fazil
/
pytorch-dann-resnet
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
Browse Source

Fixes for initial training

master
Fazil Altinel 4 years ago
parent
66e415643c
commit
eb194bff1c
4 changed files with 46 additions and 32 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Unified View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 25
      core/train.py
  2. 7
      datasets/mnist.py
  3. 0
      experiments/__init__.py
  4. 16
      experiments/mnist_mnistm.py

25
core/train.py
View File

@ -10,7 +10,7 @@ from utils.utils import save_model
import torch.backends.cudnn as cudnn import torch.backends.cudnn as cudnn
cudnn.benchmark = True cudnn.benchmark = True
def train_src(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_eval, device, logger):
def train_src(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_eval, device, logger=None):
"""Train dann.""" """Train dann."""
#################### ####################
# 1. setup network # # 1. setup network #
@ -60,6 +60,7 @@ def train_src(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_e
lr = adjust_learning_rate(optimizer, p) lr = adjust_learning_rate(optimizer, p)
else: else:
lr = adjust_learning_rate_office(optimizer, p) lr = adjust_learning_rate_office(optimizer, p)
if not logger == None:
logger.add_scalar('lr', lr, global_step) logger.add_scalar('lr', lr, global_step)
# prepare domain label # prepare domain label
@ -86,6 +87,7 @@ def train_src(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_e
global_step += 1 global_step += 1
# print step info # print step info
if not logger == None:
logger.add_scalar('loss', loss.item(), global_step) logger.add_scalar('loss', loss.item(), global_step)
if ((step + 1) % params.log_step == 0): if ((step + 1) % params.log_step == 0):
@ -96,6 +98,7 @@ def train_src(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_e
if ((epoch + 1) % params.eval_step == 0): if ((epoch + 1) % params.eval_step == 0):
src_test_loss, src_acc, src_acc_domain = test(model, src_data_loader, device, flag='source') src_test_loss, src_acc, src_acc_domain = test(model, src_data_loader, device, flag='source')
tgt_test_loss, tgt_acc, tgt_acc_domain = test(model, tgt_data_loader_eval, device, flag='target') tgt_test_loss, tgt_acc, tgt_acc_domain = test(model, tgt_data_loader_eval, device, flag='target')
if not logger == None:
logger.add_scalar('src_test_loss', src_test_loss, global_step) logger.add_scalar('src_test_loss', src_test_loss, global_step)
logger.add_scalar('src_acc', src_acc, global_step) logger.add_scalar('src_acc', src_acc, global_step)
@ -110,7 +113,7 @@ def train_src(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_e
return model return model
def train_dann(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_eval, device, logger):
def train_dann(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_eval, device, logger=None):
"""Train dann.""" """Train dann."""
#################### ####################
# 1. setup network # # 1. setup network #
@ -144,6 +147,7 @@ def train_dann(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_
# 2. train network # # 2. train network #
#################### ####################
global_step = 0 global_step = 0
bestAcc = 0.0
for epoch in range(params.num_epochs): for epoch in range(params.num_epochs):
# set train state for Dropout and BN layers # set train state for Dropout and BN layers
model.train() model.train()
@ -160,6 +164,7 @@ def train_dann(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_
lr = adjust_learning_rate(optimizer, p) lr = adjust_learning_rate(optimizer, p)
else: else:
lr = adjust_learning_rate_office(optimizer, p) lr = adjust_learning_rate_office(optimizer, p)
if not logger == None:
logger.add_scalar('lr', lr, global_step) logger.add_scalar('lr', lr, global_step)
# prepare domain label # prepare domain label
@ -196,6 +201,7 @@ def train_dann(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_
global_step += 1 global_step += 1
# print step info # print step info
if not logger == None:
logger.add_scalar('src_loss_class', src_loss_class.item(), global_step) logger.add_scalar('src_loss_class', src_loss_class.item(), global_step)
logger.add_scalar('src_loss_domain', src_loss_domain.item(), global_step) logger.add_scalar('src_loss_domain', src_loss_domain.item(), global_step)
logger.add_scalar('tgt_loss_domain', tgt_loss_domain.item(), global_step) logger.add_scalar('tgt_loss_domain', tgt_loss_domain.item(), global_step)
@ -211,6 +217,12 @@ def train_dann(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_
if ((epoch + 1) % params.eval_step == 0): if ((epoch + 1) % params.eval_step == 0):
tgt_test_loss, tgt_acc, tgt_acc_domain = test(model, tgt_data_loader_eval, device, flag='target') tgt_test_loss, tgt_acc, tgt_acc_domain = test(model, tgt_data_loader_eval, device, flag='target')
src_test_loss, src_acc, src_acc_domain = test(model, src_data_loader, device, flag='source') src_test_loss, src_acc, src_acc_domain = test(model, src_data_loader, device, flag='source')
if tgt_acc > bestAcc:
bestAcc = tgt_acc
bestAccS = src_acc
save_model(model, params.model_root,
params.src_dataset + '-' + params.tgt_dataset + "-dann-best.pt")
if not logger == None:
logger.add_scalar('src_test_loss', src_test_loss, global_step) logger.add_scalar('src_test_loss', src_test_loss, global_step)
logger.add_scalar('src_acc', src_acc, global_step) logger.add_scalar('src_acc', src_acc, global_step)
logger.add_scalar('src_acc_domain', src_acc_domain, global_step) logger.add_scalar('src_acc_domain', src_acc_domain, global_step)
@ -218,14 +230,11 @@ def train_dann(model, params, src_data_loader, tgt_data_loader, tgt_data_loader_
logger.add_scalar('tgt_acc', tgt_acc, global_step) logger.add_scalar('tgt_acc', tgt_acc, global_step)
logger.add_scalar('tgt_acc_domain', tgt_acc_domain, global_step) logger.add_scalar('tgt_acc_domain', tgt_acc_domain, global_step)
# save model parameters
if ((epoch + 1) % params.save_step == 0):
save_model(model, params.model_root,
params.src_dataset + '-' + params.tgt_dataset + "-dann-{}.pt".format(epoch + 1))
# save final model # save final model
save_model(model, params.model_root, params.src_dataset + '-' + params.tgt_dataset + "-dann-final.pt") save_model(model, params.model_root, params.src_dataset + '-' + params.tgt_dataset + "-dann-final.pt")
print('============ Summary ============= \n')
print('Accuracy of the %s dataset: %f' % (params.src_dataset, bestAccS))
print('Accuracy of the %s dataset: %f' % (params.tgtc_dataset, bestAcc))
return model return model

7
datasets/mnist.py
View File

@ -8,11 +8,11 @@ import os
def get_mnist(dataset_root, batch_size, train): def get_mnist(dataset_root, batch_size, train):
"""Get MNIST datasets loader.""" """Get MNIST datasets loader."""
# image pre-processing # image pre-processing
pre_process = transforms.Compose([transforms.Resize(32), # different img size settings for mnist(28) and svhn(32).
pre_process = transforms.Compose([transforms.Resize(28), # different img size settings for mnist(28) and svhn(32).
transforms.ToTensor(), transforms.ToTensor(),
transforms.Normalize( transforms.Normalize(
mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5)
mean=(0.5),
std=(0.5)
)]) )])
# datasets and data loader # datasets and data loader
@ -21,7 +21,6 @@ def get_mnist(dataset_root, batch_size, train):
transform=pre_process, transform=pre_process,
download=False) download=False)
mnist_data_loader = torch.utils.data.DataLoader( mnist_data_loader = torch.utils.data.DataLoader(
dataset=mnist_dataset, dataset=mnist_dataset,
batch_size=batch_size, batch_size=batch_size,

0
experiments/__init__.py
View File

16
experiments/mnist_mnistm.py
View File

@ -1,8 +1,7 @@
import os import os
import sys import sys
sys.path.append(os.path.abspath('.'))
import torch import torch
sys.path.append('../')
from models.model import MNISTmodel, MNISTmodel_plain from models.model import MNISTmodel, MNISTmodel_plain
from core.train import train_dann from core.train import train_dann
from utils.utils import get_data_loader, init_model, init_random_seed from utils.utils import get_data_loader, init_model, init_random_seed
@ -10,8 +9,13 @@ from utils.utils import get_data_loader, init_model, init_random_seed
class Config(object): class Config(object):
# params for path # params for path
dataset_root = os.path.expanduser(os.path.join('~', 'Datasets'))
model_root = os.path.expanduser(os.path.join('~', 'Models', 'pytorch-DANN'))
currentDir = os.path.dirname(os.path.realpath(__file__))
dataset_root = os.environ["DATASETDIR"]
model_root = os.path.join(currentDir, 'checkpoints')
finetune_flag = False
lr_adjust_flag = 'simple'
src_only_flag = False
# params for datasets and data loader # params for datasets and data loader
batch_size = 64 batch_size = 64
@ -53,6 +57,8 @@ class Config(object):
# params for optimizing models # params for optimizing models
lr = 2e-4 lr = 2e-4
momentum = 0.0
weight_decay = 0.0
params = Config() params = Config()
@ -70,7 +76,7 @@ tgt_data_loader = get_data_loader(params.tgt_dataset, params.dataset_root, param
tgt_data_loader_eval = get_data_loader(params.tgt_dataset, params.dataset_root, params.batch_size, train=False) tgt_data_loader_eval = get_data_loader(params.tgt_dataset, params.dataset_root, params.batch_size, train=False)
# load dann model # load dann model
dann = init_model(net=MNISTmodel_plain(), restore=None)
dann = init_model(net=MNISTmodel(), restore=None)
# train dann model # train dann model
print("Training dann model") print("Training dann model")

Write Preview
Loading…
Cancel
Save