我们从Python开源项目中，提取了以下14个代码示例，用于说明如何使用torch.nn.init.dirac()。
def dirac_delta(ni, no, k): n = min(ni, no) size = (n, n) + k repeats = (max(no // ni, 1), max(ni // no, 1)) + (1,) * len(k) return dirac(torch.Tensor(*size)).repeat(*repeats)
def dirac_delta(ni, no, k): n = min(ni, no) return dirac(torch.Tensor(n, n, k, k)).repeat(max(no // ni, 1), max(ni // no, 1), 1, 1)
def test_dirac_properties(self): for as_variable in [True, False]: for dims in [3, 4, 5]: input_tensor = self._create_random_nd_tensor(dims, size_min=1, size_max=5, as_variable=as_variable) init.dirac(input_tensor) if as_variable: input_tensor = input_tensor.data c_out, c_in = input_tensor.size(0), input_tensor.size(1) min_d = min(c_out, c_in) # Check number of nonzeros is equivalent to smallest dim assert torch.nonzero(input_tensor).size(0) == min_d # Check sum of values (can have precision issues, hence assertEqual) is also equivalent self.assertEqual(input_tensor.sum(), min_d)
def test_dirac_identity(self): batch, in_c, out_c, size, kernel_size = 8, 3, 4, 5, 3 # Test 1D input_var = Variable(torch.randn(batch, in_c, size)) filter_var = Variable(torch.zeros(out_c, in_c, kernel_size)) init.dirac(filter_var) output_var = F.conv1d(input_var, filter_var) input_tensor, output_tensor = input_var.data, output_var.data # Variables do not support nonzero self.assertEqual(input_tensor[:, :, 1:-1], output_tensor[:, :in_c, :]) # Assert in_c outputs are preserved assert torch.nonzero(output_tensor[:, in_c:, :]).numel() == 0 # Assert extra outputs are 0 # Test 2D input_var = Variable(torch.randn(batch, in_c, size, size)) filter_var = Variable(torch.zeros(out_c, in_c, kernel_size, kernel_size)) init.dirac(filter_var) output_var = F.conv2d(input_var, filter_var) input_tensor, output_tensor = input_var.data, output_var.data self.assertEqual(input_tensor[:, :, 1:-1, 1:-1], output_tensor[:, :in_c, :, :]) assert torch.nonzero(output_tensor[:, in_c:, :, :]).numel() == 0 # Test 3D input_var = Variable(torch.randn(batch, in_c, size, size, size)) filter_var = Variable(torch.zeros(out_c, in_c, kernel_size, kernel_size, kernel_size)) init.dirac(filter_var) output_var = F.conv3d(input_var, filter_var) input_tensor, output_tensor = input_var.data, output_var.data self.assertEqual(input_tensor[:, :, 1:-1, 1:-1, 1:-1], output_tensor[:, :in_c, :, :]) assert torch.nonzero(output_tensor[:, in_c:, :, :, :]).numel() == 0
def test_dirac_only_works_on_3_4_5d_inputs(self): for as_variable in [True, False]: for dims in [1, 2, 6]: with self.assertRaises(ValueError): tensor = self._create_random_nd_tensor(dims, size_min=1, size_max=3, as_variable=as_variable) init.dirac(tensor)
