File size: 41,836 Bytes
2a13495 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 |
import torch
from torch.nn import (
Module,
Conv2d,
BatchNorm2d,
Identity,
UpsamplingBilinear2d,
Mish,
ReLU,
Sequential,
)
from torch.nn.functional import interpolate, grid_sample, pad
import numpy as np
from copy import deepcopy
import os, argparse, math
import tifffile as tif
from typing import Tuple, List, Mapping
from monai.utils import (
BlendMode,
PytorchPadMode,
convert_data_type,
ensure_tuple,
fall_back_tuple,
look_up_option,
convert_to_dst_type,
)
from monai.utils.misc import ensure_tuple_size, ensure_tuple_rep, issequenceiterable
from monai.networks.layers.convutils import gaussian_1d
from monai.networks.layers.simplelayers import separable_filtering
from segmentation_models_pytorch import MAnet
from skimage.io import imread as io_imread
from skimage.util.dtype import dtype_range
from skimage._shared.utils import _supported_float_type
from scipy.ndimage import find_objects, binary_fill_holes
########################### Data Loading Modules #########################################################
DTYPE_RANGE = dtype_range.copy()
DTYPE_RANGE.update((d.__name__, limits) for d, limits in dtype_range.items())
DTYPE_RANGE.update(
{
"uint10": (0, 2 ** 10 - 1),
"uint12": (0, 2 ** 12 - 1),
"uint14": (0, 2 ** 14 - 1),
"bool": dtype_range[bool],
"float": dtype_range[np.float64],
}
)
def _output_dtype(dtype_or_range, image_dtype):
if type(dtype_or_range) in [list, tuple, np.ndarray]:
# pair of values: always return float.
return _supported_float_type(image_dtype)
if type(dtype_or_range) == type:
# already a type: return it
return dtype_or_range
if dtype_or_range in DTYPE_RANGE:
# string key in DTYPE_RANGE dictionary
try:
# if it's a canonical numpy dtype, convert
return np.dtype(dtype_or_range).type
except TypeError: # uint10, uint12, uint14
# otherwise, return uint16
return np.uint16
else:
raise ValueError(
"Incorrect value for out_range, should be a valid image data "
f"type or a pair of values, got {dtype_or_range}."
)
def intensity_range(image, range_values="image", clip_negative=False):
if range_values == "dtype":
range_values = image.dtype.type
if range_values == "image":
i_min = np.min(image)
i_max = np.max(image)
elif range_values in DTYPE_RANGE:
i_min, i_max = DTYPE_RANGE[range_values]
if clip_negative:
i_min = 0
else:
i_min, i_max = range_values
return i_min, i_max
def rescale_intensity(image, in_range="image", out_range="dtype"):
out_dtype = _output_dtype(out_range, image.dtype)
imin, imax = map(float, intensity_range(image, in_range))
omin, omax = map(
float, intensity_range(image, out_range, clip_negative=(imin >= 0))
)
image = np.clip(image, imin, imax)
if imin != imax:
image = (image - imin) / (imax - imin)
return np.asarray(image * (omax - omin) + omin, dtype=out_dtype)
else:
return np.clip(image, omin, omax).astype(out_dtype)
def _normalize(img):
non_zero_vals = img[np.nonzero(img)]
percentiles = np.percentile(non_zero_vals, [0, 99.5])
img_norm = rescale_intensity(
img, in_range=(percentiles[0], percentiles[1]), out_range="uint8"
)
return img_norm.astype(np.uint8)
def pred_transforms(filename):
# LoadImage
img = (
tif.imread(filename)
if filename.endswith(".tif") or filename.endswith(".tiff")
else io_imread(filename)
)
if len(img.shape) == 2:
img = np.repeat(np.expand_dims(img, axis=-1), 3, axis=-1)
elif len(img.shape) == 3 and img.shape[-1] > 3:
img = img[:, :, :3]
img = img.astype(np.float32)
img = _normalize(img)
img = np.moveaxis(img, -1, 0)
img = (img - img.min()) / (img.max() - img.min())
return torch.FloatTensor(img).unsqueeze(0)
################################################################################
########################### MODEL Architecture #################################
class SegformerGH(MAnet):
def __init__(
self,
encoder_name: str = "mit_b5",
encoder_weights="imagenet",
decoder_channels=(256, 128, 64, 32, 32),
decoder_pab_channels=256,
in_channels: int = 3,
classes: int = 3,
):
super(SegformerGH, self).__init__(
encoder_name=encoder_name,
encoder_weights=encoder_weights,
decoder_channels=decoder_channels,
decoder_pab_channels=decoder_pab_channels,
in_channels=in_channels,
classes=classes,
)
convert_relu_to_mish(self.encoder)
convert_relu_to_mish(self.decoder)
self.cellprob_head = DeepSegmantationHead(
in_channels=decoder_channels[-1], out_channels=1, kernel_size=3,
)
self.gradflow_head = DeepSegmantationHead(
in_channels=decoder_channels[-1], out_channels=2, kernel_size=3,
)
def forward(self, x):
"""Sequentially pass `x` trough model`s encoder, decoder and heads"""
self.check_input_shape(x)
features = self.encoder(x)
decoder_output = self.decoder(*features)
gradflow_mask = self.gradflow_head(decoder_output)
cellprob_mask = self.cellprob_head(decoder_output)
masks = torch.cat([gradflow_mask, cellprob_mask], dim=1)
return masks
class DeepSegmantationHead(Sequential):
def __init__(self, in_channels, out_channels, kernel_size=3, upsampling=1):
conv2d_1 = Conv2d(
in_channels,
in_channels // 2,
kernel_size=kernel_size,
padding=kernel_size // 2,
)
bn = BatchNorm2d(in_channels // 2)
conv2d_2 = Conv2d(
in_channels // 2,
out_channels,
kernel_size=kernel_size,
padding=kernel_size // 2,
)
mish = Mish(inplace=True)
upsampling = (
UpsamplingBilinear2d(scale_factor=upsampling)
if upsampling > 1
else Identity()
)
activation = Identity()
super().__init__(conv2d_1, mish, bn, conv2d_2, upsampling, activation)
def convert_relu_to_mish(model):
for child_name, child in model.named_children():
if isinstance(child, ReLU):
setattr(model, child_name, Mish(inplace=True))
else:
convert_relu_to_mish(child)
#####################################################################################
########################### Sliding Window Inference #################################
class GaussianFilter(Module):
def __init__(
self, spatial_dims, sigma, truncated=4.0, approx="erf", requires_grad=False,
) -> None:
if issequenceiterable(sigma):
if len(sigma) != spatial_dims: # type: ignore
raise ValueError
else:
sigma = [deepcopy(sigma) for _ in range(spatial_dims)] # type: ignore
super().__init__()
self.sigma = [
torch.nn.Parameter(
torch.as_tensor(
s,
dtype=torch.float,
device=s.device if isinstance(s, torch.Tensor) else None,
),
requires_grad=requires_grad,
)
for s in sigma # type: ignore
]
self.truncated = truncated
self.approx = approx
for idx, param in enumerate(self.sigma):
self.register_parameter(f"kernel_sigma_{idx}", param)
def forward(self, x: torch.Tensor) -> torch.Tensor:
_kernel = [
gaussian_1d(s, truncated=self.truncated, approx=self.approx)
for s in self.sigma
]
return separable_filtering(x=x, kernels=_kernel)
def compute_importance_map(
patch_size, mode=BlendMode.CONSTANT, sigma_scale=0.125, device="cpu"
):
mode = look_up_option(mode, BlendMode)
device = torch.device(device)
center_coords = [i // 2 for i in patch_size]
sigma_scale = ensure_tuple_rep(sigma_scale, len(patch_size))
sigmas = [i * sigma_s for i, sigma_s in zip(patch_size, sigma_scale)]
importance_map = torch.zeros(patch_size, device=device)
importance_map[tuple(center_coords)] = 1
pt_gaussian = GaussianFilter(len(patch_size), sigmas).to(
device=device, dtype=torch.float
)
importance_map = pt_gaussian(importance_map.unsqueeze(0).unsqueeze(0))
importance_map = importance_map.squeeze(0).squeeze(0)
importance_map = importance_map / torch.max(importance_map)
importance_map = importance_map.float()
return importance_map
def first(iterable, default=None):
for i in iterable:
return i
return default
def dense_patch_slices(image_size, patch_size, scan_interval):
num_spatial_dims = len(image_size)
patch_size = get_valid_patch_size(image_size, patch_size)
scan_interval = ensure_tuple_size(scan_interval, num_spatial_dims)
scan_num = []
for i in range(num_spatial_dims):
if scan_interval[i] == 0:
scan_num.append(1)
else:
num = int(math.ceil(float(image_size[i]) / scan_interval[i]))
scan_dim = first(
d
for d in range(num)
if d * scan_interval[i] + patch_size[i] >= image_size[i]
)
scan_num.append(scan_dim + 1 if scan_dim is not None else 1)
starts = []
for dim in range(num_spatial_dims):
dim_starts = []
for idx in range(scan_num[dim]):
start_idx = idx * scan_interval[dim]
start_idx -= max(start_idx + patch_size[dim] - image_size[dim], 0)
dim_starts.append(start_idx)
starts.append(dim_starts)
out = np.asarray([x.flatten() for x in np.meshgrid(*starts, indexing="ij")]).T
return [tuple(slice(s, s + patch_size[d]) for d, s in enumerate(x)) for x in out]
def get_valid_patch_size(image_size, patch_size):
ndim = len(image_size)
patch_size_ = ensure_tuple_size(patch_size, ndim)
# ensure patch size dimensions are not larger than image dimension, if a dimension is None or 0 use whole dimension
return tuple(min(ms, ps or ms) for ms, ps in zip(image_size, patch_size_))
class Resize:
def __init__(self, spatial_size):
self.size_mode = "all"
self.spatial_size = spatial_size
def __call__(self, img):
input_ndim = img.ndim - 1 # spatial ndim
output_ndim = len(ensure_tuple(self.spatial_size))
if output_ndim > input_ndim:
input_shape = ensure_tuple_size(img.shape, output_ndim + 1, 1)
img = img.reshape(input_shape)
spatial_size_ = fall_back_tuple(self.spatial_size, img.shape[1:])
if (
tuple(img.shape[1:]) == spatial_size_
): # spatial shape is already the desired
return img
img_, *_ = convert_data_type(img, torch.Tensor, dtype=torch.float)
resized = interpolate(
input=img_.unsqueeze(0), size=spatial_size_, mode="nearest",
)
out, *_ = convert_to_dst_type(resized.squeeze(0), img)
return out
def sliding_window_inference(
inputs,
roi_size,
sw_batch_size,
predictor,
overlap,
mode=BlendMode.CONSTANT,
sigma_scale=0.125,
padding_mode=PytorchPadMode.CONSTANT,
cval=0.0,
sw_device=None,
device=None,
roi_weight_map=None,
):
compute_dtype = inputs.dtype
num_spatial_dims = len(inputs.shape) - 2
batch_size, _, *image_size_ = inputs.shape
roi_size = fall_back_tuple(roi_size, image_size_)
# in case that image size is smaller than roi size
image_size = tuple(
max(image_size_[i], roi_size[i]) for i in range(num_spatial_dims)
)
pad_size = []
for k in range(len(inputs.shape) - 1, 1, -1):
diff = max(roi_size[k - 2] - inputs.shape[k], 0)
half = diff // 2
pad_size.extend([half, diff - half])
inputs = pad(
inputs,
pad=pad_size,
mode=look_up_option(padding_mode, PytorchPadMode).value,
value=cval,
)
scan_interval = _get_scan_interval(image_size, roi_size, num_spatial_dims, overlap)
# Store all slices in list
slices = dense_patch_slices(image_size, roi_size, scan_interval)
num_win = len(slices) # number of windows per image
total_slices = num_win * batch_size # total number of windows
# Create window-level importance map
valid_patch_size = get_valid_patch_size(image_size, roi_size)
if valid_patch_size == roi_size and (roi_weight_map is not None):
importance_map = roi_weight_map
else:
importance_map = compute_importance_map(
valid_patch_size, mode=mode, sigma_scale=sigma_scale, device=device
)
importance_map = convert_data_type(importance_map, torch.Tensor, device, compute_dtype)[0] # type: ignore
# handle non-positive weights
min_non_zero = max(importance_map[importance_map != 0].min().item(), 1e-3)
importance_map = torch.clamp(importance_map.to(torch.float32), min=min_non_zero).to(
compute_dtype
)
# Perform predictions
dict_key, output_image_list, count_map_list = None, [], []
_initialized_ss = -1
is_tensor_output = (
True # whether the predictor's output is a tensor (instead of dict/tuple)
)
# for each patch
for slice_g in range(0, total_slices, sw_batch_size):
slice_range = range(slice_g, min(slice_g + sw_batch_size, total_slices))
unravel_slice = [
[slice(int(idx / num_win), int(idx / num_win) + 1), slice(None)]
+ list(slices[idx % num_win])
for idx in slice_range
]
window_data = torch.cat([inputs[win_slice] for win_slice in unravel_slice]).to(
sw_device
)
seg_prob_out = predictor(window_data) # batched patch segmentation
# convert seg_prob_out to tuple seg_prob_tuple, this does not allocate new memory.
seg_prob_tuple: Tuple[torch.Tensor, ...]
if isinstance(seg_prob_out, torch.Tensor):
seg_prob_tuple = (seg_prob_out,)
elif isinstance(seg_prob_out, Mapping):
if dict_key is None:
dict_key = sorted(seg_prob_out.keys()) # track predictor's output keys
seg_prob_tuple = tuple(seg_prob_out[k] for k in dict_key)
is_tensor_output = False
else:
seg_prob_tuple = ensure_tuple(seg_prob_out)
is_tensor_output = False
# for each output in multi-output list
for ss, seg_prob in enumerate(seg_prob_tuple):
seg_prob = seg_prob.to(device) # BxCxMxNxP or BxCxMxN
# compute zoom scale: out_roi_size/in_roi_size
zoom_scale = []
for axis, (img_s_i, out_w_i, in_w_i) in enumerate(
zip(image_size, seg_prob.shape[2:], window_data.shape[2:])
):
_scale = out_w_i / float(in_w_i)
zoom_scale.append(_scale)
if _initialized_ss < ss: # init. the ss-th buffer at the first iteration
# construct multi-resolution outputs
output_classes = seg_prob.shape[1]
output_shape = [batch_size, output_classes] + [
int(image_size_d * zoom_scale_d)
for image_size_d, zoom_scale_d in zip(image_size, zoom_scale)
]
# allocate memory to store the full output and the count for overlapping parts
output_image_list.append(
torch.zeros(output_shape, dtype=compute_dtype, device=device)
)
count_map_list.append(
torch.zeros(
[1, 1] + output_shape[2:], dtype=compute_dtype, device=device
)
)
_initialized_ss += 1
# resizing the importance_map
resizer = Resize(spatial_size=seg_prob.shape[2:])
# store the result in the proper location of the full output. Apply weights from importance map.
for idx, original_idx in zip(slice_range, unravel_slice):
# zoom roi
original_idx_zoom = list(
original_idx
) # 4D for 2D image, 5D for 3D image
for axis in range(2, len(original_idx_zoom)):
zoomed_start = original_idx[axis].start * zoom_scale[axis - 2]
zoomed_end = original_idx[axis].stop * zoom_scale[axis - 2]
original_idx_zoom[axis] = slice(
int(zoomed_start), int(zoomed_end), None
)
importance_map_zoom = resizer(importance_map.unsqueeze(0))[0].to(
compute_dtype
)
# store results and weights
output_image_list[ss][original_idx_zoom] += (
importance_map_zoom * seg_prob[idx - slice_g]
)
count_map_list[ss][original_idx_zoom] += (
importance_map_zoom.unsqueeze(0)
.unsqueeze(0)
.expand(count_map_list[ss][original_idx_zoom].shape)
)
# account for any overlapping sections
for ss in range(len(output_image_list)):
output_image_list[ss] = (output_image_list[ss] / count_map_list.pop(0)).to(
compute_dtype
)
# remove padding if image_size smaller than roi_size
for ss, output_i in enumerate(output_image_list):
zoom_scale = [
seg_prob_map_shape_d / roi_size_d
for seg_prob_map_shape_d, roi_size_d in zip(output_i.shape[2:], roi_size)
]
final_slicing: List[slice] = []
for sp in range(num_spatial_dims):
slice_dim = slice(
pad_size[sp * 2],
image_size_[num_spatial_dims - sp - 1] + pad_size[sp * 2],
)
slice_dim = slice(
int(round(slice_dim.start * zoom_scale[num_spatial_dims - sp - 1])),
int(round(slice_dim.stop * zoom_scale[num_spatial_dims - sp - 1])),
)
final_slicing.insert(0, slice_dim)
while len(final_slicing) < len(output_i.shape):
final_slicing.insert(0, slice(None))
output_image_list[ss] = output_i[final_slicing]
if dict_key is not None: # if output of predictor is a dict
final_output = dict(zip(dict_key, output_image_list))
else:
final_output = tuple(output_image_list) # type: ignore
return final_output[0] if is_tensor_output else final_output # type: ignore
def _get_scan_interval(
image_size, roi_size, num_spatial_dims: int, overlap: float
) -> Tuple[int, ...]:
scan_interval = []
for i in range(num_spatial_dims):
if roi_size[i] == image_size[i]:
scan_interval.append(int(roi_size[i]))
else:
interval = int(roi_size[i] * (1 - overlap))
scan_interval.append(interval if interval > 0 else 1)
return tuple(scan_interval)
#####################################################################################
########################### Main Inference Functions #################################
def post_process(pred_mask, device):
dP, cellprob = pred_mask[:2], 1 / (1 + np.exp(-pred_mask[-1]))
H, W = pred_mask.shape[-2], pred_mask.shape[-1]
if np.prod(H * W) < (5000 * 5000):
pred_mask = compute_masks(
dP,
cellprob,
use_gpu=True,
flow_threshold=0.4,
device=device,
cellprob_threshold=0.4,
)[0]
else:
print("\n[Whole Slide] Grid Prediction starting...")
roi_size = 2000
# Get patch grid by roi_size
if H % roi_size != 0:
n_H = H // roi_size + 1
new_H = roi_size * n_H
else:
n_H = H // roi_size
new_H = H
if W % roi_size != 0:
n_W = W // roi_size + 1
new_W = roi_size * n_W
else:
n_W = W // roi_size
new_W = W
# Allocate values on the grid
pred_pad = np.zeros((new_H, new_W), dtype=np.uint32)
dP_pad = np.zeros((2, new_H, new_W), dtype=np.float32)
cellprob_pad = np.zeros((new_H, new_W), dtype=np.float32)
dP_pad[:, :H, :W], cellprob_pad[:H, :W] = dP, cellprob
for i in range(n_H):
for j in range(n_W):
print("Pred on Grid (%d, %d) processing..." % (i, j))
dP_roi = dP_pad[
:,
roi_size * i : roi_size * (i + 1),
roi_size * j : roi_size * (j + 1),
]
cellprob_roi = cellprob_pad[
roi_size * i : roi_size * (i + 1),
roi_size * j : roi_size * (j + 1),
]
pred_mask = compute_masks(
dP_roi,
cellprob_roi,
use_gpu=True,
flow_threshold=0.4,
device=device,
cellprob_threshold=0.4,
)[0]
pred_pad[
roi_size * i : roi_size * (i + 1),
roi_size * j : roi_size * (j + 1),
] = pred_mask
pred_mask = pred_pad[:H, :W]
cell_idx, cell_sizes = np.unique(pred_mask, return_counts=True)
cell_idx, cell_sizes = cell_idx[1:], cell_sizes[1:]
cell_drop = np.where(cell_sizes < np.mean(cell_sizes) - 2.7 * np.std(cell_sizes))
for drop_cell in cell_idx[cell_drop]:
pred_mask[pred_mask == drop_cell] = 0
return pred_mask
def hflip(x):
"""flip batch of images horizontally"""
return x.flip(3)
def vflip(x):
"""flip batch of images vertically"""
return x.flip(2)
class DualTransform:
identity_param = None
def __init__(
self, name: str, params,
):
self.params = params
self.pname = name
def apply_aug_image(self, image, *args, **params):
raise NotImplementedError
def apply_deaug_mask(self, mask, *args, **params):
raise NotImplementedError
class HorizontalFlip(DualTransform):
"""Flip images horizontally (left->right)"""
identity_param = False
def __init__(self):
super().__init__("apply", [False, True])
def apply_aug_image(self, image, apply=False, **kwargs):
if apply:
image = hflip(image)
return image
def apply_deaug_mask(self, mask, apply=False, **kwargs):
if apply:
mask = hflip(mask)
return mask
class VerticalFlip(DualTransform):
"""Flip images vertically (up->down)"""
identity_param = False
def __init__(self):
super().__init__("apply", [False, True])
def apply_aug_image(self, image, apply=False, **kwargs):
if apply:
image = vflip(image)
return image
def apply_deaug_mask(self, mask, apply=False, **kwargs):
if apply:
mask = vflip(mask)
return mask
#################### GradFlow Modules ##################################################
from scipy.ndimage.filters import maximum_filter1d
import scipy.ndimage
import fastremap
from skimage import morphology
from scipy.ndimage import mean
torch_GPU = torch.device("cuda")
torch_CPU = torch.device("cpu")
def _extend_centers_gpu(
neighbors, centers, isneighbor, Ly, Lx, n_iter=200, device=torch.device("cuda")
):
if device is not None:
device = device
nimg = neighbors.shape[0] // 9
pt = torch.from_numpy(neighbors).to(device)
T = torch.zeros((nimg, Ly, Lx), dtype=torch.double, device=device)
meds = torch.from_numpy(centers.astype(int)).to(device).long()
isneigh = torch.from_numpy(isneighbor).to(device)
for i in range(n_iter):
T[:, meds[:, 0], meds[:, 1]] += 1
Tneigh = T[:, pt[:, :, 0], pt[:, :, 1]]
Tneigh *= isneigh
T[:, pt[0, :, 0], pt[0, :, 1]] = Tneigh.mean(axis=1)
del meds, isneigh, Tneigh
T = torch.log(1.0 + T)
# gradient positions
grads = T[:, pt[[2, 1, 4, 3], :, 0], pt[[2, 1, 4, 3], :, 1]]
del pt
dy = grads[:, 0] - grads[:, 1]
dx = grads[:, 2] - grads[:, 3]
del grads
mu_torch = np.stack((dy.cpu().squeeze(), dx.cpu().squeeze()), axis=-2)
return mu_torch
def diameters(masks):
_, counts = np.unique(np.int32(masks), return_counts=True)
counts = counts[1:]
md = np.median(counts ** 0.5)
if np.isnan(md):
md = 0
md /= (np.pi ** 0.5) / 2
return md, counts ** 0.5
def masks_to_flows_gpu(masks, device=None):
if device is None:
device = torch.device("cuda")
Ly0, Lx0 = masks.shape
Ly, Lx = Ly0 + 2, Lx0 + 2
masks_padded = np.zeros((Ly, Lx), np.int64)
masks_padded[1:-1, 1:-1] = masks
# get mask pixel neighbors
y, x = np.nonzero(masks_padded)
neighborsY = np.stack((y, y - 1, y + 1, y, y, y - 1, y - 1, y + 1, y + 1), axis=0)
neighborsX = np.stack((x, x, x, x - 1, x + 1, x - 1, x + 1, x - 1, x + 1), axis=0)
neighbors = np.stack((neighborsY, neighborsX), axis=-1)
# get mask centers
slices = scipy.ndimage.find_objects(masks)
centers = np.zeros((masks.max(), 2), "int")
for i, si in enumerate(slices):
if si is not None:
sr, sc = si
ly, lx = sr.stop - sr.start + 1, sc.stop - sc.start + 1
yi, xi = np.nonzero(masks[sr, sc] == (i + 1))
yi = yi.astype(np.int32) + 1 # add padding
xi = xi.astype(np.int32) + 1 # add padding
ymed = np.median(yi)
xmed = np.median(xi)
imin = np.argmin((xi - xmed) ** 2 + (yi - ymed) ** 2)
xmed = xi[imin]
ymed = yi[imin]
centers[i, 0] = ymed + sr.start
centers[i, 1] = xmed + sc.start
# get neighbor validator (not all neighbors are in same mask)
neighbor_masks = masks_padded[neighbors[:, :, 0], neighbors[:, :, 1]]
isneighbor = neighbor_masks == neighbor_masks[0]
ext = np.array(
[[sr.stop - sr.start + 1, sc.stop - sc.start + 1] for sr, sc in slices]
)
n_iter = 2 * (ext.sum(axis=1)).max()
# run diffusion
mu = _extend_centers_gpu(
neighbors, centers, isneighbor, Ly, Lx, n_iter=n_iter, device=device
)
# normalize
mu /= 1e-20 + (mu ** 2).sum(axis=0) ** 0.5
# put into original image
mu0 = np.zeros((2, Ly0, Lx0))
mu0[:, y - 1, x - 1] = mu
mu_c = np.zeros_like(mu0)
return mu0, mu_c
def masks_to_flows(masks, use_gpu=False, device=None):
if masks.max() == 0 or (masks != 0).sum() == 1:
# dynamics_logger.warning('empty masks!')
return np.zeros((2, *masks.shape), "float32")
if use_gpu:
if use_gpu and device is None:
device = torch_GPU
elif device is None:
device = torch_CPU
masks_to_flows_device = masks_to_flows_gpu
if masks.ndim == 3:
Lz, Ly, Lx = masks.shape
mu = np.zeros((3, Lz, Ly, Lx), np.float32)
for z in range(Lz):
mu0 = masks_to_flows_device(masks[z], device=device)[0]
mu[[1, 2], z] += mu0
for y in range(Ly):
mu0 = masks_to_flows_device(masks[:, y], device=device)[0]
mu[[0, 2], :, y] += mu0
for x in range(Lx):
mu0 = masks_to_flows_device(masks[:, :, x], device=device)[0]
mu[[0, 1], :, :, x] += mu0
return mu
elif masks.ndim == 2:
mu, mu_c = masks_to_flows_device(masks, device=device)
return mu
else:
raise ValueError("masks_to_flows only takes 2D or 3D arrays")
def steps2D_interp(p, dP, niter, use_gpu=False, device=None):
shape = dP.shape[1:]
if use_gpu:
if device is None:
device = torch_GPU
shape = (
np.array(shape)[[1, 0]].astype("float") - 1
) # Y and X dimensions (dP is 2.Ly.Lx), flipped X-1, Y-1
pt = (
torch.from_numpy(p[[1, 0]].T).float().to(device).unsqueeze(0).unsqueeze(0)
) # p is n_points by 2, so pt is [1 1 2 n_points]
im = (
torch.from_numpy(dP[[1, 0]]).float().to(device).unsqueeze(0)
) # covert flow numpy array to tensor on GPU, add dimension
# normalize pt between 0 and 1, normalize the flow
for k in range(2):
im[:, k, :, :] *= 2.0 / shape[k]
pt[:, :, :, k] /= shape[k]
# normalize to between -1 and 1
pt = pt * 2 - 1
# here is where the stepping happens
for t in range(niter):
# align_corners default is False, just added to suppress warning
dPt = grid_sample(im, pt, align_corners=False)
for k in range(2): # clamp the final pixel locations
pt[:, :, :, k] = torch.clamp(
pt[:, :, :, k] + dPt[:, k, :, :], -1.0, 1.0
)
# undo the normalization from before, reverse order of operations
pt = (pt + 1) * 0.5
for k in range(2):
pt[:, :, :, k] *= shape[k]
p = pt[:, :, :, [1, 0]].cpu().numpy().squeeze().T
return p
else:
assert print("ho")
def follow_flows(dP, mask=None, niter=200, interp=True, use_gpu=True, device=None):
shape = np.array(dP.shape[1:]).astype(np.int32)
niter = np.uint32(niter)
p = np.meshgrid(np.arange(shape[0]), np.arange(shape[1]), indexing="ij")
p = np.array(p).astype(np.float32)
inds = np.array(np.nonzero(np.abs(dP[0]) > 1e-3)).astype(np.int32).T
if inds.ndim < 2 or inds.shape[0] < 5:
return p, None
if not interp:
assert print("woo")
else:
p_interp = steps2D_interp(
p[:, inds[:, 0], inds[:, 1]], dP, niter, use_gpu=use_gpu, device=device
)
p[:, inds[:, 0], inds[:, 1]] = p_interp
return p, inds
def flow_error(maski, dP_net, use_gpu=False, device=None):
if dP_net.shape[1:] != maski.shape:
print("ERROR: net flow is not same size as predicted masks")
return
# flows predicted from estimated masks
dP_masks = masks_to_flows(maski, use_gpu=use_gpu, device=device)
# difference between predicted flows vs mask flows
flow_errors = np.zeros(maski.max())
for i in range(dP_masks.shape[0]):
flow_errors += mean(
(dP_masks[i] - dP_net[i] / 5.0) ** 2,
maski,
index=np.arange(1, maski.max() + 1),
)
return flow_errors, dP_masks
def remove_bad_flow_masks(masks, flows, threshold=0.4, use_gpu=False, device=None):
merrors, _ = flow_error(masks, flows, use_gpu, device)
badi = 1 + (merrors > threshold).nonzero()[0]
masks[np.isin(masks, badi)] = 0
return masks
def get_masks(p, iscell=None, rpad=20):
pflows = []
edges = []
shape0 = p.shape[1:]
dims = len(p)
for i in range(dims):
pflows.append(p[i].flatten().astype("int32"))
edges.append(np.arange(-0.5 - rpad, shape0[i] + 0.5 + rpad, 1))
h, _ = np.histogramdd(tuple(pflows), bins=edges)
hmax = h.copy()
for i in range(dims):
hmax = maximum_filter1d(hmax, 5, axis=i)
seeds = np.nonzero(np.logical_and(h - hmax > -1e-6, h > 10))
Nmax = h[seeds]
isort = np.argsort(Nmax)[::-1]
for s in seeds:
s = s[isort]
pix = list(np.array(seeds).T)
shape = h.shape
if dims == 3:
expand = np.nonzero(np.ones((3, 3, 3)))
else:
expand = np.nonzero(np.ones((3, 3)))
for e in expand:
e = np.expand_dims(e, 1)
for iter in range(5):
for k in range(len(pix)):
if iter == 0:
pix[k] = list(pix[k])
newpix = []
iin = []
for i, e in enumerate(expand):
epix = e[:, np.newaxis] + np.expand_dims(pix[k][i], 0) - 1
epix = epix.flatten()
iin.append(np.logical_and(epix >= 0, epix < shape[i]))
newpix.append(epix)
iin = np.all(tuple(iin), axis=0)
for p in newpix:
p = p[iin]
newpix = tuple(newpix)
igood = h[newpix] > 2
for i in range(dims):
pix[k][i] = newpix[i][igood]
if iter == 4:
pix[k] = tuple(pix[k])
M = np.zeros(h.shape, np.uint32)
for k in range(len(pix)):
M[pix[k]] = 1 + k
for i in range(dims):
pflows[i] = pflows[i] + rpad
M0 = M[tuple(pflows)]
# remove big masks
uniq, counts = fastremap.unique(M0, return_counts=True)
big = np.prod(shape0) * 0.9
bigc = uniq[counts > big]
if len(bigc) > 0 and (len(bigc) > 1 or bigc[0] != 0):
M0 = fastremap.mask(M0, bigc)
fastremap.renumber(M0, in_place=True) # convenient to guarantee non-skipped labels
M0 = np.reshape(M0, shape0)
return M0
def fill_holes_and_remove_small_masks(masks, min_size=15):
""" fill holes in masks (2D/3D) and discard masks smaller than min_size (2D)
fill holes in each mask using scipy.ndimage.morphology.binary_fill_holes
(might have issues at borders between cells, todo: check and fix)
Parameters
----------------
masks: int, 2D or 3D array
labelled masks, 0=NO masks; 1,2,...=mask labels,
size [Ly x Lx] or [Lz x Ly x Lx]
min_size: int (optional, default 15)
minimum number of pixels per mask, can turn off with -1
Returns
---------------
masks: int, 2D or 3D array
masks with holes filled and masks smaller than min_size removed,
0=NO masks; 1,2,...=mask labels,
size [Ly x Lx] or [Lz x Ly x Lx]
"""
slices = find_objects(masks)
j = 0
for i,slc in enumerate(slices):
if slc is not None:
msk = masks[slc] == (i+1)
npix = msk.sum()
if min_size > 0 and npix < min_size:
masks[slc][msk] = 0
elif npix > 0:
if msk.ndim==3:
for k in range(msk.shape[0]):
msk[k] = binary_fill_holes(msk[k])
else:
msk = binary_fill_holes(msk)
masks[slc][msk] = (j+1)
j+=1
return masks
def compute_masks(
dP,
cellprob,
p=None,
niter=200,
cellprob_threshold=0.4,
flow_threshold=0.4,
interp=True,
resize=None,
use_gpu=False,
device=None,
):
"""compute masks using dynamics from dP, cellprob, and boundary"""
cp_mask = cellprob > cellprob_threshold
cp_mask = morphology.remove_small_holes(cp_mask, area_threshold=16)
cp_mask = morphology.remove_small_objects(cp_mask, min_size=16)
if np.any(cp_mask): # mask at this point is a cell cluster binary map, not labels
# follow flows
if p is None:
p, inds = follow_flows(
dP * cp_mask / 5.0,
niter=niter,
interp=interp,
use_gpu=use_gpu,
device=device,
)
if inds is None:
shape = resize if resize is not None else cellprob.shape
mask = np.zeros(shape, np.uint16)
p = np.zeros((len(shape), *shape), np.uint16)
return mask, p
# calculate masks
mask = get_masks(p, iscell=cp_mask)
# flow thresholding factored out of get_masks
shape0 = p.shape[1:]
if mask.max() > 0 and flow_threshold is not None and flow_threshold > 0:
# make sure labels are unique at output of get_masks
mask = remove_bad_flow_masks(
mask, dP, threshold=flow_threshold, use_gpu=use_gpu, device=device
)
mask = fill_holes_and_remove_small_masks(mask, min_size=15)
else: # nothing to compute, just make it compatible
shape = resize if resize is not None else cellprob.shape
mask = np.zeros(shape, np.uint16)
p = np.zeros((len(shape), *shape), np.uint16)
return mask, p
return mask, p
def main(args):
model = torch.load(args.model_path, map_location=args.device)
model.eval()
hflip_tta = HorizontalFlip()
vflip_tta = VerticalFlip()
img_names = sorted(os.listdir(args.input_path))
os.makedirs(args.output_path, exist_ok=True)
for img_name in img_names:
print(f"Segmenting {img_name}")
img_path = os.path.join(args.input_path, img_name)
img_data = pred_transforms(img_path)
img_data = img_data.to(args.device)
img_size = img_data.shape[-1] * img_data.shape[-2]
if img_size < 1150000 and 900000 < img_size:
overlap = 0.5
else:
overlap = 0.6
with torch.no_grad():
img0 = img_data
outputs0 = sliding_window_inference(
img0,
512,
4,
model,
padding_mode="reflect",
mode="gaussian",
overlap=overlap,
device="cpu",
)
outputs0 = outputs0.cpu().squeeze()
if img_size < 2000 * 2000:
model.load_state_dict(torch.load(args.model_path2, map_location=args.device))
model.eval()
img2 = hflip_tta.apply_aug_image(img_data, apply=True)
outputs2 = sliding_window_inference(
img2,
512,
4,
model,
padding_mode="reflect",
mode="gauusian",
overlap=overlap,
device="cpu",
)
outputs2 = hflip_tta.apply_deaug_mask(outputs2, apply=True)
outputs2 = outputs2.cpu().squeeze()
outputs = torch.zeros_like(outputs0)
outputs[0] = (outputs0[0] + outputs2[0]) / 2
outputs[1] = (outputs0[1] - outputs2[1]) / 2
outputs[2] = (outputs0[2] + outputs2[2]) / 2
elif img_size < 5000*5000:
# Hflip TTA
img2 = hflip_tta.apply_aug_image(img_data, apply=True)
outputs2 = sliding_window_inference(
img2,
512,
4,
model,
padding_mode="reflect",
mode="gaussian",
overlap=overlap,
device="cpu",
)
outputs2 = hflip_tta.apply_deaug_mask(outputs2, apply=True)
outputs2 = outputs2.cpu().squeeze()
img2 = img2.cpu()
##################
# #
# ensemble #
# #
##################
model.load_state_dict(torch.load(args.model_path2, map_location=args.device))
model.eval()
img1 = img_data
outputs1 = sliding_window_inference(
img1,
512,
4,
model,
padding_mode="reflect",
mode="gaussian",
overlap=overlap,
device="cpu",
)
outputs1 = outputs1.cpu().squeeze()
# Vflip TTA
img3 = vflip_tta.apply_aug_image(img_data, apply=True)
outputs3 = sliding_window_inference(
img3,
512,
4,
model,
padding_mode="reflect",
mode="gaussian",
overlap=overlap,
device="cpu",
)
outputs3 = vflip_tta.apply_deaug_mask(outputs3, apply=True)
outputs3 = outputs3.cpu().squeeze()
img3 = img3.cpu()
# Merge Results
outputs = torch.zeros_like(outputs0)
outputs[0] = (outputs0[0] + outputs1[0] + outputs2[0] - outputs3[0]) / 4
outputs[1] = (outputs0[1] + outputs1[1] - outputs2[1] + outputs3[1]) / 4
outputs[2] = (outputs0[2] + outputs1[2] + outputs2[2] + outputs3[2]) / 4
else:
outputs = outputs0
pred_mask = post_process(outputs.squeeze(0).cpu().numpy(), args.device)
file_path = os.path.join(
args.output_path, img_name.split(".")[0] + "_label.tiff"
)
tif.imwrite(file_path, pred_mask, compression="zlib")
parser = argparse.ArgumentParser("Submission for Challenge", add_help=False)
parser.add_argument("--model_path", default="./model.pt", type=str)
parser.add_argument("--model_path2", default="./model_sec.pth", type=str)
# Dataset parameters
parser.add_argument(
"-i",
"--input_path",
default="/workspace/inputs/",
type=str,
help="training data path; subfolders: images, labels",
)
parser.add_argument(
"-o", "--output_path", default="/workspace/outputs/", type=str, help="output path",
)
parser.add_argument("--device", default="cuda:0", type=str)
args = parser.parse_args()
if __name__ == "__main__":
print("Starting")
main(args)
|