add pose
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@@ -1,5 +0,0 @@
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# ignore all
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*
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# except this file
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!.gitignore
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@@ -14,9 +14,9 @@ class Mapping(tf.keras.layers.Layer):
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self.gain = 1.0
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self.gain = 1.0
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self.lrmul = 0.01
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self.lrmul = 0.01
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# self.pose_shape_embedding = PoseShapeEmbedding()
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self.pose_shape_embedding = PoseShapeEmbedding()
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# self.garment_embedding = GarmentEmbedding((self.w_dim * 2) - (216 + 11))
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self.garment_embedding = GarmentEmbedding((self.w_dim * 2) - (216 + 11))
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self.garment_embedding = GarmentEmbedding(self.w_dim)
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# self.garment_embedding = GarmentEmbedding(self.w_dim)
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self.normalize = tf.keras.layers.Lambda(lambda x: x * tf.math.rsqrt(tf.reduce_mean(tf.square(x), axis=1, keepdims=True) + 1e-8))
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self.normalize = tf.keras.layers.Lambda(lambda x: x * tf.math.rsqrt(tf.reduce_mean(tf.square(x), axis=1, keepdims=True) + 1e-8))
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@@ -31,10 +31,10 @@ class Mapping(tf.keras.layers.Layer):
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x = latents
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x = latents
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# embed label if any
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# embed label if any
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# pose_shape_latent = self.pose_shape_embedding([conditions[0], conditions[1]])
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pose_shape_latent = self.pose_shape_embedding([conditions[0], conditions[1]])
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garment_latent = self.garment_embedding(conditions[2])
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garment_latent = self.garment_embedding(conditions[2])
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# x = tf.concat([x, pose_shape_latent, garment_latent], axis=1)
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x = tf.concat([x, pose_shape_latent, garment_latent], axis=1)
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x = tf.concat([x, garment_latent], axis=1)
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# x = tf.concat([x, garment_latent], axis=1)
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# normalize inputs
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# normalize inputs
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x = self.normalize(x)
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x = self.normalize(x)
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