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| from __future__ import absolute_import | |
| import os | |
| import sys | |
| import numpy as np | |
| import tensorflow as tf | |
| import scipy.misc | |
| try: | |
| from StringIO import StringIO # Python 2.7 | |
| except ImportError: | |
| from io import BytesIO # Python 3.x | |
| from .osutils import mkdir_if_missing | |
| from config import get_args | |
| global_args = get_args(sys.argv[1:]) | |
| if global_args.run_on_remote: | |
| import moxing as mox | |
| mox.file.shift("os", "mox") | |
| class Logger(object): | |
| def __init__(self, fpath=None): | |
| self.console = sys.stdout | |
| self.file = None | |
| if fpath is not None: | |
| if global_args.run_on_remote: | |
| dir_name = os.path.dirname(fpath) | |
| if not mox.file.exists(dir_name): | |
| mox.file.make_dirs(dir_name) | |
| print('=> making dir ', dir_name) | |
| self.file = mox.file.File(fpath, 'w') | |
| # self.file = open(fpath, 'w') | |
| else: | |
| mkdir_if_missing(os.path.dirname(fpath)) | |
| self.file = open(fpath, 'w') | |
| def __del__(self): | |
| self.close() | |
| def __enter__(self): | |
| pass | |
| def __exit__(self, *args): | |
| self.close() | |
| def write(self, msg): | |
| self.console.write(msg) | |
| if self.file is not None: | |
| self.file.write(msg) | |
| def flush(self): | |
| self.console.flush() | |
| if self.file is not None: | |
| self.file.flush() | |
| os.fsync(self.file.fileno()) | |
| def close(self): | |
| self.console.close() | |
| if self.file is not None: | |
| self.file.close() | |
| class TFLogger(object): | |
| def __init__(self, log_dir=None): | |
| """Create a summary writer logging to log_dir.""" | |
| if log_dir is not None: | |
| mkdir_if_missing(log_dir) | |
| self.writer = tf.summary.FileWriter(log_dir) | |
| def scalar_summary(self, tag, value, step): | |
| """Log a scalar variable.""" | |
| 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.""" | |
| 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='%s/%d' % (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.""" | |
| # 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() | |
| def close(self): | |
| self.writer.close() |