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cifar10_utils.py
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import os
from os.path import join as oj
import torch
from torchvision import datasets, transforms
import torch.nn as nn
import torchvision.models as torch_models
MODEL_LABELS =['Res', 'Sqz', 'Den']
DATASIZE_LABELS = [str(0.01), str(0.1), str(1)]
def get_loaders():
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
trainset = datasets.CIFAR10(
root='data', train=True, download=True, transform=transform_train)
tens = list(range(0, len(trainset), 10))
sub_trainset = torch.utils.data.Subset(trainset, tens)
train_loader = torch.utils.data.DataLoader(
sub_trainset, batch_size=64, shuffle=True, num_workers=2)
testset = datasets.CIFAR10(
root='data', train=False, download=True, transform=transform_test)
test_loader = torch.utils.data.DataLoader(
testset, batch_size=100, shuffle=False, num_workers=2)
return train_loader, test_loader
from utils import cwd
def get_models(individual_N=3, exp_type='models'):
'''
Load saved models.
NOTE: Change the directories to your saved models.
'''
if exp_type == 'datasets':
models = []
exp_dir = oj('saved_models', 'CIFAR-10', 'datasets_variation', '2022-01-26-09:35')
with cwd(exp_dir):
print("Loading order of dataset proportions:", sorted(os.listdir(), key=float))
for saved_dir in sorted(os.listdir(), key=float):
for i in range(individual_N):
model = torch_models.resnet18(pretrained=False)
model.fc = nn.Linear(512, 10)
model.load_state_dict(torch.load(oj(saved_dir,'-saved_model-{}.pt'.format(i+1))))
models.append(model)
return models
elif exp_type == 'models':
models = []
exp_dir = oj('saved_models', 'CIFAR-10', 'models_variation', '2022-01-26-09:36')
with cwd(exp_dir):
for i in range(individual_N):
model = torch_models.resnet18(pretrained=False)
model.fc = nn.Linear(512, 10)
model.load_state_dict(torch.load(oj('resnet', '-saved_model-{}.pt'.format(i+1))))
models.append(model)
for i in range(individual_N):
model = torch_models.squeezenet1_0(pretrained=False)
model.classifier[1] = nn.Conv2d(512, 10, kernel_size=(1,1), stride=(1,1))
model.load_state_dict(torch.load(oj('squeezenet', '-saved_model-{}.pt'.format(i+1))))
models.append(model)
for i in range(individual_N):
model = torch_models.densenet121(pretrained=False)
num_ftrs = model.classifier.in_features
model.classifier = nn.Linear(num_ftrs, 10)
model.load_state_dict(torch.load(oj('densenet', '-saved_model-{}.pt'.format(i+1))))
models.append(model)
return models
elif exp_type == 'precise':
models = []
exp_dir = oj('saved_models', 'CIFAR-10', 'models_variation', '2022-01-26-09:36')
with cwd(exp_dir):
for i in range(individual_N):
model = torch_models.densenet121(pretrained=False)
num_ftrs = model.classifier.in_features
model.classifier = nn.Linear(num_ftrs, 10)
model.load_state_dict(torch.load(oj('densenet', '-saved_model-{}.pt'.format(i+1))))
models.append(model)
for i in range(individual_N):
model = torch_models.densenet121(pretrained=False)
num_ftrs = model.classifier.in_features
model.classifier = nn.Linear(num_ftrs, 10)
model.load_state_dict(torch.load(oj('densenet', '-saved_model-{}.pt'.format(i+1))))
models.append(model)
for i in range(individual_N):
model = torch_models.densenet121(pretrained=False)
num_ftrs = model.classifier.in_features
model.classifier = nn.Linear(num_ftrs, 10)
model.load_state_dict(torch.load(oj('densenet', '-saved_model-{}.pt'.format(i+1))))
models.append(model)
return models
else:
raise NotImplementedError(f"Experiment type: {exp_type} is not implemented.")