Python caffe.proto.caffe_pb2.TEST Examples

The following are 16 code examples of caffe.proto.caffe_pb2.TEST(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module caffe.proto.caffe_pb2 , or try the search function .
Example #1
Source File: net_generator.py    From resnet-cifar10-caffe with MIT License 6 votes vote down vote up 
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
Example #2
Source File: builder.py    From channel-pruning with MIT License 6 votes vote down vote up 
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)
Example #3
Source File: builder.py    From channel-pruning with MIT License 6 votes vote down vote up 
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)
Example #4
Source File: gen_model.py    From MobileNetv2-SSDLite with MIT License 6 votes vote down vote up 
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
Example #5
Source File: net_generator.py    From resnet-cifar10-caffe with MIT License 5 votes vote down vote up 
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 **************************
Example #6
Source File: builder.py    From channel-pruning with MIT License 5 votes vote down vote up 
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
Example #7
Source File: builder.py    From channel-pruning with MIT License 5 votes vote down vote up 
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 **************************
Example #8
Source File: builder.py    From channel-pruning with MIT License 5 votes vote down vote up 
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
Example #9
Source File: builder.py    From channel-pruning with MIT License 5 votes vote down vote up 
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)
Example #10
Source File: builder.py    From channel-pruning with MIT License 5 votes vote down vote up 
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)
Example #11
Source File: builder.py    From channel-pruning with MIT License 5 votes vote down vote up 
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)
Example #12
Source File: builder.py    From channel-pruning with MIT License 5 votes vote down vote up 
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)
Example #13
Source File: net_generator.py    From ThiNet_Code with MIT License 5 votes vote down vote up 
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 **************************
Example #14
Source File: net_generator.py    From ThiNet_Code with MIT License 5 votes vote down vote up 
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!"
Example #15
Source File: gen_model.py    From MobileNetv2-SSDLite with MIT License 5 votes vote down vote up 
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
Example #16
Source File: gen_model.py    From MobileNetv2-SSDLite with MIT License 5 votes vote down vote up 
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