Move to xautodl

xautodl/log_utils/__init__.py

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+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+# every package does not rely on pytorch or tensorflow
+# I tried to list all dependency here: os, sys, time, numpy, (possibly) matplotlib
+##################################################
+from .logger import Logger, PrintLogger
+from .meter import AverageMeter
+from .time_utils import (
+    time_for_file,
+    time_string,
+    time_string_short,
+    time_print,
+    convert_secs2time,
+)
+from .pickle_wrap import pickle_save, pickle_load

xautodl/log_utils/logger.py

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+##################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
+##################################################
+from pathlib import Path
+import importlib, warnings
+import os, sys, time, numpy as np
+
+if sys.version_info.major == 2:  # Python 2.x
+    from StringIO import StringIO as BIO
+else:  # Python 3.x
+    from io import BytesIO as BIO
+
+if importlib.util.find_spec("tensorflow"):
+    import tensorflow as tf
+
+
+class PrintLogger(object):
+    def __init__(self):
+        """Create a summary writer logging to log_dir."""
+        self.name = "PrintLogger"
+
+    def log(self, string):
+        print(string)
+
+    def close(self):
+        print("-" * 30 + " close printer " + "-" * 30)
+
+
+class Logger(object):
+    def __init__(self, log_dir, seed, create_model_dir=True, use_tf=False):
+        """Create a summary writer logging to log_dir."""
+        self.seed = int(seed)
+        self.log_dir = Path(log_dir)
+        self.model_dir = Path(log_dir) / "checkpoint"
+        self.log_dir.mkdir(parents=True, exist_ok=True)
+        if create_model_dir:
+            self.model_dir.mkdir(parents=True, exist_ok=True)
+        # self.meta_dir.mkdir(mode=0o775, parents=True, exist_ok=True)
+
+        self.use_tf = bool(use_tf)
+        self.tensorboard_dir = self.log_dir / (
+            "tensorboard-{:}".format(time.strftime("%d-%h", time.gmtime(time.time())))
+        )
+        # self.tensorboard_dir = self.log_dir / ('tensorboard-{:}'.format(time.strftime( '%d-%h-at-%H:%M:%S', time.gmtime(time.time()) )))
+        self.logger_path = self.log_dir / "seed-{:}-T-{:}.log".format(
+            self.seed, time.strftime("%d-%h-at-%H-%M-%S", time.gmtime(time.time()))
+        )
+        self.logger_file = open(self.logger_path, "w")
+
+        if self.use_tf:
+            self.tensorboard_dir.mkdir(mode=0o775, parents=True, exist_ok=True)
+            self.writer = tf.summary.FileWriter(str(self.tensorboard_dir))
+        else:
+            self.writer = None
+
+    def __repr__(self):
+        return "{name}(dir={log_dir}, use-tf={use_tf}, writer={writer})".format(
+            name=self.__class__.__name__, **self.__dict__
+        )
+
+    def path(self, mode):
+        valids = ("model", "best", "info", "log", None)
+        if mode is None:
+            return self.log_dir
+        elif mode == "model":
+            return self.model_dir / "seed-{:}-basic.pth".format(self.seed)
+        elif mode == "best":
+            return self.model_dir / "seed-{:}-best.pth".format(self.seed)
+        elif mode == "info":
+            return self.log_dir / "seed-{:}-last-info.pth".format(self.seed)
+        elif mode == "log":
+            return self.log_dir
+        else:
+            raise TypeError("Unknow mode = {:}, valid modes = {:}".format(mode, valids))
+
+    def extract_log(self):
+        return self.logger_file
+
+    def close(self):
+        self.logger_file.close()
+        if self.writer is not None:
+            self.writer.close()
+
+    def log(self, string, save=True, stdout=False):
+        if stdout:
+            sys.stdout.write(string)
+            sys.stdout.flush()
+        else:
+            print(string)
+        if save:
+            self.logger_file.write("{:}\n".format(string))
+            self.logger_file.flush()
+
+    def scalar_summary(self, tags, values, step):
+        """Log a scalar variable."""
+        if not self.use_tf:
+            warnings.warn("Do set use-tensorflow installed but call scalar_summary")
+        else:
+            assert isinstance(tags, list) == isinstance(
+                values, list
+            ), "Type : {:} vs {:}".format(type(tags), type(values))
+            if not isinstance(tags, list):
+                tags, values = [tags], [values]
+            for tag, value in zip(tags, values):
+                summary = tf.Summary(
+                    value=[tf.Summary.Value(tag=tag, simple_value=value)]
+                )
+                self.writer.add_summary(summary, step)
+                self.writer.flush()
+
+    def image_summary(self, tag, images, step):
+        """Log a list of images."""
+        import scipy
+
+        if not self.use_tf:
+            warnings.warn("Do set use-tensorflow installed but call scalar_summary")
+            return
+
+        img_summaries = []
+        for i, img in enumerate(images):
+            # Write the image to a string
+            try:
+                s = StringIO()
+            except:
+                s = BytesIO()
+            scipy.misc.toimage(img).save(s, format="png")
+
+            # Create an Image object
+            img_sum = tf.Summary.Image(
+                encoded_image_string=s.getvalue(),
+                height=img.shape[0],
+                width=img.shape[1],
+            )
+            # Create a Summary value
+            img_summaries.append(
+                tf.Summary.Value(tag="{}/{}".format(tag, i), image=img_sum)
+            )
+
+        # Create and write Summary
+        summary = tf.Summary(value=img_summaries)
+        self.writer.add_summary(summary, step)
+        self.writer.flush()
+
+    def histo_summary(self, tag, values, step, bins=1000):
+        """Log a histogram of the tensor of values."""
+        if not self.use_tf:
+            raise ValueError("Do not have tensorflow")
+        import tensorflow as tf
+
+        # Create a histogram using numpy
+        counts, bin_edges = np.histogram(values, bins=bins)
+
+        # Fill the fields of the histogram proto
+        hist = tf.HistogramProto()
+        hist.min = float(np.min(values))
+        hist.max = float(np.max(values))
+        hist.num = int(np.prod(values.shape))
+        hist.sum = float(np.sum(values))
+        hist.sum_squares = float(np.sum(values ** 2))
+
+        # Drop the start of the first bin
+        bin_edges = bin_edges[1:]
+
+        # Add bin edges and counts
+        for edge in bin_edges:
+            hist.bucket_limit.append(edge)
+        for c in counts:
+            hist.bucket.append(c)
+
+        # Create and write Summary
+        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
+        self.writer.add_summary(summary, step)
+        self.writer.flush()

xautodl/log_utils/meter.py

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+import numpy as np
+
+
+class AverageMeter(object):
+    """Computes and stores the average and current value"""
+
+    def __init__(self):
+        self.reset()
+
+    def reset(self):
+        self.val = 0.0
+        self.avg = 0.0
+        self.sum = 0.0
+        self.count = 0.0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+    def __repr__(self):
+        return "{name}(val={val}, avg={avg}, count={count})".format(
+            name=self.__class__.__name__, **self.__dict__
+        )
+
+
+class RecorderMeter(object):
+    """Computes and stores the minimum loss value and its epoch index"""
+
+    def __init__(self, total_epoch):
+        self.reset(total_epoch)
+
+    def reset(self, total_epoch):
+        assert total_epoch > 0, "total_epoch should be greater than 0 vs {:}".format(
+            total_epoch
+        )
+        self.total_epoch = total_epoch
+        self.current_epoch = 0
+        self.epoch_losses = np.zeros(
+            (self.total_epoch, 2), dtype=np.float32
+        )  # [epoch, train/val]
+        self.epoch_losses = self.epoch_losses - 1
+        self.epoch_accuracy = np.zeros(
+            (self.total_epoch, 2), dtype=np.float32
+        )  # [epoch, train/val]
+        self.epoch_accuracy = self.epoch_accuracy
+
+    def update(self, idx, train_loss, train_acc, val_loss, val_acc):
+        assert (
+            idx >= 0 and idx < self.total_epoch
+        ), "total_epoch : {} , but update with the {} index".format(
+            self.total_epoch, idx
+        )
+        self.epoch_losses[idx, 0] = train_loss
+        self.epoch_losses[idx, 1] = val_loss
+        self.epoch_accuracy[idx, 0] = train_acc
+        self.epoch_accuracy[idx, 1] = val_acc
+        self.current_epoch = idx + 1
+        return self.max_accuracy(False) == self.epoch_accuracy[idx, 1]
+
+    def max_accuracy(self, istrain):
+        if self.current_epoch <= 0:
+            return 0
+        if istrain:
+            return self.epoch_accuracy[: self.current_epoch, 0].max()
+        else:
+            return self.epoch_accuracy[: self.current_epoch, 1].max()
+
+    def plot_curve(self, save_path):
+        import matplotlib
+
+        matplotlib.use("agg")
+        import matplotlib.pyplot as plt
+
+        title = "the accuracy/loss curve of train/val"
+        dpi = 100
+        width, height = 1600, 1000
+        legend_fontsize = 10
+        figsize = width / float(dpi), height / float(dpi)
+
+        fig = plt.figure(figsize=figsize)
+        x_axis = np.array([i for i in range(self.total_epoch)])  # epochs
+        y_axis = np.zeros(self.total_epoch)
+
+        plt.xlim(0, self.total_epoch)
+        plt.ylim(0, 100)
+        interval_y = 5
+        interval_x = 5
+        plt.xticks(np.arange(0, self.total_epoch + interval_x, interval_x))
+        plt.yticks(np.arange(0, 100 + interval_y, interval_y))
+        plt.grid()
+        plt.title(title, fontsize=20)
+        plt.xlabel("the training epoch", fontsize=16)
+        plt.ylabel("accuracy", fontsize=16)
+
+        y_axis[:] = self.epoch_accuracy[:, 0]
+        plt.plot(x_axis, y_axis, color="g", linestyle="-", label="train-accuracy", lw=2)
+        plt.legend(loc=4, fontsize=legend_fontsize)
+
+        y_axis[:] = self.epoch_accuracy[:, 1]
+        plt.plot(x_axis, y_axis, color="y", linestyle="-", label="valid-accuracy", lw=2)
+        plt.legend(loc=4, fontsize=legend_fontsize)
+
+        y_axis[:] = self.epoch_losses[:, 0]
+        plt.plot(
+            x_axis, y_axis * 50, color="g", linestyle=":", label="train-loss-x50", lw=2
+        )
+        plt.legend(loc=4, fontsize=legend_fontsize)
+
+        y_axis[:] = self.epoch_losses[:, 1]
+        plt.plot(
+            x_axis, y_axis * 50, color="y", linestyle=":", label="valid-loss-x50", lw=2
+        )
+        plt.legend(loc=4, fontsize=legend_fontsize)
+
+        if save_path is not None:
+            fig.savefig(save_path, dpi=dpi, bbox_inches="tight")
+            print("---- save figure {} into {}".format(title, save_path))
+        plt.close(fig)

xautodl/log_utils/pickle_wrap.py

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+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+import pickle
+from pathlib import Path
+
+
+def pickle_save(obj, path):
+    file_path = Path(path)
+    file_dir = file_path.parent
+    file_dir.mkdir(parents=True, exist_ok=True)
+    with file_path.open("wb") as f:
+        pickle.dump(obj, f)
+
+
+def pickle_load(path):
+    if not Path(path).exists():
+        raise ValueError("{:} does not exists".format(path))
+    with Path(path).open("rb") as f:
+        data = pickle.load(f)
+    return data

xautodl/log_utils/time_utils.py

@@ -0,0 +1,49 @@
+#####################################################
+# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
+#####################################################
+import time, sys
+import numpy as np
+
+
+def time_for_file():
+    ISOTIMEFORMAT = "%d-%h-at-%H-%M-%S"
+    return "{:}".format(time.strftime(ISOTIMEFORMAT, time.gmtime(time.time())))
+
+
+def time_string():
+    ISOTIMEFORMAT = "%Y-%m-%d %X"
+    string = "[{:}]".format(time.strftime(ISOTIMEFORMAT, time.gmtime(time.time())))
+    return string
+
+
+def time_string_short():
+    ISOTIMEFORMAT = "%Y%m%d"
+    string = "{:}".format(time.strftime(ISOTIMEFORMAT, time.gmtime(time.time())))
+    return string
+
+
+def time_print(string, is_print=True):
+    if is_print:
+        print("{} : {}".format(time_string(), string))
+
+
+def convert_secs2time(epoch_time, return_str=False):
+    need_hour = int(epoch_time / 3600)
+    need_mins = int((epoch_time - 3600 * need_hour) / 60)
+    need_secs = int(epoch_time - 3600 * need_hour - 60 * need_mins)
+    if return_str:
+        str = "[{:02d}:{:02d}:{:02d}]".format(need_hour, need_mins, need_secs)
+        return str
+    else:
+        return need_hour, need_mins, need_secs
+
+
+def print_log(print_string, log):
+    # if isinstance(log, Logger): log.log('{:}'.format(print_string))
+    if hasattr(log, "log"):
+        log.log("{:}".format(print_string))
+    else:
+        print("{:}".format(print_string))
+        if log is not None:
+            log.write("{:}\n".format(print_string))
+            log.flush()