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utils.py
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utils.py
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from __future__ import print_function
from PIL import Image
import copy
import os
import os.path
import numpy as np
import sys
import imp
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.optim
import torchnet as tnt
import numbers
class FastConfusionMeter(object):
def __init__(self, k, normalized = False):
#super(FastConfusionMeter, self).__init__()
self.conf = np.ndarray((k,k), dtype=np.int32)
self.normalized = normalized
self.reset()
def reset(self):
self.conf.fill(0)
def add(self, output, target):
output = output.cpu().squeeze().numpy()
target = target.cpu().squeeze().numpy()
if np.ndim(output) == 1:
output = output[None]
onehot = np.ndim(target) != 1
assert output.shape[0] == target.shape[0], \
'number of targets and outputs do not match'
assert output.shape[1] == self.conf.shape[0], \
'number of outputs does not match size of confusion matrix'
assert not onehot or target.shape[1] == output.shape[1], \
'target should be 1D Tensor or have size of output (one-hot)'
if onehot:
assert (target >= 0).all() and (target <= 1).all(), \
'in one-hot encoding, target values should be 0 or 1'
assert (target.sum(1) == 1).all(), \
'multi-label setting is not supported'
target = target.argmax(1) if onehot else target
pred = output.argmax(1)
target = target.astype(np.int32)
pred = pred.astype(np.int32)
conf_this = np.bincount(target * self.conf.shape[0] + pred,minlength=np.prod(self.conf.shape))
conf_this = conf_this.astype(self.conf.dtype).reshape(self.conf.shape)
self.conf += conf_this
def value(self):
if self.normalized:
conf = self.conf.astype(np.float32)
return conf / conf.sum(1).clip(min=1e-12)[:,None]
else:
return self.conf
def getConfMatrixResults(matrix):
assert(len(matrix.shape)==2 and matrix.shape[0]==matrix.shape[1])
count_correct = np.diag(matrix)
count_preds = matrix.sum(1)
count_gts = matrix.sum(0)
epsilon = np.finfo(np.float32).eps
accuracies = count_correct / (count_gts + epsilon)
IoUs = count_correct / (count_gts + count_preds - count_correct + epsilon)
totAccuracy = count_correct.sum() / (matrix.sum() + epsilon)
num_valid = (count_gts > 0).sum()
meanAccuracy = accuracies.sum() / (num_valid + epsilon)
meanIoU = IoUs.sum() / (num_valid + epsilon)
result = {'totAccuracy': round(totAccuracy,4), 'meanAccuracy': round(meanAccuracy,4), 'meanIoU': round(meanIoU,4)}
if num_valid == 2:
result['IoUs_bg'] = round(IoUs[0],4)
result['IoUs_fg'] = round(IoUs[1],4)
return result
class AverageConfMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.val = np.asarray(0, dtype=np.float64)
self.avg = np.asarray(0, dtype=np.float64)
self.sum = np.asarray(0, dtype=np.float64)
self.count = 0
def update(self, val):
self.val = val
if self.count == 0:
self.sum = val.copy().astype(np.float64)
else:
self.sum += val.astype(np.float64)
self.count += 1
self.avg = getConfMatrixResults(self.sum)
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0.0
self.sum = 0.0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += float(val * n)
self.count += n
self.avg = round(self.sum / self.count,4)
class LAverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = []
self.avg = []
self.sum = []
self.count = 0
def update(self, val):
self.val = val
self.count += 1
if len(self.sum) == 0:
assert(self.count == 1)
self.sum = [v for v in val]
self.avg = [round(v,4) for v in val]
else:
assert(len(self.sum) == len(val))
for i, v in enumerate(val):
self.sum[i] += v
self.avg[i] = round(self.sum[i] / self.count,4)
class DAverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.values = {}
def update(self, values):
assert(isinstance(values, dict))
for key, val in values.items():
if isinstance(val, (float, int)):
if not (key in self.values):
self.values[key] = AverageMeter()
self.values[key].update(val)
elif isinstance(val, (tnt.meter.ConfusionMeter,FastConfusionMeter)):
if not (key in self.values):
self.values[key] = AverageConfMeter()
self.values[key].update(val.value())
elif isinstance(val, AverageConfMeter):
if not (key in self.values):
self.values[key] = AverageConfMeter()
self.values[key].update(val.sum)
elif isinstance(val, dict):
if not (key in self.values):
self.values[key] = DAverageMeter()
self.values[key].update(val)
elif isinstance(val, list):
if not (key in self.values):
self.values[key] = LAverageMeter()
self.values[key].update(val)
def average(self):
average = {}
for key, val in self.values.items():
if isinstance(val, type(self)):
average[key] = val.average()
else:
average[key] = val.avg
return average
def __str__(self):
ave_stats = self.average()
return ave_stats.__str__()
def get_git_commit_hash():
import subprocess
p = subprocess.Popen(['git', 'rev-parse', 'HEAD'], stdout=subprocess.PIPE)
git_commit, _ = p.communicate()
git_commit = git_commit.strip().decode('utf-8')
return git_commit
def save_git_diff_to_file(git_diff_file_path):
import subprocess
git_diff_file = open(git_diff_file_path, 'w')
p = subprocess.Popen(['git', 'diff', '--patch', 'HEAD'], stdout=git_diff_file)
p.wait()
class NoopContext(object):
def __enter__(self):
return self
def __exit__(self, type, value, traceback):
pass
def find_names(obj):
import gc
import inspect
frame = inspect.currentframe()
for frame in iter(lambda: frame.f_back, None):
frame.f_locals
obj_names = []
for referrer in gc.get_referrers(obj):
if isinstance(referrer, dict):
for k, v in referrer.items():
if v is obj:
obj_names.append(k)
return obj_names
def print_gpu_memory_usage():
import gc
collected = []
for obj in gc.get_objects():
try:
if torch.is_tensor(obj) or (hasattr(obj, 'data') and torch.is_tensor(obj.data)):
#print(','.join(find_names(obj)), type(obj), obj.size())
collected.append((np.prod(obj.size()), ','.join(find_names(obj)), type(obj), obj.size()))
except:
pass
for l in sorted(collected):
print(l)
def duplicate_parameters(parameters):
return [copy.deepcopy(p) for p in parameters]
def get_leading_zero_formatter(max_num):
return ':0' + str(len(str(max_num))) + 'd'
def get_leading_zero_formatted(num, max_num):
return ('{' + get_leading_zero_formatter(max_num) + '}').format(num)
class NumpySetOptions(object):
def __init__(self, *args, **kwargs):
self.args = args
self.kwargs = kwargs
def __enter__(self):
self.original = np.get_printoptions()
np.set_printoptions(*self.args, **self.kwargs)
return self
def __exit__(self, type, value, traceback):
np.set_printoptions(**self.original)
def get_network_fingerprint(network):
def get_each_fp(p):
if p.numel() == 1:
return p.item()
return p.std().item()
return [[get_each_fp(p) for p in network.parameters()], [get_each_fp(p) for p in network.buffers()]]
def flatten_dict(data, asarray=False):
flattened_data = dict()
for k in data[0].keys():
flattened_data[k] = []
for d in data:
for k, v in d.items():
flattened_data[k].append(v)
if asarray:
for k in flattened_data.keys():
flattened_data[k] = np.asarray(flattened_data[k])
return flattened_data
import time
class MeasureTime(object):
def __init__(self, key, print_func=print):
self._key = key
self._print_func = print_func
def __enter__(self):
self._time = time.time()
return self
def __exit__(self, type, value, traceback):
self._print_func('MeasureTime. {}: {}'.format(self._key, time.time() - self._time))