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bn.py
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bn.py
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import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
from pytorch2keras.converter import pytorch_to_keras
class TestConv2d(nn.Module):
"""Module for BatchNorm2d conversion testing
"""
def __init__(self, inp=10, out=16, kernel_size=3, bias=True):
super(TestConv2d, self).__init__()
self.conv2d = nn.Conv2d(inp, out, kernel_size=kernel_size, bias=bias)
self.bn = nn.BatchNorm2d(out)
def forward(self, x):
x = self.conv2d(x)
x = self.bn(x)
return x
if __name__ == '__main__':
max_error = 0
for i in range(100):
kernel_size = np.random.randint(1, 7)
inp = np.random.randint(kernel_size + 1, 100)
out = np.random.randint(1, 100)
model = TestConv2d(inp, out, kernel_size, inp % 2)
for m in model.modules():
m.training = False
input_np = np.random.uniform(0, 1, (1, inp, inp, inp))
input_var = Variable(torch.FloatTensor(input_np))
output = model(input_var)
k_model = pytorch_to_keras(model, input_var, (inp, inp, inp,), verbose=True)
pytorch_output = output.data.numpy()
keras_output = k_model.predict(input_np)
error = np.max(pytorch_output - keras_output)
print(error)
if max_error < error:
max_error = error
print('Max error: {0}'.format(max_error))