height, width, ones_flag=None):# get the mesh-grid in a special area(-1,1)# output:# @shape --> 2,H*W# @explanation --> (0,:) means all x-coordinate in a mesh# (1,:) means all y-coordinate in a meshwithtf.variable_scope('meshgrid'): ...
第一步:使用roi pool,使用x1 / width, x2/width, y1/height, y2/height获得比例用于获得部分卷积层,使用tf.image.resize_and_crop()每个边框调整后的卷积层,输入为256, 14, 14, 512, 然后使用池化层,输出的维度为256, 7, 7, 512 第二步:将输出的结果进行维度变化,适合进行全连接操作,接上3层全连接...
# 需要导入模块: import numpy [as 别名]# 或者: from numpy importmeshgrid[as 别名]defgenerate_anchors(self, image_width: int, image_height: int, num_x_anchors: int, num_y_anchors: int)-> Tensor:center_ys = np.linspace(start=0, stop=image_height, num=num_y_anchors +2)[1:-1] cen...
...效果 实现: 密集区点的标注通过牵引线的方式引出展示; 地图放大的时候更新展示; 思路 实现代码 const points = [ { "properties": {"name":"测试名称应该...canvasWidth canvas.height = canvasHeight const context = canvas.getContext('2d'); // 数据聚类处理,根据上下和左右的距离进行判断...res[...
us=torch.linspace(crop_y1+0.5*downsample_disp[0],crop_y2-0.5*downsample_disp[0],img_height//downsample_disp[0],dtype=torch.float32,device='cuda')# heightvs=torch.linspace(crop_x1+0.5*downsample_disp[1],crop_x2-0.5*downsample_disp[1],img_width//downsample_disp[1],dtype=torch.float32...
center_h = (torch.arange(in_height, device=device) + offset_h) * steps_h center_w = (torch.arange(in_width, device=device) + offset_w) * steps_w shift_y, shift_x = torch.meshgrid(center_h, center_w) shift_y, shift_x = torch.meshgrid(center_h, center_w, indexing='ij') sh...
N-D grids, given one-dimensional coordinate arrays x1, x2,…, xn.Changed in version 1.9: 1...
def generate_anchors(self, image_width: int, image_height: int, num_x_anchors: int, num_y_anchors: int) -> Tensor: center_ys = np.linspace(start=0, stop=image_height, num=num_y_anchors + 2)[1:-1] center_xs = np.linspace(start=0, stop=image_width, num=num_x_anchors + 2)...
ValueError: if height < 2 or width < 2 or the inputs have the wrong number of dimensions. """withops.name_scope(name): batch_size, height, width, channels = (array_ops.shape(image)[0], array_ops.shape(image)[1], array_ops.shape(image)[2], ...
seaborn.clustermap(data, pivot_kws=None, method='average', metric='euclidean', z_score=None, ...