[py] Small fixes in v1

bob/learn/tensorflow/models/inception_resnet_v1.py

@@ -163,8 +163,8 @@ class InceptionResnetBlock(tf.keras.layers.Layer):
             branch_1 = [Conv2D_BN(32 // n, 1, name="Branch_1/Conv2d_0a_1x1")]
             branch_1 += [Conv2D_BN(32 // n, 3, name="Branch_1/Conv2d_0b_3x3")]
             branch_2 = [Conv2D_BN(32 // n, 1, name="Branch_2/Conv2d_0a_1x1")]
-            branch_2 += [Conv2D_BN(48 // n, 3, name="Branch_2/Conv2d_0b_3x3")]
-            branch_2 += [Conv2D_BN(64 // n, 3, name="Branch_2/Conv2d_0c_3x3")]
+            branch_2 += [Conv2D_BN(32 // n, 3, name="Branch_2/Conv2d_0b_3x3")]
+            branch_2 += [Conv2D_BN(32 // n, 3, name="Branch_2/Conv2d_0c_3x3")]
             branches = [branch_0, branch_1, branch_2]
         elif block_type == "block17":
             branch_0 = [Conv2D_BN(128 // n, 1, name="Branch_0/Conv2d_1x1")]
@@ -175,8 +175,8 @@ class InceptionResnetBlock(tf.keras.layers.Layer):
         elif block_type == "block8":
             branch_0 = [Conv2D_BN(192 // n, 1, name="Branch_0/Conv2d_1x1")]
             branch_1 = [Conv2D_BN(192 // n, 1, name="Branch_1/Conv2d_0a_1x1")]
-            branch_1 += [Conv2D_BN(224 // n, (1, 3), name="Branch_1/Conv2d_0b_1x3")]
-            branch_1 += [Conv2D_BN(256 // n, (3, 1), name="Branch_1/Conv2d_0c_3x1")]
+            branch_1 += [Conv2D_BN(192 // n, (1, 3), name="Branch_1/Conv2d_0b_1x3")]
+            branch_1 += [Conv2D_BN(192 // n, (3, 1), name="Branch_1/Conv2d_0c_3x1")]
             branches = [branch_0, branch_1]
         else:
             raise ValueError(
@@ -307,14 +307,14 @@ class ReductionA(tf.keras.layers.Layer):
         config.update(
             {
                 name: getattr(self, name)
-                for name in ["padding", "k", "kl", "km", "n", "use_atrous", "name"]
+                for name in ["padding", "k", "l", "m", "n", "use_atrous", "name"]
             }
         )
         return config
 
 
 class ReductionB(tf.keras.layers.Layer):
-    """A Reduction B block for InceptionResnetV2"""
+    """A Reduction B block for InceptionResnetV1"""
 
     def __init__(
         self,
@@ -386,64 +386,6 @@ class ReductionB(tf.keras.layers.Layer):
         return config
 
 
-class InceptionA(tf.keras.layers.Layer):
-    def __init__(self, pool_filters, name="inception_a", **kwargs):
-        super().__init__(name=name, **kwargs)
-        self.pool_filters = pool_filters
-
-        self.branch1x1 = Conv2D_BN(
-            96, kernel_size=1, padding="same", name="Branch_0/Conv2d_1x1"
-        )
-
-        self.branch3x3dbl_1 = Conv2D_BN(
-            64, kernel_size=1, padding="same", name="Branch_2/Conv2d_0a_1x1"
-        )
-        self.branch3x3dbl_2 = Conv2D_BN(
-            96, kernel_size=3, padding="same", name="Branch_2/Conv2d_0b_3x3"
-        )
-        self.branch3x3dbl_3 = Conv2D_BN(
-            96, kernel_size=3, padding="same", name="Branch_2/Conv2d_0c_3x3"
-        )
-
-        self.branch5x5_1 = Conv2D_BN(
-            48, kernel_size=1, padding="same", name="Branch_1/Conv2d_0a_1x1"
-        )
-        self.branch5x5_2 = Conv2D_BN(
-            64, kernel_size=5, padding="same", name="Branch_1/Conv2d_0b_5x5"
-        )
-
-        self.branch_pool_1 = AvgPool2D(
-            pool_size=3, strides=1, padding="same", name="Branch_3/AvgPool_0a_3x3"
-        )
-        self.branch_pool_2 = Conv2D_BN(
-            pool_filters, kernel_size=1, padding="same", name="Branch_3/Conv2d_0b_1x1"
-        )
-
-        channel_axis = 1 if K.image_data_format() == "channels_first" else 3
-        self.concat = Concatenate(axis=channel_axis)
-
-    def call(self, inputs, training=None):
-        branch1x1 = self.branch1x1(inputs)
-
-        branch3x3dbl = self.branch3x3dbl_1(inputs)
-        branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl)
-        branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl)
-
-        branch5x5 = self.branch5x5_1(inputs)
-        branch5x5 = self.branch5x5_2(branch5x5)
-
-        branch_pool = self.branch_pool_1(inputs)
-        branch_pool = self.branch_pool_2(branch_pool)
-
-        outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool]
-        return self.concat(outputs)
-
-    def get_config(self):
-        config = super().get_config()
-        config.update({"pool_filters": self.pool_filters, "name": self.name})
-        return config
-
-
 def InceptionResNetV1(
     include_top=True,
     input_tensor=None,
@@ -452,7 +394,7 @@ def InceptionResNetV1(
     classes=1000,
     bottleneck=False,
     dropout_rate=0.2,
-    name="InceptionResnetV2",
+    name="InceptionResnetV1",
     **kwargs,
 ):
     """Instantiates the Inception-ResNet v1 architecture.
@@ -580,8 +522,8 @@ def InceptionResNetV1(
             scale=1.0,
             activation=None,
             block_type="block8",
-            block_idx=10,
-            name=f"Mixed_8b",
+            block_idx=5,
+            name=f"block8_5",
         )
     )