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utils.py
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import json
import logging
import os
#from PIL import Image
import glob
class Params():
"""Class that loads hyperparameters from a json file.
Example:
```
params = Params(json_path)
print(params.learning_rate)
params.learning_rate = 0.5 # change the value of learning_rate in params
```
"""
def __init__(self, json_path):
with open(json_path) as f:
params = json.load(f)
self.__dict__.update(params)
def save(self, json_path):
with open(json_path, 'w') as f:
json.dump(self.__dict__, f, indent=4)
def update(self, json_path):
"""Loads parameters from json file"""
with open(json_path) as f:
params = json.load(f)
self.__dict__.update(params)
@property
def dict(self):
"""Gives dict-like access to Params instance by `params.dict['learning_rate']"""
return self.__dict__
class RunningAverage():
"""A simple class that maintains the running average of a quantity
Example:
```
loss_avg = RunningAverage()
loss_avg.update(2)
loss_avg.update(4)
loss_avg() = 3
```
"""
def __init__(self):
self.steps = 0
self.total = 0
def update(self, val):
self.total += val
self.steps += 1
def __call__(self):
return self.total/float(self.steps)
def set_logger(log_path):
"""Set the logger to log info in terminal and file `log_path`.
In general, it is useful to have a logger so that every output to the terminal is saved
in a permanent file. Here we save it to `model_dir/train.log`.
Example:
```
logging.info("Starting training...")
```
Args:
log_path: (string) where to log
"""
logger = logging.getLogger()
logger.setLevel(logging.INFO)
if not logger.handlers:
# Logging to a file
file_handler = logging.FileHandler(log_path)
file_handler.setFormatter(logging.Formatter('%(asctime)s:%(levelname)s: %(message)s'))
logger.addHandler(file_handler)
# Logging to console
stream_handler = logging.StreamHandler()
stream_handler.setFormatter(logging.Formatter('%(message)s'))
logger.addHandler(stream_handler)
def save_dict_to_json(d, json_path):
"""Saves dict of floats in json file
Args:
d: (dict) of float-castable values (np.float, int, float, etc.)
json_path: (string) path to json file
"""
with open(json_path, 'w') as f:
# We need to convert the values to float for json (it doesn't accept np.array, np.float, )
d = {k: float(v) for k, v in d.items()}
json.dump(d, f, indent=4)
#def stitch_pictures(file_dir, ext='*.jpeg'):
# file_names = glob.glob(file_dir + ext)
# for name in file_names:
# img_groundtruth = Image.open(file_dir + name.split('_')[0] + '_groundtruth.jpeg')
# img_predicted = Image.open(file_dir + name.split('_')[0] + '_predicted.jpeg')
#
# w, h = img_groundtruth.size
# img = Image.new(img_groundtruth.mode, (w * 2, h))
# img.paste(img_groundtruth, box=(0, 0))
# img.paste(img_predicted, box=(w, 0))
#
# img_path = file_dir + name.split('_')[0] + '.jpeg'
# img.save(img_path)