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utils.py
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utils.py
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"""
Tensorboard logger code referenced from:
https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/04-utils/
Other helper functions:
https://github.com/cs230-stanford/cs230-stanford.github.io
"""
import json
import logging
import os
import shutil
import torch
from collections import OrderedDict
from torch.optim.lr_scheduler import _LRScheduler
#import tensorflow as tf
import numpy as np
import scipy.misc
try:
from StringIO import StringIO # Python 2.7
except ImportError:
from io import BytesIO # Python 3.x
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)
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val*n
self.count += n
self.avg = self.sum/self.count
def get_lr(optimizer):
for param_group in optimizer.param_groups:
return param_group['lr']
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 save_checkpoint(state, is_best, checkpoint, epoch_checkpoint = False):
"""Saves model and training parameters at checkpoint + 'last.pth.tar'. If is_best==True, also saves
checkpoint + 'best.pth.tar'
Args:
state: (dict) contains model's state_dict, may contain other keys such as epoch, optimizer state_dict
is_best: (bool) True if it is the best model seen till now
checkpoint: (string) folder where parameters are to be saved
"""
filepath = os.path.join(checkpoint, 'last.pth.tar')
if not os.path.exists(checkpoint):
print("Checkpoint Directory does not exist! Making directory {}".format(checkpoint))
os.mkdir(checkpoint)
else:
print("Checkpoint Directory exists! ")
torch.save(state, filepath)
if is_best:
shutil.copyfile(filepath, os.path.join(checkpoint, 'best.pth.tar'))
if epoch_checkpoint == True:
epoch_file = str(state['epoch']-1) + '.pth.tar'
shutil.copyfile(filepath, os.path.join(checkpoint, epoch_file))
def load_checkpoint(checkpoint, model, optimizer=None):
"""Loads model parameters (state_dict) from file_path. If optimizer is provided, loads state_dict of
optimizer assuming it is present in checkpoint.
Args:
checkpoint: (string) filename which needs to be loaded
model: (torch.nn.Module) model for which the parameters are loaded
optimizer: (torch.optim) optional: resume optimizer from checkpoint
"""
if not os.path.exists(checkpoint):
raise("File doesn't exist {}".format(checkpoint))
if torch.cuda.is_available():
checkpoint = torch.load(checkpoint)
else:
# this helps avoid errors when loading single-GPU-trained weights onto CPU-model
checkpoint = torch.load(checkpoint, map_location=lambda storage, loc: storage)
model.load_state_dict(checkpoint['state_dict'])
if optimizer:
optimizer.load_state_dict(checkpoint['optim_dict'])
return checkpoint
'''
class Board_Logger(object):
"""Tensorboard log utility"""
def __init__(self, log_dir):
"""Create a summary writer logging to 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)
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)
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
'''
class WarmUpLR(_LRScheduler):
"""warmup_training learning rate scheduler
Args:
optimizer: optimzier(e.g. SGD)
total_iters: totoal_iters of warmup phase
"""
def __init__(self, optimizer, total_iters, last_epoch=-1):
self.total_iters = total_iters
super().__init__(optimizer, last_epoch)
def get_lr(self):
"""we will use the first m batches, and set the learning
rate to base_lr * m / total_iters
"""
return [base_lr * self.last_epoch / (self.total_iters + 1e-8) for base_lr in self.base_lrs]