class InfraredDataset(Dataset): def __init__(self, dataset_dir, image_index, image_size=256): super(InfraredDataset, self).__init__() self.dataset_dir = dataset_dir self.image_index = image_index self.transformer = T.Compose([ T.Resize((int(image_size), int(image_size))), T.Gray...
time, os, decoder8fromPILimportImage9importnumpy as np10importtensorflow as tf11importpdb12importdecoder13importrandom1415'''16在汉字ocr项目中,利用基于attention的encoder-decoder(seq2seq)模型进行端对端的训练17单步调试,追踪tensorflow 对 attention-seq2seq模型的实现方式18python 中seq2seq.py的接口:tf...
nclass,bins):# bin的意义实际上是确定ROC曲线上的threshold取多少个离散值super(ROCMetric,self).__init__()self.nclass=nclassself.bins=binsself.tp_arr=np.zeros(self.bins+1)self.pos_arr=np.zeros(self.bins+1)self.fp_arr=np.zeros(self.bins+1)self.neg_arr=np.zeros(...
open(label_path) train_image = self.transformer(image) label = self.transformer(label) return paddle.cast(train_image, 'float32'), paddle.cast(label, 'float32') def __len__(self): return len(self.image_index) In [20] f = open('./sirst/idx_427/trainval.txt').readlines() ds =...
Each DS-Conv block comprises a D-Conv layer with kernels of the size of 3 × 3, and rectified linear unit (ReLU) transfer function, batch normalization (BN) layer, and P-Conv layer with kernels of the size of 1 × 1. Every D-Conv and P-Conv is followed by BN and ReLU. The den...
"test_dl = DataLoader(test_ds, batch_size=batch_size, num_workers=num_workers, shuffle=False,collate_fn=collate_spec)" ] }, { "cell_type": "markdown", "id": "7ff9c3d2", "metadata": { "papermill": { "duration": null, "end_time"...
function generateNormalVector(i): input ← [] label ← [] for x in trainSet do: input.append(x.x) If x.y = i, then label.append(1) else label.append(0) end if end for model. f it(input, output) return model.getLayer('NormalVector′).getWeight() end function During inference...