我们从Python开源项目中，提取了以下6个代码示例，用于说明如何使用network.FC。
def __init__(self, classes=None, debug=False): super(FasterRCNN, self).__init__() if classes is not None: self.classes = classes self.n_classes = len(classes) self.rpn = RPN() self.roi_pool = RoIPool(7, 7, 1.0/16) self.fc6 = FC(512 * 7 * 7, 4096) self.fc7 = FC(4096, 4096) self.score_fc = FC(4096, self.n_classes, relu=False) self.bbox_fc = FC(4096, self.n_classes * 4, relu=False) # loss self.cross_entropy = None self.loss_box = None # for log self.debug = debug
def __init__(self, classes=None, debug=False): super(FasterRCNN, self).__init__() if classes is not None: self.classes = classes self.n_classes = len(classes) self.rpn = RPN() self.roi_pool = RoIPool(7, 7, 1.0/16) self.fc6 = FC(1024 * 7 * 7, 4096) self.fc7 = FC(4096, 4096) self.score_fc = FC(4096, self.n_classes, relu=False) self.bbox_fc = FC(4096, self.n_classes * 4, relu=False) # loss self.cross_entropy = None self.loss_box = None # for log self.debug = debug
def __init__(self, ninput, nembed, nhidden, nlayers, bias, dropout): super(Img_Encoder_Structure, self).__init__() self.image_encoder = FC(ninput, nembed, relu=True) self.rnn = nn.LSTM(nembed, nhidden, nlayers, bias=bias, dropout=dropout)
def __init__(self, bn=False, num_classes=10): super(CMTL, self).__init__() self.num_classes = num_classes self.base_layer = nn.Sequential(Conv2d( 1, 16, 9, same_padding=True, NL='prelu', bn=bn), Conv2d(16, 32, 7, same_padding=True, NL='prelu', bn=bn)) self.hl_prior_1 = nn.Sequential(Conv2d( 32, 16, 9, same_padding=True, NL='prelu', bn=bn), nn.MaxPool2d(2), Conv2d(16, 32, 7, same_padding=True, NL='prelu', bn=bn), nn.MaxPool2d(2), Conv2d(32, 16, 7, same_padding=True, NL='prelu', bn=bn), Conv2d(16, 8, 7, same_padding=True, NL='prelu', bn=bn)) self.hl_prior_2 = nn.Sequential(nn.AdaptiveMaxPool2d((32,32)), Conv2d( 8, 4, 1, same_padding=True, NL='prelu', bn=bn)) self.hl_prior_fc1 = FC(4*1024,512, NL='prelu') self.hl_prior_fc2 = FC(512,256, NL='prelu') self.hl_prior_fc3 = FC(256, self.num_classes, NL='prelu') self.de_stage_1 = nn.Sequential(Conv2d( 32, 20, 7, same_padding=True, NL='prelu', bn=bn), nn.MaxPool2d(2), Conv2d(20, 40, 5, same_padding=True, NL='prelu', bn=bn), nn.MaxPool2d(2), Conv2d(40, 20, 5, same_padding=True, NL='prelu', bn=bn), Conv2d(20, 10, 5, same_padding=True, NL='prelu', bn=bn)) self.de_stage_2 = nn.Sequential(Conv2d( 18, 24, 3, same_padding=True, NL='prelu', bn=bn), Conv2d( 24, 32, 3, same_padding=True, NL='prelu', bn=bn), nn.ConvTranspose2d(32,16,4,stride=2,padding=1,output_padding=0,bias=True), nn.PReLU(), nn.ConvTranspose2d(16,8,4,stride=2,padding=1,output_padding=0,bias=True), nn.PReLU(), Conv2d(8, 1, 1, same_padding=True, NL='relu', bn=bn))
def __init__(self, classes=None, debug=False, arch='vgg16'): super(FasterRCNN, self).__init__() if classes is not None: self.classes = classes self.n_classes = len(classes) print('n_classes: {}\n{}'.format(self.n_classes, self.classes)) if arch == 'vgg16': cnn_arch = models.vgg16(pretrained=False) # w/o bn self.rpn = RPN(features=cnn_arch.features) self.fcs = nn.Sequential( nn.Linear(512 * 7 * 7, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, 4096), nn.ReLU(True), nn.Dropout() ) self.roi_pool = RoIPool(7, 7, 1.0/16) # self.fc6 = FC(512 * 7 * 7, 4096) # self.fc7 = FC(4096, 4096) self.score_fc = FC(4096, self.n_classes, relu=False) self.bbox_fc = FC(4096, self.n_classes * 4, relu=False) # loss self.cross_entropy = None self.loss_box = None # for log self.debug = debug
def __init__(self,nhidden, n_object_cats, n_predicate_cats, n_vocab, voc_sign, max_word_length, MPS_iter, use_language_loss, object_loss_weight, predicate_loss_weight, dropout=False, use_kmeans_anchors=False, gate_width=128, nhidden_caption=256, nembedding = 256, rnn_type='LSTM_normal', rnn_droptout=0.0, rnn_bias=False, use_region_reg=False, use_kernel=False): super(Hierarchical_Descriptive_Model, self).__init__(nhidden, n_object_cats, n_predicate_cats, n_vocab, voc_sign, max_word_length, MPS_iter, use_language_loss, object_loss_weight, predicate_loss_weight, dropout, use_kmeans_anchors, nhidden_caption, nembedding, rnn_type, use_region_reg) self.rpn = RPN(use_kmeans_anchors) self.roi_pool_object = RoIPool(7, 7, 1.0/16) self.roi_pool_phrase = RoIPool(7, 7, 1.0/16) self.roi_pool_region = RoIPool(7, 7, 1.0/16) self.fc6_obj = FC(512 * 7 * 7, nhidden, relu=True) self.fc7_obj = FC(nhidden, nhidden, relu=False) self.fc6_phrase = FC(512 * 7 * 7, nhidden, relu=True) self.fc7_phrase = FC(nhidden, nhidden, relu=False) self.fc6_region = FC(512 * 7 * 7, nhidden, relu=True) self.fc7_region = FC(nhidden, nhidden, relu=False) if MPS_iter == 0: self.mps = None else: self.mps = Hierarchical_Message_Passing_Structure(nhidden, dropout, gate_width=gate_width, use_kernel_function=use_kernel) # the hierarchical message passing structure network.weights_normal_init(self.mps, 0.01) self.score_obj = FC(nhidden, self.n_classes_obj, relu=False) self.bbox_obj = FC(nhidden, self.n_classes_obj * 4, relu=False) self.score_pred = FC(nhidden, self.n_classes_pred, relu=False) if self.use_region_reg: self.bbox_region = FC(nhidden, 4, relu=False) network.weights_normal_init(self.bbox_region, 0.01) else: self.bbox_region = None self.objectiveness = FC(nhidden, 2, relu=False) if use_language_loss: self.caption_prediction = \ Language_Model(rnn_type=self.rnn_type, ntoken=self.n_vocab, nimg=self.nhidden, nhidden=self.nhidden_caption, nembed=self.nembedding, nlayers=2, nseq=self.max_word_length, voc_sign = self.voc_sign, bias=rnn_bias, dropout=rnn_droptout) else: self.caption_prediction = Language_Model(rnn_type=self.rnn_type, ntoken=self.n_vocab, nimg=1, nhidden=1, nembed=1, nlayers=1, nseq=1, voc_sign = self.voc_sign) # just to make the program run network.weights_normal_init(self.score_obj, 0.01) network.weights_normal_init(self.bbox_obj, 0.005) network.weights_normal_init(self.score_pred, 0.01) network.weights_normal_init(self.objectiveness, 0.01) self.objectiveness_loss = None
