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import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import ops
from torchvision.ops import boxes as box_ops
# fmt:off
batch_size = cfg.MODEL.RPN.BATCH_SIZE_PER_IMAGE
anchor_stride = cfg.MODEL.RPN.ANCHOR_STRIDE
anchor_scales = cfg.MODEL.RPN.ANCHOR_SIZES
anchor_ratios = cfg.MODEL.RPN.ASPECT_RATIOS
num_channels = cfg.MODEL.RPN.NUM_CHANNELS
nms_thresh = cfg.MODEL.RPN.NMS_THRESH
min_size = cfg.MODEL.RPN.MIN_SIZE
num_anchors = len(anchor_scales) * len(anchor_ratios)
# fmt:on
self.pre_nms_top_n = {
True: cfg.MODEL.RPN.PRE_NMS_TOP_N_TRAIN,
False: cfg.MODEL.RPN.PRE_NMS_TOP_N_TEST,
}
self.post_nms_top_n = {
True: cfg.MODEL.RPN.POST_NMS_TOP_N_TRAIN,
False: cfg.MODEL.RPN.POST_NMS_TOP_N_TEST,
}
self.nms_thresh = nms_thresh
self.cls_logits = nn.Conv2d(num_channels, num_anchors, kernel_size=1, stride=1)
self.bbox_pred = nn.Conv2d(num_channels, num_anchors * 4, kernel_size=1, stride=1)
self.anchor_generator = AnchorGenerator(anchor_stride, (anchor_scales,), (anchor_ratios,))
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self.box_coder = BoxCoder(weights=(1.0, 1.0, 1.0, 1.0))
self.matcher = Matcher(high_threshold=0.7, low_threshold=0.3, allow_low_quality_matches=True)
self.sampler = BalancedPositiveNegativeSampler(batch_size, 0.5)
for l in [self.conv, self.cls_logits, self.bbox_pred]:
torch.nn.init.normal_(l.weight, std=0.01)
torch.nn.init.constant_(l.bias, 0)
def forward(self, images, features, img_metas, targets=None):
anchors = self.anchor_generator([features])
t = F.relu(self.conv(features))
logits = self.cls_logits(t)
bbox_reg = self.bbox_pred(t)
with torch.no_grad():
proposals = self.generate_proposals(anchors, logits, bbox_reg, img_metas)
if self.training:
objectness_loss, box_loss = self.losses(anchors, logits, bbox_reg, img_metas, targets)
loss = {
'rpn_cls_loss': objectness_loss,
'rpn_reg_loss': box_loss,
}
else:
loss = {}
return proposals, loss
def generate_proposals(self, anchors, objectness, box_regression, img_metas):
"""
Args:
anchors:
objectness: (N, A, H, W)
box_regression: (N, A * 4, H, W)
img_metas:
Returns:
"""
pre_nms_top_n = self.pre_nms_top_n[self.training]
post_nms_top_n = self.post_nms_top_n[self.training]
nms_thresh = self.nms_thresh
device = objectness.device
N, A, H, W = objectness.shape
objectness = objectness.permute(0, 2, 3, 1).reshape(N, H * W * A)
objectness = objectness.sigmoid()
box_regression = box_regression.permute(0, 2, 3, 1).reshape(N, H * W * A, 4)
concat_anchors = concat_anchors.reshape(N, A * H * W, 4)
num_anchors = A * H * W
pre_nms_top_n = min(pre_nms_top_n, num_anchors)
objectness, topk_idx = objectness.topk(pre_nms_top_n, dim=1, sorted=True)
batch_idx = torch.arange(N, device=device)[:, None]
box_regression = box_regression[batch_idx, topk_idx]
concat_anchors = concat_anchors[batch_idx, topk_idx]
proposals = self.box_coder.decode(
box_regression.view(-1, 4), concat_anchors.view(-1, 4)
)
proposals = proposals.view(N, -1, 4)
results = []
for proposal, score, img_meta in zip(proposals, objectness, img_metas):
img_width, img_height = img_meta['img_shape']
proposal = box_ops.clip_boxes_to_image(proposal, (img_height, img_width))
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proposal = proposal[keep]
score = score[keep]
keep = ops.nms(proposal, score, nms_thresh)
keep = keep[:post_nms_top_n]
proposal = proposal[keep]
score = score[keep]
results.append(proposal) # (N, 4)
return results
def losses(self, anchors, objectness, box_regression, img_metas, targets):
labels = []
regression_targets = []
for batch_id in range(len(targets)):
target = targets[batch_id]
anchors_per_image = anchors[batch_id]
img_width, img_height = img_metas[batch_id]['img_shape']
match_quality_matrix = box_ops.box_iou(target['boxes'], anchors_per_image)
matched_idxs = self.matcher(match_quality_matrix)
matched_idxs_for_target = matched_idxs.clamp(0)
target_boxes = target['boxes'][matched_idxs_for_target]
labels_per_image = (matched_idxs >= 0).to(dtype=torch.float32)
# Background (negative examples)
bg_indices = matched_idxs == Matcher.BELOW_LOW_THRESHOLD
labels_per_image[bg_indices] = 0
# discard indices that are between thresholds
inds_to_discard = matched_idxs == Matcher.BETWEEN_THRESHOLDS
labels_per_image[inds_to_discard] = -1
straddle_thresh = 0
visibility = ((anchors_per_image[..., 0] >= -straddle_thresh)
& (anchors_per_image[..., 1] >= -straddle_thresh)
& (anchors_per_image[..., 2] < img_width + straddle_thresh)
& (anchors_per_image[..., 3] < img_height + straddle_thresh))
labels_per_image[~visibility] = -1
regression_targets_per_image = self.box_coder.encode(
target_boxes, anchors_per_image
)
labels.append(labels_per_image)
regression_targets.append(regression_targets_per_image)
sampled_pos_inds, sampled_neg_inds = self.sampler(labels)
sampled_pos_inds = torch.nonzero(cat(sampled_pos_inds, dim=0)).squeeze(1)
sampled_neg_inds = torch.nonzero(cat(sampled_neg_inds, dim=0)).squeeze(1)
sampled_inds = torch.cat([sampled_pos_inds, sampled_neg_inds], dim=0)
objectness = objectness.permute(0, 2, 3, 1).reshape(-1)
box_regression = box_regression.permute(0, 2, 3, 1).reshape(-1, 4)
labels = cat(labels)
regression_targets = cat(regression_targets, dim=0)
box_loss = smooth_l1_loss(
box_regression[sampled_pos_inds],
regression_targets[sampled_pos_inds],
beta=1.0 / 9,
size_average=False,
) / (sampled_inds.numel())
objectness_loss = F.binary_cross_entropy_with_logits(
objectness[sampled_inds], labels[sampled_inds]
)
return objectness_loss, box_loss