def YOLOv3_model(mode='train', optimizer="adam"):
input_image = Input(shape=model_config.input_shape, name="input_image")
slabel = Input(shape=(52, 52, 3, 5 + model_config.class_num),
name="slabel")
sbboxes = Input(shape=(model_config.max_bbox_per_scale, 4), name="sbboxes")
mlabel = Input(shape=(26, 26, 3, 5 + model_config.class_num),
name="mlabel")
mbboxes = Input(shape=(model_config.max_bbox_per_scale, 4), name="mbboxes")
llabel = Input(shape=(13, 13, 3, 5 + model_config.class_num),
name="llabel")
lbboxes = Input(shape=(model_config.max_bbox_per_scale, 4), name="lbboxes")
all_branch = yolov3(input_image, model_config.class_num)
anchors = get_anchors("./anchors_size.txt")
# anchors=np.array(model_config.anchors_size,dtype=np.float32)
if mode == 'train':
branch_and_decodedbranch_tensor = []
for i, branch in enumerate(all_branch):
pred_tensor = Decode(
model_config.class_num,
stride=model_config.strides[i],
anchor_size=anchors[i],
anchor_num=model_config.anchor_num_per_grid)(branch)
branch_and_decodedbranch_tensor.append(branch)
branch_and_decodedbranch_tensor.append(pred_tensor)
target = [slabel, sbboxes, mlabel, mbboxes, llabel, lbboxes]
branch_and_decodedbranch_tensor.extend(target)
matched_tensor = branch_and_decodedbranch_tensor
######output losses ,metrics##############################
conf_loss = ConfidenceLoss(matched_tensor)
prob_loss = ProbabilityLoss(matched_tensor)
# bbox_loss=BboxLoss(matched_tensor)
giou_loss = GiouLoss(matched_tensor)
m_iou = MeanIou(matched_tensor)
model = Model(input=[
input_image, slabel, sbboxes, mlabel, mbboxes, llabel, lbboxes
],
output=[conf_loss, prob_loss, giou_loss])
######添加losses################################
model._losses = []
model._per_input_losses = {}
for loss_name in ["conf_loss", "prob_loss", "giou_loss"]:
layer = model.get_layer(loss_name)
if layer.output in model.losses:
continue
model.add_loss(layer.output)
model.compile(optimizer=Adam())
#######添加metric#########################################
model.metrics_names.extend(
["m_iou", "conf_loss", "prob_loss", "giou_loss", "lr"])
model.metrics_tensors.extend(
[m_iou, conf_loss, prob_loss, giou_loss, model.optimizer.lr])
####################################################
# def mIOU(y_true,y_pred):
# return y_pred
# model.compile(optimizer='adam',loss=[lambda y_true,y_pred: y_pred]*4,
# loss_weights=[1.0,0.5,0.5,0.],
# metrics={"m_iou":mIOU})
return model
elif mode == 'inference':
decodedbranch_tensor = []
for i, branch in enumerate(all_branch):
pred_tensor = Decode(
model_config.class_num,
stride=model_config.strides[i],
anchor_size=anchors[i],
anchor_num=model_config.anchor_num_per_grid)(branch)
decodedbranch_tensor.append(
Reshape(target_shape=(-1, 5 +
model_config.class_num))(pred_tensor))
all_decoded_pred_tensor = Concatenate(
axis=1, name="all_pred")(decodedbranch_tensor)
model = Model(input=input_image, output=all_decoded_pred_tensor)
return model
def deep_net(n_ch, patch_height, patch_width):
inputs = Input(shape=(n_ch, patch_height, patch_width))
# Block 1
x1_1 = Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv1',
data_format='channels_first')(inputs)
x1_2 = Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv2',
data_format='channels_first')(x1_1)
x1_pool = MaxPooling2D((2, 2),
strides=(2, 2),
name='block1_pool',
data_format='channels_first')(x1_2)
# Block 2
x2_1 = Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv1',
data_format='channels_first')(x1_pool)
x2_2 = Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv2',
data_format='channels_first')(x2_1)
x2_pool = MaxPooling2D((2, 2),
strides=(2, 2),
name='block2_pool',
data_format='channels_first')(x2_2)
# Block 3
x3_1 = Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv1',
data_format='channels_first')(x2_pool)
x3_2 = Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv2',
data_format='channels_first')(x3_1)
x3_3 = Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv3',
data_format='channels_first')(x3_2)
x3_pool = MaxPooling2D((2, 2),
strides=(2, 2),
name='block3_pool',
data_format='channels_first')(x3_3)
# Block 4
# x4_1 = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1',data_format='channels_first')(x3_3)
x4_1 = Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv1',
data_format='channels_first')(x3_pool)
#x4_drop1 = Dropout(0.5, name='dr1')(x4_1)
x4_2 = Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv2',
data_format='channels_first')(x4_1)
#x4_drop2 = Dropout(0.5, name='dr2')(x4_2)
x4_3 = Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv3',
data_format='channels_first')(x4_2)
#x4_drop3 = Dropout(0.5, name='dr3')(x4_3)
x1_2_16 = Conv2D(16, (3, 3),
name='x1_2_16',
padding='same',
data_format='channels_first')(x1_2)
x2_2_16 = Conv2D(16, (3, 3),
name='x2_2_16',
padding='same',
data_format='channels_first')(x2_2)
x3_3_16 = Conv2D(16, (3, 3),
name='x3_3_16',
padding='same',
data_format='channels_first')(x3_3)
x4_3_16 = Conv2D(16, (3, 3),
name='x4_3_16',
padding='same',
data_format='channels_first')(x4_3)
conv4_to_1 = Conv2D(2, (3, 3),
padding='same',
data_format='channels_first',
name='conv4_to_1')(x4_3)
# conv4_to_1_up=UpSampling2D(size=(8,8),data_format='channels_first')(conv4_to_1)
conv4_to_1_up = Deconv2D(filters=2,
kernel_size=16,
strides=(8, 8),
data_format='channels_first')(conv4_to_1)
# crop4to1_back = Cropping2D(cropping=(patch_height, patch_width), data_format='channels_first')(conv4_to_1_up)
# conv1_2_17=concatenate([conv4_to_1_up,x1_2_16],axis=1)
crop4to1_back = cropfunc(conv4_to_1_up, inputs)
conv1_2_17 = concatenate([crop4to1_back, x1_2_16], axis=1)
dsn1_in = Conv2D(1, (1, 1), data_format='channels_first',
name='dsn1_in')(conv1_2_17)
conv1_to_2 = Conv2D(2, (1, 1),
name='conv1_to_2',
data_format='channels_first')(x1_2_16)
# side_multi2_up=UpSampling2D(size=(2,2),data_format='channels_first')(x2_2_16)
side_multi2_up = Deconv2D(16,
4,
strides=(2, 2),
padding='same',
data_format='channels_first')(x2_2_16)
# upside_multi2 = Cropping2D(cropping=(patch_height, patch_width), data_format='channels_first')(side_multi2_up)
# conv2_2_17=concatenate([conv1_to_2,side_multi2_up],axis=1)
upside_multi2 = cropfunc(side_multi2_up, inputs)
upside_multi27 = concatenate([conv1_to_2, upside_multi2], axis=1)
# dsn2_in=Conv2D(1,(1,1),data_format='channels_first',padding='same',name='dsn2_in')(conv2_2_17)
dsn2_in = Conv2D(1, (1, 1), data_format='channels_first',
name='dsn2_in')(upside_multi27)
# conv2_to_3=Conv2D(1,(1,1),padding='same',name='conv2_to_3',data_format='channels_first')(conv2_2_17)
# side_multi3_up=UpSampling2D(size=(4,4),data_format='channels_first')(x3_3_16)
conv2_to_3 = Conv2D(1, (1, 1),
name='conv2_to_3',
data_format='channels_first')(upside_multi27)
side_multi3_up = Deconv2D(16,
8,
strides=(4, 4),
data_format='channels_first')(x3_3_16)
# upside_multi3 = Cropping2D(cropping=(patch_height, patch_width), data_format='channels_first')(side_multi3_up)
upside_multi3 = cropfunc(side_multi3_up, inputs)
# conv3_2_17=concatenate([conv2_to_3,side_multi3_up],axis=1)
upside_multi37 = concatenate([conv2_to_3, upside_multi3], axis=1)
# dsn3_in=Conv2D(1,(1,1),data_format='channels_first',padding='same',name='dsn3_in')(conv3_2_17)
dsn3_in = Conv2D(1, (1, 1), data_format='channels_first',
name='dsn3_in')(upside_multi37)
# conv3_to_4=Conv2D(1,(1,1),padding='same',name='conv3_to_4',data_format='channels_first')(conv3_2_17)
# side_multi4_up=UpSampling2D(size=(8,8),data_format='channels_first')(x4_3_16)
# conv4_2_17=concatenate([conv3_to_4,side_multi4_up],axis=1)
# dsn4_in=Conv2D(1,(1,1),data_format='channels_first',padding='same',name='dsn4_in')(conv4_2_17)
conv3_to_4 = Conv2D(1, (1, 1),
name='conv3_to_4',
data_format='channels_first')(upside_multi37)
side_multi4_up = Deconv2D(16,
16,
strides=(8, 8),
data_format='channels_first')(x4_3_16)
# upside_multi4 = Cropping2D(cropping=(patch_height, patch_width), data_format='channels_first')(side_multi4_up)
upside_multi4 = cropfunc(side_multi4_up, inputs)
upside_multi47 = concatenate([conv3_to_4, upside_multi4], axis=1)
dsn4_in = Conv2D(1, (1, 1), data_format='channels_first',
name='dsn4_in')(upside_multi47)
dsn = concatenate([dsn1_in, dsn2_in, dsn3_in, dsn4_in], axis=1)
dsn_out = Conv2D(1, (1, 1),
data_format='channels_first',
padding='same',
name='dsn_out')(dsn)
# dsn_out = Conv2D(1, (1, 1), data_format='channels_first', name='dsn_out', activation='sigmoid')(dsn)
target = Input(shape=(1, patch_height, patch_width), name="target")
MyLoss = Lambda(lambda x: MyEntropyLoss(*x), name="complex_loss")\
([target, dsn1_in, dsn2_in, dsn3_in, dsn4_in, dsn_out])
# outputs = [dsn_out, MyLoss]
outputs = [dsn_out, dsn1_in, dsn2_in, dsn3_in, dsn4_in, MyLoss]
model = Model(inputs=[inputs, target], outputs=outputs)
# model = Model(inputs=inputs, outputs=[dsn1_in, dsn2_in, dsn3_in, dsn4_in, dsn_out])
# model.add_loss(myloss)
# model.compile(loss=[None] * len(model.outputs), optimizer='SGD')
# model.compile(optimizer='SGD')
model._losses = []
model._per_input_losses = {}
for loss_name in ["complex_loss"]:
layer = model.get_layer(loss_name)
if layer.output in model.losses:
continue
loss = tf.reduce_mean(layer.output)
model.add_loss(loss)
return model
