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@pvdhove pvdhove/#readme.md
Last active Oct 9, 2018

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#!/usr/bin/env owl
open Owl
open Neural.S
open Neural.S.Graph
(* The code is heavily inspired by
* https://github.com/keras-team/keras-applications/blob/master/
* keras_applications/resnet50.py and
* https://github.com/matterport/Mask_RCNN/blob/master/mrcnn/model.py *)
let id_block input kernel_size filters stage block input_layer =
let suffix = string_of_int stage ^ block ^ "_branch" in
let conv_name = "res" ^ suffix in
let bn_name = "bn" ^ suffix in
let f1, f2, f3 = filters in
let x =
input_layer
|> conv2d [|1; 1; input; f1|] [|1; 1|] ~padding:VALID ~name:(conv_name^"2a")
|> normalisation ~axis:3 ~name:(bn_name^"2a")
|> activation Activation.Relu
|> conv2d [|kernel_size; kernel_size; f1; f2|] [|1; 1|]
~padding:SAME ~name:(conv_name^"2b")
|> normalisation ~axis:3 ~name:(bn_name^"2b")
|> activation Activation.Relu
|> conv2d [|1; 1; f2; f3|] [|1; 1|] ~padding:VALID ~name:(conv_name^"2c")
|> normalisation ~axis:3 ~name:(bn_name^"2c") in
add [|x; input_layer|]
|> activation Activation.Relu
let conv_block input kernel_size filters strides stage block input_layer =
let suffix = string_of_int stage ^ block ^ "_branch" in
let conv_name = "res" ^ suffix in
let bn_name = "bn" ^ suffix in
let f1, f2, f3 = filters in
let x =
input_layer
|> conv2d [|1; 1; input; f1|] strides ~padding:VALID ~name:(conv_name^"2a")
|> normalisation ~axis:3 ~name:(bn_name^"2a")
|> activation Activation.Relu
|> conv2d [|kernel_size; kernel_size; f1; f2|] [|1; 1|]
~padding:SAME ~name:(conv_name^"2b")
|> normalisation ~axis:3 ~name:(bn_name^"2b")
|> activation Activation.Relu
|> conv2d [|1; 1; f2; f3|] [|1; 1|] ~padding:VALID ~name:(conv_name^"2c")
|> normalisation ~axis:3 ~name:(bn_name^"2c") in
let shortcut =
input_layer
|> conv2d [|1; 1; input; f3|] strides ~name:(conv_name^"1")
|> normalisation ~axis:3 ~name:(bn_name^"1") in
add [|x; shortcut|]
|> activation Activation.Relu
let resnet50 img_size nb_classes =
input [|img_size; img_size; 3|]
|> padding2d [|[|3; 3|]; [|3; 3|]|] ~name:"conv1_pad"
|> conv2d [|7; 7; 3; 64|] [|2; 2|] ~padding:VALID ~name:"conv1"
|> normalisation ~axis:3 ~name:"bn_conv1"
|> activation Activation.Relu
|> max_pool2d [|3; 3|] [|2; 2|]
|> conv_block 64 3 (64, 64, 256) [|1; 1|] 2 "a"
|> id_block 256 3 (64, 64, 256) 2 "b"
|> id_block 256 3 (64, 64, 256) 2 "c"
|> conv_block 256 3 (128, 128, 512) [|2; 2|] 3 "a"
|> id_block 512 3 (128, 128, 512) 3 "b"
|> id_block 512 3 (128, 128, 512) 3 "c"
|> id_block 512 3 (128, 128, 512) 3 "d"
(* Here should be the change for ResNet101. *)
|> conv_block 512 3 (256, 256, 1024) [|2; 2|] 4 "a"
|> id_block 1024 3 (256, 256, 1024) 4 "b"
|> id_block 1024 3 (256, 256, 1024) 4 "c"
|> id_block 1024 3 (256, 256, 1024) 4 "d"
|> id_block 1024 3 (256, 256, 1024) 4 "e"
|> id_block 1024 3 (256, 256, 1024) 4 "f"
|> conv_block 1024 3 (512, 512, 2048) [|2; 2|] 5 "a"
|> id_block 2048 3 (512, 512, 2048) 5 "b"
|> id_block 2048 3 (512, 512, 2048) 5 "c"
|> global_avg_pool2d (* include_top *) ~name:"avg_pool"
|> linear ~act_typ:Activation.(Softmax 1) nb_classes ~name:"fc1000"
|> get_network
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