label[max_ious >= self.pos_iou_thresh] = 1 # 正标签:大于设定阈值的IOU #===正样本抽样处理=== n_pos = int(self.pos_ratio * self.n_sample) # 按照比例计算出正样本数量,pos_ratio=0.5,n_sample=256(再次抽样正标签) pos_index = np.where(label == 1)[0] # 得到所有正样本的索引 if ...
BlockArgs(kernel_size=3, num_repeat=1, input_filters=32, output_filters=16, expand_ratio=1, id_skip=True, stride=[1], se_ratio=0.25), BlockArgs(kernel_size=3, num_repeat=2, input_filters=16, output_filters=24, expand_ratio=6, id_skip=True, stride=[2], se_ratio=0.25), BlockA...
mlp_ratio=4., qkv_bias=False, qk_norm=False, proj_drop=0., attn_drop=0., init_values=None, drop_path=0., act_layer=None, norm_layer=None, mlp_layer=None ): super().__init__( hidden_size=dim, ffn_hidden_size=int(dim * mlp_ratio), num_attention_heads=num_heads, hidden_dr...
xi= [0, n_burn]#x interpmodel.gr= np.interp(ni, xi, [0.0, 1.0])#giou loss ratio (obj_loss = 1.0 or giou)accumulate= max(1, np.interp(ni, xi, [1, 64 /batch_size]).round())forj, xinenumerate(optimizer.param_groups):#pg0 pg1 pg2#bias lr falls from 0.1 to lr0, all ...
第一次看官方的代码真的很难看懂,所以自己复现了一个block的代码便于理解,这部分代码只有第一个block,如果要扩展可以改写我的Swin_Model类; 注意:没有实现Softmax(qk+B)的那个B,也就是相对位置编码,论文里面用了这个之后有小幅提升,其实不用也可以,这部分也很好加,只用把官方的对应部分加到Patch_embeding里面;...
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self.neg_pos_ratio = neg_pos_ratio self.alpha = alpha self.smooth_l1 = nn.L1Loss() self.cross_entropy = nn.CrossEntropyLoss(reduce=False) def forward(self, predicted_locs, predicted_scores, boxes, labels, img): """ Forward propagation. :param predicted_locs: predicted locations/boxes w...
(self, source, target, teacher_forcing_ratio=0.5): input_length = source.size(0) #get the input length (number of words in sentence) batch_size = target.shape[1] target_length = target.shape[0] vocab_size = self.decoder.output_dim #initialize a variable to hold the predicted outputs ...
internal_ratio=4, dropout_prob=0, bias=False, relu=True): super().__init__() internal_channels = in_channels // internal_ratio if relu: activation = nn.ReLU else: activation = nn.PReLU # Main branch - max pooling followed by feature map (channels) padding ...
nn.MaxPool2d(kernel_size=3,stride=2,padding=1) ) classFlatten(nn.Module): defforward(self,x): returnx.view(x.shape[0],-1) classSEBlock(nn.Module): def__init__(self,channels,ratio=16): super().__init__() mid_channels=channels//ratio ...