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barrier.py
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barrier.py
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import torch.backends.cudnn as cudnn
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import os
from collections import OrderedDict
import numpy as np
import random
import argparse
import pickle
import time
from models import *
from simanneal import Annealer
from google.cloud import storage
start_time = time.time()
# settings
parser = argparse.ArgumentParser(description='LMC- before/after(best)/random permutation')
parser.add_argument('--datadir', default='datasets', type=str,
help='path to the directory that contains the datasets (default: datasets)')
parser.add_argument('--arch', '-a', metavar='ARCH', default='resnet18')
parser.add_argument('--dataset', default='SVHN', type=str,
help='name of the dataset (options: MNIST | CIFAR10 | CIFAR100 | SVHN, default: CIFAR10)')
parser.add_argument('--batchsize', default=64, type=int,
help='input batch size (default: 64)')
parser.add_argument('--cpu', dest='cpu', action='store_true',
help='use cpu')
parser.add_argument('--save-dir', dest='save_dir',
help='The directory used to save the trained models',
default='save_temp', type=str)
parser.add_argument('--nlayers', default=18, type=int)
parser.add_argument('--width', default=64, type=int)
parser.add_argument('--steps', default=50000, type=int)
parser.add_argument('--tmax', default=25000, type=float)
parser.add_argument('--tmin', default=2.5, type=float)
parser.add_argument('--pair', default=1, type=int)
parser.add_argument('--rand_seed', default=1, type=int)
import timeit
def main():
start = timeit.default_timer()
global args
args = parser.parse_args()
save_dir = f'{args.arch}_{args.dataset}_{args.nlayers}_{args.width}'
from google.cloud import storage
# bucket_name = 'permutation-mlp'
# source_file_name = f'args.pkl'
# destination_blob_name = f'Neurips21_Arxiv/{save_dir}/barrier_10pairs/original/{args.seed}/{source_file_name}'
# storage_client = storage.Client()
# bucket = storage_client.bucket(bucket_name)
# blob = bucket.blob(destination_blob_name)
# pickle_out = pickle.dumps(args)
# blob.upload_from_string(pickle_out)
########### download train/test data/labels to the bucket
if (args.dataset == 'MNIST'):
if "mlp" in args.arch:
bucket_name = 'permutation-mlp'
source_file_name = 'MNIST_Train_input_org.pkl'
destination_blob_name = f'Neurips21_Arxiv/dataset_pkl/{source_file_name}'
train_inputs = download_pkl(bucket_name, destination_blob_name)
source_file_name = 'MNIST_Train_target_org.pkl'
destination_blob_name = f'Neurips21_Arxiv/dataset_pkl/{source_file_name}'
train_targets = download_pkl(bucket_name, destination_blob_name)
source_file_name = 'MNIST_Test_input_org.pkl'
destination_blob_name = f'Neurips21_Arxiv/dataset_pkl/{source_file_name}'
test_inputs = download_pkl(bucket_name, destination_blob_name)
source_file_name = 'MNIST_Test_target_org.pkl'
destination_blob_name = f'Neurips21_Arxiv/dataset_pkl/{source_file_name}'
test_targets = download_pkl(bucket_name, destination_blob_name)
else:
bucket_name = 'permutation-mlp'
source_file_name = 'MNIST3d_Train_input_org.pkl'
destination_blob_name = f'Neurips21_Arxiv/dataset_pkl/{source_file_name}'
train_inputs = download_pkl(bucket_name, destination_blob_name)
source_file_name = 'MNIST3d_Train_target_org.pkl'
destination_blob_name = f'Neurips21_Arxiv/dataset_pkl/{source_file_name}'
train_targets = download_pkl(bucket_name, destination_blob_name)
source_file_name = 'MNIST3d_Test_input_org.pkl'
destination_blob_name = f'Neurips21_Arxiv/dataset_pkl/{source_file_name}'
test_inputs = download_pkl(bucket_name, destination_blob_name)
source_file_name = 'MNIST3d_Test_target_org.pkl'
destination_blob_name = f'Neurips21_Arxiv/dataset_pkl/{source_file_name}'
test_targets = download_pkl(bucket_name, destination_blob_name)
elif (args.dataset == 'CIFAR10'):
bucket_name = 'permutation-mlp'
source_file_name = f'CIFAR10_Train_input_org.pkl'
destination_blob_name = f'Neurips21_Arxiv/dataset_pkl/{source_file_name}'
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(destination_blob_name)
pickle_in = blob.download_as_string()
train_inputs = pickle.loads(pickle_in)
bucket_name = 'permutation-mlp'
source_file_name = f'CIFAR10_Train_target_org.pkl'
destination_blob_name = f'Neurips21_Arxiv/dataset_pkl/{source_file_name}'
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(destination_blob_name)
pickle_in = blob.download_as_string()
train_targets = pickle.loads(pickle_in)
bucket_name = 'permutation-mlp'
source_file_name = f'CIFAR10_Test_input_org.pkl'
destination_blob_name = f'Neurips21_Arxiv/dataset_pkl/{source_file_name}'
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(destination_blob_name)
pickle_in = blob.download_as_string()
test_inputs = pickle.loads(pickle_in)
bucket_name = 'permutation-mlp'
source_file_name = f'CIFAR10_Test_target_org.pkl'
destination_blob_name = f'Neurips21_Arxiv/dataset_pkl/{source_file_name}'
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(destination_blob_name)
pickle_in = blob.download_as_string()
test_targets = pickle.loads(pickle_in)
elif (args.dataset == 'SVHN'):
bucket_name = 'permutation-mlp'
source_file_name = f'SVHN_Train_input_org.pkl'
destination_blob_name = f'Neurips21_Arxiv/dataset_pkl/{source_file_name}'
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(destination_blob_name)
pickle_in = blob.download_as_string()
train_inputs = pickle.loads(pickle_in)
bucket_name = 'permutation-mlp'
source_file_name = f'SVHN_Train_target_org.pkl'
destination_blob_name = f'Neurips21_Arxiv/dataset_pkl/{source_file_name}'
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(destination_blob_name)
pickle_in = blob.download_as_string()
train_targets = pickle.loads(pickle_in)
bucket_name = 'permutation-mlp'
source_file_name = f'SVHN_Test_input_org.pkl'
destination_blob_name = f'Neurips21_Arxiv/dataset_pkl/{source_file_name}'
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(destination_blob_name)
pickle_in = blob.download_as_string()
test_inputs = pickle.loads(pickle_in)
bucket_name = 'permutation-mlp'
source_file_name = f'SVHN_Test_target_org.pkl'
destination_blob_name = f'Neurips21_Arxiv/dataset_pkl/{source_file_name}'
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(destination_blob_name)
pickle_in = blob.download_as_string()
test_targets = pickle.loads(pickle_in)
elif (args.dataset == 'CIFAR100'):
bucket_name = 'permutation-mlp'
source_file_name = f'CIFAR100_Train_input_org.pkl'
destination_blob_name = f'Neurips21_Arxiv/dataset_pkl/{source_file_name}'
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(destination_blob_name)
pickle_in = blob.download_as_string()
train_inputs = pickle.loads(pickle_in)
bucket_name = 'permutation-mlp'
source_file_name = f'CIFAR100_Train_target_org.pkl'
destination_blob_name = f'Neurips21_Arxiv/dataset_pkl/{source_file_name}'
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(destination_blob_name)
pickle_in = blob.download_as_string()
train_targets = pickle.loads(pickle_in)
bucket_name = 'permutation-mlp'
source_file_name = f'CIFAR100_Test_input_org.pkl'
destination_blob_name = f'Neurips21_Arxiv/dataset_pkl/{source_file_name}'
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(destination_blob_name)
pickle_in = blob.download_as_string()
test_inputs = pickle.loads(pickle_in)
bucket_name = 'permutation-mlp'
source_file_name = f'CIFAR100_Test_target_org.pkl'
destination_blob_name = f'Neurips21_Arxiv/dataset_pkl/{source_file_name}'
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(destination_blob_name)
pickle_in = blob.download_as_string()
test_targets = pickle.loads(pickle_in)
# elif (args.dataset == 'ImageNet'):
######## models
nchannels, nclasses = 3, 10
if args.dataset == 'MNIST': nchannels = 1
if args.dataset == 'CIFAR100': nclasses = 100
if args.nlayers == 1 and "mlp" in args.arch:
model = MLP1_layer(n_units=args.width, n_channels=nchannels, n_classes=nclasses)
if args.nlayers == 2 and "mlp" in args.arch:
model = MLP2_layer(n_units=args.width, n_channels=nchannels, n_classes=nclasses)
if args.nlayers == 4 and "mlp" in args.arch:
model = MLP4_layer(n_units=args.width, n_channels=nchannels, n_classes=nclasses)
if args.nlayers == 8 and "mlp" in args.arch:
model = MLP8_layer(n_units=args.width, n_channels=nchannels, n_classes=nclasses)
if args.nlayers == 16 and "mlp" in args.arch:
model = MLP16_layer(n_units=args.width, n_channels=nchannels, n_classes=nclasses)
if args.nlayers == 18:
model = ResNet18(nclasses, args.width, nchannels)
if args.nlayers == 34:
model = ResNet34(nclasses, args.width, nchannels)
if args.nlayers == 50:
model = ResNet50(nclasses, args.width, nchannels)
if "vgg" in args.arch:
model = vgg.__dict__[args.arch](nchannels, args.width, nclasses)
if "s_conv" in args.arch and args.nlayers == 2:
model = s_conv_2layer(nchannels, args.width, nclasses)
save_dir = f'{args.arch}_nopool_{args.dataset}_{args.nlayers}_{args.width}'
if "s_conv" in args.arch and args.nlayers == 4:
model = s_conv_4layer(nchannels, args.width, nclasses)
save_dir = f'{args.arch}_nopool_{args.dataset}_{args.nlayers}_{args.width}'
if "s_conv" in args.arch and args.nlayers == 6:
model = s_conv_6layer(nchannels, args.width, nclasses)
save_dir = f'{args.arch}_nopool_{args.dataset}_{args.nlayers}_{args.width}'
if "s_conv" in args.arch and args.nlayers == 8:
model = s_conv_8layer(nchannels, args.width, nclasses)
save_dir = f'{args.arch}_nopool_{args.dataset}_{args.nlayers}_{args.width}'
print(model)
if args.cpu:
model.cpu()
else:
model.cuda()
def upload_blob(bucket_name, source_file_name, destination_blob_name):
"""Uploads a file to the bucket."""
# bucket_name = "your-bucket-name"
# source_file_name = "local/path/to/file"
# destination_blob_name = "storage-object-name"
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(destination_blob_name)
blob.upload_from_filename(source_file_name)
print(
"File {} uploaded to {}.".format(
source_file_name, destination_blob_name
)
)
cudnn.benchmark = True
nchannels, nclasses = 3, 10
if args.dataset == 'MNIST': nchannels = 1
if args.dataset == 'CIFAR100': nclasses = 100
chosen_seed = [i for i in range(1,21)]
############## form 10 pairs
random.seed(1)
def pop_random(lst):
idx = random.randrange(0, len(lst))
return lst.pop(idx)
pairs = []
lst = chosen_seed
while lst:
rand1 = pop_random(lst)
rand2 = pop_random(lst)
pair = rand1, rand2
pairs.append(pair)
dict_before = {}
print("paired models: ", pairs)
################## original barrier
# for x in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]:
# for x in [10]:
# pair = pairs[x-1]
pair = pairs[args.pair-1]
print("pair", pair)
model1_id = pair[0]
model2_id = pair[1]
############################# load selected models
bucket_name = 'permutation-mlp'
destination_blob_name = 'model_best.th'
source_file_name = f'Neurips21_Arxiv/{save_dir}/Train/{model1_id}/{destination_blob_name}'
download_blob(bucket_name, source_file_name, destination_blob_name)
checkpoint = torch.load('model_best.th', map_location=torch.device('cuda'))
sd1 = checkpoint
destination_blob_name = 'model_best.th'
source_file_name = f'Neurips21_Arxiv/{save_dir}/Train/{model2_id}/{destination_blob_name}'
download_blob(bucket_name, source_file_name, destination_blob_name)
checkpoint = torch.load('model_best.th', map_location=torch.device('cuda'))
sd2 = checkpoint
def key_transformation(old_key):
if 'module' in old_key:
return old_key[7:]
return old_key
new_state_dict1 = OrderedDict()
for key, value in sd1.items():
new_key = key_transformation(key)
new_state_dict1[new_key] = value
new_state_dict2 = OrderedDict()
for key, value in sd2.items():
new_key = key_transformation(key)
new_state_dict2[new_key] = value
sd1 = new_state_dict1
sd2 = new_state_dict2
conv_arch = False
for key in sd1:
print(key, sd1[key].shape)
if "conv" in key or "running_mean" in key:
conv_arch = True
# from torchsummary import summary
# summary(model, (1, 32, 32))
for name, param in model.named_parameters():
if param.requires_grad:
print(name, param.data.shape)
# ########################################## original barrier
dict_before = get_barrier(model, sd1, sd2, train_inputs, train_targets, test_inputs, test_targets)
barrier_train = dict_before['barrier_train']
lmc_train = dict_before['train_lmc']
test_avg_org_models = dict_before['test_avg_models'][0]
train_avg_org_models = dict_before['train_avg_models'][0]
print("barrier_train_original", barrier_train)
print("lmc_train_original", lmc_train)
source_file_name = f'dict_before_{args.pair}.pkl'
destination_blob_name = f'Neurips21_Arxiv/{save_dir}/barrier_10pairs/original/{source_file_name}'
pickle_out = pickle.dumps(dict_before)
upload_pkl(bucket_name, pickle_out, destination_blob_name)
########################################## oracle barrier
bucket_name = 'permutation-mlp'
destination_blob_name = 'model_best.th'
source_file_name = f'Neurips21_Arxiv/{save_dir}/Train/{21}/{destination_blob_name}'
download_blob(bucket_name, source_file_name, destination_blob_name)
checkpoint = torch.load('model_best.th', map_location=torch.device('cuda'))
sd1 = checkpoint
def key_transformation(old_key):
if 'module' in old_key:
return old_key[7:]
return old_key
new_state_dict1 = OrderedDict()
for key, value in sd1.items():
new_key = key_transformation(key)
new_state_dict1[new_key] = value
sd1 = new_state_dict1
w1 = []
for key in sd1.keys():
param = sd1[key]
w1.append(param.cpu().detach().numpy())
# create permutation list for mlp
if args.arch == 'mlp':
len_perm = []
for i in range(int(len(w1) / 2 - 1)):
len_perm.append(args.width)
# create permutation list for conv nets
if conv_arch:
params = []
len_perm = []
for key in sd2.keys():
param = model.state_dict()[key]
if "num_batches_tracked" not in key:
params.append(param.cpu().detach().numpy())
if len(param.shape) == 4:
len_perm.append(param.shape[0])
if len(param.shape) == 2:
len_perm.append(param.shape[0])
print("len_perm", len(len_perm))
print("len_perm", len_perm)
random_permuted_index = []
for z in len_perm:
lst = [y for y in range(z)]
random.seed(model2_id)
# random.seed(args.rand_seed)
rnd = random.sample(lst, z)
random_permuted_index.append(rnd)
init_state1 = random_permuted_index
# print(sd1["features.0.weight"][0:2])
permuted_oracle_sd1 = permute(args.arch, model, init_state1, sd1, w1, nchannels, nclasses, args.width)
# print(permuted_oracle_sd1["features.0.weight"][0:2])
# # #### sanity check if permutation is done properly: L2 Gaussian Noise
# # ##################################################
# # # hooks = {}
# # # for name, module in model.named_modules():
# # # hooks[name] = module.register_forward_hook(self, hook_fn)
# #
# #
# # activation = {}
# # def get_activation(name):
# # def hook(model, input, output):
# # activation[name] = output.detach()
# #
# # return hook
# #
# # device = torch.device('cuda')
# # torch.manual_seed(1)
# # input_g = torch.randn(256, 1, 32, 32)
# # input_g = input_g.to(device)
# # # input_g = input_g.to(device).view(input_g.size(0), -1)
# # ######################### to model1
# # model.load_state_dict(sd1)
# # model.register_forward_hook(get_activation('layer4.1.bn2'))
# # output = model(input_g)
# # print(activation['layer4.1.bn2'].shape)
# # print(torch.transpose(sd1['linear.weight'], 0, 1).shape)
# # gaussian_out1 = torch.matmul(activation['layer4.1.bn2'], torch.transpose(sd1['linear.weight'], 0, 1))
# # ######################### to model2
# # model.load_state_dict(permuted_oracle_sd)
# # model.register_forward_hook(get_activation(['layer4.1.bn2']))
# # output = model(input_g)
# # gaussian_out2 = torch.matmul(activation['layer4.1.bn2'], torch.transpose(sd1['linear.weight'], 0, 1))
# #
# # dist = np.linalg.norm(gaussian_out1.cpu() - gaussian_out2.cpu())
# # print(f"L2 noise:", dist)
# # print('{0:4f}'.format(dist))
# ##################################################
bucket_name = 'permutation-mlp'
destination_blob_name = 'model_best.th'
source_file_name = f'Neurips21_Arxiv/{save_dir}/Train/{21}/{destination_blob_name}'
download_blob(bucket_name, source_file_name, destination_blob_name)
checkpoint = torch.load('model_best.th', map_location=torch.device('cuda'))
sd1 = checkpoint
def key_transformation(old_key):
if 'module' in old_key:
return old_key[7:]
return old_key
new_state_dict1 = OrderedDict()
for key, value in sd1.items():
new_key = key_transformation(key)
new_state_dict1[new_key] = value
sd1 = new_state_dict1
dict_oracle = get_barrier(model, sd1, permuted_oracle_sd1, train_inputs, train_targets, test_inputs, test_targets)
barrier_train = dict_oracle['barrier_train']
lmc_train = dict_oracle['train_lmc']
print("barrier_train_oracle", barrier_train)
print("lmc_train_oracle", lmc_train)
# source_file_name = f'dict_oracle_{args.pair}_{args.rand_seed}.pkl'
source_file_name = f'dict_oracle_{args.pair}.pkl'
destination_blob_name = f'Neurips21_Arxiv/{save_dir}/barrier_10pairs/oracle/before/{source_file_name}'
pickle_out = pickle.dumps(dict_oracle)
upload_pkl(bucket_name, pickle_out, destination_blob_name)
# # # ########################################## SA original models: model1 and model2
# # load selected models
# bucket_name = 'permutation-mlp'
# destination_blob_name = 'model_best.th'
# source_file_name = f'Neurips21_Arxiv/{save_dir}/Train/{model1_id}/{destination_blob_name}'
# download_blob(bucket_name, source_file_name, destination_blob_name)
#
# checkpoint = torch.load('model_best.th', map_location=torch.device('cuda'))
# sd1 = checkpoint
#
# destination_blob_name = 'model_best.th'
# source_file_name = f'Neurips21_Arxiv/{save_dir}/Train/{model2_id}/{destination_blob_name}'
# download_blob(bucket_name, source_file_name, destination_blob_name)
#
# checkpoint = torch.load('model_best.th', map_location=torch.device('cuda'))
# sd2 = checkpoint
#
#
# def key_transformation(old_key):
# if 'module' in old_key:
# return old_key[7:]
# return old_key
#
# new_state_dict1 = OrderedDict()
# for key, value in sd1.items():
# new_key = key_transformation(key)
# new_state_dict1[new_key] = value
#
# new_state_dict2 = OrderedDict()
# for key, value in sd2.items():
# new_key = key_transformation(key)
# new_state_dict2[new_key] = value
# sd1 = new_state_dict1
# sd2 = new_state_dict2
#
# w2 = []
# for key in sd2.keys():
# param = sd2[key]
# w2.append(param.cpu().detach().numpy())
#
#
# # create permutation list for mlp
# if args.arch == 'mlp':
# len_perm = []
# for i in range(int(len(w2) / 2 - 1)):
# len_perm.append(args.width)
# # create permutation list for conv nets
# if conv_arch:
# params = []
# len_perm = []
# for key in sd2.keys():
# param = model.state_dict()[key]
# if "num_batches_tracked" not in key:
# params.append(param.cpu().detach().numpy())
# if len(param.shape) == 4:
# len_perm.append(param.shape[0])
# if len(param.shape) == 2:
# len_perm.append(param.shape[0])
#
# print("len_perm", len(len_perm))
# print("len_perm", len_perm)
#
#
# random_permuted_index = []
# for z in len_perm:
# lst = [y for y in range(z)]
# random.seed(model2_id)
# rnd = random.sample(lst, z)
# random_permuted_index.append(rnd)
# init_state1 = random_permuted_index
# exp_no = f'tmax{args.tmax}_tmin{args.tmin}_steps{args.steps}'
# winning_permutation = barrier_SA(args.arch, model, sd1, sd2, w2, init_state1,
# args.tmax, args.tmin, args.steps,
# train_inputs, train_targets,
# train_avg_org_models, nchannels, nclasses,
# args.width)
#
# print("winning_permutation", winning_permutation)
# winning_perm_model_sd = permute(args.arch, model, winning_permutation, sd2, w2, nchannels, nclasses, args.width)
#
# dict_after = get_barrier(model, sd1, winning_perm_model_sd, train_inputs, train_targets, test_inputs, test_targets)
#
# add_element(dict_after, 'winning_permutation', winning_permutation)
# add_element(dict_after, 'winning_perm_model_sd', winning_perm_model_sd)
#
# barrier_train = dict_after['barrier_train']
# lmc_train = dict_after['train_lmc']
#
# print("barrier_train_SA", barrier_train)
# print("lmc_train_SA", lmc_train)
#
# source_file_name = f'dict_after_pair{pair}.pkl'
# # destination_blob_name = f'Neurips21_Arxiv/{save_dir}/barrier_10pairs/SA/auto/{source_file_name}'
# destination_blob_name = f'Neurips21_Arxiv/{save_dir}/barrier_10pairs/SA/grid/{exp_no}/{source_file_name}'
# pickle_out = pickle.dumps(dict_after)
# upload_pkl(bucket_name, pickle_out, destination_blob_name)
#
# # ########################################## SA oracle: model1 and permuted model1
# # load selected models
# bucket_name = 'permutation-mlp'
# destination_blob_name = 'model_best.th'
# source_file_name = f'Neurips21_Arxiv/{save_dir}/Train/{model1_id}/{destination_blob_name}'
# download_blob(bucket_name, source_file_name, destination_blob_name)
#
# checkpoint = torch.load('model_best.th', map_location=torch.device('cuda'))
# sd1 = checkpoint
#
# destination_blob_name = 'model_best.th'
# source_file_name = f'Neurips21_Arxiv/{save_dir}/Train/{model2_id}/{destination_blob_name}'
# download_blob(bucket_name, source_file_name, destination_blob_name)
#
# checkpoint = torch.load('model_best.th', map_location=torch.device('cuda'))
# sd2 = checkpoint
#
#
# def key_transformation(old_key):
# if 'module' in old_key:
# return old_key[7:]
# return old_key
#
# new_state_dict1 = OrderedDict()
# for key, value in sd1.items():
# new_key = key_transformation(key)
# new_state_dict1[new_key] = value
#
# new_state_dict2 = OrderedDict()
# for key, value in sd2.items():
# new_key = key_transformation(key)
# new_state_dict2[new_key] = value
# sd1 = new_state_dict1
# sd2 = new_state_dict2
#
# w2 = []
# for key in permuted_oracle_sd1.keys():
# param = permuted_oracle_sd1[key]
# w2.append(param.cpu().detach().numpy())
# exp_no = f'tmax{args.tmax}_tmin{args.tmin}_steps{args.steps}'
#
# winning_permutation = barrier_SA(args.arch, model, sd1, permuted_oracle_sd1, w2, init_state1,
# args.tmax, args.tmin, args.steps,
# train_inputs, train_targets,
# train_avg_org_models, nchannels, nclasses,
# args.width)
#
# print("winning_permutation", winning_permutation)
# winning_perm_model_sd = permute(args.arch, model, winning_permutation, sd2, w2, nchannels, nclasses, args.width)
#
# dict_after = get_barrier(model, sd1, winning_perm_model_sd, train_inputs, train_targets, test_inputs, test_targets)
#
# add_element(dict_after, 'winning_permutation', winning_permutation)
# add_element(dict_after, 'winning_perm_model_sd', winning_perm_model_sd)
#
# barrier_train = dict_after['barrier_train']
# lmc_train = dict_after['train_lmc']
#
# print("barrier_train_SA_oracle", barrier_train)
# print("lmc_train_SA_oracle", lmc_train)
#
# # source_file_name = f'dict_after_pair{pair}_{args.rand_seed}.pkl'
# source_file_name = f'dict_after_pair{pair}.pkl'
# # destination_blob_name = f'Neurips21_Arxiv/{save_dir}/barrier_10pairs/oracle/SA/auto/{source_file_name}'
# destination_blob_name = f'Neurips21_Arxiv/{save_dir}/barrier_10pairs/oracle/SA/grid/{exp_no}/{source_file_name}'
# pickle_out = pickle.dumps(dict_after)
# upload_pkl(bucket_name, pickle_out, destination_blob_name)
#
#
stop = timeit.default_timer()
print('Time: ', stop - start)
def upload_blob(bucket_name, source_file_name, destination_blob_name):
"""Uploads a file to the bucket."""
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(destination_blob_name)
blob.upload_from_filename(source_file_name)
def download_blob(bucket_name, source_blob_name, destination_file_name):
"""Downloads a blob from the bucket."""
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(source_blob_name)
blob.download_to_filename(destination_file_name)
def upload_pkl(bucket_name, pickle_out, destination_blob_name):
"""Uploads a file to the bucket."""
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(destination_blob_name)
blob.upload_from_string(pickle_out)
def download_pkl(bucket_name, destination_blob_name):
"""Uploads a file to the bucket."""
storage_client = storage.Client()
bucket = storage_client.bucket(bucket_name)
blob = bucket.blob(destination_blob_name)
pickle_in = blob.download_as_string()
return pickle.loads(pickle_in)
def load_data(split, dataset_name, datadir, nchannels):
## https://gist.github.com/weiaicunzai/e623931921efefd4c331622c344d8151
if dataset_name == 'MNIST':
normalize = transforms.Normalize(mean=[0.1307], std=[0.3081])
elif dataset_name == 'SVHN':
normalize = transforms.Normalize(mean=[0.4377, 0.4438, 0.4728], std=[0.1980, 0.2010, 0.1970])
elif dataset_name == 'CIFAR10':
normalize = transforms.Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.2470, 0.2435, 0.2616])
elif dataset_name == 'CIFAR100':
normalize = transforms.Normalize(mean=[0.5071, 0.4865, 0.4409], std=[0.2673, 0.2564, 0.2762])
tr_transform = transforms.Compose([transforms.Resize(32), transforms.ToTensor(), normalize])
val_transform = transforms.Compose([transforms.Resize(32), transforms.ToTensor(), normalize])
get_dataset = getattr(datasets, dataset_name)
if dataset_name == 'SVHN':
if split == 'train':
dataset = get_dataset(root=datadir, split='train', download=True, transform=tr_transform)
else:
dataset = get_dataset(root=datadir, split='test', download=True, transform=val_transform)
else:
if split == 'train':
dataset = get_dataset(root=datadir, train=True, download=True, transform=tr_transform)
else:
dataset = get_dataset(root=datadir, train=False, download=True, transform=val_transform)
return dataset
def add_element(dict, key, value):
if key not in dict:
dict[key] = []
dict[key].append(value)
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
@property
def avg(self):
return self.sum / self.count
def calc_accuracy(output, target, topk=(1,)):
"""Computes the accuracy over the k top predictions for the specified values of k"""
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
def evaluate_model(args, model, inputs, targets):
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
losses = AverageMeter()
top1 = AverageMeter()
# switch to evaluate mode
model.eval()
with torch.no_grad():
for data, target in zip(inputs, targets):
if args.arch == 'mlp':
input = data.to(device).view(data.size(0), -1)
else:
input = data.to(device)
target = target.to(device)
# compute output
output = model(input)
# measure accuracy and record loss
acc1 = calc_accuracy(output, target, topk=(1,))[0]
top1.update(acc1[0], input.size(0))
# break
# results = dict(top1=top1.avg, loss=losses.avg, batch_time=batch_time.avg)
results = dict(top1=top1.avg)
return {key: float(val) for key, val in results.items()}
def evaluate_model_small(args, model, inputs, targets):
if torch.cuda.is_available():
device = torch.device('cuda')
else:
device = torch.device('cpu')
losses = AverageMeter()
top1 = AverageMeter()
# switch to evaluate mode
model.eval()
with torch.no_grad():
for data, target in zip(inputs, targets):
if args.arch == 'mlp':
input = data.to(device).view(data.size(0), -1)
else:
input = data.to(device)
target = target.to(device)
# compute output
output = model(input)
# measure accuracy and record loss
acc1 = calc_accuracy(output, target, topk=(1,))[0]
top1.update(acc1[0], input.size(0))
break
# results = dict(top1=top1.avg, loss=losses.avg, batch_time=batch_time.avg)
results = dict(top1=top1.avg)
return {key: float(val) for key, val in results.items()}
def interpolate_state_dicts(state_dict_1, state_dict_2, weight):
return {key: (1 - weight) * state_dict_1[key] + weight * state_dict_2[key]
for key in state_dict_1.keys()}
def get_barrier(model, sd1, sd2, train_inputs, train_targets, test_inputs, test_targets):
dict_barrier = {}
####################### get the barrier - before permutation
###### LMC
weights = np.linspace(0, 1, 11)
result_test = []
result_train = []
for i in range(len(weights)):
model.load_state_dict(interpolate_state_dicts(sd1, sd2, weights[i]))
result_train.append(evaluate_model(args, model, train_inputs, train_targets)['top1'])
result_test.append(evaluate_model(args, model, test_inputs, test_targets)['top1'])
model1_eval = result_test[0]
model2_eval = result_test[-1]
test_avg_models = (model1_eval + model2_eval) / 2
model1_eval = result_train[0]
model2_eval = result_train[-1]
train_avg_models = (model1_eval + model2_eval) / 2
add_element(dict_barrier, 'train_avg_models', train_avg_models)
add_element(dict_barrier, 'test_avg_models', test_avg_models)
add_element(dict_barrier, 'train_lmc', result_train)
add_element(dict_barrier, 'test_lmc', result_test)
add_element(dict_barrier, 'barrier_test', test_avg_models - result_test[5])
add_element(dict_barrier, 'barrier_train', train_avg_models - result_train[5])
return dict_barrier
def permute(arch, model, perm_ind, sd, w_2, nchannels, nclasses, nunits):
# 1layer
if arch == 'mlp' and len(w_2) == 4:
################################################ permutation
idx = perm_ind[0]
# print(idx)
######################### permute weights of model2, based on idx
w1 = w_2[0]
b1 = w_2[1]
w2 = w_2[2]
b2 = w_2[3]
w1_p = w1[idx, :]
b1_p = b1[idx]
w2_p = w2[:, idx]
b2_p = b2
##################### save model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = MLP1_layer(n_units=args.width, n_channels=nchannels, n_classes=nclasses)
model = model.to(device)
model.state_dict()["layers.0.weight"][:] = torch.Tensor(w1_p)
model.state_dict()["layers.0.bias"][:] = torch.Tensor(b1_p)
model.state_dict()["layers.2.weight"][:] = torch.Tensor(w2_p)
model.state_dict()["layers.2.bias"][:] = torch.Tensor(b2_p)
# 2layers
elif arch == 'mlp' and len(w_2) == 6:
######################### permute weights of model2, based on idx
w1 = w_2[0]
b1 = w_2[1]
w2 = w_2[2]
b2 = w_2[3]
w3 = w_2[4]
b3 = w_2[5]
idx1 = perm_ind[0]
w1_p = w1[idx1, :]
b1_p = b1[idx1]
idx2 = perm_ind[1]
w2_p = w2[:, idx1] ### to take care of prv permutation
w2_p = w2_p[idx2, :] ## to apply new permutation
b2_p = b2[idx2]
idx2 = perm_ind[1]
w3_p = w3[:, idx2]
b3_p = b3
##################### save model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = MLP2_layer(n_units=nunits, n_channels=nchannels, n_classes=nclasses)
model = model.to(device)
model.state_dict()["layers.0.weight"][:] = torch.Tensor(w1_p)
model.state_dict()["layers.0.bias"][:] = torch.Tensor(b1_p)
model.state_dict()["layers.2.weight"][:] = torch.Tensor(w2_p)
model.state_dict()["layers.2.bias"][:] = torch.Tensor(b2_p)
model.state_dict()["layers.4.weight"][:] = torch.Tensor(w3_p)
model.state_dict()["layers.4.bias"][:] = torch.Tensor(b3_p)
# 4layers
elif arch == 'mlp' and len(w_2) == 10:
######################### permute weights of model2, based on idx
w1 = w_2[0]
b1 = w_2[1]
w2 = w_2[2]
b2 = w_2[3]
w3 = w_2[4]
b3 = w_2[5]
w4 = w_2[6]
b4 = w_2[7]
w5 = w_2[8]
b5 = w_2[9]
idx1 = perm_ind[0]
w1_p = w1[idx1, :]
b1_p = b1[idx1]
w2_p = w2[:, idx1]
idx2 = perm_ind[1]
w2_p = w2_p[idx2, :]
b2_p = b2[idx2]
w3_p = w3[:, idx2]
idx3 = perm_ind[2]
w3_p = w3_p[idx3, :]
b3_p = b3[idx3]
w4_p = w4[:, idx3]
idx4 = perm_ind[3]
w4_p = w4_p[idx4, :]
b4_p = b4[idx4]
w5_p = w5[:, idx4]
b5_p = b5
##################### save model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
### 4layers
model = MLP4_layer(n_units=nunits, n_channels=nchannels, n_classes=nclasses)
model = model.to(device)
model.state_dict()["layers.0.weight"][:] = torch.Tensor(w1_p)
model.state_dict()["layers.0.bias"][:] = torch.Tensor(b1_p)
model.state_dict()["layers.2.weight"][:] = torch.Tensor(w2_p)
model.state_dict()["layers.2.bias"][:] = torch.Tensor(b2_p)
model.state_dict()["layers.4.weight"][:] = torch.Tensor(w3_p)
model.state_dict()["layers.4.bias"][:] = torch.Tensor(b3_p)
model.state_dict()["layers.6.weight"][:] = torch.Tensor(w4_p)
model.state_dict()["layers.6.bias"][:] = torch.Tensor(b4_p)
model.state_dict()["layers.8.weight"][:] = torch.Tensor(w5_p)
model.state_dict()["layers.8.bias"][:] = torch.Tensor(b5_p)
# 8layers
elif arch == 'mlp' and len(w_2) == 18:
######################### permute weights of model2, based on idx
w1 = w_2[0]
b1 = w_2[1]
w2 = w_2[2]
b2 = w_2[3]
w3 = w_2[4]