Merge pull request #151 from theabhirath/conv_bn

Improved time to first gradient

src/convnets/convmixer.jl

@@ -17,11 +17,11 @@ Creates a ConvMixer model.
 function convmixer(planes, depth; inchannels = 3, kernel_size = (9, 9),
                    patch_size::Dims{2} = (7, 7), activation = gelu, nclasses = 1000)
   stem = conv_bn(patch_size, inchannels, planes, activation; preact = true, stride = patch_size[1])
-  blocks = [Chain(SkipConnection(Chain(conv_bn(kernel_size, planes, planes, activation;
-                                               preact = true, groups = planes, pad = SamePad())...), +),
-                  conv_bn((1, 1), planes, planes, activation; preact = true)...) for _ in 1:depth]
+  blocks = [Chain(SkipConnection(conv_bn(kernel_size, planes, planes, activation;
+                                               preact = true, groups = planes, pad = SamePad()), +),
+                  conv_bn((1, 1), planes, planes, activation; preact = true)) for _ in 1:depth]
   head = Chain(AdaptiveMeanPool((1, 1)), MLUtils.flatten, Dense(planes, nclasses))
-  return Chain(Chain(stem..., blocks...), head)
+  return Chain(Chain(stem, Chain(blocks)), head)
 end
 
 convmixer_config = Dict(:base => Dict(:planes => 1536, :depth => 20, :kernel_size => (9, 9),

src/convnets/convnext.jl

@@ -10,7 +10,7 @@ Creates a single block of ConvNeXt.
 - `λ`: Init value for LayerScale
 """
 function convnextblock(planes, drop_path_rate = 0., λ = 1f-6)
-  layers = SkipConnection(Chain(DepthwiseConv((7, 7), planes => planes; pad = 3), 
+  layers = SkipConnection(Chain(DepthwiseConv((7, 7), planes => planes; pad = 3),
                                 swapdims((3, 1, 2, 4)),
                                 LayerNorm(planes; ϵ = 1f-6),
                                 mlp_block(planes, 4 * planes),
@@ -61,7 +61,7 @@ function convnext(depths, planes; inchannels = 3, drop_path_rate = 0., λ = 1f-6
                LayerNorm(planes[end]),
                Dense(planes[end], nclasses))
 
-  return Chain(Chain(backbone...), head)
+  return Chain(Chain(backbone), head)
 end
 
 # Configurations for ConvNeXt models

src/convnets/densenet.jl

@@ -11,10 +11,10 @@ Create a Densenet bottleneck layer
 """
 function dense_bottleneck(inplanes, outplanes)
   inner_channels = 4 * outplanes
-  m = Chain(conv_bn((1, 1), inplanes, inner_channels; bias = false, rev = true)...,
-            conv_bn((3, 3), inner_channels, outplanes; pad = 1, bias = false, rev = true)...)
+  m = Chain(conv_bn((1, 1), inplanes, inner_channels; bias = false, rev = true),
+            conv_bn((3, 3), inner_channels, outplanes; pad = 1, bias = false, rev = true))
 
-  SkipConnection(m, (mx, x) -> cat(x, mx; dims = 3))
+  SkipConnection(m, cat_channels)
 end
 
 """
@@ -28,8 +28,7 @@ Create a DenseNet transition sequence
 - `outplanes`: number of output feature maps
 """
 transition(inplanes, outplanes) =
-  [conv_bn((1, 1), inplanes, outplanes; bias = false, rev = true)...,
-   MeanPool((2, 2))]
+  Chain(conv_bn((1, 1), inplanes, outplanes; bias = false, rev = true), MeanPool((2, 2)))
 
 """
     dense_block(inplanes, growth_rates)
@@ -60,20 +59,21 @@ Create a DenseNet model
 - `nclasses`: the number of output classes
 """
 function densenet(inplanes, growth_rates; reduction = 0.5, nclasses = 1000)
-  layers = conv_bn((7, 7), 3, inplanes; stride = 2, pad = (3, 3), bias = false)
+  layers = []
+  push!(layers, conv_bn((7, 7), 3, inplanes; stride = 2, pad = (3, 3), bias = false))
   push!(layers, MaxPool((3, 3), stride = 2, pad = (1, 1)))
 
   outplanes = 0
   for (i, rates) in enumerate(growth_rates)
     outplanes = inplanes + sum(rates)
     append!(layers, dense_block(inplanes, rates))
-    (i != length(growth_rates)) && 
-      append!(layers, transition(outplanes, floor(Int, outplanes * reduction)))
+    (i != length(growth_rates)) &&
+      push!(layers, transition(outplanes, floor(Int, outplanes * reduction)))
     inplanes = floor(Int, outplanes * reduction)
   end
   push!(layers, BatchNorm(outplanes, relu))
 
-  return Chain(Chain(layers...),
+  return Chain(Chain(layers),
                Chain(AdaptiveMeanPool((1, 1)),
                      MLUtils.flatten,
                      Dense(outplanes, nclasses)))

src/convnets/googlenet.jl

@@ -15,16 +15,16 @@ Create an inception module for use in GoogLeNet
 """
 function _inceptionblock(inplanes, out_1x1, red_3x3, out_3x3, red_5x5, out_5x5, pool_proj)
     branch1 = Chain(Conv((1, 1), inplanes => out_1x1))
-  
+
     branch2 = Chain(Conv((1, 1), inplanes => red_3x3),
                     Conv((3, 3), red_3x3 => out_3x3; pad = 1))
-  
+
     branch3 = Chain(Conv((1, 1), inplanes => red_5x5),
                     Conv((5, 5), red_5x5 => out_5x5; pad = 2)) 
-  
+
     branch4 = Chain(MaxPool((3, 3), stride=1, pad = 1),
                     Conv((1, 1), inplanes => pool_proj))
-  
+
     return Parallel(cat_channels,
                     branch1, branch2, branch3, branch4)
 end

src/convnets/inception.jl

@@ -9,17 +9,17 @@ Create an Inception-v3 style-A module
 - `pool_proj`: the number of output feature maps for the pooling projection
 """
 function inception_a(inplanes, pool_proj)
-  branch1x1 = Chain(conv_bn((1, 1), inplanes, 64)...)
-
-  branch5x5 = Chain(conv_bn((1, 1), inplanes, 48)...,
-                    conv_bn((5, 5), 48, 64; pad = 2)...)
+  branch1x1 = conv_bn((1, 1), inplanes, 64)
 
-  branch3x3 = Chain(conv_bn((1, 1), inplanes, 64)..., 
-                    conv_bn((3, 3), 64, 96; pad = 1)...,
-                    conv_bn((3, 3), 96, 96; pad = 1)...)
+  branch5x5 = Chain(conv_bn((1, 1), inplanes, 48),
+                    conv_bn((5, 5), 48, 64; pad = 2))
+
+  branch3x3 = Chain(conv_bn((1, 1), inplanes, 64),
+                    conv_bn((3, 3), 64, 96; pad = 1),
+                    conv_bn((3, 3), 96, 96; pad = 1))
 
   branch_pool = Chain(MeanPool((3, 3), pad = 1, stride = 1),
-                      conv_bn((1, 1), inplanes, pool_proj)...)
+                      conv_bn((1, 1), inplanes, pool_proj))
 
   return Parallel(cat_channels,
                   branch1x1, branch5x5, branch3x3, branch_pool)
@@ -35,13 +35,13 @@ Create an Inception-v3 style-B module
 - `inplanes`: number of input feature maps
 """
 function inception_b(inplanes)
-  branch3x3_1 = Chain(conv_bn((3, 3), inplanes, 384; stride = 2)...)
+  branch3x3_1 = conv_bn((3, 3), inplanes, 384; stride = 2)
 
-  branch3x3_2 = Chain(conv_bn((1, 1), inplanes, 64)...,
-                      conv_bn((3, 3), 64, 96; pad = 1)...,
-                      conv_bn((3, 3), 96, 96; stride = 2)...)
+  branch3x3_2 = Chain(conv_bn((1, 1), inplanes, 64),
+                      conv_bn((3, 3), 64, 96; pad = 1),
+                      conv_bn((3, 3), 96, 96; stride = 2))
 
-  branch_pool = Chain(MaxPool((3, 3), stride = 2))
+  branch_pool = MaxPool((3, 3), stride = 2)
 
   return Parallel(cat_channels,
                   branch3x3_1, branch3x3_2, branch_pool)
@@ -59,20 +59,20 @@ Create an Inception-v3 style-C module
 - `n`: the "grid size" (kernel size) for the convolution layers
 """
 function inception_c(inplanes, inner_planes, n = 7)
-  branch1x1 = Chain(conv_bn((1, 1), inplanes, 192)...)
+  branch1x1 = conv_bn((1, 1), inplanes, 192)
 
-  branch7x7_1 = Chain(conv_bn((1, 1), inplanes, inner_planes)...,
-                      conv_bn((1, n), inner_planes, inner_planes; pad = (0, 3))...,
-                      conv_bn((n, 1), inner_planes, 192; pad = (3, 0))...)
+  branch7x7_1 = Chain(conv_bn((1, 1), inplanes, inner_planes),
+                      conv_bn((1, n), inner_planes, inner_planes; pad = (0, 3)),
+                      conv_bn((n, 1), inner_planes, 192; pad = (3, 0)))
 
-  branch7x7_2 = Chain(conv_bn((1, 1), inplanes, inner_planes)...,
-                      conv_bn((n, 1), inner_planes, inner_planes; pad = (3, 0))...,
-                      conv_bn((1, n), inner_planes, inner_planes; pad = (0, 3))...,
-                      conv_bn((n, 1), inner_planes, inner_planes; pad = (3, 0))...,
-                      conv_bn((1, n), inner_planes, 192; pad = (0, 3))...)
+  branch7x7_2 = Chain(conv_bn((1, 1), inplanes, inner_planes),
+                      conv_bn((n, 1), inner_planes, inner_planes; pad = (3, 0)),
+                      conv_bn((1, n), inner_planes, inner_planes; pad = (0, 3)),
+                      conv_bn((n, 1), inner_planes, inner_planes; pad = (3, 0)),
+                      conv_bn((1, n), inner_planes, 192; pad = (0, 3)))
 
-  branch_pool = Chain(MeanPool((3, 3), pad = 1, stride=1), 
-                      conv_bn((1, 1), inplanes, 192)...)
+  branch_pool = Chain(MeanPool((3, 3), pad = 1, stride=1),
+                      conv_bn((1, 1), inplanes, 192))
 
   return Parallel(cat_channels,
                   branch1x1, branch7x7_1, branch7x7_2, branch_pool)
@@ -88,15 +88,15 @@ Create an Inception-v3 style-D module
 - `inplanes`: number of input feature maps
 """
 function inception_d(inplanes)
-  branch3x3 = Chain(conv_bn((1, 1), inplanes, 192)...,
-                    conv_bn((3, 3), 192, 320; stride = 2)...)
+  branch3x3 = Chain(conv_bn((1, 1), inplanes, 192),
+                    conv_bn((3, 3), 192, 320; stride = 2))
 
-  branch7x7x3 = Chain(conv_bn((1, 1), inplanes, 192)...,
-                      conv_bn((1, 7), 192, 192; pad = (0, 3))...,
-                      conv_bn((7, 1), 192, 192; pad = (3, 0))...,
-                      conv_bn((3, 3), 192, 192; stride = 2)...)
+  branch7x7x3 = Chain(conv_bn((1, 1), inplanes, 192),
+                      conv_bn((1, 7), 192, 192; pad = (0, 3)),
+                      conv_bn((7, 1), 192, 192; pad = (3, 0)),
+                      conv_bn((3, 3), 192, 192; stride = 2))
 
-  branch_pool = Chain(MaxPool((3, 3), stride=2))
+  branch_pool = MaxPool((3, 3), stride=2)
 
   return Parallel(cat_channels,
                   branch3x3, branch7x7x3, branch_pool)
@@ -112,26 +112,26 @@ Create an Inception-v3 style-E module
 - `inplanes`: number of input feature maps
 """
 function inception_e(inplanes)
-  branch1x1 = Chain(conv_bn((1, 1), inplanes, 320)...)
+  branch1x1 = conv_bn((1, 1), inplanes, 320)
 
-  branch3x3_1 = Chain(conv_bn((1, 1), inplanes, 384)...)
-  branch3x3_1a = Chain(conv_bn((1, 3), 384, 384; pad = (0, 1))...)
-  branch3x3_1b = Chain(conv_bn((3, 1), 384, 384; pad = (1, 0))...)
+  branch3x3_1 = conv_bn((1, 1), inplanes, 384)
+  branch3x3_1a = conv_bn((1, 3), 384, 384; pad = (0, 1))
+  branch3x3_1b = conv_bn((3, 1), 384, 384; pad = (1, 0))
 
-  branch3x3_2 = Chain(conv_bn((1, 1), inplanes, 448)...,
-                      conv_bn((3, 3), 448, 384; pad = 1)...)
-  branch3x3_2a = Chain(conv_bn((1, 3), 384, 384; pad = (0, 1))...)
-  branch3x3_2b = Chain(conv_bn((3, 1), 384, 384; pad = (1, 0))...)
+  branch3x3_2 = Chain(conv_bn((1, 1), inplanes, 448),
+                      conv_bn((3, 3), 448, 384; pad = 1))
+  branch3x3_2a = conv_bn((1, 3), 384, 384; pad = (0, 1))
+  branch3x3_2b = conv_bn((3, 1), 384, 384; pad = (1, 0))
 
   branch_pool = Chain(MeanPool((3, 3), pad = 1, stride = 1),
-                      conv_bn((1, 1), inplanes, 192)...)
+                      conv_bn((1, 1), inplanes, 192))
 
   return Parallel(cat_channels,
                   branch1x1,
                   Chain(branch3x3_1,
                         Parallel(cat_channels,
                                   branch3x3_1a, branch3x3_1b)),
-      
+
                   Chain(branch3x3_2,
                         Parallel(cat_channels,
                                   branch3x3_2a, branch3x3_2b)),
@@ -150,12 +150,12 @@ Create an Inception-v3 model ([reference](https://arxiv.org/abs/1512.00567v3)).
     `inception3` does not currently support pretrained weights.
 """
 function inception3(; nclasses = 1000)
-  layer = Chain(Chain(conv_bn((3, 3), 3, 32; stride = 2)...,
-                      conv_bn((3, 3), 32, 32)...,
-                      conv_bn((3, 3), 32, 64; pad = 1)...,
+  layer = Chain(Chain(conv_bn((3, 3), 3, 32; stride = 2),
+                      conv_bn((3, 3), 32, 32),
+                      conv_bn((3, 3), 32, 64; pad = 1),
                       MaxPool((3, 3), stride = 2),
-                      conv_bn((1, 1), 64, 80)...,
-                      conv_bn((3, 3), 80, 192)...,
+                      conv_bn((1, 1), 64, 80),
+                      conv_bn((3, 3), 80, 192),
                       MaxPool((3, 3), stride = 2),
                       inception_a(192, 32),
                       inception_a(256, 64),

src/convnets/mobilenet.jl

@@ -31,17 +31,15 @@ function mobilenetv1(width_mult, config;
   for (dw, outch, stride, repeats) in config
     outch = Int(outch * width_mult)
     for _ in 1:repeats
-      layer = if dw
-        depthwise_sep_conv_bn((3, 3), inchannels, outch, activation; stride = stride, pad = 1)
-      else
-        conv_bn((3, 3), inchannels, outch, activation; stride = stride, pad = 1)
-      end
-      append!(layers, layer)
+      layer = dw ? depthwise_sep_conv_bn((3, 3), inchannels, outch, activation;
+                                         stride = stride, pad = 1) :
+                   conv_bn((3, 3), inchannels, outch, activation; stride = stride, pad = 1)
+      push!(layers, layer)
       inchannels = outch
     end
   end
 
-  return Chain(Chain(layers...),
+  return Chain(Chain(layers),
                Chain(GlobalMeanPool(),
                      MLUtils.flatten,
                      Dense(inchannels, fcsize, activation),
@@ -120,7 +118,7 @@ function mobilenetv2(width_mult, configs; max_width = 1280, nclasses = 1000)
   # building first layer
   inplanes = _round_channels(32 * width_mult, width_mult == 0.1 ? 4 : 8)
   layers = []
-  append!(layers, conv_bn((3, 3), 3, inplanes, stride = 2))
+  push!(layers, conv_bn((3, 3), 3, inplanes, stride = 2))
 
   # building inverted residual blocks
   for (t, c, n, s, a) in configs
@@ -136,8 +134,7 @@ function mobilenetv2(width_mult, configs; max_width = 1280, nclasses = 1000)
   outplanes = (width_mult > 1) ? _round_channels(max_width * width_mult, width_mult == 0.1 ? 4 : 8) :
                                  max_width
 
-  return Chain(Chain(layers...,
-                     conv_bn((1, 1), inplanes, outplanes, relu6, bias = false)...),
+  return Chain(Chain(Chain(layers), conv_bn((1, 1), inplanes, outplanes, relu6, bias = false)),
                Chain(AdaptiveMeanPool((1, 1)), MLUtils.flatten, Dense(outplanes, nclasses)))
 end
 
@@ -186,7 +183,7 @@ end
 (m::MobileNetv2)(x) = m.layers(x)
 
 backbone(m::MobileNetv2) = m.layers[1]
-classifier(m::MobileNetv2) = m.layers[2:end]
+classifier(m::MobileNetv2) = m.layers[2]
 
 # MobileNetv3
 
@@ -214,7 +211,7 @@ function mobilenetv3(width_mult, configs; max_width = 1024, nclasses = 1000)
   # building first layer
   inplanes = _round_channels(16 * width_mult, 8)
   layers = []
-  append!(layers, conv_bn((3, 3), 3, inplanes, hardswish; stride = 2))
+  push!(layers, conv_bn((3, 3), 3, inplanes, hardswish; stride = 2))
   explanes = 0
   # building inverted residual blocks
   for (k, t, c, r, a, s) in configs
@@ -229,13 +226,12 @@ function mobilenetv3(width_mult, configs; max_width = 1024, nclasses = 1000)
   # building last several layers
   output_channel = max_width
   output_channel = width_mult > 1.0 ? _round_channels(output_channel * width_mult, 8) : output_channel
-  classifier = (Dense(explanes, output_channel, hardswish),
-                Dropout(0.2),
-                Dense(output_channel, nclasses))
+  classifier = Chain(Dense(explanes, output_channel, hardswish),
+                     Dropout(0.2),
+                     Dense(output_channel, nclasses))
 
-  return Chain(Chain(layers...,
-                     conv_bn((1, 1), inplanes, explanes, hardswish, bias = false)...),
-               Chain(AdaptiveMeanPool((1, 1)), MLUtils.flatten, classifier...))
+  return Chain(Chain(Chain(layers), conv_bn((1, 1), inplanes, explanes, hardswish, bias = false)),
+               Chain(AdaptiveMeanPool((1, 1)), MLUtils.flatten, classifier))
 end
 
 # Configurations for small and large mode for MobileNetv3
@@ -310,4 +306,4 @@ end
 (m::MobileNetv3)(x) = m.layers(x)
 
 backbone(m::MobileNetv3) = m.layers[1]
-classifier(m::MobileNetv3) = m.layers[2:end]
+classifier(m::MobileNetv3) = m.layers[2]

src/convnets/resnet.jl

@@ -12,8 +12,8 @@ Create a basic residual block
 """
 function basicblock(inplanes, outplanes, downsample = false)
   stride = downsample ? 2 : 1
-  Chain(conv_bn((3, 3), inplanes, outplanes[1]; stride = stride, pad = 1, bias = false)...,
-        conv_bn((3, 3), outplanes[1], outplanes[2], identity; stride = 1, pad = 1, bias = false)...)
+  Chain(conv_bn((3, 3), inplanes, outplanes[1]; stride = stride, pad = 1, bias = false),
+        conv_bn((3, 3), outplanes[1], outplanes[2], identity; stride = 1, pad = 1, bias = false))
 end
 
 """
@@ -36,9 +36,9 @@ The original paper uses `stride == [2, 1, 1]` when `downsample == true` instead.
 """
 function bottleneck(inplanes, outplanes, downsample = false;
                     stride = [1, (downsample ? 2 : 1), 1])
-  Chain(conv_bn((1, 1), inplanes, outplanes[1]; stride = stride[1], bias = false)...,
-        conv_bn((3, 3), outplanes[1], outplanes[2]; stride = stride[2], pad = 1, bias = false)...,
-        conv_bn((1, 1), outplanes[2], outplanes[3], identity; stride = stride[3], bias = false)...)
+  Chain(conv_bn((1, 1), inplanes, outplanes[1]; stride = stride[1], bias = false),
+        conv_bn((3, 3), outplanes[1], outplanes[2]; stride = stride[2], pad = 1, bias = false),
+        conv_bn((1, 1), outplanes[2], outplanes[3], identity; stride = stride[3], bias = false))
 end
 
 
@@ -82,7 +82,7 @@ function resnet(block, residuals::AbstractVector{<:NTuple{2, Any}}, connection =
   inplanes = 64
   baseplanes = 64
   layers = []
-  append!(layers, conv_bn((7, 7), 3, inplanes; stride = 2, pad = 3, bias = false))
+  push!(layers, conv_bn((7, 7), 3, inplanes; stride = 2, pad = 3, bias = false))
   push!(layers, MaxPool((3, 3), stride = (2, 2), pad = (1, 1)))
   for (i, nrepeats) in enumerate(block_config)
     # output planes within a block
@@ -102,7 +102,7 @@ function resnet(block, residuals::AbstractVector{<:NTuple{2, Any}}, connection =
     baseplanes *= 2
   end
 
-  return Chain(Chain(layers...),
+  return Chain(Chain(layers),
                Chain(AdaptiveMeanPool((1, 1)), MLUtils.flatten, Dense(inplanes, nclasses)))
 end
 
@@ -246,7 +246,7 @@ function ResNet(depth::Int = 50; pretrain = false, nclasses = 1000)
     model
 end
 
-# Compat with Methalhead 0.6; remove in 0.7
+# Compat with Metalhead 0.6; remove in 0.7
 @deprecate ResNet18(; kw...) ResNet(18; kw...)
 @deprecate ResNet34(; kw...) ResNet(34; kw...)
 @deprecate ResNet50(; kw...) ResNet(50; kw...)