More declarative interface for ResNet

Less keywords for the user to worry about

src/convnets/efficientnet.jl

@@ -6,19 +6,21 @@ Create an EfficientNet model ([reference](https://arxiv.org/abs/1905.11946v5)).
 
 # Arguments
 
-- `scalings`: global width and depth scaling (given as a tuple)
-- `block_config`: configuration for each inverted residual block,
-                  given as a vector of tuples with elements:
-    - `n`: number of block repetitions (will be scaled by global depth scaling)
-    - `k`: kernel size
-    - `s`: kernel stride
-    - `e`: expansion ratio
-    - `i`: block input channels (will be scaled by global width scaling)
-    - `o`: block output channels (will be scaled by global width scaling)
-- `inchannels`: number of input channels
-- `nclasses`: number of output classes
-- `max_width`: maximum number of output channels before the fully connected
-               classification blocks
+  - `scalings`: global width and depth scaling (given as a tuple)
+
+  - `block_config`: configuration for each inverted residual block,
+    given as a vector of tuples with elements:
+    
+      + `n`: number of block repetitions (will be scaled by global depth scaling)
+      + `k`: kernel size
+      + `s`: kernel stride
+      + `e`: expansion ratio
+      + `i`: block input channels (will be scaled by global width scaling)
+      + `o`: block output channels (will be scaled by global width scaling)
+  - `inchannels`: number of input channels
+  - `nclasses`: number of output classes
+  - `max_width`: maximum number of output channels before the fully connected
+    classification blocks
 """
 function efficientnet(scalings, block_config;
                       inchannels = 3, nclasses = 1000, max_width = 1280)
@@ -64,34 +66,33 @@ end
 # i: block input channels
 # o: block output channels
 const efficientnet_block_configs = [
-#   (n, k, s, e,   i,   o)
-    (1, 3, 1, 1,  32,  16),
-    (2, 3, 2, 6,  16,  24),
-    (2, 5, 2, 6,  24,  40),
-    (3, 3, 2, 6,  40,  80),
-    (3, 5, 1, 6,  80, 112),
+    # (n, k, s, e, i, o)
+    (1, 3, 1, 1, 32, 16),
+    (2, 3, 2, 6, 16, 24),
+    (2, 5, 2, 6, 24, 40),
+    (3, 3, 2, 6, 40, 80),
+    (3, 5, 1, 6, 80, 112),
     (4, 5, 2, 6, 112, 192),
-    (1, 3, 1, 6, 192, 320)
+    (1, 3, 1, 6, 192, 320),
 ]
 
 # w: width scaling
 # d: depth scaling
 # r: image resolution
 const efficientnet_global_configs = Dict(
-#          (  r, (  w,   d))
-    :b0 => (224, (1.0, 1.0)),
-    :b1 => (240, (1.0, 1.1)),
-    :b2 => (260, (1.1, 1.2)),
-    :b3 => (300, (1.2, 1.4)),
-    :b4 => (380, (1.4, 1.8)),
-    :b5 => (456, (1.6, 2.2)),
-    :b6 => (528, (1.8, 2.6)),
-    :b7 => (600, (2.0, 3.1)),
-    :b8 => (672, (2.2, 3.6))
-)
+                                         # (r, (w, d))
+                                         :b0 => (224, (1.0, 1.0)),
+                                         :b1 => (240, (1.0, 1.1)),
+                                         :b2 => (260, (1.1, 1.2)),
+                                         :b3 => (300, (1.2, 1.4)),
+                                         :b4 => (380, (1.4, 1.8)),
+                                         :b5 => (456, (1.6, 2.2)),
+                                         :b6 => (528, (1.8, 2.6)),
+                                         :b7 => (600, (2.0, 3.1)),
+                                         :b8 => (672, (2.2, 3.6)))
 
 struct EfficientNet
-  layers::Any
+    layers::Any
 end
 
 """
@@ -103,27 +104,29 @@ See also [`efficientnet`](#).
 
 # Arguments
 
-- `scalings`: global width and depth scaling (given as a tuple)
-- `block_config`: configuration for each inverted residual block,
-                  given as a vector of tuples with elements:
-    - `n`: number of block repetitions (will be scaled by global depth scaling)
-    - `k`: kernel size
-    - `s`: kernel stride
-    - `e`: expansion ratio
-    - `i`: block input channels (will be scaled by global width scaling)
-    - `o`: block output channels (will be scaled by global width scaling)
-- `inchannels`: number of input channels
-- `nclasses`: number of output classes
-- `max_width`: maximum number of output channels before the fully connected
-               classification blocks
+  - `scalings`: global width and depth scaling (given as a tuple)
+
+  - `block_config`: configuration for each inverted residual block,
+    given as a vector of tuples with elements:
+    
+      + `n`: number of block repetitions (will be scaled by global depth scaling)
+      + `k`: kernel size
+      + `s`: kernel stride
+      + `e`: expansion ratio
+      + `i`: block input channels (will be scaled by global width scaling)
+      + `o`: block output channels (will be scaled by global width scaling)
+  - `inchannels`: number of input channels
+  - `nclasses`: number of output classes
+  - `max_width`: maximum number of output channels before the fully connected
+    classification blocks
 """
 function EfficientNet(scalings, block_config;
                       inchannels = 3, nclasses = 1000, max_width = 1280)
-  layers = efficientnet(scalings, block_config;
-                        inchannels = inchannels,
-                        nclasses = nclasses,
-                        max_width = max_width)
-  return EfficientNet(layers)
+    layers = efficientnet(scalings, block_config;
+                          inchannels = inchannels,
+                          nclasses = nclasses,
+                          max_width = max_width)
+    return EfficientNet(layers)
 end
 
 @functor EfficientNet
@@ -141,13 +144,13 @@ See also [`efficientnet`](#).
 
 # Arguments
 
-- `name`: name of default configuration
-          (can be `:b0`, `:b1`, `:b2`, `:b3`, `:b4`, `:b5`, `:b6`, `:b7`, `:b8`)
-- `pretrain`: set to `true` to load the pre-trained weights for ImageNet
+  - `name`: name of default configuration
+    (can be `:b0`, `:b1`, `:b2`, `:b3`, `:b4`, `:b5`, `:b6`, `:b7`, `:b8`)
+  - `pretrain`: set to `true` to load the pre-trained weights for ImageNet
 """
 function EfficientNet(name::Symbol; pretrain = false)
     @assert name in keys(efficientnet_global_configs)
-        "`name` must be one of $(sort(collect(keys(efficientnet_global_configs))))"
+    "`name` must be one of $(sort(collect(keys(efficientnet_global_configs))))"
 
     model = EfficientNet(efficientnet_global_configs[name][2], efficientnet_block_configs)
     pretrain && loadpretrain!(model, string("efficientnet-", name))

src/convnets/mobilenet.jl

@@ -28,7 +28,7 @@ Create a MobileNetv1 model ([reference](https://arxiv.org/abs/1704.04861v1)).
 function mobilenetv1(width_mult, config;
                      activation = relu,
                      inchannels = 3,
-					     fcsize = 1024,
+                     fcsize = 1024,
                      nclasses = 1000)
     layers = []
     for (dw, outch, stride, nrepeats) in config