SGRRN/utils.py at master · DreamtaleCore/SGRRN · GitHub

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import multiprocessing

from PIL import Image
from torch.utils.data import DataLoader
from torch.autograd import Variable
from torch.optim import lr_scheduler
from data import LowLevelImageFolder
import torch
import os
import math
import torchvision.utils as vutils
import yaml
import numpy as np
import torch.nn.init as init
import time
import easydict
import queue
import collections
import threading


def to_number(data):
    if type(data) is torch.Tensor:
        return data.item()
    else:
        return data


def get_local_time():
    return time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())


def get_reflection_data_loader(conf, train_mode='train'):
    data_root = conf['data_root'].replace('train', train_mode)
    train_set = LowLevelImageFolder(root=data_root, sub_folders=['input', 'background', 'reflection',
                                                                 'bg-semantic', 'rf-semantic'],
                                    new_size=conf['new_size'], mode=conf['mode'])
    train_loader = DataLoader(train_set, batch_size=conf['batch_size'])
    return train_loader


def get_config(config):
    with open(config, 'r') as stream:
        return easydict.EasyDict(yaml.load(stream))


def eformat(f, prec):
    s = "%.*e" % (prec, f)
    mantissa, exp = s.split('e')
    # add 1 to digits as 1 is taken by sign +/-
    return "%se%d" % (mantissa, int(exp))


def __write_images(image_outputs, display_image_num, file_name):
    image_outputs = [images.expand(-1, 3, -1, -1) for images in image_outputs]  # expand gray-scale images to 3 channels
    image_tensor = torch.cat([images[:display_image_num] for images in image_outputs], 0)
    image_grid = vutils.make_grid(image_tensor.data, nrow=display_image_num, padding=0, normalize=False)
    vutils.save_image(image_grid, file_name, nrow=1)


def write_2images(image_outputs, display_image_num, image_directory, postfix, image_name='saved_images'):
    n = len(image_outputs)
    __write_images(image_outputs, display_image_num,
                   '%s/{}-%s.jpg'.format(image_name) % (image_directory, postfix))


def prepare_sub_folder(output_directory):
    image_directory = os.path.join(output_directory, 'images')
    if not os.path.exists(image_directory):
        print("Creating directory: {}".format(image_directory))
        os.makedirs(image_directory)
    checkpoint_directory = os.path.join(output_directory, 'checkpoints')
    if not os.path.exists(checkpoint_directory):
        print("Creating directory: {}".format(checkpoint_directory))
        os.makedirs(checkpoint_directory)
    return checkpoint_directory, image_directory


def write_one_row_html(html_file, iterations, img_filename, all_size):
    html_file.write("<h3>iteration [%d] (%s)</h3>" % (iterations, img_filename.split('/')[-1]))
    html_file.write("""
        <p><a href="%s">
          <img src="%s" style="width:%dpx">
        </a><br>
        <p>
        """ % (img_filename, img_filename, all_size))
    return


def write_html(filename, iterations, image_save_iterations, image_directory, all_size=1536):
    html_file = open(filename, "w")
    html_file.write('''
    <!DOCTYPE html>
    <html>
    <head>
      <title>Experiment name = %s</title>
      <meta http-equiv="refresh" content="30">
    </head>
    <body>
    ''' % os.path.basename(filename))
    html_file.write("<h3>current</h3>")
    write_one_row_html(html_file, iterations, '%s/gen_a2b_train_current.jpg' % (image_directory), all_size)
    write_one_row_html(html_file, iterations, '%s/gen_b2a_train_current.jpg' % (image_directory), all_size)
    for j in range(iterations, image_save_iterations - 1, -1):
        if j % image_save_iterations == 0:
            write_one_row_html(html_file, j, '%s/gen_a2b_test_%08d.jpg' % (image_directory, j), all_size)
            write_one_row_html(html_file, j, '%s/gen_b2a_test_%08d.jpg' % (image_directory, j), all_size)
            write_one_row_html(html_file, j, '%s/gen_a2b_train_%08d.jpg' % (image_directory, j), all_size)
            write_one_row_html(html_file, j, '%s/gen_b2a_train_%08d.jpg' % (image_directory, j), all_size)
    html_file.write("</body></html>")
    html_file.close()


def write_loss(iterations, trainer, train_writer):
    members = [attr for attr in dir(trainer) \
               if not callable(getattr(trainer, attr)) and not attr.startswith("__") and (
                       'loss' in attr or 'grad' in attr or 'nwd' in attr)]
    for m in members:
        train_writer.add_scalar(m, getattr(trainer, m), iterations + 1)


def slerp(val, low, high):
    """
    original: Animating Rotation with Quaternion Curves, Ken Shoemake
    https://arxiv.org/abs/1609.04468
    Code: https://github.com/soumith/dcgan.torch/issues/14, Tom White
    """
    omega = np.arccos(np.dot(low / np.linalg.norm(low), high / np.linalg.norm(high)))
    so = np.sin(omega)
    return np.sin((1.0 - val) * omega) / so * low + np.sin(val * omega) / so * high


def get_slerp_interp(nb_latents, nb_interp, z_dim):
    """
    modified from: PyTorch inference for "Progressive Growing of GANs" with CelebA snapshot
    https://github.com/ptrblck/prog_gans_pytorch_inference
    """

    latent_interps = np.empty(shape=(0, z_dim), dtype=np.float32)
    for _ in range(nb_latents):
        low = np.random.randn(z_dim)
        high = np.random.randn(z_dim)  # low + np.random.randn(512) * 0.7
        interp_vals = np.linspace(0, 1, num=nb_interp)
        latent_interp = np.array([slerp(v, low, high) for v in interp_vals],
                                 dtype=np.float32)
        latent_interps = np.vstack((latent_interps, latent_interp))

    return latent_interps[:, :, np.newaxis, np.newaxis]


# Get model list for resume
def get_model_list(dirname, key):
    if os.path.exists(dirname) is False:
        return None
    gen_models = [os.path.join(dirname, f) for f in os.listdir(dirname) if
                  os.path.isfile(os.path.join(dirname, f)) and key in f and ".pt" in f]
    if len(gen_models) == 0:
        return None
    gen_models.sort()
    last_model_name = gen_models[-1]
    return last_model_name


def vgg_preprocess(batch):
    tensortype = type(batch.data)
    (r, g, b) = torch.chunk(batch, 3, dim=1)
    batch = torch.cat((b, g, r), dim=1)  # convert RGB to BGR
    batch = (batch + 1) * 255 * 0.5  # [-1, 1] -> [0, 255]
    mean = tensortype(batch.data.size()).cuda()
    mean[:, 0, :, :] = 103.939
    mean[:, 1, :, :] = 116.779
    mean[:, 2, :, :] = 123.680
    batch = batch.sub(Variable(mean))  # subtract mean
    return batch


def get_scheduler(optimizer, hyperparameters, iterations=-1):
    if 'lr_policy' not in hyperparameters or hyperparameters['lr_policy'] == 'constant':
        scheduler = None  # constant scheduler
    elif hyperparameters['lr_policy'] == 'step':
        scheduler = lr_scheduler.StepLR(optimizer, step_size=hyperparameters['step_size'],
                                        gamma=hyperparameters['gamma'], last_epoch=iterations)
    else:
        return NotImplementedError('learning rate policy [%s] is not implemented', hyperparameters['lr_policy'])
    return scheduler


def weights_init(init_type='gaussian'):
    def init_fun(m):
        classname = m.__class__.__name__
        if (classname.find('Conv') == 0 or classname.find('Linear') == 0) and hasattr(m, 'weight'):
            # print m.__class__.__name__
            if init_type == 'gaussian':
                init.normal_(m.weight.data, 0.0, 0.02)
            elif init_type == 'xavier':
                init.xavier_normal_(m.weight.data, gain=math.sqrt(2))
            elif init_type == 'kaiming':
                init.kaiming_normal_(m.weight.data, a=0, mode='fan_in')
            elif init_type == 'orthogonal':
                init.orthogonal_(m.weight.data, gain=math.sqrt(2))
            elif init_type == 'default':
                pass
            else:
                assert 0, "Unsupported initialization: {}".format(init_type)
            if hasattr(m, 'bias') and m.bias is not None:
                init.constant_(m.bias.data, 0.0)

    return init_fun


class Timer:
    def __init__(self, msg):
        self.msg = msg
        self.start_time = None

    def __enter__(self):
        self.start_time = time.time()

    def __exit__(self, exc_type, exc_value, exc_tb):
        print(self.msg % (time.time() - self.start_time))


class FutureResult(object):
    """A thread-safe future implementation. Used only as one-to-one pipe."""

    def __init__(self):
        self._result = None
        self._lock = threading.Lock()
        self._cond = threading.Condition(self._lock)

    def put(self, result):
        with self._lock:
            assert self._result is None, 'Previous result has\'t been fetched.'
            self._result = result
            self._cond.notify()

    def get(self):
        with self._lock:
            if self._result is None:
                self._cond.wait()

            res = self._result
            self._result = None
            return res


_MasterRegistry = collections.namedtuple('MasterRegistry', ['result'])
_SlavePipeBase = collections.namedtuple('_SlavePipeBase', ['identifier', 'queue', 'result'])


class SlavePipe(_SlavePipeBase):
    """Pipe for master-slave communication."""

    def run_slave(self, msg):
        self.queue.put((self.identifier, msg))
        ret = self.result.get()
        self.queue.put(True)
        return ret


class SyncMaster(object):
    """An abstract `SyncMaster` object.

    - During the replication, as the data parallel will trigger an callback of each module, all slave devices should
    call `register(id)` and obtain an `SlavePipe` to communicate with the master.
    - During the forward pass, master device invokes `run_master`, all messages from slave devices will be collected,
    and passed to a registered callback.
    - After receiving the messages, the master device should gather the information and determine to message passed
    back to each slave devices.
    """

    def __init__(self, master_callback):
        """

        Args:
            master_callback: a callback to be invoked after having collected messages from slave devices.
        """
        self._master_callback = master_callback
        self._queue = queue.Queue()
        self._registry = collections.OrderedDict()
        self._activated = False

    def __getstate__(self):
        return {'master_callback': self._master_callback}

    def __setstate__(self, state):
        self.__init__(state['master_callback'])

    def register_slave(self, identifier):
        """
        Register an slave device.

        Args:
            identifier: an identifier, usually is the device id.

        Returns: a `SlavePipe` object which can be used to communicate with the master device.

        """
        if self._activated:
            assert self._queue.empty(), 'Queue is not clean before next initialization.'
            self._activated = False
            self._registry.clear()
        future = FutureResult()
        self._registry[identifier] = _MasterRegistry(future)
        return SlavePipe(identifier, self._queue, future)

    def run_master(self, master_msg):
        """
        Main entry for the master device in each forward pass.
        The messages were first collected from each devices (including the master device), and then
        an callback will be invoked to compute the message to be sent back to each devices
        (including the master device).

        Args:
            master_msg: the message that the master want to send to itself. This will be placed as the first
            message when calling `master_callback`. For detailed usage, see `_SynchronizedBatchNorm` for an example.

        Returns: the message to be sent back to the master device.

        """
        self._activated = True

        intermediates = [(0, master_msg)]
        for i in range(self.nr_slaves):
            intermediates.append(self._queue.get())

        results = self._master_callback(intermediates)
        assert results[0][0] == 0, 'The first result should belongs to the master.'

        for i, res in results:
            if i == 0:
                continue
            self._registry[i].result.put(res)

        for i in range(self.nr_slaves):
            assert self._queue.get() is True

        return results[0][1]

    @property
    def nr_slaves(self):
        return len(self._registry)


def __colormap(N):
    """Get the map from label index to color

    Args:
        N: number of class

        return: a Nx3 matrix

    """
    cmap = np.zeros((N, 3), dtype=np.uint8)

    def uint82bin(n, count=8):
        """returns the binary of integer n, count refers to amount of bits"""
        return ''.join([str((n >> y) & 1) for y in range(count - 1, -1, -1)])

    for i in range(N):
        r = 0
        g = 0
        b = 0
        idx = i
        for j in range(7):
            str_id = uint82bin(idx)
            r = r ^ (np.uint8(str_id[-1]) << (7 - j))
            g = g ^ (np.uint8(str_id[-2]) << (7 - j))
            b = b ^ (np.uint8(str_id[-3]) << (7 - j))
            idx = idx >> 3
        cmap[i, 0] = r
        cmap[i, 1] = g
        cmap[i, 2] = b
    return cmap


def label2colormap(label):
    m = label.astype(np.uint8)
    r, c = m.shape
    cmap = np.zeros((r, c, 3), dtype=np.uint8)
    cmap[:, :, 0] = (m & 1) << 7 | (m & 8) << 3
    cmap[:, :, 1] = (m & 2) << 6 | (m & 16) << 2
    cmap[:, :, 2] = (m & 4) << 5
    return cmap


def label2colormap_batch(tf_label):
    labels_color = []
    for bi in range(tf_label.shape[0]):
        label = tf_label[bi].squeeze().cpu().numpy()
        label_color = label2colormap(label).transpose((2, 0, 1))
        labels_color.append(torch.from_numpy(label_color).unsqueeze(0))
    colormap = torch.cat(labels_color)
    return colormap


def compute_miou(pred, label, n_classes=21):
    TP = np.zeros(n_classes, np.uint64)
    P = np.zeros(n_classes, np.uint64)
    T = np.zeros(n_classes, np.uint64)
    predict = pred.detach().cpu().numpy()
    gt = label.detach().cpu().numpy()
    cal = gt < 255
    mask = (predict == gt) & cal
    for i in range(n_classes):
        P[i] += np.sum((predict == i) * cal)
        T[i] += np.sum((gt == i) * cal)
        TP[i] += np.sum((gt == i) * mask)
    TP = TP.astype(np.float64)
    T = T.astype(np.float64)
    P = P.astype(np.float64)
    IoU = TP / (T + P - TP)

    miou = np.mean(IoU[np.argwhere(np.isfinite(IoU))])
    return miou, IoU


def do_python_eval(param, model_id):
    predict_folder = os.path.join(param.rst_dir, '%s_%s_cls' % (model_id, param.period))
    gt_folder = param.seg_dir
    TP = []
    P = []
    T = []
    for i in range(param.cfg.MODEL_NUM_CLASSES):
        TP.append(multiprocessing.Value('i', 0, lock=True))
        P.append(multiprocessing.Value('i', 0, lock=True))
        T.append(multiprocessing.Value('i', 0, lock=True))

    def compare(start, step, TP, P, T):
        for idx in range(start, len(param.name_list), step):
            print('%d/%d' % (idx, len(param.name_list)))
            name = param.name_list[idx]
            predict_file = os.path.join(predict_folder, '%s.png' % name)
            gt_file = os.path.join(gt_folder, '%s.png' % name)
            predict = np.array(Image.open(predict_file))  # cv2.imread(predict_file)
            gt = np.array(Image.open(gt_file))
            cal = gt < 255
            mask = (predict == gt) * cal

            for i in range(param.cfg.MODEL_NUM_CLASSES):
                P[i].acquire()
                P[i].value += np.sum((predict == i) * cal)
                P[i].release()
                T[i].acquire()
                T[i].value += np.sum((gt == i) * cal)
                T[i].release()
                TP[i].acquire()
                TP[i].value += np.sum((gt == i) * mask)
                TP[i].release()

    p_list = []
    for i in range(8):
        p = multiprocessing.Process(target=compare, args=(i, 8, TP, P, T))
        p.start()
        p_list.append(p)
    for p in p_list:
        p.join()
    IoU = []
    for i in range(param.cfg.MODEL_NUM_CLASSES):
        IoU.append(TP[i].value / (T[i].value + P[i].value - TP[i].value + 1e-10))
    for i in range(param.cfg.MODEL_NUM_CLASSES):
        if i == 0:
            print('%11s:%7.3f%%' % ('backbound', IoU[i] * 100), end='\t')
        else:
            if i % 2 != 1:
                print('%11s:%7.3f%%' % (param.categories[i - 1], IoU[i] * 100), end='\t')
            else:
                print('%11s:%7.3f%%' % (param.categories[i - 1], IoU[i] * 100))

    miou = np.mean(np.array(IoU))
    print('\n======================================================')
    print('%11s:%7.3f%%' % ('mIoU', miou * 100))


def test_miou():
    import cv2
    img_a = cv2.imread('/media/ros/Workshop/ws/Datasets/RRdataset/val/bg-semantic/1-person-pottedplant.png', -1)
    img_b = cv2.imread('/media/ros/Workshop/ws/Datasets/RRdataset/val/bg-semantic/2-person-pottedplant.png', -1)

    cv2.imshow('a', label2colormap(img_a)[:, :, ::-1])
    cv2.imshow('b', label2colormap(img_b)[:, :, ::-1])
    cv2.waitKey()

    miou, iou = compute_miou(torch.from_numpy(img_a).cuda(), torch.from_numpy(img_b).cuda())

    print(miou)
    print(iou)


if __name__ == '__main__':
    test_miou()