trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev) weights = slim.variable('weights', shape=[3 , 3], #形状 #参数初始化 initializer=trunc_normal(0.1), ) init = tf.global_variables_initializer() with tf.Session() as sess: sess.run(init) print(sess.run(weights))...
问题:最近需要初始化一个模型的参数,但在pytorch中没有类似可直接使用的类似tf.truncnormal()函数,一开始是直接尝试用torch.nn.init.normal_() 来代替tf.truncnormal()。效果相差较远,简单的正态分布并不能代替截断正态分布的作用。故考虑自己实现,借鉴了 discuss.pytorch.org/t/i的一个实现, 实现代码如下: 实...
model_ft.head=torch.nn.Linear(numftr,classes) nn.init.trunc_normal_(model_ft.head.weight, std=2e-5)# 将参数初始化为整态分布 timm也提供了trunc_normal_,导入方法: from timm.models.layers import trunc_normal_ 调用方法同上。
>>> nn.init.trunc_normal_(w) """return_no_grad_trunc_normal_(tensor,mean,std,a,b) 何时使用,比如我们使用ImageNet的预训练权重,修改了类别,这时候就要对其参数做初始化,调用方法: model_ft=convvit_base_patch16()model_ft.load_state_dict(torch.load('checkpoint.pth'),strict=False)numftr=model...
确认torch.nn.init模块中是否存在trunc_normal_属性: 在某些 PyTorch 版本中,torch.nn.init 确实没有 trunc_normal_ 这个属性。这是因为 trunc_normal_ 是后来才添加到 PyTorch 中的,用于初始化张量,使其符合截断正态分布。 查找PyTorch官方文档或相关资源: 根据PyTorch 的官方文档,trunc_normal_ 是用于初始化...
Hi, guys, I see there is a method of torch.nn.init.trunc_normal_(), but I do not find the description of it in the documentation. So is there a description for this torch.nn.init.trunc_normal_()? I am a little confused. Suggest a potential alternative/fix No responseContributor...
In pytorch's official docs:https://pytorch.org/docs/stable/index.html or https://pytorch.org/docs/1.9.0/,i can not get any results when i search trunc_normal_. Though i can use nn.init.trunc_normal_() in my code without reporting error,i can not find it's doc to get more ...
I am trying to visualize the result of YCB-V inference and get this error. Could you please suggest a solution? (gdrnpp) mona@ada:~/gdrnpp_bop2022$ python core/gdrn_modeling/tools/ycbv/ycbv_3_vis_poses_full.py /home/mona/.local/lib/python3.10/site-packages/mmcv/__init__.py:20:...
示例1: __init__ ▲點讚 6▼ # 需要導入模塊: import cv2 [as 別名]# 或者: from cv2 importTHRESH_TRUNC[as 別名]def__init__(self):self.resize = ResizeClip(resize_shape = [2,2]) self.crop = CropClip(0,0,0,0, crop_shape=[2,2]) ...