().set_scope("activation_3").set_input([6])) # activation cell 3
# self.nodes[7]['subgraph'].name = "activation_3"
self.update_edges(
update_func=lambda edge: self._set_ops(edge, 16),
scope=f"activation_{3}",
private_edge_data=True, )
self.add_edges_from([
(1, 2, EdgeData()),
(2, 3, EdgeData()),
(3, 4, EdgeData()),
(4, 5, EdgeData()),
(5, 6, EdgeData()),
(3, 6, EdgeData()),
(6, 7, EdgeData())
])
self.edges[1, 2].set('op',
ops.Sequential(
nn.Conv2d(3, 16, 3, padding=1), nn.BatchNorm2d(16))) # convolutional edge
self.edges[3, 4].set('op',
ops.Sequential(
nn.Conv2d(16, 16, 3, padding=1), nn.BatchNorm2d(16))) # convolutional edge
self.edges[5, 6].set('op',
ops.Sequential(
nn.Conv2d(16, 16, 3, padding=1), nn.BatchNorm2d(16))) # convolutional edge
conv_option = {
"in_channels": 16,
"out_channels": 16,
"kernel_size": 3,
"padding": 1
}
self._create_base_block(7, 4, activation_cell, conv_option)
self._create_base_block(11, 6, activation_cell, conv_option)
# add head
self.add_node(16) # 34 + 5
self.add_edges_from([
(15, 16, EdgeData())
])
self.edges[15, 16].set('op',
ops.Sequential(
nn.AvgPool2d(8),
nn.Flatten(),
nn.Linear(256, 10),
nn.Softmax()
)) # convolutional edge
self.add_node(17)
self.add_edges_from([
(16, 17, EdgeData().finalize())
])
def _create_base_block(self, start: int, stage: int, cell, conv_option: dict):
self.add_node(start + 1)
self.add_node(start + 2, subgraph=cell.copy().set_scope(f"activation_{stage}").set_input(
[start + 1])) # activation cell 3
# self.nodes[start + 2]['subgraph'].name = f"activation_{stage}"
self.update_edges(
update_func=lambda edge: self._set_ops(edge, conv_option["out_channels"]),
scope=f"activation_{stage}",
private_edge_data=True, )
self.add_node(start + 3)
self.add_node(start + 4, subgraph=cell.copy().set_scope(f"activation_{stage + 1}").set_input(
[start + 3])) # activation cell 3
# self.nodes[start + 4]['subgraph'].name = f"activation_{stage + 1}"
self.update_edges(
update_func=lambda edge: self._set_ops(edge, conv_option["out_channels"]),
scope=f"activation_{stage + 1}",
private_edge_data=True, )
self.add_edges_from([
(start, start + 1, EdgeData()),
(start, start + 3, EdgeData()),
(start + 1, start + 2, EdgeData()),
(start + 2, start + 3, EdgeData()),
(start + 3, start + 4, EdgeData()),
])
self.edges[start, start + 1].set('op',
ops.Sequential(
nn.Conv2d(**conv_option),
nn.BatchNorm2d(conv_option["out_channels"]), )) # convolutional edge
self.edges[start + 2, start + 3].set('op',
ops.Sequential(
nn.Conv2d(**conv_option),
nn.BatchNorm2d(conv_option["out_channels"]), )) # convolutional edge
def _create_reduction_block(self, start: int, stage: int, cell, conv_option_a: dict, conv_option_b: dict):
self.add_node(start + 1)
self.add_node(start + 2, subgraph=cell.copy().set_scope(f"activation_{stage}").set_input(
[start + 1])) # activation cell 3
# self.nodes[start + 2]['subgraph'].name = f"activation_{stage}"
self.update_edges(
update_func=lambda edge: self._set_ops(edge, conv_option_a["out_channels"]),
scope=f"activation_{stage}",
private_edge_data=True, )
self.add_node(start + 3)
self.add_node(start + 4, subgraph=cell.copy().set_scope(f"activation_{stage + 1}").set_input(
[start + 3])) # activation cell 3
# self.nodes[start + 4]['subgraph'].name = f"activation_{stage + 1}"
self.update_edges(
update_func=lambda edge: self._set_ops(edge, conv_option_b["out_channels"]),
scope=f"activation_{stage + 1}",
private_edge_data=True, )
self.add_edges_from([
(start, start + 1, EdgeData()),
(start, start + 3, EdgeData()), # add conv
(start + 1, start + 2, EdgeData()),
(start + 2, start + 3, EdgeData()),
(start + 3, start + 4, EdgeData()),
])
self.edges[start, start + 1].set('op',
ops.Sequential(
nn.Conv2d(**conv_option_a),
nn.BatchNorm2d(conv_option_a["out_channels"]))) # convolutional edge
conv_option_a["in_channels"] = conv_option_a["out_channels"]
conv_option_a["stride"] = 1
self.edges[start, start + 3].set('op',
ops.Sequential(
nn.Conv2d(**conv_option_b),
nn.BatchNorm2d(conv_option_b["out_channels"]), )) # convolutional edge
self.edges[start + 2, start + 3].set('op',
ops.Sequential(
nn.Conv2d(**conv_option_a),
nn.BatchNorm2d(conv_option_a["out_channels"]), )) # convolutional edge
def _set_ops(self, edge, channels=32):
# unary (1, 2), (1, 3), (1, 8), (6, 7)
if (edge.head, edge.tail) in {(1, 2), (1, 3), (1, 8), (6, 7)}:
edge.data.set("op", [
ops.Identity(),
ops.Zero(stride=1),
Power(2),
Power(3),
Sqrt(),
Sin(),
Cos(),
Abs_op(),
Sign(),
Beta_mul(channels=channels),
Beta_add(channels=channels),
Log(),
Exp(),
Sinh(),
Cosh(),
Tanh(),
Asinh(),
Atan(),
Sinc(),
Maximum0(),
Minimum0(),
Sigmoid(),
LogExp(),
Exp2(),
Erf(),
Beta(channels=channels),
])
# binary (4, 5), (9, 10)
elif (edge.head, edge.tail) in {(4, 5), (9, 10)}:
edge.data.set("op", [
Add(),
Sub(),
Mul(),
Div(),
Maximum(),
Minimum(),
SigMul(),
ExpBetaSub2(channels=channels),
ExpBetaSubAbs(channels=channels),
BetaMix(channels=channels),
])
if __name__ == '__main__':
config = utils.get_config_from_args(config_type='nas')
config.optimizer = 'darts' # 'gdas', 'drnas'
utils.set_seed(config.seed)
config.search.batch_size = 64
config.search.epochs = 100
config.search.lr = 0.025
config.run_id = time.time()
config.save = f'{config.out_dir}/{config.dataset}/{config.optimizer}/{config.run_id}'
config.evaluation.epochs = 100
clear_output(wait=True)
utils.log_args(config)
utils.create_exp_dir(config.save)
utils.create_exp_dir(config.save + "/search")
utils.create_exp_dir(config.save + "/eval")
torch.manual_seed(config.search.seed)
logger = setup_logger(config.save + '/log.log')
logger.setLevel(logging.INFO)
search_space = ActivationFuncResNet8SearchSpace()
# nx.draw_kamada_kawai(search_space)
# plt.show()
optimizer = DARTSOptimizer(config)
optimizer.adapt_search_space(search_space)
# with torch.autograd.set_detect_anomaly(True):
trainer = Trainer(optimizer, config)
trainer.search()
trainer.evaluate(retrain=False)