Fifth refactor is a charm

Also, we aren't using the skips anymore

src/convnets/resnets/core.jl

@@ -1,9 +1,8 @@
 """
-    basicblock(inplanes, planes; stride = 1, downsample = identity,
-               reduction_factor = 1, dilation = 1, first_dilation = dilation,
-               activation = relu, connection = addact\$activation,
-               norm_layer = BatchNorm, drop_block = identity, drop_path = identity,
-               attn_fn = planes -> identity)
+    basicblock(inplanes, planes; stride = 1, reduction_factor = 1, activation = relu,
+                    norm_layer = BatchNorm, prenorm = false,
+                    drop_block = identity, drop_path = identity,
+                    attn_fn = planes -> identity)
 
 Creates a basic ResNet block.
 
@@ -12,24 +11,19 @@ Creates a basic ResNet block.
   - `inplanes`: number of input feature maps
   - `planes`: number of feature maps for the block
   - `stride`: the stride of the block
-  - `downsample`: the downsampling function to use
   - `reduction_factor`: the reduction factor that the input feature maps are reduced by before the first
     convolution.
-  - `dilation`: the dilation of the second convolution.
-  - `first_dilation`: the dilation of the first convolution.
   - `activation`: the activation function to use.
-  - `connection`: the function applied to the output of residual and skip paths in
-    a block. See [`addact`](#) and [`actadd`](#) for an example. Note that this uses
-    PartialFunctions.jl to pass in the activation function with the notation `addact\$activation`.
   - `norm_layer`: the normalization layer to use.
   - `drop_block`: the drop block layer. This is usually initialised in the `_make_blocks`
     function and passed in.
   - `drop_path`: the drop path layer. This is usually initialised in the `_make_blocks`
     function and passed in.
   - `attn_fn`: the attention function to use. See [`squeeze_excite`](#) for an example.
 """
-function basicblock(inplanes, planes; stride = 1, reduction_factor = 1, activation = relu,
-                    norm_layer = BatchNorm, prenorm = false,
+function basicblock(inplanes::Integer, planes::Integer; stride::Integer = 1,
+                    reduction_factor::Integer = 1, activation = relu,
+                    norm_layer = BatchNorm, prenorm::Bool = false,
                     drop_block = identity, drop_path = identity,
                     attn_fn = planes -> identity)
     first_planes = planes ÷ reduction_factor
@@ -45,11 +39,11 @@ end
 expansion_factor(::typeof(basicblock)) = 1
 
 """
-    bottleneck(inplanes, planes; stride = 1, downsample = identity, cardinality = 1,
-               base_width = 64, reduction_factor = 1, first_dilation = 1,
-               activation = relu, connection = addact\$activation,
-               norm_layer = BatchNorm, drop_block = identity, drop_path = identity,
-               attn_fn = planes -> identity)
+    bottleneck(inplanes, planes; stride = 1, cardinality = 1, base_width = 64,
+                    reduction_factor = 1, activation = relu,
+                    norm_layer = BatchNorm, prenorm = false,
+                    drop_block = identity, drop_path = identity,
+                    attn_fn = planes -> identity)
 
 Creates a bottleneck ResNet block.
 
@@ -58,26 +52,22 @@ Creates a bottleneck ResNet block.
   - `inplanes`: number of input feature maps
   - `planes`: number of feature maps for the block
   - `stride`: the stride of the block
-  - `downsample`: the downsampling function to use
   - `cardinality`: the number of groups in the convolution.
   - `base_width`: the number of output feature maps for each convolutional group.
   - `reduction_factor`: the reduction factor that the input feature maps are reduced by before the first
     convolution.
-  - `first_dilation`: the dilation of the 3x3 convolution.
   - `activation`: the activation function to use.
-  - `connection`: the function applied to the output of residual and skip paths in
-    a block. See [`addact`](#) and [`actadd`](#) for an example. Note that this uses
-    PartialFunctions.jl to pass in the activation function with the notation `addact\$activation`.
   - `norm_layer`: the normalization layer to use.
   - `drop_block`: the drop block layer. This is usually initialised in the `_make_blocks`
     function and passed in.
   - `drop_path`: the drop path layer. This is usually initialised in the `_make_blocks`
     function and passed in.
   - `attn_fn`: the attention function to use. See [`squeeze_excite`](#) for an example.
 """
-function bottleneck(inplanes, planes; stride = 1, cardinality = 1, base_width = 64,
-                    reduction_factor = 1, activation = relu,
-                    norm_layer = BatchNorm, prenorm = false,
+function bottleneck(inplanes::Integer, planes::Integer; stride::Integer,
+                    cardinality::Integer = 1, base_width::Integer = 64,
+                    reduction_factor::Integer = 1, activation = relu,
+                    norm_layer = BatchNorm, prenorm::Bool = false,
                     drop_block = identity, drop_path = identity,
                     attn_fn = planes -> identity)
     width = floor(Int, planes * (base_width / 64)) * cardinality
@@ -113,6 +103,7 @@ end
 
 # Downsample layer which is an identity projection. Uses max pooling
 # when the output size is more than the input size.
+# TODO - figure out how to make this work when outplanes < inplanes
 function downsample_identity(inplanes::Integer, outplanes::Integer; kwargs...)
     if outplanes > inplanes
         return Chain(MaxPool((1, 1); stride = 2),
@@ -174,8 +165,8 @@ on how to use this function.
   - `activation`: The activation function used in the stem.
 """
 function resnet_stem(stem_type::Symbol = :default; inchannels::Integer = 3,
-                     replace_pool::Bool = false, norm_layer = BatchNorm, prenorm = false,
-                     activation = relu)
+                     replace_pool::Bool = false, activation = relu,
+                     norm_layer = BatchNorm, prenorm::Bool = false)
     @assert stem_type in [:default, :deep, :deep_tiered]
     "Stem type must be one of [:default, :deep, :deep_tiered]"
     # Main stem
@@ -203,65 +194,70 @@ function resnet_stem(stem_type::Symbol = :default; inchannels::Integer = 3,
                                prenorm,
                                stride = 2, pad = 1, bias = false)...) :
                MaxPool((3, 3); stride = 2, pad = 1)
-    return Chain(conv1, bn1, stempool), inplanes
-end
-
-# Templating builders for the blocks and the downsampling layers
-function template_builder(block_fn; kwargs...)
-    function (inplanes, planes; _kwargs...)
-        return block_fn(inplanes, planes; kwargs..., _kwargs...)
-    end
-end
-
-function template_builder(::typeof(basicblock); reduction_factor::Integer = 1,
-                          activation = relu, norm_layer = BatchNorm, prenorm::Bool = false,
-                          attn_fn = planes -> identity, kargs...)
-    return (args...; kwargs...) -> basicblock(args...; kwargs..., reduction_factor,
-                                              activation, norm_layer, prenorm, attn_fn)
+    return Chain(conv1, bn1, stempool)
 end
 
-function template_builder(::typeof(bottleneck); cardinality::Integer = 1,
-                          base_width::Integer = 64,
-                          reduction_factor::Integer = 1, activation = relu,
-                          norm_layer = BatchNorm, prenorm::Bool = false,
-                          attn_fn = planes -> identity, kargs...)
-    return (args...; kwargs...) -> bottleneck(args...; kwargs..., cardinality, base_width,
-                                              reduction_factor, activation,
-                                              norm_layer, prenorm, attn_fn)
-end
+resnet_planes(stage_idx::Integer) = 64 * 2^(stage_idx - 1)
 
-function template_builder(downsample_fn::Union{typeof(downsample_conv),
-                                               typeof(downsample_pool),
-                                               typeof(downsample_identity)};
-                          norm_layer = BatchNorm, prenorm = false)
-    return (args...; kwargs...) -> downsample_fn(args...; kwargs..., norm_layer, prenorm)
+function basicblock_builder(block_repeats::Vector{<:Integer}; inplanes::Integer = 64,
+                            reduction_factor::Integer = 1, expansion::Integer = 1,
+                            norm_layer = BatchNorm, prenorm::Bool = false,
+                            activation = relu, attn_fn = planes -> identity,
+                            drop_block_rate = 0.0, drop_path_rate = 0.0,
+                            stride_fn = get_stride, planes_fn = resnet_planes,
+                            downsample_tuple = (downsample_conv, downsample_identity))
+    pathschedule = linear_scheduler(drop_path_rate; depth = sum(block_repeats))
+    blockschedule = linear_scheduler(drop_block_rate; depth = sum(block_repeats))
+    # closure over `idxs`
+    function get_layers(stage_idx::Integer, block_idx::Integer)
+        planes = planes_fn(stage_idx)
+        # `get_stride` is a callback that the user can tweak to change the stride of the
+        # blocks. It defaults to the standard behaviour as in the paper
+        stride = stride_fn(stage_idx, block_idx)
+        downsample_fn = (stride != 1 || inplanes != planes * expansion) ?
+                        downsample_tuple[1] : downsample_tuple[2]
+        # DropBlock, DropPath both take in rates based on a linear scaling schedule
+        schedule_idx = sum(block_repeats[1:(stage_idx - 1)]) + block_idx
+        drop_path = DropPath(pathschedule[schedule_idx])
+        drop_block = DropBlock(blockschedule[schedule_idx])
+        block = basicblock(inplanes, planes; stride, reduction_factor, activation,
+                           norm_layer, prenorm, attn_fn, drop_path, drop_block)
+        downsample = downsample_fn(inplanes, planes * expansion; stride)
+        # inplanes increases by expansion after each block
+        inplanes = planes * expansion
+        return block, downsample
+    end
+    return get_layers
 end
 
-resnet_planes(stage_idx::Integer) = 64 * 2^(stage_idx - 1)
-
-function configure_resnet_block(block_template, expansion, block_repeats::Vector{<:Integer};
-                                stride_fn = get_stride, plane_fn = resnet_planes,
-                                downsample_templates::NTuple{2, Any},
-                                inplanes::Integer = 64,
-                                drop_path_rate = 0.0, drop_block_rate = 0.0, kwargs...)
+function bottleneck_builder(block_repeats::Vector{<:Integer}; inplanes::Integer = 64,
+                            cardinality::Integer = 1, base_width::Integer = 64,
+                            reduction_factor::Integer = 1, expansion::Integer = 4,
+                            norm_layer = BatchNorm, prenorm::Bool = false,
+                            activation = relu, attn_fn = planes -> identity,
+                            drop_block_rate = 0.0, drop_path_rate = 0.0,
+                            stride_fn = get_stride, planes_fn = resnet_planes,
+                            downsample_tuple = (downsample_conv, downsample_identity))
     pathschedule = linear_scheduler(drop_path_rate; depth = sum(block_repeats))
     blockschedule = linear_scheduler(drop_block_rate; depth = sum(block_repeats))
     # closure over `idxs`
     function get_layers(stage_idx::Integer, block_idx::Integer)
-        planes = plane_fn(stage_idx)
+        planes = planes_fn(stage_idx)
         # `get_stride` is a callback that the user can tweak to change the stride of the
         # blocks. It defaults to the standard behaviour as in the paper
         stride = stride_fn(stage_idx, block_idx)
-        downsample_template = (stride != 1 || inplanes != planes * expansion) ?
-                              downsample_templates[1] : downsample_templates[2]
+        downsample_fn = (stride != 1 || inplanes != planes * expansion) ?
+                        downsample_tuple[1] : downsample_tuple[2]
         # DropBlock, DropPath both take in rates based on a linear scaling schedule
         schedule_idx = sum(block_repeats[1:(stage_idx - 1)]) + block_idx
         drop_path = DropPath(pathschedule[schedule_idx])
         drop_block = DropBlock(blockschedule[schedule_idx])
-        block = block_template(inplanes, planes; stride, drop_path, drop_block)
-        downsample = downsample_template(inplanes, planes * expansion; stride)
+        block = bottleneck(inplanes, planes; stride, cardinality, base_width,
+                           reduction_factor, activation, norm_layer, prenorm,
+                           attn_fn, drop_path, drop_block)
+        downsample = downsample_fn(inplanes, planes * expansion; stride)
         # inplanes increases by expansion after each block
-        inplanes = (planes * expansion)
+        inplanes = planes * expansion
         return block, downsample
     end
     return get_layers
@@ -283,41 +279,59 @@ function resnet_stages(get_layers, block_repeats::Vector{<:Integer}, connection)
     return Chain(stages...)
 end
 
-function resnet(connection, get_layers, block_repeats::Vector{<:Integer}, stem, classifier)
-    stage_blocks = resnet_stages(get_layers, block_repeats, connection)
-    return Chain(Chain(stem, stage_blocks), classifier)
+function resnet(block_type::Symbol, block_repeats::Vector{<:Integer};
+                downsample_opt::NTuple{2, Any} = (downsample_conv, downsample_identity),
+                cardinality::Integer = 1, base_width::Integer = 64, inplanes::Integer = 64,
+                reduction_factor::Integer = 1, imsize::Dims{2} = (256, 256),
+                inchannels::Integer = 3, stem_fn = resnet_stem,
+                connection = addact, activation = relu, norm_layer = BatchNorm,
+                prenorm::Bool = false, attn_fn = planes -> identity,
+                pool_layer = AdaptiveMeanPool((1, 1)), use_conv::Bool = false,
+                drop_block_rate = 0.0, drop_path_rate = 0.0, dropout_rate = 0.0,
+                nclasses::Integer = 1000)
+    # Build stem
+    stem = stem_fn(; inchannels)
+    # Block builder
+    if block_type == :basicblock
+        @assert cardinality==1 "Cardinality must be 1 for `basicblock`"
+        @assert base_width==64 "Base width must be 64 for `basicblock`"
+        get_layers = basicblock_builder(block_repeats; inplanes, reduction_factor,
+                                        activation, norm_layer, prenorm, attn_fn,
+                                        drop_block_rate, drop_path_rate,
+                                        stride_fn = get_stride, planes_fn = resnet_planes,
+                                        downsample_tuple = downsample_opt)
+    elseif block_type == :bottleneck
+        get_layers = bottleneck_builder(block_repeats; inplanes, cardinality, base_width,
+                                        reduction_factor, activation, norm_layer,
+                                        prenorm, attn_fn, drop_block_rate, drop_path_rate,
+                                        stride_fn = get_stride, planes_fn = resnet_planes,
+                                        downsample_tuple = downsample_opt)
+    else
+        throw(ArgumentError("Unknown block type $block_type"))
+    end
+    classifier_fn = nfeatures -> create_classifier(nfeatures, nclasses; dropout_rate,
+                                                   pool_layer, use_conv)
+    return resnet((imsize..., inchannels), stem, connection$activation, get_layers,
+                  block_repeats, classifier_fn)
+end
+function resnet(block_fn, block_repeats, downsample_opt::Symbol = :B; kwargs...)
+    return resnet(block_fn, block_repeats, shortcut_dict[downsample_opt]; kwargs...)
 end
 
-function resnet(block_fn, block_repeats::Vector{<:Integer},
-                downsample_opt::NTuple{2, Any} = (downsample_conv, downsample_identity);
-                imsize::Dims{2} = (256, 256), inchannels::Integer = 3,
-                stem = first(resnet_stem(; inchannels)), inplanes::Integer = 64,
-                connection = addact, activation = relu,
-                pool_layer = AdaptiveMeanPool((1, 1)), use_conv::Bool = false,
-                dropout_rate = 0.0, nclasses::Integer = 1000, kwargs...)
-    # Configure downsample templates
-    downsample_templates = map(template_builder, downsample_opt)
-    # Configure block templates
-    block_template = template_builder(block_fn; kwargs...)
-    get_layers = configure_resnet_block(block_template, expansion_factor(block_fn),
-                                        block_repeats; inplanes, downsample_templates,
-                                        kwargs...)
+function resnet(img_dims, stem, connection, get_layers, block_repeats::Vector{<:Integer},
+                classifier_fn)
     # Build stages of the ResNet
-    stage_blocks = resnet_stages(get_layers, block_repeats, connection$activation)
+    stage_blocks = resnet_stages(get_layers, block_repeats, connection)
     backbone = Chain(stem, stage_blocks)
     # Build the classifier head
-    nfeaturemaps = Flux.outputsize(backbone, (imsize..., inchannels); padbatch = true)[3]
-    classifier = create_classifier(nfeaturemaps, nclasses; dropout_rate, pool_layer,
-                                   use_conv)
+    nfeaturemaps = Flux.outputsize(backbone, img_dims; padbatch = true)[3]
+    classifier = classifier_fn(nfeaturemaps)
     return Chain(backbone, classifier)
 end
-function resnet(block_fn, block_repeats, downsample_opt::Symbol = :B; kwargs...)
-    return resnet(block_fn, block_repeats, shortcut_dict[downsample_opt], kwargs...)
-end
 
 # block-layer configurations for ResNet-like models
-const resnet_configs = Dict(18 => (basicblock, [2, 2, 2, 2]),
-                            34 => (basicblock, [3, 4, 6, 3]),
-                            50 => (bottleneck, [3, 4, 6, 3]),
-                            101 => (bottleneck, [3, 4, 23, 3]),
-                            152 => (bottleneck, [3, 8, 36, 3]))
+const resnet_configs = Dict(18 => (:basicblock, [2, 2, 2, 2]),
+                            34 => (:basicblock, [3, 4, 6, 3]),
+                            50 => (:bottleneck, [3, 4, 6, 3]),
+                            101 => (:bottleneck, [3, 4, 23, 3]),
+                            152 => (:bottleneck, [3, 8, 36, 3]))

src/layers/Layers.jl

@@ -26,7 +26,7 @@ include("normalise.jl")
 export prenorm, ChannelLayerNorm
 
 include("conv.jl")
-export conv_norm, depthwise_sep_conv_bn, invertedresidual, skip_identity, skip_projection
+export conv_norm, depthwise_sep_conv_bn, invertedresidual
 
 include("drop.jl")
 export DropBlock, DropPath, droppath_rates