#打卡学习第14天#
1. 模型介绍
SSD，全称Single Shot MultiBox Detector，是Wei Liu在ECCV 2016上提出的一种目标检测算法。使用Nvidia Titan X在VOC 2007测试集上，SSD对于输入尺寸300x300的网络，达到74.3%mAP(mean Average Precision)以及59FPS；对于512x512的网络，达到了76.9%mAP ，超越当时最强的Faster RCNN(73.2%mAP)。 SSD目标检测主流算法分成可以两个类型：
-
two-stage方法：RCNN系列
通过算法产生候选框，然后再对这些候选框进行分类和回归。
-
one-stage方法：YOLO和SSD
直接通过主干网络给出类别位置信息，不需要区域生成。
SSD是单阶段的目标检测算法，通过卷积神经网络进行特征提取，取不同的特征层进行检测输出，所以SSD是一种多尺度的检测方法。在需要检测的特征层，直接使用一个3 ×× 3卷积，进行通道的变换。SSD采用了anchor的策略，预设不同长宽比例的anchor，每一个输出特征层基于anchor预测多个检测框（4或者6）。采用了多尺度检测方法，浅层用于检测小目标，深层用于检测大目标。SSD的框架如下图：
1.1 模型结构
SSD采用VGG16作为基础模型，然后在VGG16的基础上新增了卷积层来获得更多的特征图以用于检测。SSD的网络结构如图所示。上面是SSD模型，下面是YOLO模型，可以明显看到SSD利用了多尺度的特征图做检测。
1.2 模型特点
-
多尺度检测
在SSD的网络结构图中我们可以看到，SSD使用了多个特征层，特征层的尺寸分别是38 ×× 38，19 ×× 19，10 ×× 10，5 ×× 5，3 ×× 3，1 ×× 1，一共6种不同的特征图尺寸。大尺度特征图（较靠前的特征图）可以用来检测小物体，而小尺度特征图（较靠后的特征图）用来检测大物体。多尺度检测的方式，可以使得检测更加充分（SSD属于密集检测），更能检测出小目标。
-
采用卷积进行检测
与YOLO最后采用全连接层不同，SSD直接采用卷积对不同的特征图来进行提取检测结果。对于形状为m ×× n ×× p的特征图，只需要采用3 ×× 3 ×× p这样比较小的卷积核得到检测值。
-
预设anchor
在YOLOv1中，直接由网络预测目标的尺寸，这种方式使得预测框的长宽比和尺寸没有限制，难以训练。在SSD中，采用预设边界框，我们习惯称它为anchor（在SSD论文中叫default bounding boxes），预测框的尺寸在anchor的指导下进行微调。
2. 模型数据
2.1 数据采样
为了使模型对于各种输入对象大小和形状更加鲁棒，SSD算法每个训练图像通过以下选项之一随机采样：
-
使用整个原始输入图像
-
采样一个区域，使采样区域和原始图片最小的交并比重叠为0.1,0.3,0.5,0.7或0.9
-
随机采样一个区域
每个采样区域的大小为原始图像大小的[0.3,1]，长宽比在1/2和2之间。如果真实标签框中心在采样区域内，则保留两者重叠部分作为新图片的真实标注框。在上述采样步骤之后，将每个采样区域大小调整为固定大小，并以0.5的概率水平翻转。
import cv2
import numpy as np
def _rand(a=0., b=1.):
return np.random.rand() * (b - a) + a
def intersect(box_a, box_b):
"""Compute the intersect of two sets of boxes."""
max_yx = np.minimum(box_a[:, 2:4], box_b[2:4])
min_yx = np.maximum(box_a[:, :2], box_b[:2])
inter = np.clip((max_yx - min_yx), a_min=0, a_max=np.inf)
return inter[:, 0] * inter[:, 1]
def jaccard_numpy(box_a, box_b):
"""Compute the jaccard overlap of two sets of boxes."""
inter = intersect(box_a, box_b)
area_a = ((box_a[:, 2] - box_a[:, 0]) *
(box_a[:, 3] - box_a[:, 1]))
area_b = ((box_b[2] - box_b[0]) *
(box_b[3] - box_b[1]))
union = area_a + area_b - inter
return inter / union
def random_sample_crop(image, boxes):
"""Crop images and boxes randomly."""
height, width, _ = image.shape
min_iou = np.random.choice([None, 0.1, 0.3, 0.5, 0.7, 0.9])
if min_iou is None:
return image, boxes
for _ in range(50):
image_t = image
w = _rand(0.3, 1.0) * width
h = _rand(0.3, 1.0) * height
# aspect ratio constraint b/t .5 & 2
if h / w < 0.5 or h / w > 2:
continue
left = _rand() * (width - w)
top = _rand() * (height - h)
rect = np.array([int(top), int(left), int(top + h), int(left + w)])
overlap = jaccard_numpy(boxes, rect)
# dropout some boxes
drop_mask = overlap > 0
if not drop_mask.any():
continue
if overlap[drop_mask].min() < min_iou and overlap[drop_mask].max() > (min_iou + 0.2):
continue
image_t = image_t[rect[0]:rect[2], rect[1]:rect[3], :]
centers = (boxes[:, :2] + boxes[:, 2:4]) / 2.0
m1 = (rect[0] < centers[:, 0]) * (rect[1] < centers[:, 1])
m2 = (rect[2] > centers[:, 0]) * (rect[3] > centers[:, 1])
# mask in that both m1 and m2 are true
mask = m1 * m2 * drop_mask
# have any valid boxes? try again if not
if not mask.any():
continue
# take only matching gt boxes
boxes_t = boxes[mask, :].copy()
boxes_t[:, :2] = np.maximum(boxes_t[:, :2], rect[:2])
boxes_t[:, :2] -= rect[:2]
boxes_t[:, 2:4] = np.minimum(boxes_t[:, 2:4], rect[2:4])
boxes_t[:, 2:4] -= rect[:2]
return image_t, boxes_t
return image, boxes
def ssd_bboxes_encode(boxes):
"""Labels anchors with ground truth inputs."""
def jaccard_with_anchors(bbox):
"""Compute jaccard score a box and the anchors."""
# Intersection bbox and volume.
ymin = np.maximum(y1, bbox[0])
xmin = np.maximum(x1, bbox[1])
ymax = np.minimum(y2, bbox[2])
xmax = np.minimum(x2, bbox[3])
w = np.maximum(xmax - xmin, 0.)
h = np.maximum(ymax - ymin, 0.)
# Volumes.
inter_vol = h * w
union_vol = vol_anchors + (bbox[2] - bbox[0]) * (bbox[3] - bbox[1]) - inter_vol
jaccard = inter_vol / union_vol
return np.squeeze(jaccard)
pre_scores = np.zeros((8732), dtype=np.float32)
t_boxes = np.zeros((8732, 4), dtype=np.float32)
t_label = np.zeros((8732), dtype=np.int64)
for bbox in boxes:
label = int(bbox[4])
scores = jaccard_with_anchors(bbox)
idx = np.argmax(scores)
scores[idx] = 2.0
mask = (scores > matching_threshold)
mask = mask & (scores > pre_scores)
pre_scores = np.maximum(pre_scores, scores * mask)
t_label = mask * label + (1 - mask) * t_label
for i in range(4):
t_boxes[:, i] = mask * bbox[i] + (1 - mask) * t_boxes[:, i]
index = np.nonzero(t_label)
# Transform to tlbr.
bboxes = np.zeros((8732, 4), dtype=np.float32)
bboxes[:, [0, 1]] = (t_boxes[:, [0, 1]] + t_boxes[:, [2, 3]]) / 2
bboxes[:, [2, 3]] = t_boxes[:, [2, 3]] - t_boxes[:, [0, 1]]
# Encode features.
bboxes_t = bboxes[index]
default_boxes_t = default_boxes[index]
bboxes_t[:, :2] = (bboxes_t[:, :2] - default_boxes_t[:, :2]) / (default_boxes_t[:, 2:] * 0.1)
tmp = np.maximum(bboxes_t[:, 2:4] / default_boxes_t[:, 2:4], 0.000001)
bboxes_t[:, 2:4] = np.log(tmp) / 0.2
bboxes[index] = bboxes_t
num_match = np.array([len(np.nonzero(t_label)[0])], dtype=np.int32)
return bboxes, t_label.astype(np.int32), num_match
def preprocess_fn(img_id, image, box, is_training):
"""Preprocess function for dataset."""
cv2.setNumThreads(2)
def _infer_data(image, input_shape):
img_h, img_w, _ = image.shape
input_h, input_w = input_shape
image = cv2.resize(image, (input_w, input_h))
# When the channels of image is 1
if len(image.shape) == 2:
image = np.expand_dims(image, axis=-1)
image = np.concatenate([image, image, image], axis=-1)
return img_id, image, np.array((img_h, img_w), np.float32)
def _data_aug(image, box, is_training, image_size=(300, 300)):
ih, iw, _ = image.shape
h, w = image_size
if not is_training:
return _infer_data(image, image_size)
# Random crop
box = box.astype(np.float32)
image, box = random_sample_crop(image, box)
ih, iw, _ = image.shape
# Resize image
image = cv2.resize(image, (w, h))
# Flip image or not
flip = _rand() < .5
if flip:
image = cv2.flip(image, 1, dst=None)
# When the channels of image is 1
if len(image.shape) == 2:
image = np.expand_dims(image, axis=-1)
image = np.concatenate([image, image, image], axis=-1)
box[:, [0, 2]] = box[:, [0, 2]] / ih
box[:, [1, 3]] = box[:, [1, 3]] / iw
if flip:
box[:, [1, 3]] = 1 - box[:, [3, 1]]
box, label, num_match = ssd_bboxes_encode(box)
return image, box, label, num_match
return _data_aug(image, box, is_training, image_size=[300, 300])2.2 数据集创建
from mindspore import Tensor
from mindspore.dataset import MindDataset
from mindspore.dataset.vision import Decode, HWC2CHW, Normalize, RandomColorAdjust
def create_ssd_dataset(mindrecord_file, batch_size=32, device_num=1, rank=0,
is_training=True, num_parallel_workers=1, use_multiprocessing=True):
"""Create SSD dataset with MindDataset."""
dataset = MindDataset(mindrecord_file, columns_list=["img_id", "image", "annotation"], num_shards=device_num,
shard_id=rank, num_parallel_workers=num_parallel_workers, shuffle=is_training)
decode = Decode()
dataset = dataset.map(operations=decode, input_columns=["image"])
change_swap_op = HWC2CHW()
# Computed from random subset of ImageNet training images
normalize_op = Normalize(mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
color_adjust_op = RandomColorAdjust(brightness=0.4, contrast=0.4, saturation=0.4)
compose_map_func = (lambda img_id, image, annotation: preprocess_fn(img_id, image, annotation, is_training))
if is_training:
output_columns = ["image", "box", "label", "num_match"]
trans = [color_adjust_op, normalize_op, change_swap_op]
else:
output_columns = ["img_id", "image", "image_shape"]
trans = [normalize_op, change_swap_op]
dataset = dataset.map(operations=compose_map_func, input_columns=["img_id", "image", "annotation"],
output_columns=output_columns, python_multiprocessing=use_multiprocessing,
num_parallel_workers=num_parallel_workers)
dataset = dataset.map(operations=trans, input_columns=["image"], python_multiprocessing=use_multiprocessing,
num_parallel_workers=num_parallel_workers)
dataset = dataset.batch(batch_size, drop_remainder=True)
return dataset3. 模型构建
import mindspore as ms
import mindspore.nn as nn
import mindspore.ops as ops
def _make_layer(channels):
in_channels = channels[0]
layers = []
for out_channels in channels[1:]:
layers.append(nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=3))
layers.append(nn.ReLU())
in_channels = out_channels
return nn.SequentialCell(layers)
class Vgg16(nn.Cell):
"""VGG16 module."""
def __init__(self):
super(Vgg16, self).__init__()
self.b1 = _make_layer([3, 64, 64])
self.b2 = _make_layer([64, 128, 128])
self.b3 = _make_layer([128, 256, 256, 256])
self.b4 = _make_layer([256, 512, 512, 512])
self.b5 = _make_layer([512, 512, 512, 512])
self.m1 = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode='SAME')
self.m2 = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode='SAME')
self.m3 = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode='SAME')
self.m4 = nn.MaxPool2d(kernel_size=2, stride=2, pad_mode='SAME')
self.m5 = nn.MaxPool2d(kernel_size=3, stride=1, pad_mode='SAME')
def construct(self, x):
# block1
x = self.b1(x)
x = self.m1(x)
# block2
x = self.b2(x)
x = self.m2(x)
# block3
x = self.b3(x)
x = self.m3(x)
# block4
x = self.b4(x)
block4 = x
x = self.m4(x)
# block5
x = self.b5(x)
x = self.m5(x)
return block4, x
def _last_conv2d(in_channel, out_channel, kernel_size=3, stride=1, pad_mod='same', pad=0):
in_channels = in_channel
out_channels = in_channel
depthwise_conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, pad_mode='same',
padding=pad, group=in_channels)
conv = nn.Conv2d(in_channel, out_channel, kernel_size=1, stride=1, padding=0, pad_mode='same', has_bias=True)
bn = nn.BatchNorm2d(in_channel, eps=1e-3, momentum=0.97,
gamma_init=1, beta_init=0, moving_mean_init=0, moving_var_init=1)
return nn.SequentialCell([depthwise_conv, bn, nn.ReLU6(), conv])
class FlattenConcat(nn.Cell):
"""FlattenConcat module."""
def __init__(self):
super(FlattenConcat, self).__init__()
self.num_ssd_boxes = 8732
def construct(self, inputs):
output = ()
batch_size = ops.shape(inputs[0])[0]
for x in inputs:
x = ops.transpose(x, (0, 2, 3, 1))
output += (ops.reshape(x, (batch_size, -1)),)
res = ops.concat(output, axis=1)
return ops.reshape(res, (batch_size, self.num_ssd_boxes, -1))
class MultiBox(nn.Cell):
"""
Multibox conv layers. Each multibox layer contains class conf scores and localization predictions.
"""
def __init__(self):
super(MultiBox, self).__init__()
num_classes = 81
out_channels = [512, 1024, 512, 256, 256, 256]
num_default = [4, 6, 6, 6, 4, 4]
loc_layers = []
cls_layers = []
for k, out_channel in enumerate(out_channels):
loc_layers += [_last_conv2d(out_channel, 4 * num_default[k],
kernel_size=3, stride=1, pad_mod='same', pad=0)]
cls_layers += [_last_conv2d(out_channel, num_classes * num_default[k],
kernel_size=3, stride=1, pad_mod='same', pad=0)]
self.multi_loc_layers = nn.CellList(loc_layers)
self.multi_cls_layers = nn.CellList(cls_layers)
self.flatten_concat = FlattenConcat()
def construct(self, inputs):
loc_outputs = ()
cls_outputs = ()
for i in range(len(self.multi_loc_layers)):
loc_outputs += (self.multi_loc_layers[i](inputs[i]),)
cls_outputs += (self.multi_cls_layers[i](inputs[i]),)
return self.flatten_concat(loc_outputs), self.flatten_concat(cls_outputs)
class SSD300Vgg16(nn.Cell):
"""SSD300Vgg16 module."""
def __init__(self):
super(SSD300Vgg16, self).__init__()
# VGG16 backbone: block1~5
self.backbone = Vgg16()
# SSD blocks: block6~7
self.b6_1 = nn.Conv2d(in_channels=512, out_channels=1024, kernel_size=3, padding=6, dilation=6, pad_mode='pad')
self.b6_2 = nn.Dropout(p=0.5)
self.b7_1 = nn.Conv2d(in_channels=1024, out_channels=1024, kernel_size=1)
self.b7_2 = nn.Dropout(p=0.5)
# Extra Feature Layers: block8~11
self.b8_1 = nn.Conv2d(in_channels=1024, out_channels=256, kernel_size=1, padding=1, pad_mode='pad')
self.b8_2 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, stride=2, pad_mode='valid')
self.b9_1 = nn.Conv2d(in_channels=512, out_channels=128, kernel_size=1, padding=1, pad_mode='pad')
self.b9_2 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, stride=2, pad_mode='valid')
self.b10_1 = nn.Conv2d(in_channels=256, out_channels=128, kernel_size=1)
self.b10_2 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, pad_mode='valid')
self.b11_1 = nn.Conv2d(in_channels=256, out_channels=128, kernel_size=1)
self.b11_2 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, pad_mode='valid')
# boxes
self.multi_box = MultiBox()
def construct(self, x):
# VGG16 backbone: block1~5
block4, x = self.backbone(x)
# SSD blocks: block6~7
x = self.b6_1(x) # 1024
x = self.b6_2(x)
x = self.b7_1(x) # 1024
x = self.b7_2(x)
block7 = x
# Extra Feature Layers: block8~11
x = self.b8_1(x) # 256
x = self.b8_2(x) # 512
block8 = x
x = self.b9_1(x) # 128
x = self.b9_2(x) # 256
block9 = x
x = self.b10_1(x) # 128
x = self.b10_2(x) # 256
block10 = x
x = self.b11_1(x) # 128
x = self.b11_2(x) # 256
block11 = x
# boxes
multi_feature = (block4, block7, block8, block9, block10, block11)
pred_loc, pred_label = self.multi_box(multi_feature)
if not self.training:
pred_label = ops.sigmoid(pred_label)
pred_loc = pred_loc.astype(ms.float32)
pred_label = pred_label.astype(ms.float32)
return pred_loc, pred_label3.1 损失函数
def class_loss(logits, label):
"""Calculate category losses."""
label = ops.one_hot(label, ops.shape(logits)[-1], Tensor(1.0, ms.float32), Tensor(0.0, ms.float32))
weight = ops.ones_like(logits)
pos_weight = ops.ones_like(logits)
sigmiod_cross_entropy = ops.binary_cross_entropy_with_logits(logits, label, weight.astype(ms.float32), pos_weight.astype(ms.float32))
sigmoid = ops.sigmoid(logits)
label = label.astype(ms.float32)
p_t = label * sigmoid + (1 - label) * (1 - sigmoid)
modulating_factor = ops.pow(1 - p_t, 2.0)
alpha_weight_factor = label * 0.75 + (1 - label) * (1 - 0.75)
focal_loss = modulating_factor * alpha_weight_factor * sigmiod_cross_entropy
return focal_loss3.2 Metrics
import json
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
def apply_eval(eval_param_dict):
net = eval_param_dict["net"]
net.set_train(False)
ds = eval_param_dict["dataset"]
anno_json = eval_param_dict["anno_json"]
coco_metrics = COCOMetrics(anno_json=anno_json,
classes=train_cls,
num_classes=81,
max_boxes=100,
nms_threshold=0.6,
min_score=0.1)
for data in ds.create_dict_iterator(output_numpy=True, num_epochs=1):
img_id = data['img_id']
img_np = data['image']
image_shape = data['image_shape']
output = net(Tensor(img_np))
for batch_idx in range(img_np.shape[0]):
pred_batch = {
"boxes": output[0].asnumpy()[batch_idx],
"box_scores": output[1].asnumpy()[batch_idx],
"img_id": int(np.squeeze(img_id[batch_idx])),
"image_shape": image_shape[batch_idx]
}
coco_metrics.update(pred_batch)
eval_metrics = coco_metrics.get_metrics()
return eval_metrics
def apply_nms(all_boxes, all_scores, thres, max_boxes):
"""Apply NMS to bboxes."""
y1 = all_boxes[:, 0]
x1 = all_boxes[:, 1]
y2 = all_boxes[:, 2]
x2 = all_boxes[:, 3]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = all_scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
if len(keep) >= max_boxes:
break
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
ovr = inter / (areas[i] + areas[order[1:]] - inter)
inds = np.where(ovr <= thres)[0]
order = order[inds + 1]
return keep
class COCOMetrics:
"""Calculate mAP of predicted bboxes."""
def __init__(self, anno_json, classes, num_classes, min_score, nms_threshold, max_boxes):
self.num_classes = num_classes
self.classes = classes
self.min_score = min_score
self.nms_threshold = nms_threshold
self.max_boxes = max_boxes
self.val_cls_dict = {i: cls for i, cls in enumerate(classes)}
self.coco_gt = COCO(anno_json)
cat_ids = self.coco_gt.loadCats(self.coco_gt.getCatIds())
self.class_dict = {cat['name']: cat['id'] for cat in cat_ids}
self.predictions = []
self.img_ids = []
def update(self, batch):
pred_boxes = batch['boxes']
box_scores = batch['box_scores']
img_id = batch['img_id']
h, w = batch['image_shape']
final_boxes = []
final_label = []
final_score = []
self.img_ids.append(img_id)
for c in range(1, self.num_classes):
class_box_scores = box_scores[:, c]
score_mask = class_box_scores > self.min_score
class_box_scores = class_box_scores[score_mask]
class_boxes = pred_boxes[score_mask] * [h, w, h, w]
if score_mask.any():
nms_index = apply_nms(class_boxes, class_box_scores, self.nms_threshold, self.max_boxes)
class_boxes = class_boxes[nms_index]
class_box_scores = class_box_scores[nms_index]
final_boxes += class_boxes.tolist()
final_score += class_box_scores.tolist()
final_label += [self.class_dict[self.val_cls_dict[c]]] * len(class_box_scores)
for loc, label, score in zip(final_boxes, final_label, final_score):
res = {}
res['image_id'] = img_id
res['bbox'] = [loc[1], loc[0], loc[3] - loc[1], loc[2] - loc[0]]
res['score'] = score
res['category_id'] = label
self.predictions.append(res)
def get_metrics(self):
with open('predictions.json', 'w') as f:
json.dump(self.predictions, f)
coco_dt = self.coco_gt.loadRes('predictions.json')
E = COCOeval(self.coco_gt, coco_dt, iouType='bbox')
E.params.imgIds = self.img_ids
E.evaluate()
E.accumulate()
E.summarize()
return E.stats[0]
class SsdInferWithDecoder(nn.Cell):
"""
SSD Infer wrapper to decode the bbox locations."""
def __init__(self, network, default_boxes, ckpt_path):
super(SsdInferWithDecoder, self).__init__()
param_dict = ms.load_checkpoint(ckpt_path)
ms.load_param_into_net(network, param_dict)
self.network = network
self.default_boxes = default_boxes
self.prior_scaling_xy = 0.1
self.prior_scaling_wh = 0.2
def construct(self, x):
pred_loc, pred_label = self.network(x)
default_bbox_xy = self.default_boxes[..., :2]
default_bbox_wh = self.default_boxes[..., 2:]
pred_xy = pred_loc[..., :2] * self.prior_scaling_xy * default_bbox_wh + default_bbox_xy
pred_wh = ops.exp(pred_loc[..., 2:] * self.prior_scaling_wh) * default_bbox_wh
pred_xy_0 = pred_xy - pred_wh / 2.0
pred_xy_1 = pred_xy + pred_wh / 2.0
pred_xy = ops.concat((pred_xy_0, pred_xy_1), -1)
pred_xy = ops.maximum(pred_xy, 0)
pred_xy = ops.minimum(pred_xy, 1)
return pred_xy, pred_label4. 训练过程
（1）先验框匹配
在训练过程中，首先要确定训练图片中的ground truth（真实目标）与哪个先验框来进行匹配，与之匹配的先验框所对应的边界框将负责预测它。
（2）损失函数
损失函数使用的是上文提到的位置损失函数和置信度损失函数的加权和。
（3）数据增强
使用之前定义好的数据增强方式，对创建好的数据增强方式进行数据增强。
模型训练时，设置模型训练的epoch次数为60，然后通过create_ssd_dataset类创建了训练集和验证集。batch_size大小为5，图像尺寸统一调整为300×300。损失函数使用位置损失函数和置信度损失函数的加权和，优化器使用Momentum，并设置初始学习率为0.001。回调函数方面使用了LossMonitor和TimeMonitor来监控训练过程中每个epoch结束后，损失值Loss的变化情况以及每个epoch、每个step的运行时间。设置每训练10个epoch保存一次模型。
import math
import itertools as it
import time
from mindspore.amp import DynamicLossScaler
from mindspore import set_seed
from mindspore.common.initializer import initializer, TruncatedNormal
class GeneratDefaultBoxes():
"""
Generate Default boxes for SSD, follows the order of (W, H, archor_sizes).
`self.default_boxes` has a shape of [archor_sizes, H, W, 4], the last dimension is [y, x, h, w].
`self.default_boxes_tlbr` has a shape as `self.default_boxes`, the last dimension is [y1, x1, y2, x2].
"""
def __init__(self):
fk = 300 / np.array([8, 16, 32, 64, 100, 300])
scale_rate = (0.95 - 0.1) / (len([4, 6, 6, 6, 4, 4]) - 1)
scales = [0.1 + scale_rate * i for i in range(len([4, 6, 6, 6, 4, 4]))] + [1.0]
self.default_boxes = []
for idex, feature_size in enumerate([38, 19, 10, 5, 3, 1]):
sk1 = scales[idex]
sk2 = scales[idex + 1]
sk3 = math.sqrt(sk1 * sk2)
if idex == 0 and not [[2], [2, 3], [2, 3], [2, 3], [2], [2]][idex]:
w, h = sk1 * math.sqrt(2), sk1 / math.sqrt(2)
all_sizes = [(0.1, 0.1), (w, h), (h, w)]
else:
all_sizes = [(sk1, sk1)]
for aspect_ratio in [[2], [2, 3], [2, 3], [2, 3], [2], [2]][idex]:
w, h = sk1 * math.sqrt(aspect_ratio), sk1 / math.sqrt(aspect_ratio)
all_sizes.append((w, h))
all_sizes.append((h, w))
all_sizes.append((sk3, sk3))
assert len(all_sizes) == [4, 6, 6, 6, 4, 4][idex]
for i, j in it.product(range(feature_size), repeat=2):
for w, h in all_sizes:
cx, cy = (j + 0.5) / fk[idex], (i + 0.5) / fk[idex]
self.default_boxes.append([cy, cx, h, w])
def to_tlbr(cy, cx, h, w):
return cy - h / 2, cx - w / 2, cy + h / 2, cx + w / 2
# For IoU calculation
self.default_boxes_tlbr = np.array(tuple(to_tlbr(*i) for i in self.default_boxes), dtype='float32')
self.default_boxes = np.array(self.default_boxes, dtype='float32')
default_boxes_tlbr = GeneratDefaultBoxes().default_boxes_tlbr
default_boxes = GeneratDefaultBoxes().default_boxes
y1, x1, y2, x2 = np.split(default_boxes_tlbr[:, :4], 4, axis=-1)
vol_anchors = (x2 - x1) * (y2 - y1)
matching_threshold = 0.5
def init_net_param(network, initialize_mode='TruncatedNormal'):
"""Init the parameters in net."""
params = network.trainable_params()
for p in params:
if 'beta' not in p.name and 'gamma' not in p.name and 'bias' not in p.name:
if initialize_mode == 'TruncatedNormal':
p.set_data(initializer(TruncatedNormal(0.02), p.data.shape, p.data.dtype))
else:
p.set_data(initialize_mode, p.data.shape, p.data.dtype)
def get_lr(global_step, lr_init, lr_end, lr_max, warmup_epochs, total_epochs, steps_per_epoch):
""" generate learning rate array"""
lr_each_step = []
total_steps = steps_per_epoch * total_epochs
warmup_steps = steps_per_epoch * warmup_epochs
for i in range(total_steps):
if i < warmup_steps:
lr = lr_init + (lr_max - lr_init) * i / warmup_steps
else:
lr = lr_end + (lr_max - lr_end) * (1. + math.cos(math.pi * (i - warmup_steps) / (total_steps - warmup_steps))) / 2.
if lr < 0.0:
lr = 0.0
lr_each_step.append(lr)
current_step = global_step
lr_each_step = np.array(lr_each_step).astype(np.float32)
learning_rate = lr_each_step[current_step:]
return learning_rate
set_seed(1)
# load data
mindrecord_dir = "./datasets/MindRecord_COCO"
mindrecord_file = "./datasets/MindRecord_COCO/ssd.mindrecord0"
dataset = create_ssd_dataset(mindrecord_file, batch_size=5, rank=0, use_multiprocessing=True)
dataset_size = dataset.get_dataset_size()
image, get_loc, gt_label, num_matched_boxes = next(dataset.create_tuple_iterator())
# Network definition and initialization
network = SSD300Vgg16()
init_net_param(network)
# Define the learning rate
lr = Tensor(get_lr(global_step=0 * dataset_size,
lr_init=0.001, lr_end=0.001 * 0.05, lr_max=0.05,
warmup_epochs=2, total_epochs=60, steps_per_epoch=dataset_size))
# Define the optimizer
opt = nn.Momentum(filter(lambda x: x.requires_grad, network.get_parameters()), lr,
0.9, 0.00015, float(1024))
# Define the forward procedure
def forward_fn(x, gt_loc, gt_label, num_matched_boxes):
pred_loc, pred_label = network(x)
mask = ops.less(0, gt_label).astype(ms.float32)
num_matched_boxes = ops.sum(num_matched_boxes.astype(ms.float32))
# Positioning loss
mask_loc = ops.tile(ops.expand_dims(mask, -1), (1, 1, 4))
smooth_l1 = nn.SmoothL1Loss()(pred_loc, gt_loc) * mask_loc
loss_loc = ops.sum(ops.sum(smooth_l1, -1), -1)
# Category loss
loss_cls = class_loss(pred_label, gt_label)
loss_cls = ops.sum(loss_cls, (1, 2))
return ops.sum((loss_cls + loss_loc) / num_matched_boxes)
grad_fn = ms.value_and_grad(forward_fn, None, opt.parameters, has_aux=False)
loss_scaler = DynamicLossScaler(1024, 2, 1000)
# Gradient updates
def train_step(x, gt_loc, gt_label, num_matched_boxes):
loss, grads = grad_fn(x, gt_loc, gt_label, num_matched_boxes)
opt(grads)
return loss
print("=================== Starting Training =====================")
for epoch in range(60):
network.set_train(True)
begin_time = time.time()
for step, (image, get_loc, gt_label, num_matched_boxes) in enumerate(dataset.create_tuple_iterator()):
loss = train_step(image, get_loc, gt_label, num_matched_boxes)
end_time = time.time()
times = end_time - begin_time
print(f"Epoch:[{int(epoch + 1)}/{int(60)}], "
f"loss:{loss} , "
f"time:{times}s ")
ms.save_checkpoint(network, "ssd-60_9.ckpt")
print("=================== Training Success =====================")
5. 评估
自定义eval_net()类对训练好的模型进行评估，调用了上述定义的SsdInferWithDecoder类返回预测的坐标及标签，然后分别计算了在不同的IoU阈值、area和maxDets设置下的Average Precision（AP）和Average Recall（AR）。使用COCOMetrics类计算mAP。
mindrecord_file = "./datasets/MindRecord_COCO/ssd_eval.mindrecord0"
def ssd_eval(dataset_path, ckpt_path, anno_json):
"""SSD evaluation."""
batch_size = 1
ds = create_ssd_dataset(dataset_path, batch_size=batch_size,
is_training=False, use_multiprocessing=False)
network = SSD300Vgg16()
print("Load Checkpoint!")
net = SsdInferWithDecoder(network, Tensor(default_boxes), ckpt_path)
net.set_train(False)
total = ds.get_dataset_size() * batch_size
print("\n========================================\n")
print("total images num: ", total)
eval_param_dict = {"net": net, "dataset": ds, "anno_json": anno_json}
mAP = apply_eval(eval_param_dict)
print("\n========================================\n")
print(f"mAP: {mAP}")
def eval_net():
print("Start Eval!")
ssd_eval(mindrecord_file, "./ssd-60_9.ckpt", anno_json)
eval_net()6. 总结
1. SSD（Single Shot MultiBox Detector）是一种高效的目标检测网络模型，它在2016年由Wei Liu等人提出。SSD的主要特点是它能够在单次前向传播过程中直接预测图像中多个目标的位置和类别，从而实现快速且准确的目标检测。
2. SSD的网络结构通常基于一个预训练的卷积神经网络（如VGG16），并在其基础上添加额外的卷积层来生成多尺度的特征图。这些额外的卷积层使得网络能够捕捉到更丰富的特征信息，从而提高检测的准确性。
3. SSD在速度和准确性之间取得了很好的平衡，尤其在实时目标检测任务中表现出色。它在多个基准数据集（如PASCAL VOC、COCO等）上取得了领先的结果。
