本文整理汇总了Python中torch.functional方法的典型用法代码示例。如果您正苦于以下问题：Python torch.functional方法的具体用法？Python torch.functional怎么用？Python torch.functional使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类torch的用法示例。
在下文中一共展示了torch.functional方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: build_header_str
# 需要导入模块: import torch [as 别名]
# 或者: from torch import functional [as 别名]
def build_header_str(net_name, rgb_mean, rgb_std, im_size, uses_functional,
debug_mode):
"""Generate source code header - constructs the header source
code for the network definition file.
Args:
net_name (str): name of the network architecture
debug_mode (bool): whether to generate additional debugging code
rgb_mean (List): average rgb image of training data
rgb_std (List): standard deviation of rgb images in training data
im_size (List): spatial dimensions of the training input image size
uses_functional (bool): whether the network requires the
torch.functional module
Returns:
(str) : source code header string.
"""
imports = '''
import torch
import torch.nn as nn
'''
if uses_functional:
imports = imports + '''
import torch.nn.functional as F
'''
header = imports + '''
class {0}(nn.Module):
def __init__(self):
super({0}, self).__init__()
self.meta = {{'mean': {1},
'std': {2},
'imageSize': {3}}}
'''
if debug_mode:
header = header + '''
from collections import OrderedDict
self.debug_feats = OrderedDict() # only used for feature verification
'''
return header.format(net_name, rgb_mean, rgb_std, im_size) 示例2: _compute_conv_grad_sample
# 需要导入模块: import torch [as 别名]
# 或者: from torch import functional [as 别名]
def _compute_conv_grad_sample(layer, A, B, batch_dim=0):
n = A.shape[0]
layer_type = get_layer_type(layer)
# get A and B in shape depending on the Conv layer
if layer_type == "Conv2d":
A = torch.nn.functional.unfold(
A, layer.kernel_size, padding=layer.padding, stride=layer.stride
)
B = B.reshape(n, -1, A.shape[-1])
elif layer_type == "Conv1d":
# unfold doesn't work for 3D tensors; so force it to be 4D
A = A.unsqueeze(-2) # add the H dimension
# set arguments to tuples with appropriate second element
A = torch.nn.functional.unfold(
A,
(1, layer.kernel_size[0]),
padding=(0, layer.padding[0]),
stride=(1, layer.stride[0]),
)
B = B.reshape(n, -1, A.shape[-1])
try:
# n=batch_sz; o=num_out_channels; p=num_in_channels*kernel_sz
grad_sample = (
torch.einsum("noq,npq->nop", B, A)
if layer.groups == 1
else torch.einsum("njk,njk->nj", B, A)
)
shape = [n] + list(layer.weight.shape)
_create_or_extend_grad_sample(
layer.weight, grad_sample.reshape(shape), batch_dim
)
except Exception as e:
raise type(e)(
f"{e} There is probably a problem with {layer_type}.groups"
+ "It should be either 1 or in_channel"
)
if layer.bias is not None:
_create_or_extend_grad_sample(layer.bias, torch.sum(B, dim=2), batch_dim)