Skip to content
Branch: master
Find file Copy path
Find file Copy path
Fetching contributors…
Cannot retrieve contributors at this time
221 lines (169 sloc) 6.73 KB
import logging
import os
import shutil
import sys
import scipy.sparse as sparse
import numpy as np
import torch
def save_checkpoint(state, is_best, checkpoint_dir, logger=None):
"""Saves model and training parameters at '{checkpoint_dir}/last_checkpoint.pytorch'.
If is_best==True saves '{checkpoint_dir}/best_checkpoint.pytorch' as well.
state (dict): contains model's state_dict, optimizer's state_dict, epoch
and best evaluation metric value so far
is_best (bool): if True state contains the best model seen so far
checkpoint_dir (string): directory where the checkpoint are to be saved
def log_info(message):
if logger is not None:
if not os.path.exists(checkpoint_dir):
f"Checkpoint directory does not exists. Creating {checkpoint_dir}")
last_file_path = os.path.join(checkpoint_dir, 'last_checkpoint.pytorch')
log_info(f"Saving last checkpoint to '{last_file_path}'"), last_file_path)
if is_best:
best_file_path = os.path.join(checkpoint_dir, 'best_checkpoint.pytorch')
log_info(f"Saving best checkpoint to '{best_file_path}'")
shutil.copyfile(last_file_path, best_file_path)
def load_checkpoint(checkpoint_path, model, optimizer=None):
"""Loads model and training parameters from a given checkpoint_path
If optimizer is provided, loads optimizer's state_dict of as well.
checkpoint_path (string): path to the checkpoint to be loaded
model (torch.nn.Module): model into which the parameters are to be copied
optimizer (torch.optim.Optimizer) optional: optimizer instance into
which the parameters are to be copied
if not os.path.exists(checkpoint_path):
raise IOError(f"Checkpoint '{checkpoint_path}' does not exist")
state = torch.load(checkpoint_path)
if optimizer is not None:
return state
def get_logger(name, level=logging.INFO):
logger = logging.getLogger(name)
# Logging to console
stream_handler = logging.StreamHandler(sys.stdout)
formatter = logging.Formatter(
'%(asctime)s [%(threadName)s] %(levelname)s %(name)s - %(message)s')
return logger
def get_number_of_learnable_parameters(model):
model_parameters = filter(lambda p: p.requires_grad, model.parameters())
return sum([ for p in model_parameters])
class RunningAverage:
"""Computes and stores the average
def __init__(self):
self.count = 0
self.sum = 0
self.avg = 0
def update(self, value, n=1):
self.count += n
self.sum += value * n
self.avg = self.sum / self.count
def find_maximum_patch_size(model, device):
"""Tries to find the biggest patch size that can be send to GPU for inference
without throwing CUDA out of memory"""
logger = get_logger('PatchFinder')
in_channels = model.in_channels
patch_shapes = [(64, 128, 128), (96, 128, 128),
(64, 160, 160), (96, 160, 160),
(64, 192, 192), (96, 192, 192)]
for shape in patch_shapes:
# generate random patch of a given size
patch = np.random.randn(*shape).astype('float32')
patch = torch \
.from_numpy(patch) \
.view((1, in_channels) + patch.shape) \
.to(device)"Current patch size: {shape}")
def unpad(probs, index, shape, pad_width=8):
def _new_slices(slicing, max_size):
if slicing.start == 0:
p_start = 0
i_start = 0
p_start = pad_width
i_start = slicing.start + pad_width
if slicing.stop == max_size:
p_stop = None
i_stop = max_size
p_stop = -pad_width
i_stop = slicing.stop - pad_width
return slice(p_start, p_stop), slice(i_start, i_stop)
D, H, W = shape
i_c, i_z, i_y, i_x = index
p_c = slice(0, probs.shape[0])
p_z, i_z = _new_slices(i_z, D)
p_y, i_y = _new_slices(i_y, H)
p_x, i_x = _new_slices(i_x, W)
probs_index = (p_c, p_z, p_y, p_x)
index = (i_c, i_z, i_y, i_x)
return probs[probs_index], index
def create_feature_maps(init_channel_number, number_of_fmaps):
return [init_channel_number * 2 ** k for k in range(number_of_fmaps)]
# Code taken from
def adapted_rand(seg, gt, all_stats=False):
"""Compute Adapted Rand error as defined by the SNEMI3D contest [1]
Formula is given as 1 - the maximal F-score of the Rand index
(excluding the zero component of the original labels). Adapted
from the SNEMI3D MATLAB script, hence the strange style.
seg : np.ndarray
the segmentation to score, where each value is the label at that point
gt : np.ndarray, same shape as seg
the groundtruth to score against, where each value is a label
all_stats : boolean, optional
whether to also return precision and recall as a 3-tuple with rand_error
are : float
The adapted Rand error; equal to $1 - \frac{2pr}{p + r}$,
where $p$ and $r$ are the precision and recall described below.
prec : float, optional
The adapted Rand precision. (Only returned when `all_stats` is ``True``.)
rec : float, optional
The adapted Rand recall. (Only returned when `all_stats` is ``True``.)
# just to prevent division by 0
epsilon = 1e-6
# segA is truth, segB is query
segA = np.ravel(gt)
segB = np.ravel(seg)
n = segA.size
n_labels_A = np.amax(segA) + 1
n_labels_B = np.amax(segB) + 1
ones_data = np.ones(n)
p_ij = sparse.csr_matrix((ones_data, (segA[:], segB[:])), shape=(n_labels_A, n_labels_B))
a = p_ij[1:n_labels_A, :]
b = p_ij[1:n_labels_A, 1:n_labels_B]
c = p_ij[1:n_labels_A, 0].todense()
d = b.multiply(b)
a_i = np.array(a.sum(1))
b_i = np.array(b.sum(0))
sumA = np.sum(a_i * a_i)
sumB = np.sum(b_i * b_i) + (np.sum(c) / n)
sumAB = np.sum(d) + (np.sum(c) / n)
precision = sumAB / max(sumB, epsilon)
recall = sumAB / max(sumA, epsilon)
fScore = 2.0 * precision * recall / max(precision + recall, epsilon)
are = 1.0 - fScore
if all_stats:
return are, precision, recall
return are
You can’t perform that action at this time.