(<tf.Tensor: id=9, shape=(), dtype=int32, numpy=1>, <tf.Tensor: id=10, shape=(), dtype=int32, numpy=10>) (<tf.Tensor: id=11, shape=(), dtype=int32, numpy=2>, <tf.Tensor: id=12, shape=(), dtype=int32, numpy=20>) (<tf.Tensor: id=13, shape=(), dtype=int32, ...
print('Image name: {}'.format(img_name)) print('Landmarks shape: {}'.format(landmarks.shape)) print('First 4 Landmarks: {}'.format(landmarks[:4])) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 输出如下所示: 接着写一个辅助函数来显示人脸图片及其关键点,代码如下所示: def show_land...
创建如下python脚本并执行: fromtorch.utils.tensorboardimportSummaryWriter# 参数:log_dir,日志存放的路径名称writer=SummaryWriter("logs")# 记录曲线 y = x^2# 参数一:tag,数据标识符# 参数二:scalar_value,标量值,y轴数据# 参数三:global_step,步骤,x轴数据foriinrange(100):writer.add_scalar("y = x^...
train_dataloader = DataLoader(training_data, batch_size=batch_size) test_dataloader = DataLoader(test_data, batch_size=batch_size) for X, y in test_dataloader: print(f"Shape of X [N, C, H, W]: {X.shape}") print(f"Shape of y: {y.shape} {y.dtype}") break ### ###...
shape[:2] # 判断给定大小的形式,tuple 还是 int 类型 if isinstance(self.output_size, int): # int 类型,给定大小作为最短边,最大边长根据原来尺寸比例进行调整 if h > w: new_h, new_w = self.output_size * h / w, self.output_size else: new_h, new_w = self.output_size, self.output...
self.images_aux = [None]*n_pairsforiinrange(len(files_main)): main_ = cv2.imread(files_main[i], -1)# gray imageaux_ = cv2.imread(files_aux[i], -1)# gray imageassertlen(main_.shape)==2assertlen(aux_.shape)==2assertmain_.shape[0] == aux_.shape[0]assertmain_.shape[1] ...
: print('point0.shape:\n', point[0].shape) # ([2, 2500, 3]) print('point1.shape:\n', point[1].shape) # [2, 1]) 大部件的标签 print('point2.shape:\n', point[2].shape) # torch.Size([2, 2500]) 部件类别标签,每个点的标签 #print('label.shape:\n', label.shape) ...
关于shapenet数据读入的数据集,我参考的是PotinNet的数据集部分。 填写好路径,先来测试一些输出部分。可以发现,shapenet数据集会给我们返回三个输出。第一个就是每个点云集合下采样后后 的xyz坐标,每个大类别的标签,以及每个点云集中每个点的类别(2,2500)。
> If label_mode is None, it yields float32 tensors of shape (batch_size, image_size[0], image_size[1], num_channels), encoding > > ``` > > 图像(有关 num_channels 的规则,请参见下文)。否则,它会生成一个元组 (images, labels),其中 images 具有形状 (batch_size, image_size\[0\]...
to each workerwds.tarfile_to_samples(),# this shuffles the samples in memorywds.shuffle(shuffle_buffer),# this decodes the images and jsonwds.decode("pil"),wds.to_tuple("png","json"),wds.map(preprocess),wds.batched(16) )batch=next(iter(dataset))batch[0].shape,batch[1].shape ...