release code

Dassl.ProGrad.pytorch/datasets/da/cifar_stl.py

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+import sys
+import pprint as pp
+import os.path as osp
+from torchvision.datasets import STL10, CIFAR10
+
+from dassl.utils import mkdir_if_missing
+
+cifar_label2name = {
+    0: "airplane",
+    1: "car",  # the original name was 'automobile'
+    2: "bird",
+    3: "cat",
+    4: "deer",
+    5: "dog",
+    6: "frog",  # conflict class
+    7: "horse",
+    8: "ship",
+    9: "truck",
+}
+
+stl_label2name = {
+    0: "airplane",
+    1: "bird",
+    2: "car",
+    3: "cat",
+    4: "deer",
+    5: "dog",
+    6: "horse",
+    7: "monkey",  # conflict class
+    8: "ship",
+    9: "truck",
+}
+
+new_name2label = {
+    "airplane": 0,
+    "bird": 1,
+    "car": 2,
+    "cat": 3,
+    "deer": 4,
+    "dog": 5,
+    "horse": 6,
+    "ship": 7,
+    "truck": 8,
+}
+
+
+def extract_and_save_image(dataset, save_dir, discard, label2name):
+    if osp.exists(save_dir):
+        print('Folder "{}" already exists'.format(save_dir))
+        return
+
+    print('Extracting images to "{}" ...'.format(save_dir))
+    mkdir_if_missing(save_dir)
+
+    for i in range(len(dataset)):
+        img, label = dataset[i]
+        if label == discard:
+            continue
+        class_name = label2name[label]
+        label_new = new_name2label[class_name]
+        class_dir = osp.join(
+            save_dir,
+            str(label_new).zfill(3) + "_" + class_name
+        )
+        mkdir_if_missing(class_dir)
+        impath = osp.join(class_dir, str(i + 1).zfill(5) + ".jpg")
+        img.save(impath)
+
+
+def download_and_prepare(name, root, discarded_label, label2name):
+    print("Dataset: {}".format(name))
+    print("Root: {}".format(root))
+    print("Old labels:")
+    pp.pprint(label2name)
+    print("Discarded label: {}".format(discarded_label))
+    print("New labels:")
+    pp.pprint(new_name2label)
+
+    if name == "cifar":
+        train = CIFAR10(root, train=True, download=True)
+        test = CIFAR10(root, train=False)
+    else:
+        train = STL10(root, split="train", download=True)
+        test = STL10(root, split="test")
+
+    train_dir = osp.join(root, name, "train")
+    test_dir = osp.join(root, name, "test")
+
+    extract_and_save_image(train, train_dir, discarded_label, label2name)
+    extract_and_save_image(test, test_dir, discarded_label, label2name)
+
+
+if __name__ == "__main__":
+    download_and_prepare("cifar", sys.argv[1], 6, cifar_label2name)
+    download_and_prepare("stl", sys.argv[1], 7, stl_label2name)

Dassl.ProGrad.pytorch/datasets/da/digit5.py

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+import os
+import numpy as np
+import os.path as osp
+import argparse
+from PIL import Image
+from scipy.io import loadmat
+
+
+def mkdir_if_missing(directory):
+    if not osp.exists(directory):
+        os.makedirs(directory)
+
+
+def extract_and_save(data, label, save_dir):
+    for i, (x, y) in enumerate(zip(data, label)):
+        if x.shape[2] == 1:
+            x = np.repeat(x, 3, axis=2)
+        if y == 10:
+            y = 0
+        x = Image.fromarray(x, mode="RGB")
+        save_path = osp.join(
+            save_dir,
+            str(i + 1).zfill(6) + "_" + str(y) + ".jpg"
+        )
+        x.save(save_path)
+
+
+def load_mnist(data_dir, raw_data_dir):
+    filepath = osp.join(raw_data_dir, "mnist_data.mat")
+    data = loadmat(filepath)
+
+    train_data = np.reshape(data["train_32"], (55000, 32, 32, 1))
+    test_data = np.reshape(data["test_32"], (10000, 32, 32, 1))
+
+    train_label = np.nonzero(data["label_train"])[1]
+    test_label = np.nonzero(data["label_test"])[1]
+
+    return train_data, test_data, train_label, test_label
+
+
+def load_mnist_m(data_dir, raw_data_dir):
+    filepath = osp.join(raw_data_dir, "mnistm_with_label.mat")
+    data = loadmat(filepath)
+
+    train_data = data["train"]
+    test_data = data["test"]
+
+    train_label = np.nonzero(data["label_train"])[1]
+    test_label = np.nonzero(data["label_test"])[1]
+
+    return train_data, test_data, train_label, test_label
+
+
+def load_svhn(data_dir, raw_data_dir):
+    train = loadmat(osp.join(raw_data_dir, "svhn_train_32x32.mat"))
+    train_data = train["X"].transpose(3, 0, 1, 2)
+    train_label = train["y"][:, 0]
+
+    test = loadmat(osp.join(raw_data_dir, "svhn_test_32x32.mat"))
+    test_data = test["X"].transpose(3, 0, 1, 2)
+    test_label = test["y"][:, 0]
+
+    return train_data, test_data, train_label, test_label
+
+
+def load_syn(data_dir, raw_data_dir):
+    filepath = osp.join(raw_data_dir, "syn_number.mat")
+    data = loadmat(filepath)
+
+    train_data = data["train_data"]
+    test_data = data["test_data"]
+
+    train_label = data["train_label"][:, 0]
+    test_label = data["test_label"][:, 0]
+
+    return train_data, test_data, train_label, test_label
+
+
+def load_usps(data_dir, raw_data_dir):
+    filepath = osp.join(raw_data_dir, "usps_28x28.mat")
+    data = loadmat(filepath)["dataset"]
+
+    train_data = data[0][0].transpose(0, 2, 3, 1)
+    test_data = data[1][0].transpose(0, 2, 3, 1)
+
+    train_data *= 255
+    test_data *= 255
+
+    train_data = train_data.astype(np.uint8)
+    test_data = test_data.astype(np.uint8)
+
+    train_label = data[0][1][:, 0]
+    test_label = data[1][1][:, 0]
+
+    return train_data, test_data, train_label, test_label
+
+
+def main(data_dir):
+    data_dir = osp.abspath(osp.expanduser(data_dir))
+    raw_data_dir = osp.join(data_dir, "Digit-Five")
+
+    if not osp.exists(data_dir):
+        raise FileNotFoundError('"{}" does not exist'.format(data_dir))
+
+    datasets = ["mnist", "mnist_m", "svhn", "syn", "usps"]
+
+    for name in datasets:
+        print("Creating {}".format(name))
+
+        output = eval("load_" + name)(data_dir, raw_data_dir)
+        train_data, test_data, train_label, test_label = output
+
+        print("# train: {}".format(train_data.shape[0]))
+        print("# test: {}".format(test_data.shape[0]))
+
+        train_dir = osp.join(data_dir, name, "train_images")
+        mkdir_if_missing(train_dir)
+        test_dir = osp.join(data_dir, name, "test_images")
+        mkdir_if_missing(test_dir)
+
+        extract_and_save(train_data, train_label, train_dir)
+        extract_and_save(test_data, test_label, test_dir)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "data_dir", type=str, help="directory containing Digit-Five/"
+    )
+    args = parser.parse_args()
+    main(args.data_dir)

Dassl.ProGrad.pytorch/datasets/da/visda17.sh

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+# ------------------------------------------------------------------------
+# ROOT is the root directory where you put your domain datasets.
+# 
+# Suppose you wanna put the dataset under $DATA, which stores all the
+# domain datasets, run the following command in your terminal to
+# download VisDa17:
+#
+# $ sh visda17.sh $DATA
+#------------------------------------------------------------------------
+
+ROOT=$1
+mkdir $ROOT/visda17
+cd $ROOT/visda17
+
+wget http://csr.bu.edu/ftp/visda17/clf/train.tar
+tar xvf train.tar
+
+wget http://csr.bu.edu/ftp/visda17/clf/validation.tar
+tar xvf validation.tar  
+
+wget http://csr.bu.edu/ftp/visda17/clf/test.tar
+tar xvf test.tar
+
+wget https://raw.githubusercontent.com/VisionLearningGroup/taskcv-2017-public/master/classification/data/image_list.txt -O test/image_list.txt