68 lines
1.3 KiB
Python
68 lines
1.3 KiB
Python
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from __future__ import print_function
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import os
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import sys
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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import torchvision
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from torchvision import datasets, transforms
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import torchvision.models as models
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import matplotlib.pyplot as plt
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import numpy as np
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import struct
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from struct import Struct
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CurrentPath = os.path.split(os.path.realpath(__file__))[0]+"/"
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# resnet50 = models.resnet50(pretrained=True)
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# torch.save(resnet50, CurrentPath+'params.pth')
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resnet50 = torch.load(CurrentPath+'params.pth')
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resnet50.eval()
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weightfile = open(CurrentPath+'ResNet50Weight.cc', 'r')
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binaryfile = open(CurrentPath+'ResNet50Weight.bin', 'rb')
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bindata = binaryfile.read()
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def toNumpy(start, end):
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global bindata
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byte = []
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for i in range(start,end,4):
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byte.append(struct.unpack("f", bindata[i:i+4])[0])
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return np.array(byte)
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weight = toNumpy(38656,55039)
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inp = toNumpy(125573760,126376575)
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outp = toNumpy(126376576,127179391)
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m = resnet50.layer1._modules['0'].conv1
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weight = weight.reshape(m.weight.shape)
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inp = inp.reshape(1, 64, 56, 56,)
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outp = outp.reshape( 64, 56, 56,)
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m.weight.data = torch.from_numpy(weight)
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output = m(torch.from_numpy(inp))
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output = output.cpu().detach().numpy()
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print(output[0,0,:])
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print(outp[0,0,:])
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i = 0
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