def tiny_yolo3_peleenet_body(inputs, num_anchors, num_classes):
'''Create Tiny YOLO_v3 PeleeNet model CNN body in keras.'''
peleenet = PeleeNet(input_tensor=inputs,
weights='imagenet',
include_top=False)
print('backbone layers number: {}'.format(len(peleenet.layers)))
# input: 416 x 416 x 3
# re_lu_338(layer 365, final feature map): 13 x 13 x 704
# re_lu_307(layer 265, end of stride 16) : 26 x 26 x 512
# re_lu_266(layer 133, end of stride 8) : 52 x 52 x 256
# NOTE: activation layer name may different for TF1.x/2.x, so we
# use index to fetch layer
# f1: 13 x 13 x 704
f1 = peleenet.layers[365].output
# f2: 26 x 26 x 512
f2 = peleenet.layers[265].output
# f3: 52 x 52 x 256
f3 = peleenet.layers[133].output
f1_channel_num = 704
f2_channel_num = 512
y1, y2 = tiny_yolo3_predictions((f1, f2), (f1_channel_num, f2_channel_num),
num_anchors, num_classes)
return Model(inputs, [y1, y2])
def get_base_model(model_type, model_input_shape, weights='imagenet'):
input_tensor = Input(shape=model_input_shape + (3, ), name='image_input')
if model_type == 'mobilenet':
model = MobileNet(input_tensor=input_tensor,
input_shape=model_input_shape + (3, ),
weights=weights,
pooling=None,
include_top=False,
alpha=0.5)
elif model_type == 'mobilenetv2':
model = MobileNetV2(input_tensor=input_tensor,
input_shape=model_input_shape + (3, ),
weights=weights,
pooling=None,
include_top=False,
alpha=0.5)
elif model_type == 'mobilenetv3large':
model = MobileNetV3Large(input_tensor=input_tensor,
input_shape=model_input_shape + (3, ),
weights=weights,
pooling=None,
include_top=False,
alpha=0.75)
elif model_type == 'mobilenetv3small':
model = MobileNetV3Small(input_tensor=input_tensor,
input_shape=model_input_shape + (3, ),
weights=weights,
pooling=None,
include_top=False,
alpha=0.75)
elif model_type == 'peleenet':
model = PeleeNet(input_tensor=input_tensor,
input_shape=model_input_shape + (3, ),
weights=weights,
pooling=None,
include_top=False)
elif model_type == 'ghostnet':
model = GhostNet(input_tensor=input_tensor,
input_shape=model_input_shape + (3, ),
weights=weights,
pooling=None,
include_top=False)
elif model_type == 'squeezenet':
model = SqueezeNet(input_tensor=input_tensor,
input_shape=model_input_shape + (3, ),
weights=weights,
pooling=None,
include_top=False)
elif model_type == 'mobilevit_s':
model = MobileViT_S(input_tensor=input_tensor,
input_shape=model_input_shape + (3, ),
weights=weights,
pooling=None,
include_top=False)
elif model_type == 'mobilevit_xs':
model = MobileViT_XS(input_tensor=input_tensor,
input_shape=model_input_shape + (3, ),
weights=weights,
pooling=None,
include_top=False)
elif model_type == 'mobilevit_xxs':
model = MobileViT_XXS(input_tensor=input_tensor,
input_shape=model_input_shape + (3, ),
weights=weights,
pooling=None,
include_top=False)
elif model_type == 'resnet50':
model = ResNet50(input_tensor=input_tensor,
input_shape=model_input_shape + (3, ),
weights=weights,
pooling=None,
include_top=False)
elif model_type == 'simple_cnn':
model = SimpleCNN(input_tensor=input_tensor,
input_shape=model_input_shape + (3, ),
weights=None,
pooling=None,
include_top=False)
elif model_type == 'simple_cnn_lite':
model = SimpleCNNLite(input_tensor=input_tensor,
input_shape=model_input_shape + (3, ),
weights=None,
pooling=None,
include_top=False)
else:
raise ValueError('Unsupported model type')
return model