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Tensor library for Gleam/BEAM with a pure Gleam API, zero-copy views, and optional native acceleration
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src/viva_tensor@vision@augmentations.erl
-module(viva_tensor@vision@augmentations).
-compile([no_auto_import, nowarn_unused_vars, nowarn_unused_function, nowarn_nomatch, inline]).
-define(FILEPATH, "src/viva_tensor/vision/augmentations.gleam").
-export([color_jitter_init/4, color_jitter_forward/2, mixup/4, cutmix/4]).
-export_type([color_jitter_config/0]).
-if(?OTP_RELEASE >= 27).
-define(MODULEDOC(Str), -moduledoc(Str)).
-define(DOC(Str), -doc(Str)).
-else.
-define(MODULEDOC(Str), -compile([])).
-define(DOC(Str), -compile([])).
-endif.
?MODULEDOC(false).
-type color_jitter_config() :: {color_jitter_config,
float(),
float(),
float(),
float()}.
-file("src/viva_tensor/vision/augmentations.gleam", 43).
?DOC(false).
-spec color_jitter_init(float(), float(), float(), float()) -> color_jitter_config().
color_jitter_init(Brightness, Contrast, Saturation, Hue) ->
{color_jitter_config, Brightness, Contrast, Saturation, Hue}.
-file("src/viva_tensor/vision/augmentations.gleam", 525).
?DOC(false).
-spec uniform_in(float(), float()) -> float().
uniform_in(Lo, Hi) ->
Lo + ((Hi - Lo) * viva_tensor@core@ffi:random_uniform()).
-file("src/viva_tensor/vision/augmentations.gleam", 204).
?DOC(false).
-spec apply_hue(list(float()), list(float()), list(float()), float()) -> {list(float()),
list(float()),
list(float())}.
apply_hue(R, G, B, Strength) ->
case Strength =< +0.0 of
true ->
{R, G, B};
false ->
Theta = uniform_in(+0.0 - Strength, Strength),
C = viva_tensor@core@ffi:cos(Theta),
S = viva_tensor@core@ffi:sin(Theta),
One_minus_c_third = (1.0 - C) / 3.0,
Sqrt3 = viva_tensor@core@ffi:sqrt(3.0),
S_over_sqrt3 = case Sqrt3 of
+0.0 -> +0.0;
-0.0 -> -0.0;
Gleam@denominator -> S / Gleam@denominator
end,
Diag = C + One_minus_c_third,
Off1 = One_minus_c_third - S_over_sqrt3,
Off2 = One_minus_c_third + S_over_sqrt3,
Triples = gleam@list:zip(R, gleam@list:zip(G, B)),
Mixed = gleam@list:map(
Triples,
fun(T) ->
{Rv, {Gv, Bv}} = T,
Rn = ((Diag * Rv) + (Off1 * Gv)) + (Off2 * Bv),
Gn = ((Off2 * Rv) + (Diag * Gv)) + (Off1 * Bv),
Bn = ((Off1 * Rv) + (Off2 * Gv)) + (Diag * Bv),
{Rn, Gn, Bn}
end
),
R_out = gleam@list:map(
Mixed,
fun(T@1) -> erlang:element(1, T@1) end
),
G_out = gleam@list:map(
Mixed,
fun(T@2) -> erlang:element(2, T@2) end
),
B_out = gleam@list:map(
Mixed,
fun(T@3) -> erlang:element(3, T@3) end
),
{R_out, G_out, B_out}
end.
-file("src/viva_tensor/vision/augmentations.gleam", 177).
?DOC(false).
-spec apply_saturation(list(float()), list(float()), list(float()), float()) -> {list(float()),
list(float()),
list(float())}.
apply_saturation(R, G, B, Strength) ->
case Strength =< +0.0 of
true ->
{R, G, B};
false ->
S = uniform_in(1.0 - Strength, 1.0 + Strength),
Luma_for = gleam@list:map(
gleam@list:zip(R, gleam@list:zip(G, B)),
fun(Triple) ->
{Rv, {Gv, Bv}} = Triple,
((0.299 * Rv) + (0.587 * Gv)) + (0.114 * Bv)
end
),
Lerp_one = fun(Channel) ->
gleam@list:map(
gleam@list:zip(Channel, Luma_for),
fun(Pair) ->
{V, Y} = Pair,
Y + (S * (V - Y))
end
)
end,
{Lerp_one(R), Lerp_one(G), Lerp_one(B)}
end.
-file("src/viva_tensor/vision/augmentations.gleam", 152).
?DOC(false).
-spec apply_contrast(list(float()), list(float()), list(float()), float()) -> {list(float()),
list(float()),
list(float())}.
apply_contrast(R, G, B, Strength) ->
case Strength =< +0.0 of
true ->
{R, G, B};
false ->
Factor = uniform_in(1.0 - Strength, 1.0 + Strength),
N = erlang:float(erlang:length(R) * 3),
Total = (gleam@list:fold(R, +0.0, fun(Acc, V) -> Acc + V end) + gleam@list:fold(
G,
+0.0,
fun(Acc@1, V@1) -> Acc@1 + V@1 end
))
+ gleam@list:fold(B, +0.0, fun(Acc@2, V@2) -> Acc@2 + V@2 end),
Mean = case N > +0.0 of
true ->
case N of
+0.0 -> +0.0;
-0.0 -> -0.0;
Gleam@denominator -> Total / Gleam@denominator
end;
false ->
+0.0
end,
Shift = fun(V@3) -> ((V@3 - Mean) * Factor) + Mean end,
{gleam@list:map(R, Shift),
gleam@list:map(G, Shift),
gleam@list:map(B, Shift)}
end.
-file("src/viva_tensor/vision/augmentations.gleam", 133).
?DOC(false).
-spec apply_brightness(list(float()), list(float()), list(float()), float()) -> {list(float()),
list(float()),
list(float())}.
apply_brightness(R, G, B, Strength) ->
case Strength =< +0.0 of
true ->
{R, G, B};
false ->
Factor = uniform_in(1.0 - Strength, 1.0 + Strength),
{gleam@list:map(R, fun(V) -> V * Factor end),
gleam@list:map(G, fun(V@1) -> V@1 * Factor end),
gleam@list:map(B, fun(V@2) -> V@2 * Factor end)}
end.
-file("src/viva_tensor/vision/augmentations.gleam", 458).
?DOC(false).
-spec slice(list(float()), integer(), integer()) -> list(float()).
slice(Data, Start, Length) ->
_pipe = Data,
_pipe@1 = gleam@list:drop(_pipe, Start),
gleam@list:take(_pipe@1, Length).
-file("src/viva_tensor/vision/augmentations.gleam", 664).
?DOC(false).
-spec range_loop(integer(), integer(), list(integer())) -> list(integer()).
range_loop(From, To, Acc) ->
case From > To of
true ->
lists:reverse(Acc);
false ->
range_loop(From + 1, To, [From | Acc])
end.
-file("src/viva_tensor/vision/augmentations.gleam", 660).
?DOC(false).
-spec range_int(integer(), integer()) -> list(integer()).
range_int(From, To) ->
range_loop(From, To, []).
-file("src/viva_tensor/vision/augmentations.gleam", 393).
?DOC(false).
-spec parse_image_shape(binary(), list(integer())) -> {ok,
{integer(), integer(), integer(), integer()}} |
{error, viva_tensor@core@error:tensor_error()}.
parse_image_shape(Op, Shape) ->
case Shape of
[B, C, H, W] ->
{ok, {B, C, H, W}};
[C@1, H@1, W@1] ->
{ok, {1, C@1, H@1, W@1}};
_ ->
{error,
{operand_shape_mismatch,
Op,
<<"image"/utf8>>,
<<"[C, H, W] or [B, C, H, W]"/utf8>>,
Shape}}
end.
-file("src/viva_tensor/vision/augmentations.gleam", 79).
?DOC(false).
-spec color_jitter_forward(color_jitter_config(), viva_tensor@tensor:tensor()) -> {ok,
viva_tensor@tensor:tensor()} |
{error, viva_tensor@core@error:tensor_error()}.
color_jitter_forward(Config, Image) ->
Shape = viva_tensor@tensor:shape(Image),
gleam@result:'try'(
parse_image_shape(<<"color_jitter_forward"/utf8>>, Shape),
fun(_use0) ->
{Batch, Channels, Height, Width} = _use0,
case Channels =:= 3 of
true ->
Data = viva_tensor@tensor:to_list(Image),
Plane = Height * Width,
Stride = Channels * Plane,
Total_strength = ((gleam@float:absolute_value(
erlang:element(2, Config)
)
+ gleam@float:absolute_value(erlang:element(3, Config)))
+ gleam@float:absolute_value(erlang:element(4, Config)))
+ gleam@float:absolute_value(erlang:element(5, Config)),
case Total_strength =< +0.0 of
true ->
{ok, Image};
false ->
Processed = begin
_pipe = range_int(0, Batch - 1),
gleam@list:flat_map(
_pipe,
fun(B) ->
Start = B * Stride,
R = slice(Data, Start, Plane),
G = slice(Data, Start + Plane, Plane),
B_ch = slice(
Data,
Start + (2 * Plane),
Plane
),
{R1, G1, B1} = apply_brightness(
R,
G,
B_ch,
erlang:element(2, Config)
),
{R2, G2, B2} = apply_contrast(
R1,
G1,
B1,
erlang:element(3, Config)
),
{R3, G3, B3} = apply_saturation(
R2,
G2,
B2,
erlang:element(4, Config)
),
{R4, G4, B4} = apply_hue(
R3,
G3,
B3,
erlang:element(5, Config)
),
lists:append(R4, lists:append(G4, B4))
end
)
end,
{ok, {tensor, Processed, Shape}}
end;
false ->
{error,
{operand_shape_mismatch,
<<"color_jitter_forward"/utf8>>,
<<"image"/utf8>>,
<<"channel dim = 3 (RGB)"/utf8>>,
Shape}}
end
end
).
-file("src/viva_tensor/vision/augmentations.gleam", 462).
?DOC(false).
-spec mix_batched(list(float()), list(integer()), integer(), float()) -> list(float()).
mix_batched(Data, Perm, Per_sample, Lambda) ->
_pipe = gleam@list:index_map(
Perm,
fun(P, I) ->
A = slice(Data, I * Per_sample, Per_sample),
B = slice(Data, P * Per_sample, Per_sample),
gleam@list:map(
gleam@list:zip(A, B),
fun(Pair) ->
{Va, Vb} = Pair,
(Lambda * Va) + ((1.0 - Lambda) * Vb)
end
)
end
),
lists:append(_pipe).
-file("src/viva_tensor/vision/augmentations.gleam", 454).
?DOC(false).
-spec element_count(list(integer())) -> integer().
element_count(Shape) ->
gleam@list:fold(Shape, 1, fun(Acc, Dim) -> Acc * Dim end).
-file("src/viva_tensor/vision/augmentations.gleam", 563).
?DOC(false).
-spec list_get(list(integer()), integer(), integer()) -> integer().
list_get(Xs, I, Default) ->
case gleam@list:drop(Xs, I) of
[V | _] ->
V;
[] ->
Default
end.
-file("src/viva_tensor/vision/augmentations.gleam", 546).
?DOC(false).
-spec swap_at(list(integer()), integer(), integer()) -> list(integer()).
swap_at(Xs, I, J) ->
case I =:= J of
true ->
Xs;
false ->
Xi = list_get(Xs, I, 0),
Xj = list_get(Xs, J, 0),
gleam@list:index_map(Xs, fun(V, K) -> case {K =:= I, K =:= J} of
{true, _} ->
Xj;
{_, true} ->
Xi;
{_, _} ->
V
end end)
end.
-file("src/viva_tensor/vision/augmentations.gleam", 535).
?DOC(false).
-spec fisher_yates(list(integer()), integer()) -> list(integer()).
fisher_yates(Xs, I) ->
case I =< 0 of
true ->
Xs;
false ->
J = gleam@int:random(I + 1),
Swapped = swap_at(Xs, I, J),
fisher_yates(Swapped, I - 1)
end.
-file("src/viva_tensor/vision/augmentations.gleam", 529).
?DOC(false).
-spec random_permutation(integer()) -> list(integer()).
random_permutation(N) ->
Initial = range_int(0, N - 1),
fisher_yates(Initial, N - 1).
-file("src/viva_tensor/vision/augmentations.gleam", 638).
?DOC(false).
-spec standard_normal() -> float().
standard_normal() ->
U1 = gleam@float:max(viva_tensor@core@ffi:random_uniform(), 1.0e-12),
U2 = viva_tensor@core@ffi:random_uniform(),
viva_tensor@core@ffi:sqrt(-2.0 * viva_tensor@core@ffi:log(U1)) * viva_tensor@core@ffi:cos(
(2.0 * 3.14159265358979323846) * U2
).
-file("src/viva_tensor/vision/augmentations.gleam", 614).
?DOC(false).
-spec marsaglia_loop(float(), float(), integer()) -> float().
marsaglia_loop(D, C, Attempts) ->
case Attempts >= 100 of
true ->
D;
false ->
X = standard_normal(),
V_base = 1.0 + (C * X),
case V_base =< +0.0 of
true ->
marsaglia_loop(D, C, Attempts + 1);
false ->
V = (V_base * V_base) * V_base,
U = gleam@float:max(
viva_tensor@core@ffi:random_uniform(),
1.0e-12
),
Lhs = U,
Rhs = viva_tensor@core@ffi:exp(
((((0.5 * X) * X) + D) - (D * V)) + (D * viva_tensor@core@ffi:log(
V
))
),
case Lhs < Rhs of
true ->
D * V;
false ->
marsaglia_loop(D, C, Attempts + 1)
end
end
end.
-file("src/viva_tensor/vision/augmentations.gleam", 608).
?DOC(false).
-spec marsaglia_tsang(float()) -> float().
marsaglia_tsang(Shape) ->
D = Shape - (1.0 / 3.0),
C = case viva_tensor@core@ffi:sqrt(9.0 * D) of
+0.0 -> +0.0;
-0.0 -> -0.0;
Gleam@denominator -> 1.0 / Gleam@denominator
end,
marsaglia_loop(D, C, 0).
-file("src/viva_tensor/vision/augmentations.gleam", 595).
?DOC(false).
-spec sample_gamma(float()) -> float().
sample_gamma(Shape) ->
case Shape < 1.0 of
true ->
Boosted = marsaglia_tsang(Shape + 1.0),
U = gleam@float:max(viva_tensor@core@ffi:random_uniform(), 1.0e-12),
Boosted * viva_tensor@core@ffi:pow(U, case Shape of
+0.0 -> +0.0;
-0.0 -> -0.0;
Gleam@denominator -> 1.0 / Gleam@denominator
end);
false ->
marsaglia_tsang(Shape)
end.
-file("src/viva_tensor/vision/augmentations.gleam", 580).
?DOC(false).
-spec sample_beta(float(), float()) -> float().
sample_beta(A, B) ->
case (A =< +0.0) orelse (B =< +0.0) of
true ->
1.0;
false ->
X = sample_gamma(A),
Y = sample_gamma(B),
Total = X + Y,
case Total =< +0.0 of
true ->
0.5;
false ->
case Total of
+0.0 -> +0.0;
-0.0 -> -0.0;
Gleam@denominator -> X / Gleam@denominator
end
end
end.
-file("src/viva_tensor/vision/augmentations.gleam", 441).
?DOC(false).
-spec one_hot_from_indices(list(float()), integer()) -> list(float()).
one_hot_from_indices(Indices, Num_classes) ->
gleam@list:flat_map(
Indices,
fun(Idx_f) ->
Idx = erlang:round(Idx_f),
_pipe = range_int(0, Num_classes - 1),
gleam@list:map(_pipe, fun(K) -> case K =:= Idx of
true ->
1.0;
false ->
+0.0
end end)
end
).
-file("src/viva_tensor/vision/augmentations.gleam", 410).
?DOC(false).
-spec normalize_labels(
binary(),
viva_tensor@tensor:tensor(),
integer(),
integer()
) -> {ok, list(float())} | {error, viva_tensor@core@error:tensor_error()}.
normalize_labels(Op, Labels, Batch, Num_classes) ->
case Num_classes =< 0 of
true ->
{error,
{invalid_shape,
<<<<Op/binary, ": num_classes must be positive, got "/utf8>>/binary,
(erlang:integer_to_binary(Num_classes))/binary>>}};
false ->
Label_shape = viva_tensor@tensor:shape(Labels),
Label_data = viva_tensor@tensor:to_list(Labels),
case Label_shape of
[B] when B =:= Batch ->
{ok, one_hot_from_indices(Label_data, Num_classes)};
[B@1, K] when (B@1 =:= Batch) andalso (K =:= Num_classes) ->
{ok, Label_data};
_ ->
{error,
{operand_shape_mismatch,
Op,
<<"labels"/utf8>>,
<<"[B] indices or [B, num_classes] one-hot"/utf8>>,
Label_shape}}
end
end.
-file("src/viva_tensor/vision/augmentations.gleam", 267).
?DOC(false).
-spec mixup(
viva_tensor@tensor:tensor(),
viva_tensor@tensor:tensor(),
integer(),
float()
) -> {ok, {viva_tensor@tensor:tensor(), viva_tensor@tensor:tensor()}} |
{error, viva_tensor@core@error:tensor_error()}.
mixup(Images, Labels, Num_classes, Alpha) ->
Image_shape = viva_tensor@tensor:shape(Images),
gleam@result:'try'(
parse_image_shape(<<"mixup"/utf8>>, Image_shape),
fun(_use0) ->
{Batch, _, _, _} = _use0,
gleam@result:'try'(
normalize_labels(<<"mixup"/utf8>>, Labels, Batch, Num_classes),
fun(Label_matrix) ->
Lambda = sample_beta(Alpha, Alpha),
Perm = random_permutation(Batch),
Image_data = viva_tensor@tensor:to_list(Images),
Per_image_size = case Batch of
0 -> 0;
Gleam@denominator -> element_count(Image_shape) div Gleam@denominator
end,
Mixed_image_data = mix_batched(
Image_data,
Perm,
Per_image_size,
Lambda
),
Mixed_label_data = mix_batched(
Label_matrix,
Perm,
Num_classes,
Lambda
),
{ok,
{{tensor, Mixed_image_data, Image_shape},
{tensor, Mixed_label_data, [Batch, Num_classes]}}}
end
)
end
).
-file("src/viva_tensor/vision/augmentations.gleam", 479).
?DOC(false).
-spec paste_box(
list(float()),
list(integer()),
integer(),
integer(),
integer(),
integer(),
integer(),
integer(),
integer(),
integer()
) -> list(float()).
paste_box(Data, Perm, Batch, Channels, Height, Width, X1, X2, Y1, Y2) ->
Plane = Height * Width,
Stride = Channels * Plane,
_pipe = range_int(0, Batch - 1),
gleam@list:flat_map(
_pipe,
fun(I) ->
P = case gleam@list:drop(Perm, I) of
[Head | _] ->
Head;
[] ->
I
end,
_pipe@1 = range_int(0, Channels - 1),
gleam@list:flat_map(
_pipe@1,
fun(C) ->
Dst_base = (I * Stride) + (C * Plane),
Src_base = (P * Stride) + (C * Plane),
_pipe@2 = range_int(0, Height - 1),
gleam@list:flat_map(
_pipe@2,
fun(Y) -> _pipe@3 = range_int(0, Width - 1),
gleam@list:map(
_pipe@3,
fun(X) ->
Pick_src = (((Y >= Y1) andalso (Y < Y2))
andalso (X >= X1))
andalso (X < X2),
Offset = (Y * Width) + X,
Base = case Pick_src of
true ->
Src_base;
false ->
Dst_base
end,
case gleam@list:drop(Data, Base + Offset) of
[V | _] ->
V;
[] ->
+0.0
end
end
) end
)
end
)
end
).
-file("src/viva_tensor/vision/augmentations.gleam", 645).
?DOC(false).
-spec int_clamp(integer(), integer(), integer()) -> integer().
int_clamp(Value, Lo, Hi) ->
case Value < Lo of
true ->
Lo;
false ->
case Value > Hi of
true ->
Hi;
false ->
Value
end
end.
-file("src/viva_tensor/vision/augmentations.gleam", 656).
?DOC(false).
-spec float_to_round(float()) -> integer().
float_to_round(Value) ->
erlang:round(Value).
-file("src/viva_tensor/vision/augmentations.gleam", 322).
?DOC(false).
-spec cutmix(
viva_tensor@tensor:tensor(),
viva_tensor@tensor:tensor(),
integer(),
float()
) -> {ok, {viva_tensor@tensor:tensor(), viva_tensor@tensor:tensor()}} |
{error, viva_tensor@core@error:tensor_error()}.
cutmix(Images, Labels, Num_classes, Alpha) ->
Image_shape = viva_tensor@tensor:shape(Images),
gleam@result:'try'(
parse_image_shape(<<"cutmix"/utf8>>, Image_shape),
fun(_use0) ->
{Batch, Channels, Height, Width} = _use0,
gleam@result:'try'(
normalize_labels(<<"cutmix"/utf8>>, Labels, Batch, Num_classes),
fun(Label_matrix) ->
Lambda_initial = sample_beta(Alpha, Alpha),
Cut_ratio = viva_tensor@core@ffi:sqrt(
gleam@float:max(+0.0, 1.0 - Lambda_initial)
),
Cut_w = float_to_round(erlang:float(Width) * Cut_ratio),
Cut_h = float_to_round(erlang:float(Height) * Cut_ratio),
Rx = case Width =< 0 of
true ->
0;
false ->
gleam@int:random(Width)
end,
Ry = case Height =< 0 of
true ->
0;
false ->
gleam@int:random(Height)
end,
X1 = int_clamp(Rx - (Cut_w div 2), 0, Width),
X2 = int_clamp(Rx + (Cut_w div 2), 0, Width),
Y1 = int_clamp(Ry - (Cut_h div 2), 0, Height),
Y2 = int_clamp(Ry + (Cut_h div 2), 0, Height),
Box_w = X2 - X1,
Box_h = Y2 - Y1,
Lambda = case (Width * Height) =< 0 of
true ->
1.0;
false ->
1.0 - (case erlang:float(Width * Height) of
+0.0 -> +0.0;
-0.0 -> -0.0;
Gleam@denominator -> erlang:float(Box_w * Box_h)
/ Gleam@denominator
end)
end,
Perm = random_permutation(Batch),
Image_data = viva_tensor@tensor:to_list(Images),
Mixed_image_data = case (Box_w =:= 0) orelse (Box_h =:= 0) of
true ->
Image_data;
false ->
paste_box(
Image_data,
Perm,
Batch,
Channels,
Height,
Width,
X1,
X2,
Y1,
Y2
)
end,
Mixed_label_data = mix_batched(
Label_matrix,
Perm,
Num_classes,
Lambda
),
{ok,
{{tensor, Mixed_image_data, Image_shape},
{tensor, Mixed_label_data, [Batch, Num_classes]}}}
end
)
end
).