常用网络层

一 卷积层

二 池化、线性、激活函数层

import os
import torch
import random
import numpy as np
import torchvision
import torch.nn as nn
from torchvision import transforms
from matplotlib import pyplot as plt
from PIL import Image
from tools.common_tools import transform_invert, set_seed

set_seed(1)  # 设置随机种子

# ================================= load img ==================================
path_img = os.path.join(os.path.dirname(os.path.abspath(__file__)), "lena.png")
img = Image.open(path_img).convert('RGB')  # 0~255

# convert to tensor
img_transform = transforms.Compose([transforms.ToTensor()])
img_tensor = img_transform(img)
img_tensor.unsqueeze_(dim=0)    # C*H*W to B*C*H*W

# ================================= create convolution layer ==================================

# ================ maxpool
# flag = 1
flag = 0
if flag:
    maxpool_layer = nn.MaxPool2d((2, 2), stride=(2, 2))   # input:(i, o, size) weights:(o, i , h, w)
    img_pool = maxpool_layer(img_tensor)

# ================ avgpool
# flag = 1
flag = 0
if flag:
    avgpoollayer = nn.AvgPool2d((2, 2), stride=(2, 2))   # input:(i, o, size) weights:(o, i , h, w)
    img_pool = avgpoollayer(img_tensor)

# ================ avgpool divisor_override
# flag = 1
flag = 0
if flag:
    img_tensor = torch.ones((1, 1, 4, 4))
    avgpool_layer = nn.AvgPool2d((2, 2), stride=(2, 2), divisor_override=3)
    img_pool = avgpool_layer(img_tensor)

    print("raw_img:\n{}\npooling_img:\n{}".format(img_tensor, img_pool))


# ================ max unpool
# flag = 1
flag = 0
if flag:
    # pooling
    img_tensor = torch.randint(high=5, size=(1, 1, 4, 4), dtype=torch.float)
    maxpool_layer = nn.MaxPool2d((2, 2), stride=(2, 2), return_indices=True)
    img_pool, indices = maxpool_layer(img_tensor)

    # unpooling
    img_reconstruct = torch.randn_like(img_pool, dtype=torch.float)
    maxunpool_layer = nn.MaxUnpool2d((2, 2), stride=(2, 2))
    img_unpool = maxunpool_layer(img_reconstruct, indices)

    print("raw_img:\n{}\nimg_pool:\n{}".format(img_tensor, img_pool))
    print("img_reconstruct:\n{}\nimg_unpool:\n{}".format(img_reconstruct, img_unpool))


# ================ linear
flag = 1
# flag = 0
if flag:
    inputs = torch.tensor([[1., 2, 3]])
    linear_layer = nn.Linear(3, 4)
    linear_layer.weight.data = torch.tensor([[1., 1., 1.],
                                             [2., 2., 2.],
                                             [3., 3., 3.],
                                             [4., 4., 4.]])

    linear_layer.bias.data.fill_(0.5)
    output = linear_layer(inputs)
    print(inputs, inputs.shape)
    print(linear_layer.weight.data, linear_layer.weight.data.shape)
    print(output, output.shape)


# ================================= visualization ==================================
# print("池化前尺寸:{}\n池化后尺寸:{}".format(img_tensor.shape, img_pool.shape))
# img_pool = transform_invert(img_pool[0, 0:3, ...], img_transform)
# img_raw = transform_invert(img_tensor.squeeze(), img_transform)
# plt.subplot(122).imshow(img_pool)
# plt.subplot(121).imshow(img_raw)
# plt.show()

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