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()






weightfile = open(CurrentPath+'ResNet50Weight.cc', 'r')
binaryfile = open(CurrentPath+'ResNet50Weight.bin', 'rb')
bindata = binaryfile.read()



def toNumpy(start, end):
  global bindata
  byte = []
  for i in range(start,end,4):
    byte.append(struct.unpack("f", bindata[i:i+4])[0])
  return np.array(byte)







weight = toNumpy(38656,55039)
inp = toNumpy(125573760,126376575)
outp = toNumpy(126376576,127179391)

m = resnet50.layer1._modules['0'].conv1

weight = weight.reshape(m.weight.shape)
inp = inp.reshape(1, 64, 56, 56,)
outp = outp.reshape( 64, 56, 56,)


m.weight.data = torch.from_numpy(weight)
output = m(torch.from_numpy(inp))

output = output.cpu().detach().numpy()
print(output[0,0,:])
print(outp[0,0,:])
i = 0