def generator_network_w_label(x, labels, data_dim, label_dim, base_n_count):
x = layers.concatenate([x, labels])
x = layers.Dense(base_n_count * 1, activation='relu')(x) # 1
x = layers.Dense(base_n_count * 2, activation='relu')(x) # 2
x = layers.Dense(base_n_count * 4, activation='relu')(x)
# x = layers.Dense(base_n_count*4, activation='relu')(x) # extra
# x = layers.Dense(base_n_count*4, activation='relu')(x) # extra
x = layers.Dense(data_dim)(x)
x = layers.concatenate([x, labels])
return x
def NN_huaweiv1(maxlen, embedding_matrix=None, class_num1=17, class_num2=12):
emb_layer = Embedding(
embedding_matrix.shape[0],
embedding_matrix.shape[1],
input_length=maxlen,
weights=[embedding_matrix],
trainable=False,
)
seq1 = Input(shape=(maxlen, ))
x1 = emb_layer(seq1)
sdrop = SpatialDropout1D(rate=0.2)
lstm_layer = Bidirectional(CuDNNGRU(128, return_sequences=True))
gru_layer = Bidirectional(CuDNNGRU(128, return_sequences=True))
cnn1d_layer = Conv1D(64,
kernel_size=3,
padding="same",
kernel_initializer="he_uniform")
x1 = sdrop(x1)
lstm1 = lstm_layer(x1)
gru1 = gru_layer(lstm1)
att_1 = Attention(maxlen)(lstm1)
att_2 = Attention(maxlen)(gru1)
cnn1 = cnn1d_layer(lstm1)
avg_pool = GlobalAveragePooling1D()
max_pool = GlobalMaxPooling1D()
x1 = concatenate([
att_1, att_2,
Attention(maxlen)(cnn1),
avg_pool(cnn1),
max_pool(cnn1)
])
x = Dropout(0.2)(Activation(activation="relu")(BatchNormalization()(
Dense(128)(x1))))
x = Activation(activation="relu")(BatchNormalization()(Dense(64)(x)))
pred1_d = Dense(class_num1)(x)
pred1 = Activation(activation='sigmoid', name='pred1')(pred1_d)
y = concatenate([x1, x])
y = Activation(activation="relu")(BatchNormalization()(Dense(64)(x)))
pred2_d = Dense(class_num2)(y)
pred2 = Activation(activation='sigmoid', name='pred2')(pred2_d)
z = Dropout(0.2)(Activation(activation="relu")(BatchNormalization()(
Dense(128)(x1))))
z = concatenate([pred1_d, pred2_d, z])
pred3 = Dense(class_num1 + class_num2, activation='sigmoid',
name='pred3')(z)
model = Model(inputs=seq1, outputs=[pred1, pred2, pred3])
return model
def inception_block_1c(X):
X_3x3 = fr_utils.conv2d_bn(X,
layer='inception_3c_3x3',
cv1_out=128,
cv1_filter=(1, 1),
cv2_out=256,
cv2_filter=(3, 3),
cv2_strides=(2, 2),
padding=(1, 1))
X_5x5 = fr_utils.conv2d_bn(X,
layer='inception_3c_5x5',
cv1_out=32,
cv1_filter=(1, 1),
cv2_out=64,
cv2_filter=(5, 5),
cv2_strides=(2, 2),
padding=(2, 2))
X_pool = MaxPooling2D(pool_size=3, strides=2, data_format='channels_first')(X)
X_pool = ZeroPadding2D(padding=((0, 1), (0, 1)), data_format='channels_first')(X_pool)
inception = concatenate([X_3x3, X_5x5, X_pool], axis=1)
return inception
def inception_block_1b(X):
X_3x3 = Conv2D(96, (1, 1), data_format='channels_first', name='inception_3b_3x3_conv1')(X)
X_3x3 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3b_3x3_bn1')(X_3x3)
X_3x3 = Activation('relu')(X_3x3)
X_3x3 = ZeroPadding2D(padding=(1, 1), data_format='channels_first')(X_3x3)
X_3x3 = Conv2D(128, (3, 3), data_format='channels_first', name='inception_3b_3x3_conv2')(X_3x3)
X_3x3 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3b_3x3_bn2')(X_3x3)
X_3x3 = Activation('relu')(X_3x3)
X_5x5 = Conv2D(32, (1, 1), data_format='channels_first', name='inception_3b_5x5_conv1')(X)
X_5x5 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3b_5x5_bn1')(X_5x5)
X_5x5 = Activation('relu')(X_5x5)
X_5x5 = ZeroPadding2D(padding=(2, 2), data_format='channels_first')(X_5x5)
X_5x5 = Conv2D(64, (5, 5), data_format='channels_first', name='inception_3b_5x5_conv2')(X_5x5)
X_5x5 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3b_5x5_bn2')(X_5x5)
X_5x5 = Activation('relu')(X_5x5)
X_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3), data_format='channels_first')(X)
X_pool = Conv2D(64, (1, 1), data_format='channels_first', name='inception_3b_pool_conv')(X_pool)
X_pool = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3b_pool_bn')(X_pool)
X_pool = Activation('relu')(X_pool)
X_pool = ZeroPadding2D(padding=(4, 4), data_format='channels_first')(X_pool)
X_1x1 = Conv2D(64, (1, 1), data_format='channels_first', name='inception_3b_1x1_conv')(X)
X_1x1 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3b_1x1_bn')(X_1x1)
X_1x1 = Activation('relu')(X_1x1)
inception = concatenate([X_3x3, X_5x5, X_pool, X_1x1], axis=1)
return inception
def inception_block_2a(X):
X_3x3 = fr_utils.conv2d_bn(X,
layer='inception_4a_3x3',
cv1_out=96,
cv1_filter=(1, 1),
cv2_out=192,
cv2_filter=(3, 3),
cv2_strides=(1, 1),
padding=(1, 1))
X_5x5 = fr_utils.conv2d_bn(X,
layer='inception_4a_5x5',
cv1_out=32,
cv1_filter=(1, 1),
cv2_out=64,
cv2_filter=(5, 5),
cv2_strides=(1, 1),
padding=(2, 2))
X_pool = AveragePooling2D(pool_size=(3, 3), strides=(3, 3), data_format='channels_first')(X)
X_pool = fr_utils.conv2d_bn(X_pool,
layer='inception_4a_pool',
cv1_out=128,
cv1_filter=(1, 1),
padding=(2, 2))
X_1x1 = fr_utils.conv2d_bn(X,
layer='inception_4a_1x1',
cv1_out=256,
cv1_filter=(1, 1))
inception = concatenate([X_3x3, X_5x5, X_pool, X_1x1], axis=1)
return inception
def inception_block_1a(X):
"""
Implementation of an inception block
"""
X_3x3 = Conv2D(96, (1, 1), data_format='channels_first', name='inception_3a_3x3_conv1')(X)
X_3x3 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3a_3x3_bn1')(X_3x3)
X_3x3 = Activation('relu')(X_3x3)
X_3x3 = ZeroPadding2D(padding=(1, 1), data_format='channels_first')(X_3x3)
X_3x3 = Conv2D(128, (3, 3), data_format='channels_first', name='inception_3a_3x3_conv2')(X_3x3)
X_3x3 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3a_3x3_bn2')(X_3x3)
X_3x3 = Activation('relu')(X_3x3)
X_5x5 = Conv2D(16, (1, 1), data_format='channels_first', name='inception_3a_5x5_conv1')(X)
X_5x5 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3a_5x5_bn1')(X_5x5)
X_5x5 = Activation('relu')(X_5x5)
X_5x5 = ZeroPadding2D(padding=(2, 2), data_format='channels_first')(X_5x5)
X_5x5 = Conv2D(32, (5, 5), data_format='channels_first', name='inception_3a_5x5_conv2')(X_5x5)
X_5x5 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3a_5x5_bn2')(X_5x5)
X_5x5 = Activation('relu')(X_5x5)
X_pool = MaxPooling2D(pool_size=3, strides=2, data_format='channels_first')(X)
X_pool = Conv2D(32, (1, 1), data_format='channels_first', name='inception_3a_pool_conv')(X_pool)
X_pool = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3a_pool_bn')(X_pool)
X_pool = Activation('relu')(X_pool)
X_pool = ZeroPadding2D(padding=((3, 4), (3, 4)), data_format='channels_first')(X_pool)
X_1x1 = Conv2D(64, (1, 1), data_format='channels_first', name='inception_3a_1x1_conv')(X)
X_1x1 = BatchNormalization(axis=1, epsilon=0.00001, name='inception_3a_1x1_bn')(X_1x1)
X_1x1 = Activation('relu')(X_1x1)
# CONCAT
inception = concatenate([X_3x3, X_5x5, X_pool, X_1x1], axis=1)
return inception
def call(self, x, mask=None):
with tf.name_scope('MDN'):
mdn_out = layers.concatenate([self.mdn_mus(x),
self.mdn_sigmas(x),
self.mdn_pi(x)],
name='mdn_outputs')
return mdn_out
def multi_gpu_model(model, gpus):
if isinstance(gpus, (list, tuple)):
num_gpus = len(gpus)
target_gpu_ids = gpus
else:
num_gpus = gpus
target_gpu_ids = range(num_gpus)
def get_slice(data, i, parts):
shape = tf.shape(data)
batch_size = shape[:1]
input_shape = shape[1:]
step = batch_size // parts
if i == num_gpus - 1:
size = batch_size - step * i
else:
size = step
size = tf.concat([size, input_shape], axis=0)
stride = tf.concat([step, input_shape * 0], axis=0)
start = stride * i
return tf.slice(data, start, size)
all_outputs = []
for i in range(len(model.outputs)):
all_outputs.append([])
# Place a copy of the model on each GPU,
# each getting a slice of the inputs.
for i, gpu_id in enumerate(target_gpu_ids):
with tf.device('/gpu:%d' % gpu_id):
with tf.name_scope('replica_%d' % gpu_id):
inputs = []
# Retrieve a slice of the input.
for x in model.inputs:
input_shape = tuple(x.get_shape().as_list())[1:]
slice_i = Lambda(get_slice,
output_shape=input_shape,
arguments={
'i': i,
'parts': num_gpus
})(x)
inputs.append(slice_i)
# Apply model on slice
# (creating a model replica on the target device).
outputs = model(inputs)
if not isinstance(outputs, list):
outputs = [outputs]
# Save the outputs for merging back together later.
for o in range(len(outputs)):
all_outputs[o].append(outputs[o])
# Merge outputs on CPU.
with tf.device('/cpu:0'):
merged = []
for name, outputs in zip(model.output_names, all_outputs):
merged.append(concatenate(outputs, axis=0, name=name))
return Model(model.inputs, merged)
def NN_huaweiv1(maxlen, embedding_matrix=None, class_num1=17, class_num2=12):
emb_layer = Embedding(
embedding_matrix.shape[0],
embedding_matrix.shape[1],
input_length=maxlen,
weights=[embedding_matrix],
trainable=False,
)
seq1 = Input(shape=(maxlen, ))
emb = emb_layer(seq1)
sdrop = SpatialDropout1D(rate=0.2)
lstm_layer = Bidirectional(CuDNNGRU(128, return_sequences=True))
gru_layer = Bidirectional(CuDNNGRU(128, return_sequences=True))
cnn1d_layer = Conv1D(64,
kernel_size=3,
padding="same",
kernel_initializer="he_uniform")
sd = sdrop(emb)
lstm1 = lstm_layer(sd)
gru1 = gru_layer(lstm1)
cnn1 = cnn1d_layer(gru1)
gru1 = concatenate([lstm1, gru1, cnn1])
att_1 = Attention(maxlen)(gru1)
att_2 = Attention(maxlen)(gru1)
att_3 = Attention(maxlen)(gru1)
att_4 = Attention(maxlen)(gru1)
x1 = Activation(activation="relu")(BatchNormalization()(Dense(128)(att_1)))
x2 = Activation(activation="relu")(BatchNormalization()(Dense(128)(att_2)))
x3 = Activation(activation="relu")(BatchNormalization()(Dense(128)(att_3)))
x4 = Activation(activation="relu")(BatchNormalization()(Dense(128)(att_4)))
pred1_1 = Dense(class_num1 - 10, activation='sigmoid')(x1)
pred1_2 = Dense(10, activation='sigmoid')(x2)
pred1 = concatenate([pred1_1, pred1_2], axis=-1, name='pred1')
pred2_1 = Dense(class_num2 - 9, activation='sigmoid')(x3)
pred2_2 = Dense(9, activation='sigmoid')(x4)
pred2 = concatenate(
[pred2_1, pred2_2], axis=-1, name='pred2'
) # Dense(class_num2, activation='sigmoid',name='pred2')(y)
model = Model(inputs=seq1, outputs=[pred1, pred2])
return model
def triplet_impl(input_shape, output_size):
inputs = [Input(shape=shape) for shape in input_shape]
net = modelf(input_shape[0], output_size)
output = concatenate(list(map(net, inputs)))
y = TripletLossLayer()(output)
model = Model(inputs, y, name="triplet({})".format(net.name))
model.compile(optimizer="adam",
loss=triplet_loss,
metrics=[neg_minus_pos])
return model
def create_critic_network(self):
# parallel 1
state_input = Input(shape = [self.obs_dim])
w1 = Dense(self.hidden_dim, activation = 'relu')(state_input)
h1 = Dense(self.hidden_dim, activation = 'linear')(w1)
# parallel 2
action_input = Input(shape = [self.act_dim], name = 'action2')
a1 = Dense(self.hidden_dim, activation = 'linear')(action_input)
# merge
#h2 = concatenate([h1, a1], mode = 'sum')
h2 = concatenate([h1, a1])
h3 = Dense(self.hidden_dim, activation = 'relu')(h2)
value_out = Dense(self.act_dim, activation = 'linear')(h3)
model = Model(inputs = [state_input, action_input], outputs = [value_out])
adam = Adam(self.lr)
model.compile(loss = 'mse', optimizer = adam)
return model, action_input, state_input
def inception_block_3b(X):
X_3x3 = fr_utils.conv2d_bn(X,
layer='inception_5b_3x3',
cv1_out=96,
cv1_filter=(1, 1),
cv2_out=384,
cv2_filter=(3, 3),
cv2_strides=(1, 1),
padding=(1, 1))
X_pool = MaxPooling2D(pool_size=3, strides=2, data_format='channels_first')(X)
X_pool = fr_utils.conv2d_bn(X_pool,
layer='inception_5b_pool',
cv1_out=96,
cv1_filter=(1, 1))
X_pool = ZeroPadding2D(padding=(1, 1), data_format='channels_first')(X_pool)
X_1x1 = fr_utils.conv2d_bn(X,
layer='inception_5b_1x1',
cv1_out=256,
cv1_filter=(1, 1))
inception = concatenate([X_3x3, X_pool, X_1x1], axis=1)
return inception
def create_yolov3_model(nb_class, anchors, max_box_per_image, max_grid,
batch_size, warmup_batches, ignore_thresh, grid_scales,
obj_scale, noobj_scale, xywh_scale, class_scale):
input_image = Input(shape=(None, None, 3)) # net_h, net_w, 3
true_boxes = Input(shape=(1, 1, 1, max_box_per_image, 4))
true_yolo_1 = Input(
shape=(None, None, len(anchors) // 6,
4 + 1 + nb_class)) # grid_h, grid_w, nb_anchor, 5+nb_class
true_yolo_2 = Input(
shape=(None, None, len(anchors) // 6,
4 + 1 + nb_class)) # grid_h, grid_w, nb_anchor, 5+nb_class
true_yolo_3 = Input(
shape=(None, None, len(anchors) // 6,
4 + 1 + nb_class)) # grid_h, grid_w, nb_anchor, 5+nb_class
# Layer 0 => 4
x = _conv_block(input_image, [{
'filter': 32,
'kernel': 3,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 0
}, {
'filter': 64,
'kernel': 3,
'stride': 2,
'bnorm': True,
'leaky': True,
'layer_idx': 1
}, {
'filter': 32,
'kernel': 1,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 2
}, {
'filter': 64,
'kernel': 3,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 3
}])
# Layer 5 => 8
x = _conv_block(x, [{
'filter': 128,
'kernel': 3,
'stride': 2,
'bnorm': True,
'leaky': True,
'layer_idx': 5
}, {
'filter': 64,
'kernel': 1,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 6
}, {
'filter': 128,
'kernel': 3,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 7
}])
# Layer 9 => 11
x = _conv_block(x, [{
'filter': 64,
'kernel': 1,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 9
}, {
'filter': 128,
'kernel': 3,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 10
}])
# Layer 12 => 15
x = _conv_block(x, [{
'filter': 256,
'kernel': 3,
'stride': 2,
'bnorm': True,
'leaky': True,
'layer_idx': 12
}, {
'filter': 128,
'kernel': 1,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 13
}, {
'filter': 256,
'kernel': 3,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 14
}])
# Layer 16 => 36
for i in range(7):
x = _conv_block(x, [{
'filter': 128,
'kernel': 1,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 16 + i * 3
}, {
'filter': 256,
'kernel': 3,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 17 + i * 3
}])
skip_36 = x
# Layer 37 => 40
x = _conv_block(x, [{
'filter': 512,
'kernel': 3,
'stride': 2,
'bnorm': True,
'leaky': True,
'layer_idx': 37
}, {
'filter': 256,
'kernel': 1,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 38
}, {
'filter': 512,
'kernel': 3,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 39
}])
# Layer 41 => 61
for i in range(7):
x = _conv_block(x, [{
'filter': 256,
'kernel': 1,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 41 + i * 3
}, {
'filter': 512,
'kernel': 3,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 42 + i * 3
}])
skip_61 = x
# Layer 62 => 65
x = _conv_block(x, [{
'filter': 1024,
'kernel': 3,
'stride': 2,
'bnorm': True,
'leaky': True,
'layer_idx': 62
}, {
'filter': 512,
'kernel': 1,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 63
}, {
'filter': 1024,
'kernel': 3,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 64
}])
# Layer 66 => 74
for i in range(3):
x = _conv_block(x, [{
'filter': 512,
'kernel': 1,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 66 + i * 3
}, {
'filter': 1024,
'kernel': 3,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 67 + i * 3
}])
# Layer 75 => 79
x = _conv_block(x, [{
'filter': 512,
'kernel': 1,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 75
}, {
'filter': 1024,
'kernel': 3,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 76
}, {
'filter': 512,
'kernel': 1,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 77
}, {
'filter': 1024,
'kernel': 3,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 78
}, {
'filter': 512,
'kernel': 1,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 79
}],
do_skip=False)
# Layer 80 => 82
pred_yolo_1 = _conv_block(x, [{
'filter': 1024,
'kernel': 3,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 80
}, {
'filter': (3 * (5 + nb_class)),
'kernel': 1,
'stride': 1,
'bnorm': False,
'leaky': False,
'layer_idx': 81
}],
do_skip=False)
loss_yolo_1 = YoloLayer(
anchors[12:], [1 * num
for num in max_grid], batch_size, warmup_batches,
ignore_thresh, grid_scales[0], obj_scale, noobj_scale, xywh_scale,
class_scale)([input_image, pred_yolo_1, true_yolo_1, true_boxes])
# Layer 83 => 86
x = _conv_block(x, [{
'filter': 256,
'kernel': 1,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 84
}],
do_skip=False)
x = UpSampling2D(2)(x)
x = concatenate([x, skip_61])
# Layer 87 => 91
x = _conv_block(x, [{
'filter': 256,
'kernel': 1,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 87
}, {
'filter': 512,
'kernel': 3,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 88
}, {
'filter': 256,
'kernel': 1,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 89
}, {
'filter': 512,
'kernel': 3,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 90
}, {
'filter': 256,
'kernel': 1,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 91
}],
do_skip=False)
# Layer 92 => 94
pred_yolo_2 = _conv_block(x, [{
'filter': 512,
'kernel': 3,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 92
}, {
'filter': (3 * (5 + nb_class)),
'kernel': 1,
'stride': 1,
'bnorm': False,
'leaky': False,
'layer_idx': 93
}],
do_skip=False)
loss_yolo_2 = YoloLayer(
anchors[6:12], [2 * num
for num in max_grid], batch_size, warmup_batches,
ignore_thresh, grid_scales[1], obj_scale, noobj_scale, xywh_scale,
class_scale)([input_image, pred_yolo_2, true_yolo_2, true_boxes])
# Layer 95 => 98
x = _conv_block(x, [{
'filter': 128,
'kernel': 1,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 96
}],
do_skip=False)
x = UpSampling2D(2)(x)
x = concatenate([x, skip_36])
# Layer 99 => 106
pred_yolo_3 = _conv_block(x, [{
'filter': 128,
'kernel': 1,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 99
}, {
'filter': 256,
'kernel': 3,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 100
}, {
'filter': 128,
'kernel': 1,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 101
}, {
'filter': 256,
'kernel': 3,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 102
}, {
'filter': 128,
'kernel': 1,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 103
}, {
'filter': 256,
'kernel': 3,
'stride': 1,
'bnorm': True,
'leaky': True,
'layer_idx': 104
}, {
'filter': (3 * (5 + nb_class)),
'kernel': 1,
'stride': 1,
'bnorm': False,
'leaky': False,
'layer_idx': 105
}],
do_skip=False)
loss_yolo_3 = YoloLayer(
anchors[:6], [4 * num for num in max_grid], batch_size, warmup_batches,
ignore_thresh, grid_scales[2], obj_scale, noobj_scale, xywh_scale,
class_scale)([input_image, pred_yolo_3, true_yolo_3, true_boxes])
train_model = Model(
[input_image, true_boxes, true_yolo_1, true_yolo_2, true_yolo_3],
[loss_yolo_1, loss_yolo_2, loss_yolo_3])
infer_model = Model(input_image, [pred_yolo_1, pred_yolo_2, pred_yolo_3])
return [train_model, infer_model]
