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run_pruning.py
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run_pruning.py
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import os
import shutil
import torch
import torch.utils.data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import argparse
from helpers import makedir
import model
import push
import prune
import train_and_test as tnt
import save
from log import create_logger
from preprocess import mean, std, preprocess_input_function
parser = argparse.ArgumentParser()
parser.add_argument('-gpuid', nargs=1, type=str, default='0')
parser.add_argument('-modeldir', nargs=1, type=str)
parser.add_argument('-model', nargs=1, type=str)
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpuid[0]
optimize_last_layer = True
# pruning parameters
k = 6
prune_threshold = 3
original_model_dir = args.modeldir[0] #'./saved_models/densenet161/003/'
original_model_name = args.model[0] #'10_16push0.8007.pth'
need_push = ('nopush' in original_model_name)
if need_push:
assert(False) # pruning must happen after push
else:
epoch = original_model_name.split('push')[0]
if '_' in epoch:
epoch = int(epoch.split('_')[0])
else:
epoch = int(epoch)
model_dir = os.path.join(original_model_dir, 'pruned_prototypes_epoch{}_k{}_pt{}'.format(epoch,
k,
prune_threshold))
makedir(model_dir)
shutil.copy(src=os.path.join(os.getcwd(), __file__), dst=model_dir)
log, logclose = create_logger(log_filename=os.path.join(model_dir, 'prune.log'))
ppnet = torch.load(original_model_dir + original_model_name)
ppnet = ppnet.cuda()
ppnet_multi = torch.nn.DataParallel(ppnet)
class_specific = True
# load the data
from settings import train_dir, test_dir, train_push_dir
train_batch_size = 80
test_batch_size = 100
img_size = 224
train_push_batch_size = 80
normalize = transforms.Normalize(mean=mean,
std=std)
# train set
train_dataset = datasets.ImageFolder(
train_dir,
transforms.Compose([
transforms.Resize(size=(img_size, img_size)),
transforms.ToTensor(),
normalize,
]))
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=train_batch_size, shuffle=True,
num_workers=4, pin_memory=False)
# test set
test_dataset = datasets.ImageFolder(
test_dir,
transforms.Compose([
transforms.Resize(size=(img_size, img_size)),
transforms.ToTensor(),
normalize,
]))
test_loader = torch.utils.data.DataLoader(
test_dataset, batch_size=test_batch_size, shuffle=False,
num_workers=4, pin_memory=False)
log('training set size: {0}'.format(len(train_loader.dataset)))
log('test set size: {0}'.format(len(test_loader.dataset)))
log('batch size: {0}'.format(train_batch_size))
# push set: needed for pruning because it is unnormalized
train_push_dataset = datasets.ImageFolder(
train_push_dir,
transforms.Compose([
transforms.Resize(size=(img_size, img_size)),
transforms.ToTensor(),
]))
train_push_loader = torch.utils.data.DataLoader(
train_push_dataset, batch_size=train_push_batch_size, shuffle=False,
num_workers=4, pin_memory=False)
log('push set size: {0}'.format(len(train_push_loader.dataset)))
tnt.test(model=ppnet_multi, dataloader=test_loader,
class_specific=class_specific, log=log)
# prune prototypes
log('prune')
prune.prune_prototypes(dataloader=train_push_loader,
prototype_network_parallel=ppnet_multi,
k=k,
prune_threshold=prune_threshold,
preprocess_input_function=preprocess_input_function, # normalize
original_model_dir=original_model_dir,
epoch_number=epoch,
#model_name=None,
log=log,
copy_prototype_imgs=True)
accu = tnt.test(model=ppnet_multi, dataloader=test_loader,
class_specific=class_specific, log=log)
save.save_model_w_condition(model=ppnet, model_dir=model_dir,
model_name=original_model_name.split('push')[0] + 'prune',
accu=accu,
target_accu=0.70, log=log)
# last layer optimization
if optimize_last_layer:
last_layer_optimizer_specs = [{'params': ppnet.last_layer.parameters(), 'lr': 1e-4}]
last_layer_optimizer = torch.optim.Adam(last_layer_optimizer_specs)
coefs = {
'crs_ent': 1,
'clst': 0.8,
'sep': -0.08,
'l1': 1e-4,
}
log('optimize last layer')
tnt.last_only(model=ppnet_multi, log=log)
for i in range(100):
log('iteration: \t{0}'.format(i))
_ = tnt.train(model=ppnet_multi, dataloader=train_loader, optimizer=last_layer_optimizer,
class_specific=class_specific, coefs=coefs, log=log)
accu = tnt.test(model=ppnet_multi, dataloader=test_loader,
class_specific=class_specific, log=log)
save.save_model_w_condition(model=ppnet, model_dir=model_dir,
model_name=original_model_name.split('push')[0] + '_' + str(i) + 'prune',
accu=accu,
target_accu=0.70, log=log)
logclose()