def _processBinayMasks(self, ann):
boxes = []
masks = []
labels = []
def mask_to_tight_box(mask):
a = mask.nonzero()
bbox = [
jt.min(a[:, 1]),
jt.min(a[:, 0]),
jt.max(a[:, 1]),
jt.max(a[:, 0]),
]
bbox = list(map(int, bbox))
return bbox # xmin, ymin, xmax, ymax
# Sort for consistent order between instances as the polygon annotation
_, instIds = jt.argsort(jt.unique(ann))
for instId in instIds.numpy():
if instId < 1000: # group labels
continue
mask = ann == instId
label = int(instId / 1000)
label = self.cityscapesID_to_ind[label]
box = mask_to_tight_box(mask)
boxes.append(box)
masks.append(mask)
labels.append(label)
return boxes, masks, labels
def check_cub_argsort(shape, dim, descending = False):
with jt.log_capture_scope(
log_silent=1,
log_v=0, log_vprefix="op.cc=100"
) as raw_log:
x = jt.random(shape)
y, y_key = jt.argsort(x, dim=dim, descending=descending)
v = []
for i in range(len(shape)):
if (i == dim):
v.append(y)
else:
v.append(jt.index(shape, dim=i))
yk = jt.reindex(x, v)
yk_ = yk.data
y_key_ = y_key.data
logs = find_log_with_re(raw_log, "(Jit op key (not )?found: " + "cub_argsort" + ".*)")
assert len(logs)==1
x__ = x.data
if descending:
x__ = -x__
yk__ = np.sort(x__, axis=dim)
if descending:
yk__ = -yk__
assert np.allclose(y_key_, yk__)
assert np.allclose(yk_, yk__)
def select_top_predictions(self, predictions):
"""
Select only predictions which have a `score` > self.confidence_threshold,
and returns the predictions in descending order of score
Arguments:
predictions (BoxList): the result of the computation by the model.
It should contain the field `scores`.
Returns:
prediction (BoxList): the detected objects. Additional information
of the detection properties can be found in the fields of
the BoxList via `prediction.fields()`
"""
if predictions.has_field("mask_scores"):
scores = predictions.get_field("mask_scores")
else:
scores = predictions.get_field("scores")
if scores.shape[0]==0:
return None
keep = jt.nonzero(scores>self.confidence_threshold).squeeze(1)
predictions = predictions[keep]
scores = predictions.get_field("scores")
idx,_ = jt.argsort(scores,0, descending=True)
return predictions[idx]
def fast_nms(self,
boxes,
masks,
scores,
iou_threshold: float = 0.5,
top_k: int = 200,
second_threshold: bool = False):
idx, scores = scores.argsort(1, descending=True)
idx = idx[:, :top_k]
scores = scores[:, :top_k]
num_classes, num_dets = idx.shape
boxes = boxes[idx.view(-1)].view(num_classes, num_dets, 4)
masks = masks[idx.view(-1)].view(num_classes, num_dets, -1)
iou = jaccard(boxes, boxes)
iou = iou.triu_(diagonal=1)
iou_max = iou.max(dim=1)
# Now just filter out the ones higher than the threshold
keep = (iou_max <= iou_threshold)
# We should also only keep detections over the confidence threshold, but at the cost of
# maxing out your detection count for every image, you can just not do that. Because we
# have such a minimal amount of computation per detection (matrix mulitplication only),
# this increase doesn't affect us much (+0.2 mAP for 34 -> 33 fps), so we leave it out.
# However, when you implement this in your method, you should do this second threshold.
if second_threshold:
keep *= (scores > self.conf_thresh)
# Assign each kept detection to its corresponding class
classes = jt.arange(num_classes).unsqueeze(1).expand_as(keep)
classes = classes[keep]
boxes = boxes[keep]
masks = masks[keep]
scores = scores[keep]
#print('keep finish')
# Only keep the top cfg.max_num_detections highest scores across all classes
idx, scores = jt.argsort(scores, dim=0, descending=True)
#print('argsort finish')
idx = idx[:cfg.max_num_detections]
scores = scores[:cfg.max_num_detections]
classes = classes[idx]
boxes = boxes[idx]
masks = masks[idx]
'''
scores = scores[:cfg.max_num_detections]
classes = classes[:cfg.max_num_detections]
boxes = boxes[:cfg.max_num_detections]
masks = masks[:cfg.max_num_detections]
'''
return boxes, masks, classes, scores
def topk(input, k, dim=None, largest=True, sorted=True):
if input.numel()==0:
return jt.array([],dtype=input.dtype),jt.array([],dtype='int32')
if dim is None:
dim = -1
if dim<0:
dim+=input.ndim
index,values = jt.argsort(input,dim=dim,descending=largest)
dims = (slice(None),)*dim+(slice(0,k),)
indices = index[dims]
values = values[dims]
return values,indices
def kthvalue(input, k, dim=None, keepdim=False):
if dim is None:
dim = -1
if dim<0:
dim+=input.ndim
index,values = jt.argsort(input,dim=dim)
dims = (slice(None),)*dim+(slice(k-1,k),)
indices = index[dims]
values = values[dims]
if not keepdim and indices.ndim>1:
indices = indices.squeeze(dim)
values = values.squeeze(dim)
return values,indices
def unique(x):
r'''
Returns the unique elements of the input tensor.
Args:
x– the input tensor.
'''
x = x.reshape(-1)
_, x = jt.argsort(x)
index, = jt.index((x.shape[0], ))
y = x[1:][x[index[1:]] != x[index[:-1]]]
x = jt.contrib.concat([x[:1], y], dim=0)
return x
def topk(input, k, dim=None, largest=True, sorted=True):
if dim is None:
dim = -1
if dim < 0:
dim += input.ndim
transpose_dims = [i for i in range(input.ndim)]
transpose_dims[0] = dim
transpose_dims[dim] = 0
input = input.transpose(transpose_dims)
index, values = jt.argsort(input, dim=0, descending=largest)
indices = index[:k]
values = values[:k]
indices = indices.transpose(transpose_dims)
values = values.transpose(transpose_dims)
return [values, indices]
def unique(x):
r'''
Returns the unique elements of the input tensor.
Args:
x– the input tensor.
'''
x = x.reshape(-1)
_,x = jt.argsort(x)
index2 = [i for i in range(1,x.shape[0])]
index1 = [i for i in range(x.shape[0]-1)]
y = x[1:][x[index2] != x[index1]]
x = jt.contrib.concat([x[:1],y],dim=0)
return x
def nms(dets,thresh):
'''
dets jt.array [x1,y1,x2,y2,score]
x(:,0)->x1,x(:,1)->y1,x(:,2)->x2,x(:,3)->y2,x(:,4)->score
'''
threshold = str(thresh)
order = jt.argsort(dets[:,4],descending=True)[0]
dets = dets[order]
s_1 = '(@x(j,2)-@x(j,0)+1)*(@x(j,3)-@x(j,1)+1)'
s_2 = '(@x(i,2)-@x(i,0)+1)*(@x(i,3)-@x(i,1)+1)'
s_inter_w = 'max((Tx)0,min(@x(j,2),@x(i,2))-max(@x(j,0),@x(i,0))+1)'
s_inter_h = 'max((Tx)0,min(@x(j,3),@x(i,3))-max(@x(j,1),@x(i,1))+1)'
s_inter = s_inter_h+'*'+s_inter_w
iou = s_inter + '/(' + s_1 +'+' + s_2 + '-' + s_inter + ')'
fail_cond = iou+'>'+threshold
selected = jt.candidate(dets, fail_cond)
return order[selected]
def lovasz_hinge_flat(self, logits, labels):
"""
Binary Lovasz hinge loss
logits: [P] Variable, logits at each prediction (between -\infty and +\infty)
labels: [P] Tensor, binary ground truth labels (0 or 1)
ignore: label to ignore
"""
if len(labels) == 0:
# only void pixels, the gradients should be 0
return logits.sum() * 0.
signs = 2. * labels.float() - 1.
errors = (1. - logits * jt.array(signs))
perm, errors_sorted = jt.argsort(errors, dim=0, descending=True)
perm = perm.data
gt_sorted = labels[perm]
grad = lovasz_grad(gt_sorted)
loss = jt.dot(nn.relu(errors_sorted), jt.array(grad))
return loss
def execute(self, xyz1, xyz2, points1, points2):
"""
Input:
xyz1: input points position data, [B, C, N]
xyz2: sampled input points position data, [B, C, S]
points1: input points data, [B, D, N]
points2: input points data, [B, D, S]
Return:
new_points: upsampled points data, [B, D', N]
"""
# xyz1 = xyz1.permute(0, 2, 1)
# xyz2 = xyz2.permute(0, 2, 1)
# points2 = points2.permute(0, 2, 1)
B, N, C = xyz1.shape
_, S, _ = xyz2.shape
if S == 1:
interpolated_points = points2.repeat(1, N, 1)
else:
dists = square_distance(xyz1, xyz2)
idx, dists = jt.argsort(dists, dim=-1)
dists, idx = dists[:, :, :3], idx[:, :, :3] # [B, N, 3]
dist_recip = 1.0 / (dists + 1e-8)
norm = jt.sum(dist_recip, dim=2, keepdims=True)
weight = dist_recip / norm
interpolated_points = jt.sum(index_points(points2, idx) *
weight.view(B, N, 3, 1),
dim=2)
if points1 is not None:
# points1 = points1.permute(0, 2, 1)
new_points = concat([points1, interpolated_points], dim=-1)
else:
new_points = interpolated_points
new_points = new_points.permute(0, 2, 1)
# l = len(self.mlp_convs)
for i, conv in self.mlp_convs.layers.items():
# conv = self.mlp_convs[i]
bn = self.mlp_bns[i]
new_points = self.relu(bn(conv(new_points)))
return new_points.permute(0, 2, 1)
def check_argsort(shape, dim, descending = False):
x = jt.random(shape)
y, y_key = jt.argsort(x, dim=dim, descending=descending)
v = []
for i in range(len(shape)):
if (i == dim):
v.append(y)
else:
v.append(jt.index(shape, dim=i))
yk = jt.reindex(x, v)
yk_ = yk.data
y_key_ = y_key.data
x__ = x.data
if descending:
x__ = -x__
yk__ = np.sort(x__, axis=dim)
if descending:
yk__ = -yk__
assert np.allclose(y_key_, yk__)
assert np.allclose(yk_, yk__)
def randperm(n, dtype="int32"):
key = jt.random((n, ))
index, _ = jt.argsort(key)
return index.cast(dtype)
def run_model(config_file, img_f=None):
original_image = load(img_f)
from detectron.config import cfg
from detectron.modeling.detector import build_detection_model
from detectron.utils.checkpoint import DetectronCheckpointer
from detectron.structures.image_list import to_image_list
from detectron.modeling.roi_heads.mask_head.inference import Masker
from jittor import transform as T
from jittor import nn
import jittor as jt
from jittor_utils import auto_diff
jt.flags.use_cuda = 1
confidence_threshold = 0.0
cfg.merge_from_file(config_file)
model = build_detection_model(cfg)
checkpointer = DetectronCheckpointer(cfg, model, save_dir=cfg.OUTPUT_DIR)
_ = checkpointer.load(cfg.MODEL.WEIGHT)
name = config_file.split('/')[-1].split('.')[0]
# hook = auto_diff.Hook(name)
# hook.hook_module(model)
model.eval()
class Resize(object):
def __init__(self, min_size, max_size):
self.min_size = min_size
self.max_size = max_size
# modified from torchvision to add support for max size
def get_size(self, image_size):
w, h = image_size
size = self.min_size
max_size = self.max_size
if max_size is not None:
min_original_size = float(min((w, h)))
max_original_size = float(max((w, h)))
if max_original_size / min_original_size * size > max_size:
size = int(
round(max_size * min_original_size /
max_original_size))
if (w <= h and w == size) or (h <= w and h == size):
return (h, w)
if w < h:
ow = size
oh = int(size * h / w)
else:
oh = size
ow = int(size * w / h)
return (oh, ow)
def __call__(self, image):
size = self.get_size(image.size)
image = T.resize(image, size)
return image
def build_transform():
if cfg.INPUT.TO_BGR255:
to_bgr_transform = T.Lambda(lambda x: x * 255)
else:
to_bgr_transform = T.Lambda(lambda x: x[[2, 1, 0]])
normalize_transform = T.ImageNormalize(mean=cfg.INPUT.PIXEL_MEAN,
std=cfg.INPUT.PIXEL_STD)
min_size = cfg.INPUT.MIN_SIZE_TEST
max_size = cfg.INPUT.MAX_SIZE_TEST
transform = T.Compose([
T.ToPILImage(),
Resize(min_size, max_size),
T.ToTensor(),
to_bgr_transform,
normalize_transform,
])
return transform
transforms = build_transform()
image = transforms(original_image)
image_list = to_image_list(image, cfg.DATALOADER.SIZE_DIVISIBILITY)
predictions = model(image_list)
predictions = predictions[0]
if predictions.has_field("mask_scores"):
scores = predictions.get_field("mask_scores")
else:
scores = predictions.get_field("scores")
keep = jt.nonzero(scores > confidence_threshold).squeeze(1)
predictions = predictions[keep]
scores = predictions.get_field("scores")
idx, _ = jt.argsort(scores, 0, descending=True)
predictions = predictions[idx]
result_diff(predictions)
def check_backward(shape, dim, descending = False):
x = jt.random(shape)
y, y_key = jt.argsort(x, dim=dim, descending=descending)
loss = (y_key * y_key).sum()
gs = jt.grad(loss, x)
assert np.allclose(x.data*2, gs.data)
def execute(self, loc, score, anchor, img_size, scale=1.):
"""input should be ndarray
Propose RoIs.
Inputs :obj:`loc, score, anchor` refer to the same anchor when indexed
by the same index.
On notations, :math:`R` is the total number of anchors. This is equal
to product of the height and the width of an image and the number of
anchor bases per pixel.
Type of the output is same as the inputs.
Args:
loc (array): Predicted offsets and scaling to anchors.
Its shape is :math:`(R, 4)`.
score (array): Predicted foreground probability for anchors.
Its shape is :math:`(R,)`.
anchor (array): Coordinates of anchors. Its shape is
:math:`(R, 4)`.
img_size (tuple of ints): A tuple :obj:`height, width`,
which contains image size after scaling.
scale (float): The scaling factor used to scale an image after
reading it from a file.
Returns:
array:
An array of coordinates of proposal boxes.
Its shape is :math:`(S, 4)`. :math:`S` is less than
:obj:`self.n_test_post_nms` in test time and less than
:obj:`self.n_train_post_nms` in train time. :math:`S` depends on
the size of the predicted bounding boxes and the number of
bounding boxes discarded by NMS.
"""
# NOTE: when test, remember
if self.is_training():
n_pre_nms = self.n_train_pre_nms
n_post_nms = self.n_train_post_nms
else:
n_pre_nms = self.n_test_pre_nms
n_post_nms = self.n_test_post_nms
# Convert anchors into proposal via bbox transformations.
roi = loc2bbox(anchor, loc)
# Clip predicted boxes to image.
roi[:, 0] = jt.clamp(roi[:, 0], min_v=0, max_v=img_size[0])
roi[:, 2] = jt.clamp(roi[:, 2], min_v=0, max_v=img_size[0])
roi[:, 1] = jt.clamp(roi[:, 1], min_v=0, max_v=img_size[1])
roi[:, 3] = jt.clamp(roi[:, 3], min_v=0, max_v=img_size[1])
# Remove predicted boxes with either height or width < threshold.
min_size = self.min_size * scale
hs = roi[:, 2] - roi[:, 0]
ws = roi[:, 3] - roi[:, 1]
keep = jt.where((hs >= min_size) & (ws >= min_size))[0]
roi = roi[keep, :]
score = score[keep]
# Sort all (proposal, score) pairs by score from highest to lowest.
# Take top pre_nms_topN (e.g. 6000).
order, _ = jt.argsort(score, descending=True)
if n_pre_nms > 0:
order = order[:n_pre_nms]
roi = roi[order, :]
score = score[order]
# Apply nms (e.g. threshold = 0.7).
# Take after_nms_topN (e.g. 300).
dets = jt.contrib.concat([roi, score.unsqueeze(1)], dim=1)
keep = jt.nms(dets, self.nms_thresh)
if n_post_nms > 0:
keep = keep[:n_post_nms]
roi = roi[keep]
return roi
def execute(self, xyz1, xyz2, points1, points2):
"""
Input:
xyz1: input points position data, [B, C, N]
xyz2: sampled input points position data, [B, C, S]
points1: input points data, [B, D, N]
points2: input points data, [B, D, S]
Return:
new_points: upsampled points data, [B, D', N]
"""
# print ('xyz1.shape, xyz2.shape')
# print (xyz1.shape, xyz2.shape, points1.shape, points2.shape)
xyz1 = xyz1.permute(0, 2, 1)
xyz2 = xyz2.permute(0, 2, 1)
points1 = points1.permute(0, 2, 1)
points2 = points2.permute(0, 2, 1)
B, N, C = xyz1.shape
_, S, _ = xyz2.shape
# points2 = points2.permute(0, 2, 1)
# print (xyz1.shape, xyz2.shape)
dists = square_distance(xyz1, xyz2)
idx, dists = jt.argsort(dists, dim=-1)
dists, idx = dists[:, :, :3], idx[:, :, :3] # [B, N, 3]
dist_recip = 1.0 / (dists + 1e-8)
norm = jt.sum(dist_recip, dim=2, keepdims=True)
weight = dist_recip / norm
interpolated_points = jt.sum(index_points(points2, idx) *
weight.view(B, N, 3, 1),
dim=2)
# print ('interpolated_points shape', interpolated_points.shape)
xyz_density = compute_density(xyz1, self.bandwidth)
density_scale = self.densitynet(xyz_density)
new_xyz, new_points, grouped_xyz_norm, _, grouped_density = sample_and_group(
N, self.nsample, xyz1, interpolated_points,
density_scale.reshape(B, N, 1))
new_points = new_points.permute(0, 3, 2, 1) # [B, C+D, nsample,npoint]
for i in range(len(self.mlp_convs)):
conv = self.mlp_convs[i]
bn = self.mlp_bns[i]
# print ('new new new point shape', new_points.shape)
new_points = self.relu(bn(conv(new_points)))
grouped_xyz = grouped_xyz_norm.permute(0, 3, 2, 1)
weights = self.weightnet(grouped_xyz)
new_points = new_points * grouped_density.permute(0, 3, 2, 1)
new_points = jt.matmul(new_points.permute(0, 3, 1, 2),
weights.permute(0, 3, 2, 1)).reshape(B, N, -1)
new_points = self.linear(new_points)
new_points = self.bn_linear(new_points.permute(0, 2, 1))
new_points = self.relu(new_points)
new_xyz = new_xyz.permute(0, 2, 1)
return new_points
def render_rays(ray_batch,
network_fn,
network_query_fn,
N_samples,
retraw=False,
lindisp=False,
perturb=0.,
N_importance=0,
network_fine=None,
white_bkgd=False,
raw_noise_std=0.,
verbose=False):
"""Volumetric rendering.
Args:
ray_batch: array of shape [batch_size, ...]. All information necessary
for sampling along a ray, including: ray origin, ray direction, min
dist, max dist, and unit-magnitude viewing direction.
network_fn: function. Model for predicting RGB and density at each point
in space.
network_query_fn: function used for passing queries to network_fn.
N_samples: int. Number of different times to sample along each ray.
retraw: bool. If True, include model's raw, unprocessed predictions.
lindisp: bool. If True, sample linearly in inverse depth rather than in depth.
perturb: float, 0 or 1. If non-zero, each ray is sampled at stratified
random points in time.
N_importance: int. Number of additional times to sample along each ray.
These samples are only passed to network_fine.
network_fine: "fine" network with same spec as network_fn.
white_bkgd: bool. If True, assume a white background.
raw_noise_std: ...
verbose: bool. If True, print more debugging info.
Returns:
rgb_map: [num_rays, 3]. Estimated RGB color of a ray. Comes from fine model.
disp_map: [num_rays]. Disparity map. 1 / depth.
acc_map: [num_rays]. Accumulated opacity along each ray. Comes from fine model.
raw: [num_rays, num_samples, 4]. Raw predictions from model.
rgb0: See rgb_map. Output for coarse model.
disp0: See disp_map. Output for coarse model.
acc0: See acc_map. Output for coarse model.
z_std: [num_rays]. Standard deviation of distances along ray for each
sample.
"""
N_rays = ray_batch.shape[0]
rays_o, rays_d = ray_batch[:, 0:3], ray_batch[:, 3:6] # [N_rays, 3] each
viewdirs = ray_batch[:, -3:] if ray_batch.shape[-1] > 8 else None
bounds = jt.reshape(ray_batch[..., 6:8], [-1, 1, 2])
near, far = bounds[..., 0], bounds[..., 1] # [-1,1]
z_vals = sample(N_rays, N_samples, lindisp, perturb, near, far)
pts = rays_o.unsqueeze(-2) + rays_d.unsqueeze(-2) * z_vals.unsqueeze(
-1) # [N_rays, N_samples, 3]
raw = network_query_fn(pts, viewdirs, network_fn)
rgb_map, disp_map, acc_map, weights, depth_map = integrator(
raw, z_vals, rays_d, raw_noise_std, white_bkgd)
rgb_map_0, disp_map_0, acc_map_0 = rgb_map, disp_map, acc_map
#usefulRayIndex = jt.nonzero(acc_map > 0.1)
if N_importance > 0:
# importance sampling
z_vals_mid = .5 * (z_vals[..., 1:] + z_vals[..., :-1])
z_samples = sample_pdf(z_vals_mid,
weights[..., 1:-1],
N_importance,
det=(perturb == 0.))
z_samples = z_samples.detach()
_, z_vals = jt.argsort(jt.concat([z_vals, z_samples], -1), -1)
pts = rays_o.unsqueeze(-2) + rays_d.unsqueeze(-2) * z_vals.unsqueeze(
-1) # [N_rays, N_samples + N_importance, 3]
run_fn = network_fn if network_fine is None else network_fine
raw = network_query_fn(pts, viewdirs, run_fn)
rgb_map, disp_map, acc_map, weights, depth_map = integrator(
raw, z_vals, rays_d, raw_noise_std, white_bkgd)
ret = {'rgb_map': rgb_map, 'disp_map': disp_map, 'acc_map': acc_map}
if retraw:
ret['raw'] = raw
if N_importance > 0:
ret['rgb0'] = rgb_map_0
ret['disp0'] = disp_map_0
ret['acc0'] = acc_map_0
return ret
