import os.path
from typing import Iterator

import numpy as np
import torch
import cv2
from PIL import Image
from torch.utils.data import Dataset,DataLoader
import re
from functools import reduce
from torch.utils.tensorboard import SummaryWriter as Writer
from torchvision import transforms
import torchvision as tv
from torch import nn
import torch.nn.functional as F

#为true时，网络参数为在数据集上训练好的
vgg16=tv.models.vgg16(True)
#在features的后面增加一层，classifier为一属性
vgg16.features.add_module('extra',nn.Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)))
# VGG(
#   (features): Sequential(
#     (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
#     (1): ReLU(inplace=True)
#     (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
#     (3): ReLU(inplace=True)
#     (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
#     (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
#     (6): ReLU(inplace=True)
#     (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
#     (8): ReLU(inplace=True)
#     (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
#     (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
#     (11): ReLU(inplace=True)
#     (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
#     (13): ReLU(inplace=True)
#     (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
#     (15): ReLU(inplace=True)
#     (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
#     (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
#     (18): ReLU(inplace=True)
#     (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
#     (20): ReLU(inplace=True)
#     (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
#     (22): ReLU(inplace=True)
#     (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
#     (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
#     (25): ReLU(inplace=True)
#     (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
#     (27): ReLU(inplace=True)
#     (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
#     (29): ReLU(inplace=True)
#     (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
#     (extra): Conv2d(100, 100, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
#   )
#   (avgpool): AdaptiveAvgPool2d(output_size=(7, 7))
#   (classifier): Sequential(
#     (0): Linear(in_features=25088, out_features=4096, bias=True)
#     (1): ReLU(inplace=True)
#     (2): Dropout(p=0.5, inplace=False)
#     (3): Linear(in_features=4096, out_features=4096, bias=True)
#     (4): ReLU(inplace=True)
#     (5): Dropout(p=0.5, inplace=False)
#     (6): Linear(in_features=4096, out_features=1000, bias=True)
#   )
# )
#修改指定层多的信息：可用序号修改指定的层:
vgg16.features[31]=nn.Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
#删除掉features的最后一层：
vgg16.features=nn.Sequential(*list(vgg16.features.children())[:-1])
print(vgg16)
#冻结指定层的预训练参数：
vgg16.feature[26].weight.requires_grad = False
#optimizer = optim.Adam(filter(lambda p: p.requires_grad, net.parameters()), lr=0.1)