remove legacy code.

core/pretrain.py

@@ -1,61 +0,0 @@
-"""Train classifier for source dataset."""
-
-import torch.nn as nn
-import torch.optim as optim
-
-from utils.utils import save_model
-from core.test import test
-
-
-def train_src(model, params, data_loader, device):
-    """Train classifier for source domain."""
-    ####################
-    # 1. setup network #
-    ####################
-
-    # set train state for Dropout and BN layers
-    model.train()
-
-    # setup criterion and optimizer
-    optimizer = optim.Adam(model.parameters(), lr=params.lr)
-    loss_class = nn.NLLLoss()
-
-    ####################
-    # 2. train network #
-    ####################
-
-    for epoch in range(params.num_epochs_src):
-        for step, (images, labels) in enumerate(data_loader):
-            # make images and labels variable
-            images = images.to(device)
-            labels = labels.squeeze_().to(device)
-
-            # zero gradients for optimizer
-            optimizer.zero_grad()
-
-            # compute loss for critic
-            preds_class, _ = model(images)
-            loss = loss_class(preds_class, labels)
-
-            # optimize source classifier
-            loss.backward()
-            optimizer.step()
-
-            # print step info
-            if ((step + 1) % params.log_step_src == 0):
-                print("Epoch [{}/{}] Step [{}/{}]: loss={}".format(epoch + 1, params.num_epochs_src, step + 1,
-                                                                   len(data_loader), loss.data[0]))
-
-        # eval model on test set
-        if ((epoch + 1) % params.eval_step_src == 0):
-            test(model, data_loader, flag='source')
-            model.train()
-
-        # save model parameters
-        if ((epoch + 1) % params.save_step_src == 0):
-            save_model(model, params.src_dataset + "-source-classifier-{}.pt".format(epoch + 1))
-
-    # save final model
-    save_model(model, params.src_dataset + "-source-classifier-final.pt")
-
-    return model

datasets/gtsrb_legacy.py

@@ -1,45 +0,0 @@
-"""Dataset setting and data loader for GTSRB."""
-
-import os
-import torch
-from torchvision import datasets, transforms
-import torch.utils.data as data
-from torch.utils.data.sampler import SubsetRandomSampler
-import numpy as np
-
-def get_gtsrb(dataset_root, batch_size, train):
-    """Get GTSRB datasets loader."""
-    shuffle_dataset = True
-    random_seed = 42
-    train_size = 31367
-
-    # image pre-processing
-    pre_process = transforms.Compose([
-        transforms.Resize((40, 40)),
-        transforms.ToTensor(),
-        transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
-    ])
-
-    # datasets and data_loader
-    gtsrb_dataset = datasets.ImageFolder(
-        os.path.join(dataset_root, 'Final_Training', 'Images'), transform=pre_process)
-
-    dataset_size = len(gtsrb_dataset)
-    indices = list(range(dataset_size))
-    if shuffle_dataset:
-        np.random.seed(random_seed)
-        np.random.shuffle(indices)
-    train_indices, val_indices = indices[:train_size], indices[train_size:]
-
-    # Creating PT data samplers and loaders:
-    train_sampler = SubsetRandomSampler(train_indices)
-    valid_sampler = SubsetRandomSampler(val_indices)
-
-    if train:
-        gtsrb_dataloader_train = torch.utils.data.DataLoader(gtsrb_dataset, batch_size=batch_size,
-                                               sampler=train_sampler)
-        return gtsrb_dataloader_train
-    else:
-        gtsrb_dataloader_test = torch.utils.data.DataLoader(gtsrb_dataset, batch_size=batch_size,
-                                                    sampler=valid_sampler)
-        return gtsrb_dataloader_test