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witnn/CNNDemo/Resnet50.py

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from __future__ import print_function
import os
import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
from torchvision import datasets, transforms
import torchvision.models as models
import matplotlib.pyplot as plt
import numpy as np
import struct
from struct import Struct
CurrentPath = os.path.split(os.path.realpath(__file__))[0]+"/"
# resnet50 = models.resnet50(pretrained=True)
# torch.save(resnet50, CurrentPath+'params.pth')
resnet50 = torch.load(CurrentPath+'params.pth')
resnet50.eval()
print("===========================")
print("==========START============")
print("===========================")
# print(resnet50)
ResNet50 = {
"conv1": "Conv2d",
"bn1": "BatchNorm2d",
"relu": "ReLU",
"maxpool": "MaxPool2d",
"layer1": {
"_modules": {
"0": {
"conv1": "Conv2d",
"bn1": "BatchNorm2d",
"conv2": "Conv2d",
"bn2": "BatchNorm2d",
"conv3": "Conv2d",
"bn3": "BatchNorm2d",
"relu": "ReLU",
"downsample": {
"_modules": {
"0": "Conv2d",
"1": "BatchNorm2d",
}
}
},
"1": {
"conv1": "Conv2d",
"bn1": "BatchNorm2d",
"conv2": "Conv2d",
"bn2": "BatchNorm2d",
"conv3": "Conv2d",
"bn3": "BatchNorm2d",
"relu": "ReLU",
},
"2": {
"conv1": "Conv2d",
"bn1": "BatchNorm2d",
"conv2": "Conv2d",
"bn2": "BatchNorm2d",
"conv3": "Conv2d",
"bn3": "BatchNorm2d",
"relu": "ReLU",
},
}
},
"layer2": {
"_modules": {
"0": {
"conv1": "Conv2d",
"bn1": "BatchNorm2d",
"conv2": "Conv2d",
"bn2": "BatchNorm2d",
"conv3": "Conv2d",
"bn3": "BatchNorm2d",
"relu": "ReLU",
"downsample": {
"_modules": {
"0": "Conv2d",
"1": "BatchNorm2d",
}
}
},
"1": {
"conv1": "Conv2d",
"bn1": "BatchNorm2d",
"conv2": "Conv2d",
"bn2": "BatchNorm2d",
"conv3": "Conv2d",
"bn3": "BatchNorm2d",
"relu": "ReLU",
},
"2": {
"conv1": "Conv2d",
"bn1": "BatchNorm2d",
"conv2": "Conv2d",
"bn2": "BatchNorm2d",
"conv3": "Conv2d",
"bn3": "BatchNorm2d",
"relu": "ReLU",
},
"3": {
"conv1": "Conv2d",
"bn1": "BatchNorm2d",
"conv2": "Conv2d",
"bn2": "BatchNorm2d",
"conv3": "Conv2d",
"bn3": "BatchNorm2d",
"relu": "ReLU",
}
}
},
"layer3": {
"_modules": {
"0": {
"conv1": "Conv2d",
"bn1": "BatchNorm2d",
"conv2": "Conv2d",
"bn2": "BatchNorm2d",
"conv3": "Conv2d",
"bn3": "BatchNorm2d",
"relu": "ReLU",
"downsample": {
"_modules": {
"0": "Conv2d",
"1": "BatchNorm2d",
}
}
},
"1": {
"conv1": "Conv2d",
"bn1": "BatchNorm2d",
"conv2": "Conv2d",
"bn2": "BatchNorm2d",
"conv3": "Conv2d",
"bn3": "BatchNorm2d",
"relu": "ReLU",
},
"2": {
"conv1": "Conv2d",
"bn1": "BatchNorm2d",
"conv2": "Conv2d",
"bn2": "BatchNorm2d",
"conv3": "Conv2d",
"bn3": "BatchNorm2d",
"relu": "ReLU",
},
"3": {
"conv1": "Conv2d",
"bn1": "BatchNorm2d",
"conv2": "Conv2d",
"bn2": "BatchNorm2d",
"conv3": "Conv2d",
"bn3": "BatchNorm2d",
"relu": "ReLU",
},
"4": {
"conv1": "Conv2d",
"bn1": "BatchNorm2d",
"conv2": "Conv2d",
"bn2": "BatchNorm2d",
"conv3": "Conv2d",
"bn3": "BatchNorm2d",
"relu": "ReLU",
},
"5": {
"conv1": "Conv2d",
"bn1": "BatchNorm2d",
"conv2": "Conv2d",
"bn2": "BatchNorm2d",
"conv3": "Conv2d",
"bn3": "BatchNorm2d",
"relu": "ReLU",
}
}
},
"layer4": {
"_modules": {
"0": {
"conv1": "Conv2d",
"bn1": "BatchNorm2d",
"conv2": "Conv2d",
"bn2": "BatchNorm2d",
"conv3": "Conv2d",
"bn3": "BatchNorm2d",
"relu": "ReLU",
"downsample": {
"_modules": {
"0": "Conv2d",
"1": "BatchNorm2d",
}
}
},
"1": {
"conv1": "Conv2d",
"bn1": "BatchNorm2d",
"conv2": "Conv2d",
"bn2": "BatchNorm2d",
"conv3": "Conv2d",
"bn3": "BatchNorm2d",
"relu": "ReLU",
},
"2": {
"conv1": "Conv2d",
"bn1": "BatchNorm2d",
"conv2": "Conv2d",
"bn2": "BatchNorm2d",
"conv3": "Conv2d",
"bn3": "BatchNorm2d",
"relu": "ReLU",
}
}
},
"avgpool": "AdaptiveAvgPool2d",
"fc": "Linear"
}
weightfile = open(CurrentPath+'ResNet50Weight.cc', 'w')
shapefile = open(CurrentPath+'ResNet50WeightShape.cc', 'w')
binaryfile = open(CurrentPath+'ResNet50Weight.bin', 'wb')
currentbyte = 0
strg = ''
strgShape = ''
def genData(name, data):
global currentbyte
global binaryfile
global strg
global strgShape
strg = strg + "int "+name+"[] = { "
array = data.cpu().detach().numpy().reshape(-1)
strg += str(currentbyte) + ","
for a in array:
bs = struct.pack("f", a)
binaryfile.write(bs)
currentbyte = currentbyte+4
strg += str(currentbyte-1)
strg = strg + " };\n"
strgShape = strgShape + "int "+name+"_shape[] = { "
array = data.cpu().detach().numpy().shape
for a in array:
strgShape += str(a) + ", "
strgShape = strgShape + " };\n"
def hook_fn(m, i, o):
print(m)
print("------------Input Grad------------")
for grad in i:
try:
print(grad.shape)
except AttributeError:
print ("None found for Gradient")
print("------------Output Grad------------")
for grad in o:
try:
print(grad.shape)
except AttributeError:
print ("None found for Gradient")
print("\n")
def hook_print(name, m, i, o):
global currentbyte
global binaryfile
global strg
genData(name+"_input", i[0])
genData(name+"_output", o[0])
def printDick(d, head, obj):
global strg
for item in d:
if type(d[item]).__name__ == 'dict':
objsub = getattr(obj, item, '')
if objsub == '':
objsub = obj[item]
printDick(d[item], head+"_"+item, objsub)
else:
objsub = getattr(obj, item, '')
if objsub == '':
objsub = obj[item]
if d[item] == "Conv2d":
genData(head+"_"+item+"_weight", objsub.weight)
strg = strg + "\n"
if d[item] == "BatchNorm2d":
genData(head+"_"+item+"_running_mean", objsub.running_mean)
genData(head+"_"+item+"_running_var", objsub.running_var)
genData(head+"_"+item+"_weight", objsub.weight)
genData(head+"_"+item+"_bias", objsub.bias)
strg = strg + "\n"
if d[item] == "Linear":
genData(head+"_"+item+"_weight", objsub.weight)
genData(head+"_"+item+"_bias", objsub.bias)
strg = strg + "\n"
printDick(ResNet50, "RN50", resnet50)
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
val_loader = torch.utils.data.DataLoader(
datasets.ImageFolder(CurrentPath+'ImageNet/', transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
normalize,
])),
batch_size=1, shuffle=False,
num_workers=1, pin_memory=True)
strg = strg + "\n"
strg = strg + "\n"
strg = strg + "\n"
strg = strg + "\n"
strg = strg + "// val data 0-9 \n"
for batch_idx, (data, target) in enumerate(val_loader):
genData("input_"+str(batch_idx), data)
out = resnet50(data)
genData("output_"+str(batch_idx), out)
strg = strg + "\n"
strg = strg + "\n"
strg = strg + "\n"
strg = strg + "\n"
strg = strg + "// input 0 layer output \n"
for batch_idx, (data, target) in enumerate(val_loader):
genData("verify_input", data)
x = resnet50.conv1(data)
genData("verify_conv1", x)
x = resnet50.bn1(x)
genData("verify_bn1", x)
x = resnet50.relu(x)
genData("verify_relu", x)
x = resnet50.maxpool(x)
genData("verify_maxpool", x)
x = resnet50.layer1(x)
genData("verify_layer1", x)
x = resnet50.layer2(x)
genData("verify_layer2", x)
x = resnet50.layer3(x)
genData("verify_layer3", x)
x = resnet50.layer4(x)
genData("verify_layer4", x)
x = resnet50.avgpool(x)
genData("verify_avgpool", x)
x = torch.flatten(x, 1)
x = resnet50.fc(x)
genData("verify_fc", x)
break
resnet50.layer1._modules['0'].bn1.register_forward_hook(lambda m, i, o: hook_print("layer1_block0_bn1", m, i, o))
resnet50.layer1._modules['0'].bn2.register_forward_hook(lambda m, i, o: hook_print("layer1_block0_bn2", m, i, o))
resnet50.layer1._modules['0'].bn3.register_forward_hook(lambda m, i, o: hook_print("layer1_block0_bn3", m, i, o))
resnet50.layer1._modules['0'].conv1.register_forward_hook(lambda m, i, o: hook_print("layer1_block0_conv1", m, i, o))
resnet50.layer1._modules['0'].conv2.register_forward_hook(lambda m, i, o: hook_print("layer1_block0_conv2", m, i, o))
resnet50.layer1._modules['0'].conv3.register_forward_hook(lambda m, i, o: hook_print("layer1_block0_conv3", m, i, o))
resnet50.layer1._modules['0'].downsample._modules['0'].register_forward_hook(lambda m, i, o: hook_print("layer1_block0_downsample_conv", m, i, o))
resnet50.layer1._modules['0'].downsample._modules['1'].register_forward_hook(lambda m, i, o: hook_print("layer1_block0_downsample_bn", m, i, o))
resnet50.layer1._modules['1'].bn1.register_forward_hook(lambda m, i, o: hook_print("layer1_block1_bn1", m, i, o))
resnet50.layer1._modules['1'].bn2.register_forward_hook(lambda m, i, o: hook_print("layer1_block1_bn2", m, i, o))
resnet50.layer1._modules['1'].bn3.register_forward_hook(lambda m, i, o: hook_print("layer1_block1_bn3", m, i, o))
resnet50.layer1._modules['1'].conv1.register_forward_hook(lambda m, i, o: hook_print("layer1_block1_conv1", m, i, o))
resnet50.layer1._modules['1'].conv2.register_forward_hook(lambda m, i, o: hook_print("layer1_block1_conv2", m, i, o))
resnet50.layer1._modules['1'].conv3.register_forward_hook(lambda m, i, o: hook_print("layer1_block1_conv3", m, i, o))
resnet50.layer1._modules['2'].bn1.register_forward_hook(lambda m, i, o: hook_print("layer1_block2_bn1", m, i, o))
resnet50.layer1._modules['2'].bn2.register_forward_hook(lambda m, i, o: hook_print("layer1_block2_bn2", m, i, o))
resnet50.layer1._modules['2'].bn3.register_forward_hook(lambda m, i, o: hook_print("layer1_block2_bn3", m, i, o))
resnet50.layer1._modules['2'].conv1.register_forward_hook(lambda m, i, o: hook_print("layer1_block2_conv1", m, i, o))
resnet50.layer1._modules['2'].conv2.register_forward_hook(lambda m, i, o: hook_print("layer1_block2_conv2", m, i, o))
resnet50.layer1._modules['2'].conv3.register_forward_hook(lambda m, i, o: hook_print("layer1_block2_conv3", m, i, o))
resnet50.layer2._modules['0'].bn1.register_forward_hook(lambda m, i, o: hook_print("layer2_block0_bn1", m, i, o))
resnet50.layer2._modules['0'].bn2.register_forward_hook(lambda m, i, o: hook_print("layer2_block0_bn2", m, i, o))
resnet50.layer2._modules['0'].bn3.register_forward_hook(lambda m, i, o: hook_print("layer2_block0_bn3", m, i, o))
resnet50.layer2._modules['0'].conv1.register_forward_hook(lambda m, i, o: hook_print("layer2_block0_conv1", m, i, o))
resnet50.layer2._modules['0'].conv2.register_forward_hook(lambda m, i, o: hook_print("layer2_block0_conv2", m, i, o))
resnet50.layer2._modules['0'].conv3.register_forward_hook(lambda m, i, o: hook_print("layer2_block0_conv3", m, i, o))
resnet50.layer2._modules['0'].downsample._modules['0'].register_forward_hook(lambda m, i, o: hook_print("layer2_block0_downsample_conv", m, i, o))
resnet50.layer2._modules['0'].downsample._modules['1'].register_forward_hook(lambda m, i, o: hook_print("layer2_block0_downsample_bn", m, i, o))
resnet50.layer2._modules['1'].bn1.register_forward_hook(lambda m, i, o: hook_print("layer2_block1_bn1", m, i, o))
resnet50.layer2._modules['1'].bn2.register_forward_hook(lambda m, i, o: hook_print("layer2_block1_bn2", m, i, o))
resnet50.layer2._modules['1'].bn3.register_forward_hook(lambda m, i, o: hook_print("layer2_block1_bn3", m, i, o))
resnet50.layer2._modules['1'].conv1.register_forward_hook(lambda m, i, o: hook_print("layer2_block1_conv1", m, i, o))
resnet50.layer2._modules['1'].conv2.register_forward_hook(lambda m, i, o: hook_print("layer2_block1_conv2", m, i, o))
resnet50.layer2._modules['1'].conv3.register_forward_hook(lambda m, i, o: hook_print("layer2_block1_conv3", m, i, o))
resnet50.layer2._modules['2'].bn1.register_forward_hook(lambda m, i, o: hook_print("layer2_block2_bn1", m, i, o))
resnet50.layer2._modules['2'].bn2.register_forward_hook(lambda m, i, o: hook_print("layer2_block2_bn2", m, i, o))
resnet50.layer2._modules['2'].bn3.register_forward_hook(lambda m, i, o: hook_print("layer2_block2_bn3", m, i, o))
resnet50.layer2._modules['2'].conv1.register_forward_hook(lambda m, i, o: hook_print("layer2_block2_conv1", m, i, o))
resnet50.layer2._modules['2'].conv2.register_forward_hook(lambda m, i, o: hook_print("layer2_block2_conv2", m, i, o))
resnet50.layer2._modules['2'].conv3.register_forward_hook(lambda m, i, o: hook_print("layer2_block2_conv3", m, i, o))
resnet50.layer2._modules['3'].bn1.register_forward_hook(lambda m, i, o: hook_print("layer2_block3_bn1", m, i, o))
resnet50.layer2._modules['3'].bn2.register_forward_hook(lambda m, i, o: hook_print("layer2_block3_bn2", m, i, o))
resnet50.layer2._modules['3'].bn3.register_forward_hook(lambda m, i, o: hook_print("layer2_block3_bn3", m, i, o))
resnet50.layer2._modules['3'].conv1.register_forward_hook(lambda m, i, o: hook_print("layer2_block3_conv1", m, i, o))
resnet50.layer2._modules['3'].conv2.register_forward_hook(lambda m, i, o: hook_print("layer2_block3_conv2", m, i, o))
resnet50.layer2._modules['3'].conv3.register_forward_hook(lambda m, i, o: hook_print("layer2_block3_conv3", m, i, o))
resnet50.layer3._modules['0'].bn1.register_forward_hook(lambda m, i, o: hook_print("layer3_block0_bn1", m, i, o))
resnet50.layer3._modules['0'].bn2.register_forward_hook(lambda m, i, o: hook_print("layer3_block0_bn2", m, i, o))
resnet50.layer3._modules['0'].bn3.register_forward_hook(lambda m, i, o: hook_print("layer3_block0_bn3", m, i, o))
resnet50.layer3._modules['0'].conv1.register_forward_hook(lambda m, i, o: hook_print("layer3_block0_conv1", m, i, o))
resnet50.layer3._modules['0'].conv2.register_forward_hook(lambda m, i, o: hook_print("layer3_block0_conv2", m, i, o))
resnet50.layer3._modules['0'].conv3.register_forward_hook(lambda m, i, o: hook_print("layer3_block0_conv3", m, i, o))
resnet50.layer3._modules['0'].downsample._modules['0'].register_forward_hook(lambda m, i, o: hook_print("layer3_block0_downsample_conv", m, i, o))
resnet50.layer3._modules['0'].downsample._modules['1'].register_forward_hook(lambda m, i, o: hook_print("layer3_block0_downsample_bn", m, i, o))
resnet50.layer3._modules['1'].bn1.register_forward_hook(lambda m, i, o: hook_print("layer3_block1_bn1", m, i, o))
resnet50.layer3._modules['1'].bn2.register_forward_hook(lambda m, i, o: hook_print("layer3_block1_bn2", m, i, o))
resnet50.layer3._modules['1'].bn3.register_forward_hook(lambda m, i, o: hook_print("layer3_block1_bn3", m, i, o))
resnet50.layer3._modules['1'].conv1.register_forward_hook(lambda m, i, o: hook_print("layer3_block1_conv1", m, i, o))
resnet50.layer3._modules['1'].conv2.register_forward_hook(lambda m, i, o: hook_print("layer3_block1_conv2", m, i, o))
resnet50.layer3._modules['1'].conv3.register_forward_hook(lambda m, i, o: hook_print("layer3_block1_conv3", m, i, o))
resnet50.layer3._modules['2'].bn1.register_forward_hook(lambda m, i, o: hook_print("layer3_block2_bn1", m, i, o))
resnet50.layer3._modules['2'].bn2.register_forward_hook(lambda m, i, o: hook_print("layer3_block2_bn2", m, i, o))
resnet50.layer3._modules['2'].bn3.register_forward_hook(lambda m, i, o: hook_print("layer3_block2_bn3", m, i, o))
resnet50.layer3._modules['2'].conv1.register_forward_hook(lambda m, i, o: hook_print("layer3_block2_conv1", m, i, o))
resnet50.layer3._modules['2'].conv2.register_forward_hook(lambda m, i, o: hook_print("layer3_block2_conv2", m, i, o))
resnet50.layer3._modules['2'].conv3.register_forward_hook(lambda m, i, o: hook_print("layer3_block2_conv3", m, i, o))
resnet50.layer3._modules['3'].bn1.register_forward_hook(lambda m, i, o: hook_print("layer3_block3_bn1", m, i, o))
resnet50.layer3._modules['3'].bn2.register_forward_hook(lambda m, i, o: hook_print("layer3_block3_bn2", m, i, o))
resnet50.layer3._modules['3'].bn3.register_forward_hook(lambda m, i, o: hook_print("layer3_block3_bn3", m, i, o))
resnet50.layer3._modules['3'].conv1.register_forward_hook(lambda m, i, o: hook_print("layer3_block3_conv1", m, i, o))
resnet50.layer3._modules['3'].conv2.register_forward_hook(lambda m, i, o: hook_print("layer3_block3_conv2", m, i, o))
resnet50.layer3._modules['3'].conv3.register_forward_hook(lambda m, i, o: hook_print("layer3_block3_conv3", m, i, o))
resnet50.layer3._modules['4'].bn1.register_forward_hook(lambda m, i, o: hook_print("layer3_block4_bn1", m, i, o))
resnet50.layer3._modules['4'].bn2.register_forward_hook(lambda m, i, o: hook_print("layer3_block4_bn2", m, i, o))
resnet50.layer3._modules['4'].bn3.register_forward_hook(lambda m, i, o: hook_print("layer3_block4_bn3", m, i, o))
resnet50.layer3._modules['4'].conv1.register_forward_hook(lambda m, i, o: hook_print("layer3_block4_conv1", m, i, o))
resnet50.layer3._modules['4'].conv2.register_forward_hook(lambda m, i, o: hook_print("layer3_block4_conv2", m, i, o))
resnet50.layer3._modules['4'].conv3.register_forward_hook(lambda m, i, o: hook_print("layer3_block4_conv3", m, i, o))
resnet50.layer3._modules['5'].bn1.register_forward_hook(lambda m, i, o: hook_print("layer3_block5_bn1", m, i, o))
resnet50.layer3._modules['5'].bn2.register_forward_hook(lambda m, i, o: hook_print("layer3_block5_bn2", m, i, o))
resnet50.layer3._modules['5'].bn3.register_forward_hook(lambda m, i, o: hook_print("layer3_block5_bn3", m, i, o))
resnet50.layer3._modules['5'].conv1.register_forward_hook(lambda m, i, o: hook_print("layer3_block5_conv1", m, i, o))
resnet50.layer3._modules['5'].conv2.register_forward_hook(lambda m, i, o: hook_print("layer3_block5_conv2", m, i, o))
resnet50.layer3._modules['5'].conv3.register_forward_hook(lambda m, i, o: hook_print("layer3_block5_conv3", m, i, o))
resnet50.layer4._modules['0'].bn1.register_forward_hook(lambda m, i, o: hook_print("layer4_block0_bn1", m, i, o))
resnet50.layer4._modules['0'].bn2.register_forward_hook(lambda m, i, o: hook_print("layer4_block0_bn2", m, i, o))
resnet50.layer4._modules['0'].bn3.register_forward_hook(lambda m, i, o: hook_print("layer4_block0_bn3", m, i, o))
resnet50.layer4._modules['0'].conv1.register_forward_hook(lambda m, i, o: hook_print("layer4_block0_conv1", m, i, o))
resnet50.layer4._modules['0'].conv2.register_forward_hook(lambda m, i, o: hook_print("layer4_block0_conv2", m, i, o))
resnet50.layer4._modules['0'].conv3.register_forward_hook(lambda m, i, o: hook_print("layer4_block0_conv3", m, i, o))
resnet50.layer4._modules['0'].downsample._modules['0'].register_forward_hook(lambda m, i, o: hook_print("layer4_block0_downsample_conv", m, i, o))
resnet50.layer4._modules['0'].downsample._modules['1'].register_forward_hook(lambda m, i, o: hook_print("layer4_block0_downsample_bn", m, i, o))
resnet50.layer4._modules['1'].bn1.register_forward_hook(lambda m, i, o: hook_print("layer4_block1_bn1", m, i, o))
resnet50.layer4._modules['1'].bn2.register_forward_hook(lambda m, i, o: hook_print("layer4_block1_bn2", m, i, o))
resnet50.layer4._modules['1'].bn3.register_forward_hook(lambda m, i, o: hook_print("layer4_block1_bn3", m, i, o))
resnet50.layer4._modules['1'].conv1.register_forward_hook(lambda m, i, o: hook_print("layer4_block1_conv1", m, i, o))
resnet50.layer4._modules['1'].conv2.register_forward_hook(lambda m, i, o: hook_print("layer4_block1_conv2", m, i, o))
resnet50.layer4._modules['1'].conv3.register_forward_hook(lambda m, i, o: hook_print("layer4_block1_conv3", m, i, o))
resnet50.layer4._modules['2'].bn1.register_forward_hook(lambda m, i, o: hook_print("layer4_block2_bn1", m, i, o))
resnet50.layer4._modules['2'].bn2.register_forward_hook(lambda m, i, o: hook_print("layer4_block2_bn2", m, i, o))
resnet50.layer4._modules['2'].bn3.register_forward_hook(lambda m, i, o: hook_print("layer4_block2_bn3", m, i, o))
resnet50.layer4._modules['2'].conv1.register_forward_hook(lambda m, i, o: hook_print("layer4_block2_conv1", m, i, o))
resnet50.layer4._modules['2'].conv2.register_forward_hook(lambda m, i, o: hook_print("layer4_block2_conv2", m, i, o))
resnet50.layer4._modules['2'].conv3.register_forward_hook(lambda m, i, o: hook_print("layer4_block2_conv3", m, i, o))
# for batch_idx, (data, target) in enumerate(val_loader):
# out = resnet50(data)
# break
weightfile.write(strg)
shapefile.write(strgShape)
binaryfile.close()
weightfile.close()
print("===========================")
print("============END============")
print("===========================")
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