# Copyright (c) Facebook, Inc. and its affiliates. # All rights reserved. # # This source code is licensed under the license found in the # LICENSE file in the root directory of this source tree. # 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 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') if 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()