From ee0cdb0ead0e4bfdff7a3bf7a5dc26e715089b6a Mon Sep 17 00:00:00 2001
From: Falco Weichselbaum <f.weichselbaum@fz-juelich.de>
Date: Mon, 25 Oct 2021 12:18:21 +0200
Subject: [PATCH] uncommented imports that were commented for testing in
model_class.py and fixed InputSpec import in advanced_paddings.py
---
mlair/model_modules/advanced_paddings.py | 44 ++++++++++++++++++++++++
mlair/model_modules/model_class.py | 4 +--
2 files changed, 46 insertions(+), 2 deletions(-)
diff --git a/mlair/model_modules/advanced_paddings.py b/mlair/model_modules/advanced_paddings.py
index b0f6c9c2..7a16c2b6 100644
--- a/mlair/model_modules/advanced_paddings.py
+++ b/mlair/model_modules/advanced_paddings.py
@@ -8,6 +8,7 @@ from typing import Union, Tuple
import numpy as np
import tensorflow as tf
+from tensorflow.keras import backend as K
# from tensorflow.keras.backend.common import normalize_data_format
from tensorflow.keras.layers import ZeroPadding2D
# from tensorflow.keras.layers.convolutional import _ZeroPadding
@@ -194,6 +195,49 @@ class PadUtils:
return normalized_padding
+class InputSpec(object):
+ """Specifies the ndim, dtype and shape of every input to a layer.
+ Every layer should expose (if appropriate) an `input_spec` attribute:
+ a list of instances of InputSpec (one per input tensor).
+ A None entry in a shape is compatible with any dimension,
+ a None shape is compatible with any shape.
+ # Arguments
+ dtype: Expected datatype of the input.
+ shape: Shape tuple, expected shape of the input
+ (may include None for unchecked axes).
+ ndim: Integer, expected rank of the input.
+ max_ndim: Integer, maximum rank of the input.
+ min_ndim: Integer, minimum rank of the input.
+ axes: Dictionary mapping integer axes to
+ a specific dimension value.
+ """
+
+ def __init__(self, dtype=None,
+ shape=None,
+ ndim=None,
+ max_ndim=None,
+ min_ndim=None,
+ axes=None):
+ self.dtype = dtype
+ self.shape = shape
+ if shape is not None:
+ self.ndim = len(shape)
+ else:
+ self.ndim = ndim
+ self.max_ndim = max_ndim
+ self.min_ndim = min_ndim
+ self.axes = axes or {}
+
+ def __repr__(self):
+ spec = [('dtype=' + str(self.dtype)) if self.dtype else '',
+ ('shape=' + str(self.shape)) if self.shape else '',
+ ('ndim=' + str(self.ndim)) if self.ndim else '',
+ ('max_ndim=' + str(self.max_ndim)) if self.max_ndim else '',
+ ('min_ndim=' + str(self.min_ndim)) if self.min_ndim else '',
+ ('axes=' + str(self.axes)) if self.axes else '']
+ return 'InputSpec(%s)' % ', '.join(x for x in spec if x)
+
+
class _ZeroPadding(Layer):
"""Abstract nD ZeroPadding layer (private, used as implementation base).
# Arguments
diff --git a/mlair/model_modules/model_class.py b/mlair/model_modules/model_class.py
index be4f4b22..1165999e 100644
--- a/mlair/model_modules/model_class.py
+++ b/mlair/model_modules/model_class.py
@@ -123,9 +123,9 @@ __date__ = '2020-05-12'
import tensorflow.keras as keras
from mlair.model_modules import AbstractModelClass
-#from mlair.model_modules.inception_model import InceptionModelBase
+from mlair.model_modules.inception_model import InceptionModelBase
from mlair.model_modules.flatten import flatten_tail
-#from mlair.model_modules.advanced_paddings import PadUtils, Padding2D, SymmetricPadding2D
+from mlair.model_modules.advanced_paddings import PadUtils, Padding2D, SymmetricPadding2D
from mlair.model_modules.loss import l_p_loss
--
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