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Upgrade API of NAS-Bench-201

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This commit is contained in:
D-X-Y
2020-03-10 19:08:56 +11:00
parent c8f2a93ecf
commit d783193392
10 changed files with 623 additions and 178 deletions
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3
lib/nas_201_api/__init__.py
View File

@@ -4,4 +4,5 @@
from .api import NASBench201API
from .api import ArchResults, ResultsCount
NAS_BENCH_201_API_VERSION="v1.1" # [2020.02.25]
# NAS_BENCH_201_API_VERSION="v1.1" # [2020.02.25]
NAS_BENCH_201_API_VERSION="v1.2" # [2020.03.09]

378
lib/nas_201_api/api.py
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@@ -8,7 +8,7 @@
#
#
import os, copy, random, torch, numpy as np
from typing import List, Text, Union, Dict, Any
from typing import List, Text, Union, Dict
from collections import OrderedDict, defaultdict
@@ -19,8 +19,7 @@ def print_information(information, extra_info=None, show=False):
return 'loss = {:.3f}, top1 = {:.2f}%'.format(loss, acc)
for ida, dataset in enumerate(dataset_names):
#flop, param, latency = information.get_comput_costs(dataset)
metric = information.get_comput_costs(dataset)
metric = information.get_compute_costs(dataset)
flop, param, latency = metric['flops'], metric['params'], metric['latency']
str1 = '{:14s} FLOP={:6.2f} M, Params={:.3f} MB, latency={:} ms.'.format(dataset, flop, param, '{:.2f}'.format(latency*1000) if latency is not None and latency > 0 else None)
train_info = information.get_metrics(dataset, 'train')
@@ -80,6 +79,7 @@ class NASBench201API(object):
return ('{name}({num}/{total} architectures)'.format(name=self.__class__.__name__, num=len(self.evaluated_indexes), total=len(self.meta_archs)))
def random(self):
"""Return a random index of all architectures."""
return random.randint(0, len(self.meta_archs)-1)
# This function is used to query the index of an architecture in the search space.
@@ -166,7 +166,7 @@ class NASBench201API(object):
else : basestr, arch2infos = '200epochs', self.arch2infos_full
best_index, highest_accuracy = -1, None
for i, idx in enumerate(self.evaluated_indexes):
info = arch2infos[idx].get_comput_costs(dataset)
info = arch2infos[idx].get_compute_costs(dataset)
flop, param, latency = info['flops'], info['params'], info['latency']
if FLOP_max is not None and flop > FLOP_max : continue
if Param_max is not None and param > Param_max: continue
@@ -178,38 +178,40 @@ class NASBench201API(object):
best_index, highest_accuracy = idx, accuracy
return best_index, highest_accuracy
# return the topology structure of the `index`-th architecture
def arch(self, index: int):
"""Return the topology structure of the `index`-th architecture."""
assert 0 <= index < len(self.meta_archs), 'invalid index : {:} vs. {:}.'.format(index, len(self.meta_archs))
return copy.deepcopy(self.meta_archs[index])
"""
This function is used to obtain the trained weights of the `index`-th architecture on `dataset` with the seed of `seed`
Args [seed]:
-- None : return a dict containing the trained weights of all trials, where each key is a seed and its corresponding value is the weights.
-- a interger : return the weights of a specific trial, whose seed is this interger.
Args [use_12epochs_result]:
-- True : train the model by 12 epochs
-- False : train the model by 200 epochs
"""
def get_net_param(self, index, dataset, seed, use_12epochs_result=False):
if use_12epochs_result: basestr, arch2infos = '12epochs' , self.arch2infos_less
else : basestr, arch2infos = '200epochs', self.arch2infos_full
archresult = arch2infos[index]
return archresult.get_net_param(dataset, seed)
"""
This function is used to obtain the trained weights of the `index`-th architecture on `dataset` with the seed of `seed`
Args [seed]:
-- None : return a dict containing the trained weights of all trials, where each key is a seed and its corresponding value is the weights.
-- a interger : return the weights of a specific trial, whose seed is this interger.
Args [use_12epochs_result]:
-- True : train the model by 12 epochs
-- False : train the model by 200 epochs
"""
if use_12epochs_result: arch2infos = self.arch2infos_less
else: arch2infos = self.arch2infos_full
arch_result = arch2infos[index]
return arch_result.get_net_param(dataset, seed)
"""
This function is used to obtain the configuration for the `index`-th architecture on `dataset`.
Args [dataset] (4 possible options):
-- cifar10-valid : training the model on the CIFAR-10 training set.
-- cifar10 : training the model on the CIFAR-10 training + validation set.
-- cifar100 : training the model on the CIFAR-100 training set.
-- ImageNet16-120 : training the model on the ImageNet16-120 training set.
This function will return a dict.
========= Some examlpes for using this function:
config = api.get_net_config(128, 'cifar10')
"""
def get_net_config(self, index, dataset):
def get_net_config(self, index: int, dataset: Text):
"""
This function is used to obtain the configuration for the `index`-th architecture on `dataset`.
Args [dataset] (4 possible options):
-- cifar10-valid : training the model on the CIFAR-10 training set.
-- cifar10 : training the model on the CIFAR-10 training + validation set.
-- cifar100 : training the model on the CIFAR-100 training set.
-- ImageNet16-120 : training the model on the ImageNet16-120 training set.
This function will return a dict.
========= Some examlpes for using this function:
config = api.get_net_config(128, 'cifar10')
"""
archresult = self.arch2infos_full[index]
all_results = archresult.query(dataset, None)
if len(all_results) == 0: raise ValueError('can not find one valid trial for the {:}-th architecture on {:}'.format(index, dataset))
@@ -218,12 +220,25 @@ class NASBench201API(object):
#print ('SEED [{:}] : {:}'.format(seed, result))
raise ValueError('Impossible to reach here!')
# obtain the cost metric for the `index`-th architecture on a dataset
def get_cost_info(self, index, dataset, use_12epochs_result=False):
if use_12epochs_result: basestr, arch2infos = '12epochs' , self.arch2infos_less
else : basestr, arch2infos = '200epochs', self.arch2infos_full
archresult = arch2infos[index]
return archresult.get_comput_costs(dataset)
def get_cost_info(self, index: int, dataset: Text, use_12epochs_result: bool = False) -> Dict[Text, float]:
"""To obtain the cost metric for the `index`-th architecture on a dataset."""
if use_12epochs_result: arch2infos = self.arch2infos_less
else: arch2infos = self.arch2infos_full
arch_result = arch2infos[index]
return arch_result.get_compute_costs(dataset)
def get_latency(self, index: int, dataset: Text, use_12epochs_result: bool = False) -> float:
"""
To obtain the latency of the network (by default it will return the latency with the batch size of 256).
:param index: the index of the target architecture
:param dataset: the dataset name (cifar10-valid, cifar10, cifar100, ImageNet16-120)
:return: return a float value in seconds
"""
cost_dict = self.get_cost_info(index, dataset, use_12epochs_result)
return cost_dict['latency']
# obtain the metric for the `index`-th architecture
# `dataset` indicates the dataset:
@@ -298,12 +313,15 @@ class NASBench201API(object):
xifo['est-valid-accuracy'] = est_valid_info['accuracy']
return xifo
"""
This function will print the information of a specific (or all) architecture(s).
If the index < 0: it will loop for all architectures and print their information one by one.
else: it will print the information of the 'index'-th archiitecture.
"""
def show(self, index: int = -1) -> None:
"""
This function will print the information of a specific (or all) architecture(s).
:param index: If the index < 0: it will loop for all architectures and print their information one by one.
else: it will print the information of the 'index'-th archiitecture.
:return: nothing
"""
if index < 0: # show all architectures
print(self)
for i, idx in enumerate(self.evaluated_indexes):
@@ -330,19 +348,27 @@ class NASBench201API(object):
else:
print('This index ({:}) is out of range (0~{:}).'.format(index, len(self.meta_archs)))
# This func shows how to read the string-based architecture encoding
# the same as the `str2structure` func in `AutoDL-Projects/lib/models/cell_searchs/genotypes.py`
# Usage:
# arch = api.str2lists( '|nor_conv_1x1~0|+|none~0|none~1|+|none~0|none~1|skip_connect~2|' )
# print ('there are {:} nodes in this arch'.format(len(arch)+1)) # arch is a list
# for i, node in enumerate(arch):
# print('the {:}-th node is the sum of these {:} nodes with op: {:}'.format(i+1, len(node), node))
@staticmethod
def str2lists(xstr: Text) -> List[Any]:
# assert isinstance(xstr, str), 'must take string (not {:}) as input'.format(type(xstr))
nodestrs = xstr.split('+')
def str2lists(arch_str: Text) -> List[tuple]:
"""
This function shows how to read the string-based architecture encoding.
It is the same as the `str2structure` func in `AutoDL-Projects/lib/models/cell_searchs/genotypes.py`
:param
arch_str: the input is a string indicates the architecture topology, such as
|nor_conv_1x1~0|+|none~0|none~1|+|none~0|none~1|skip_connect~2|
:return: a list of tuple, contains multiple (op, input_node_index) pairs.
:usage
arch = api.str2lists( '|nor_conv_1x1~0|+|none~0|none~1|+|none~0|none~1|skip_connect~2|' )
print ('there are {:} nodes in this arch'.format(len(arch)+1)) # arch is a list
for i, node in enumerate(arch):
print('the {:}-th node is the sum of these {:} nodes with op: {:}'.format(i+1, len(node), node))
"""
node_strs = arch_str.split('+')
genotypes = []
for i, node_str in enumerate(nodestrs):
for i, node_str in enumerate(node_strs):
inputs = list(filter(lambda x: x != '', node_str.split('|')))
for xinput in inputs: assert len(xinput.split('~')) == 2, 'invalid input length : {:}'.format(xinput)
inputs = ( xi.split('~') for xi in inputs )
@@ -350,40 +376,47 @@ class NASBench201API(object):
genotypes.append( input_infos )
return genotypes
# This func shows how to convert the string-based architecture encoding to the encoding strategy in NAS-Bench-101
# Usage:
# # this will return a numpy matrix (2-D np.array)
# matrix = api.str2matrix( '|nor_conv_1x1~0|+|none~0|none~1|+|none~0|none~1|skip_connect~2|' )
# # This matrix is 4-by-4 matrix representing a cell with 4 nodes (only the lower left triangle is useful).
# [ [0, 0, 0, 0], # the first line represents the input (0-th) node
# [2, 0, 0, 0], # the second line represents the 1-st node, is calculated by 2-th-op( 0-th-node )
# [0, 0, 0, 0], # the third line represents the 2-nd node, is calculated by 0-th-op( 0-th-node ) + 0-th-op( 1-th-node )
# [0, 0, 1, 0] ] # the fourth line represents the 3-rd node, is calculated by 0-th-op( 0-th-node ) + 0-th-op( 1-th-node ) + 1-th-op( 2-th-node )
# In NAS-Bench-201 search space, 0-th-op is 'none', 1-th-op is 'skip_connect'
# 2-th-op is 'nor_conv_1x1', 3-th-op is 'nor_conv_3x3', 4-th-op is 'avg_pool_3x3'.
@staticmethod
def str2matrix(xstr):
assert isinstance(xstr, str), 'must take string (not {:}) as input'.format(type(xstr))
# this only support NAS-Bench-201 search space
# this defination will be consistant with this line https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/models/cell_operations.py#L24
# If a node has two input-edges from the same node, this function does not work. One edge will be overleaped.
NAS_BENCH_201 = ['none', 'skip_connect', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3']
nodestrs = xstr.split('+')
num_nodes = len(nodestrs) + 1
matrix = np.zeros((num_nodes,num_nodes))
for i, node_str in enumerate(nodestrs):
def str2matrix(arch_str: Text,
search_space: List[Text] = ['none', 'skip_connect', 'nor_conv_1x1', 'nor_conv_3x3', 'avg_pool_3x3']) -> np.ndarray:
"""
This func shows how to convert the string-based architecture encoding to the encoding strategy in NAS-Bench-101.
:param
arch_str: the input is a string indicates the architecture topology, such as
|nor_conv_1x1~0|+|none~0|none~1|+|none~0|none~1|skip_connect~2|
search_space: a list of operation string, the default list is the search space for NAS-Bench-201
the default value should be be consistent with this line https://github.com/D-X-Y/AutoDL-Projects/blob/master/lib/models/cell_operations.py#L24
:return
the numpy matrix (2-D np.ndarray) representing the DAG of this architecture topology
:usage
matrix = api.str2matrix( '|nor_conv_1x1~0|+|none~0|none~1|+|none~0|none~1|skip_connect~2|' )
This matrix is 4-by-4 matrix representing a cell with 4 nodes (only the lower left triangle is useful).
[ [0, 0, 0, 0], # the first line represents the input (0-th) node
[2, 0, 0, 0], # the second line represents the 1-st node, is calculated by 2-th-op( 0-th-node )
[0, 0, 0, 0], # the third line represents the 2-nd node, is calculated by 0-th-op( 0-th-node ) + 0-th-op( 1-th-node )
[0, 0, 1, 0] ] # the fourth line represents the 3-rd node, is calculated by 0-th-op( 0-th-node ) + 0-th-op( 1-th-node ) + 1-th-op( 2-th-node )
In NAS-Bench-201 search space, 0-th-op is 'none', 1-th-op is 'skip_connect',
2-th-op is 'nor_conv_1x1', 3-th-op is 'nor_conv_3x3', 4-th-op is 'avg_pool_3x3'.
:(NOTE)
If a node has two input-edges from the same node, this function does not work. One edge will be overlapped.
"""
node_strs = arch_str.split('+')
num_nodes = len(node_strs) + 1
matrix = np.zeros((num_nodes, num_nodes))
for i, node_str in enumerate(node_strs):
inputs = list(filter(lambda x: x != '', node_str.split('|')))
for xinput in inputs: assert len(xinput.split('~')) == 2, 'invalid input length : {:}'.format(xinput)
for xi in inputs:
op, idx = xi.split('~')
if op not in NAS_BENCH_201: raise ValueError('this op ({:}) is not in {:}'.format(op, NAS_BENCH_201))
op_idx, node_idx = NAS_BENCH_201.index(op), int(idx)
if op not in search_space: raise ValueError('this op ({:}) is not in {:}'.format(op, search_space))
op_idx, node_idx = search_space.index(op), int(idx)
matrix[i+1, node_idx] = op_idx
return matrix
class ArchResults(object):
def __init__(self, arch_index, arch_str):
@@ -393,15 +426,15 @@ class ArchResults(object):
self.dataset_seed = dict()
self.clear_net_done = False
def get_comput_costs(self, dataset):
def get_compute_costs(self, dataset):
x_seeds = self.dataset_seed[dataset]
results = [self.all_results[ (dataset, seed) ] for seed in x_seeds]
flops = [result.flop for result in results]
params = [result.params for result in results]
lantencies = [result.get_latency() for result in results]
lantencies = [x for x in lantencies if x > 0]
mean_latency = np.mean(lantencies) if len(lantencies) > 0 else None
flops = [result.flop for result in results]
params = [result.params for result in results]
latencies = [result.get_latency() for result in results]
latencies = [x for x in latencies if x > 0]
mean_latency = np.mean(latencies) if len(latencies) > 0 else None
time_infos = defaultdict(list)
for result in results:
time_info = result.get_times()
@@ -416,38 +449,38 @@ class ArchResults(object):
else: info[key] = None
return info
"""
This `get_metrics` function is used to obtain obtain the loss, accuracy, etc information on a specific dataset.
If not specify, each set refer to the proposed split in NAS-Bench-201 paper.
If some args return None or raise error, then it is not avaliable.
========================================
Args [dataset] (4 possible options):
-- cifar10-valid : training the model on the CIFAR-10 training set.
-- cifar10 : training the model on the CIFAR-10 training + validation set.
-- cifar100 : training the model on the CIFAR-100 training set.
-- ImageNet16-120 : training the model on the ImageNet16-120 training set.
Args [setname] (each dataset has different setnames):
-- When dataset = cifar10-valid, you can use 'train', 'x-valid', 'ori-test'
------ 'train' : the metric on the training set.
------ 'x-valid' : the metric on the validation set.
------ 'ori-test' : the metric on the test set.
-- When dataset = cifar10, you can use 'train', 'ori-test'.
------ 'train' : the metric on the training + validation set.
------ 'ori-test' : the metric on the test set.
-- When dataset = cifar100 or ImageNet16-120, you can use 'train', 'ori-test', 'x-valid', 'x-test'
------ 'train' : the metric on the training set.
------ 'x-valid' : the metric on the validation set.
------ 'x-test' : the metric on the test set.
------ 'ori-test' : the metric on the validation + test set.
Args [iepoch] (None or an integer in [0, the-number-of-total-training-epochs)
------ None : return the metric after the last training epoch.
------ an integer i : return the metric after the i-th training epoch.
Args [is_random]:
------ True : return the metric of a randomly selected trial.
------ False : return the averaged metric of all avaliable trials.
------ an integer indicating the 'seed' value : return the metric of a specific trial (whose random seed is 'is_random').
"""
def get_metrics(self, dataset, setname, iepoch=None, is_random=False):
"""
This `get_metrics` function is used to obtain obtain the loss, accuracy, etc information on a specific dataset.
If not specify, each set refer to the proposed split in NAS-Bench-201 paper.
If some args return None or raise error, then it is not avaliable.
========================================
Args [dataset] (4 possible options):
-- cifar10-valid : training the model on the CIFAR-10 training set.
-- cifar10 : training the model on the CIFAR-10 training + validation set.
-- cifar100 : training the model on the CIFAR-100 training set.
-- ImageNet16-120 : training the model on the ImageNet16-120 training set.
Args [setname] (each dataset has different setnames):
-- When dataset = cifar10-valid, you can use 'train', 'x-valid', 'ori-test'
------ 'train' : the metric on the training set.
------ 'x-valid' : the metric on the validation set.
------ 'ori-test' : the metric on the test set.
-- When dataset = cifar10, you can use 'train', 'ori-test'.
------ 'train' : the metric on the training + validation set.
------ 'ori-test' : the metric on the test set.
-- When dataset = cifar100 or ImageNet16-120, you can use 'train', 'ori-test', 'x-valid', 'x-test'
------ 'train' : the metric on the training set.
------ 'x-valid' : the metric on the validation set.
------ 'x-test' : the metric on the test set.
------ 'ori-test' : the metric on the validation + test set.
Args [iepoch] (None or an integer in [0, the-number-of-total-training-epochs)
------ None : return the metric after the last training epoch.
------ an integer i : return the metric after the i-th training epoch.
Args [is_random]:
------ True : return the metric of a randomly selected trial.
------ False : return the averaged metric of all avaliable trials.
------ an integer indicating the 'seed' value : return the metric of a specific trial (whose random seed is 'is_random').
"""
x_seeds = self.dataset_seed[dataset]
results = [self.all_results[ (dataset, seed) ] for seed in x_seeds]
infos = defaultdict(list)
@@ -483,20 +516,55 @@ class ArchResults(object):
def get_dataset_seeds(self, dataset):
return copy.deepcopy( self.dataset_seed[dataset] )
"""
This function will return the trained network's weights on the 'dataset'.
When the 'seed' is None, it will return the weights for every run trial in the form of a dict.
When the
"""
def get_net_param(self, dataset, seed=None):
def get_net_param(self, dataset: Text, seed: Union[None, int] =None):
"""
This function will return the trained network's weights on the 'dataset'.
:arg
dataset: one of 'cifar10-valid', 'cifar10', 'cifar100', and 'ImageNet16-120'.
seed: an integer indicates the seed value or None that indicates returing all trials.
"""
if seed is None:
x_seeds = self.dataset_seed[dataset]
return {seed: self.all_results[(dataset, seed)].get_net_param() for seed in x_seeds}
else:
return self.all_results[(dataset, seed)].get_net_param()
# get the total number of training epochs
def reset_latency(self, dataset: Text, seed: Union[None, Text], latency: float) -> None:
"""This function is used to reset the latency in all corresponding ResultsCount(s)."""
if seed is None:
for seed in self.dataset_seed[dataset]:
self.all_results[(dataset, seed)].update_latency([latency])
else:
self.all_results[(dataset, seed)].update_latency([latency])
def reset_pseudo_train_times(self, dataset: Text, seed: Union[None, Text], estimated_per_epoch_time: float) -> None:
"""This function is used to reset the train-times in all corresponding ResultsCount(s)."""
if seed is None:
for seed in self.dataset_seed[dataset]:
self.all_results[(dataset, seed)].reset_pseudo_train_times(estimated_per_epoch_time)
else:
self.all_results[(dataset, seed)].reset_pseudo_train_times(estimated_per_epoch_time)
def reset_pseudo_eval_times(self, dataset: Text, seed: Union[None, Text], eval_name: Text, estimated_per_epoch_time: float) -> None:
"""This function is used to reset the eval-times in all corresponding ResultsCount(s)."""
if seed is None:
for seed in self.dataset_seed[dataset]:
self.all_results[(dataset, seed)].reset_pseudo_eval_times(eval_name, estimated_per_epoch_time)
else:
self.all_results[(dataset, seed)].reset_pseudo_eval_times(eval_name, estimated_per_epoch_time)
def get_latency(self, dataset: Text) -> float:
"""Get the latency of a model on the target dataset. [Timestamp: 2020.03.09]"""
latencies = []
for seed in self.dataset_seed[dataset]:
latency = self.all_results[(dataset, seed)].get_latency()
if not isinstance(latency, float) or latency <= 0:
raise ValueError('invalid latency of {:} for {:} with {:}'.format(dataset))
latencies.append(latency)
return sum(latencies) / len(latencies)
def get_total_epoch(self, dataset=None):
"""Return the total number of training epochs."""
if dataset is None:
epochss = []
for xdata, x_seeds in self.dataset_seed.items():
@@ -509,13 +577,13 @@ class ArchResults(object):
if len(set(epochss)) > 1: raise ValueError('Each trial mush have the same number of training epochs : {:}'.format(epochss))
return epochss[-1]
# return the ResultsCount object (containing all information of a single trial) for 'dataset' and 'seed'
def query(self, dataset, seed=None):
"""Return the ResultsCount object (containing all information of a single trial) for 'dataset' and 'seed'"""
if seed is None:
x_seeds = self.dataset_seed[dataset]
return {seed: self.all_results[ (dataset, seed) ] for seed in x_seeds}
return {seed: self.all_results[(dataset, seed)] for seed in x_seeds}
else:
return self.all_results[ (dataset, seed) ]
return self.all_results[(dataset, seed)]
def arch_idx_str(self):
return '{:06d}'.format(self.arch_index)
@@ -573,7 +641,18 @@ class ArchResults(object):
def clear_params(self):
for key, result in self.all_results.items():
result.net_state_dict = None
self.clear_net_done = True
self.clear_net_done = True
def debug_test(self):
"""This function is used for me to debug and test, which will call most methods."""
all_dataset = ['cifar10-valid', 'cifar10', 'cifar100', 'ImageNet16-120']
for dataset in all_dataset:
print('---->>>> {:}'.format(dataset))
print('The latency on {:} is {:} s'.format(dataset, self.get_latency(dataset)))
for seed in self.dataset_seed[dataset]:
result = self.all_results[(dataset, seed)]
print(' ==>> result = {:}'.format(result))
print(' ==>> cost = {:}'.format(result.get_times()))
def __repr__(self):
return ('{name}(arch-index={index}, arch={arch}, {num} runs, clear={clear})'.format(name=self.__class__.__name__, index=self.arch_index, arch=self.arch_str, num=len(self.all_results), clear=self.clear_net_done))
@@ -603,12 +682,25 @@ class ResultsCount(object):
# evaluation results
self.reset_eval()
def update_train_info(self, train_acc1es, train_acc5es, train_losses, train_times):
def update_train_info(self, train_acc1es, train_acc5es, train_losses, train_times) -> None:
self.train_acc1es = train_acc1es
self.train_acc5es = train_acc5es
self.train_losses = train_losses
self.train_times = train_times
def reset_pseudo_train_times(self, estimated_per_epoch_time: float) -> None:
"""Assign the training times."""
train_times = OrderedDict()
for i in range(self.epochs):
train_times[i] = estimated_per_epoch_time
self.train_times = train_times
def reset_pseudo_eval_times(self, eval_name: Text, estimated_per_epoch_time: float) -> None:
"""Assign the evaluation times."""
if eval_name not in self.eval_names: raise ValueError('invalid eval name : {:}'.format(eval_name))
for i in range(self.epochs):
self.eval_times['{:}@{:}'.format(eval_name,i)] = estimated_per_epoch_time
def reset_eval(self):
self.eval_names = []
self.eval_acc1es = {}
@@ -618,6 +710,11 @@ class ResultsCount(object):
def update_latency(self, latency):
self.latency = copy.deepcopy( latency )
def get_latency(self) -> float:
"""Return the latency value in seconds. -1 represents not avaliable ; otherwise it should be a float value"""
if self.latency is None: return -1.0
else: return sum(self.latency) / len(self.latency)
def update_eval(self, accs, losses, times): # new version
data_names = set([x.split('@')[0] for x in accs.keys()])
for data_name in data_names:
@@ -642,28 +739,22 @@ class ResultsCount(object):
set_name = '[' + ', '.join(self.eval_names) + ']'
return ('{name}({xname}, arch={arch}, FLOP={flop:.2f}M, Param={param:.3f}MB, seed={seed}, {num_eval} eval-sets: {set_name})'.format(name=self.__class__.__name__, xname=self.name, arch=self.arch_config['arch_str'], flop=self.flop, param=self.params, seed=self.seed, num_eval=num_eval, set_name=set_name))
# get the total number of training epochs
def get_total_epoch(self):
return copy.deepcopy(self.epochs)
# get the latency
# -1 represents not avaliable ; otherwise it should be a float value
def get_latency(self):
if self.latency is None: return -1
else: return sum(self.latency) / len(self.latency)
# get the information regarding time
def get_times(self):
"""Obtain the information regarding both training and evaluation time."""
if self.train_times is not None and isinstance(self.train_times, dict):
train_times = list( self.train_times.values() )
time_info = {'T-train@epoch': np.mean(train_times), 'T-train@total': np.sum(train_times)}
for name in self.eval_names:
else:
time_info = {'T-train@epoch': None, 'T-train@total': None }
for name in self.eval_names:
try:
xtimes = [self.eval_times['{:}@{:}'.format(name,i)] for i in range(self.epochs)]
time_info['T-{:}@epoch'.format(name)] = np.mean(xtimes)
time_info['T-{:}@total'.format(name)] = np.sum(xtimes)
else:
time_info = {'T-train@epoch': None, 'T-train@total': None }
for name in self.eval_names:
except:
time_info['T-{:}@epoch'.format(name)] = None
time_info['T-{:}@total'.format(name)] = None
return time_info
@@ -699,18 +790,19 @@ class ResultsCount(object):
'cur_time': xtime,
'all_time': atime}
def get_net_param(self):
return self.net_state_dict
def get_net_param(self, clone=False):
if clone: return copy.deepcopy(self.net_state_dict)
else: return self.net_state_dict
# This function is used to obtain the config dict for this architecture.
def get_config(self, str2structure):
if str2structure is None:
return {'name': 'infer.tiny', 'C': self.arch_config['channel'], \
'N' : self.arch_config['num_cells'], \
return {'name': 'infer.tiny', 'C': self.arch_config['channel'],
'N' : self.arch_config['num_cells'],
'arch_str': self.arch_config['arch_str'], 'num_classes': self.arch_config['class_num']}
else:
return {'name': 'infer.tiny', 'C': self.arch_config['channel'], \
'N' : self.arch_config['num_cells'], \
return {'name': 'infer.tiny', 'C': self.arch_config['channel'],
'N' : self.arch_config['num_cells'],
'genotype': str2structure(self.arch_config['arch_str']), 'num_classes': self.arch_config['class_num']}
def state_dict(self):

1
lib/procedures/__init__.py
View File

@@ -5,6 +5,7 @@ from .starts import prepare_seed, prepare_logger, get_machine_info, save_che
from .optimizers import get_optim_scheduler
from .funcs_nasbench import evaluate_for_seed as bench_evaluate_for_seed
from .funcs_nasbench import pure_evaluate as bench_pure_evaluate
from .funcs_nasbench import get_nas_bench_loaders
def get_procedures(procedure):
from .basic_main import basic_train, basic_valid

76
lib/procedures/funcs_nasbench.py
View File

@@ -1,14 +1,17 @@
#####################################################
# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.08 #
#####################################################
import time, torch
import os, time, copy, torch, pathlib
import datasets
from config_utils import load_config
from procedures import prepare_seed, get_optim_scheduler
from utils import get_model_infos, obtain_accuracy
from log_utils import AverageMeter, time_string, convert_secs2time
from models import get_cell_based_tiny_net
__all__ = ['evaluate_for_seed', 'pure_evaluate']
__all__ = ['evaluate_for_seed', 'pure_evaluate', 'get_nas_bench_loaders']
def pure_evaluate(xloader, network, criterion=torch.nn.CrossEntropyLoss()):
@@ -127,3 +130,72 @@ def evaluate_for_seed(arch_config, opt_config, train_loader, valid_loaders, seed
'finish-train': True
}
return info_seed
def get_nas_bench_loaders(workers):
torch.set_num_threads(workers)
root_dir = (pathlib.Path(__file__).parent / '..' / '..').resolve()
torch_dir = pathlib.Path(os.environ['TORCH_HOME'])
# cifar
cifar_config_path = root_dir / 'configs' / 'nas-benchmark' / 'CIFAR.config'
cifar_config = load_config(cifar_config_path, None, None)
get_datasets = datasets.get_datasets # a function to return the dataset
break_line = '-' * 150
print ('{:} Create data-loader for all datasets'.format(time_string()))
print (break_line)
TRAIN_CIFAR10, VALID_CIFAR10, xshape, class_num = get_datasets('cifar10', str(torch_dir/'cifar.python'), -1)
print ('original CIFAR-10 : {:} training images and {:} test images : {:} input shape : {:} number of classes'.format(len(TRAIN_CIFAR10), len(VALID_CIFAR10), xshape, class_num))
cifar10_splits = load_config(root_dir / 'configs' / 'nas-benchmark' / 'cifar-split.txt', None, None)
assert cifar10_splits.train[:10] == [0, 5, 7, 11, 13, 15, 16, 17, 20, 24] and cifar10_splits.valid[:10] == [1, 2, 3, 4, 6, 8, 9, 10, 12, 14]
temp_dataset = copy.deepcopy(TRAIN_CIFAR10)
temp_dataset.transform = VALID_CIFAR10.transform
# data loader
trainval_cifar10_loader = torch.utils.data.DataLoader(TRAIN_CIFAR10, batch_size=cifar_config.batch_size, shuffle=True , num_workers=workers, pin_memory=True)
train_cifar10_loader = torch.utils.data.DataLoader(TRAIN_CIFAR10, batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar10_splits.train), num_workers=workers, pin_memory=True)
valid_cifar10_loader = torch.utils.data.DataLoader(temp_dataset , batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar10_splits.valid), num_workers=workers, pin_memory=True)
test__cifar10_loader = torch.utils.data.DataLoader(VALID_CIFAR10, batch_size=cifar_config.batch_size, shuffle=False, num_workers=workers, pin_memory=True)
print ('CIFAR-10 : trval-loader has {:3d} batch with {:} per batch'.format(len(trainval_cifar10_loader), cifar_config.batch_size))
print ('CIFAR-10 : train-loader has {:3d} batch with {:} per batch'.format(len(train_cifar10_loader), cifar_config.batch_size))
print ('CIFAR-10 : valid-loader has {:3d} batch with {:} per batch'.format(len(valid_cifar10_loader), cifar_config.batch_size))
print ('CIFAR-10 : test--loader has {:3d} batch with {:} per batch'.format(len(test__cifar10_loader), cifar_config.batch_size))
print (break_line)
# CIFAR-100
TRAIN_CIFAR100, VALID_CIFAR100, xshape, class_num = get_datasets('cifar100', str(torch_dir/'cifar.python'), -1)
print ('original CIFAR-100: {:} training images and {:} test images : {:} input shape : {:} number of classes'.format(len(TRAIN_CIFAR100), len(VALID_CIFAR100), xshape, class_num))
cifar100_splits = load_config(root_dir / 'configs' / 'nas-benchmark' / 'cifar100-test-split.txt', None, None)
assert cifar100_splits.xvalid[:10] == [1, 3, 4, 5, 8, 10, 13, 14, 15, 16] and cifar100_splits.xtest[:10] == [0, 2, 6, 7, 9, 11, 12, 17, 20, 24]
train_cifar100_loader = torch.utils.data.DataLoader(TRAIN_CIFAR100, batch_size=cifar_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True)
valid_cifar100_loader = torch.utils.data.DataLoader(VALID_CIFAR100, batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xvalid), num_workers=workers, pin_memory=True)
test__cifar100_loader = torch.utils.data.DataLoader(VALID_CIFAR100, batch_size=cifar_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(cifar100_splits.xtest) , num_workers=workers, pin_memory=True)
print ('CIFAR-100 : train-loader has {:3d} batch'.format(len(train_cifar100_loader)))
print ('CIFAR-100 : valid-loader has {:3d} batch'.format(len(valid_cifar100_loader)))
print ('CIFAR-100 : test--loader has {:3d} batch'.format(len(test__cifar100_loader)))
print (break_line)
imagenet16_config_path = 'configs/nas-benchmark/ImageNet-16.config'
imagenet16_config = load_config(imagenet16_config_path, None, None)
TRAIN_ImageNet16_120, VALID_ImageNet16_120, xshape, class_num = get_datasets('ImageNet16-120', str(torch_dir/'cifar.python'/'ImageNet16'), -1)
print ('original TRAIN_ImageNet16_120: {:} training images and {:} test images : {:} input shape : {:} number of classes'.format(len(TRAIN_ImageNet16_120), len(VALID_ImageNet16_120), xshape, class_num))
imagenet_splits = load_config(root_dir / 'configs' / 'nas-benchmark' / 'imagenet-16-120-test-split.txt', None, None)
assert imagenet_splits.xvalid[:10] == [1, 2, 3, 6, 7, 8, 9, 12, 16, 18] and imagenet_splits.xtest[:10] == [0, 4, 5, 10, 11, 13, 14, 15, 17, 20]
train_imagenet_loader = torch.utils.data.DataLoader(TRAIN_ImageNet16_120, batch_size=imagenet16_config.batch_size, shuffle=True, num_workers=workers, pin_memory=True)
valid_imagenet_loader = torch.utils.data.DataLoader(VALID_ImageNet16_120, batch_size=imagenet16_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_splits.xvalid), num_workers=workers, pin_memory=True)
test__imagenet_loader = torch.utils.data.DataLoader(VALID_ImageNet16_120, batch_size=imagenet16_config.batch_size, sampler=torch.utils.data.sampler.SubsetRandomSampler(imagenet_splits.xtest) , num_workers=workers, pin_memory=True)
print ('ImageNet-16-120 : train-loader has {:3d} batch with {:} per batch'.format(len(train_imagenet_loader), imagenet16_config.batch_size))
print ('ImageNet-16-120 : valid-loader has {:3d} batch with {:} per batch'.format(len(valid_imagenet_loader), imagenet16_config.batch_size))
print ('ImageNet-16-120 : test--loader has {:3d} batch with {:} per batch'.format(len(test__imagenet_loader), imagenet16_config.batch_size))
# 'cifar10', 'cifar100', 'ImageNet16-120'
loaders = {'cifar10@trainval': trainval_cifar10_loader,
'cifar10@train' : train_cifar10_loader,
'cifar10@valid' : valid_cifar10_loader,
'cifar10@test' : test__cifar10_loader,
'cifar100@train' : train_cifar100_loader,
'cifar100@valid' : valid_cifar100_loader,
'cifar100@test' : test__cifar100_loader,
'ImageNet16-120@train': train_imagenet_loader,
'ImageNet16-120@valid': valid_imagenet_loader,
'ImageNet16-120@test' : test__imagenet_loader}
return loaders