Spaces:
Sleeping
Sleeping
File size: 16,032 Bytes
749745d |
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 |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import math
import numpy as np
import torch
from torch import nn
from maskrcnn_benchmark.structures.bounding_box import BoxList
from maskrcnn_benchmark.structures.image_list import ImageList
from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist
class BufferList(nn.Module):
"""
Similar to nn.ParameterList, but for buffers
"""
def __init__(self, buffers=None):
super(BufferList, self).__init__()
if buffers is not None:
self.extend(buffers)
def extend(self, buffers):
offset = len(self)
for i, buffer in enumerate(buffers):
self.register_buffer(str(offset + i), buffer)
return self
def __len__(self):
return len(self._buffers)
def __iter__(self):
return iter(self._buffers.values())
class AnchorGenerator(nn.Module):
"""
For a set of image sizes and feature maps, computes a set
of anchors
"""
def __init__(
self,
sizes=(128, 256, 512),
aspect_ratios=(0.5, 1.0, 2.0),
anchor_strides=(8, 16, 32),
straddle_thresh=0,
):
super(AnchorGenerator, self).__init__()
if len(anchor_strides) == 1:
anchor_stride = anchor_strides[0]
cell_anchors = [generate_anchors(anchor_stride, sizes, aspect_ratios).float()]
else:
if len(anchor_strides) != len(sizes):
raise RuntimeError("FPN should have #anchor_strides == #sizes")
cell_anchors = [
generate_anchors(
anchor_stride, size if isinstance(size, (tuple, list)) else (size,), aspect_ratios
).float()
for anchor_stride, size in zip(anchor_strides, sizes)
]
self.strides = anchor_strides
self.cell_anchors = BufferList(cell_anchors)
self.straddle_thresh = straddle_thresh
def num_anchors_per_location(self):
return [len(cell_anchors) for cell_anchors in self.cell_anchors]
def grid_anchors(self, grid_sizes):
anchors = []
for size, stride, base_anchors in zip(grid_sizes, self.strides, self.cell_anchors):
grid_height, grid_width = size
device = base_anchors.device
shifts_x = torch.arange(0, grid_width * stride, step=stride, dtype=torch.float32, device=device)
shifts_y = torch.arange(0, grid_height * stride, step=stride, dtype=torch.float32, device=device)
shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
shift_x = shift_x.reshape(-1)
shift_y = shift_y.reshape(-1)
shifts = torch.stack((shift_x, shift_y, shift_x, shift_y), dim=1)
anchors.append((shifts.view(-1, 1, 4) + base_anchors.view(1, -1, 4)).reshape(-1, 4))
return anchors
def add_visibility_to(self, boxlist):
image_width, image_height = boxlist.size
anchors = boxlist.bbox
if self.straddle_thresh >= 0:
inds_inside = (
(anchors[..., 0] >= -self.straddle_thresh)
& (anchors[..., 1] >= -self.straddle_thresh)
& (anchors[..., 2] < image_width + self.straddle_thresh)
& (anchors[..., 3] < image_height + self.straddle_thresh)
)
else:
device = anchors.device
inds_inside = torch.ones(anchors.shape[0], dtype=torch.bool, device=device)
boxlist.add_field("visibility", inds_inside)
def forward(self, image_list, feature_maps):
grid_sizes = [feature_map.shape[-2:] for feature_map in feature_maps]
anchors_over_all_feature_maps = self.grid_anchors(grid_sizes)
anchors = []
if isinstance(image_list, ImageList):
for i, (image_height, image_width) in enumerate(image_list.image_sizes):
anchors_in_image = []
for anchors_per_feature_map in anchors_over_all_feature_maps:
boxlist = BoxList(anchors_per_feature_map, (image_width, image_height), mode="xyxy")
self.add_visibility_to(boxlist)
anchors_in_image.append(boxlist)
anchors.append(anchors_in_image)
else:
image_height, image_width = [int(x) for x in image_list.size()[-2:]]
anchors_in_image = []
for anchors_per_feature_map in anchors_over_all_feature_maps:
boxlist = BoxList(anchors_per_feature_map, (image_width, image_height), mode="xyxy")
self.add_visibility_to(boxlist)
anchors_in_image.append(boxlist)
anchors.append(anchors_in_image)
return anchors
def make_anchor_generator(config):
anchor_sizes = config.MODEL.RPN.ANCHOR_SIZES
aspect_ratios = config.MODEL.RPN.ASPECT_RATIOS
anchor_stride = config.MODEL.RPN.ANCHOR_STRIDE
straddle_thresh = config.MODEL.RPN.STRADDLE_THRESH
if config.MODEL.RPN.USE_FPN:
assert len(anchor_stride) == len(anchor_sizes), "FPN should have len(ANCHOR_STRIDE) == len(ANCHOR_SIZES)"
else:
assert len(anchor_stride) == 1, "Non-FPN should have a single ANCHOR_STRIDE"
anchor_generator = AnchorGenerator(anchor_sizes, aspect_ratios, anchor_stride, straddle_thresh)
return anchor_generator
def make_anchor_generator_complex(config):
anchor_sizes = config.MODEL.RPN.ANCHOR_SIZES
aspect_ratios = config.MODEL.RPN.ASPECT_RATIOS
anchor_strides = config.MODEL.RPN.ANCHOR_STRIDE
straddle_thresh = config.MODEL.RPN.STRADDLE_THRESH
octave = config.MODEL.RPN.OCTAVE
scales_per_octave = config.MODEL.RPN.SCALES_PER_OCTAVE
if config.MODEL.RPN.USE_FPN:
assert len(anchor_strides) == len(anchor_sizes), "Only support FPN now"
new_anchor_sizes = []
for size in anchor_sizes:
per_layer_anchor_sizes = []
for scale_per_octave in range(scales_per_octave):
octave_scale = octave ** (scale_per_octave / float(scales_per_octave))
per_layer_anchor_sizes.append(octave_scale * size)
new_anchor_sizes.append(tuple(per_layer_anchor_sizes))
else:
assert len(anchor_strides) == 1, "Non-FPN should have a single ANCHOR_STRIDE"
new_anchor_sizes = anchor_sizes
anchor_generator = AnchorGenerator(tuple(new_anchor_sizes), aspect_ratios, anchor_strides, straddle_thresh)
return anchor_generator
class CenterAnchorGenerator(nn.Module):
"""
For a set of image sizes and feature maps, computes a set
of anchors
"""
def __init__(
self,
sizes=(128, 256, 512),
aspect_ratios=(0.5, 1.0, 2.0),
anchor_strides=(8, 16, 32),
straddle_thresh=0,
anchor_shift=(0.0, 0.0, 0.0, 0.0),
use_relative=False,
):
super(CenterAnchorGenerator, self).__init__()
self.sizes = sizes
self.aspect_ratios = aspect_ratios
self.strides = anchor_strides
self.straddle_thresh = straddle_thresh
self.anchor_shift = anchor_shift
self.use_relative = use_relative
def add_visibility_to(self, boxlist):
image_width, image_height = boxlist.size
anchors = boxlist.bbox
if self.straddle_thresh >= 0:
inds_inside = (
(anchors[..., 0] >= -self.straddle_thresh)
& (anchors[..., 1] >= -self.straddle_thresh)
& (anchors[..., 2] < image_width + self.straddle_thresh)
& (anchors[..., 3] < image_height + self.straddle_thresh)
)
else:
device = anchors.device
inds_inside = torch.ones(anchors.shape[0], dtype=torch.uint8, device=device)
boxlist.add_field("visibility", inds_inside)
def forward(self, centers, image_sizes, feature_maps):
shift_left, shift_top, shift_right, shift_down = self.anchor_shift
grid_sizes = [feature_map.shape[-2:] for feature_map in feature_maps]
anchors = []
for i, ((image_height, image_width), center_bbox) in enumerate(zip(image_sizes, centers)):
center = center_bbox.get_field("centers")
boxlist_per_level = []
for size, fsize in zip(self.sizes, grid_sizes):
for ratios in self.aspect_ratios:
size_ratios = size * size / ratios
ws = np.round(np.sqrt(size_ratios))
hs = np.round(ws * ratios)
anchors_per_level = torch.cat(
(
center[:, 0, None] - 0.5 * (1 + shift_left) * (ws - 1),
center[:, 1, None] - 0.5 * (1 + shift_top) * (hs - 1),
center[:, 0, None] + 0.5 * (1 + shift_right) * (ws - 1),
center[:, 1, None] + 0.5 * (1 + shift_down) * (hs - 1),
),
dim=1,
)
boxlist = BoxList(anchors_per_level, (image_width, image_height), mode="xyxy")
boxlist.add_field("cbox", center_bbox)
self.add_visibility_to(boxlist)
boxlist_per_level.append(boxlist)
if self.use_relative:
area = center_bbox.area()
for ratios in self.aspect_ratios:
size_ratios = area / ratios
ws = torch.round(torch.sqrt(size_ratios))
hs = torch.round(ws * ratios)
anchors_per_level = torch.stack(
(
center[:, 0] - (1 + shift_left) * ws,
center[:, 1] - (1 + shift_top) * hs,
center[:, 0] + (1 + shift_right) * ws,
center[:, 1] + (1 + shift_down) * hs,
),
dim=1,
)
boxlist = BoxList(anchors_per_level, (image_width, image_height), mode="xyxy")
boxlist.add_field("cbox", center_bbox)
self.add_visibility_to(boxlist)
boxlist_per_level.append(boxlist)
anchors_in_image = cat_boxlist(boxlist_per_level)
anchors.append(anchors_in_image)
return anchors
def make_center_anchor_generator(config):
anchor_sizes = config.MODEL.RPN.ANCHOR_SIZES
aspect_ratios = config.MODEL.RPN.ASPECT_RATIOS
anchor_strides = config.MODEL.RPN.ANCHOR_STRIDE
straddle_thresh = config.MODEL.RPN.STRADDLE_THRESH
octave = config.MODEL.RPN.OCTAVE
scales_per_octave = config.MODEL.RPN.SCALES_PER_OCTAVE
anchor_shift = config.MODEL.RPN.ANCHOR_SHIFT
use_relative = config.MODEL.RPN.USE_RELATIVE_SIZE
if config.MODEL.RPN.USE_FPN:
assert len(anchor_strides) == len(anchor_sizes), "Only support FPN now"
new_anchor_sizes = []
for size in anchor_sizes:
per_layer_anchor_sizes = []
for scale_per_octave in range(scales_per_octave):
octave_scale = octave ** (scale_per_octave / float(scales_per_octave))
per_layer_anchor_sizes.append(octave_scale * size)
new_anchor_sizes.append(tuple(per_layer_anchor_sizes))
else:
assert len(anchor_strides) == 1, "Non-FPN should have a single ANCHOR_STRIDE"
new_anchor_sizes = anchor_sizes
anchor_generator = CenterAnchorGenerator(
tuple(new_anchor_sizes), aspect_ratios, anchor_strides, straddle_thresh, anchor_shift, use_relative
)
return anchor_generator
# Copyright (c) 2017-present, Facebook, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
##############################################################################
#
# Based on:
# --------------------------------------------------------
# Faster R-CNN
# Copyright (c) 2015 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ross Girshick and Sean Bell
# --------------------------------------------------------
# Verify that we compute the same anchors as Shaoqing's matlab implementation:
#
# >> load output/rpn_cachedir/faster_rcnn_VOC2007_ZF_stage1_rpn/anchors.mat
# >> anchors
#
# anchors =
#
# -83 -39 100 56
# -175 -87 192 104
# -359 -183 376 200
# -55 -55 72 72
# -119 -119 136 136
# -247 -247 264 264
# -35 -79 52 96
# -79 -167 96 184
# -167 -343 184 360
# array([[ -83., -39., 100., 56.],
# [-175., -87., 192., 104.],
# [-359., -183., 376., 200.],
# [ -55., -55., 72., 72.],
# [-119., -119., 136., 136.],
# [-247., -247., 264., 264.],
# [ -35., -79., 52., 96.],
# [ -79., -167., 96., 184.],
# [-167., -343., 184., 360.]])
def generate_anchors(stride=16, sizes=(32, 64, 128, 256, 512), aspect_ratios=(0.5, 1, 2)):
"""Generates a matrix of anchor boxes in (x1, y1, x2, y2) format. Anchors
are centered on stride / 2, have (approximate) sqrt areas of the specified
sizes, and aspect ratios as given.
"""
return _generate_anchors(
stride,
np.array(sizes, dtype=np.float) / stride,
np.array(aspect_ratios, dtype=np.float),
)
def _generate_anchors(base_size, scales, aspect_ratios):
"""Generate anchor (reference) windows by enumerating aspect ratios X
scales wrt a reference (0, 0, base_size - 1, base_size - 1) window.
"""
anchor = np.array([1, 1, base_size, base_size], dtype=np.float) - 1
anchors = _ratio_enum(anchor, aspect_ratios)
anchors = np.vstack([_scale_enum(anchors[i, :], scales) for i in range(anchors.shape[0])])
return torch.from_numpy(anchors)
def _whctrs(anchor):
"""Return width, height, x center, and y center for an anchor (window)."""
w = anchor[2] - anchor[0] + 1
h = anchor[3] - anchor[1] + 1
x_ctr = anchor[0] + 0.5 * (w - 1)
y_ctr = anchor[1] + 0.5 * (h - 1)
return w, h, x_ctr, y_ctr
def _mkanchors(ws, hs, x_ctr, y_ctr):
"""Given a vector of widths (ws) and heights (hs) around a center
(x_ctr, y_ctr), output a set of anchors (windows).
"""
ws = ws[:, np.newaxis]
hs = hs[:, np.newaxis]
anchors = np.hstack(
(
x_ctr - 0.5 * (ws - 1),
y_ctr - 0.5 * (hs - 1),
x_ctr + 0.5 * (ws - 1),
y_ctr + 0.5 * (hs - 1),
)
)
return anchors
def _ratio_enum(anchor, ratios):
"""Enumerate a set of anchors for each aspect ratio wrt an anchor."""
w, h, x_ctr, y_ctr = _whctrs(anchor)
size = w * h
size_ratios = size / ratios
ws = np.round(np.sqrt(size_ratios))
hs = np.round(ws * ratios)
anchors = _mkanchors(ws, hs, x_ctr, y_ctr)
return anchors
def _scale_enum(anchor, scales):
"""Enumerate a set of anchors for each scale wrt an anchor."""
w, h, x_ctr, y_ctr = _whctrs(anchor)
ws = w * scales
hs = h * scales
anchors = _mkanchors(ws, hs, x_ctr, y_ctr)
return anchors
|