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main.py
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main.py
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import os
import shutil
import torch
import torch.utils.data
# import torch.utils.data.distributed
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import argparse
import re
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
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-gpuid', nargs=1, type=str, default='0') # python3 main.py -gpuid=0,1,2,3
args = parser.parse_args()
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpuid[0] # [0]
print(os.environ['CUDA_VISIBLE_DEVICES'])
# book keeping namings and code
from settings import base_architecture, img_size, prototype_shape, num_classes, \
prototype_activation_function, add_on_layers_type, experiment_run
base_architecture_type = re.match('^[a-z]*', base_architecture).group(0)
model_dir = './saved_models/' + base_architecture + '/' + experiment_run + '/'
makedir(model_dir)
shutil.copy(src=os.path.join(os.getcwd(), __file__), dst=model_dir)
shutil.copy(src=os.path.join(os.getcwd(), 'settings.py'), dst=model_dir)
shutil.copy(src=os.path.join(os.getcwd(), base_architecture_type + '_features.py'), dst=model_dir)
shutil.copy(src=os.path.join(os.getcwd(), 'model.py'), dst=model_dir)
shutil.copy(src=os.path.join(os.getcwd(), 'train_and_test.py'), dst=model_dir)
log, logclose = create_logger(log_filename=os.path.join(model_dir, 'train.log'))
img_dir = os.path.join(model_dir, 'img')
makedir(img_dir)
weight_matrix_filename = 'outputL_weights'
prototype_img_filename_prefix = 'prototype-img'
prototype_self_act_filename_prefix = 'prototype-self-act'
proto_bound_boxes_filename_prefix = 'bb'
# load the data
from settings import train_dir, test_dir, train_push_dir, \
train_batch_size, test_batch_size, train_push_batch_size
normalize = transforms.Normalize(mean=mean,
std=std)
# all datasets
# 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)
# push set
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)
# 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)
# we should look into distributed sampler more carefully at torch.utils.data.distributed.DistributedSampler(train_dataset)
log('training set size: {0}'.format(len(train_loader.dataset)))
log('push set size: {0}'.format(len(train_push_loader.dataset)))
log('test set size: {0}'.format(len(test_loader.dataset)))
log('batch size: {0}'.format(train_batch_size))
# construct the model
ppnet = model.construct_PPNet(base_architecture=base_architecture,
pretrained=True, img_size=img_size,
prototype_shape=prototype_shape,
num_classes=num_classes,
prototype_activation_function=prototype_activation_function,
add_on_layers_type=add_on_layers_type)
#if prototype_activation_function == 'linear':
# ppnet.set_last_layer_incorrect_connection(incorrect_strength=0)
ppnet = ppnet.cuda()
ppnet_multi = torch.nn.DataParallel(ppnet)
class_specific = True
# define optimizer
from settings import joint_optimizer_lrs, joint_lr_step_size
joint_optimizer_specs = \
[{'params': ppnet.features.parameters(), 'lr': joint_optimizer_lrs['features'], 'weight_decay': 1e-3}, # bias are now also being regularized
{'params': ppnet.add_on_layers.parameters(), 'lr': joint_optimizer_lrs['add_on_layers'], 'weight_decay': 1e-3},
{'params': ppnet.prototype_vectors, 'lr': joint_optimizer_lrs['prototype_vectors']},
]
joint_optimizer = torch.optim.Adam(joint_optimizer_specs)
joint_lr_scheduler = torch.optim.lr_scheduler.StepLR(joint_optimizer, step_size=joint_lr_step_size, gamma=0.1)
from settings import warm_optimizer_lrs
warm_optimizer_specs = \
[{'params': ppnet.add_on_layers.parameters(), 'lr': warm_optimizer_lrs['add_on_layers'], 'weight_decay': 1e-3},
{'params': ppnet.prototype_vectors, 'lr': warm_optimizer_lrs['prototype_vectors']},
]
warm_optimizer = torch.optim.Adam(warm_optimizer_specs)
from settings import last_layer_optimizer_lr
last_layer_optimizer_specs = [{'params': ppnet.last_layer.parameters(), 'lr': last_layer_optimizer_lr}]
last_layer_optimizer = torch.optim.Adam(last_layer_optimizer_specs)
# weighting of different training losses
from settings import coefs
# number of training epochs, number of warm epochs, push start epoch, push epochs
from settings import num_train_epochs, num_warm_epochs, push_start, push_epochs
# train the model
log('start training')
import copy
for epoch in range(num_train_epochs):
log('epoch: \t{0}'.format(epoch))
if epoch < num_warm_epochs:
tnt.warm_only(model=ppnet_multi, log=log)
_ = tnt.train(model=ppnet_multi, dataloader=train_loader, optimizer=warm_optimizer,
class_specific=class_specific, coefs=coefs, log=log)
else:
tnt.joint(model=ppnet_multi, log=log)
joint_lr_scheduler.step()
_ = tnt.train(model=ppnet_multi, dataloader=train_loader, optimizer=joint_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=str(epoch) + 'nopush', accu=accu,
target_accu=0.70, log=log)
if epoch >= push_start and epoch in push_epochs:
push.push_prototypes(
train_push_loader, # pytorch dataloader (must be unnormalized in [0,1])
prototype_network_parallel=ppnet_multi, # pytorch network with prototype_vectors
class_specific=class_specific,
preprocess_input_function=preprocess_input_function, # normalize if needed
prototype_layer_stride=1,
root_dir_for_saving_prototypes=img_dir, # if not None, prototypes will be saved here
epoch_number=epoch, # if not provided, prototypes saved previously will be overwritten
prototype_img_filename_prefix=prototype_img_filename_prefix,
prototype_self_act_filename_prefix=prototype_self_act_filename_prefix,
proto_bound_boxes_filename_prefix=proto_bound_boxes_filename_prefix,
save_prototype_class_identity=True,
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=str(epoch) + 'push', accu=accu,
target_accu=0.70, log=log)
if prototype_activation_function != 'linear':
tnt.last_only(model=ppnet_multi, log=log)
for i in range(20):
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=str(epoch) + '_' + str(i) + 'push', accu=accu,
target_accu=0.70, log=log)
logclose()