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witnn/CNNDemo/Resnet50.py

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add ResNet weight dump
2020-07-18 11:23:58 +08:00
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')
print("===========================")
print("===========================")
print("===========================")
print(resnet50)
print("===========================")
print("===========================")
print("===========================")
# ss = resnet50.conv1.weight.cpu().detach().numpy().reshape(-1)
# ss = ss.tolist()
# strs = ''
# # for s in ss:
# # strs += str(s) + ","
# bs = struct.pack("f",1.0)
# f = open('data.hex', 'wb')
# f.write(bs)
# f.close()
# print(strs)
# ssa = ss.array()
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",
}
}
}
}
},
"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')
binaryfile = open(CurrentPath+'ResNet50Weight.bin', 'wb')
currentbyte = 0
def printDick(d, head, obj):
global currentbyte
global binaryfile
strg = ""
for item in d:
if type(d[item]).__name__ == 'dict':
objsub = getattr(obj, item, '')
if objsub == '':
objsub = obj[item]
strg = strg + printDick(d[item], head+"_"+item, objsub)
else:
objsub = getattr(obj, item, '')
if objsub == '':
objsub = obj[item]
if d[item] == "Conv2d":
strg = strg + "int "+head+"_"+item+"_weight[]={"
array = objsub.weight.cpu().detach().numpy().reshape(-1)
strg += str(currentbyte) + ","
for a in array:
bs = struct.pack("f", a)
binaryfile.write(bs)
currentbyte = currentbyte+1
strg += str(currentbyte-1) + ","
strg = strg + "}\n"
if d[item] == "BatchNorm2d":
strg = strg + "int "+head+"_"+item+"_running_mean[]={"
array = objsub.running_mean.cpu().detach().numpy().reshape(-1)
strg += str(currentbyte) + ","
for a in array:
bs = struct.pack("f", a)
binaryfile.write(bs)
currentbyte = currentbyte+1
strg += str(currentbyte-1) + ","
strg = strg + "}\n"
strg = strg + "int "+head+"_"+item+"_running_var[]={"
array = objsub.running_var.cpu().detach().numpy().reshape(-1)
strg += str(currentbyte) + ","
for a in array:
bs = struct.pack("f", a)
binaryfile.write(bs)
currentbyte = currentbyte+1
strg += str(currentbyte-1) + ","
strg = strg + "}\n"
if d[item] == "Linear":
strg = strg + "int "+head+"_"+item+"_weight[]={"
array = objsub.weight.cpu().detach().numpy().reshape(-1)
strg += str(currentbyte) + ","
for a in array:
bs = struct.pack("f", a)
binaryfile.write(bs)
currentbyte = currentbyte+1
strg += str(currentbyte-1) + ","
strg = strg + "}\n"
strg = strg + "int "+head+"_"+item+"_bias[]={"
array = objsub.bias.cpu().detach().numpy().reshape(-1)
strg += str(currentbyte) + ","
for a in array:
bs = struct.pack("f", a)
binaryfile.write(bs)
currentbyte = currentbyte+1
strg += str(currentbyte-1) + ","
strg = strg + "}\n"
return strg
ss = printDick(ResNet50, "RN50", resnet50)
weightfile.write(ss)
binaryfile.close()
weightfile.close()
print(ss)
print("===========================")
print("===========================")
print("===========================")
# ResNet(
# (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
# (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (relu): ReLU(inplace=True)
# (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
# (layer1): Sequential(
# (0): Bottleneck(
# (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
# (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (relu): ReLU(inplace=True)
# (downsample): Sequential(
# (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# )
# )
# (1): Bottleneck(
# (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
# (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (relu): ReLU(inplace=True)
# )
# (2): Bottleneck(
# (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
# (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (relu): ReLU(inplace=True)
# )
# )
# (layer2): Sequential(
# (0): Bottleneck(
# (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
# (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (relu): ReLU(inplace=True)
# (downsample): Sequential(
# (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
# (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# )
# )
# (1): Bottleneck(
# (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
# (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (relu): ReLU(inplace=True)
# )
# (2): Bottleneck(
# (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
# (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (relu): ReLU(inplace=True)
# )
# (3): Bottleneck(
# (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
# (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (relu): ReLU(inplace=True)
# )
# )
# (layer3): Sequential(
# (0): Bottleneck(
# (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
# (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (relu): ReLU(inplace=True)
# (downsample): Sequential(
# (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)
# (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# )
# )
# (1): Bottleneck(
# (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
# (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (relu): ReLU(inplace=True)
# )
# (2): Bottleneck(
# (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
# (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (relu): ReLU(inplace=True)
# )
# (3): Bottleneck(
# (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
# (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (relu): ReLU(inplace=True)
# )
# (4): Bottleneck(
# (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
# (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (relu): ReLU(inplace=True)
# )
# (5): Bottleneck(
# (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
# (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (relu): ReLU(inplace=True)
# )
# )
# (layer4): Sequential(
# (0): Bottleneck(
# (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
# (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (relu): ReLU(inplace=True)
# (downsample): Sequential(
# (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)
# (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# )
# )
# (1): Bottleneck(
# (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
# (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (relu): ReLU(inplace=True)
# )
# (2): Bottleneck(
# (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
# (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)
# (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
# (relu): ReLU(inplace=True)
# )
# )
# (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
# (fc): Linear(in_features=2048, out_features=1000, bias=True)
# )