我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用caffe.NetSpec()。
def archiEncoder(convoList, fullList, lmdb, batch_size): n = caffe.NetSpec() n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb, transform_param=dict(scale=1./255), ntop=2) n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=convoList[0], weight_filler=dict(type='xavier')) n.relu1 = L.ReLU(n.conv1, in_place=True) n.pool1 = L.Pooling(n.relu1, kernel_size=2, stride=2, pool=P.Pooling.MAX) n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=convoList[1], weight_filler=dict(type='xavier')) n.relu2 = L.ReLU(n.conv2, in_place=True) n.pool2 = L.Pooling(n.relu2, kernel_size=2, stride=2, pool=P.Pooling.MAX) n.ip2 = L.InnerProduct(n.pool2, num_output=10, weight_filler=dict(type='xavier')) n.loss = L.SoftmaxWithLoss(n.ip2, n.label) return n.to_proto()
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()
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()
def make_joint(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] last = network.build_context( net, last, 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()
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()
def _new_model(self): self.netspec = caffe.NetSpec() if self.cnn == 'AlexNet': from cnns import AlexNet self.base_cnn = AlexNet(netspec=self.netspec) self.feat_layer = 'fc7' self.feat_dim = 4096 elif self.cnn == 'GoogLeNet': from cnns import InceptionV1 self.base_cnn = InceptionV1(netspec=self.netspec) self.feat_layer = 'pool5' self.feat_dim = 1024 elif self.cnn == 'VGG19': from cnns import VGG19 self.base_cnn = VGG19(netspec=self.netspec) self.feat_layer = 'fc7' self.feat_dim = 4096 return self.netspec
def lenet(lmdb,batch_size): #define Lenet n = caffe.NetSpec() n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb, 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.fc1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier')) n.relu1 = L.ReLU(n.fc1, in_place=True) n.score = L.InnerProduct(n.relu1,num_output=10, weight_filler=dict(type='xavier')) n.loss = L.SoftmaxWithLoss(n.score, n.label) return n.to_proto()
def LeNet(lmdb, batch_size): # our version of LeNet: a series of linear and simple nonlinear transformations n = caffe.NetSpec() n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb, 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.fc1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier')) n.relu1 = L.ReLU(n.fc1, in_place=True) n.score = L.InnerProduct(n.relu1, num_output=10, weight_filler=dict(type='xavier')) n.loss = L.SoftmaxWithLoss(n.score, n.label) proto = n.to_proto() proto.name = 'LeNet' return proto
def __create_network_prototxt(self, deploy): n = caffe.NetSpec() top = self.__create_data_layer(n, deploy=deploy) top = self.__create_first_convolutional_layer(n, top) # Create Residual Units for i in range(len(self.__num_residual_units)): stride = 1 if i == 0 else 2 top = self.__layer(n, top, 'res%d' % (2+i), self.__wide_basic, self.__num_feature_maps[i], self.__num_feature_maps[i+1], self.__num_residual_units[i], stride, deploy) top = self.__create_fully_connected_layer(n, top, deploy=deploy) self.__create_output_layer(n, top, deploy=deploy) # Return prototxt return str(n.to_proto()).replace('__test', '')
def construc_net(): net = caffe.NetSpec() net.data = L.Input(shape = dict(dim = [10,3,224,224])) block_1 = _block_crp('1', 2, net, net.data, 64) block_2 = _block_crp('2', 2, net, block_1, 128) block_3 = _block_crp('3', 4, net, block_2, 256) block_4 = _block_crp('4', 4, net, block_3, 512) block_5 = _block_crp('5', 4, net, block_4, 512) block_6 = _block_frd('6', net, block_5, 4096) block_7 = _block_frd('7', net, block_6, 4096) net.fc8 = L.InnerProduct(block_7, num_output = 1000) net.prob = L.Softmax(net.fc8) return net.to_proto()
def construc_net(): net = caffe.NetSpec() net.data = L.Input(shape = dict(dim = [10,3,224,224])) block_1 = _block_crp('1', 2, net, net.data, 64) block_2 = _block_crp('2', 2, net, block_1, 128) block_3 = _block_crp('3', 3, net, block_2, 256) block_4 = _block_crp('4', 3, net, block_3, 512) block_5 = _block_crp('5', 3, net, block_4, 512) block_6 = _block_frd('6', net, block_5, 4096) block_7 = _block_frd('7', net, block_6, 4096) net.fc8 = L.InnerProduct(block_7, num_output = 1000) net.prob = L.Softmax(net.fc8) return net.to_proto()
def overall_net(batch_size, channels, height, width, action_size, net_type): # param = learned_param n=caffe.NetSpec() # action n.frames = L.Input(shape=dict(dim=[batch_size, channels, height, width])) # Image feature if net_type == 'action': param = learned_param else: param = frozen_param n.conv1, n.relu1 = conv_relu(n.frames, 8, 32, stride=4, param=param) n.conv2, n.relu2 = conv_relu(n.relu1, 4, 64, stride=2, param=param) n.conv3, n.relu3 = conv_relu(n.relu2, 3, 64, stride=1, param=param) n.fc4, n.relu4 = fc_relu(n.relu3, 512, param=param) n.value_q = L.InnerProduct(n.relu4, num_output=action_size, param=param, weight_filler=dict(type='gaussian', std=0.005), bias_filler=dict(type='constant', value=1)) if net_type == 'test': return n.to_proto() n.filter = L.Input(shape=dict(dim=[batch_size, action_size])) # operation 0: PROD n.filtered_value_q = L.Eltwise(n.value_q, n.filter, operation=0) n.target = L.Input(shape=dict(dim=[batch_size, action_size])) n.loss = L.EuclideanLoss(n.filtered_value_q, n.target) return n.to_proto() ### define solver
def __init__(self): self.net = caffe.NetSpec() self.testnet = caffe.NetSpec()
def make_frontend_vgg(options): deploy_net = caffe.NetSpec() deploy_net.data = network.make_input_data(options.input_size) last, final_name = network.build_frontend_vgg( deploy_net, deploy_net.data, options.classes) if options.up: deploy_net.upsample = network.make_upsample(last, options.classes) last = deploy_net.upsample deploy_net.prob = network.make_prob(last) deploy_net = deploy_net.to_proto() return deploy_net, final_name
def make_context(options): deploy_net = caffe.NetSpec() deploy_net.data = network.make_input_data( options.input_size, options.classes) last, final_name = network.build_context( deploy_net, deploy_net.data, options.classes, options.layers) if options.up: deploy_net.upsample = network.make_upsample(last, options.classes) last = deploy_net.upsample deploy_net.prob = network.make_prob(last) deploy_net = deploy_net.to_proto() return deploy_net, final_name
def deploy_net(conf, batch_size, class_num) : ''' :param conf: the data_set_config information, defined in data_info_set.item :param batch_size: the batch_size of prototxt :param class_num: the class_num of the data_set :param channels: the channels of hyperspectral data, maybe it is 224,448 or 103,206 :param kernel_size: the kernel_size of the convolution layer, often is 1/9 of the channels :return: deploy file handle ''' n = caffe.NetSpec() if conf.use_CK is True: n.data, n.label = L.DummyData(shape= {'dim' : [batch_size, 1, conf.CK_channels, 1]}, ntop = 2) n.conv1 = L.Convolution(n.data, kernel_h=conf.CK_kernel_size, kernel_w=1, num_output=20, weight_filler=dict(type='gaussian', std=0.05), bias_filler=dict(type='constant', value=0.1)) else: n.data, n.label = L.DummyData(shape= {'dim' : [batch_size, 1, conf.channels, 1]}, ntop = 2) n.conv1 = L.Convolution(n.data, kernel_h = conf.kernel_size, kernel_w = 1, num_output = 20, weight_filler = dict(type = 'gaussian', std = 0.05), bias_filler = dict(type = 'constant', value = 0.1)) n.bn1 = L.BatchNorm(n.conv1, use_global_stats = 1, in_place = True) n.relu1 = L.PReLU(n.bn1, in_place = True) n.ip1 = L.InnerProduct(n.relu1, num_output = 100, weight_filler = dict(type = 'gaussian', std = 0.05), bias_filler = dict(type = 'constant', value = 0.1)) n.drop1 = L.Dropout(n.ip1, dropout_ratio = 0.1, in_place = True) n.relu2 = L.PReLU(n.drop1, in_place = True) n.ip2 = L.InnerProduct(n.relu2, num_output = class_num, weight_filler = dict(type = 'gaussian', std = 0.05), bias_filler = dict(type = 'constant', value = 0.1)) return n.to_proto()
def lenet_proto(self, batch_size, phase='TRAIN'): n = caffe.NetSpec() if phase == 'TRAIN': source_data = self.train_data mirror = False else: source_data = self.test_data mirror = False n.data, n.label = L.Data(source=source_data, backend=P.Data.LMDB, batch_size=batch_size, ntop=2, transform_param=dict(scale=0.00390625, mirror=mirror)) n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, stride=1, weight_filler=dict(type='xavier'), bias_filler=dict(type='constant')) n.pool1 = L.Pooling(n.conv1, pool=P.Pooling.MAX, kernel_size=2, stride=2) n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, stride=1, weight_filler=dict(type='xavier'), bias_filler=dict(type='constant')) n.pool2 = L.Pooling(n.conv2, pool=P.Pooling.MAX, kernel_size=2, stride=2) n.ip1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'), bias_filler=dict(type='constant')) n.relu1 = L.ReLU(n.ip1, in_place=True) n.ip2 = L.InnerProduct(n.relu1, num_output=self.classifier_num, weight_filler=dict(type='xavier'), bias_filler=dict(type='constant')) n.loss = L.SoftmaxWithLoss(n.ip2, n.label) if phase == 'TRAIN': pass else: n.accuracy = L.Accuracy(n.ip2, n.label, include=dict(phase=1)) return n.to_proto()
def lenet_bn_proto(self, batch_size, phase='TRAIN'): n = caffe.NetSpec() if phase == 'TRAIN': source_data = self.train_data mirror = False else: source_data = self.test_data mirror = False n.data, n.label = L.Data(source=source_data, backend=P.Data.LMDB, batch_size=batch_size, ntop=2, transform_param=dict(scale=0.00390625, mirror=mirror)) n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, stride=1, weight_filler=dict(type='xavier'), bias_filler=dict(type='constant')) n.bn1 = L.BatchNorm(n.conv1, use_global_stats=False) n.pool1 = L.Pooling(n.bn1, pool=P.Pooling.MAX, kernel_size=2, stride=2) n.conv2 = L.Convolution(n.pool1, kernel_size=5, num_output=50, stride=1, weight_filler=dict(type='xavier'), bias_filler=dict(type='constant')) n.bn2 = L.BatchNorm(n.conv2, use_global_stats=False) n.pool2 = L.Pooling(n.bn2, pool=P.Pooling.MAX, kernel_size=2, stride=2) n.ip1 = L.InnerProduct(n.pool2, num_output=500, weight_filler=dict(type='xavier'), bias_filler=dict(type='constant')) n.relu1 = L.ReLU(n.ip1, in_place=True) n.ip2 = L.InnerProduct(n.relu1, num_output=self.classifier_num, weight_filler=dict(type='xavier'), bias_filler=dict(type='constant')) n.loss = L.SoftmaxWithLoss(n.ip2, n.label) if phase == 'TRAIN': pass else: n.accuracy = L.Accuracy(n.ip2, n.label, include=dict(phase=1)) return n.to_proto()
def alexnet_proto(self, batch_size, phase='TRAIN'): n = caffe.NetSpec() if phase == 'TRAIN': source_data = self.train_data mirror = True else: source_data = self.test_data mirror = False n.data, n.label = L.Data(source=source_data, backend=P.Data.LMDB, batch_size=batch_size, ntop=2, transform_param=dict(crop_size=227, mean_value=[104, 117, 123], mirror=mirror)) n.conv1, n.relu1 = conv_relu(n.data, num_output=96, kernel_size=11, stride=4) # 96x55x55 n.norm1 = L.LRN(n.conv1, local_size=5, alpha=0.0001, beta=0.75) n.pool1 = L.Pooling(n.norm1, kernel_size=3, stride=2, pool=P.Pooling.MAX) # 96x27x27 n.conv2, n.relu2 = conv_relu(n.pool1, num_output=256, kernel_size=5, pad=2, group=2) # 256x27x27 n.norm2 = L.LRN(n.conv2, local_size=5, alpha=0.0001, beta=0.75) n.pool2 = L.Pooling(n.norm2, kernel_size=3, stride=2, pool=P.Pooling.MAX) # 256x13x13 n.conv3, n.relu3 = conv_relu(n.pool2, num_output=384, kernel_size=3, pad=1) # 384x13x13 n.conv4, n.relu4 = conv_relu(n.conv3, num_output=384, kernel_size=3, pad=1, group=2) # 384x13x13 n.conv5, n.relu5 = conv_relu(n.conv4, num_output=256, kernel_size=3, pad=1, group=2) # 256x13x13 n.pool5 = L.Pooling(n.conv5, kernel_size=3, stride=2, pool=P.Pooling.MAX) # 256x6x16 n.fc6, n.relu6, n.drop6 = fc_relu_drop(n.pool5, num_output=4096) # 4096x1x1 n.fc7, n.relu7, n.drop7 = fc_relu_drop(n.fc6, num_output=4096) # 4096x1x1 n.fc8 = L.InnerProduct(n.fc7, num_output=self.classifier_num, param=[dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)], weight_filler=dict(type='gaussian', std=0.01), bias_filler=dict(type='constant', value=0)) n.loss = L.SoftmaxWithLoss(n.fc8, n.label) if phase == 'TRAIN': pass else: n.accuracy_top1, n.accuracy_top5 = accuracy_top1_top5(n.fc8, n.label) return n.to_proto()
def net(hdf5, batch_size): n = caffe.NetSpec() n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5, ntop=2) n.ip1 = L.InnerProduct(n.data, num_output=1024, weight_filler=dict(type='xavier')) n.relu1 = L.ReLU(n.ip1, in_place=True) n.ip2 = L.InnerProduct(n.relu1, num_output=1024, weight_filler=dict(type='xavier')) n.relu2 = L.ReLU(n.ip2, in_place=True) n.ip3 = L.InnerProduct(n.relu2, num_output=2, weight_filler=dict(type='xavier')) n.loss = L.SigmoidCrossEntropyLoss(n.ip3, n.label) return n.to_proto()
def test_train2deploy(self): """ Test train to deploy conversion. """ def network(lmdb_path, batch_size): net = caffe.NetSpec() net.data, net.labels = caffe.layers.Data(batch_size = batch_size, backend = caffe.params.Data.LMDB, source = lmdb_path, transform_param = dict(scale = 1./255), ntop = 2) net.conv1 = caffe.layers.Convolution(net.data, kernel_size = 5, num_output = 20, weight_filler = dict(type = 'xavier')) net.pool1 = caffe.layers.Pooling(net.conv1, kernel_size = 2, stride = 2, pool = caffe.params.Pooling.MAX) net.conv2 = caffe.layers.Convolution(net.pool1, kernel_size = 5, num_output = 50, weight_filler = dict(type = 'xavier')) net.pool2 = caffe.layers.Pooling(net.conv2, kernel_size = 2, stride = 2, pool = caffe.params.Pooling.MAX) net.fc1 = caffe.layers.InnerProduct(net.pool2, num_output = 500, weight_filler = dict(type = 'xavier')) net.relu1 = caffe.layers.ReLU(net.fc1, in_place = True) net.score = caffe.layers.InnerProduct(net.relu1, num_output = 10, weight_filler = dict(type = 'xavier')) net.loss = caffe.layers.SoftmaxWithLoss(net.score, net.labels) return net.to_proto() train_prototxt_path = 'tests/train.prototxt' deploy_prototxt_path = 'tests/deploy.prototxt' with open(train_prototxt_path, 'w') as f: f.write(str(network('tests/train_lmdb', 128))) tools.prototxt.train2deploy(train_prototxt_path, (128, 3, 28, 28), deploy_prototxt_path)
def _update_deploy_net(self): """ Update deploy net. """ self._deploy_net = caffe.NetSpec() data = self._add_input_layers(self._deploy_net, deploy=True) bottom_dict = self._add_intermediate_layers(self._deploy_net, data, deploy=True) self._add_output_layers(self._deploy_net, bottom_dict, deploy=True) # add input definition strings. self._deploy_str='input: {}\ninput_dim: {}\ninput_dim: {}\ninput_dim: {}\ninput_dim: {}'.\ format('"'+self.io.data_name+'"', 1, 3, 224, 224)
def _update_trainval_net(self): """ Update trainval net. """ self._train_net = caffe.NetSpec() self._val_net = caffe.NetSpec() in_nets = {'train': self._train_net, 'val': self._val_net} data = self._add_input_layers(in_nets, deploy=False) bottom_dict = self._add_intermediate_layers(self._train_net, data, deploy=False) self._add_output_layers(self._train_net, bottom_dict, deploy=False)
def caffenet(data, label=None, train=True, num_classes=1000, classifier_name='fc8', learn_all=False): """Returns a NetSpec specifying CaffeNet, following the original proto text specification (./models/bvlc_reference_caffenet/train_val.prototxt).""" n = caffe.NetSpec() n.data = data param = learned_param if learn_all else frozen_param n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4, param=param) n.pool1 = max_pool(n.relu1, 3, stride=2) n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75) n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2, param=param) n.pool2 = max_pool(n.relu2, 3, stride=2) n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75) n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1, param=param) n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2, param=param) n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2, param=param) n.pool5 = max_pool(n.relu5, 3, stride=2) n.fc6, n.relu6 = fc_relu(n.pool5, 4096, param=param) if train: n.drop6 = fc7input = L.Dropout(n.relu6, in_place=True) else: fc7input = n.relu6 n.fc7, n.relu7 = fc_relu(fc7input, 4096, param=param) if train: n.drop7 = fc8input = L.Dropout(n.relu7, in_place=True) else: fc8input = n.relu7 # always learn fc8 (param=learned_param) fc8 = L.InnerProduct(fc8input, num_output=num_classes, param=learned_param) # give fc8 the name specified by argument `classifier_name` n.__setattr__(classifier_name, fc8) if not train: n.probs = L.Softmax(fc8) if label is not None: n.label = label n.loss = L.SoftmaxWithLoss(fc8, n.label) n.acc = L.Accuracy(fc8, n.label) # write the net to a temporary file and return its filename with open('/home/ldy/workspace/caffe/models/finetune_UCMerced_LandUse/deploy.prototxt','w') as f: f.write(str(n.to_proto())) return f.name
def customNet(lmdb,batch_size): #define Lenet n = caffe.NetSpec() n.data, n.label = L.Data(batch_size=batch_size, backend=P.Data.LMDB, source=lmdb, transform_param=dict(scale=1./255),ntop=2) n.conv1 = L.Convolution(n.data, kernel_size=3, num_output=20, weight_filler=dict(type='xavier')) n.bn1 = L.BatchNorm(n.conv1,in_place=True) n.scale1 = L.Scale(n.bn1,bias_term=True,bias_filler=dict(value=0),filler=dict(value=1)) n.conv2 = L.Convolution(n.scale1, kernel_size=3,stride=2, num_output=20, weight_filler=dict(type='xavier')) n.bn2 = L.BatchNorm(n.conv2,in_place=True) n.scale2 = L.Scale(n.bn2,bias_term=True,bias_filler=dict(value=0),filler=dict(value=1)) # n.pool1 = L.Pooling(n.scale2, kernel_size=2, stride=2, pool=P.Pooling.MAX) n.conv3 = L.Convolution(n.scale2, kernel_size=3, num_output=50, weight_filler = dict(type='xavier')) n.bn3 = L.BatchNorm(n.conv3,in_place=True) n.scale3 = L.Scale(n.bn3,bias_term=True,bias_filler=dict(value=0),filler=dict(value=1)) n.conv4 = L.Convolution(n.scale3, kernel_size=3, stride=2,num_output=50, weight_filler=dict(type='xavier')) n.bn4 = L.BatchNorm(n.conv4,in_place=True) n.scale4 = L.Scale(n.bn4,bias_term=True,bias_filler=dict(value=0),filler=dict(value=1)) # n.pool2 = L.Pooling(n.scale4, kernel_size=2, stride=2, pool=P.Pooling.MAX) n.fc1 = L.InnerProduct(n.scale4, num_output=500, weight_filler=dict(type='xavier')) n.relu1 = L.ReLU(n.fc1, in_place=True) n.fc2 = L.InnerProduct(n.relu1, num_output=100, weight_filler=dict(type='xavier')) n.relu2 = L.ReLU(n.fc2, in_place=True) n.score = L.InnerProduct(n.relu2,num_output=10, weight_filler=dict(type='xavier')) n.loss = L.SoftmaxWithLoss(n.score, n.label) return n.to_proto()
def caffenet_multilabel(data_layer_params, datalayer): # setup the python data layer n = caffe.NetSpec() n.data, n.label = L.Python(module = 'pascal_multilabel_datalayers', layer = datalayer, ntop = 2, param_str=str(data_layer_params)) # the net itself n.conv1, n.relu1 = conv_relu(n.data, 11, 96, stride=4) n.pool1 = max_pool(n.relu1, 3, stride=2) n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75) n.conv2, n.relu2 = conv_relu(n.norm1, 5, 256, pad=2, group=2) n.pool2 = max_pool(n.relu2, 3, stride=2) n.norm2 = L.LRN(n.pool2, local_size=5, alpha=1e-4, beta=0.75) n.conv3, n.relu3 = conv_relu(n.norm2, 3, 384, pad=1) n.conv4, n.relu4 = conv_relu(n.relu3, 3, 384, pad=1, group=2) n.conv5, n.relu5 = conv_relu(n.relu4, 3, 256, pad=1, group=2) n.pool5 = max_pool(n.relu5, 3, stride=2) n.fc6, n.relu6 = fc_relu(n.pool5, 4096) n.drop6 = L.Dropout(n.relu6, in_place=True) n.fc7, n.relu7 = fc_relu(n.drop6, 4096) n.drop7 = L.Dropout(n.relu7, in_place=True) n.score = L.InnerProduct(n.drop7, num_output=20) n.loss = L.SigmoidCrossEntropyLoss(n.score, n.label) return str(n.to_proto()) # crete net and solver prototxts.
def logreg(hdf5, batch_size): # logistic regression: data, matrix multiplication, and 2-class softmax loss n=caffe.NetSpec() n.data,n.label = L.HDF5Data(batch_size=batch_size,source=hdf5,ntop=2) n.ip1 = L.InnerProduct(n.data,num_output=2,weight_filler=dict(type='xavier')) n.accuracy = L.Accuracy(n.ip1,n.label) n.loss = L.SoftmaxWithLoss(n.ip1,n.label) return n.to_proto()
def SketchTriplet_anchor(out_dim): n = caffe.NetSpec() n.data_a = L.Input(name='data', shape=dict(dim=[1,1,225,225])) n.conv1_a, n.relu1_a = conv_relu_triplet_dep(n.data_a, 15, 64, stride = 3) n.pool1_a = pooling(n.relu1_a, 3, stride=2) n.conv2_a, n.relu2_a = conv_relu_triplet_dep(n.pool1_a, 5, 128) n.pool2_a = pooling(n.relu2_a, 3, stride=2) n.conv3_a, n.relu3_a = conv_relu_triplet_dep(n.pool2_a, 3, 256) n.conv4_a, n.relu4_a = conv_relu_triplet_dep(n.relu3_a, 3, 256) n.conv5_a, n.relu5_a = conv_relu_triplet_dep(n.relu4_a, 3, 256) n.pool5_a = pooling(n.relu5_a, 3, stride=2) n.fc6_a, n.relu6_a = fc_relu_triplet_dep(n.pool5_a, 512) n.fc7_a, n.relu7_a = fc_relu_triplet_dep(n.relu6_a, 512) #n.fc8_a, n.feat_a = fc_norm_triplet_dep(n.relu7_a, out_dim) n.feat_a = fc_triplet_dep(n.relu7_a, out_dim) proto = n.to_proto() proto.name = 'SketchTriplet' return proto
def SketchTriplet_pos(out_dim): n = caffe.NetSpec() n.data_p = L.Input(name='data', shape=dict(dim=[1,1,225,225])) n.conv1_p, n.relu1_p = conv_relu_triplet_dep(n.data_p, 15, 64, stride = 3) n.pool1_p = pooling(n.relu1_p, 3, stride=2) n.conv2_p, n.relu2_p = conv_relu_triplet_dep(n.pool1_p, 5, 128) n.pool2_p = pooling(n.relu2_p, 3, stride=2) n.conv3_p, n.relu3_p = conv_relu_triplet_dep(n.pool2_p, 3, 256) n.conv4_p, n.relu4_p = conv_relu_triplet_dep(n.relu3_p, 3, 256) n.conv5_p, n.relu5_p = conv_relu_triplet_dep(n.relu4_p, 3, 256) n.pool5_p = pooling(n.relu5_p, 3, stride=2) n.fc6_p, n.relu6_p = fc_relu_triplet_dep(n.pool5_p, 512) n.fc7_p, n.relu7_p = fc_relu_triplet_dep(n.relu6_p, 512) #n.fc8_p, n.feat_p = fc_norm_triplet_dep(n.relu7_p, out_dim) n.feat_p = fc_triplet_dep(n.relu7_p, out_dim) proto = n.to_proto() proto.name = 'SketchTriplet' return proto
def SketchTriplet_anchor(out_dim): n = caffe.NetSpec() n.data_a = L.Input(name='data', shape=dict(dim=[1,1,225,225])) n.conv1_a, n.relu1_a = conv_relu_triplet_dep(n.data_a, 15, 64, stride = 3) n.pool1_a = pooling(n.relu1_a, 3, stride=2) n.conv2_a, n.relu2_a = conv_relu_triplet_dep(n.pool1_a, 5, 128) n.pool2_a = pooling(n.relu2_a, 3, stride=2) n.conv3_a, n.relu3_a = conv_relu_triplet_dep(n.pool2_a, 3, 256) n.conv4_a, n.relu4_a = conv_relu_triplet_dep(n.relu3_a, 3, 256) n.conv5_a, n.relu5_a = conv_relu_triplet_dep(n.relu4_a, 3, 256) n.pool5_a = pooling(n.relu5_a, 3, stride=2) n.fc6_a, n.relu6_a = fc_relu_triplet_dep(n.pool5_a, 512) n.fc7_a, n.relu7_a = fc_relu_triplet_dep(n.relu6_a, 512) #n.fc8_a, n.feat_a = fc_norm_triplet_dep(n.relu7_a, out_dim) n.feat_a = fc_triplet_dep(n.relu7_a, out_dim) n.norm_a = L.Normalize(n.feat_a,in_place=True) proto = n.to_proto() proto.name = 'SketchTriplet' return proto
def siamese_anchor(out_dim=100): n = caffe.NetSpec() n.data_a = L.Input(name='data', shape=dict(dim=[1,1,225,225])) n.conv1_a, n.relu1_a = conv_relu_triplet_dep(n.data_a, 15, 64, stride = 3) n.pool1_a = pooling(n.relu1_a, 3, stride=2) n.conv2_a, n.relu2_a = conv_relu_triplet_dep(n.pool1_a, 5, 128) n.pool2_a = pooling(n.relu2_a, 3, stride=2) n.conv3_a, n.relu3_a = conv_relu_triplet_dep(n.pool2_a, 3, 256) n.conv4_a, n.relu4_a = conv_relu_triplet_dep(n.relu3_a, 3, 256) n.conv5_a, n.relu5_a = conv_relu_triplet_dep(n.relu4_a, 3, 256) n.pool5_a = pooling(n.relu5_a, 3, stride=2) n.fc6_a, n.relu6_a = fc_relu_triplet_dep(n.pool5_a, 512) n.fc7_a, n.relu7_a = fc_relu_triplet_dep(n.relu6_a, 512) #n.fc8_a, n.feat_a = fc_norm_triplet_dep(n.relu7_a, out_dim) n.feat_a = fc_triplet_dep(n.relu7_a, out_dim) #n.norm_a = L.Normalize(n.feat_a,in_place=True) proto = n.to_proto() proto.name = 'SketchTriplet' return proto
def siamese_pos(out_dim=100): n = caffe.NetSpec() n.data_p = L.Input(name='data', shape=dict(dim=[1,1,225,225])) n.conv1_p, n.relu1_p = conv_relu_triplet_dep(n.data_p, 15, 64, stride = 3) n.pool1_p = pooling(n.relu1_p, 3, stride=2) n.conv2_p, n.relu2_p = conv_relu_triplet_dep(n.pool1_p, 5, 128) n.pool2_p = pooling(n.relu2_p, 3, stride=2) n.conv3_p, n.relu3_p = conv_relu_triplet_dep(n.pool2_p, 3, 256) n.conv4_p, n.relu4_p = conv_relu_triplet_dep(n.relu3_p, 3, 256) n.conv5_p, n.relu5_p = conv_relu_triplet_dep(n.relu4_p, 3, 256) n.pool5_p = pooling(n.relu5_p, 3, stride=2) n.fc6_p, n.relu6_p = fc_relu_triplet_dep(n.pool5_p, 512) n.fc7_p, n.relu7_p = fc_relu_triplet_dep(n.relu6_p, 512) #n.fc8_p, n.feat_p = fc_norm_triplet_dep(n.relu7_p, out_dim) n.feat_p = fc_triplet_dep(n.relu7_p, out_dim) #n.norm_p = L.Normalize(n.feat_p,in_place=True) proto = n.to_proto() proto.name = 'SketchTriplet' return proto
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()
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()
def lenethdf5(hdf5 , batch_size): # Net: a series of linear and simple nonlinear transformations n = caffe.NetSpec() n.data, n.label = L.HDF5Data(batch_size=batch_size, source=hdf5,ntop=2) # the base net n.conv1, n.relu1 = conv_relu(n.data, 32) n.pool1 = max_pool(n.relu1) n.conv2, n.relu2 = conv_relu(n.pool1, 64) n.pool2 = max_pool(n.relu2) n.conv3, n.relu3 = conv_relu(n.pool2, 128) n.pool3 = max_pool(n.relu3) # fully convolutional n.fc1, n.rlfc1 = conv_relu(n.pool3, 512, ks=3, pad=1) n.decov5 = deconv(n.rlfc1, 128, pad=1) n.relu5, n.conv5 = relu_conv(n.decov5, 128, pad=0) n.decov6 = deconv(n.conv5, 64, pad=1) n.relu6, n.conv6 = relu_conv(n.decov6, 64, pad=0) n.decov7 = deconv(n.conv6, 32, pad=1) n.relu7, n.conv7 = relu_conv(n.decov7, 32, pad=0) n.relu8, n.conv8 = relu_conv(n.conv7, 2, pad=0) n.accuracy= L.Accuracy(n.conv8, n.label) n.loss = L.SoftmaxWithLoss(n.conv8, n.label) return n.to_proto() # create file prototxt for training and validation
def lenethdf5d(): # our version of LeNet: a series of linear and simple nonlinear transformations n = caffe.NetSpec() #cambiano il primo e gli ultimi 2 strati n.data = L.Input(input_param=dict(shape=dict(dim=[1,1,128,128]))) # the base net n.conv1, n.relu1 = conv_relu(n.data, 32) n.pool1 = max_pool(n.relu1) #n.norm1 = L.LRN(n.pool1, local_size=5, alpha=1e-4, beta=0.75) n.conv2, n.relu2 = conv_relu(n.pool1, 64) n.pool2 = max_pool(n.relu2) n.conv3, n.relu3 = conv_relu(n.pool2, 128) n.pool3 = max_pool(n.relu3) # fully convolutional n.fc1, n.rlfc1 = conv_relu(n.pool3, 512, ks=3, pad=1) n.decov5 = deconv(n.rlfc1, 128, pad=1) n.relu5, n.conv5 = relu_conv(n.decov5, 128, pad=0) n.decov6 = deconv(n.conv5, 64, pad=1) n.relu6, n.conv6 = relu_conv(n.decov6, 64, pad=0) n.decov7 = deconv(n.conv6, 32, pad=1) n.relu7, n.conv7 = relu_conv(n.decov7, 32, pad=0) n.relu8, n.conv8 = relu_conv(n.conv7, 2, pad=0) n.prob = L.Softmax(n.conv8) return n.to_proto() # create file prototxt for deployment
def construc_net(widening_factor, num_block_per_stage): net = caffe.NetSpec() net.data = L.Input(input_param = dict(shape = dict(dim = [1,3,32,32]))) net.conv1 = L.Convolution(net.data, num_output = 16, kernel_size = 3, stride = 1, pad = 1, bias_term = False) # stage 1 num_out = widening_factor * 16 block_pre = _block('2_1', net, net.conv1, num_out, has_branch1=True, increasing_dims=False) for idx in xrange(2,num_block_per_stage+1,1): flag = '2_{}'.format(idx) block_pre = _block(flag, net, block_pre, num_out) # stage 2 num_out = widening_factor * 32 block_pre = _block('3_1', net, block_pre, num_out, has_branch1=True) for idx in xrange(2,num_block_per_stage+1,1): flag = '3_{}'.format(idx) block_pre = _block(flag, net, block_pre, num_out) # stage 3 num_out = widening_factor * 64 block_pre = _block('4_1', net, block_pre, num_out, has_branch1=True) for idx in xrange(2,num_block_per_stage+1,1): flag = '4_{}'.format(idx) block_pre = _block(flag, net, block_pre, num_out) net.bn5 = L.BatchNorm(block_pre) net.scale5 = L.Scale(net.bn5, bias_term = True, in_place=True) net.relu5 = L.ReLU(net.scale5, in_place = True) net.pool5 = L.Pooling(net.relu5, pool = P.Pooling.AVE, global_pooling=True) net.fc6 = L.InnerProduct(net.pool5, num_output = 10) net.prob = L.Softmax(net.fc6) return net.to_proto()
def construc_net(): net = caffe.NetSpec() net.data = L.Input(input_param = dict(shape = dict(dim = [1,3,224,224]))) block1 = _block_first(net, net.data) net.pool1 = L.Pooling(block1, pool = P.Pooling.MAX, kernel_size = 3, stride = 2) branch_2a = _branch('2a', net, net.pool1, 64, has_branch1 = True, is_branch_2a = True) branch_2b = _branch('2b', net, branch_2a, 64) branch_2c = _branch('2c', net, branch_2b, 64) branch_3a = _branch('3a', net, branch_2c, 128, has_branch1 = True) branch_3b = _branch('3b', net, branch_3a, 128) branch_3c = _branch('3c', net, branch_3b, 128) branch_3d = _branch('3d', net, branch_3c, 128) branch_4a = _branch('4a', net, branch_3d, 256, has_branch1 = True) branch_pre = branch_4a for idx in xrange(1,23,1): # conv4_x total 1+22=23 flag = '4b{}'.format(idx) branch_pre = _branch(flag, net, branch_pre, 256) branch_5a = _branch('5a', net, branch_pre, 512, has_branch1 = True) branch_5b = _branch('5b', net, branch_5a, 512) branch_5c = _branch('5c', net, branch_5b, 512) net.pool5 = L.Pooling(branch_5c, pool = P.Pooling.AVE, kernel_size = 7, stride = 1) net.fc6 = L.InnerProduct(net.pool5, num_output = 1000) net.prob = L.Softmax(net.fc6) return net.to_proto()
def construc_net(): net = caffe.NetSpec() net.data = L.Input(input_param = dict(shape = dict(dim = [1,1,224,224]))) block1 = _block_first(net, net.data) net.pool1 = L.Pooling(block1, pool = P.Pooling.MAX, kernel_size = 3, stride = 2) branch_2a = _branch('2a', net, net.pool1, 64, has_branch1 = True, is_branch_2a = True) branch_2b = _branch('2b', net, branch_2a, 64) branch_2c = _branch('2c', net, branch_2b, 64) branch_3a = _branch('3a', net, branch_2c, 128, has_branch1 = True) branch_3b = _branch('3b', net, branch_3a, 128) branch_3c = _branch('3c', net, branch_3b, 128) branch_3d = _branch('3d', net, branch_3c, 128) branch_4a = _branch('4a', net, branch_3d, 256, has_branch1 = True) branch_4b = _branch('4b', net, branch_4a, 256) branch_4c = _branch('4c', net, branch_4b, 256) branch_4d = _branch('4d', net, branch_4c, 256) branch_4e = _branch('4e', net, branch_4d, 256) branch_4f = _branch('4f', net, branch_4e, 256) branch_5a = _branch('5a', net, branch_4f, 512, has_branch1 = True) branch_5b = _branch('5b', net, branch_5a, 512) branch_5c = _branch('5c', net, branch_5b, 512) net.pool5 = L.Pooling(branch_5c, pool = P.Pooling.AVE, kernel_size = 7, stride = 1) net.fc6 = L.InnerProduct(net.pool5, num_output = 1000) net.prob = L.Softmax(net.fc6) return net.to_proto()
def construc_net(): net = caffe.NetSpec() net.data = L.Input(input_param = dict(shape = dict(dim = [1,3,224,224]))) block1 = _block_first(net, net.data) net.pool1 = L.Pooling(block1, pool = P.Pooling.MAX, kernel_size = 3, stride = 2) branch_2a = _branch('2a', net, net.pool1, 64, has_branch1 = True, is_branch_2a = True) branch_2b = _branch('2b', net, branch_2a, 64) branch_2c = _branch('2c', net, branch_2b, 64) branch_3a = _branch('3a', net, branch_2c, 128, has_branch1 = True) branch_pre = branch_3a for idx in xrange(1,8,1): # conv3_x total 1+7=8 flag = '3b{}'.format(idx) branch_pre = _branch(flag, net, branch_pre, 128) branch_4a = _branch('4a', net, branch_pre, 256, has_branch1 = True) branch_pre = branch_4a for idx in xrange(1,36,1): # conv4_x total 1+35=36 flag = '4b{}'.format(idx) branch_pre = _branch(flag, net, branch_pre, 256) branch_5a = _branch('5a', net, branch_pre, 512, has_branch1 = True) branch_5b = _branch('5b', net, branch_5a, 512) branch_5c = _branch('5c', net, branch_5b, 512) net.pool5 = L.Pooling(branch_5c, pool = P.Pooling.AVE, kernel_size = 7, stride = 1) net.fc6 = L.InnerProduct(net.pool5, num_output = 1000) net.prob = L.Softmax(net.fc6) return net.to_proto()
def construc_net(): net = caffe.NetSpec() net.data = L.Input(shape = dict(dim = [10,3,224,224])) block_cr_1 = _block_cr('conv1', '7x7_s2', net, net.data, 64, 3, 7, 2) pool_layer_1 = 'pool1/3x3_s2' net[pool_layer_1] = L.Pooling(block_cr_1, pool = P.Pooling.MAX, kernel_size = 3, stride = 2) ##LRN block_cr_2_reduce = _block_cr('conv2', '3x3_reduce', net, net[pool_layer_1], 64, 0, 1, 1) block_cr_2 = _block_cr('conv2', '3x3', net, block_cr_2_reduce, 192, 1, 3, 1) ##LRN pool_layer_2 = 'pool2/3x3_s2' net[pool_layer_2] = L.Pooling(block_cr_2, pool = P.Pooling.MAX, kernel_size = 3, stride = 2) inception_3a = _inception_v1('inception_3a', net, net[pool_layer_2], [64,96,128,16,32,32]) inception_3b = _inception_v1('inception_3b', net, inception_3a, [128,128,192,32,96,64]) pool_layer_3 = 'pool3/3x3_s2' net[pool_layer_3] = L.Pooling(inception_3b, pool = P.Pooling.MAX, kernel_size = 3, stride = 2) inception_4a = _inception_v1('inception_4a', net, net[pool_layer_3], [192,96,208,16,48,64]) inception_4b = _inception_v1('inception_4b', net, inception_4a, [160,112,224,24,64,64]) inception_4c = _inception_v1('inception_4c', net, inception_4b, [128,128,256,24,64,64]) inception_4d = _inception_v1('inception_4d', net, inception_4c, [112,144,288,32,64,64]) inception_4e = _inception_v1('inception_4e', net, inception_4d, [256,160,320,32,128,128]) pool_layer_4 = 'pool4/3x3_s2' net[pool_layer_4] = L.Pooling(inception_4e, pool = P.Pooling.MAX, kernel_size = 3, stride = 2) inception_5a = _inception_v1('inception_5a', net, net[pool_layer_4], [256,160,320,32,128,128]) inception_5b = _inception_v1('inception_5b', net, inception_5a, [384,192,384,48,128,128]) pool_layer_5 = 'pool5/7x7_s1' net[pool_layer_5] = L.Pooling(inception_5b, pool = P.Pooling.AVE, kernel_size = 7, stride = 1) pool_layer_5_drop = 'pool5/drop_7x7_s1' net[pool_layer_5_drop] = L.Dropout(net[pool_layer_5], dropout_ratio = 0.4, in_place = True) fc_layer = 'loos3/classifier' net[fc_layer] = L.InnerProduct(net[pool_layer_5_drop], num_output = 1000) net.prob = L.Softmax(net[fc_layer]) return net.to_proto()
def construc_net(): net = caffe.NetSpec() net.data = L.Input(input_param = dict(shape = dict(dim = [1,3,224,224]))) block1 = _block_first(net, net.data) net.pool1 = L.Pooling(block1, pool = P.Pooling.MAX, kernel_size = 3, stride = 2) branch_2a = _branch('2a', net, net.pool1, 128, has_branch1 = True, is_branch_2a = True) branch_2b = _branch('2b', net, branch_2a, 128) branch_2c = _branch('2c', net, branch_2b, 128) branch_3a = _branch('3a', net, branch_2c, 256, has_branch1 = True) branch_3b = _branch('3b', net, branch_3a, 256) branch_3c = _branch('3c', net, branch_3b, 256) branch_3d = _branch('3d', net, branch_3c, 256) branch_4a = _branch('4a', net, branch_3d, 512, has_branch1 = True) branch_pre = branch_4a for idx in xrange(1,23,1): # conv4_x total 1+22=23 flag = '4b{}'.format(idx) branch_pre = _branch(flag, net, branch_pre, 512) branch_5a = _branch('5a', net, branch_pre, 1024, has_branch1 = True) branch_5b = _branch('5b', net, branch_5a, 1024) branch_5c = _branch('5c', net, branch_5b, 1024) net.pool5 = L.Pooling(branch_5c, pool = P.Pooling.AVE, kernel_size = 7, stride = 1) net.fc6 = L.InnerProduct(net.pool5, num_output = 1000) net.prob = L.Softmax(net.fc6) return net.to_proto()
def construc_net(): net = caffe.NetSpec() net.data = L.Input(input_param = dict(shape = dict(dim = [1,3,224,224]))) block1 = _block_first(net, net.data) net.pool1 = L.Pooling(block1, pool = P.Pooling.MAX, kernel_size = 3, stride = 2) branch_2a = _branch('2a', net, net.pool1, 128, has_branch1 = True, is_branch_2a = True) branch_2b = _branch('2b', net, branch_2a, 128) branch_2c = _branch('2c', net, branch_2b, 128) branch_3a = _branch('3a', net, branch_2c, 256, has_branch1 = True) branch_3b = _branch('3b', net, branch_3a, 256) branch_3c = _branch('3c', net, branch_3b, 256) branch_3d = _branch('3d', net, branch_3c, 256) branch_4a = _branch('4a', net, branch_3d, 512, has_branch1 = True) branch_4b = _branch('4b', net, branch_4a, 512) branch_4c = _branch('4c', net, branch_4b, 512) branch_4d = _branch('4d', net, branch_4c, 512) branch_4e = _branch('4e', net, branch_4d, 512) branch_4f = _branch('4f', net, branch_4e, 512) branch_5a = _branch('5a', net, branch_4f, 1024, has_branch1 = True) branch_5b = _branch('5b', net, branch_5a, 1024) branch_5c = _branch('5c', net, branch_5b, 1024) net.pool5 = L.Pooling(branch_5c, pool = P.Pooling.AVE, kernel_size = 7, stride = 1) net.fc6 = L.InnerProduct(net.pool5, num_output = 1000) net.prob = L.Softmax(net.fc6) return net.to_proto()
