def inference (self, net_input, num_classes, is_training):
if FLAGS.patch_slim:
fuck_slim.patch(is_training)
network = None
init_fn = None
if FLAGS.net == "FC-DenseNet56" or FLAGS.net == "FC-DenseNet67" or FLAGS.net == "FC-DenseNet103":
with slim.arg_scope(aardvark.default_argscope(is_training)):
network = build_fc_densenet(net_input, preset_model = FLAGS.net, num_classes=num_classes)
elif FLAGS.net == "RefineNet-Res50" or FLAGS.net == "RefineNet-Res101" or FLAGS.net == "RefineNet-Res152":
with slim.arg_scope(aardvark.default_argscope(is_training)):
# RefineNet requires pre-trained ResNet weights
network, init_fn = build_refinenet(net_input, preset_model = FLAGS.net, num_classes=num_classes, is_training=is_training)
elif FLAGS.net == "FRRN-A" or FLAGS.net == "FRRN-B":
with slim.arg_scope(aardvark.default_argscope(is_training)):
network = build_frrn(net_input, preset_model = FLAGS.net, num_classes=num_classes)
elif FLAGS.net == "Encoder-Decoder" or FLAGS.net == "Encoder-Decoder-Skip":
with slim.arg_scope(aardvark.default_argscope(is_training)):
network = build_encoder_decoder(net_input, preset_model = FLAGS.net, num_classes=num_classes)
elif FLAGS.net == "MobileUNet" or FLAGS.net == "MobileUNet-Skip":
with slim.arg_scope(aardvark.default_argscope(is_training)):
network = build_mobile_unet(net_input, preset_model = FLAGS.net, num_classes=num_classes)
elif FLAGS.net == "PSPNet-Res50" or FLAGS.net == "PSPNet-Res101" or FLAGS.net == "PSPNet-Res152":
with slim.arg_scope(aardvark.default_argscope(is_training)):
# Image size is required for PSPNet
# PSPNet requires pre-trained ResNet weights
network, init_fn = build_pspnet(net_input, label_size=[args.crop_height, args.crop_width], preset_model = FLAGS.net, num_classes=num_classes, is_training=is_training)
elif FLAGS.net == "GCN-Res50" or FLAGS.net == "GCN-Res101" or FLAGS.net == "GCN-Res152":
with slim.arg_scope(aardvark.default_argscope(is_training)):
# GCN requires pre-trained ResNet weights
network, init_fn = build_gcn(net_input, preset_model = FLAGS.net, num_classes=num_classes, is_training=is_training)
elif FLAGS.net == "DeepLabV3-Res50" or FLAGS.net == "DeepLabV3-Res101" or FLAGS.net == "DeepLabV3-Res152":
with slim.arg_scope(aardvark.default_argscope(is_training)):
# DeepLabV requires pre-trained ResNet weights
network, init_fn = build_deeplabv3(net_input, preset_model = FLAGS.net, num_classes=num_classes, is_training=is_training)
elif FLAGS.net == "DeepLabV3_plus-Res50" or FLAGS.net == "DeepLabV3_plus-Res101" or FLAGS.net == "DeepLabV3_plus-Res152":
# DeepLabV3+ requires pre-trained ResNet weights
with slim.arg_scope(aardvark.default_argscope(is_training)):
network, init_fn = build_deeplabv3_plus(net_input, preset_model = FLAGS.net, num_classes=num_classes, is_training=is_training)
elif FLAGS.net == "AdapNet":
with slim.arg_scope(aardvark.default_argscope(is_training)):
network = build_adaptnet(net_input, num_classes=num_classes)
else:
raise ValueError("Error: the model %d is not available. Try checking which models are available using the command python main.py --help")
self.init_fn = init_fn
return network
if args.model == "FC-DenseNet56" or args.model == "FC-DenseNet67" or args.model == "FC-DenseNet103":
network = build_fc_densenet(net_input,
preset_model=args.model,
num_classes=num_classes)
elif args.model == "RefineNet-Res50" or args.model == "RefineNet-Res101" or args.model == "RefineNet-Res152":
# RefineNet requires pre-trained ResNet weights
network, init_fn = build_refinenet(net_input,
preset_model=args.model,
num_classes=num_classes)
elif args.model == "FRRN-A" or args.model == "FRRN-B":
network = build_frrn(net_input,
preset_model=args.model,
num_classes=num_classes)
elif args.model == "Encoder-Decoder" or args.model == "Encoder-Decoder-Skip":
network = build_encoder_decoder(net_input,
preset_model=args.model,
num_classes=num_classes)
elif args.model == "MobileUNet" or args.model == "MobileUNet-Skip":
network = build_mobile_unet(net_input,
preset_model=args.model,
num_classes=num_classes)
elif args.model == "PSPNet-Res50" or args.model == "PSPNet-Res101" or args.model == "PSPNet-Res152":
# Image size is required for PSPNet
# PSPNet requires pre-trained ResNet weights
network, init_fn = build_pspnet(
net_input,
label_size=[args.crop_height, args.crop_width],
preset_model=args.model,
num_classes=num_classes)
elif args.model == "GCN-Res50" or args.model == "GCN-Res101" or args.model == "GCN-Res152":
# GCN requires pre-trained ResNet weights
with tf.device('/gpu:' + str(args.gpu)):
input = tf.placeholder(tf.float32, shape=[None, None, None, 3])
output = tf.placeholder(tf.float32, shape=[None, None, None, num_classes])
if args.model == "FC-DenseNet56" or args.model == "FC-DenseNet67" or args.model == "FC-DenseNet103" or args.model == "FC-DenseNet158" or args.model == "FC-DenseNet232":
network = build_fc_densenet(input,
preset_model=args.model,
num_classes=num_classes,
dropout_p=0.5)
elif args.model == "RefineNet-Res101" or args.model == "RefineNet-Res152":
network = build_refinenet(input,
preset_model=args.model,
num_classes=num_classes)
elif args.model == "Encoder-Decoder":
network = build_encoder_decoder(input, num_classes)
elif args.model == "Encoder-Decoder-Skip":
network = build_encoder_decoder_skip(input, num_classes)
elif args.model == "HF-FCN":
network = build_hf_fcn(input,
preset_model='FC-DenseNet103',
num_classes=num_classes)
elif args.model == "custom":
network = build_custom(input, num_classes)
# Compute your (unweighted) softmax cross entropy loss
if not args.balanced_weight:
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=network,
labels=output))
else:
def buildNetwork(model, net_input, num_class):
# Get the selected model.
# Some of them require pre-trained ResNet
if "Res50" in model and not os.path.isfile("models/resnet_v2_50.ckpt"):
download_checkpoints("ResnetV2", "50")
if "Res101" in model and not os.path.isfile("models/resnet_v2_101.ckpt"):
utils.download_checkpoints("ResnetV2", "101")
if "Res152" in model and not os.path.isfile("models/resnet_v2_152.ckpt"):
utils.download_checkpoints("ResnetV2", "152")
network = None
init_fn = None
if model == "FC-DenseNet56" or model == "FC-DenseNet67" or model == "FC-DenseNet103":
network = build_fc_densenet(net_input,
preset_model=model,
num_classes=num_class)
elif model == "RefineNet-Res50" or model == "RefineNet-Res101" or model == "RefineNet-Res152":
# RefineNet requires pre-trained ResNet weights
network, init_fn = build_refinenet(net_input,
preset_model=model,
num_classes=num_class)
elif model == "FRRN-A" or model == "FRRN-B":
network = build_frrn(net_input,
preset_model=model,
num_classes=num_class)
elif model == "Encoder-Decoder" or model == "Encoder-Decoder-Skip":
network = build_encoder_decoder(net_input,
preset_model=model,
num_classes=num_class)
elif model == "MobileUNet" or model == "MobileUNet-Skip":
network = build_mobile_unet(net_input,
preset_model=model,
num_classes=num_class)
elif model == "PSPNet-Res50" or model == "PSPNet-Res101" or model == "PSPNet-Res152":
# Image size is required for PSPNet
# PSPNet requires pre-trained ResNet weights
network, init_fn = build_pspnet(
net_input,
label_size=[args.crop_height, args.crop_width],
preset_model=model,
num_classes=num_class)
elif model == "GCN-Res50" or model == "GCN-Res101" or model == "GCN-Res152":
# GCN requires pre-trained ResNet weights
network, init_fn = build_gcn(net_input,
preset_model=model,
num_classes=num_class)
elif model == "DeepLabV3-Res50" or model == "DeepLabV3-Res101" or model == "DeepLabV3-Res152":
# DeepLabV requires pre-trained ResNet weights
network, init_fn = build_deeplabv3(net_input,
preset_model=model,
num_classes=num_class)
elif model == "DeepLabV3_plus-Res50" or model == "DeepLabV3_plus-Res101" or model == "DeepLabV3_plus-Res152":
# DeepLabV3+ requires pre-trained ResNet weights
network, init_fn = build_deeplabv3_plus(net_input,
preset_model=model,
num_classes=num_class)
elif model == "AdapNet":
network = build_adaptnet(net_input, num_classes=num_class)
elif model == "custom":
network = build_custom(net_input, num_class)
else:
raise ValueError(
"Error: the model %d is not available. Try checking which models are available using the command python main.py --help"
)
return network, init_fn
def build_model(model_name,
net_input,
num_classes,
frontend="ResNet101",
is_training=True):
# Get the selected model.
# Some of them require pre-trained ResNet
print("Preparing the model ...")
if model_name not in SUPPORTED_MODELS:
raise ValueError(
"The model you selelect is not supported. The following models are currently supported: {0}"
.format(SUPPORTED_MODELS))
if frontend not in SUPPORTED_FRONTENDS:
raise ValueError(
"The frontend you selelect is not supported. The following models are currently supported: {0}"
.format(SUPPORTED_FRONTENDS))
if "ResNet50" == frontend and not os.path.isfile(
"models/resnet_v2_50.ckpt"):
download_checkpoints("ResNet50")
if "ResNet101" == frontend and not os.path.isfile(
"models/resnet_v2_101.ckpt"):
download_checkpoints("ResNet101")
if "ResNet152" == frontend and not os.path.isfile(
"models/resnet_v2_152.ckpt"):
download_checkpoints("ResNet152")
if "MobileNetV2" == frontend and not os.path.isfile(
"models/mobilenet_v2_1.4_224.ckpt.data-00000-of-00001"):
download_checkpoints("MobileNetV2")
if "InceptionV4" == frontend and not os.path.isfile(
"models/inception_v4.ckpt"):
download_checkpoints("InceptionV4")
network = None
init_fn = None
if model_name == "FC-DenseNet56" or model_name == "FC-DenseNet67" or model_name == "FC-DenseNet103":
network = build_fc_densenet(net_input,
preset_model=model_name,
num_classes=num_classes)
elif model_name == "RefineNet":
# RefineNet requires pre-trained ResNet weights
network, init_fn = build_refinenet(net_input,
preset_model=model_name,
frontend=frontend,
num_classes=num_classes,
is_training=is_training)
elif model_name == "FRRN-A" or model_name == "FRRN-B":
network = build_frrn(net_input,
preset_model=model_name,
num_classes=num_classes)
elif model_name == "Encoder-Decoder" or model_name == "Encoder-Decoder-Skip":
network = build_encoder_decoder(net_input,
preset_model=model_name,
num_classes=num_classes)
elif model_name == "MobileUNet" or model_name == "MobileUNet-Skip":
network = build_mobile_unet(net_input,
preset_model=model_name,
num_classes=num_classes)
elif model_name == "PSPNet":
# Image size is required for PSPNet
# PSPNet requires pre-trained ResNet weights
network, init_fn = build_pspnet(
net_input,
label_size=[args.crop_height, args.crop_width],
preset_model=model_name,
frontend=frontend,
num_classes=num_classes,
is_training=is_training)
elif model_name == "GCN":
# GCN requires pre-trained ResNet weights
network, init_fn = build_gcn(net_input,
preset_model=model_name,
frontend=frontend,
num_classes=num_classes,
is_training=is_training)
elif model_name == "DeepLabV3":
# DeepLabV requires pre-trained ResNet weights
network, init_fn = build_deeplabv3(net_input,
preset_model=model_name,
frontend=frontend,
num_classes=num_classes,
is_training=is_training)
elif model_name == "DeepLabV3_plus":
# DeepLabV3+ requires pre-trained ResNet weights
network, init_fn = build_deeplabv3_plus(net_input,
preset_model=model_name,
frontend=frontend,
num_classes=num_classes,
is_training=is_training)
elif model_name == "AdapNet":
network = build_adaptnet(net_input, num_classes=num_classes)
elif model_name == "custom":
network = build_custom(net_input, num_classes)
else:
raise ValueError(
"Error: the model %d is not available. Try checking which models are available using the command python main.py --help"
)
return network, init_fn
