Adding new kTypes.

Author: joaopauloschuler

cai/layers.py

@@ -33,6 +33,8 @@ def kT3_32ch(): return 23
 def kT3_64ch(): return 25
 def kT3_128ch(): return 27
 
+def kT3v3_4ch(): return 47
+def kT3v3_8ch(): return 48
 def kT3v3_16ch(): return 36
 def kT3v3_32ch(): return 37
 def kT3v3_64ch(): return 38
@@ -655,9 +657,9 @@ def kConv2D(last_tensor, filters=32, channel_axis=3, name=None, activation=None,
         return kConv2DType0(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, kernel_size=kernel_size, stride_size=stride_size, padding=padding)
     elif kType == 1:
         return kConv2DType1(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, kernel_size=kernel_size, stride_size=stride_size, padding=padding)
-    elif kType == 2:
+    elif kType == D6_16ch():
         return kConv2DType2(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, kernel_size=kernel_size, stride_size=stride_size, padding=padding, min_channels_per_group=16)
-    elif kType == 3:
+    elif kType == kT3_16ch():
         return kConv2DType3(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, kernel_size=kernel_size, stride_size=stride_size, padding=padding)
     elif kType == 4:
         return kConv2DType4(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, kernel_size=kernel_size, stride_size=stride_size, padding=padding)
@@ -677,11 +679,11 @@ def kConv2D(last_tensor, filters=32, channel_axis=3, name=None, activation=None,
         return kConv2DType7(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, kernel_size=kernel_size, stride_size=stride_size, padding=padding, bin_conv_count=5)
     elif kType == 12:
         return kConv2DType7(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, kernel_size=kernel_size, stride_size=stride_size, padding=padding, bin_conv_count=6)
-    elif kType == 13:
+    elif kType == D6_32ch():
         return kConv2DType2(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, kernel_size=kernel_size, stride_size=stride_size, padding=padding, min_channels_per_group=32)
-    elif kType == 14:
+    elif kType == D6_8ch():
         return kConv2DType2(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, kernel_size=kernel_size, stride_size=stride_size, padding=padding, min_channels_per_group=8)
-    elif kType == 15:
+    elif kType == D6_4ch():
         return kConv2DType2(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, kernel_size=kernel_size, stride_size=stride_size, padding=padding, min_channels_per_group=4)
     elif kType == 16:
         if prev_layer_channel_count >= filters:
@@ -706,15 +708,15 @@ def kConv2D(last_tensor, filters=32, channel_axis=3, name=None, activation=None,
         return kConv2DType8(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, kernel_size=kernel_size, stride_size=stride_size, padding=padding, min_channels_per_group=16, always_intergroup=True)
     elif kType == 22:
         return kConv2DType8(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, kernel_size=kernel_size, stride_size=stride_size, padding=padding, min_channels_per_group=32, always_intergroup=True)
-    elif kType == 23:
+    elif kType == kT3_32ch():
         return kConv2DType3(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, kernel_size=kernel_size, stride_size=stride_size, padding=padding, min_channels_per_group=32)
-    elif kType == 24:
+    elif kType == D6_64ch():
         return kConv2DType2(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, kernel_size=kernel_size, stride_size=stride_size, padding=padding, min_channels_per_group=64)
-    elif kType == 25:
+    elif kType == kT3_64ch():
         return kConv2DType3(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, kernel_size=kernel_size, stride_size=stride_size, padding=padding, min_channels_per_group=64)
-    elif kType == 26:
+    elif kType == D6_128ch():
         return kConv2DType2(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, kernel_size=kernel_size, stride_size=stride_size, padding=padding, min_channels_per_group=128)
-    elif kType == 27:
+    elif kType == kT3_128ch():
         return kConv2DType3(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, kernel_size=kernel_size, stride_size=stride_size, padding=padding, min_channels_per_group=128)
     elif kType == 28:
         return kConv2DType9(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, kernel_size=kernel_size, stride_size=stride_size, padding=padding, min_channels_per_group=16, always_intergroup=True)
@@ -724,36 +726,40 @@ def kConv2D(last_tensor, filters=32, channel_axis=3, name=None, activation=None,
         return kConv2DType9(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, kernel_size=kernel_size, stride_size=stride_size, padding=padding, min_channels_per_group=64, always_intergroup=True)
     elif kType == 31:
         return kConv2DType9(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, kernel_size=kernel_size, stride_size=stride_size, padding=padding, min_channels_per_group=128, always_intergroup=True)
-    elif kType == 32:
+    elif kType == D6v3_16ch():
         return kConv2DType10(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, min_channels_per_group=16, kernel_size=kernel_size, stride_size=stride_size, padding=padding)
-    elif kType == 33:
+    elif kType == D6v3_32ch():
         return kConv2DType10(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, min_channels_per_group=32, kernel_size=kernel_size, stride_size=stride_size, padding=padding)
-    elif kType == 34:
+    elif kType == D6v3_64ch():
         return kConv2DType10(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, min_channels_per_group=64, kernel_size=kernel_size, stride_size=stride_size, padding=padding)
-    elif kType == 35:
+    elif kType == D6v3_128ch():
         return kConv2DType10(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, min_channels_per_group=128, kernel_size=kernel_size, stride_size=stride_size, padding=padding)
-    elif kType == 36:
+    elif kType == kT3v3_16ch():
         return kConv2DType10(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, min_channels_per_group=16, kernel_size=kernel_size, stride_size=stride_size, padding=padding, never_intergroup=True)
-    elif kType == 37:
+    elif kType == kT3v3_32ch():
         return kConv2DType10(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, min_channels_per_group=32, kernel_size=kernel_size, stride_size=stride_size, padding=padding, never_intergroup=True)
-    elif kType == 38:
+    elif kType == kT3v3_64ch():
         return kConv2DType10(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, min_channels_per_group=64, kernel_size=kernel_size, stride_size=stride_size, padding=padding, never_intergroup=True)
-    elif kType == 39:
+    elif kType == kT3v3_128ch():
         return kConv2DType10(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, min_channels_per_group=128, kernel_size=kernel_size, stride_size=stride_size, padding=padding, never_intergroup=True)
-    elif kType == 40:
+    elif kType == D6_12ch():
         return kConv2DType2(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, kernel_size=kernel_size, stride_size=stride_size, padding=padding, min_channels_per_group=12)
-    elif kType == 41:
+    elif kType == D6_24ch():
         return kConv2DType2(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, kernel_size=kernel_size, stride_size=stride_size, padding=padding, min_channels_per_group=24)
-    elif kType == 42:
+    elif kType == D6v3_12ch():
         return kConv2DType10(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, min_channels_per_group=12, kernel_size=kernel_size, stride_size=stride_size, padding=padding)
-    elif kType == 43:
+    elif kType == D6v3_24ch():
         return kConv2DType10(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, min_channels_per_group=24, kernel_size=kernel_size, stride_size=stride_size, padding=padding)
-    elif kType == 44:
+    elif kType == D6v3_8ch():
         return kConv2DType10(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, min_channels_per_group=8, kernel_size=kernel_size, stride_size=stride_size, padding=padding)
-    elif kType == 45:
+    elif kType == D6v3_4ch():
         return kConv2DType10(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, min_channels_per_group=4, kernel_size=kernel_size, stride_size=stride_size, padding=padding)
-    elif kType == 46:
+    elif kType == D6v3_2ch():
         return kConv2DType10(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, min_channels_per_group=2, kernel_size=kernel_size, stride_size=stride_size, padding=padding)
+    elif kType == kT3v3_4ch():
+        return kConv2DType10(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, min_channels_per_group=4, kernel_size=kernel_size, stride_size=stride_size, padding=padding, never_intergroup=True)
+    elif kType == kT3v3_8ch():
+        return kConv2DType10(last_tensor, filters=filters, channel_axis=channel_axis, name=name, activation=activation, has_batch_norm=has_batch_norm, has_batch_scale=has_batch_scale, use_bias=use_bias, min_channels_per_group=8, kernel_size=kernel_size, stride_size=stride_size, padding=padding, never_intergroup=True)
 
 def kPointwiseConv2D(last_tensor, filters=32, channel_axis=3, name=None, activation=None, has_batch_norm=True, has_batch_scale=True, use_bias=True, kType=2):
     """