def bbox_transform_inv(self, boxes, deltas):
"""All boxes and deltas is a Tensor name on IPU.
args:
boxes: [1, 12960, 4]
deltas: [1, 12960, 4]
"""
#
with gcop.variable_scope("bbox_transform_inv"):
widths = boxes[:, :, 2] - boxes[:, :, 0] + 1.0
heights = boxes[:, :, 3] - boxes[:, :, 1] + 1.0
ctr_x = boxes[:, :, 0] + 0.5 * widths
ctr_y = boxes[:, :, 1] + 0.5 * heights
dx = deltas[:, :, 0]
dy = deltas[:, :, 1]
dw = deltas[:, :, 2]
dh = deltas[:, :, 3]
pred_ctr_x = dx * widths + ctr_x
pred_ctr_y = dy * heights + ctr_y
pred_w = gcop.exp(dw) * widths
pred_h = gcop.exp(dh) * heights
x1 = gcop.expand_dims(pred_ctr_x - 0.5 * pred_w, -1)
y1 = gcop.expand_dims(pred_ctr_y - 0.5 * pred_h, -1)
x2 = gcop.expand_dims(0.5 * pred_w + pred_ctr_x, -1)
y2 = gcop.expand_dims(0.5 * pred_h + pred_ctr_y, -1)
pred_boxes = gcop.concat([x1, y1, x2, y2], 2)
return pred_boxes
def __forward__(self, x, training):
"""Algorithm:
1:for each (H, W) location i
generate A anchor boxes centered on cell i.
2:apply predicted bbox deltas at cell i to each of the A anchors
clip predicted boxes to image.
3:remove predicted boxes with either height or width < threshold
sort all (proposal, score) pairs by score from highest to lowest.
4:take top pre_nms_topN proposals before NMS.
5:apply NMS with threshold 0.7 to remaining proposals
take after_nms_topN proposals after NMS.
return the top proposals (-> RoIs top, scores top)
args:
x[0]: rpn_cls_prob [1, 18, 30, 48]
x[1]: rpn_bbox_pred [1, 36, 30, 48]
"""
# the first set of _num_anchors channels are bg probs
# the second set are the fg probs
with gcop.variable_scope("Proposal"):
x[0] = x[0].detach()
x[1] = x[1].detach()
scores = x[0][:, self.num_anchors:, :, :]
bbox_deltas = x[1]
im_info = x[2]
bbox_deltas = gcop.reshape(bbox_deltas, [self.batch_size, -1, 4])
scores = gcop.transpose(scores, perm=[0, 2, 3, 1])
B, C, H, W = scores.shape.as_list()
scores = gcop.reshape(scores, [B, C * H * W])
proposals = self.bbox_transform_inv(self.anchors, bbox_deltas)
clipped_proposals = self.clip_boxes(proposals, im_info)
valid_area_boxes = get_valid_area_mask(clipped_proposals)
self.add_output('clipped_valid_area_boxes',
gcop.reduce_sum(valid_area_boxes))
if cfg.TRAIN.RPN_PRE_NMS_TOP_N > 0:
rpn_pre_nms_top_n = min(cfg.TRAIN.RPN_PRE_NMS_TOP_N,
scores.squeeze(0).pureShape[0])
sorted_scores, order = gcop.nn.top_k(scores.squeeze(0),
k=rpn_pre_nms_top_n)
sorted_clipped_proposals = gcop.gather(clipped_proposals,
order,
axis=1)
else:
sorted_scores = scores.squeeze(0)
sorted_clipped_proposals = clipped_proposals
output_boxes, output_keeps, _ = nms(
sorted_scores.unsqueeze(0),
sorted_clipped_proposals,
numDetections=cfg.TRAIN.RPN_POST_NMS_TOP_N
if training else cfg.TEST.RPN_POST_NMS_TOP_N)
valid_area_output_boxes = get_valid_area_mask(output_boxes)
self.add_output('valid_area_output_boxes',
gcop.reduce_sum(valid_area_output_boxes))
return output_boxes, output_keeps
def subsample(self, indicator, batch_size, positive_flags, negative_flags,
boxes_keep_arr):
"""Returns subsampled minibatch.
Args:
indicator: boolean tensor of shape [N] whose True entries can be sampled.
boxes_keep_arr: some box's area is zero, this boolen array: False for zero area box, True for non-zero area box
batch_size: desired batch size. If None, keeps all positive samples and
randomly selects negative samples so that the positive sample fraction
matches self._positive_fraction. It cannot be None is is_static is True.
positive_flags: boolean tensor of shape [N] denoting positive(=True) and negative and unsampled (=False) examples.
negative_flags: boolean tensor of shape [N] denoting negative(=True) and positive and unsampled (=False) examples.
scope: name scope.
Returns:
sampled_idx_indicator: boolean tensor of shape [N], True for entries which
are sampled.
Raises:
ValueError: if labels and indicator are not 1D boolean tensors.
"""
if len(indicator.shape) != 1:
raise ValueError(
'indicator must be 1 dimensional, got a tensor of '
'shape %s' % indicator.shape)
if len(positive_flags.shape) != 1:
raise ValueError(
'positive_flags must be 1 dimensional, got a tensor of '
'shape %s' % positive_flags.shape)
if len(negative_flags.shape) != 1:
raise ValueError(
'negative_flags must be 1 dimensional, got a tensor of '
'shape %s' % negative_flags.shape)
if positive_flags.dtype != gcop.bool:
raise ValueError(
'positive_flags should be of type bool. Received: %s' %
positive_flags.dtype)
if negative_flags.dtype != gcop.bool:
raise ValueError(
'negative_flags should be of type bool. Received: %s' %
negative_flags.dtype)
if indicator.dtype != gcop.bool:
raise ValueError('indicator should be of type bool. Received: %s' %
indicator.dtype)
with gcop.variable_scope('BalancedPositiveNegativeSampler'):
if self._is_static:
return self._static_subsample(indicator, batch_size,
positive_flags, negative_flags,
boxes_keep_arr)
else:
raise RuntimeError('only static sampler can be use')
def _add_class_assignments(self, iou, gt_boxes, gt_labels):
"""Computes object category assignment for each box.
Args:
iou: a tensor for the iou matrix with a shape of
[batch_size, K, MAX_NUM_INSTANCES]. K is the number of post-nms RoIs
(i.e., rpn_post_nms_topn).
gt_boxes: a tensor with a shape of [batch_size, MAX_NUM_INSTANCES, 4].
This tensor might have paddings with negative values. The coordinates
of gt_boxes are in the pixel coordinates of the scaled image scale.
gt_labels: a tensor with a shape of [batch_size, MAX_NUM_INSTANCES]. This
tensor might have paddings with a value of -1.
Returns:
max_boxes: a tensor with a shape of [batch_size, K, 4], representing
the ground truth coordinates of each roi.
max_classes: a int32 tensor with a shape of [batch_size, K], representing
the ground truth class of each roi.
max_overlap: a tensor with a shape of [batch_size, K], representing
the maximum overlap of each roi.
argmax_iou: a tensor with a shape of [batch_size, K], representing the iou
argmax.
"""
with gcop.variable_scope('add_class_assignments'):
batch_size, _, _ = iou.shape.as_list()
argmax_iou = gcop.argmax(iou, axis=2)
local_interval = gcop.constant(
np.array(gt_labels.shape.as_list()[1]))
indices = gcop.reshape(
argmax_iou.cast(gcop.int32) + gcop.expand_dims(
gcop.range(batch_size, dtype=gcop.int32) *
local_interval.cast(gcop.int32), 1), [-1]).cast(gcop.int32)
max_classes = gcop.reshape(
gcop.gather(gcop.reshape(gt_labels, [-1, 1]), indices, axis=0),
[batch_size, -1])
max_overlap = gcop.reduce_max(iou, axis=2)
bg_mask = gcop.math.equal(max_overlap,
gcop.zeros_like(max_overlap))
max_classes = gcop.where(bg_mask, gcop.zeros_like(max_classes),
max_classes)
max_boxes = gcop.reshape(
gcop.gather(gcop.reshape(gt_boxes, [-1, 4]), indices, axis=0),
[batch_size, -1, 4])
max_boxes = gcop.where(
gcop.tile(gcop.expand_dims(bg_mask, axis=2), [1, 1, 4]),
gcop.zeros_like(max_boxes), max_boxes)
return max_boxes, max_classes, max_overlap, argmax_iou
def smooth_l1_loss(bbox_pred,
bbox_targets,
bbox_inside_weights,
bbox_outside_weights,
sigma=1.0,
reduceDim=None,
debugPrefix=''):
"""SmoothL1(x) = 0.5 * (sigma * x)^2, if |x| < 1 / sigma^2
|x| - 0.5 / sigma^2, otherwise
"""
sigma2 = sigma * sigma # 1.0
#
if bbox_inside_weights is None:
inside_mul = bbox_pred - bbox_targets
else:
inside_sub = bbox_pred - bbox_targets
inside_mul = bbox_inside_weights * inside_sub
dst_type = inside_mul.dtype
smooth_l1_sign = gcop.less(
gcop.abs(inside_mul), gcop.constant(np.asarray(1.0 / sigma2),
dst_type))
smooth_l1_sign = smooth_l1_sign.cast(inside_mul.dtype).detach()
smooth_l1_option1 = inside_mul * inside_mul * gcop.constant(
np.asarray(0.5 * sigma2), dst_type)
smooth_l1_option2 = gcop.abs(inside_mul) - gcop.constant(
np.asarray(0.5 / sigma2), dst_type)
smooth_l1_result = smooth_l1_option1 * smooth_l1_sign + smooth_l1_option2 * (
gcop.abs(smooth_l1_sign - gcop.constant(np.asarray(1.0), dst_type)))
if bbox_outside_weights is None:
outside_mul = smooth_l1_result
else:
outside_mul = bbox_outside_weights * smooth_l1_result
with gcop.variable_scope(debugPrefix):
outside_mul = gcop.reduce_sum(outside_mul, reduceDim, keepdims=0)
rest_dims = list(range(len(outside_mul.shape.as_list())))
if len(rest_dims) > 0:
outside_mul = gcop.reduce_mean(outside_mul, rest_dims, keepdims=0)
return outside_mul
def clip_boxes(self, boxes, im_info):
with gcop.variable_scope("clip_boxes"):
x1 = gcop.clip_by_value(boxes[:, :, 0],
clip_value_min=0,
clip_value_max=im_info[1] -
1).unsqueeze(-1)
y1 = gcop.clip_by_value(boxes[:, :, 1],
clip_value_min=0,
clip_value_max=im_info[0] -
1).unsqueeze(-1)
x2 = gcop.clip_by_value(boxes[:, :, 2],
clip_value_min=0,
clip_value_max=im_info[1] -
1).unsqueeze(-1)
y2 = gcop.clip_by_value(boxes[:, :, 3],
clip_value_min=0,
clip_value_max=im_info[0] -
1).unsqueeze(-1)
boxes = gcop.concat([x1, y1, x2, y2], 2)
return boxes
def matmul_gather_on_zeroth_axis(self, params, indices, scope=None):
"""Matrix multiplication based implementation of self.gather on zeroth axis.
TODO(rathodv, jonathanhuang): enable sparse matmul option.
Args:
params: A float32 Tensor. The tensor from which to gather values.
Must be at least rank 1.
indices: A Tensor. Must be one of the following types: int32, int64.
Must be in range [0, params.shape[0])
scope: A name for the operation (optional).
Returns:
A Tensor. Has the same type as params. Values from params gathered
from indices given by indices, with shape indices.shape + params.shape[1:].
"""
with gcop.variable_scope('MatMulGather'):
params_shape = self.combined_static_and_dynamic_shape(params)
indices_shape = self.combined_static_and_dynamic_shape(indices)
params2d = gcop.reshape(params, [params_shape[0], -1])
indicator_matrix = gcop.one_hot(indices.cast(gcop.int32),
params_shape[0])
gathered_result_flattened = gcop.matmul(
indicator_matrix.cast(gcop.float32),
params2d.cast(gcop.float32))
return gcop.reshape(gathered_result_flattened,
indices_shape + params_shape[1:])
def forward(self, x, im_info=None, rpn_data=None, stage_configs='0'):
if cfg.MODEL.RPN_CONV_FP16_ON:
x = x.cast(gcop.float16)
else:
x = x.cast(gcop.float32)
with gcop.variable_scope("rpn"):
x = gcop.cF.conv2d(x,
self.rpn_channel,
ksize=3,
train=True,
strides=[1, 1],
padding_mode='same',
fp16_on=None,
weights_fp16_on=cfg.MODEL.RPN_CONV_FP16_ON,
filters_data=self.normal_init(
[self.rpn_channel, x.pureShape[1], 3, 3],
0,
0.01,
dtype=self.dtype),
debugContext='conv')
x = gcop.nn.relu(x)
with gcop.variable_scope("rpn_cls"):
rpn_cls_score = gcop.cF.conv2d(
x,
self.nc_score_out,
ksize=1,
train=True,
strides=[1, 1],
padding_mode='same',
fp16_on=None,
filters_data=self.normal_init(
[self.nc_score_out, x.pureShape[1], 1, 1],
0,
0.01,
dtype=self.dtype))
B, C, H, W = rpn_cls_score.shape.as_list()
target_shape = [B, 2, -1]
rpn_cls_score_reshape = gcop.reshape(rpn_cls_score,
target_shape)
rpn_cls_prob_premute = gcop.transpose(rpn_cls_score_reshape,
perm=[0, 2, 1])
rpn_cls_prob_premute_reshape = gcop.reshape(
rpn_cls_prob_premute, [-1, 2])
rpn_cls_prob_premute_reshape = rpn_cls_prob_premute_reshape.cast(
gcop.float32)
logits = gcop.nn.softmax(rpn_cls_prob_premute_reshape)
# get rpn offsets to the anchor boxes
with gcop.variable_scope("rpn_box"):
rpn_bbox_pred = gcop.cF.conv2d(
x,
self.nc_score_out * 2,
ksize=1,
train=True,
strides=[1, 1],
padding_mode='same',
fp16_on=None,
filters_data=self.normal_init(
[self.nc_score_out * 2, x.pureShape[1], 1, 1],
0,
0.01,
dtype=self.dtype))
rpn_bbox_pred = gcop.transpose(rpn_bbox_pred, [0, 2, 3, 1])
if cfg.MODEL.RPN_CONV_FP16_ON:
rpn_bbox_pred = rpn_bbox_pred.cast(gcop.float32)
logits = logits.cast(gcop.float32)
rpn_cls_prob_premute_reshape = rpn_cls_prob_premute_reshape.cast(
gcop.float32)
with gcop.device(stage_configs):
if self.training:
_rpn_label, rpn_keep, rpn_bbox_targets, rpn_bbox_inside_weights, rpn_bbox_outside_weights = rpn_data
rpn_keep = rpn_keep.squeeze(0)
rpn_scores = gcop.gather(
rpn_cls_prob_premute_reshape,
rpn_keep.cast(gcop.int32),
)
rpn_label = gcop.gather(
gcop.reshape(_rpn_label, [-1]),
rpn_keep.cast(gcop.int32),
).cast(gcop.int32)
rpn_scores, rpn_bbox_pred = [
ele.cast(gcop.float32)
for ele in [rpn_scores, rpn_bbox_pred]
]
self.rpn_loss_cls = gcop.nn.sparse_softmax_cross_entropy_with_logits(
labels=rpn_label, logits=rpn_scores, name="rpn_loss_cls")
self.rpn_loss_box = smooth_l1_loss(rpn_bbox_pred,
rpn_bbox_targets,
rpn_bbox_inside_weights,
rpn_bbox_outside_weights,
sigma=3,
reduceDim=[0, 1, 2, 3],
debugPrefix='rpn_loss_box')
else:
self.rpn_loss_cls, self.rpn_loss_box = 0, 0
logits_transpose = gcop.transpose(logits, perm=[1, 0])
rpn_cls_prob = gcop.reshape(logits_transpose, [B, C, H, W])
fixed_length_roi, roi_keeps = self.proposal(
[rpn_cls_prob, rpn_bbox_pred, im_info], self.training)
return fixed_length_roi, roi_keeps, self.rpn_loss_cls, self.rpn_loss_box
def rois_sampler(self,
boxes,
gt_boxes,
gt_labels,
batch_size_per_im=512,
fg_fraction=0.25,
fg_thresh=0.5,
bg_thresh_hi=0.5,
bg_thresh_lo=0.0):
"""Assigns the proposals with ground truth labels and performs subsmpling.
Given proposal `boxes`, `gt_boxes`, and `gt_labels`, the function uses the
following algorithm to generate the final `batch_size_per_im` RoIs.
1. Calculates the IoU between each proposal box and each gt_boxes.
2. Assigns each proposal box with a ground truth class and box label by
choosing the largest overlap.
3. Samples `batch_size_per_im` boxes from all proposal boxes, and returns
box_targets, class_targets, and RoIs.
The reference implementations of #1 and #2 are here: https://github.com/facebookresearch/Detectron/blob/master/detectron/datasets/json_dataset.py # pylint: disable=line-too-long
The reference implementation of #3 is here: https://github.com/facebookresearch/Detectron/blob/master/detectron/roi_data/fast_rcnn.py. # pylint: disable=line-too-long
Args:
boxes: a tensor with a shape of [batch_size, N, 4]. N is the number of
proposals before groundtruth assignment (e.g., rpn_post_nms_topn). The
last dimension is the pixel coordinates of scaled images in
[ymin, xmin, ymax, xmax] form.
gt_boxes: a tensor with a shape of [batch_size, MAX_NUM_INSTANCES, 4]. This
tensor might have paddings with a value of -1. The coordinates of gt_boxes
are in the pixel coordinates of the scaled image.
gt_labels: a tensor with a shape of [batch_size, MAX_NUM_INSTANCES]. This
tensor might have paddings with a value of -1.
batch_size_per_im: an integer represents RoI minibatch size per image.
fg_fraction: a float represents the target fraction of RoI minibatch that
is labeled foreground (i.e., class > 0).
fg_thresh: a float represents the overlap threshold for an RoI to be
considered foreground (if >= fg_thresh).
bg_thresh_hi: a float represents the overlap threshold for an RoI to be
considered background (class = 0 if overlap in [LO, HI)).
bg_thresh_lo: a float represents the overlap threshold for an RoI to be
considered background (class = 0 if overlap in [LO, HI)).
Returns:
box_targets: a tensor with a shape of [batch_size, K, 4]. The tensor
contains the ground truth pixel coordinates of the scaled images for each
roi. K is the number of sample RoIs (e.g., batch_size_per_im).
class_targets: an integer tensor with a shape of [batch_size, K]. The tensor
contains the ground truth class for each roi. Note, 0 for background, 1 to N
represent N obj classes.
rois: a tensor with a shape of [batch_size, K, 4], representing the
coordinates of the selected RoI.
proposal_to_label_map: a tensor with a shape of [batch_size, K]. This tensor
keeps the mapping between proposal to labels. proposal_to_label_map[i]
means the index of the ground truth instance for the i-th proposal. For example,
-1 for no obj, 0 for first instance, 1 for second instance.
"""
with gcop.variable_scope('ProposalTargetLayer'):
batch_size = boxes.shape.as_list()[0]
# The reference implementation intentionally includes ground truth boxes in
# the proposals. see https://github.com/facebookresearch/Detectron/blob/master/detectron/datasets/json_dataset.py#L359. # pylint: disable=line-too-long
if cfg.TRAIN.ADD_GT_BOX_IN_SAMPLER:
boxes = gcop.concat([boxes, gt_boxes], axis=1)
else:
pass
boxes_keep_arr = self.get_valid_area_flags(boxes)
gt_boxes_keep_arr = self.get_valid_area_flags(gt_boxes)
iou = bbox_overlaps_torch(boxes[0], gt_boxes[0])
iou = iou.unsqueeze(0)
iou_keep_arr = (boxes_keep_arr.cast(
gcop.float32).unsqueeze(-1)) * (gt_boxes_keep_arr.cast(
gcop.float32).unsqueeze(1))
iou = iou * iou_keep_arr
(pre_sample_box_targets, pre_sample_class_targets, max_overlap,
proposal_to_label_map) = self._add_class_assignments(
iou, gt_boxes, gt_labels)
# Generates a random sample of RoIs comprising foreground and background
# examples. reference: https://github.com/facebookresearch/Detectron/blob/master/detectron/roi_data/fast_rcnn.py#L132 # pylint: disable=line-too-long
positives = gcop.math.greater_equal(
max_overlap.cast(gcop.float32),
(fg_thresh * gcop.ones_like(max_overlap)).cast(gcop.float32))
negatives = gcop.math.logical_and(
gcop.math.greater_equal(
max_overlap, bg_thresh_lo * gcop.ones_like(max_overlap)),
gcop.less(max_overlap,
bg_thresh_hi * gcop.ones_like(max_overlap)))
pre_sample_class_targets = gcop.where(
negatives, gcop.zeros_like(pre_sample_class_targets),
pre_sample_class_targets)
proposal_to_label_map = gcop.where(
negatives,
gcop.ones_like(proposal_to_label_map).cast(gcop.int32) * -1,
proposal_to_label_map.cast(gcop.int32),
) # -1 for no instance in current proposal,
# 0 for first instance(not class, there might be one class but 888 instances) of input targets
# Handles ground truth paddings.
ignore_mask = gcop.less(gcop.reduce_min(iou, axis=2),
gcop.zeros_like(max_overlap))
# indicator includes both positive and negative labels.
# labels includes only positives labels.
# positives = indicator & labels.
# negatives = indicator & !labels.
# ignore = !indicator.
positive_flags = positives
negative_flags = negatives
pos_or_neg = gcop.math.logical_or(positives, negatives)
indicator = gcop.math.logical_and(
pos_or_neg, gcop.math.logical_not(ignore_mask))
all_samples = []
sampler = (balanced_positive_negative_sampler.
BalancedPositiveNegativeSampler(
fp16_on=self.fp16_on,
training=True,
positive_fraction=fg_fraction))
# Batch-unroll the sub-sampling process.
for i in range(batch_size):
samples = sampler.subsample(indicator[i], batch_size_per_im,
positive_flags[i],
negative_flags[i],
boxes_keep_arr[i])
all_samples.append(samples)
all_samples = gcop.stack(all_samples, axis=0)
# A workaround to get the indices from the boolean tensors.
_, samples_indices = gcop.nn.top_k(all_samples.cast(gcop.int32),
k=batch_size_per_im,
sorted=True)
# Contructs indices for gather.
samples_indices = gcop.reshape(
samples_indices.cast(gcop.int32) + gcop.expand_dims(
gcop.range(batch_size, dtype=gcop.int32).cast(gcop.int32) *
boxes.shape.as_list()[1], 1).cast(gcop.int32),
[-1]).cast(gcop.int32)
rois = gcop.reshape(
gcop.gather(gcop.reshape(boxes, [-1, 4]), samples_indices),
[batch_size, -1, 4])
class_targets = gcop.reshape(
gcop.gather(gcop.reshape(pre_sample_class_targets, [-1, 1]),
samples_indices), [batch_size, -1])
sample_box_targets = gcop.reshape(
gcop.gather(gcop.reshape(pre_sample_box_targets, [-1, 4]),
samples_indices), [batch_size, -1, 4])
sample_proposal_to_label_map = gcop.reshape(
gcop.gather(gcop.reshape(proposal_to_label_map, [-1, 1]),
samples_indices), [batch_size, -1])
encoded_boxes_result = self._compute_targets_pytorch(
rois, sample_box_targets)
return sample_box_targets, class_targets, rois, sample_proposal_to_label_map, encoded_boxes_result
