DL之RetinaNet：基于RetinaNet算法(keras框架)利用resnet50_coco数据集(.h5文件)实现目标检测
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DL之RetinaNet：RetinaNet算法的简介(论文介绍)、架构详解、案例应用等配图集合之详细攻略 之6、ResNet50RetinaNet在程序中如何实现的？——结构框图详解
输出结果
设计思路
更新……
核心代码
更新……
def __create_pyramid_features(C3, C4, C5, feature_size=256):
""" Creates the FPN layers on top of the backbone features.
在ResNet基础上创建FPN金字塔特征：参照博客的框架图，输入[C3,C4,C5]，返回5个特征级别[P3, P4, P5, P6, P7]
参考博客：https://yunyaniu.blog.csdn.net/article/details/100010853
Args
C3 : Feature stage C3 from the backbone.
C4 : Feature stage C4 from the backbone.
C5 : Feature stage C5 from the backbone.
feature_size : The feature size to use for the resulting feature levels.
Returns
A list of feature levels [P3, P4, P5, P6, P7].
"""
# upsample C5 to get P5 from the FPN paper
P5 = keras.layers.Conv2D(feature_size, kernel_size=1, strides=1, padding='same', name='C5_reduced')(C5)
P5_upsampled = layers.UpsampleLike(name='P5_upsampled')([P5, C4])
P5 = keras.layers.Conv2D(feature_size, kernel_size=3, strides=1, padding='same', name='P5')(P5)
# add P5 elementwise to C4
P4 = keras.layers.Conv2D(feature_size, kernel_size=1, strides=1, padding='same', name='C4_reduced')(C4)
P4 = keras.layers.Add(name='P4_merged')([P5_upsampled, P4])
P4_upsampled = layers.UpsampleLike(name='P4_upsampled')([P4, C3])
P4 = keras.layers.Conv2D(feature_size, kernel_size=3, strides=1, padding='same', name='P4')(P4)
# add P4 elementwise to C3
P3 = keras.layers.Conv2D(feature_size, kernel_size=1, strides=1, padding='same', name='C3_reduced')(C3)
P3 = keras.layers.Add(name='P3_merged')([P4_upsampled, P3])
P3 = keras.layers.Conv2D(feature_size, kernel_size=3, strides=1, padding='same', name='P3')(P3)
# "P6 is obtained via a 3x3 stride-2 conv on C5"
P6 = keras.layers.Conv2D(feature_size, kernel_size=3, strides=2, padding='same', name='P6')(C5)
# "P7 is computed by applying ReLU followed by a 3x3 stride-2 conv on P6"
P7 = keras.layers.Activation('relu', name='C6_relu')(P6)
P7 = keras.layers.Conv2D(feature_size, kernel_size=3, strides=2, padding='same', name='P7')(P7)
return [P3, P4, P5, P6, P7]
def default_submodels(num_classes, num_anchors):
""" Create a list of default submodels used for object detection.
两个子模型：目标分类子模型default_classification_model、框回归子模型default_regression_model
The default submodels contains a regression submodel and a classification submodel.
Args
num_classes : Number of classes to use.
num_anchors : Number of base anchors.
Returns
A list of tuple, where the first element is the name of the submodel and the second element is the submodel itself.
"""
return [
('regression', default_regression_model(4, num_anchors)),
('classification', default_classification_model(num_classes, num_anchors))
]
def __build_model_pyramid(name, model, features):
""" Applies a single submodel to each FPN level.
真正的构造金字塔模型
Args
name : Name of the submodel.
model : The submodel to evaluate.
features : The FPN features.
Returns
A tensor containing the response from the submodel on the FPN features.
"""
return keras.layers.Concatenate(axis=1, name=name)([model(f) for f in features])
"""
The default anchor parameters. 默认的anchors参数，组合以后有9个anchors
"""
AnchorParameters.default = AnchorParameters(
sizes = [32, 64, 128, 256, 512],
strides = [8, 16, 32, 64, 128],
ratios = np.array([0.5, 1, 2], keras.backend.floatx()),
scales = np.array([2 ** 0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)], keras.backend.floatx()),
)
def anchor_targets_bbox(
anchors,
image_group,
annotations_group,
num_classes,
#negative_overlap和positive_overlap，根据IOU区分
negative_overlap=0.4,
positive_overlap=0.5
):
def focal(alpha=0.25, gamma=2.0):
""" Create a functor for computing the focal loss.
Args
alpha: Scale the focal weight with alpha.
gamma: Take the power of the focal weight with gamma.
Returns
A functor that computes the focal loss using the alpha and gamma.
"""
def _focal(y_true, y_pred):
""" Compute the focal loss given the target tensor and the predicted tensor.
As defined in https:///abs/1708.02002
Args
y_true: Tensor of target data from the generator with shape (B, N, num_classes).
y_pred: Tensor of predicted data from the network with shape (B, N, num_classes).
Returns
The focal loss of y_pred w.r.t. y_true.
"""
labels = y_true[:, :, :-1]
anchor_state = y_true[:, :, -1] # -1 for ignore, 0 for background, 1 for object
classification = y_pred
# filter out "ignore" anchors
indices = backend.where(keras.backend.not_equal(anchor_state, -1))
labels = backend.gather_nd(labels, indices)
classification = backend.gather_nd(classification, indices)
# compute the focal loss
alpha_factor = keras.backend.ones_like(labels) * alpha
alpha_factor = backend.where(keras.backend.equal(labels, 1), alpha_factor, 1 - alpha_factor)
focal_weight = backend.where(keras.backend.equal(labels, 1), 1 - classification, classification)
focal_weight = alpha_factor * focal_weight ** gamma
#定义分类损失： 权重*原来的交叉熵损失
cls_loss = focal_weight * keras.backend.binary_crossentropy(labels, classification)
# compute the normalizer: the number of positive anchors
normalizer = backend.where(keras.backend.equal(anchor_state, 1))
normalizer = keras.backend.cast(keras.backend.shape(normalizer)[0], keras.backend.floatx())
normalizer = keras.backend.maximum(keras.backend.cast_to_floatx(1.0), normalizer)
return keras.backend.sum(cls_loss) / normalizer
return _focal
def smooth_l1(sigma=3.0): #框回归损失采用smooth_l1函数
""" Create a smooth L1 loss functor.
Args
sigma: This argument defines the point where the loss changes from L2 to L1.
Returns
A functor for computing the smooth L1 loss given target data and predicted data.
"""
sigma_squared = sigma ** 2
def _smooth_l1(y_true, y_pred):
""" Compute the smooth L1 loss of y_pred w.r.t. y_true.
Args
y_true: Tensor from the generator of shape (B, N, 5). The last value for each box is the state of the anchor (ignore, negative, positive).
y_pred: Tensor from the network of shape (B, N, 4).
Returns
The smooth L1 loss of y_pred w.r.t. y_true.
"""
# separate target and state
regression = y_pred
regression_target = y_true[:, :, :-1]
anchor_state = y_true[:, :, -1]
# filter out "ignore" anchors
indices = backend.where(keras.backend.equal(anchor_state, 1))
regression = backend.gather_nd(regression, indices)
regression_target = backend.gather_nd(regression_target, indices)
# compute smooth L1 loss
# f(x) = 0.5 * (sigma * x)^2 if |x| < 1 / sigma / sigma
# |x| - 0.5 / sigma / sigma otherwise
regression_diff = regression - regression_target
regression_diff = keras.backend.abs(regression_diff)
regression_loss = backend.where(
keras.backend.less(regression_diff, 1.0 / sigma_squared),
0.5 * sigma_squared * keras.backend.pow(regression_diff, 2),
regression_diff - 0.5 / sigma_squared
)
# compute the normalizer: the number of positive anchors
normalizer = keras.backend.maximum(1, keras.backend.shape(indices)[0])
normalizer = keras.backend.cast(normalizer, dtype=keras.backend.floatx())
return keras.backend.sum(regression_loss) / normalizer
return _smooth_l1
更多输出
Using TensorFlow backend.
2019-08-27 21:56:31.376015:
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, None, None, 3 0
__________________________________________________________________________________________________
padding_conv1 (ZeroPadding2D) (None, None, None, 3 0 input_1[0][0]
__________________________________________________________________________________________________
conv1 (Conv2D) (None, None, None, 6 9408 padding_conv1[0][0]
__________________________________________________________________________________________________
bn_conv1 (BatchNormalization) (None, None, None, 6 256 conv1[0][0]
__________________________________________________________________________________________________
conv1_relu (Activation) (None, None, None, 6 0 bn_conv1[0][0]
__________________________________________________________________________________________________
pool1 (MaxPooling2D) (None, None, None, 6 0 conv1_relu[0][0]
__________________________________________________________________________________________________
res2a_branch2a (Conv2D) (None, None, None, 6 4096 pool1[0][0]
__________________________________________________________________________________________________
bn2a_branch2a (BatchNormalizati (None, None, None, 6 256 res2a_branch2a[0][0]
__________________________________________________________________________________________________
res2a_branch2a_relu (Activation (None, None, None, 6 0 bn2a_branch2a[0][0]
__________________________________________________________________________________________________
padding2a_branch2b (ZeroPadding (None, None, None, 6 0 res2a_branch2a_relu[0][0]
__________________________________________________________________________________________________
res2a_branch2b (Conv2D) (None, None, None, 6 36864 padding2a_branch2b[0][0]
__________________________________________________________________________________________________
bn2a_branch2b (BatchNormalizati (None, None, None, 6 256 res2a_branch2b[0][0]
__________________________________________________________________________________________________
res2a_branch2b_relu (Activation (None, None, None, 6 0 bn2a_branch2b[0][0]
__________________________________________________________________________________________________
res2a_branch2c (Conv2D) (None, None, None, 2 16384 res2a_branch2b_relu[0][0]
__________________________________________________________________________________________________
res2a_branch1 (Conv2D) (None, None, None, 2 16384 pool1[0][0]
__________________________________________________________________________________________________
bn2a_branch2c (BatchNormalizati (None, None, None, 2 1024 res2a_branch2c[0][0]
__________________________________________________________________________________________________
bn2a_branch1 (BatchNormalizatio (None, None, None, 2 1024 res2a_branch1[0][0]
__________________________________________________________________________________________________
res2a (Add) (None, None, None, 2 0 bn2a_branch2c[0][0]
bn2a_branch1[0][0]
__________________________________________________________________________________________________
res2a_relu (Activation) (None, None, None, 2 0 res2a[0][0]
__________________________________________________________________________________________________
res2b_branch2a (Conv2D) (None, None, None, 6 16384 res2a_relu[0][0]
__________________________________________________________________________________________________
bn2b_branch2a (BatchNormalizati (None, None, None, 6 256 res2b_branch2a[0][0]
__________________________________________________________________________________________________
res2b_branch2a_relu (Activation (None, None, None, 6 0 bn2b_branch2a[0][0]
__________________________________________________________________________________________________
padding2b_branch2b (ZeroPadding (None, None, None, 6 0 res2b_branch2a_relu[0][0]
__________________________________________________________________________________________________
res2b_branch2b (Conv2D) (None, None, None, 6 36864 padding2b_branch2b[0][0]
__________________________________________________________________________________________________
bn2b_branch2b (BatchNormalizati (None, None, None, 6 256 res2b_branch2b[0][0]
__________________________________________________________________________________________________
res2b_branch2b_relu (Activation (None, None, None, 6 0 bn2b_branch2b[0][0]
__________________________________________________________________________________________________
res2b_branch2c (Conv2D) (None, None, None, 2 16384 res2b_branch2b_relu[0][0]
__________________________________________________________________________________________________
bn2b_branch2c (BatchNormalizati (None, None, None, 2 1024 res2b_branch2c[0][0]
__________________________________________________________________________________________________
res2b (Add) (None, None, None, 2 0 bn2b_branch2c[0][0]
res2a_relu[0][0]
__________________________________________________________________________________________________
res2b_relu (Activation) (None, None, None, 2 0 res2b[0][0]
__________________________________________________________________________________________________
res2c_branch2a (Conv2D) (None, None, None, 6 16384 res2b_relu[0][0]
__________________________________________________________________________________________________
bn2c_branch2a (BatchNormalizati (None, None, None, 6 256 res2c_branch2a[0][0]
__________________________________________________________________________________________________
res2c_branch2a_relu (Activation (None, None, None, 6 0 bn2c_branch2a[0][0]
__________________________________________________________________________________________________
padding2c_branch2b (ZeroPadding (None, None, None, 6 0 res2c_branch2a_relu[0][0]
__________________________________________________________________________________________________
res2c_branch2b (Conv2D) (None, None, None, 6 36864 padding2c_branch2b[0][0]
__________________________________________________________________________________________________
bn2c_branch2b (BatchNormalizati (None, None, None, 6 256 res2c_branch2b[0][0]
__________________________________________________________________________________________________
res2c_branch2b_relu (Activation (None, None, None, 6 0 bn2c_branch2b[0][0]
__________________________________________________________________________________________________
res2c_branch2c (Conv2D) (None, None, None, 2 16384 res2c_branch2b_relu[0][0]
__________________________________________________________________________________________________
bn2c_branch2c (BatchNormalizati (None, None, None, 2 1024 res2c_branch2c[0][0]
__________________________________________________________________________________________________
res2c (Add) (None, None, None, 2 0 bn2c_branch2c[0][0]
res2b_relu[0][0]
__________________________________________________________________________________________________
res2c_relu (Activation) (None, None, None, 2 0 res2c[0][0]
__________________________________________________________________________________________________
res3a_branch2a (Conv2D) (None, None, None, 1 32768 res2c_relu[0][0]
__________________________________________________________________________________________________
bn3a_branch2a (BatchNormalizati (None, None, None, 1 512 res3a_branch2a[0][0]
__________________________________________________________________________________________________
res3a_branch2a_relu (Activation (None, None, None, 1 0 bn3a_branch2a[0][0]
__________________________________________________________________________________________________
padding3a_branch2b (ZeroPadding (None, None, None, 1 0 res3a_branch2a_relu[0][0]
__________________________________________________________________________________________________
res3a_branch2b (Conv2D) (None, None, None, 1 147456 padding3a_branch2b[0][0]
__________________________________________________________________________________________________
bn3a_branch2b (BatchNormalizati (None, None, None, 1 512 res3a_branch2b[0][0]
__________________________________________________________________________________________________
res3a_branch2b_relu (Activation (None, None, None, 1 0 bn3a_branch2b[0][0]
__________________________________________________________________________________________________
res3a_branch2c (Conv2D) (None, None, None, 5 65536 res3a_branch2b_relu[0][0]
__________________________________________________________________________________________________
res3a_branch1 (Conv2D) (None, None, None, 5 131072 res2c_relu[0][0]
__________________________________________________________________________________________________
bn3a_branch2c (BatchNormalizati (None, None, None, 5 2048 res3a_branch2c[0][0]
__________________________________________________________________________________________________
bn3a_branch1 (BatchNormalizatio (None, None, None, 5 2048 res3a_branch1[0][0]
__________________________________________________________________________________________________
res3a (Add) (None, None, None, 5 0 bn3a_branch2c[0][0]
bn3a_branch1[0][0]
__________________________________________________________________________________________________
res3a_relu (Activation) (None, None, None, 5 0 res3a[0][0]
__________________________________________________________________________________________________
res3b_branch2a (Conv2D) (None, None, None, 1 65536 res3a_relu[0][0]
__________________________________________________________________________________________________
bn3b_branch2a (BatchNormalizati (None, None, None, 1 512 res3b_branch2a[0][0]
__________________________________________________________________________________________________
res3b_branch2a_relu (Activation (None, None, None, 1 0 bn3b_branch2a[0][0]
__________________________________________________________________________________________________
padding3b_branch2b (ZeroPadding (None, None, None, 1 0 res3b_branch2a_relu[0][0]
__________________________________________________________________________________________________
res3b_branch2b (Conv2D) (None, None, None, 1 147456 padding3b_branch2b[0][0]
__________________________________________________________________________________________________
bn3b_branch2b (BatchNormalizati (None, None, None, 1 512 res3b_branch2b[0][0]
__________________________________________________________________________________________________
res3b_branch2b_relu (Activation (None, None, None, 1 0 bn3b_branch2b[0][0]
__________________________________________________________________________________________________
res3b_branch2c (Conv2D) (None, None, None, 5 65536 res3b_branch2b_relu[0][0]
__________________________________________________________________________________________________
bn3b_branch2c (BatchNormalizati (None, None, None, 5 2048 res3b_branch2c[0][0]
__________________________________________________________________________________________________
res3b (Add) (None, None, None, 5 0 bn3b_branch2c[0][0]
res3a_relu[0][0]
__________________________________________________________________________________________________
res3b_relu (Activation) (None, None, None, 5 0 res3b[0][0]
__________________________________________________________________________________________________
res3c_branch2a (Conv2D) (None, None, None, 1 65536 res3b_relu[0][0]
__________________________________________________________________________________________________
bn3c_branch2a (BatchNormalizati (None, None, None, 1 512 res3c_branch2a[0][0]
__________________________________________________________________________________________________
res3c_branch2a_relu (Activation (None, None, None, 1 0 bn3c_branch2a[0][0]
__________________________________________________________________________________________________
padding3c_branch2b (ZeroPadding (None, None, None, 1 0 res3c_branch2a_relu[0][0]
__________________________________________________________________________________________________
res3c_branch2b (Conv2D) (None, None, None, 1 147456 padding3c_branch2b[0][0]
__________________________________________________________________________________________________
bn3c_branch2b (BatchNormalizati (None, None, None, 1 512 res3c_branch2b[0][0]
__________________________________________________________________________________________________
res3c_branch2b_relu (Activation (None, None, None, 1 0 bn3c_branch2b[0][0]
__________________________________________________________________________________________________
res3c_branch2c (Conv2D) (None, None, None, 5 65536 res3c_branch2b_relu[0][0]
__________________________________________________________________________________________________
bn3c_branch2c (BatchNormalizati (None, None, None, 5 2048 res3c_branch2c[0][0]
__________________________________________________________________________________________________
res3c (Add) (None, None, None, 5 0 bn3c_branch2c[0][0]
res3b_relu[0][0]
__________________________________________________________________________________________________
res3c_relu (Activation) (None, None, None, 5 0 res3c[0][0]
__________________________________________________________________________________________________
res3d_branch2a (Conv2D) (None, None, None, 1 65536 res3c_relu[0][0]
__________________________________________________________________________________________________
bn3d_branch2a (BatchNormalizati (None, None, None, 1 512 res3d_branch2a[0][0]
__________________________________________________________________________________________________
res3d_branch2a_relu (Activation (None, None, None, 1 0 bn3d_branch2a[0][0]
__________________________________________________________________________________________________
padding3d_branch2b (ZeroPadding (None, None, None, 1 0 res3d_branch2a_relu[0][0]
__________________________________________________________________________________________________
res3d_branch2b (Conv2D) (None, None, None, 1 147456 padding3d_branch2b[0][0]
__________________________________________________________________________________________________
bn3d_branch2b (BatchNormalizati (None, None, None, 1 512 res3d_branch2b[0][0]
__________________________________________________________________________________________________
res3d_branch2b_relu (Activation (None, None, None, 1 0 bn3d_branch2b[0][0]
__________________________________________________________________________________________________
res3d_branch2c (Conv2D) (None, None, None, 5 65536 res3d_branch2b_relu[0][0]
__________________________________________________________________________________________________
bn3d_branch2c (BatchNormalizati (None, None, None, 5 2048 res3d_branch2c[0][0]
__________________________________________________________________________________________________
res3d (Add) (None, None, None, 5 0 bn3d_branch2c[0][0]
res3c_relu[0][0]
__________________________________________________________________________________________________
res3d_relu (Activation) (None, None, None, 5 0 res3d[0][0]
__________________________________________________________________________________________________
res4a_branch2a (Conv2D) (None, None, None, 2 131072 res3d_relu[0][0]
__________________________________________________________________________________________________
bn4a_branch2a (BatchNormalizati (None, None, None, 2 1024 res4a_branch2a[0][0]
__________________________________________________________________________________________________
res4a_branch2a_relu (Activation (None, None, None, 2 0 bn4a_branch2a[0][0]
__________________________________________________________________________________________________
padding4a_branch2b (ZeroPadding (None, None, None, 2 0 res4a_branch2a_relu[0][0]
__________________________________________________________________________________________________
res4a_branch2b (Conv2D) (None, None, None, 2 589824 padding4a_branch2b[0][0]
__________________________________________________________________________________________________
bn4a_branch2b (BatchNormalizati (None, None, None, 2 1024 res4a_branch2b[0][0]
__________________________________________________________________________________________________
res4a_branch2b_relu (Activation (None, None, None, 2 0 bn4a_branch2b[0][0]
__________________________________________________________________________________________________
res4a_branch2c (Conv2D) (None, None, None, 1 262144 res4a_branch2b_relu[0][0]
__________________________________________________________________________________________________
res4a_branch1 (Conv2D) (None, None, None, 1 524288 res3d_relu[0][0]
__________________________________________________________________________________________________
bn4a_branch2c (BatchNormalizati (None, None, None, 1 4096 res4a_branch2c[0][0]
__________________________________________________________________________________________________
bn4a_branch1 (BatchNormalizatio (None, None, None, 1 4096 res4a_branch1[0][0]
__________________________________________________________________________________________________
res4a (Add) (None, None, None, 1 0 bn4a_branch2c[0][0]
bn4a_branch1[0][0]
__________________________________________________________________________________________________
res4a_relu (Activation) (None, None, None, 1 0 res4a[0][0]
__________________________________________________________________________________________________
res4b_branch2a (Conv2D) (None, None, None, 2 262144 res4a_relu[0][0]
__________________________________________________________________________________________________
bn4b_branch2a (BatchNormalizati (None, None, None, 2 1024 res4b_branch2a[0][0]
__________________________________________________________________________________________________
res4b_branch2a_relu (Activation (None, None, None, 2 0 bn4b_branch2a[0][0]
__________________________________________________________________________________________________
padding4b_branch2b (ZeroPadding (None, None, None, 2 0 res4b_branch2a_relu[0][0]
__________________________________________________________________________________________________
res4b_branch2b (Conv2D) (None, None, None, 2 589824 padding4b_branch2b[0][0]
__________________________________________________________________________________________________
bn4b_branch2b (BatchNormalizati (None, None, None, 2 1024 res4b_branch2b[0][0]
__________________________________________________________________________________________________
res4b_branch2b_relu (Activation (None, None, None, 2 0 bn4b_branch2b[0][0]
__________________________________________________________________________________________________
res4b_branch2c (Conv2D) (None, None, None, 1 262144 res4b_branch2b_relu[0][0]
__________________________________________________________________________________________________
bn4b_branch2c (BatchNormalizati (None, None, None, 1 4096 res4b_branch2c[0][0]
__________________________________________________________________________________________________
res4b (Add) (None, None, None, 1 0 bn4b_branch2c[0][0]
res4a_relu[0][0]
__________________________________________________________________________________________________
res4b_relu (Activation) (None, None, None, 1 0 res4b[0][0]
__________________________________________________________________________________________________
res4c_branch2a (Conv2D) (None, None, None, 2 262144 res4b_relu[0][0]
__________________________________________________________________________________________________
bn4c_branch2a (BatchNormalizati (None, None, None, 2 1024 res4c_branch2a[0][0]
__________________________________________________________________________________________________
res4c_branch2a_relu (Activation (None, None, None, 2 0 bn4c_branch2a[0][0]
__________________________________________________________________________________________________
padding4c_branch2b (ZeroPadding (None, None, None, 2 0 res4c_branch2a_relu[0][0]
__________________________________________________________________________________________________
res4c_branch2b (Conv2D) (None, None, None, 2 589824 padding4c_branch2b[0][0]
__________________________________________________________________________________________________
bn4c_branch2b (BatchNormalizati (None, None, None, 2 1024 res4c_branch2b[0][0]
__________________________________________________________________________________________________
res4c_branch2b_relu (Activation (None, None, None, 2 0 bn4c_branch2b[0][0]
__________________________________________________________________________________________________
res4c_branch2c (Conv2D) (None, None, None, 1 262144 res4c_branch2b_relu[0][0]
__________________________________________________________________________________________________
bn4c_branch2c (BatchNormalizati (None, None, None, 1 4096 res4c_branch2c[0][0]
__________________________________________________________________________________________________
res4c (Add) (None, None, None, 1 0 bn4c_branch2c[0][0]
res4b_relu[0][0]
__________________________________________________________________________________________________
res4c_relu (Activation) (None, None, None, 1 0 res4c[0][0]
__________________________________________________________________________________________________
res4d_branch2a (Conv2D) (None, None, None, 2 262144 res4c_relu[0][0]
__________________________________________________________________________________________________
bn4d_branch2a (BatchNormalizati (None, None, None, 2 1024 res4d_branch2a[0][0]
__________________________________________________________________________________________________
res4d_branch2a_relu (Activation (None, None, None, 2 0 bn4d_branch2a[0][0]
__________________________________________________________________________________________________
padding4d_branch2b (ZeroPadding (None, None, None, 2 0 res4d_branch2a_relu[0][0]
__________________________________________________________________________________________________
res4d_branch2b (Conv2D) (None, None, None, 2 589824 padding4d_branch2b[0][0]
__________________________________________________________________________________________________
bn4d_branch2b (BatchNormalizati (None, None, None, 2 1024 res4d_branch2b[0][0]
__________________________________________________________________________________________________
res4d_branch2b_relu (Activation (None, None, None, 2 0 bn4d_branch2b[0][0]
__________________________________________________________________________________________________
res4d_branch2c (Conv2D) (None, None, None, 1 262144 res4d_branch2b_relu[0][0]
__________________________________________________________________________________________________
bn4d_branch2c (BatchNormalizati (None, None, None, 1 4096 res4d_branch2c[0][0]
__________________________________________________________________________________________________
res4d (Add) (None, None, None, 1 0 bn4d_branch2c[0][0]
res4c_relu[0][0]
__________________________________________________________________________________________________
res4d_relu (Activation) (None, None, None, 1 0 res4d[0][0]
__________________________________________________________________________________________________
res4e_branch2a (Conv2D) (None, None, None, 2 262144 res4d_relu[0][0]
__________________________________________________________________________________________________
bn4e_branch2a (BatchNormalizati (None, None, None, 2 1024 res4e_branch2a[0][0]
__________________________________________________________________________________________________
res4e_branch2a_relu (Activation (None, None, None, 2 0 bn4e_branch2a[0][0]
__________________________________________________________________________________________________
padding4e_branch2b (ZeroPadding (None, None, None, 2 0 res4e_branch2a_relu[0][0]
__________________________________________________________________________________________________
res4e_branch2b (Conv2D) (None, None, None, 2 589824 padding4e_branch2b[0][0]
__________________________________________________________________________________________________
bn4e_branch2b (BatchNormalizati (None, None, None, 2 1024 res4e_branch2b[0][0]
__________________________________________________________________________________________________
res4e_branch2b_relu (Activation (None, None, None, 2 0 bn4e_branch2b[0][0]
__________________________________________________________________________________________________
res4e_branch2c (Conv2D) (None, None, None, 1 262144 res4e_branch2b_relu[0][0]
__________________________________________________________________________________________________
bn4e_branch2c (BatchNormalizati (None, None, None, 1 4096 res4e_branch2c[0][0]
__________________________________________________________________________________________________
res4e (Add) (None, None, None, 1 0 bn4e_branch2c[0][0]
res4d_relu[0][0]
__________________________________________________________________________________________________
res4e_relu (Activation) (None, None, None, 1 0 res4e[0][0]
__________________________________________________________________________________________________
res4f_branch2a (Conv2D) (None, None, None, 2 262144 res4e_relu[0][0]
__________________________________________________________________________________________________
bn4f_branch2a (BatchNormalizati (None, None, None, 2 1024 res4f_branch2a[0][0]
__________________________________________________________________________________________________
res4f_branch2a_relu (Activation (None, None, None, 2 0 bn4f_branch2a[0][0]
__________________________________________________________________________________________________
padding4f_branch2b (ZeroPadding (None, None, None, 2 0 res4f_branch2a_relu[0][0]
__________________________________________________________________________________________________
res4f_branch2b (Conv2D) (None, None, None, 2 589824 padding4f_branch2b[0][0]
__________________________________________________________________________________________________
bn4f_branch2b (BatchNormalizati (None, None, None, 2 1024 res4f_branch2b[0][0]
__________________________________________________________________________________________________
res4f_branch2b_relu (Activation (None, None, None, 2 0 bn4f_branch2b[0][0]
__________________________________________________________________________________________________
res4f_branch2c (Conv2D) (None, None, None, 1 262144 res4f_branch2b_relu[0][0]
__________________________________________________________________________________________________
bn4f_branch2c (BatchNormalizati (None, None, None, 1 4096 res4f_branch2c[0][0]
__________________________________________________________________________________________________
res4f (Add) (None, None, None, 1 0 bn4f_branch2c[0][0]
res4e_relu[0][0]
__________________________________________________________________________________________________
res4f_relu (Activation) (None, None, None, 1 0 res4f[0][0]
__________________________________________________________________________________________________
res5a_branch2a (Conv2D) (None, None, None, 5 524288 res4f_relu[0][0]
__________________________________________________________________________________________________
bn5a_branch2a (BatchNormalizati (None, None, None, 5 2048 res5a_branch2a[0][0]
__________________________________________________________________________________________________
res5a_branch2a_relu (Activation (None, None, None, 5 0 bn5a_branch2a[0][0]
__________________________________________________________________________________________________
padding5a_branch2b (ZeroPadding (None, None, None, 5 0 res5a_branch2a_relu[0][0]
__________________________________________________________________________________________________
res5a_branch2b (Conv2D) (None, None, None, 5 2359296 padding5a_branch2b[0][0]
__________________________________________________________________________________________________
bn5a_branch2b (BatchNormalizati (None, None, None, 5 2048 res5a_branch2b[0][0]
__________________________________________________________________________________________________
res5a_branch2b_relu (Activation (None, None, None, 5 0 bn5a_branch2b[0][0]
__________________________________________________________________________________________________
res5a_branch2c (Conv2D) (None, None, None, 2 1048576 res5a_branch2b_relu[0][0]
__________________________________________________________________________________________________
res5a_branch1 (Conv2D) (None, None, None, 2 2097152 res4f_relu[0][0]
__________________________________________________________________________________________________
bn5a_branch2c (BatchNormalizati (None, None, None, 2 8192 res5a_branch2c[0][0]
__________________________________________________________________________________________________
bn5a_branch1 (BatchNormalizatio (None, None, None, 2 8192 res5a_branch1[0][0]
__________________________________________________________________________________________________
res5a (Add) (None, None, None, 2 0 bn5a_branch2c[0][0]
bn5a_branch1[0][0]
__________________________________________________________________________________________________
res5a_relu (Activation) (None, None, None, 2 0 res5a[0][0]
__________________________________________________________________________________________________
res5b_branch2a (Conv2D) (None, None, None, 5 1048576 res5a_relu[0][0]
__________________________________________________________________________________________________
bn5b_branch2a (BatchNormalizati (None, None, None, 5 2048 res5b_branch2a[0][0]
__________________________________________________________________________________________________
res5b_branch2a_relu (Activation (None, None, None, 5 0 bn5b_branch2a[0][0]
__________________________________________________________________________________________________
padding5b_branch2b (ZeroPadding (None, None, None, 5 0 res5b_branch2a_relu[0][0]
__________________________________________________________________________________________________
res5b_branch2b (Conv2D) (None, None, None, 5 2359296 padding5b_branch2b[0][0]
__________________________________________________________________________________________________
bn5b_branch2b (BatchNormalizati (None, None, None, 5 2048 res5b_branch2b[0][0]
__________________________________________________________________________________________________
res5b_branch2b_relu (Activation (None, None, None, 5 0 bn5b_branch2b[0][0]
__________________________________________________________________________________________________
res5b_branch2c (Conv2D) (None, None, None, 2 1048576 res5b_branch2b_relu[0][0]
__________________________________________________________________________________________________
bn5b_branch2c (BatchNormalizati (None, None, None, 2 8192 res5b_branch2c[0][0]
__________________________________________________________________________________________________
res5b (Add) (None, None, None, 2 0 bn5b_branch2c[0][0]
res5a_relu[0][0]
__________________________________________________________________________________________________
res5b_relu (Activation) (None, None, None, 2 0 res5b[0][0]
__________________________________________________________________________________________________
res5c_branch2a (Conv2D) (None, None, None, 5 1048576 res5b_relu[0][0]
__________________________________________________________________________________________________
bn5c_branch2a (BatchNormalizati (None, None, None, 5 2048 res5c_branch2a[0][0]
__________________________________________________________________________________________________
res5c_branch2a_relu (Activation (None, None, None, 5 0 bn5c_branch2a[0][0]
__________________________________________________________________________________________________
padding5c_branch2b (ZeroPadding (None, None, None, 5 0 res5c_branch2a_relu[0][0]
__________________________________________________________________________________________________
res5c_branch2b (Conv2D) (None, None, None, 5 2359296 padding5c_branch2b[0][0]
__________________________________________________________________________________________________
bn5c_branch2b (BatchNormalizati (None, None, None, 5 2048 res5c_branch2b[0][0]
__________________________________________________________________________________________________
res5c_branch2b_relu (Activation (None, None, None, 5 0 bn5c_branch2b[0][0]
__________________________________________________________________________________________________
res5c_branch2c (Conv2D) (None, None, None, 2 1048576 res5c_branch2b_relu[0][0]
__________________________________________________________________________________________________
bn5c_branch2c (BatchNormalizati (None, None, None, 2 8192 res5c_branch2c[0][0]
__________________________________________________________________________________________________
res5c (Add) (None, None, None, 2 0 bn5c_branch2c[0][0]
res5b_relu[0][0]
__________________________________________________________________________________________________
res5c_relu (Activation) (None, None, None, 2 0 res5c[0][0]
__________________________________________________________________________________________________
C5_reduced (Conv2D) (None, None, None, 2 524544 res5c_relu[0][0]
__________________________________________________________________________________________________
P5_upsampled (UpsampleLike) (None, None, None, 2 0 C5_reduced[0][0]
res4f_relu[0][0]
__________________________________________________________________________________________________
C4_reduced (Conv2D) (None, None, None, 2 262400 res4f_relu[0][0]
__________________________________________________________________________________________________
P4_merged (Add) (None, None, None, 2 0 P5_upsampled[0][0]
C4_reduced[0][0]
__________________________________________________________________________________________________
P4_upsampled (UpsampleLike) (None, None, None, 2 0 P4_merged[0][0]
res3d_relu[0][0]
__________________________________________________________________________________________________
C3_reduced (Conv2D) (None, None, None, 2 131328 res3d_relu[0][0]
__________________________________________________________________________________________________
P6 (Conv2D) (None, None, None, 2 4718848 res5c_relu[0][0]
__________________________________________________________________________________________________
P3_merged (Add) (None, None, None, 2 0 P4_upsampled[0][0]
C3_reduced[0][0]
__________________________________________________________________________________________________
C6_relu (Activation) (None, None, None, 2 0 P6[0][0]
__________________________________________________________________________________________________
P3 (Conv2D) (None, None, None, 2 590080 P3_merged[0][0]
__________________________________________________________________________________________________
P4 (Conv2D) (None, None, None, 2 590080 P4_merged[0][0]
__________________________________________________________________________________________________
P5 (Conv2D) (None, None, None, 2 590080 C5_reduced[0][0]
__________________________________________________________________________________________________
P7 (Conv2D) (None, None, None, 2 590080 C6_relu[0][0]
__________________________________________________________________________________________________
anchors_0 (Anchors) (None, None, 4) 0 P3[0][0]
__________________________________________________________________________________________________
anchors_1 (Anchors) (None, None, 4) 0 P4[0][0]
__________________________________________________________________________________________________
anchors_2 (Anchors) (None, None, 4) 0 P5[0][0]
__________________________________________________________________________________________________
anchors_3 (Anchors) (None, None, 4) 0 P6[0][0]
__________________________________________________________________________________________________
anchors_4 (Anchors) (None, None, 4) 0 P7[0][0]
__________________________________________________________________________________________________
regression_submodel (Model) (None, None, 4) 2443300 P3[0][0]
P4[0][0]
P5[0][0]
P6[0][0]
P7[0][0]
__________________________________________________________________________________________________
anchors (Concatenate) (None, None, 4) 0 anchors_0[0][0]
anchors_1[0][0]
anchors_2[0][0]
anchors_3[0][0]
anchors_4[0][0]
__________________________________________________________________________________________________
regression (Concatenate) (None, None, 4) 0 regression_submodel[1][0]
regression_submodel[2][0]
regression_submodel[3][0]
regression_submodel[4][0]
regression_submodel[5][0]
__________________________________________________________________________________________________
boxes (RegressBoxes) (None, None, 4) 0 anchors[0][0]
regression[0][0]
__________________________________________________________________________________________________
classification_submodel (Model) (None, None, 80) 4019920 P3[0][0]
P4[0][0]
P5[0][0]
P6[0][0]
P7[0][0]
__________________________________________________________________________________________________
clipped_boxes (ClipBoxes) (None, None, 4) 0 input_1[0][0]
boxes[0][0]
__________________________________________________________________________________________________
classification (Concatenate) (None, None, 80) 0 classification_submodel[1][0]
classification_submodel[2][0]
classification_submodel[3][0]
classification_submodel[4][0]
classification_submodel[5][0]
__________________________________________________________________________________________________
filtered_detections (FilterDete [(None, 300, 4), (No 0 clipped_boxes[0][0]
classification[0][0]
==================================================================================================
Total params: 38,021,812
Trainable params: 37,915,572
Non-trainable params: 106,240
__________________________________________________________________________________________________
None
processing time: 16.85137176513672
当前目标为： bicycle
当前目标为： person
当前目标为： car
当前目标为： person
