Python caffe_pb2.TEST属性代码示例

# 需要导入模块: from caffe.proto import caffe_pb2 [as 别名]
# 或者: from caffe.proto.caffe_pb2 import TEST [as 别名]
def transform_param(self, 
            mean_value=128, 
            batch_size=128, 
            scale=1., #.0078125, 
            mirror=1, crop_size=None, mean_file_size=None, phase=None):

        new_transform_param = self.this.transform_param
        if scale != 1.:
            new_transform_param.scale = scale
        new_transform_param.mean_value.extend([mean_value])
        if phase is not None and phase == 'TEST':
            return

        new_transform_param.mirror = mirror
        if crop_size is not None:
            new_transform_param.crop_size = crop_size 

# 需要导入模块: from caffe.proto import caffe_pb2 [as 别名]
# 或者: from caffe.proto.caffe_pb2 import TEST [as 别名]
def resnet(n=3, num_output = 16):
    """6n+2, n=3 9 18 coresponds to 20 56 110 layers"""    
    net_name = "resnet-"    
    pt_folder = osp.join(osp.abspath(osp.curdir), net_name +str(6*n+2))
    name = net_name+str(6*n+2)+'-cifar10'

    if n > 18:
        # warm up
        solver = Solver(solver_name="solver_warm.prototxt", folder=pt_folder, lr_policy=Solver.policy.fixed)
        solver.p.base_lr = 0.01
        solver.set_max_iter(500)
        solver.write()
        del solver
    
    solver = Solver(folder=pt_folder)
    solver.write()
    del solver

    builder = Net(name)
    builder.Data('cifar-10-batches-py/train', phase='TRAIN', crop_size=32)
    builder.Data('cifar-10-batches-py/test', phase='TEST')
    builder.resnet_cifar(n, num_output=num_output)
    builder.write(folder=pt_folder) 

# 需要导入模块: from caffe.proto import caffe_pb2 [as 别名]
# 或者: from caffe.proto.caffe_pb2 import TEST [as 别名]
def resnet_orth(n=3):
    """6n+2, n=3 9 18 coresponds to 20 56 110 layers"""    
    net_name = "resnet-orth-"    
    pt_folder = osp.join(osp.abspath(osp.curdir), net_name +str(6*n+2))
    name = net_name+str(6*n+2)+'-cifar10'

    if n > 18:
        # warm up
        solver = Solver(solver_name="solver_warm.prototxt", folder=pt_folder, lr_policy=Solver.policy.fixed)
        solver.p.base_lr = 0.01
        solver.set_max_iter(500)
        solver.write()
        del solver
    
    solver = Solver(folder=pt_folder)
    solver.write()
    del solver

    builder = Net(name)
    builder.Data('cifar-10-batches-py/train', phase='TRAIN', crop_size=32)
    builder.Data('cifar-10-batches-py/test', phase='TEST')
    builder.resnet_cifar(n, orth=True)
    builder.write(folder=pt_folder) 

# 需要导入模块: from caffe.proto import caffe_pb2 [as 别名]
# 或者: from caffe.proto.caffe_pb2 import TEST [as 别名]
def data_test_ssd(self):
        layer = self.net.layer.add()
        layer.name = "data"
        layer.type = "AnnotatedData"
        layer.top.append("data")
        layer.top.append("label")
        layer.include.add().phase = caffe_pb2.TEST
        layer.transform_param.scale = 0.007843
        layer.transform_param.mirror = True
        layer.transform_param.mean_value.append(127.5)
        layer.transform_param.mean_value.append(127.5)
        layer.transform_param.mean_value.append(127.5)
        layer.transform_param.resize_param.prob = 1.0
        layer.transform_param.resize_param.resize_mode = caffe_pb2.ResizeParameter.WARP
        layer.transform_param.resize_param.height = self.input_size
        layer.transform_param.resize_param.width = self.input_size
        layer.transform_param.resize_param.interp_mode.append(caffe_pb2.ResizeParameter.LINEAR)

        layer.data_param.source = ""
        layer.data_param.batch_size = 8
        layer.data_param.backend = caffe_pb2.DataParameter.LMDB
        layer.annotated_data_param.label_map_file = self.label_map 

# 需要导入模块: from caffe.proto import caffe_pb2 [as 别名]
# 或者: from caffe.proto.caffe_pb2 import TEST [as 别名]
def include(self, phase='TRAIN'):
        if phase is not None:
            includes = self.this.include.add()
            if phase == 'TRAIN':
                includes.phase = caffe_pb2.TRAIN
            elif phase == 'TEST':
                includes.phase = caffe_pb2.TEST
        else:
            NotImplementedError


    #************************** inplace ************************** 

# 需要导入模块: from caffe.proto import caffe_pb2 [as 别名]
# 或者: from caffe.proto.caffe_pb2 import TEST [as 别名]
def transform_param(self, mean_value=128, batch_size=128, scale=.0078125, mirror=1, crop_size=None, mean_file_size=None, phase=None):

        new_transform_param = self.this.transform_param
        if scale != 1:
            new_transform_param.scale = scale
        if isinstance(mean_value, list):
            new_transform_param.mean_value.extend(mean_value)
        else:
            new_transform_param.mean_value.extend([mean_value])
        if phase is not None and phase == 'TEST':
            return

        new_transform_param.mirror = mirror
        if crop_size is not None:
            new_transform_param.crop_size = crop_size 

# 需要导入模块: from caffe.proto import caffe_pb2 [as 别名]
# 或者: from caffe.proto.caffe_pb2 import TEST [as 别名]
def BatchNorm(self, name=None, inplace=True,eps=1e-5):
        moving_average_fraction = 0
        if not inplace:
            bottom = self.this.name
        # train
        bn_name = self.suffix('bn', name)
        self.setup(bn_name, 'BatchNorm', inplace=inplace)
        # self.include()

        self.param(lr_mult=0, decay_mult=0)
        self.param(lr_mult=0, decay_mult=0)
        self.param(lr_mult=0, decay_mult=0)
        batch_norm_param = self.this.batch_norm_param
        if eps != 1e-5:
            batch_norm_param.eps = eps

        return bn_name
        # batch_norm_param.use_global_stats = False
        #batch_norm_param.moving_average_fraction = moving_average_fraction

        # test 
        # if not inplace:
        #     self.setup(bn_name, 'BatchNorm', inplace=inplace, bottom=[bottom])
        # else:
        #     self.setup(bn_name, 'BatchNorm', inplace=inplace)

        # self.include(phase='TEST')

        # self.param(lr_mult=0, decay_mult=0)
        # self.param(lr_mult=0, decay_mult=0)
        # self.param(lr_mult=0, decay_mult=0)
        # batch_norm_param = self.this.batch_norm_param
        # batch_norm_param.use_global_stats = True
        # batch_norm_param.moving_average_fraction = moving_average_fraction 

# 需要导入模块: from caffe.proto import caffe_pb2 [as 别名]
# 或者: from caffe.proto.caffe_pb2 import TEST [as 别名]
def plain(n=3):
    """6n+2, n=3 9 18 coresponds to 20 56 110 layers"""
    net_name = "plain"
    pt_folder = osp.join(osp.abspath(osp.curdir), net_name +str(6*n+2))
    name = net_name+str(6*n+2)+'-cifar10'

    solver = Solver(folder=pt_folder)
    solver.write()
    del solver

    builder = Net(name)
    builder.Data('cifar-10-batches-py/train', phase='TRAIN', crop_size=32)
    builder.Data('cifar-10-batches-py/test', phase='TEST')
    builder.plain_cifar(n, num_output = 16)
    builder.write(folder=pt_folder) 

# 需要导入模块: from caffe.proto import caffe_pb2 [as 别名]
# 或者: from caffe.proto.caffe_pb2 import TEST [as 别名]
def plain_orth(n=3):
    """6n+2, n=3 5 7 9 18 coresponds to 20 56 110 layers"""
    net_name = "plain-orth"
    pt_folder = osp.join(osp.abspath(osp.curdir), net_name +str(6*n+2))
    name = net_name+str(6*n+2)+'-cifar10'

    solver = Solver(folder=pt_folder)
    solver.write()
    del solver

    builder = Net(name)
    builder.Data('cifar-10-batches-py/train', phase='TRAIN', crop_size=32)
    builder.Data('cifar-10-batches-py/test', phase='TEST')
    builder.plain_cifar(n, orth=True)
    builder.write(folder=pt_folder) 

# 需要导入模块: from caffe.proto import caffe_pb2 [as 别名]
# 或者: from caffe.proto.caffe_pb2 import TEST [as 别名]
def plain_orth_v1(n=3):
    """6n+2, n=3 5 7 9 18 coresponds to 20 32 44 56 110 layers"""
    net_name = "plain-orth-v1-"
    pt_folder = osp.join(osp.abspath(osp.curdir), net_name +str(6*n+2))
    name = net_name+str(6*n+2)+'-cifar10'

    solver = Solver(folder=pt_folder)
    solver.write()
    del solver

    builder = Net(name)
    builder.Data('cifar-10-batches-py/train', phase='TRAIN', crop_size=32)
    builder.Data('cifar-10-batches-py/test', phase='TEST')
    builder.plain_cifar(n, orth=True, inplace=False, num_output = 16)
    builder.write(folder=pt_folder) 

# 需要导入模块: from caffe.proto import caffe_pb2 [as 别名]
# 或者: from caffe.proto.caffe_pb2 import TEST [as 别名]
def acc(n=3):
    """6n+2, n=3 9 18 coresponds to 20 56 110 layers"""
    net_name = "plain"
    pt_folder = osp.join(osp.abspath(osp.curdir), net_name +str(6*n+2))
    name = net_name+str(6*n+2)+'-cifar10'

    solver = Solver(folder=pt_folder)
    solver.write()
    del solver

    builder = Net(name)
    builder.Data('cifar-10-batches-py/train', phase='TRAIN', crop_size=32)
    builder.Data('cifar-10-batches-py/test', phase='TEST')
    builder.plain_cifar(n, num_output = 16, inplace=False)
    builder.write(folder=pt_folder) 

# 需要导入模块: from caffe.proto import caffe_pb2 [as 别名]
# 或者: from caffe.proto.caffe_pb2 import TEST [as 别名]
def include(self, phase='TRAIN'):
        if phase is not None:
            includes = self.this.include.add()
            if phase == 'TRAIN':
                includes.phase = caffe_pb2.TRAIN
            elif phase == 'TEST':
                includes.phase = caffe_pb2.TEST
        else:
            NotImplementedError

    #************************** inplace ************************** 

# 需要导入模块: from caffe.proto import caffe_pb2 [as 别名]
# 或者: from caffe.proto.caffe_pb2 import TEST [as 别名]
def solver_and_prototxt(compress_layer, compress_rate, compress_block):
    layers = ['2a', '2b', '2c', '3a', '3b', '3c', '3d',
              '4a', '4b', '4c', '4d', '4e', '4f', '5a', '5b', '5c']
    pt_folder = layers[compress_layer] + '_' + str(compress_block)
    if not os.path.exists(pt_folder):
        os.mkdir(pt_folder)
    name = 'resnet-' + layers[compress_layer] + str(compress_block) +'-ImageNet'

    solver = Solver(folder=pt_folder, b=compress_layer, compress_block=compress_block)
    solver.write()

    builder = Net(name)
    builder.Data('/opt/luojh/Dataset/ImageNet/lmdb/ilsvrc12_train_lmdb', backend='LMDB', phase='TRAIN', mirror=True,
                 crop_size=224, batch_size=32)
    builder.Data('/opt/luojh/Dataset/ImageNet/lmdb/ilsvrc12_val_lmdb', backend='LMDB', phase='TEST', mirror=False,
                 crop_size=224, batch_size=10)
    builder.resnet_50(layers, compress_layer, compress_rate, compress_block)
    builder.write(name='trainval.prototxt', folder=pt_folder)

    if compress_block == 0:
        compress_block = 1
        compress_layer -= 1
    else:
        compress_block =0

    builder = Net(name + '-old')
    builder.setup('data', 'Data', top=['data'])
    builder.resnet_50(layers, compress_layer, compress_rate, compress_block, deploy=True)
    builder.write(name='deploy.prototxt', folder=pt_folder, deploy=True)
    print "Finished net prototxt generation!" 

# 需要导入模块: from caffe.proto import caffe_pb2 [as 别名]
# 或者: from caffe.proto.caffe_pb2 import TEST [as 别名]
def ssd_predict(self):
        layer = self.net.layer.add()
        layer.name = "mbox_conf_reshape"
        layer.type = "Reshape"
        layer.bottom.append("mbox_conf")
        layer.top.append("mbox_conf_reshape")
        layer.reshape_param.shape.dim.append(0)
        layer.reshape_param.shape.dim.append(-1)
        layer.reshape_param.shape.dim.append(self.class_num)

        layer = self.net.layer.add()
        layer.name = "mbox_conf_sigmoid"
        layer.type = "Sigmoid"
        layer.bottom.append("mbox_conf_reshape")
        layer.top.append("mbox_conf_sigmoid")
        layer = self.net.layer.add()
        layer.name = "mbox_conf_flatten"
        layer.type = "Flatten"
        layer.bottom.append("mbox_conf_sigmoid")
        layer.top.append("mbox_conf_flatten")
        layer.flatten_param.axis = 1

        layer = self.net.layer.add()
        layer.name = "detection_out"
        layer.type = "DetectionOutput"
        layer.bottom.append("mbox_loc")
        layer.bottom.append("mbox_conf_flatten")
        layer.bottom.append("mbox_priorbox")
        layer.top.append("detection_out")
        layer.include.add().phase = caffe_pb2.TEST

        layer.detection_output_param.num_classes = self.class_num
        layer.detection_output_param.share_location = True
        layer.detection_output_param.background_label_id = 0
        layer.detection_output_param.nms_param.nms_threshold = 0.45
        layer.detection_output_param.nms_param.top_k = 100
        layer.detection_output_param.code_type = caffe_pb2.PriorBoxParameter.CENTER_SIZE
        layer.detection_output_param.keep_top_k = 100
        layer.detection_output_param.confidence_threshold = 0.35 

# 需要导入模块: from caffe.proto import caffe_pb2 [as 别名]
# 或者: from caffe.proto.caffe_pb2 import TEST [as 别名]
def ssd_test(self):
        self.ssd_predict()
        layer = self.net.layer.add() 
        layer.name = "detection_eval"
        layer.type = "DetectionEvaluate"
        layer.bottom.append("detection_out")
        layer.bottom.append("label")
        layer.top.append("detection_eval")
        layer.include.add().phase = caffe_pb2.TEST
        layer.detection_evaluate_param.num_classes = self.class_num
        layer.detection_evaluate_param.background_label_id = 0
        layer.detection_evaluate_param.overlap_threshold = 0.5
        layer.detection_evaluate_param.evaluate_difficult_gt = False