Python caffe.NetSpec方法代碼示例

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import NetSpec [as 別名]
def fcn(split, tops):
    n = caffe.NetSpec()
    n.color, n.hha, n.label = L.Python(module='nyud_layers',
            layer='NYUDSegDataLayer', ntop=3,
            param_str=str(dict(nyud_dir='../data/nyud', split=split,
                tops=tops, seed=1337)))
    n = modality_fcn(n, 'color', 'color')
    n = modality_fcn(n, 'hha', 'hha')
    n.score_fused = L.Eltwise(n.score_frcolor, n.score_frhha,
            operation=P.Eltwise.SUM, coeff=[0.5, 0.5])
    n.upscore = L.Deconvolution(n.score_fused,
        convolution_param=dict(num_output=40, kernel_size=64, stride=32,
            bias_term=False),
        param=[dict(lr_mult=0)])
    n.score = crop(n.upscore, n.color)
    n.loss = L.SoftmaxWithLoss(n.score, n.label,
            loss_param=dict(normalize=False, ignore_label=255))
    return n.to_proto() 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import NetSpec [as 別名]
def make_frontend_vgg(options, is_training):
    batch_size = options.train_batch if is_training else options.test_batch
    image_path = options.train_image if is_training else options.test_image
    label_path = options.train_label if is_training else options.test_label
    net = caffe.NetSpec()
    net.data, net.label = network.make_image_label_data(
        image_path, label_path, batch_size,
        is_training, options.crop_size, options.mean)
    last = network.build_frontend_vgg(
        net, net.data, options.classes)[0]
    if options.up:
        net.upsample = network.make_upsample(last, options.classes)
        last = net.upsample
    net.loss = network.make_softmax_loss(last, net.label)
    if not is_training:
        net.accuracy = network.make_accuracy(last, net.label)
    return net.to_proto() 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import NetSpec [as 別名]
def make_context(options, is_training):
    batch_size = options.train_batch if is_training else options.test_batch
    image_path = options.train_image if is_training else options.test_image
    label_path = options.train_label if is_training else options.test_label
    net = caffe.NetSpec()
    net.data, net.label = network.make_bin_label_data(
        image_path, label_path, batch_size,
        options.label_shape, options.label_stride)
    last = network.build_context(
        net, net.data, options.classes, options.layers)[0]
    if options.up:
        net.upsample = network.make_upsample(last, options.classes)
        last = net.upsample
    net.loss = network.make_softmax_loss(last, net.label)
    if not is_training:
        net.accuracy = network.make_accuracy(last, net.label)
    return net.to_proto() 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import NetSpec [as 別名]
def lenet(batch_size):
    n = caffe.NetSpec()
    n.data, n.label = L.DummyData(shape=[dict(dim=[batch_size, 1, 28, 28]),
                                         dict(dim=[batch_size, 1, 1, 1])],
                                  transform_param=dict(scale=1./255), ntop=2)
    n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20,
        weight_filler=dict(type='xavier'))
    n.pool1 = L.Pooling(n.conv1, kernel_size=2, stride=2, pool=P.Pooling.MAX)
    n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50,
        weight_filler=dict(type='xavier'))
    n.pool2 = L.Pooling(n.conv2, kernel_size=2, stride=2, pool=P.Pooling.MAX)
    n.ip1 = L.InnerProduct(n.pool2, num_output=500,
        weight_filler=dict(type='xavier'))
    n.relu1 = L.ReLU(n.ip1, in_place=True)
    n.ip2 = L.InnerProduct(n.relu1, num_output=10,
        weight_filler=dict(type='xavier'))
    n.loss = L.SoftmaxWithLoss(n.ip2, n.label)
    return n.to_proto() 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import NetSpec [as 別名]
def coord_net_spec(ks=3, stride=1, pad=0, pool=2, dstride=2, dpad=0):
    """
    Define net spec for simple conv-pool-deconv pattern common to all
    coordinate mapping tests.
    """
    n = caffe.NetSpec()
    n.data = L.Input(shape=dict(dim=[2, 1, 100, 100]))
    n.aux = L.Input(shape=dict(dim=[2, 1, 20, 20]))
    n.conv = L.Convolution(
        n.data, num_output=10, kernel_size=ks, stride=stride, pad=pad)
    n.pool = L.Pooling(
        n.conv, pool=P.Pooling.MAX, kernel_size=pool, stride=pool, pad=0)
    # for upsampling kernel size is 2x stride
    try:
        deconv_ks = [s*2 for s in dstride]
    except:
        deconv_ks = dstride*2
    n.deconv = L.Deconvolution(
        n.pool, num_output=10, kernel_size=deconv_ks, stride=dstride, pad=dpad)
    return n 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import NetSpec [as 別名]
def test_nd_conv(self):
        """
        ND conv maps the same way in more dimensions.
        """
        n = caffe.NetSpec()
        # define data with 3 spatial dimensions, otherwise the same net
        n.data = L.Input(shape=dict(dim=[2, 3, 100, 100, 100]))
        n.conv = L.Convolution(
            n.data, num_output=10, kernel_size=[3, 3, 3], stride=[1, 1, 1],
            pad=[0, 1, 2])
        n.pool = L.Pooling(
            n.conv, pool=P.Pooling.MAX, kernel_size=2, stride=2, pad=0)
        n.deconv = L.Deconvolution(
            n.pool, num_output=10, kernel_size=4, stride=2, pad=0)
        ax, a, b = coord_map_from_to(n.deconv, n.data)
        self.assertEquals(ax, 1)
        self.assertTrue(len(a) == len(b))
        self.assertTrue(np.all(a == 1))
        self.assertEquals(b[0] - 1, b[1])
        self.assertEquals(b[1] - 1, b[2]) 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import NetSpec [as 別名]
def gen_net(batch_size=512, use_bn=True):
    n=NetSpec();
    n.data = L.DummyData(shape={"dim":[batch_size,3,96,96]})
    n.select1 = L.DummyData(shape={"dim":[2]})
    n.select2 = L.DummyData(shape={"dim":[2]})
    n.label = L.DummyData(shape={"dim":[2]})
    caffenet_stack(n.data, n, use_bn)
    n.first = L.BatchReindex(n.relu6, n.select1)
    n.second = L.BatchReindex(n.relu6, n.select2)
    n.fc6_concat=L.Concat(n.first, n.second);

    fc_relu(n, n.fc6_concat, '7', 4096, batchnorm=use_bn);
    fc_relu(n, n.relu7, '8', 4096);
    n.fc9 = L.InnerProduct(n.relu8, num_output=8,
                            weight_filler=dict(type='xavier'));
    n.loss = L.SoftmaxWithLoss(n.fc9, n.label, loss_param=dict(normalization=P.Loss.NONE));

    prot=n.to_proto()
    prot.debug_info=True
    return prot;

# image preprocessing.  Note that the input image will modified. 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import NetSpec [as 別名]
def get_phocnet(self, word_image_lmdb_path, phoc_lmdb_path,
                    phoc_size=604, generate_deploy=False):
        '''
        Returns a NetSpec definition of the PHOCNet. The definition can then be transformed
        into a protobuffer message by casting it into a str.
        '''
        n = NetSpec()
        # Data
        self.set_phocnet_data(n=n, generate_deploy=generate_deploy,
                              word_image_lmdb_path=word_image_lmdb_path,
                              phoc_lmdb_path=phoc_lmdb_path)

        # Conv Part
        self.set_phocnet_conv_body(n=n, relu_in_place=True)

        # FC Part
        n.spp5 = L.SPP(n.relu4_3, spp_param=dict(pool=P.SPP.MAX, pyramid_height=3, engine=self.spp_engine))
        n.fc6, n.relu6, n.drop6 = self.fc_relu(bottom=n.spp5, layer_size=4096,
                                               dropout_ratio=0.5, relu_in_place=True)
        n.fc7, n.relu7, n.drop7 = self.fc_relu(bottom=n.drop6, layer_size=4096,
                                               dropout_ratio=0.5, relu_in_place=True)
        n.fc8 = L.InnerProduct(n.drop7, num_output=phoc_size,
                               weight_filler=dict(type=self.initialization),
                               bias_filler=dict(type='constant'))
        n.sigmoid = L.Sigmoid(n.fc8, include=dict(phase=self.phase_test))

        # output part
        if not generate_deploy:
            n.silence = L.Silence(n.sigmoid, ntop=0, include=dict(phase=self.phase_test))
            n.loss = L.SigmoidCrossEntropyLoss(n.fc8, n.phocs)

        return n.to_proto() 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import NetSpec [as 別名]
def get_tpp_phocnet(self, word_image_lmdb_path, phoc_lmdb_path, phoc_size, tpp_levels=5,
                        generate_deploy=False):
        '''
        Returns a NetSpec definition of the TPP-PHOCNet. The definition can then be transformed
        into a protobuffer message by casting it into a str.
        '''
        n = NetSpec()
        # Data
        self.set_phocnet_data(n=n, generate_deploy=generate_deploy,
                              word_image_lmdb_path=word_image_lmdb_path,
                              phoc_lmdb_path=phoc_lmdb_path)

        # Conv Part
        self.set_phocnet_conv_body(n=n, relu_in_place=True)

        # FC Part
        n.tpp5 = L.TPP(n.relu4_3, tpp_param=dict(pool=P.TPP.MAX, pyramid_layer=range(1, tpp_levels + 1), engine=self.spp_engine))
        n.fc6, n.relu6, n.drop6 = self.fc_relu(bottom=n.tpp5, layer_size=4096,
                                               dropout_ratio=0.5, relu_in_place=True)
        n.fc7, n.relu7, n.drop7 = self.fc_relu(bottom=n.drop6, layer_size=4096,
                                               dropout_ratio=0.5, relu_in_place=True)
        n.fc8 = L.InnerProduct(n.drop7, num_output=phoc_size,
                               weight_filler=dict(type=self.initialization),
                               bias_filler=dict(type='constant'))
        n.sigmoid = L.Sigmoid(n.fc8, include=dict(phase=self.phase_test))

        # output part
        if not generate_deploy:
            n.silence = L.Silence(n.sigmoid, ntop=0, include=dict(phase=self.phase_test))
            n.loss = L.SigmoidCrossEntropyLoss(n.fc8, n.phocs)

        return n.to_proto() 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import NetSpec [as 別名]
def approx_bilinear_net(input_shapes, hdf5_txt_fname='', batch_size=1, net_name='ApproxBilinearNet', phase=None):
    assert len(input_shapes) == 2
    image_shape, vel_shape = input_shapes
    assert len(image_shape) == 3
    assert len(vel_shape) == 1
    _, height, width = image_shape
    y_dim = height * width

    fc_kwargs = dict(param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=0, decay_mult=0)],
                     num_output=y_dim,
                     weight_filler=dict(type='gaussian', std=0.001),
                     bias_filler=dict(type='constant', value=0))

    n = caffe.NetSpec()
    data_kwargs = dict(name='data', ntop=3, batch_size=batch_size, source=hdf5_txt_fname)
    if phase is not None:
        data_kwargs.update(dict(include=dict(phase=phase)))
    n.image_curr, n.image_diff, n.vel = L.HDF5Data(**data_kwargs)
    u = n.vel
    n.y = L.Flatten(n.image_curr, name='flatten1')
    n.y_diff = L.Flatten(n.image_diff, name='flatten2')
    n.fc1_y = L.InnerProduct(n.y, name='fc1', **fc_kwargs)
    n.fc2_u = L.InnerProduct(u, name='fc2', **fc_kwargs)
    n.fc3_u = L.InnerProduct(u, name='fc3', **fc_kwargs)
    n.prod_y_u = L.Eltwise(n.fc1_y, n.fc2_u, name='prod', operation=P.Eltwise.PROD)
    n.y_diff_pred = L.Eltwise(n.prod_y_u, n.fc3_u, name='sum', operation=P.Eltwise.SUM)
    n.loss = L.EuclideanLoss(n.y_diff_pred, n.y_diff, name='loss')

    net = n.to_proto()
    net.name = net_name
    return net, None 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import NetSpec [as 別名]
def bilinear_net(input_shapes, hdf5_txt_fname='', batch_size=1, net_name='BilinearNet', phase=None, **kwargs):
    assert len(input_shapes) == 2
    image_shape, vel_shape = input_shapes
    assert len(image_shape) == 3
    assert len(vel_shape) == 1
    y_dim = np.prod(image_shape)
    u_dim, = vel_shape

    fc_kwargs = dict(param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=0, decay_mult=0)],
                     weight_filler=dict(type='gaussian', std=0.001),
                     bias_filler=dict(type='constant', value=0))

    n = caffe.NetSpec()
    data_kwargs = dict(name='data', ntop=3, batch_size=batch_size, source=hdf5_txt_fname)
    if phase is not None:
        data_kwargs.update(dict(include=dict(phase=phase)))
    n.image_curr, n.image_diff, n.vel = L.HDF5Data(**data_kwargs)
    u = n.vel
    n.y = L.Flatten(n.image_curr)
    n.y_diff_pred = Bilinear(n, n.y, u, y_dim, u_dim, **fc_kwargs)
    n.y_diff = L.Flatten(n.image_diff)
    n.loss = L.EuclideanLoss(n.y_diff_pred, n.y_diff, name='loss')

    n.image_diff_pred = L.Reshape(n.y_diff_pred, shape=dict(dim=[batch_size] + list(image_shape)))
    n.image_next_pred = L.Eltwise(n.image_curr, n.image_diff_pred, operation=P.Eltwise.SUM)

    net = n.to_proto()
    net.name = net_name
    return net, None 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import NetSpec [as 別名]
def bilinear_constrained_net(input_shapes, hdf5_txt_fname='', batch_size=1, net_name='BilinearConstrainedNet', phase=None, **kwargs):
    assert len(input_shapes) == 2
    image_shape, vel_shape = input_shapes
    assert len(image_shape) == 3
    assert len(vel_shape) == 1
    _, height, width = image_shape
    y_dim = height * width
    u_dim = vel_shape[0]

    fc_kwargs = dict(param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=0, decay_mult=0)],
                     weight_filler=dict(type='gaussian', std=0.001),
                     bias_filler=dict(type='constant', value=0))

    n = caffe.NetSpec()
    data_kwargs = dict(name='data', ntop=3, batch_size=batch_size, source=hdf5_txt_fname)
    if phase is not None:
        data_kwargs.update(dict(include=dict(phase=phase)))
    n.image_curr, n.image_diff, n.vel = L.HDF5Data(**data_kwargs)
    u = n.vel
    n.y = L.Flatten(n.image_curr)
    n.y_diff_pred = Bilinear(n, n.y, u, y_dim, u_dim, **fc_kwargs)
    n.image_diff_pred = L.Reshape(n.y_diff_pred, shape=dict(dim=[batch_size] + list(image_shape)))
    n.image_next_pred_unconstrained = L.Eltwise(n.image_curr, n.image_diff_pred, operation=P.Eltwise.SUM)
    n.image_next_pred = L.TanH(n.image_next_pred_unconstrained)
    n.image_next = L.Eltwise(n.image_curr, n.image_diff, operation=P.Eltwise.SUM)
    n.loss = L.EuclideanLoss(n.image_next, n.image_next_pred, name='loss')

    net = n.to_proto()
    net.name = net_name
    return net, None 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import NetSpec [as 別名]
def convert_to_caffe(self, name):
        caffe_net = caffe.NetSpec()
        layer = L.Input(shape=dict(dim=[1, 3, args.image_hw, args.image_hw]))
        caffe_net.tops['data'] = layer
        slim.generate_caffe_prototxt(self, caffe_net, layer)
        print(caffe_net.to_proto())
        with open(name + '.prototxt', 'wb') as f:
            f.write(str(caffe_net.to_proto()).encode())
        caffe_net = caffe.Net(name + '.prototxt', caffe.TEST)
        slim.convert_pytorch_to_caffe(self, caffe_net)
        caffe_net.save(name + '.caffemodel') 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import NetSpec [as 別名]
def convert_to_caffe(self, name):
            caffe_net = caffe.NetSpec()
            layer = L.Input(shape=dict(dim=[1, 3, 224, 224]))
            caffe_net.tops['data'] = layer
            generate_caffe_prototxt(self, caffe_net, layer)
            print(caffe_net.to_proto())
            with open(name + '.prototxt', 'wb') as f:
                f.write(str(caffe_net.to_proto()))
            caffe_net = caffe.Net(name + '.prototxt', caffe.TEST)
            convert_pytorch_to_caffe(self, caffe_net)
            caffe_net.save(name + '.caffemodel') 

# 需要導入模塊: import caffe [as 別名]
# 或者: from caffe import NetSpec [as 別名]
def header_code(self):
        return """from __future__ import print_function
import numpy as np
import sys, argparse
import caffe
from caffe import layers as L
from caffe import params as P
from caffe import to_proto
from six import text_type as _text_type


__weights_dict = dict()

def load_weights(weight_file):
    if weight_file == None:
        return

    try:
        weights_dict = np.load(weight_file, allow_pickle=True).item()
    except:
        weights_dict = np.load(weight_file, allow_pickle=True, encoding='bytes').item()

    return weights_dict


def KitModel(weight_file = None):
    n = caffe.NetSpec()
"""