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import math
import torch
import torch.nn.functional as F
from torch import nn
from .inference import make_atss_postprocessor
from .loss import make_atss_loss_evaluator
from maskrcnn_benchmark.structures.boxlist_ops import cat_boxlist
from maskrcnn_benchmark.layers import Scale, DFConv2d, DYReLU, SELayer
from .anchor_generator import make_anchor_generator_complex
class BoxCoder(object):
def __init__(self, cfg):
self.cfg = cfg
def encode(self, gt_boxes, anchors):
TO_REMOVE = 1 # TODO remove
ex_widths = anchors[:, 2] - anchors[:, 0] + TO_REMOVE
ex_heights = anchors[:, 3] - anchors[:, 1] + TO_REMOVE
ex_ctr_x = (anchors[:, 2] + anchors[:, 0]) / 2
ex_ctr_y = (anchors[:, 3] + anchors[:, 1]) / 2
gt_widths = gt_boxes[:, 2] - gt_boxes[:, 0] + TO_REMOVE
gt_heights = gt_boxes[:, 3] - gt_boxes[:, 1] + TO_REMOVE
gt_ctr_x = (gt_boxes[:, 2] + gt_boxes[:, 0]) / 2
gt_ctr_y = (gt_boxes[:, 3] + gt_boxes[:, 1]) / 2
wx, wy, ww, wh = (10.0, 10.0, 5.0, 5.0)
targets_dx = wx * (gt_ctr_x - ex_ctr_x) / ex_widths
targets_dy = wy * (gt_ctr_y - ex_ctr_y) / ex_heights
targets_dw = ww * torch.log(gt_widths / ex_widths)
targets_dh = wh * torch.log(gt_heights / ex_heights)
targets = torch.stack((targets_dx, targets_dy, targets_dw, targets_dh), dim=1)
return targets
def decode(self, preds, anchors):
anchors = anchors.to(preds.dtype)
TO_REMOVE = 1 # TODO remove
widths = anchors[:, 2] - anchors[:, 0] + TO_REMOVE
heights = anchors[:, 3] - anchors[:, 1] + TO_REMOVE
ctr_x = (anchors[:, 2] + anchors[:, 0]) / 2
ctr_y = (anchors[:, 3] + anchors[:, 1]) / 2
wx, wy, ww, wh = (10.0, 10.0, 5.0, 5.0)
dx = preds[:, 0::4] / wx
dy = preds[:, 1::4] / wy
dw = preds[:, 2::4] / ww
dh = preds[:, 3::4] / wh
# Prevent sending too large values into torch.exp()
dw = torch.clamp(dw, max=math.log(1000.0 / 16))
dh = torch.clamp(dh, max=math.log(1000.0 / 16))
pred_ctr_x = dx * widths[:, None] + ctr_x[:, None]
pred_ctr_y = dy * heights[:, None] + ctr_y[:, None]
pred_w = torch.exp(dw) * widths[:, None]
pred_h = torch.exp(dh) * heights[:, None]
pred_boxes = torch.zeros_like(preds)
pred_boxes[:, 0::4] = pred_ctr_x - 0.5 * (pred_w - 1)
pred_boxes[:, 1::4] = pred_ctr_y - 0.5 * (pred_h - 1)
pred_boxes[:, 2::4] = pred_ctr_x + 0.5 * (pred_w - 1)
pred_boxes[:, 3::4] = pred_ctr_y + 0.5 * (pred_h - 1)
return pred_boxes
class ATSSHead(torch.nn.Module):
def __init__(self, cfg):
super(ATSSHead, self).__init__()
self.cfg = cfg
num_classes = cfg.MODEL.ATSS.NUM_CLASSES - 1
num_anchors = len(cfg.MODEL.RPN.ASPECT_RATIOS) * cfg.MODEL.RPN.SCALES_PER_OCTAVE
in_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS
channels = cfg.MODEL.ATSS.CHANNELS
use_gn = cfg.MODEL.ATSS.USE_GN
use_bn = cfg.MODEL.ATSS.USE_BN
use_dcn_in_tower = cfg.MODEL.ATSS.USE_DFCONV
use_dyrelu = cfg.MODEL.ATSS.USE_DYRELU
use_se = cfg.MODEL.ATSS.USE_SE
cls_tower = []
bbox_tower = []
for i in range(cfg.MODEL.ATSS.NUM_CONVS):
if use_dcn_in_tower and i == cfg.MODEL.ATSS.NUM_CONVS - 1:
conv_func = DFConv2d
else:
conv_func = nn.Conv2d
cls_tower.append(
conv_func(in_channels if i == 0 else channels, channels, kernel_size=3, stride=1, padding=1, bias=True)
)
if use_gn:
cls_tower.append(nn.GroupNorm(32, channels))
if use_bn:
cls_tower.append(nn.BatchNorm2d(channels))
if use_se:
cls_tower.append(SELayer(channels))
if use_dyrelu:
cls_tower.append(DYReLU(channels, channels))
else:
cls_tower.append(nn.ReLU())
bbox_tower.append(
conv_func(in_channels if i == 0 else channels, channels, kernel_size=3, stride=1, padding=1, bias=True)
)
if use_gn:
bbox_tower.append(nn.GroupNorm(32, channels))
if use_bn:
bbox_tower.append(nn.BatchNorm2d(channels))
if use_se:
bbox_tower.append(SELayer(channels))
if use_dyrelu:
bbox_tower.append(DYReLU(channels, channels))
else:
bbox_tower.append(nn.ReLU())
self.add_module("cls_tower", nn.Sequential(*cls_tower))
self.add_module("bbox_tower", nn.Sequential(*bbox_tower))
self.cls_logits = nn.Conv2d(channels, num_anchors * num_classes, kernel_size=3, stride=1, padding=1)
self.bbox_pred = nn.Conv2d(channels, num_anchors * 4, kernel_size=3, stride=1, padding=1)
self.centerness = nn.Conv2d(channels, num_anchors * 1, kernel_size=3, stride=1, padding=1)
# initialization
for modules in [self.cls_tower, self.bbox_tower, self.cls_logits, self.bbox_pred, self.centerness]:
for l in modules.modules():
if isinstance(l, nn.Conv2d):
torch.nn.init.normal_(l.weight, std=0.01)
torch.nn.init.constant_(l.bias, 0)
# initialize the bias for focal loss
prior_prob = cfg.MODEL.ATSS.PRIOR_PROB
bias_value = -math.log((1 - prior_prob) / prior_prob)
torch.nn.init.constant_(self.cls_logits.bias, bias_value)
self.scales = nn.ModuleList([Scale(init_value=1.0) for _ in range(5)])
def forward(self, x):
logits = []
bbox_reg = []
centerness = []
for l, feature in enumerate(x):
cls_tower = self.cls_tower(feature)
box_tower = self.bbox_tower(feature)
logits.append(self.cls_logits(cls_tower))
bbox_pred = self.scales[l](self.bbox_pred(box_tower))
bbox_reg.append(bbox_pred)
centerness.append(self.centerness(box_tower))
return logits, bbox_reg, centerness
class ATSSModule(torch.nn.Module):
def __init__(self, cfg):
super(ATSSModule, self).__init__()
self.cfg = cfg
self.head = ATSSHead(cfg)
box_coder = BoxCoder(cfg)
self.loss_evaluator = make_atss_loss_evaluator(cfg, box_coder)
self.box_selector_train = make_atss_postprocessor(cfg, box_coder, is_train=True)
self.box_selector_test = make_atss_postprocessor(cfg, box_coder, is_train=False)
self.anchor_generator = make_anchor_generator_complex(cfg)
def forward(self, images, features, targets=None):
box_cls, box_regression, centerness = self.head(features)
anchors = self.anchor_generator(images, features)
if self.training:
return self._forward_train(box_cls, box_regression, centerness, targets, anchors)
else:
return self._forward_test(box_cls, box_regression, centerness, anchors)
def _forward_train(self, box_cls, box_regression, centerness, targets, anchors):
loss_box_cls, loss_box_reg, loss_centerness = self.loss_evaluator(
box_cls, box_regression, centerness, targets, anchors
)
losses = {"loss_cls": loss_box_cls, "loss_reg": loss_box_reg, "loss_centerness": loss_centerness}
if self.cfg.MODEL.RPN_ONLY:
return None, losses
else:
boxes = self.box_selector_train(box_cls, box_regression, centerness, anchors)
train_boxes = []
for b, a in zip(boxes, anchors):
a = cat_boxlist(a)
b.add_field("visibility", torch.ones(b.bbox.shape[0], dtype=torch.bool, device=b.bbox.device))
del b.extra_fields["scores"]
del b.extra_fields["labels"]
train_boxes.append(cat_boxlist([b, a]))
return train_boxes, losses
def _forward_test(self, box_cls, box_regression, centerness, anchors):
boxes = self.box_selector_test(box_cls, box_regression, centerness, anchors)
return boxes, {}
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