package torch
Torch bindings for OCaml
Install
Dune Dependency
Authors
Maintainers
Sources
torch-v0.16.0.tar.gz
sha256=ccd9ef3b630bdc7c41e363e71d8ecb86c316460cbf79afe67546c6ff22c19da4
doc/src/torch.vision/densenet.ml.html
Source file densenet.ml
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open Base open Torch let sub = Var_store.sub let conv2d ?(stride = 1) ?(padding = 0) = Layer.conv2d_ ~stride ~padding ~use_bias:false let dense_layer vs ~bn_size ~growth_rate ~input_dim = let inter_dim = bn_size * growth_rate in let bn1 = Layer.batch_norm2d (sub vs "norm1") input_dim in let conv1 = conv2d (sub vs "conv1") ~ksize:1 ~input_dim inter_dim in let bn2 = Layer.batch_norm2d (sub vs "norm2") inter_dim in let conv2 = conv2d (sub vs "conv2") ~ksize:3 ~padding:1 ~input_dim:inter_dim growth_rate in Layer.of_fn_ (fun xs ~is_training -> Layer.forward_ bn1 xs ~is_training |> Tensor.relu |> Layer.forward conv1 |> Layer.forward_ bn2 ~is_training |> Tensor.relu |> Layer.forward conv2 |> fun ys -> Tensor.concat [ xs; ys ] ~dim:1) ;; let dense_block vs ~bn_size ~growth_rate ~num_layers ~input_dim = List.init num_layers ~f:(fun i -> let vs = sub vs (Printf.sprintf "denselayer%d" (1 + i)) in dense_layer vs ~bn_size ~growth_rate ~input_dim:(input_dim + (i * growth_rate))) |> Layer.sequential_ ;; let transition vs ~input_dim output_dim = let bn = Layer.batch_norm2d (sub vs "norm") input_dim in let conv = conv2d (sub vs "conv") ~ksize:1 ~input_dim output_dim in Layer.of_fn_ (fun xs ~is_training -> Layer.forward_ bn xs ~is_training |> Tensor.relu |> Layer.forward conv |> Tensor.avg_pool2d ~stride:(2, 2) ~ksize:(2, 2)) ;; let densenet vs ~growth_rate ~block_config ~init_dim ~bn_size ~num_classes = let features_vs = sub vs "features" in let conv0 = conv2d (sub features_vs "conv0") ~ksize:7 ~stride:2 ~padding:3 ~input_dim:3 init_dim in let bn0 = Layer.batch_norm2d (sub features_vs "norm0") init_dim in let num_features, layers = let last_index = List.length block_config - 1 in List.foldi block_config ~init:(init_dim, Layer.id_) ~f:(fun i (num_features, acc) num_layers -> let block = dense_block (Printf.sprintf "denseblock%d" (1 + i) |> sub features_vs) ~bn_size ~growth_rate ~num_layers ~input_dim:num_features in let num_features = num_features + (num_layers * growth_rate) in if i <> last_index then ( let trans = transition (Printf.sprintf "transition%d" (1 + i) |> sub features_vs) ~input_dim:num_features (num_features / 2) in num_features / 2, Layer.sequential_ [ acc; block; trans ]) else num_features, Layer.sequential_ [ acc; block ]) in let bn5 = Layer.batch_norm2d (sub features_vs "norm5") num_features in let linear = Layer.linear (sub vs "classifier") ~input_dim:num_features num_classes in Layer.of_fn_ (fun xs ~is_training -> Layer.forward conv0 xs |> Layer.forward_ bn0 ~is_training |> Tensor.relu |> Tensor.max_pool2d ~padding:(1, 1) ~stride:(2, 2) ~ksize:(3, 3) |> Layer.forward_ layers ~is_training |> Layer.forward_ bn5 ~is_training |> fun features -> Tensor.relu features |> Tensor.avg_pool2d ~stride:(1, 1) ~ksize:(7, 7) |> Tensor.view ~size:[ Tensor.shape features |> List.hd_exn; -1 ] |> Layer.forward linear) ;; let densenet121 vs = densenet vs ~growth_rate:32 ~init_dim:64 ~block_config:[ 6; 12; 24; 16 ] ~bn_size:4 ;; let densenet161 vs = densenet vs ~growth_rate:48 ~init_dim:96 ~block_config:[ 6; 12; 36; 24 ] ~bn_size:4 ;; let densenet169 vs = densenet vs ~growth_rate:32 ~init_dim:64 ~block_config:[ 6; 12; 32; 32 ] ~bn_size:4 ;; let densenet201 vs = densenet vs ~growth_rate:32 ~init_dim:64 ~block_config:[ 6; 12; 48; 32 ] ~bn_size:4 ;;
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