resnet imported w/o Pad layer

KnetML · Oct 27, 2023 · b53d8d9 · b53d8d9
1 parent 638b284
commit b53d8d9
Showing 1 changed file with 36 additions and 36 deletions.
diff --git a/src/pretrained.jl b/src/pretrained.jl
@@ -211,18 +211,18 @@ function get_resnet50v2(; filters_only=false, trainable=true)
     h5 = HDF5.h5open(local_file)
 
     filter_layers = Chain(
-        Pad(3),
-        Conv(h5, "conv1_conv", trainable=trainable, stride=2, actf=identity),
-        Pad(1),
-        Pool(;window=3, stride=2),
+        #Pad(3),
+        Conv(h5, "conv1_conv", trainable=trainable, stride=2, actf=identity, padding=3),
+        #Pad(1),
+        Pool(;window=3, stride=2, padding=1),
         BatchNorm(h5, "conv2_block1_preact_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
         Relu(),
         ResNetBlock([
                     Conv(h5, "conv2_block1_1_conv", use_bias=false, trainable=trainable, actf=identity),
                     BatchNorm( h5, "conv2_block1_1_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
-                    Pad(1),
-                    Conv(h5, "conv2_block1_2_conv", use_bias=false, trainable=trainable, actf=identity),
+                    #Pad(1),
+                    Conv(h5, "conv2_block1_2_conv", use_bias=false, trainable=trainable, actf=identity, padding=1),
                     BatchNorm( h5, "conv2_block1_2_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
                     Conv(h5, "conv2_block1_3_conv", trainable=trainable, actf=identity),
@@ -236,8 +236,8 @@ function get_resnet50v2(; filters_only=false, trainable=true)
                     Conv(h5, "conv2_block2_1_conv", use_bias=false, trainable=trainable, actf=identity),
                     BatchNorm( h5, "conv2_block2_1_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
-                    Pad(1),
-                    Conv(h5, "conv2_block2_2_conv", use_bias=false, trainable=trainable, actf=identity),
+                    #Pad(1),
+                    Conv(h5, "conv2_block2_2_conv", use_bias=false, trainable=trainable, actf=identity, padding=1),
                     BatchNorm( h5, "conv2_block2_2_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
                     Conv(h5, "conv2_block2_3_conv", trainable=trainable, actf=identity),
@@ -248,8 +248,8 @@ function get_resnet50v2(; filters_only=false, trainable=true)
                     Conv(h5, "conv2_block3_1_conv",use_bias=false,  trainable=trainable, actf=identity),
                     BatchNorm( h5, "conv2_block3_1_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
-                    Pad(1),
-                    Conv(h5, "conv2_block3_2_conv", use_bias=false, stride=2, trainable=trainable, actf=identity),
+                    #Pad(1),
+                    Conv(h5, "conv2_block3_2_conv", use_bias=false, stride=2, trainable=trainable, actf=identity, padding=1),
                     BatchNorm( h5, "conv2_block3_2_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
                     Conv(h5, "conv2_block3_3_conv", trainable=trainable, actf=identity),
@@ -265,8 +265,8 @@ function get_resnet50v2(; filters_only=false, trainable=true)
                     Conv(h5, "conv3_block1_1_conv", use_bias=false, trainable=trainable, actf=identity),
                     BatchNorm(h5, "conv3_block1_1_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
-                    Pad(1),
-                    Conv(h5, "conv3_block1_2_conv", use_bias=false, trainable=trainable, actf=identity),
+                    #Pad(1),
+                    Conv(h5, "conv3_block1_2_conv", use_bias=false, trainable=trainable, actf=identity, padding=1),
                     BatchNorm( h5, "conv3_block1_2_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
                     Conv(h5, "conv3_block1_3_conv", trainable=trainable, actf=identity),
@@ -280,8 +280,8 @@ function get_resnet50v2(; filters_only=false, trainable=true)
                     Conv(h5, "conv3_block2_1_conv"; use_bias=false, trainable=trainable, actf=identity),
                     BatchNorm(h5, "conv3_block2_1_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
-                    Pad(1),
-                    Conv(h5, "conv3_block2_2_conv", use_bias=false, trainable=trainable, actf=identity),
+                    #Pad(1),
+                    Conv(h5, "conv3_block2_2_conv", use_bias=false, trainable=trainable, actf=identity, padding=1),
                     BatchNorm( h5, "conv3_block2_2_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
                     Conv(h5, "conv3_block2_3_conv", trainable=trainable, actf=identity),
@@ -292,8 +292,8 @@ function get_resnet50v2(; filters_only=false, trainable=true)
                     Conv(h5, "conv3_block3_1_conv", use_bias=false, trainable=trainable, actf=identity),
                     BatchNorm(h5, "conv3_block3_1_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
-                    Pad(1),
-                    Conv(h5, "conv3_block3_2_conv", use_bias=false, trainable=trainable, actf=identity),
+                    #Pad(1),
+                    Conv(h5, "conv3_block3_2_conv", use_bias=false, trainable=trainable, actf=identity, padding=1),
                     BatchNorm( h5, "conv3_block3_2_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
                     Conv(h5, "conv3_block3_3_conv", trainable=trainable, actf=identity),
@@ -304,8 +304,8 @@ function get_resnet50v2(; filters_only=false, trainable=true)
                     Conv(h5, "conv3_block4_1_conv", use_bias=false, trainable=trainable, actf=identity),
                     BatchNorm( h5, "conv3_block4_1_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
-                    Pad(1),
-                    Conv(h5, "conv3_block4_2_conv", use_bias=false, trainable=trainable, stride=2, actf=identity),
+                    #Pad(1),
+                    Conv(h5, "conv3_block4_2_conv", use_bias=false, trainable=trainable, stride=2, actf=identity, padding=1),
                     BatchNorm( h5, "conv3_block4_2_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
                     Conv(h5, "conv3_block4_3_conv", trainable=trainable, actf=identity),
@@ -321,8 +321,8 @@ function get_resnet50v2(; filters_only=false, trainable=true)
                     Conv(h5, "conv4_block1_1_conv", use_bias=false, trainable=trainable, actf=identity),
                     BatchNorm( h5, "conv4_block1_1_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
-                    Pad(1),
-                    Conv(h5, "conv4_block1_2_conv", use_bias=false, trainable=trainable, actf=identity),
+                    #Pad(1),
+                    Conv(h5, "conv4_block1_2_conv", use_bias=false, trainable=trainable, actf=identity, padding=1),
                     BatchNorm( h5, "conv4_block1_2_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
                     Conv(h5, "conv4_block1_3_conv", trainable=trainable, actf=identity),
@@ -336,8 +336,8 @@ function get_resnet50v2(; filters_only=false, trainable=true)
                     Conv(h5, "conv4_block2_1_conv", use_bias=false, trainable=trainable, padding=0, stride=1, actf=identity),
                     BatchNorm(h5, "conv4_block2_1_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
-                    Pad(1),
-                    Conv(h5, "conv4_block2_2_conv", use_bias=false, trainable=trainable, actf=identity),
+                    #Pad(1),
+                    Conv(h5, "conv4_block2_2_conv", use_bias=false, trainable=trainable, actf=identity, padding=1),
                     BatchNorm( h5, "conv4_block2_2_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
                     Conv(h5, "conv4_block2_3_conv", trainable=trainable, padding=0, stride=1, actf=identity),
@@ -348,8 +348,8 @@ function get_resnet50v2(; filters_only=false, trainable=true)
                     Conv(h5, "conv4_block3_1_conv", use_bias=false, trainable=trainable, actf=identity),
                     BatchNorm(h5, "conv4_block3_1_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
-                    Pad(1),
-                    Conv(h5, "conv4_block3_2_conv", use_bias=false, trainable=trainable, actf=identity),
+                    #Pad(1),
+                    Conv(h5, "conv4_block3_2_conv", use_bias=false, trainable=trainable, actf=identity, padding=1),
                     BatchNorm( h5, "conv4_block3_2_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
                     Conv(h5, "conv4_block3_3_conv", trainable=trainable, actf=identity),
@@ -360,8 +360,8 @@ function get_resnet50v2(; filters_only=false, trainable=true)
                     Conv(h5, "conv4_block4_1_conv", use_bias=false, trainable=trainable, actf=identity),
                     BatchNorm(h5, "conv4_block4_1_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
-                    Pad(1),
-                    Conv(h5, "conv4_block4_2_conv", use_bias=false, trainable=trainable, actf=identity),
+                    #Pad(1),
+                    Conv(h5, "conv4_block4_2_conv", use_bias=false, trainable=trainable, actf=identity, padding=1),
                     BatchNorm( h5, "conv4_block4_2_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
                     Conv(h5, "conv4_block4_3_conv", trainable=trainable, padding=0, stride=1, actf=identity),
@@ -372,8 +372,8 @@ function get_resnet50v2(; filters_only=false, trainable=true)
                     Conv(h5, "conv4_block5_1_conv", use_bias=false, trainable=trainable, actf=identity),
                     BatchNorm(h5, "conv4_block5_1_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
-                    Pad(1),
-                    Conv(h5, "conv4_block5_2_conv", use_bias=false, trainable=trainable, actf=identity),
+                    #Pad(1),
+                    Conv(h5, "conv4_block5_2_conv", use_bias=false, trainable=trainable, actf=identity, padding=1),
                     BatchNorm( h5, "conv4_block5_2_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
                     Conv(h5, "conv4_block5_3_conv", trainable=trainable, padding=0, stride=1, actf=identity),
@@ -384,8 +384,8 @@ function get_resnet50v2(; filters_only=false, trainable=true)
                     Conv(h5, "conv4_block6_1_conv", use_bias=false, trainable=trainable, actf=identity),
                     BatchNorm( h5, "conv4_block6_1_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
-                    Pad(1),
-                    Conv(h5, "conv4_block6_2_conv", use_bias=false, trainable=trainable, stride=2, actf=identity),
+                    #Pad(1),
+                    Conv(h5, "conv4_block6_2_conv", use_bias=false, trainable=trainable, stride=2, actf=identity, padding=1),
                     BatchNorm( h5, "conv4_block6_2_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
                     Conv(h5, "conv4_block6_3_conv", trainable=trainable, padding=0, stride=1, actf=identity),
@@ -401,8 +401,8 @@ function get_resnet50v2(; filters_only=false, trainable=true)
                     Conv(h5, "conv5_block1_1_conv", use_bias=false, trainable=trainable, actf=identity),
                     BatchNorm( h5, "conv5_block1_1_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
-                    Pad(1),
-                    Conv(h5, "conv5_block1_2_conv", use_bias=false, trainable=trainable, actf=identity),
+                    #Pad(1),
+                    Conv(h5, "conv5_block1_2_conv", use_bias=false, trainable=trainable, actf=identity, padding=1),
                     BatchNorm( h5, "conv5_block1_2_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
                     Conv(h5, "conv5_block1_3_conv", trainable=trainable, actf=identity),
@@ -416,8 +416,8 @@ function get_resnet50v2(; filters_only=false, trainable=true)
                     Conv(h5, "conv5_block2_1_conv", use_bias=false, trainable=trainable, actf=identity),
                     BatchNorm(h5, "conv5_block2_1_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
-                    Pad(1),
-                    Conv(h5, "conv5_block2_2_conv", use_bias=false, trainable=trainable, actf=identity),
+                    #Pad(1),
+                    Conv(h5, "conv5_block2_2_conv", use_bias=false, trainable=trainable, actf=identity, padding=1),
                     BatchNorm( h5, "conv5_block2_2_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
                     Conv(h5, "conv5_block2_3_conv", trainable=trainable, actf=identity),
@@ -428,8 +428,8 @@ function get_resnet50v2(; filters_only=false, trainable=true)
                     Conv(h5, "conv5_block3_1_conv", use_bias=false, trainable=trainable, actf=identity),
                     BatchNorm(h5, "conv5_block3_1_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
-                    Pad(1),
-                    Conv(h5, "conv5_block3_2_conv", use_bias=false, trainable=trainable, actf=identity),
+                    #Pad(1),
+                    Conv(h5, "conv5_block3_2_conv", use_bias=false, trainable=trainable, actf=identity, padding=1),
                     BatchNorm( h5, "conv5_block3_2_bn", trainable=trainable, momentum=0.99, ε=1.001e-5),
                     Relu(),
                     Conv(h5, "conv5_block3_3_conv", trainable=trainable, actf=identity),