# Input is (seq, batch, input)
x = torch.autograd.Variable(torch.randn(2, 1, 10)).cuda()
h0 = None
###
print('Testing WeightDrop')
print('=-=-=-=-=-=-=-=-=-=')
###
print('Testing WeightDrop with Linear')
lin = WeightDrop(torch.nn.Linear(10, 10), ['weight'], dropout=0.9)
lin.cuda()
run1 = [x.sum() for x in lin(x).data]
run2 = [x.sum() for x in lin(x).data]
print('All items should be different')
print('Run 1:', run1)
print('Run 2:', run2)
assert run1[0] != run2[0]
assert run1[1] != run2[1]
print('---')
###
print('Testing WeightDrop with LSTM')
# Input is (seq, batch, input)
x = torch.autograd.Variable(torch.randn(2, 1, 10)).cuda()
h0 = None
###
print('Testing WeightDrop')
print('=-=-=-=-=-=-=-=-=-=')
###
print('Testing WeightDrop with Linear')
lin = WeightDrop(torch.nn.Linear(10, 10), ['weight'], dropout=0.9)
lin.cuda()
run1 = [x.sum() for x in lin(x).data]
run2 = [x.sum() for x in lin(x).data]
print('All items should be different')
print('Run 1:', run1)
print('Run 2:', run2)
assert run1[0] != run2[0]
assert run1[1] != run2[1]
print('---')
###
print('Testing WeightDrop with LSTM')
# Input is (seq, batch, input)
x = torch.autograd.Variable(torch.randn(2, 1, 10)).cuda()
h0 = None
###
print('Testing WeightDrop')
print('=-=-=-=-=-=-=-=-=-=')
###
print('Testing WeightDrop with Linear')
lin = WeightDrop(torch.nn.Linear(10, 10), ['weight'], dropout=0.9)
lin.cuda()
run1 = [x.sum() for x in lin(x).data]
run2 = [x.sum() for x in lin(x).data]
print('All items should be different')
print('Run 1:', run1)
print('Run 2:', run2)
assert run1[0] != run2[0]
assert run1[1] != run2[1]
print('---')
###
print('Testing WeightDrop with LSTM')
