• max_pool2d
2020-09-10 13:37:47
class DeepWise_MaxPool(nn.MaxPool1d):
def __init__(self, channels):
super(DeepWise_MaxPool, self).__init__(channels)

def forward(self, input):
n, c, h, w = input.size()
input = input.view(n, c, h * w).permute(0, 2, 1)
pooled = torch.nn.functional.max_pool1d(input, self.kernel_size, self.stride,
_, _, c = pooled.size()
pooled = pooled.permute(0, 2, 1)
return pooled.view(n, c, h, w)

在模型中使用该类作为基本结构的一部分时，网络反向传播报错，使用contiguous()将permute()操作之后的tensor变为连续。

Returns a contiguous in memory tensor containing the same data as self tensor. If self tensor is already in the specified memory format, this function returns the self tensor
class DeepWise_MaxPool(nn.MaxPool1d):
def __init__(self, channels):
super(DeepWise_MaxPool, self).__init__(channels)

def forward(self, input):
n, c, h, w = input.size()
input = input.view(n, c, h * w).permute(0, 2, 1).contiguous()
pooled = torch.nn.functional.max_pool1d(input, self.kernel_size, self.stride,
_, _, c = pooled.size()
pooled = pooled.permute(0, 2, 1).contiguous()
return pooled.view(n, c, h, w)

更多相关内容
• 本文整理汇总了Python中torch.nn.functional.max_pool2d方法的典型用法代码示例。如果您正苦于以下问题：Python functional.max_pool2d方法的具体用法？Python functional.max_pool2d怎么用？Python functional.max_...

本文整理汇总了Python中torch.nn.functional.max_pool2d方法的典型用法代码示例。如果您正苦于以下问题：Python functional.max_pool2d方法的具体用法？Python functional.max_pool2d怎么用？Python functional.max_pool2d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模块torch.nn.functional的用法示例。

在下文中一共展示了functional.max_pool2d方法的14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，您的评价将有助于我们的系统推荐出更棒的Python代码示例。

示例1: test_resize_methods

​点赞 6

# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import max_pool2d [as 别名]

def test_resize_methods():

inputs_x = torch.randn([2, 256, 128, 128])

target_resize_sizes = [(128, 128), (256, 256)]

resize_methods_list = ['nearest', 'bilinear']

for method in resize_methods_list:

merge_cell = BaseMergeCell(upsample_mode=method)

for target_size in target_resize_sizes:

merge_cell_out = merge_cell._resize(inputs_x, target_size)

gt_out = F.interpolate(inputs_x, size=target_size, mode=method)

assert merge_cell_out.equal(gt_out)

target_size = (64, 64) # resize to a smaller size

merge_cell = BaseMergeCell()

merge_cell_out = merge_cell._resize(inputs_x, target_size)

kernel_size = inputs_x.shape[-1] // target_size[-1]

gt_out = F.max_pool2d(

inputs_x, kernel_size=kernel_size, stride=kernel_size)

assert (merge_cell_out == gt_out).all()

开发者ID:open-mmlab，项目名称:mmdetection，代码行数:21，

示例2: forward

​点赞 6

# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import max_pool2d [as 别名]

def forward(self, x):

out = F.relu(self.conv1(x))

out = self.bnm1(out)

out = F.relu(self.conv2(out))

out = self.bnm2(out)

out = F.max_pool2d(out, 2)

out = F.relu(self.conv3(out))

out = self.bnm3(out)

out = F.relu(self.conv4(out))

out = self.bnm4(out)

out = F.max_pool2d(out, 2)

out = out.view(out.size(0), -1)

#out = self.dropout1(out)

out = F.relu(self.fc1(out))

#out = self.dropout2(out)

out = self.bnm5(out)

out = F.relu(self.fc2(out))

#out = self.dropout3(out)

out = self.bnm6(out)

out = self.fc3(out)

return (out)

开发者ID:StephanZheng，项目名称:neural-fingerprinting，代码行数:23，

示例3: apply

​点赞 6

# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import max_pool2d [as 别名]

def apply(features: Tensor, proposal_bboxes: Tensor, proposal_batch_indices: Tensor, mode: Mode) -> Tensor:

_, _, feature_map_height, feature_map_width = features.shape

scale = 1 / 16

output_size = (7 * 2, 7 * 2)

if mode == Pooler.Mode.POOLING:

pool = []

for (proposal_bbox, proposal_batch_index) in zip(proposal_bboxes, proposal_batch_indices):

start_x = max(min(round(proposal_bbox[0].item() * scale), feature_map_width - 1), 0) # [0, feature_map_width)

start_y = max(min(round(proposal_bbox[1].item() * scale), feature_map_height - 1), 0) # (0, feature_map_height]

end_x = max(min(round(proposal_bbox[2].item() * scale) + 1, feature_map_width), 1) # [0, feature_map_width)

end_y = max(min(round(proposal_bbox[3].item() * scale) + 1, feature_map_height), 1) # (0, feature_map_height]

roi_feature_map = features[proposal_batch_index, :, start_y:end_y, start_x:end_x]

pool = torch.stack(pool, dim=0)

elif mode == Pooler.Mode.ALIGN:

pool = ROIAlign(output_size, spatial_scale=scale, sampling_ratio=0)(

features,

torch.cat([proposal_batch_indices.view(-1, 1).float(), proposal_bboxes], dim=1)

)

else:

raise ValueError

pool = F.max_pool2d(input=pool, kernel_size=2, stride=2)

return pool

开发者ID:potterhsu，项目名称:easy-faster-rcnn.pytorch，代码行数:27，

示例4: forward

​点赞 6

# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import max_pool2d [as 别名]

def forward(self, X):

h = F.relu(self.conv1_1(X))

h = F.relu(self.conv1_2(h))

relu1_2 = h

h = F.max_pool2d(h, kernel_size=2, stride=2)

h = F.relu(self.conv2_1(h))

h = F.relu(self.conv2_2(h))

relu2_2 = h

h = F.max_pool2d(h, kernel_size=2, stride=2)

h = F.relu(self.conv3_1(h))

h = F.relu(self.conv3_2(h))

h = F.relu(self.conv3_3(h))

relu3_3 = h

h = F.max_pool2d(h, kernel_size=2, stride=2)

h = F.relu(self.conv4_1(h))

h = F.relu(self.conv4_2(h))

h = F.relu(self.conv4_3(h))

relu4_3 = h

return [relu1_2, relu2_2, relu3_3, relu4_3]

## Weights init function

开发者ID:AlexiaJM，项目名称:Deep-learning-with-cats，代码行数:26，

示例5: forward

​点赞 6

# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import max_pool2d [as 别名]

def forward(self, inputs, y=None):

# Apply convs

theta = self.theta(inputs)

phi = F.max_pool2d(self.phi(inputs), [2, 2])

g = F.max_pool2d(self.g(inputs), [2, 2])

# Perform reshapes

theta = theta.view(-1, self.channels // self.heads, inputs.shape[2] * inputs.shape[3])

phi = phi.view(-1, self.channels // self.heads, inputs.shape[2] * inputs.shape[3] // 4)

g = g.view(-1, self.channels // 2, inputs.shape[2] * inputs.shape[3] // 4)

# Matmul and softmax to get attention maps

beta = F.softmax(torch.bmm(theta.transpose(1, 2), phi), -1)

# Attention map times g path

o = self.o(torch.bmm(g, beta.transpose(1, 2)).view(-1, self.channels // 2, inputs.shape[2],

inputs.shape[3]))

outputs = self.gamma * o + inputs

return outputs

开发者ID:bayesiains，项目名称:nsf，代码行数:18，

示例6: forward

​点赞 6

# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import max_pool2d [as 别名]

def forward(self, x):

x = F.relu(self.bn1_a(self.conv1_a(x)))

x_pool1b = F.max_pool2d(F.relu(self.bn1_b(self.conv1_b(x))),2, stride=2)

x = self.layer1(x_pool1b)

x = F.max_pool2d(F.relu(self.bn2(self.conv2(x))),2, stride=2)

x = self.layer2(x)

x_pool3 = F.max_pool2d(F.relu(self.bn3(self.conv3(x))),2, stride=2)

x = self.layer3(x_pool3)

x = F.max_pool2d(F.relu(self.bn4(self.conv4(x))),2, stride=2)

x = self.layer4(x)

x = x.view(-1, self.num_flat_features(x))

x = self.fc5_new(x)

# x1 = x1.view(1,-1,512)

# x1, hn1 = self.lstm1(x1, (self.h1, self.c1))

x = self.fc8_final(x)

return x

开发者ID:XiaoYee，项目名称:emotion_classification，代码行数:23，

示例7: forward

​点赞 5

# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import max_pool2d [as 别名]

def forward(self, x):

x = self.conv1(x)

x = F.relu(x)

x = self.conv2(x)

x = F.max_pool2d(x, 2)

x = torch.flatten(x, 1)

x = self.fc1(x)

x = F.normalize(x)

return x

开发者ID:peisuke，项目名称:MomentumContrast.pytorch，代码行数:11，

示例8: forward

​点赞 5

# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import max_pool2d [as 别名]

def forward(self, x):

"""Forward input images through the network to generate heatmaps."""

x = F.max_pool2d(F.relu(self.bn1(self.conv1(x))), 2)

x = F.max_pool2d(F.relu(self.bn2(self.conv2(x))), 2)

x = F.max_pool2d(F.relu(self.bn3(self.conv3(x))), 2)

x = F.relu(self.bn4(self.conv4(x)))

x = F.relu(self.bn5(self.conv5(x)))

x = F.relu(self.bn6(self.conv6(x)))

x = F.sigmoid(self.conv7(x))

return x

开发者ID:aleju，项目名称:cat-bbs，代码行数:12，

示例9: _crop_pool_layer

​点赞 5

# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import max_pool2d [as 别名]

def _crop_pool_layer(self, bottom, rois, max_pool=True): # done

# implement it using stn

# box to affine

# input (x1,y1,x2,y2)

"""

[ x2-x1 x1 + x2 - W + 1 ]

[ ----- 0 --------------- ]

[ W - 1 W - 1 ]

[ ]

[ y2-y1 y1 + y2 - H + 1 ]

[ 0 ----- --------------- ]

[ H - 1 H - 1 ]

"""

rois = rois.detach()

x1 = rois[:, 1::4] / 16.0

y1 = rois[:, 2::4] / 16.0

x2 = rois[:, 3::4] / 16.0

y2 = rois[:, 4::4] / 16.0

height = bottom.size(2)

width = bottom.size(3)

# affine theta

theta = Variable(rois.data.new(rois.size(0), 2, 3).zero_())

theta[:, 0, 0] = (x2 - x1) / (width - 1)

theta[:, 0 ,2] = (x1 + x2 - width + 1) / (width - 1)

theta[:, 1, 1] = (y2 - y1) / (height - 1)

theta[:, 1, 2] = (y1 + y2 - height + 1) / (height - 1)

if max_pool:

pre_pool_size = cfg.POOLING_SIZE * 2

grid = F.affine_grid(theta, torch.Size((rois.size(0), 1, pre_pool_size, pre_pool_size)))

crops = F.grid_sample(bottom.expand(rois.size(0), bottom.size(1), bottom.size(2), bottom.size(3)), grid)

crops = F.max_pool2d(crops, 2, 2)

else:

grid = F.affine_grid(theta, torch.Size((rois.size(0), 1, cfg.POOLING_SIZE, cfg.POOLING_SIZE)))

crops = F.grid_sample(bottom.expand(rois.size(0), bottom.size(1), bottom.size(2), bottom.size(3)), grid)

return crops

开发者ID:Sunarker，项目名称:Collaborative-Learning-for-Weakly-Supervised-Object-Detection，代码行数:42，

示例10: _resize

​点赞 5

# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import max_pool2d [as 别名]

def _resize(self, x, size):

if x.shape[-2:] == size:

return x

elif x.shape[-2:] < size:

return F.interpolate(x, size=size, mode=self.upsample_mode)

else:

assert x.shape[-2] % size[-2] == 0 and x.shape[-1] % size[-1] == 0

kernel_size = x.shape[-1] // size[-1]

x = F.max_pool2d(x, kernel_size=kernel_size, stride=kernel_size)

return x

开发者ID:open-mmlab，项目名称:mmdetection，代码行数:12，

示例11: forward

​点赞 5

# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import max_pool2d [as 别名]

def forward(self, x):

# Left branch

y1 = self.sep_conv1(x)

y2 = self.sep_conv2(x)

# Right branch

y3 = F.max_pool2d(x, kernel_size=3, stride=self.stride, padding=1)

if self.stride==2:

y3 = self.bn1(self.conv1(y3))

y4 = self.sep_conv3(x)

# Concat & reduce channels

b1 = F.relu(y1+y2)

b2 = F.relu(y3+y4)

y = torch.cat([b1,b2], 1)

return F.relu(self.bn2(self.conv2(y)))

开发者ID:StephanZheng，项目名称:neural-fingerprinting，代码行数:16，

示例12: forward

​点赞 5

# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import max_pool2d [as 别名]

def forward(self, x):

out = F.relu(self.conv1(x))

out = F.max_pool2d(out, 2)

out = F.relu(self.conv2(out))

out = F.max_pool2d(out, 2)

out = out.view(out.size(0), -1)

out = F.relu(self.fc1(out))

out = F.relu(self.fc2(out))

out = self.fc3(out)

return (out,F.log_softmax(out))

开发者ID:StephanZheng，项目名称:neural-fingerprinting，代码行数:12，

示例13: forward

​点赞 5

# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import max_pool2d [as 别名]

def forward(self, x):

x = F.relu(F.max_pool2d(self.conv1(x), 2))

x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))

x = x.view(-1, 320)

x = F.relu(self.fc1(x))

x = F.dropout(x, training=self.training)

x = self.fc2(x)

return F.log_softmax(x)

开发者ID:StephanZheng，项目名称:neural-fingerprinting，代码行数:10，

示例14: forward

​点赞 5

# 需要导入模块: from torch.nn import functional [as 别名]

# 或者: from torch.nn.functional import max_pool2d [as 别名]

def forward(self, x):

y1 = self.sep_conv1(x)

y2 = F.max_pool2d(x, kernel_size=3, stride=self.stride, padding=1)

if self.stride==2:

y2 = self.bn1(self.conv1(y2))

return F.relu(y1+y2)

开发者ID:StephanZheng，项目名称:neural-fingerprinting，代码行数:8，

展开全文
• torch.nn.MaxPool2d和torch.nn.functional.max_pool2d，在pytorch构建模型中，都可以作为最大池化层的引入，但前者为类模块，后者为函数，在使用上存在不同。 1. torch.nn.functional.max_pool2d pytorch中的函数，...

# 引言

torch.nn.MaxPool2dtorch.nn.functional.max_pool2d，在pytorch构建模型中，都可以作为最大池化层的引入，但前者为类模块，后者为函数，在使用上存在不同。

# 1. torch.nn.functional.max_pool2d

pytorch中的函数，可以直接调用，源码如下：

def max_pool2d_with_indices(
ceil_mode: bool = False,
return_indices: bool = False
) -> Tuple[Tensor, Tensor]:
r"""Applies a 2D max pooling over an input signal composed of several input
planes.

See :class:~torch.nn.MaxPool2d for details.
"""
if has_torch_function_unary(input):
return handle_torch_function(
max_pool2d_with_indices,
(input,),
input,
kernel_size,
stride=stride,
dilation=dilation,
ceil_mode=ceil_mode,
return_indices=return_indices,
)
if stride is None:
stride = torch.jit.annotate(List[int], [])

def _max_pool2d(
ceil_mode: bool = False,
return_indices: bool = False
) -> Tensor:
if has_torch_function_unary(input):
return handle_torch_function(
max_pool2d,
(input,),
input,
kernel_size,
stride=stride,
dilation=dilation,
ceil_mode=ceil_mode,
return_indices=return_indices,
)
if stride is None:
stride = torch.jit.annotate(List[int], [])

max_pool2d = boolean_dispatch(
arg_name="return_indices",
arg_index=6,
default=False,
if_true=max_pool2d_with_indices,
if_false=_max_pool2d,
module_name=__name__,
func_name="max_pool2d",
)

使用如下：

import torch.nn.functional as F
input = torch.randn(20, 16, 50, 32)  # 输入张量
"""
其中：
Shape:
- Input: :math:(N, C, H_{in}, W_{in})
- Output: :math:(N, C, H_{out}, W_{out}), where
"""

# 2. torch.nn.MaxPool2d

pytorch中的类模块，先实例化，再调用其函数，源码如下（笔者已将源码中的注释简化）：

class MaxPool2d(_MaxPoolNd):

kernel_size: _size_2_t
stride: _size_2_t
dilation: _size_2_t

def forward(self, input: Tensor) -> Tensor:
return F.max_pool2d(input, self.kernel_size, self.stride,
self.return_indices)

使用如下：

import torch
m = torch.nn.MaxPool2d(3, stride=2)  # 实例化
# 或者
m = torch.nn.MaxPool2d((3, 2), stride=(2, 1))  # 实例化
input = torch.randn(20, 16, 50, 32)  # 输入张量
output = m(input) # 使用该类
"""
Shape:
- Input: :math:(N, C, H_{in}, W_{in})
- Output: :math:(N, C, H_{out}, W_{out}), where
"""

# 3. 对比类和函数的使用

torch.nn.MaxPool2d，要先实例化，并在forward调用了torch.nn.functional.max_pool2d函数。
综上：torch.nn.functional.max_pool2d函数包含于torch.nn.MaxPool2d类模块中，可以单独使用，也可以实例化类再使用。
在模型构建下的使用：
（1）使用类模块

import torch
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = torch.nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = torch.nn.Conv2d(10, 20, kernel_size=5)
self.pooling = torch.nn.MaxPool2d(2)  # kernel_size = 2，实例化
self.fc = torch.nn.Linear(320, 10)

def forward(self, x):
# Flatten data from(n,1,28,28) to (n,784)
batch_size = x.s(0)
x = F.relu(self.pooling(self.conv1(x)))
x = F.relu(self.pooling(self.conv2(x)))
x = x.view(batch_size, -1)
x = self.fc(x)
return x

说明：kernel_size 是必须要指定的参数，否则会报错
笔者修改了torch.nn.MaxPool2d的源码，说明传入参数要求（记得改回来！）：

import torch
pooling1 = torch.nn.MaxPool2d(1,2,3,4)
print(pooling1)
pooling2 = torch.nn.MaxPool2d(1)
print(pooling2)

输出为

（2）直接调用函数

import torch
import torch.nn.functional as F
class Net(torch.nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = torch.nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = torch.nn.Conv2d(10, 20, kernel_size=5)
# 最大池化层无需实例化，直接在forward中调用
self.fc = torch.nn.Linear(320, 10)

def forward(self, x):
# Flatten data from(n,1,28,28) to (n,784)
batch_size = x.s(0)
x = F.relu(F.max_pool2d(self.conv1(x), kernel_size=2))  # 一定要指定kernel_size
x = F.relu(F.max_pool2d(self.conv2(x), kernel_size=2))
x = x.view(batch_size, -1)
x = self.fc(x)
return x

展开全文
• torch.nn.MaxPool2d和torch.nn.functional.max_pool2d两者本质上是一样的，具体可以参考torch.nn.MaxPool2d的源代码，核心源代码如下所示： from .. import functional as F class MaxPool2d(_MaxPoolNd): kernel_...

# 两者之间的区别与联系

首先给出结论，torch.nn.MaxPool2dtorch.nn.functional.max_pool2d两者本质上是一样的。
具体可以参考torch.nn.MaxPool2d源代码，核心源代码如下所示：

from .. import functional as F

class MaxPool2d(_MaxPoolNd):
kernel_size: _size_2_t
stride: _size_2_t
dilation: _size_2_t

def forward(self, input: Tensor) -> Tensor:
return F.max_pool2d(input, self.kernel_size, self.stride,
self.return_indices)

由此可见，torch.nn.MaxPool2d在自己的forward()方法中调用了torch.nn.functional.max_pool2d
而pytorch官网在介绍torch.nn.functional.max_pool2d时，也是直接说

See MaxPool2d for details.

因此两者本质上是一样的。

至于为什么同一个任务定义两种方法，可能是为了符合不同人使用PyTorch的编码风格。

# 资料参考

https://pytorch.org/docs/stable/generated/torch.nn.functional.max_pool2d.html#torch.nn.functional.max_pool2d

https://stackoverflow.com/questions/58514197/difference-between-nn-maxpool2d-vs-nn-functional-max-pool2d?noredirect=1

展开全文
• # def max_pool2d(inputs, # kernel_size, # stride=2, # padding='VALID', # data_format=DATA_FORMAT_NHWC, # outputs_collections...
• 一、 如果在做卷积过程中出现WARNING:tensorflow:From D:\Anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py:4070: The name tf.... Please use tf.nn.max_pool2d instead.**** ，就是如下图所示的...
• RuntimeError: max_pool2d_with_indices_out_cuda_frame failed with error code 0 问题描述 pytorch前向推理模型, 在1.3版本下可以运行, 但切换到1.4后报RuntimeError: max_pool2d_with_indices_out_cuda_frame ...
• 在学习python时，遇到了以下报错，有大佬遇到过吗？ 可以帮忙解读一下这是什么原因造成的吗？ 标题
• # def max_pool2d(inputs, # kernel_size, # stride=2, # padding='VALID', # data_format=DATA_FORMAT_NHWC, # outputs_collectio...
• tf.nn.max_pool 原生的池化操作，而tf.layers.max_pooling2d是进行了封装，对许多参数进行了设置，使用起来更方便 tf.layers.max_pooling2d： 用于2D输入的最大池化层(例如图像). 参数： inputs：池的张量,秩...
• 参考自tf2.1官方文档: https://www.tensorflow.org/api_docs/python/tf/nn/max_pool2d Performs the max pooling on the input. ...tf.nn.max_pool2d( input, ksize, strides, padding, data_forma...
• 今天小编就为大家分享一篇浅谈pytorch池化maxpool2D注意事项，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧
•  valid_pad = tf.nn.max_pool(x, [1, 3, 3, 1], [1, 2, 2, 1], padding='VALID')  same_pad = tf.nn.max_pool(x, [1, 3, 3, 1], [1, 2, 2, 1], padding='SAME') with tf.Session(graph=graph) as sess:  print...
• ## torch.nn.MaxPool2d详解

万次阅读 多人点赞 2020-11-22 20:18:38
_) class MaxPool2d(_MaxPoolNd): kernel_size: _size_2_t stride: _size_2_t padding: _size_2_t dilation: _size_2_t def forward(self, input: Tensor) -> Tensor: return F.max_pool2d(input, self.kernel_size,...
• : max pool with 2x2 kernel, stride 2 and SAME padding (this is the classic way to go) The output shapes are: valid_pad : here, no padding so the output shape is [1, 1] same_pad : here, ...
• ## tf.nn.max_pool3d用法

千次阅读 2020-09-16 20:01:52
tf.nn.max_pool3d：对输入执行最大池化。 在tf 1.x中为tf.compat.v1.nn.max_pool3d tf.nn.max_pool3d( input, ksize, strides, padding, data_format='NDHWC', name=None ) Args input 由data_...
• pytorch 中nn.MaxPool1d() 和nn.MaxPool2d()对比
• 记录帖 nn.conv2d中的参数SAME和VALID https://blog.csdn.net/flashlau/article/details/82944536 tf.nn.max_pool中的参数SAME和VALID https://vimsky.com/article/3881.html
• 1.tf.nn.maxpool2d()函数介绍 tf.nn.max_pool2d(input, ksize, strides, padding, data_format='NHWC', name=None) 参数说明： Args input A 4-DTensorof the format specified bydata_format. ...
• Pytorch nn.MaxPool1d； nn.functional.max_pool1d pytorch 中nn.MaxPool1d() 和nn.MaxPool2d()对比
• MaxPool2d的使用方法。 API官网文档 MaxPool2d 参数介绍 kernel_size ：表示做最大池化的窗口大小，可以是单个值，也可以是tuple元组 stride ：步长，可以是单个值，也可以是tuple元组 padding ：填充，可以是...
• tf.nn.max_pool(value, ksize, strides, padding, name=None) 参数是四个，和卷积很类似： 第一个参数value：需要池化的输入，一般池化层接在卷积层后面，所以输入通常是feature map，依然是[batch, height, width, ...
• MaxPool2d 的使用 此处我们仍然使用官网自带的数据集进行训练,最后将其可视化 加载数据集和可视化部分在此处不在介绍,若需要了解: 加载数据集:torch.utils.data中的DataLoader数据加载器(附代码)_硕大的蛋的博客-...
• ## tf.nn.max_pool参数含义和用法

万次阅读 多人点赞 2017-09-19 00:05:24
转自：... max pooling是CNN当中的最大值池化操作，其实用法和卷积很类似 ...有些地方可以从卷积去参考【TensorFlow】tf.nn.conv2d是怎样实现卷积的？...tf.nn.max_pool(value, ksize, strid
• 在上一节中我们详细的阐述了Conv2d的计算原理，今天我们来讲述下Pytorch中其他比较常见的操作！...在pytorch中使用Pooling操作来实现采样，常见的pool操作包含Max_pool，Avg_poolMax_pool x = t.rand(1,3,7,7...
• ## Pytorch MaxPool2d

千次阅读 2020-08-07 12:24:42
再说说我学nn.MaxPool2d时遇到的问题： import torch import torch.nn as nn m=nn.MaxPool2d(3,stride=2) input=torch.randn(6,6) output=m(input) 然后就会报这个错： RuntimeError: non-empty 3D or 4D (batch ...

...