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ssd_r34.py
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ssd_r34.py
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import torch
import torch.nn as nn
from models.base_model_r34 import ResNet34
import numpy as np
from math import sqrt, ceil
import itertools
import torch.nn.functional as F
##Inspired by https://github.com/kuangliu/pytorch-ssd
class Encoder(object):
"""
Transform between (bboxes, lables) <-> SSD output
dboxes: default boxes in size 8732 x 4,
encoder: input ltrb format, output xywh format
decoder: input xywh format, output ltrb format
decode:
input : bboxes_in (Tensor 8732 x 4), scores_in (Tensor 8732 x nitems)
output : bboxes_out (Tensor nboxes x 4), labels_out (Tensor nboxes)
criteria : IoU threshold of bboexes
max_output : maximum number of output bboxes
"""
def __init__(self, dboxes):
self.dboxes = dboxes(order="ltrb")
self.dboxes_xywh = dboxes(order="xywh").unsqueeze(dim=0)
self.nboxes = self.dboxes.size(0)
#print("# Bounding boxes: {}".format(self.nboxes))
self.scale_xy = torch.tensor(dboxes.scale_xy)
self.scale_wh = torch.tensor(dboxes.scale_wh)
def decode_batch(self, bboxes_in, scores_in, criteria = 0.45, max_output=200):
self.dboxes = self.dboxes.to(bboxes_in)
self.dboxes_xywh = self.dboxes_xywh.to(bboxes_in)
bboxes, probs = scale_back_batch(bboxes_in, scores_in,self.scale_xy,self.scale_wh,self.dboxes_xywh)
boxes = []; labels=[]; scores=[]
for bbox, prob in zip(bboxes.split(1, 0), probs.split(1, 0)):
bbox = bbox.squeeze(0)
prob = prob.squeeze(0)
dbox,dlabel,dscore=self.decode_single(bbox, prob, criteria, max_output)
boxes.append(dbox)
labels.append(dlabel)
scores.append(dscore)
return [boxes,labels,scores]
# perform non-maximum suppression
def decode_single(self, bboxes_in, scores_in, criteria, max_output, max_num=200):
# Reference to https://github.com/amdegroot/ssd.pytorch
bboxes_out = []
scores_out = []
labels_out = []
for i, score in enumerate(scores_in.split(1, 1)):
# skip background
if i == 0: continue
score = score.squeeze(1)
mask = score > 0.05
bboxes, score = bboxes_in[mask, :], score[mask]
if score.size(0) == 0: continue
score_sorted, score_idx_sorted = score.sort(dim=0)
# select max_output indices
score_idx_sorted = score_idx_sorted[-max_num:]
candidates = []
while score_idx_sorted.numel() > 0:
idx = score_idx_sorted[-1].item()
bboxes_sorted = bboxes[score_idx_sorted, :]
bboxes_idx = bboxes[idx, :].unsqueeze(dim=0)
iou_sorted = calc_iou_tensor(bboxes_sorted, bboxes_idx).squeeze()
# we only need iou < criteria
score_idx_sorted = score_idx_sorted[iou_sorted < criteria]
candidates.append(idx)
bboxes_out.append(bboxes[candidates, :])
scores_out.append(score[candidates])
labels_out.extend([i]*len(candidates))
bboxes_out, labels_out, scores_out = torch.cat(bboxes_out, dim=0), \
torch.tensor(labels_out, dtype=torch.long), \
torch.cat(scores_out, dim=0)
_, max_ids = scores_out.sort(dim=0)
max_ids = max_ids[-max_output:]
return bboxes_out[max_ids, :], labels_out[max_ids], scores_out[max_ids]
@torch.jit.script
def calc_iou_tensor(box1, box2):
""" Calculation of IoU based on two boxes tensor,
Reference to https://github.com/kuangliu/pytorch-ssd
input:
box1 (N, 4)
box2 (M, 4)
output:
IoU (N, M)
"""
N = box1.size(0)
M = box2.size(0)
be1 = box1.unsqueeze(1).expand(-1, M, -1)
be2 = box2.unsqueeze(0).expand(N, -1, -1)
# Left Top & Right Bottom
lt = torch.max(be1[:,:,:2], be2[:,:,:2])
rb = torch.min(be1[:,:,2:], be2[:,:,2:])
delta = rb - lt
delta.clone().masked_fill_(delta < 0,0)
intersect = delta[:,:,0]*delta[:,:,1]
delta1 = be1[:,:,2:] - be1[:,:,:2]
area1 = delta1[:,:,0]*delta1[:,:,1]
delta2 = be2[:,:,2:] - be2[:,:,:2]
area2 = delta2[:,:,0]*delta2[:,:,1]
iou = intersect/(area1 + area2 - intersect)
return iou
@torch.jit.script
def scale_back_batch(bboxes_in, scores_in,scale_xy,scale_wh,dboxes_xywh):
"""
Do scale and transform from xywh to ltrb
suppose input Nx4xnum_bbox Nxlabel_numxnum_bbox
"""
bboxes_in = bboxes_in.permute(0, 2, 1)
scores_in = scores_in.permute(0, 2, 1)
bboxes_in[:, :, :2] = scale_xy*bboxes_in[:, :, :2]
bboxes_in[:, :, 2:] = scale_wh*bboxes_in[:, :, 2:]
bboxes_in[:, :, :2] = bboxes_in[:, :, :2]*dboxes_xywh[:, :, 2:] + dboxes_xywh[:, :, :2]
bboxes_in[:, :, 2:] = bboxes_in[:, :, 2:].exp()*dboxes_xywh[:, :, 2:]
# Transform format to ltrb
l, t, r, b = bboxes_in[:, :, 0] - 0.5*bboxes_in[:, :, 2],\
bboxes_in[:, :, 1] - 0.5*bboxes_in[:, :, 3],\
bboxes_in[:, :, 0] + 0.5*bboxes_in[:, :, 2],\
bboxes_in[:, :, 1] + 0.5*bboxes_in[:, :, 3]
bboxes_in[:, :, 0] = l
bboxes_in[:, :, 1] = t
bboxes_in[:, :, 2] = r
bboxes_in[:, :, 3] = b
return bboxes_in, F.softmax(scores_in, dim=-1)
class DefaultBoxes(object):
def __init__(self, fig_size, feat_size, steps, scales, aspect_ratios, \
scale_xy=0.1, scale_wh=0.2):
self.feat_size = feat_size
self.fig_size_w,self.fig_size_h = fig_size
self.scale_xy_ = scale_xy
self.scale_wh_ = scale_wh
# According to https://github.com/weiliu89/caffe
# Calculation method slightly different from paper
self.steps_w = [st[0] for st in steps]
self.steps_h = [st[1] for st in steps]
self.scales = scales
fkw = self.fig_size_w//np.array(self.steps_w)
fkh = self.fig_size_h//np.array(self.steps_h)
self.aspect_ratios = aspect_ratios
self.default_boxes = []
# size of feature and number of feature
for idx, sfeat in enumerate(self.feat_size):
sfeat_w,sfeat_h=sfeat
sk1 = scales[idx][0]/self.fig_size_w
sk2 = scales[idx+1][1]/self.fig_size_h
sk3 = sqrt(sk1*sk2)
all_sizes = [(sk1, sk1), (sk3, sk3)]
for alpha in aspect_ratios[idx]:
w, h = sk1*sqrt(alpha), sk1/sqrt(alpha)
all_sizes.append((w, h))
all_sizes.append((h, w))
for w, h in all_sizes:
for i, j in itertools.product(range(sfeat_w), range(sfeat_h)):
cx, cy = (j+0.5)/fkh[idx], (i+0.5)/fkw[idx]
self.default_boxes.append((cx, cy, w, h))
self.dboxes = torch.tensor(self.default_boxes)
self.dboxes.clamp_(min=0, max=1)
# For IoU calculation
self.dboxes_ltrb = self.dboxes.clone()
self.dboxes_ltrb[:, 0] = self.dboxes[:, 0] - 0.5*self.dboxes[:, 2]
self.dboxes_ltrb[:, 1] = self.dboxes[:, 1] - 0.5*self.dboxes[:, 3]
self.dboxes_ltrb[:, 2] = self.dboxes[:, 0] + 0.5*self.dboxes[:, 2]
self.dboxes_ltrb[:, 3] = self.dboxes[:, 1] + 0.5*self.dboxes[:, 3]
@property
def scale_xy(self):
return self.scale_xy_
@property
def scale_wh(self):
return self.scale_wh_
def __call__(self, order="ltrb"):
if order == "ltrb": return self.dboxes_ltrb
if order == "xywh": return self.dboxes
def dboxes_R34_coco(figsize,strides):
feat_size = [[50, 50], [25, 25], [13, 13], [7, 7], [3, 3], [3, 3]]
steps=[(int(figsize[0]/fs[0]),int(figsize[1]/fs[1])) for fs in feat_size]
scales = [(int(s*figsize[0]/300),int(s*figsize[1]/300)) for s in [21, 45, 99, 153, 207, 261, 315]]
aspect_ratios = [[2], [2, 3], [2, 3], [2, 3], [2], [2]]
dboxes = DefaultBoxes(figsize, feat_size, steps, scales, aspect_ratios)
return dboxes
class SSD_R34(nn.Module):
"""
Build a SSD module to take 300x300 image input,
and output 8732 per class bounding boxes
vggt: pretrained vgg16 (partial) model
label_num: number of classes (including background 0)
"""
def __init__(self, label_num=81, backbone='resnet34', model_path="./resnet34-333f7ec4.pth",strides=[3,3 ,2 ,2 ,2 ,2],extract_shapes=False):
super(SSD_R34, self).__init__()
self.label_num = label_num
self.strides = strides
if backbone == 'resnet34':
self.model = ResNet34()
out_channels = 256
self.out_chan = [out_channels, 512, 512, 256, 256, 256]
else:
raise ValueError('Invalid backbone chosen')
self._build_additional_features(self.out_chan)
self.extract_shapes=extract_shapes
# after l2norm, conv7, conv8_2, conv9_2, conv10_2, conv11_2
# classifer 1, 2, 3, 4, 5 ,6
self.num_defaults = [4, 6, 6, 6, 4, 4]
self.loc = []
self.conf = []
for nd, oc in zip(self.num_defaults, self.out_chan):
self.loc.append(nn.Conv2d(oc, nd*4, kernel_size=3, padding=1,stride=self.strides[0]))
self.conf.append(nn.Conv2d(oc, nd*label_num, kernel_size=3, padding=1,stride=self.strides[1]))
self.loc = nn.ModuleList(self.loc)
self.conf = nn.ModuleList(self.conf)
if not extract_shapes:
self.size=(1200,1200)
dboxes = dboxes_R34_coco(list(self.size),[3,3,2,2,2,2])
self.encoder = Encoder(dboxes)
# intitalize all weights
self._init_weights()
self.device = 1
def _build_additional_features(self, input_channels):
idx = 0
self.additional_blocks = []
self.additional_blocks.append(nn.Sequential(
nn.Conv2d(input_channels[idx], 256, kernel_size=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, input_channels[idx+1], kernel_size=3, padding=1,stride=self.strides[2]),
nn.ReLU(inplace=True),
))
idx += 1
self.additional_blocks.append(nn.Sequential(
nn.Conv2d(input_channels[idx], 256, kernel_size=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, input_channels[idx+1], kernel_size=3, padding=1, stride=self.strides[3]),
nn.ReLU(inplace=True),
))
idx += 1
# conv9_1, conv9_2
self.additional_blocks.append(nn.Sequential(
nn.Conv2d(input_channels[idx], 128, kernel_size=1),
nn.ReLU(inplace=True),
nn.Conv2d(128, input_channels[idx+1], kernel_size=3, padding=1, stride=self.strides[4]),
nn.ReLU(inplace=True),
))
idx += 1
# conv10_1, conv10_2
self.additional_blocks.append(nn.Sequential(
nn.Conv2d(input_channels[idx], 128, kernel_size=1),
nn.ReLU(inplace=True),
nn.Conv2d(128, input_channels[idx+1], kernel_size=3,stride=self.strides[5]),
nn.ReLU(inplace=True),
))
idx += 1
# conv11_1, conv11_2
self.additional_blocks.append(nn.Sequential(
nn.Conv2d(input_channels[idx], 128, kernel_size=1),
nn.ReLU(inplace=True),
nn.Conv2d(128, input_channels[idx+1], kernel_size=3),
nn.ReLU(inplace=True),
))
self.additional_blocks = nn.ModuleList(self.additional_blocks)
def _init_weights(self):
layers = [
*self.additional_blocks,
*self.loc, *self.conf]
for layer in layers:
for param in layer.parameters():
if param.dim() > 1: nn.init.xavier_uniform_(param)
# Shape the classifier to the view of bboxes
def bbox_view(self, src, loc, conf,extract_shapes=False):
ret = []
features_shapes = []
for s, l, c in zip(src, loc, conf):
ret.append((l(s).view(s.size(0), 4, -1), c(s).view(s.size(0), self.label_num, -1)))
# extract shapes for prior box initliziation
if extract_shapes:
ls=l(s)
features_shapes.append([ls.shape[2],ls.shape[3]])
locs, confs = list(zip(*ret))
locs, confs = torch.cat(locs, 2).contiguous(), torch.cat(confs, 2).contiguous()
return locs, confs,features_shapes
def forward(self, data):
layers = self.model(data)
# last result from network goes into additional blocks
x = layers[-1]
additional_results = []
for i, l in enumerate(self.additional_blocks):
x = l(x)
additional_results.append(x)
src = [*layers, *additional_results]
# Feature maps sizes depend on the image size. For 300x300 with strides=[1,1,2,2,2,1] it is 38x38x4, 19x19x6, 10x10x6, 5x5x6, 3x3x4, 1x1x4
locs, confs,features_shapes = self.bbox_view(src, self.loc, self.conf,extract_shapes=self.extract_shapes)
if self.extract_shapes:
return locs, confs,features_shapes
else:
# For SSD 300 with strides=[1,1,2,2,2,1] , shall return nbatch x 8732 x {nlabels, nlocs} results
results=self.encoder.decode_batch(locs, confs, 0.50, 200) #[0]
return results #locs, confs,features_shapes