Python caffe_pb2.TRAIN屬性代碼示例

# 需要導入模塊: from caffe.proto import caffe_pb2 [as 別名]
# 或者: from caffe.proto.caffe_pb2 import TRAIN [as 別名]
def orth_loss_v2(self, bottom_name):
        # self.Python('orth_loss', 'orthLossLayer', loss_weight=1, bottom=[bottom_name], top=[name], name=name)
        # , bottom=[bottom+'_MVN']
        # save bottom
        mainpath = self.bottom

        bottom = bottom_name #'NormLayer', 
        # self.MVN(bottom=[bottom])
        layer = "TransposeLayer"
        layername = bottom_name+'_' + layer
        outputs = [layername]
        self.Python(layer, layer, top=outputs, bottom=[bottom], name=layername, phase='TRAIN')
        self.Matmul()
        
        outputs = [self.this.name]
        self.EuclideanLoss(name=bottom_name+'_euclidean', bottom=outputs, loss_weight=1e-1, phase='TRAIN')
        
        # restore bottom
        self.cur = mainpath 

# 需要導入模塊: from caffe.proto import caffe_pb2 [as 別名]
# 或者: from caffe.proto.caffe_pb2 import TRAIN [as 別名]
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) 

# 需要導入模塊: from caffe.proto import caffe_pb2 [as 別名]
# 或者: from caffe.proto.caffe_pb2 import TRAIN [as 別名]
def resnet_orth_v2(n=3):
    """6n+2, n=3 9 18 coresponds to 20 56 110 layers"""    
    net_name = "resnet-orth-v2"    
    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, v2=True)
    builder.write(folder=pt_folder) 

# 需要導入模塊: from caffe.proto import caffe_pb2 [as 別名]
# 或者: from caffe.proto.caffe_pb2 import TRAIN [as 別名]
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 ************************** 

# 需要導入模塊: from caffe.proto import caffe_pb2 [as 別名]
# 或者: from caffe.proto.caffe_pb2 import TRAIN [as 別名]
def MVN(self, name=None, bottom=[], normalize_variance=True, across_channels=False, phase='TRAIN'):
        if across_channels:
            NotImplementedError
        if not normalize_variance:
            NotImplementedError
        self.setup(self.suffix('MVN', name),bottom=bottom, layer_type='MVN')
        if phase!='TRAIN':
            NotImplementedError
        self.include() 

# 需要導入模塊: from caffe.proto import caffe_pb2 [as 別名]
# 或者: from caffe.proto.caffe_pb2 import TRAIN [as 別名]
def plain_func(self, name, num_output, up=False, **kwargs):
        self.conv_bn_relu(name+'_conv0', num_output=num_output, stride=1+int(up), **kwargs)
        self.conv_bn_relu(name+'_conv1', num_output=num_output, **kwargs)
    
    # def orth_loss(self, bottom_name):
    #     # self.Python('orth_loss', 'orthLossLayer', loss_weight=1, bottom=[bottom_name], top=[name], name=name)
    #     # , bottom=[bottom+'_MVN']

    #     # save bottom
    #     mainpath = self.bottom

    #     bottom = bottom_name #'NormLayer', 
    #     # self.MVN(bottom=[bottom])
    #     layer = "TransposeLayer"
    #     layername = bottom_name+'_' + layer
    #     outputs = [layername]#, bottom_name+'_zerolike']
    #     self.Python(layer, layer, top=outputs, bottom=[bottom], name=layername, phase='TRAIN')
    #     self.Matmul()
    #     # layer="diagLayer"
    #     # layername = bottom_name+'_' + layer
        
    #     # self.Python(layer, layer, top=[layername], name=layername, phase='TRAIN')
    #     outputs = [self.this.name]#, bottom_name+'_zerolike']
    #     self.EuclideanLoss(name=bottom_name+'_euclidean', bottom=outputs, loss_weight=1e-3, phase='TRAIN')
        
    #     # restore bottom
    #     self.cur = mainpath 

# 需要導入模塊: from caffe.proto import caffe_pb2 [as 別名]
# 或者: from caffe.proto.caffe_pb2 import TRAIN [as 別名]
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) 

# 需要導入模塊: from caffe.proto import caffe_pb2 [as 別名]
# 或者: from caffe.proto.caffe_pb2 import TRAIN [as 別名]
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) 

# 需要導入模塊: from caffe.proto import caffe_pb2 [as 別名]
# 或者: from caffe.proto.caffe_pb2 import TRAIN [as 別名]
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) 

# 需要導入模塊: from caffe.proto import caffe_pb2 [as 別名]
# 或者: from caffe.proto.caffe_pb2 import TRAIN [as 別名]
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) 

# 需要導入模塊: from caffe.proto import caffe_pb2 [as 別名]
# 或者: from caffe.proto.caffe_pb2 import TRAIN [as 別名]
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 ************************** 

# 需要導入模塊: from caffe.proto import caffe_pb2 [as 別名]
# 或者: from caffe.proto.caffe_pb2 import TRAIN [as 別名]
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!" 

# 需要導入模塊: from caffe.proto import caffe_pb2 [as 別名]
# 或者: from caffe.proto.caffe_pb2 import TRAIN [as 別名]
def ssd_loss(self):
        layer = self.net.layer.add() 
        layer.name = "mbox_loss"
        layer.type = "MultiBoxLoss"
        layer.bottom.append("mbox_loc")
        layer.bottom.append("mbox_conf")
        layer.bottom.append("mbox_priorbox")
        layer.bottom.append("label")
        layer.top.append("mbox_loss")
        layer.include.add().phase = caffe_pb2.TRAIN
        layer.propagate_down.append(True)
        layer.propagate_down.append(True)
        layer.propagate_down.append(False)
        layer.propagate_down.append(False)
        layer.loss_param.normalization = caffe_pb2.LossParameter.VALID
        layer.multibox_loss_param.loc_loss_type = caffe_pb2.MultiBoxLossParameter.SMOOTH_L1
        layer.multibox_loss_param.conf_loss_type = caffe_pb2.MultiBoxLossParameter.LOGISTIC
        layer.multibox_loss_param.loc_weight = 1.0
        layer.multibox_loss_param.num_classes = self.class_num
        layer.multibox_loss_param.share_location = True
        layer.multibox_loss_param.match_type = caffe_pb2.MultiBoxLossParameter.PER_PREDICTION
        layer.multibox_loss_param.overlap_threshold = 0.5
        layer.multibox_loss_param.use_difficult_gt = True
        layer.multibox_loss_param.neg_pos_ratio = 3.0
        layer.multibox_loss_param.neg_overlap = 0.5
        layer.multibox_loss_param.code_type = caffe_pb2.PriorBoxParameter.CENTER_SIZE
        layer.multibox_loss_param.ignore_cross_boundary_bbox = False
        layer.multibox_loss_param.mining_type = caffe_pb2.MultiBoxLossParameter.MAX_NEGATIVE 

# 需要導入模塊: from caffe.proto import caffe_pb2 [as 別名]
# 或者: from caffe.proto.caffe_pb2 import TRAIN [as 別名]
def data_train_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.TRAIN

        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.transform_param.resize_param.interp_mode.append(caffe_pb2.ResizeParameter.AREA)
        layer.transform_param.resize_param.interp_mode.append(caffe_pb2.ResizeParameter.NEAREST)
        layer.transform_param.resize_param.interp_mode.append(caffe_pb2.ResizeParameter.CUBIC)
        layer.transform_param.resize_param.interp_mode.append(caffe_pb2.ResizeParameter.LANCZOS4)
        layer.transform_param.emit_constraint.emit_type = caffe_pb2.EmitConstraint.CENTER
        layer.transform_param.distort_param.brightness_prob = 0.5
        layer.transform_param.distort_param.brightness_delta = 32.0
        layer.transform_param.distort_param.contrast_lower = 0.5
        layer.transform_param.distort_param.contrast_upper = 1.5
        layer.transform_param.distort_param.hue_prob = 0.5
        layer.transform_param.distort_param.hue_delta = 18.0
        layer.transform_param.distort_param.saturation_prob = 0.5
        layer.transform_param.distort_param.saturation_lower = 0.5
        layer.transform_param.distort_param.saturation_upper = 1.5
        layer.transform_param.distort_param.random_order_prob = 0.0
        layer.transform_param.expand_param.prob = 0.5
        layer.transform_param.expand_param.max_expand_ratio = 4.0


        layer.data_param.source = self.lmdb
        layer.data_param.batch_size = 64
        layer.data_param.backend = caffe_pb2.DataParameter.LMDB

        sampler = layer.annotated_data_param.batch_sampler.add()
        sampler.max_sample = 1
        sampler.max_trials = 1
        for overlap in [0.1, 0.3, 0.5, 0.7, 0.9, 1.0]:
            sampler = layer.annotated_data_param.batch_sampler.add()
            sampler.sampler.min_scale = 0.3
            sampler.sampler.max_scale = 1.0
            sampler.sampler.min_aspect_ratio = 0.5
            sampler.sampler.max_aspect_ratio = 2.0
            sampler.sample_constraint.min_jaccard_overlap = overlap
            sampler.max_sample = 1
            sampler.max_trials = 50
        layer.annotated_data_param.label_map_file = self.label_map