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utils.py
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utils.py
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#!/usr/bin/env python3.6
import argparse
from random import random, uniform, randint
from pathlib import Path
from multiprocessing.pool import Pool
from typing import Any, Callable, Iterable, List, Set, Tuple, TypeVar, Union
import torch
import numpy as np
from tqdm import tqdm
from torch import einsum
from torch import Tensor
from functools import partial, reduce
from skimage.io import imsave
from PIL import Image, ImageOps
from scipy.ndimage import distance_transform_edt as distance
from scipy.spatial.distance import directed_hausdorff
colors = ["c", "r", "g", "b", "m", 'y', 'k', 'chartreuse', 'coral', 'gold', 'lavender',
'silver', 'tan', 'teal', 'wheat', 'orchid', 'orange', 'tomato']
# functions redefinitions
tqdm_ = partial(tqdm, ncols=175,
leave=False,
bar_format='{l_bar}{bar}| {n_fmt}/{total_fmt} [' '{rate_fmt}{postfix}]')
A = TypeVar("A")
B = TypeVar("B")
T = TypeVar("T", Tensor, np.ndarray)
def str2bool(v):
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
def map_(fn: Callable[[A], B], iter: Iterable[A]) -> List[B]:
return list(map(fn, iter))
def mmap_(fn: Callable[[A], B], iter: Iterable[A]) -> List[B]:
return Pool().map(fn, iter)
def uc_(fn: Callable) -> Callable:
return partial(uncurry, fn)
def uncurry(fn: Callable, args: List[Any]) -> Any:
return fn(*args)
def id_(x):
return x
def flatten_(to_flat: Iterable[Iterable[A]]) -> List[A]:
return [e for l in to_flat for e in l]
def depth(e: List) -> int:
"""
Compute the depth of nested lists
"""
if type(e) == list and e:
return 1 + depth(e[0])
return 0
def compose(fns, init):
return reduce(lambda acc, f: f(acc), fns, init)
def compose_acc(fns, init):
return reduce(lambda acc, f: acc + [f(acc[-1])], fns, [init])
# fns
def soft_size(a: Tensor) -> Tensor:
return torch.einsum("bcwh->bc", a)[..., None]
def batch_soft_size(a: Tensor) -> Tensor:
return torch.einsum("bcwh->c", a)[..., None]
def soft_centroid(a: Tensor) -> Tensor:
b, c, w, h = a.shape
ws, hs = map_(lambda e: Tensor(e).to(a.device).type(torch.float32), np.mgrid[0:w, 0:h])
assert ws.shape == hs.shape == (w, h)
flotted = a.type(torch.float32)
tot = einsum("bcwh->bc", flotted) + 1e-10
assert tot.dtype == torch.float32
cw = einsum("bcwh,wh->bc", flotted, ws) / tot
ch = einsum("bcwh,wh->bc", flotted, hs) / tot
assert cw.dtype == ch.dtype == torch.float32
res = torch.stack([cw, ch], dim=2)
assert res.shape == (b, c, 2)
assert res.dtype == torch.float32
return res
# Assert utils
def uniq(a: Tensor) -> Set:
return set(torch.unique(a.cpu()).numpy())
def sset(a: Tensor, sub: Iterable) -> bool:
return uniq(a).issubset(sub)
def eq(a: Tensor, b) -> bool:
return torch.eq(a, b).all()
def simplex(t: Tensor, axis=1) -> bool:
_sum = t.sum(axis).type(torch.float32)
_ones = torch.ones_like(_sum, dtype=torch.float32)
return torch.allclose(_sum, _ones)
def one_hot(t: Tensor, axis=1) -> bool:
return simplex(t, axis) and sset(t, [0, 1])
# # Metrics and shitz
def meta_dice(sum_str: str, label: Tensor, pred: Tensor, smooth: float = 1e-8) -> float:
assert label.shape == pred.shape
assert one_hot(label)
assert one_hot(pred)
inter_size: Tensor = einsum(sum_str, [intersection(label, pred)]).type(torch.float32)
sum_sizes: Tensor = (einsum(sum_str, [label]) + einsum(sum_str, [pred])).type(torch.float32)
dices: Tensor = (2 * inter_size + smooth) / (sum_sizes + smooth)
return dices
dice_coef = partial(meta_dice, "bcwh->bc")
dice_batch = partial(meta_dice, "bcwh->c") # used for 3d dice
def intersection(a: Tensor, b: Tensor) -> Tensor:
assert a.shape == b.shape
assert sset(a, [0, 1])
assert sset(b, [0, 1])
return a & b
def union(a: Tensor, b: Tensor) -> Tensor:
assert a.shape == b.shape
assert sset(a, [0, 1])
assert sset(b, [0, 1])
return a | b
def inter_sum(a: Tensor, b: Tensor) -> Tensor:
return einsum("bcwh->bc", intersection(a, b).type(torch.float32))
def union_sum(a: Tensor, b: Tensor) -> Tensor:
return einsum("bcwh->bc", union(a, b).type(torch.float32))
def haussdorf(preds: Tensor, target: Tensor) -> Tensor:
assert preds.shape == target.shape
assert one_hot(preds)
assert one_hot(target)
B, C, _, _ = preds.shape
res = torch.zeros((B, C), dtype=torch.float32, device=preds.device)
n_pred = preds.cpu().numpy()
n_target = target.cpu().numpy()
for b in range(B):
if C == 2:
res[b, :] = numpy_haussdorf(n_pred[b, 0], n_target[b, 0])
continue
for c in range(C):
res[b, c] = numpy_haussdorf(n_pred[b, c], n_target[b, c])
return res
def numpy_haussdorf(pred: np.ndarray, target: np.ndarray) -> float:
assert len(pred.shape) == 2
assert pred.shape == target.shape
return max(directed_hausdorff(pred, target)[0], directed_hausdorff(target, pred)[0])
def iIoU(pred: Tensor, target: Tensor) -> Tensor:
IoUs = inter_sum(pred, target) / (union_sum(pred, target) + 1e-10)
assert IoUs.shape == pred.shape[:2]
return IoUs
# switch between representations
def probs2class(probs: Tensor) -> Tensor:
b, _, w, h = probs.shape # type: Tuple[int, int, int, int]
assert simplex(probs)
res = probs.argmax(dim=1)
assert res.shape == (b, w, h)
return res
def class2one_hot(seg: Tensor, C: int) -> Tensor:
if len(seg.shape) == 2: # Only w, h, used by the dataloader
seg = seg.unsqueeze(dim=0)
assert sset(seg, list(range(C)))
assert len(seg.shape) == 3, seg.shape
b, w, h = seg.shape # type: Tuple[int, int, int]
res = torch.stack([seg == c for c in range(C)], dim=1).type(torch.int32)
assert res.shape == (b, C, w, h)
assert one_hot(res)
return res
def fast_np_class2one_hot(seg: Tensor, C: int) -> Tensor:
if len(seg.shape) == 2: # Only w, h, used by the dataloader
return fast_np_class2one_hot(seg[None, ...], C)[0]
assert set(np.unique(seg)).issubset(list(range(C)))
b, w, h = seg.shape # type: Tuple[int, int, int]
res = np.zeros((b, C, w, h), dtype=np.int32)
np.put_along_axis(res, seg[:, None, ...], 1, axis=1)
assert res.shape == (b, C, w, h)
assert np.all(res.sum(axis=1) == 1)
assert set(np.unique(res)).issubset([0, 1])
return res
def probs2one_hot(probs: Tensor) -> Tensor:
_, C, _, _ = probs.shape
assert simplex(probs)
res = class2one_hot(probs2class(probs), C)
assert res.shape == probs.shape
assert one_hot(res)
return res
def one_hot2dist(seg: np.ndarray) -> np.ndarray:
assert one_hot(torch.Tensor(seg), axis=0)
C: int = len(seg)
res = np.zeros_like(seg)
for c in range(C):
posmask = seg[c].astype(np.bool)
if posmask.any():
negmask = ~posmask
res[c] = distance(negmask) * negmask - (distance(posmask) - 1) * posmask
# The idea is to leave blank the negative classes
# since this is one-hot encoded, another class will supervise that pixel
# else:
# padded = np.pad(~posmask, [[1, 1], [1, 1]], mode='constant', constant_values=[[0, 0], [0, 0]])
# res[c] = distance(padded)[1:-1, 1:-1]
# if c == 3:
# import matplotlib.pyplot as plt
# Fig, axes = plt.subplots(nrows=1, ncols=2)
# for axe, fig in zip(axes, [posmask, res[c]]):
# im = axe.imshow(fig)
# Fig.colorbar(im)
# plt.show()
return res
# Misc utils
def save_images(segs: Tensor, names: Iterable[str], root: str, mode: str, iter: int) -> None:
b, w, h = segs.shape # Since we have the class numbers, we do not need a C axis
for seg, name in zip(segs, names):
save_path = Path(root, f"iter{iter:03d}", mode, name).with_suffix(".png")
save_path.parent.mkdir(parents=True, exist_ok=True)
imsave(str(save_path), seg.cpu().numpy())
def augment(*arrs: Union[np.ndarray, Image.Image], rotate_angle: float = 45,
flip: bool = True, mirror: bool = True,
rotate: bool = True, scale: bool = False) -> List[Image.Image]:
imgs: List[Image.Image] = map_(Image.fromarray, arrs) if isinstance(arrs[0], np.ndarray) else list(arrs)
if flip and random() > 0.5:
imgs = map_(ImageOps.flip, imgs)
if mirror and random() > 0.5:
imgs = map_(ImageOps.mirror, imgs)
if rotate and random() > 0.5:
angle: float = uniform(-rotate_angle, rotate_angle)
imgs = map_(lambda e: e.rotate(angle), imgs)
if scale and random() > 0.5:
scale_factor: float = uniform(1, 1.2)
w, h = imgs[0].size # Tuple[int, int]
nw, nh = int(w * scale_factor), int(h * scale_factor) # Tuple[int, int]
# Resize
imgs = map_(lambda i: i.resize((nw, nh)), imgs)
# Now need to crop to original size
bw, bh = randint(0, nw - w), randint(0, nh - h) # Tuple[int, int]
imgs = map_(lambda i: i.crop((bw, bh, bw + w, bh + h)), imgs)
assert all(i.size == (w, h) for i in imgs)
return imgs
def augment_arr(*arrs_a: np.ndarray) -> List[np.ndarray]:
arrs = list(arrs_a) # manoucherie type check
if random() > 0.5:
arrs = map_(np.flip, arrs)
if random() > 0.5:
arrs = map_(np.fliplr, arrs)
# if random() > 0.5:
# orig_shape = arrs[0].shape
# angle = random() * 90 - 45
# arrs = map_(lambda e: sp.ndimage.rotate(e, angle, order=1), arrs)
# arrs = get_center(orig_shape, *arrs)
return arrs
def get_center(shape: Tuple, *arrs: np.ndarray) -> List[np.ndarray]:
def g_center(arr):
if arr.shape == shape:
return arr
dx = (arr.shape[0] - shape[0]) // 2
dy = (arr.shape[1] - shape[1]) // 2
if dx == 0 or dy == 0:
return arr[:shape[0], :shape[1]]
res = arr[dx:-dx, dy:-dy][:shape[0], :shape[1]] # Deal with off-by-one errors
assert res.shape == shape, (res.shape, shape, dx, dy)
return res
return [g_center(arr) for arr in arrs]
colormap = np.asarray([[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0],
[0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128],
[64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0],
[64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128],
[0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0],
[0, 64, 128]])
assert(colormap.shape == (21, 3))
simplexmap = np.asarray([[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0],
[0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128],
[64, 0, 0], [192, 0, 0], [64, 128, 0], [192, 128, 0],
[64, 0, 128], [192, 0, 128], [64, 128, 128], [192, 128, 128],
[0, 64, 0], [128, 64, 0], [0, 192, 0], [128, 192, 0],
[0, 64, 128], [255, 255, 255]])
assert simplexmap.shape == (22, 3)
def simplex2colors(mask: np.ndarray) -> np.ndarray:
C, W, H = mask.shape
res: np.ndarray
if C == 21:
res = np.moveaxis(mask, 0, -1).dot(colormap)
elif C == 22:
res = np.moveaxis(mask, 0, -1).dot(simplexmap)
else:
raise ValueError(f"Invalid number of class: {C}")
assert res.shape == (W, H, 3)
return res