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Riccardo231Riccardo231
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added tests; to note that they don't work
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5 files changed

+169
-6
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5 files changed

+169
-6
lines changed

‎src/nf/nf_layer_submodule.f90‎

Lines changed: 4 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -340,6 +340,10 @@ impure elemental module subroutine init(self, input)
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self % layer_shape = shape(this_layer % output)
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type is(maxpool2d_layer)
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self % layer_shape = shape(this_layer % output)
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type is(locally_connected_1d_layer)
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self % layer_shape = shape(this_layer % output)
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type is(maxpool1d_layer)
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self % layer_shape = shape(this_layer % output)
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type is(flatten_layer)
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self % layer_shape = shape(this_layer % output)
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end select

‎src/nf/nf_network_submodule.f90‎

Lines changed: 9 additions & 6 deletions
Original file line numberDiff line numberDiff line change
@@ -73,18 +73,21 @@ module function network_from_layers(layers) result(res)
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type is(conv2d_layer)
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res % layers = [res % layers(:n-1), flatten(), res % layers(n:)]
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n = n + 1
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!type is(locally_connected_1d_layer)
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!res % layers = [res % layers(:n-1), flatten(), res % layers(n:)]
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!n = n + 1
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type is(maxpool2d_layer)
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res % layers = [res % layers(:n-1), flatten(), res % layers(n:)]
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n = n + 1
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type is(reshape3d_layer)
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res % layers = [res % layers(:n-1), flatten(), res % layers(n:)]
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n = n + 1
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type is(maxpool1d_layer)
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res % layers = [res % layers(:n-1), flatten(), res % layers(n:)]
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n = n + 1
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type is(reshape2d_layer)
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res % layers = [res % layers(:n-1), flatten(), res % layers(n:)]
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n = n + 1
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!type is(maxpool1d_layer)
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! res % layers = [res % layers(:n-1), flatten(), res % layers(n:)]
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! n = n + 1
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!type is(reshape2d_layer)
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! res % layers = [res % layers(:n-1), flatten(), res % layers(n:)]
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! n = n + 1
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class default
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n = n + 1
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end select

‎test/CMakeLists.txt‎

Lines changed: 2 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -5,10 +5,12 @@ foreach(execid
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parametric_activation
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dense_layer
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conv2d_layer
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maxpool1d_layer
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maxpool2d_layer
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flatten_layer
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insert_flatten
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reshape_layer
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reshape2d_layer
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dense_network
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get_set_network_params
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conv2d_network

‎test/test_maxpool1d_layer.f90‎

Lines changed: 100 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,100 @@
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program test_maxpool1d_layer
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use iso_fortran_env, only: stderr => error_unit
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use nf, only: maxpool1d, input, layer
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use nf_input2d_layer, only: input2d_layer
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use nf_maxpool1d_layer, only: maxpool1d_layer
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implicit none
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type(layer) :: maxpool_layer, input_layer
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integer, parameter :: pool_size = 2, stride = 2
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integer, parameter :: channels = 3, length = 32
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integer, parameter :: input_shape(2) = [channels, length]
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integer, parameter :: output_shape(2) = [channels, length / 2]
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real, allocatable :: sample_input(:,:), output(:,:), gradient(:,:)
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integer :: i
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logical :: ok = .true., gradient_ok = .true.
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maxpool_layer = maxpool1d(pool_size)
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if (.not. maxpool_layer % name == 'maxpool1d') then
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ok = .false.
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write(stderr, '(a)') 'maxpool1d layer has its name set correctly.. failed'
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end if
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if (maxpool_layer % initialized) then
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ok = .false.
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write(stderr, '(a)') 'maxpool1d layer should not be marked as initialized yet.. failed'
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end if
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input_layer = input(channels, length)
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call maxpool_layer % init(input_layer)
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if (.not. maxpool_layer % initialized) then
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ok = .false.
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write(stderr, '(a)') 'maxpool1d layer should now be marked as initialized.. failed'
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end if
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if (.not. all(maxpool_layer % input_layer_shape == input_shape)) then
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ok = .false.
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write(stderr, '(a)') 'maxpool1d layer input layer shape should be correct.. failed'
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end if
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if (.not. all(maxpool_layer % layer_shape == output_shape)) then
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ok = .false.
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write(stderr, '(a)') 'maxpool1d layer output layer shape should be correct.. failed'
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end if
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! Allocate and initialize sample input data
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allocate(sample_input(channels, length))
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do concurrent(i = 1:length)
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sample_input(:,i) = i
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end do
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select type(this_layer => input_layer % p); type is(input2d_layer)
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call this_layer % set(sample_input)
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end select
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call maxpool_layer % forward(input_layer)
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call maxpool_layer % get_output(output)
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do i = 1, length / 2
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! Since input is i, maxpool1d output must be stride*i
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if (.not. all(output(:,i) == stride * i)) then
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ok = .false.
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write(stderr, '(a)') 'maxpool1d layer forward pass correctly propagates the max value.. failed'
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end if
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end do
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! Test the backward pass
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! Allocate and initialize the downstream gradient field
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allocate(gradient, source=output)
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! Make a backward pass
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call maxpool_layer % backward(input_layer, gradient)
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select type(this_layer => maxpool_layer % p); type is(maxpool1d_layer)
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do i = 1, length
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if (mod(i,2) == 0) then
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if (.not. all(sample_input(:,i) == this_layer % gradient(:,i))) gradient_ok = .false.
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else
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if (.not. all(this_layer % gradient(:,i) == 0)) gradient_ok = .false.
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end if
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end do
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end select
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if (.not. gradient_ok) then
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ok = .false.
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write(stderr, '(a)') 'maxpool1d layer backward pass produces the correct dL/dx.. failed'
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end if
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if (ok) then
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print '(a)', 'test_maxpool1d_layer: All tests passed.'
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else
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write(stderr, '(a)') 'test_maxpool1d_layer: One or more tests failed.'
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stop 1
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end if
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end program test_maxpool1d_layer
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‎test/test_reshape2d_layer.f90‎

Lines changed: 54 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -0,0 +1,54 @@
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program test_reshape2d_layer
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use iso_fortran_env, only: stderr => error_unit
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use nf, only: input, network, reshape2d_layer => reshape2d
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use nf_datasets, only: download_and_unpack, keras_reshape_url
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implicit none
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type(network) :: net
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real, allocatable :: sample_input(:), output(:,:)
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integer, parameter :: output_shape(2) = [32, 32]
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integer, parameter :: input_size = product(output_shape)
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character(*), parameter :: keras_reshape_path = 'keras_reshape.h5'
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logical :: file_exists
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logical :: ok = .true.
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! Create the network
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net = network([ &
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input(input_size), &
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reshape2d_layer(output_shape) &
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])
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if (.not. size(net % layers) == 2) then
24+
write(stderr, '(a)') 'the network should have 2 layers.. failed'
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ok = .false.
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end if
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! Initialize test data
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allocate(sample_input(input_size))
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call random_number(sample_input)
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! Propagate forward and get the output
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call net % forward(sample_input)
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call net % layers(2) % get_output(output)
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if (.not. all(shape(output) == output_shape)) then
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write(stderr, '(a)') 'the reshape layer produces expected output shape.. failed'
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ok = .false.
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end if
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if (.not. all(reshape(sample_input, output_shape) == output)) then
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write(stderr, '(a)') 'the reshape layer produces expected output values.. failed'
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ok = .false.
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end if
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if (ok) then
47+
print '(a)', 'test_reshape2d_layer: All tests passed.'
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else
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write(stderr, '(a)') 'test_reshape2d_layer: One or more tests failed.'
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stop 1
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end if
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end program test_reshape2d_layer
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