def make_one_net_model(self, cf, in_shape, loss, metrics, optimizer):
# Assertions
if 'tiramisu' in cf.model_name:
input_rows, input_cols = cf.target_size_train[0], cf.target_size_train[1]
multiple = 2 ** 5 # 5 transition blocks
if input_rows is not None:
if input_rows % multiple != 0:
raise ValueError('The number of rows of the input data must be a multiple of {}'.format(multiple))
if input_cols is not None:
if input_cols % multiple != 0:
raise ValueError(
'The number of columns of the input data must be a multiple of {}'.format(multiple))
# Create the *Keras* model
if cf.model_name == 'fcn8':
model = build_fcn8(in_shape, cf.dataset.n_classes, cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
# path_weights='weights/pascal-fcn8s-dag.mat')
path_weights=None)
elif cf.model_name == 'dilation':
model = build_dilation(in_shape, cf.dataset.n_classes, cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
# path_weights='weights/pascal-fcn8s-dag.mat')
path_weights=None)
elif cf.model_name == 'segnet_basic':
model = build_segnet(in_shape, cf.dataset.n_classes, cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None, basic=True)
elif cf.model_name == 'segnet_vgg':
model = build_segnet(in_shape, cf.dataset.n_classes, cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None, basic=False)
elif cf.model_name == 'densenetFCN':
model = build_densenetFCN(in_shape, cf.dataset.n_classes, cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'vgg16':
model = build_vgg(in_shape, cf.dataset.n_classes, 16, cf.weight_decay,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'vgg19':
model = build_vgg(in_shape, cf.dataset.n_classes, 19, cf.weight_decay,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'resnet50':
model = build_resnet50(in_shape, cf.dataset.n_classes, cf.weight_decay,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'yolo':
model = build_yolo(in_shape, cf.dataset.n_classes,
cf.dataset.n_priors,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from, tiny=False)
elif cf.model_name == 'tiny-yolo':
model = build_yolo(in_shape, cf.dataset.n_classes,
cf.dataset.n_priors,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from, tiny=True)
elif cf.model_name == 'ssd300':
model = build_ssd300(in_shape, cf.dataset.n_classes + 1, cf.weight_decay,
load_pretrained=cf.load_imageNet, freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'deeplabV2':
model = build_deeplabv2(in_shape, nclasses=cf.dataset.n_classes, load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from, weight_decay=cf.weight_decay)
elif cf.model_name == 'ssd300':
model = build_ssd300(in_shape, cf.dataset.n_classes + 1, cf.weight_decay,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'tiramisu_fc56':
model = build_tiramisu_fc56(in_shape, cf.dataset.n_classes, cf.weight_decay,
compression=0, dropout=0.2, nb_filter=48,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'tiramisu_fc67':
model = build_tiramisu_fc67(in_shape, cf.dataset.n_classes, cf.weight_decay,
compression=0, dropout=0.2, nb_filter=48,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'tiramisu_fc103':
model = build_tiramisu_fc103(in_shape, cf.dataset.n_classes, cf.weight_decay,
compression=0, dropout=0.2, nb_filter=48,
freeze_layers_from=cf.freeze_layers_from)
else:
raise ValueError('Unknown model')
# Load pretrained weights
if cf.load_pretrained:
print(' loading model weights from: ' + cf.weights_file + '...')
model.load_weights(cf.weights_file, by_name=True)
# Compile model
model.compile(loss=loss, metrics=metrics, optimizer=optimizer)
# Show model structure
if cf.show_model:
model.summary()
plot(model, to_file=os.path.join(cf.savepath, 'model.png'))
# Output the model
print (' Model: ' + cf.model_name)
# model is a keras model, Model is a class wrapper so that we can have
# other models (like GANs) made of a pair of keras models, with their
# own ways to train, test and predict
return One_Net_Model(model, cf, optimizer)
def make_one_net_model(self, cf, in_shape, loss, metrics, optimizer):
# Create the *Keras* model
if cf.model_name == 'fcn8':
model = build_fcn8(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=cf.load_imageNet)
elif cf.model_name == 'unet':
model = build_unet(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None)
elif cf.model_name == 'segnet_basic':
model = build_segnet(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None,
basic=True)
elif cf.model_name == 'segnet_vgg':
model = build_segnet(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None,
basic=False)
elif cf.model_name == 'resnetFCN':
model = build_resnetFCN(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None)
elif cf.model_name == 'densenetFCN':
model = build_densenetFCN(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None)
elif cf.model_name == 'lenet':
model = build_lenet(in_shape, cf.dataset.n_classes,
cf.weight_decay)
elif cf.model_name == 'alexNet':
model = build_alexNet(in_shape, cf.dataset.n_classes,
cf.weight_decay)
elif cf.model_name == 'vgg16':
model = build_vgg(in_shape,
cf.dataset.n_classes,
16,
cf.weight_decay,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'vgg19':
model = build_vgg(in_shape,
cf.dataset.n_classes,
19,
cf.weight_decay,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'resnet50':
model = build_resnet50(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'InceptionV3':
model = build_inceptionV3(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'yolo':
model = build_yolo(in_shape,
cf.dataset.n_classes,
cf.dataset.n_priors,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from,
tiny=False)
elif cf.model_name == 'tiny-yolo':
model = build_yolo(in_shape,
cf.dataset.n_classes,
cf.dataset.n_priors,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from,
tiny=True)
else:
raise ValueError('Unknown model')
# Load pretrained weights
if cf.load_pretrained:
print(' loading model weights from: ' + cf.weights_file + '...')
model.load_weights(cf.weights_file, by_name=True)
# Compile model
model.compile(loss=loss, metrics=metrics, optimizer=optimizer)
# Show model structure
if cf.show_model:
model.summary()
plot_model(model, to_file=os.path.join(cf.savepath, 'model.png'))
# Output the model
print(' Model: ' + cf.model_name)
# model is a keras model, Model is a class wrapper so that we can have
# other models (like GANs) made of a pair of keras models, with their
# own ways to train, test and predict
return One_Net_Model(model, cf, optimizer)
def make_one_net_model(self, cf, in_shape, loss, metrics, optimizer):
# Create the *Keras* model
if cf.model_name == 'fcn8':
model = build_fcn8(in_shape, cf.dataset.n_classes, cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=cf.load_imageNet)
elif cf.model_name == 'unet':
model = build_unet(in_shape, cf.dataset.n_classes, cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None)
elif cf.model_name == 'segnet_basic':
model = build_segnet(in_shape, cf.dataset.n_classes, cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
basic=True)
elif cf.model_name == 'segnet_vgg':
model = build_segnet(in_shape, cf.dataset.n_classes, cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
basic=False)
elif cf.model_name == 'resnetFCN':
model = build_resnetFCN(in_shape, cf.dataset.n_classes, cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None)
elif cf.model_name == 'inceptionFCN':
model = build_inceptionFCN(in_shape, cf.dataset.n_classes, cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None)
elif cf.model_name == 'densenet':
model = build_densenet(in_shape, cf.dataset.n_classes, cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None)
elif cf.model_name == 'lenet':
model = build_lenet(in_shape, cf.dataset.n_classes, cf.weight_decay)
elif cf.model_name == 'alexNet':
model = build_alexNet(in_shape, cf.dataset.n_classes, cf.weight_decay)
elif cf.model_name == 'vgg16':
model = build_vgg(in_shape, cf.dataset.n_classes, 16, cf.weight_decay,
load_imageNet=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'vgg19':
model = build_vgg(in_shape, cf.dataset.n_classes, 19, cf.weight_decay,
load_imageNet=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'resnet50':
model = build_resnet50(in_shape, cf.dataset.n_classes, cf.weight_decay,
load_imageNet=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'InceptionV3':
model = build_inceptionV3(in_shape, cf.dataset.n_classes,
cf.weight_decay,
load_imageNet=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'yolo':
model = build_yolo(in_shape, cf.dataset.n_classes,
cf.dataset.n_priors,
load_imageNet=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from, tiny=False)
elif cf.model_name == 'tiny-yolo':
model = build_yolo(in_shape, cf.dataset.n_classes,
cf.dataset.n_priors,
load_imageNet=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from, tiny=True)
elif cf.model_name == 'ssd':
model = build_ssd(in_shape, cf.dataset.n_classes+1,
cf.dataset.n_priors,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'densenet_segmentation':
model = build_densenet_segmentation(in_shape, cf.dataset.n_classes, weight_decay = cf.weight_decay,
freeze_layers_from = cf.freeze_layers_from, path_weights = cf.load_imageNet)
else:
raise ValueError('Unknown model')
# Load pretrained weights
if cf.load_pretrained:
print(' loading model weights from: ' + cf.weights_file + '...')
# If the weights are from different datasets
if cf.different_datasets:
if cf.freeze_layers_from == 'base_model':
raise TypeError('Please, enter the layer id instead of "base_model"'
' for the freeze_layers_from config parameter')
croppedmodel = model_from_json(model.to_json())
# Remove not frozen layers
for i in range(len(model.layers[cf.freeze_layers_from:])):
croppedmodel.layers.pop()
# Load weights only for the frozen layers
croppedmodel.load_weights(cf.weights_file, by_name=True)
model.set_weights(croppedmodel.get_weights())
else:
model.load_weights(cf.weights_file, by_name=True)
# Compile model
model.compile(loss=loss, metrics=metrics, optimizer=optimizer)
# Show model structure
if cf.show_model:
model.summary()
plot(model, to_file=os.path.join(cf.savepath, 'model.png'))
# Output the model
print (' Model: ' + cf.model_name)
# model is a keras model, Model is a class wrapper so that we can have
# other models (like GANs) made of a pair of keras models, with their
# own ways to train, test and predict
return One_Net_Model(model, cf, optimizer)
def vgg16_tt100k_base_network(input_shape, pretrained_weigths):
"""VGG16 base model with pretrained weights
`pretrained_weigths` is an hdf5 file with the weights from a VGG16 model
pretrained on the TT100K dataset.
NOTE: expected input_shape for the model must be (64, 64, 3). It is the
input shape we used for our trained vgg16 models.
`input_shape` is a tuple (height, width, channels).
Return a dict with the layers to use from the base model.
"""
base_model = vgg.build_vgg(img_shape=(64, 64, 3),
n_classes=45,
n_layers=16,
freeze_layers_from=None)
base_model.load_weights(pretrained_weigths)
net = {}
net['input'] = base_model.input
net['conv4_3'] = base_model.get_layer('block4_conv3').output
net['pool5'] = base_model.get_layer('block5_pool').output
# Block 6
net['conv6'] = AtrousConvolution2D(1024,
3,
3,
atrous_rate=(6, 6),
activation='relu',
border_mode='same',
name='conv6')(net['pool5'])
# Block 7
net['conv7'] = Convolution2D(1024,
1,
1,
activation='relu',
border_mode='same',
name='conv7')(net['conv6'])
# Block 8
net['conv8_1'] = Convolution2D(256,
1,
1,
activation='relu',
border_mode='same',
name='conv8_1')(net['conv7'])
net['conv8_2'] = Convolution2D(512,
3,
3,
subsample=(2, 2),
activation='relu',
border_mode='same',
name='conv8_2')(net['conv8_1'])
# Block 9
net['conv9_1'] = Convolution2D(128,
1,
1,
activation='relu',
border_mode='same',
name='conv9_1')(net['conv8_2'])
net['conv9_2'] = Convolution2D(256,
3,
3,
subsample=(2, 2),
activation='relu',
border_mode='same',
name='conv9_2')(net['conv9_1'])
# Block 10
net['conv10_1'] = Convolution2D(128,
1,
1,
activation='relu',
border_mode='same',
name='conv10_1')(net['conv9_2'])
net['conv10_2'] = Convolution2D(256,
3,
3,
activation='relu',
border_mode='same',
name='conv10_2')(net['conv10_1'])
# Block 11
net['conv11_1'] = Convolution2D(128,
1,
1,
activation='relu',
border_mode='same',
name='conv11_1')(net['conv10_2'])
net['conv11_2'] = Convolution2D(256,
3,
3,
activation='relu',
border_mode='same',
name='conv11_2')(net['conv11_1'])
# Add extra layer on top of conv4_3 to normalize its output according to
# the paper
net['conv4_3_norm'] = Normalize(20, name='conv4_3_norm')(net['conv4_3'])
return net
def make_one_net_model(self, cf, in_shape, loss, metrics, optimizer):
# Create the *Keras* model
if cf.model_name == 'fcn8':
model = build_fcn8(
in_shape,
cf.dataset.n_classes,
cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
#path_weights='weights/pascal-fcn8s-dag.mat')
path_weights=None)
elif cf.model_name == 'unet':
model = build_unet(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None)
elif cf.model_name == 'segnet_basic':
model = build_segnet(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None,
basic=True)
elif cf.model_name == 'segnet_vgg':
model = build_segnet(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None,
basic=False)
elif cf.model_name == 'resnetFCN':
model = build_resnetFCN(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None)
elif cf.model_name == 'densenetFCN':
model = build_densenetFCN(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None)
elif cf.model_name == 'lenet':
model = build_lenet(in_shape, cf.dataset.n_classes,
cf.weight_decay)
elif cf.model_name == 'alexNet':
model = build_alexNet(in_shape, cf.dataset.n_classes,
cf.weight_decay)
elif cf.model_name == 'vgg16':
model = build_vgg(in_shape,
cf.dataset.n_classes,
16,
cf.weight_decay,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'vgg19':
model = build_vgg(in_shape,
cf.dataset.n_classes,
19,
cf.weight_decay,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'resnet50Keras':
model = build_resnet50(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'resnet18':
model = ResnetBuilder.build_resnet_18(in_shape,
cf.dataset.n_classes)
elif cf.model_name == 'resnet34':
model = ResnetBuilder.build_resnet_34(in_shape,
cf.dataset.n_classes)
elif cf.model_name == 'resnet50':
model = ResnetBuilder.build_resnet_50(in_shape,
cf.dataset.n_classes)
elif cf.model_name == 'resnet101':
model = ResnetBuilder.build_resnet_101(in_shape,
cf.dataset.n_classes)
elif cf.model_name == 'resnet152':
model = ResnetBuilder.build_resnet_152(in_shape,
cf.dataset.n_classes)
elif cf.model_name == 'InceptionV3':
model = build_inceptionV3(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'densenet':
model = build_densenet(in_shape, cf.dataset.n_classes,
cf.weight_decay)
elif cf.model_name == 'yolo':
model = build_yolo(in_shape,
cf.dataset.n_classes,
cf.dataset.n_priors,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from,
tiny=False)
elif cf.model_name == 'tiny-yolo':
model = build_yolo(in_shape,
cf.dataset.n_classes,
cf.dataset.n_priors,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from,
tiny=True)
elif cf.model_name == 'ssd':
model = build_SSD300(in_shape, cf.dataset.n_classes)
if cf.load_imageNet:
# Rename last layer to not load pretrained weights
model.layers[-1].name += '_new'
model.load_weights('weights/weights_SSD300.hdf5', by_name=True)
else:
raise ValueError('Unknown model')
# Load pretrained weights
if cf.load_pretrained:
print(' loading model weights from: ' + cf.weights_file)
#old_name=model.layers[-2].name
#model.layers[-2].name=model.layers[-2].name+'_replaced'
model.load_weights(cf.weights_file, by_name=True)
#model.layers[-2].name=old_name
# Compile model
model.compile(loss=loss, metrics=metrics, optimizer=optimizer)
# Show model structure
if cf.show_model:
model.summary()
plot(model, to_file=os.path.join(cf.savepath, 'model.png'))
# Output the model
print(' Model: ' + cf.model_name)
# model is a keras model, Model is a class wrapper so that we can have
# other models (like GANs) made of a pair of keras models, with their
# own ways to train, test and predict
return One_Net_Model(model, cf, optimizer)
def make_one_net_model(self, cf, in_shape, loss, metrics, optimizer):
# Create the *Keras* model
model_name = cf.model_name
if cf.model_name == 'fcn8':
model = build_fcn8(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=cf.load_imageNet)
elif cf.model_name == 'unet':
model = build_unet(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None)
elif cf.model_name == 'segnet_basic':
model = build_segnet(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None,
basic=True)
elif cf.model_name == 'segnet_vgg':
model = build_segnet(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None,
basic=False)
elif cf.model_name == 'resnetFCN':
model = build_resnetFCN(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None)
elif cf.model_name == 'densenetFCN':
model = build_densenetFCN(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None)
elif cf.model_name == 'densenet_fc':
model = DenseNetFCN((224, 224, 3),
nb_dense_block=5,
growth_rate=16,
nb_layers_per_block=4,
upsampling_type='upsampling',
classes=cf.dataset.n_classes)
elif cf.model_name == 'lenet':
model = build_lenet(in_shape, cf.dataset.n_classes,
cf.weight_decay)
elif cf.model_name == 'alexNet':
model = build_alexNet(in_shape, cf.dataset.n_classes,
cf.weight_decay)
elif cf.model_name == 'vgg16':
model = build_vgg(in_shape,
cf.dataset.n_classes,
16,
cf.weight_decay,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'vgg19':
model = build_vgg(in_shape,
cf.dataset.n_classes,
19,
cf.weight_decay,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'resnet50Keras':
model = build_resnet50(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'resnet18':
model = ResnetBuilder.build_resnet_18(in_shape,
cf.dataset.n_classes)
elif cf.model_name == 'resnet34':
model = ResnetBuilder.build_resnet_34(in_shape,
cf.dataset.n_classes)
elif cf.model_name == 'resnet50':
model = ResnetBuilder.build_resnet_50(in_shape,
cf.dataset.n_classes)
elif cf.model_name == 'resnet101':
model = ResnetBuilder.build_resnet_101(in_shape,
cf.dataset.n_classes)
elif cf.model_name == 'resnet152':
model = ResnetBuilder.build_resnet_152(in_shape,
cf.dataset.n_classes)
elif cf.model_name == 'InceptionV3':
model = build_inceptionV3(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'densenet':
model = build_densenet(in_shape, cf.dataset.n_classes,
cf.weight_decay)
elif cf.model_name == 'yolo':
model = build_yolo(in_shape,
cf.dataset.n_classes,
cf.dataset.n_priors,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from,
typeNet='Regular')
elif cf.model_name == 'tiny-yolo':
if hasattr(cf, 'lookTwice'):
yolt = cf.lookTwice
if yolt:
model_name = 'Tiny-YOLT'
else:
yolt = False
model = build_yolo(in_shape,
cf.dataset.n_classes,
cf.dataset.n_priors,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from,
typeNet='Tiny',
lookTwice=yolt)
elif cf.model_name == 'yolt':
model = build_yolo(in_shape,
cf.dataset.n_classes,
cf.dataset.n_priors,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from,
typeNet='YOLT')
elif cf.model_name == 'ssd':
model = Build_SSD(in_shape,
cf.dataset.n_classes + 1,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from)
else:
raise ValueError('Unknown model')
# Load pretrained weights
if cf.load_pretrained:
print(' loading model weights from: ' + cf.weights_file)
model.load_weights(cf.weights_file, by_name=True)
else:
try:
if cf.load_transferlearning:
print(' loading model weights from: ' + cf.weights_file)
old_name = model.layers[-2].name
model.layers[-2].name = model.layers[-2].name + '_replaced'
model.load_weights(cf.weights_file, by_name=True)
model.layers[-2].name = old_name
except:
pass
# Compile model
model.compile(loss=loss, metrics=metrics, optimizer=optimizer)
# Show model structure
if cf.show_model:
model.summary()
plot(model, to_file=os.path.join(cf.savepath, 'model.png'))
# Output the model
print(' Model: ' + model_name)
# model is a keras model, Model is a class wrapper so that we can have
# other models (like GANs) made of a pair of keras models, with their
# own ways to train, test and predict
return One_Net_Model(model, cf, optimizer)
def make_one_net_model(self, cf, in_shape, loss, metrics, optimizer):
# Create the *Keras* model
if cf.model_name == 'fcn8':
model = build_fcn8(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=('weights/pascal-fcn8s-dag.mat'
if cf.load_pascalVOC else None))
elif cf.model_name == 'unet':
model = build_unet(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None)
elif cf.model_name == 'segnet_basic':
model = build_segnet(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None,
basic=True)
elif cf.model_name == 'segnet_vgg':
model = build_segnet(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None,
basic=False)
elif cf.model_name == 'resnetFCN':
model = build_resnetFCN(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None)
elif cf.model_name == 'densenetFCN':
model = build_densenetFCN(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
freeze_layers_from=cf.freeze_layers_from,
path_weights=None)
elif cf.model_name == 'lenet':
model = build_lenet(in_shape, cf.dataset.n_classes,
cf.weight_decay)
elif cf.model_name == 'alexNet':
model = build_alexNet(in_shape, cf.dataset.n_classes,
cf.weight_decay)
elif cf.model_name == 'vgg16':
model = build_vgg(in_shape,
cf.dataset.n_classes,
16,
cf.weight_decay,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'vgg19':
model = build_vgg(in_shape,
cf.dataset.n_classes,
19,
cf.weight_decay,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'resnet50':
model = build_resnet50(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'InceptionV3':
model = build_inceptionV3(in_shape,
cf.dataset.n_classes,
cf.weight_decay,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from)
elif cf.model_name == 'densenet':
model = build_densenet(
in_shape,
cf.dataset.n_classes,
layers_in_dense_block=cf.layers_in_dense_block,
initial_filters=cf.initial_filters,
growth_rate=cf.growth_rate,
n_bottleneck=cf.n_bottleneck,
compression=cf.compression,
dropout=cf.dropout,
weight_decay=cf.weight_decay)
elif cf.model_name == 'yolo':
model = build_yolo(in_shape,
cf.dataset.n_classes,
cf.dataset.n_priors,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from,
tiny=False)
elif cf.model_name == 'tiny-yolo':
model = build_yolo(in_shape,
cf.dataset.n_classes,
cf.dataset.n_priors,
load_pretrained=cf.load_imageNet,
freeze_layers_from=cf.freeze_layers_from,
tiny=True)
elif cf.model_name == 'ssd300':
model = build_ssd300(in_shape, cf.dataset.n_classes + 1)
# TODO: find best parameters
ssd_utils.initialize_module(model,
in_shape,
cf.dataset.n_classes + 1,
overlap_threshold=0.5,
nms_thresh=0.45,
top_k=400)
elif cf.model_name == 'tiramisu':
model = build_tiramisu(
in_shape,
cf.dataset.n_classes,
layers_in_dense_block=cf.layers_in_dense_block,
initial_filters=cf.initial_filters,
growth_rate=cf.growth_rate,
n_bottleneck=cf.n_bottleneck,
compression=cf.compression,
dropout=cf.dropout,
weight_decay=cf.weight_decay)
elif cf.model_name == 'ssd300_pretrained':
model = build_ssd300_pretrained(in_shape, cf.dataset.n_classes + 1)
# TODO: find best parameters
ssd_utils.initialize_module(model,
in_shape,
cf.dataset.n_classes + 1,
overlap_threshold=0.5,
nms_thresh=0.45,
top_k=400)
elif cf.model_name == 'ssd_resnet50':
model = build_ssd_resnet50(in_shape, cf.dataset.n_classes + 1)
# TODO: find best parameters
ssd_utils.initialize_module(model,
in_shape,
cf.dataset.n_classes + 1,
overlap_threshold=0.5,
nms_thresh=0.45,
top_k=400)
else:
raise ValueError('Unknown model')
# Load pretrained weights
if cf.load_pretrained:
print(' loading model weights from: ' + cf.weights_file + '...')
model.load_weights(cf.weights_file, by_name=True)
# Compile model
model.compile(loss=loss, metrics=metrics, optimizer=optimizer)
# Show model structure
if cf.show_model:
model.summary()
plot(model, to_file=os.path.join(cf.savepath, 'model.png'))
# Output the model
print(' Model: ' + cf.model_name)
# model is a keras model, Model is a class wrapper so that we can have
# other models (like GANs) made of a pair of keras models, with their
# own ways to train, test and predict
return One_Net_Model(model, cf, optimizer)
