def build_frontend(inputs, frontend, is_training=True):
inputs = tf.to_float(inputs)
if frontend == 'ResNet50':
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
logits, end_points = resnet_v2.resnet_v2_50(
inputs, is_training=is_training, scope='resnet_v2_50')
frontend_scope = 'resnet_v2_50'
elif frontend == 'ResNet101':
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
logits, end_points = resnet_v2.resnet_v2_101(
inputs, is_training=is_training, scope='resnet_v2_101')
frontend_scope = 'resnet_v2_101'
elif frontend == 'ResNet152':
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
logits, end_points = resnet_v2.resnet_v2_152(
inputs, is_training=is_training, scope='resnet_v2_152')
frontend_scope = 'resnet_v2_152'
elif frontend == 'MobileNetV2':
with slim.arg_scope(mobilenet_v2.training_scope()):
logits, end_points = mobilenet_v2.mobilenet(
inputs, is_training=is_training, scope='mobilenet_v2', base_only=True)
frontend_scope = 'mobilenet_v2'
elif frontend == 'InceptionV4':
with slim.arg_scope(inception_v4.inception_v4_arg_scope()):
logits, end_points = inception_v4.inception_v4(
inputs, is_training=is_training, scope='inception_v4')
frontend_scope = 'inception_v4'
else:
raise ValueError(
"Unsupported fronetnd model '%s'. This function only supports ResNet50, ResNet101, ResNet152, and MobileNetV2" % (frontend))
return logits, end_points, frontend_scope
def build_frontend(inputs, frontend, is_training=True, pretrained_dir="checkpoints"):
if frontend == 'ResNet50':
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
logits, end_points = resnet_v2.resnet_v2_50(inputs, is_training=is_training, scope='resnet_v2_50')
frontend_scope='resnet_v2_50'
init_fn = slim.assign_from_checkpoint_fn("D:/codepython/Segmentation/checkpoints/resnet_v2_50.ckpt", var_list=slim.get_model_variables('resnet_v2_50'), ignore_missing_vars=True)
elif frontend == 'ResNet101':
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
logits, end_points = resnet_v2.resnet_v2_101(inputs, is_training=is_training, scope='resnet_v2_101')
frontend_scope='resnet_v2_101'
init_fn = slim.assign_from_checkpoint_fn(model_path=os.path.join(pretrained_dir, 'resnet_v2_101.ckpt'), var_list=slim.get_model_variables('resnet_v2_101'), ignore_missing_vars=True)
elif frontend == 'ResNet152':
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
logits, end_points = resnet_v2.resnet_v2_152(inputs, is_training=is_training, scope='resnet_v2_152')
frontend_scope='resnet_v2_152'
init_fn = slim.assign_from_checkpoint_fn(model_path=os.path.join(pretrained_dir, 'resnet_v2_152.ckpt'), var_list=slim.get_model_variables('resnet_v2_152'), ignore_missing_vars=True)
elif frontend == 'MobileNetV2':
with slim.arg_scope(mobilenet_v2.training_scope()):
logits, end_points = mobilenet_v2.mobilenet(inputs, is_training=is_training, scope='MobilenetV2', base_only=True)
frontend_scope='MobilenetV2'
init_fn = slim.assign_from_checkpoint_fn(model_path=os.path.join(pretrained_dir, 'mobilenet_v2_1.0_224.ckpt'), var_list=slim.get_model_variables('MobilenetV2'), ignore_missing_vars=True)
elif frontend == 'InceptionV4':
with slim.arg_scope(inception_v4.inception_v4_arg_scope()):
logits, end_points = inception_v4.inception_v4(inputs, is_training=is_training, scope='inception_v4')
frontend_scope='inception_v4'
init_fn = slim.assign_from_checkpoint_fn(model_path=os.path.join(pretrained_dir, 'inception_v4.ckpt'), var_list=slim.get_model_variables('inception_v4'), ignore_missing_vars=True)
else:
raise ValueError("Unsupported fronetnd model '%s'. This function only supports ResNet50, ResNet101, ResNet152, and MobileNetV2" % (frontend))
return logits, end_points, frontend_scope, init_fn
def build_frontend(inputs, frontend_config, is_training=True, reuse=False):
frontend = frontend_config['frontend']
pretrained_dir = frontend_config['pretrained_dir']
if "ResNet50" == frontend and not os.path.isfile("pretrain/resnet_v2_50.ckpt"):
download_checkpoints("ResNet50")
if "ResNet101" == frontend and not os.path.isfile("pretrain/resnet_v2_101.ckpt"):
download_checkpoints("ResNet101")
if "ResNet152" == frontend and not os.path.isfile("pretrain/resnet_v2_152.ckpt"):
download_checkpoints("ResNet152")
if "MobileNetV2" == frontend and not os.path.isfile("pretrain/mobilenet_v2.ckpt.data-00000-of-00001"):
download_checkpoints("MobileNetV2")
if "InceptionV4" == frontend and not os.path.isfile("pretrain/inception_v4.ckpt"):
download_checkpoints("InceptionV4")
if frontend == 'ResNet50':
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
logits, end_points = resnet_v2.resnet_v2_50(inputs, is_training=is_training, scope='resnet_v2_50', reuse=reuse)
frontend_scope='resnet_v2_50'
init_fn = slim.assign_from_checkpoint_fn(model_path=os.path.join(pretrained_dir, 'resnet_v2_50.ckpt'), var_list=slim.get_model_variables('resnet_v2_50'), ignore_missing_vars=True)
elif frontend == 'ResNet101':
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
logits, end_points = resnet_v2.resnet_v2_101(inputs, is_training=is_training, scope='resnet_v2_101', reuse=reuse)
frontend_scope='resnet_v2_101'
init_fn = slim.assign_from_checkpoint_fn(model_path=os.path.join(pretrained_dir, 'resnet_v2_101.ckpt'), var_list=slim.get_model_variables('resnet_v2_101'), ignore_missing_vars=True)
elif frontend == 'ResNet152':
with slim.arg_scope(resnet_v2.resnet_arg_scope()):
logits, end_points = resnet_v2.resnet_v2_152(inputs, is_training=is_training, scope='resnet_v2_152', reuse=reuse)
frontend_scope='resnet_v2_152'
init_fn = slim.assign_from_checkpoint_fn(model_path=os.path.join(pretrained_dir, 'resnet_v2_152.ckpt'), var_list=slim.get_model_variables('resnet_v2_152'), ignore_missing_vars=True)
elif frontend == 'MobileNetV2':
with slim.arg_scope(mobilenet_v2.training_scope()):
logits, end_points = mobilenet_v2.mobilenet(inputs, is_training=is_training, scope='mobilenet_v2', base_only=True, reuse=reuse)
frontend_scope='mobilenet_v2'
init_fn = slim.assign_from_checkpoint_fn(model_path=os.path.join(pretrained_dir, 'mobilenet_v2.ckpt'), var_list=slim.get_model_variables('mobilenet_v2'), ignore_missing_vars=True)
elif frontend == 'InceptionV4':
with slim.arg_scope(inception_v4.inception_v4_arg_scope()):
logits, end_points = inception_v4.inception_v4(inputs, is_training=is_training, scope='inception_v4', reuse=reuse)
frontend_scope='inception_v4'
init_fn = slim.assign_from_checkpoint_fn(model_path=os.path.join(pretrained_dir, 'inception_v4.ckpt'), var_list=slim.get_model_variables('inception_v4'), ignore_missing_vars=True)
elif frontend == 'DenseNet121':
with slim.arg_scope(densenet.densenet_arg_scope()):
logits, end_points = densenet.densenet121(inputs, is_training=is_training, scope='densenet121', reuse=reuse)
frontend_scope ='densenet121'
init_fn = slim.assign_from_checkpoint_fn(model_path=os.path.join(pretrained_dir, 'tf-densenet121/tf-densenet121.ckpt'), var_list=slim.get_model_variables('densenet121'), ignore_missing_vars=True)
elif frontend == 'DenseNet161':
with slim.arg_scope(densenet.densenet_arg_scope()):
logits, end_points = densenet.densenet121(inputs, is_training=is_training, scope='densenet161', reuse=reuse)
frontend_scope='densenet161'
init_fn = slim.assign_from_checkpoint_fn(model_path=os.path.join(pretrained_dir, 'tf-densenet161.ckpt'), var_list=slim.get_model_variables('densenet161'), ignore_missing_vars=True)
elif frontend == 'DenseNet169':
with slim.arg_scope(densenet.densenet_arg_scope()):
logits, end_points= densenet.densenet121(inputs, is_training=is_training, scope='densenet169', reuse=reuse)
frontend_scope='densenet169'
init_fn = slim.assign_from_checkpoint_fn(model_path=os.path.join(pretrained_dir, 'tf-densenet169.ckpt'), var_list=slim.get_model_variables('densenet169'), ignore_missing_vars=True)
elif frontend == 'Xception39':
with slim.arg_scope(xception.xception_arg_scope()):
logits, end_points = xception.xception39(inputs, is_training=is_training, scope='xception39', reuse=reuse)
frontend_scope='Xception39'
init_fn = None
else:
raise ValueError("Unsupported fronetnd model '%s'. This function only supports ResNet50, ResNet101, ResNet152, and MobileNetV2" % (frontend))
return logits, end_points, frontend_scope, init_fn