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update svhnmodel structure, basically the same as paper, except for the last conv layers, kernel change from 5x5 to 4x4, get better result 75%.

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wogong 5 years ago
parent
5e65946d13
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6a9e484102
4 changed files with 38 additions and 35 deletions
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  1. 5
      datasets/mnist.py
  2. 6
      datasets/svhn.py
  3. 41
      experiments/svhn_mnist.py
  4. 21
      models/model.py

5
datasets/mnist.py
View File

@ -8,14 +8,15 @@ import os
def get_mnist(dataset_root, batch_size, train):
"""Get MNIST datasets loader."""
# image pre-processing
pre_process = transforms.Compose([transforms.ToTensor(),
pre_process = transforms.Compose([transforms.Resize(32), # different img size settings for mnist(28) and svhn(32).
transforms.ToTensor(),
transforms.Normalize(
mean=(0.5, 0.5, 0.5),
std=(0.5, 0.5, 0.5)
)])
# datasets and data loader
mnist_dataset = datasets.MNIST(root=os.path.join(dataset_root,'mnist'),
mnist_dataset = datasets.MNIST(root=os.path.join(dataset_root),
train=train,
transform=pre_process,
download=False)

6
datasets/svhn.py
View File

@ -8,7 +8,7 @@ import os
def get_svhn(dataset_root, batch_size, train):
"""Get SVHN datasets loader."""
# image pre-processing
pre_process = transforms.Compose([transforms.Resize(28),
pre_process = transforms.Compose([transforms.Resize(32),
transforms.ToTensor(),
transforms.Normalize(
mean=(0.5, 0.5, 0.5),
@ -17,12 +17,12 @@ def get_svhn(dataset_root, batch_size, train):
# datasets and data loader
if train:
svhn_dataset = datasets.SVHN(root=os.path.join(dataset_root,'svhn'),
svhn_dataset = datasets.SVHN(root=os.path.join(dataset_root),
split='train',
transform=pre_process,
download=True)
else:
svhn_dataset = datasets.SVHN(root=os.path.join(dataset_root,'svhn'),
svhn_dataset = datasets.SVHN(root=os.path.join(dataset_root),
split='test',
transform=pre_process,
download=True)

41
experiments/svhn_mnist.py
View File

@ -1,8 +1,9 @@
import os
import sys
import datetime
from tensorboardX import SummaryWriter
import torch
sys.path.append('../')
from models.model import SVHNmodel
from core.train import train_dann
@ -11,29 +12,32 @@ from utils.utils import get_data_loader, init_model, init_random_seed
class Config(object):
# params for path
dataset_root = os.path.expanduser(os.path.join('~', 'Datasets'))
model_name = "svhn-mnist"
model_base = '/home/wogong/models/pytorch-dann'
model_root = os.path.expanduser(os.path.join('~', 'Models', 'pytorch-DANN', model_name))
note = 'paper-structure'
model_root = os.path.join(model_base, model_name, note + '_' + datetime.datetime.now().strftime('%m%d_%H%M%S'))
os.makedirs(model_root)
config = os.path.join(model_root, 'config.txt')
finetune_flag = False
lr_adjust_flag = 'simple'
src_only_flag = False
# params for datasets and data loader
batch_size = 128
# params for source dataset
src_dataset = "svhn"
src_image_root = os.path.join('/home/wogong/datasets', 'svhn')
src_model_trained = True
src_classifier_restore = os.path.join(model_root, src_dataset + '-source-classifier-final.pt')
# params for target dataset
tgt_dataset = "mnist"
tgt_image_root = os.path.join('/home/wogong/datasets', 'mnist')
tgt_model_trained = True
dann_restore = os.path.join(model_root, src_dataset + '-' + tgt_dataset + '-dann-final.pt')
# params for pretrain
num_epochs_src = 100
log_step_src = 10
save_step_src = 50
eval_step_src = 20
# params for training dann
gpu_id = '0'
@ -41,7 +45,7 @@ class Config(object):
num_epochs = 200
log_step = 50
save_step = 100
eval_step = 5
eval_step = 1
## for office
# num_epochs = 1000
@ -53,21 +57,22 @@ class Config(object):
alpha = 0
# params for optimizing models
lr = 2e-4
lr = 0.01
momentum = 0.9
weight_decay = 1e-6
params = Config()
logger = SummaryWriter(params.model_root)
device = torch.device("cuda:" + params.gpu_id if torch.cuda.is_available() else "cpu")
# init random seed
init_random_seed(params.manual_seed)
# init device
device = torch.device("cuda:" + params.gpu_id if torch.cuda.is_available() else "cpu")
# load dataset
src_data_loader = get_data_loader(params.src_dataset, params.dataset_root, params.batch_size, train=True)
src_data_loader_eval = get_data_loader(params.src_dataset, params.dataset_root, params.batch_size, train=False)
tgt_data_loader = get_data_loader(params.tgt_dataset, params.dataset_root, params.batch_size, train=True)
tgt_data_loader_eval = get_data_loader(params.tgt_dataset, params.dataset_root, params.batch_size, train=False)
src_data_loader = get_data_loader(params.src_dataset, params.src_image_root, params.batch_size, train=True)
src_data_loader_eval = get_data_loader(params.src_dataset, params.src_image_root, params.batch_size, train=False)
tgt_data_loader = get_data_loader(params.tgt_dataset, params.tgt_image_root, params.batch_size, train=True)
tgt_data_loader_eval = get_data_loader(params.tgt_dataset, params.tgt_image_root, params.batch_size, train=False)
# load dann model
dann = init_model(net=SVHNmodel(), restore=None)
@ -75,4 +80,4 @@ dann = init_model(net=SVHNmodel(), restore=None)
# train dann model
print("Training dann model")
if not (dann.restored and params.dann_restore):
dann = train_dann(dann, params, src_data_loader, tgt_data_loader, tgt_data_loader_eval, device)
dann = train_dann(dann, params, src_data_loader, tgt_data_loader, tgt_data_loader_eval, device, logger)

21
models/model.py
View File

@ -150,8 +150,6 @@ class MNISTmodel_plain(nn.Module):
class SVHNmodel(nn.Module):
""" SVHN architecture
I don't know how to implement the paper's structure
"""
def __init__(self):
@ -159,22 +157,21 @@ class SVHNmodel(nn.Module):
self.restored = False
self.feature = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=64, kernel_size=(
5, 5), stride=(1, 1)), # 3 28 28, 64 24 24
nn.Conv2d(in_channels=3, out_channels=64, kernel_size=(5, 5)), # 28
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=(2, 2)), # 64 12 12
nn.Conv2d(in_channels=64, out_channels=64,
kernel_size=(5, 5)), # 64 8 8
nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2)), # 14
nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(5, 5)), # 10
nn.BatchNorm2d(64),
nn.Dropout2d(),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=(2, 2), stride=(2, 2)), # 64 4 4
nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2)), # 5
nn.ReLU(inplace=True),
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(4, 4)), # 1
)
self.classifier = nn.Sequential(
nn.Linear(64*4*4, 1024),
nn.Linear(128 * 1 * 1, 1024),
nn.BatchNorm1d(1024),
nn.ReLU(inplace=True),
nn.Linear(1024, 256),
@ -184,7 +181,7 @@ class SVHNmodel(nn.Module):
)
self.discriminator = nn.Sequential(
nn.Linear(64*4*4, 1024),
nn.Linear(128 * 1 * 1, 1024),
nn.BatchNorm1d(1024),
nn.ReLU(inplace=True),
nn.Linear(1024, 256),
@ -194,9 +191,9 @@ class SVHNmodel(nn.Module):
)
def forward(self, input_data, alpha = 1.0):
input_data = input_data.expand(input_data.data.shape[0], 3, 28, 28)
input_data = input_data.expand(input_data.data.shape[0], 3, 32, 32)
feature = self.feature(input_data)
feature = feature.view(-1, 64 * 4 * 4)
feature = feature.view(-1, 128 * 1 * 1)
reverse_feature = ReverseLayerF.apply(feature, alpha)
class_output = self.classifier(feature)
domain_output = self.discriminator(reverse_feature)

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