[KERAS]Upsample3d & ZeroPadding3d op (#5125)

* [KERAS]upsampling3d and zeropadding3d op

* [KERAS]upsampling3d and zeropadding3d test case

* Review comments updated

diff --git a/python/tvm/relay/frontend/keras.py b/python/tvm/relay/frontend/keras.py
index 7c189f2..f86092d 100644 (file)
--- a/python/tvm/relay/frontend/keras.py
+++ b/python/tvm/relay/frontend/keras.py
@@ -569,14 +569,6 @@ def _convert_upsample(inexpr, keras_layer, etab):
                 params['method'] = 'nearest_neighbor'
             else:
                 params['method'] = 'bilinear'
-
-    elif upsample_type == 'UpSampling3D':
-        h, w, d = keras_layer.size
-        if h != w or w != d:
-            raise tvm.error.OpAttributeInvalid(
-                'Height, width, and depth must all be equal for operator Upsample.')
-        params['scale_h'] = h
-        params['scale_w'] = h
     else:
         raise tvm.error.OpNotImplemented(
             'Operator {} is not supported for frontend Keras.'.format(upsample_type))
@@ -585,6 +577,18 @@ def _convert_upsample(inexpr, keras_layer, etab):
     return out
 
 
+def _convert_upsample3d(inexpr, keras_layer, etab):
+    _check_data_format(keras_layer)
+    params = {}
+    d, h, w = keras_layer.size
+    params['scale_d'] = d
+    params['scale_h'] = h
+    params['scale_w'] = w
+    params['layout'] = etab.data_layout
+    out = _op.nn.upsampling3d(inexpr, **params)
+    return out
+
+
 def _convert_cropping(inexpr, keras_layer, _):
     _check_data_format(keras_layer)
     crop_type = type(keras_layer).__name__
@@ -663,6 +667,36 @@ def _convert_padding(inexpr, keras_layer, etab):
         return _op.nn.pad(data=inexpr, pad_width=((0, 0), (0, 0), (top, bottom), (left, right)))
     return _op.nn.pad(data=inexpr, pad_width=((0, 0), (top, bottom), (left, right), (0, 0)))
 
+def _convert_padding3d(inexpr, keras_layer, etab):
+    _check_data_format(keras_layer)
+    padding = keras_layer.padding
+
+    d_pad = h_pad = w_pad = [0, 0]
+
+    # padding can be 'int' or 'tuple of 3 ints' or 'tuple of 3 tuples of 2 ints' or 'tuple
+    # of 3 tuples of 2 ints different values'. In all these scenarios keras will send 3
+    # tuples of 2 ints.
+    if isinstance(padding, tuple) and isinstance(padding[0], tuple):
+        d_pad = padding[0]
+        h_pad = padding[1]
+        w_pad = padding[2]
+    else:
+        msg = 'Value {} in attribute "padding" of operator ZeroPadding3D is ' \
+              'not valid.'
+        raise tvm.error.OpAttributeInvalid(msg.format(str(padding)))
+
+    if etab.data_layout == 'NCDHW':
+        out = _op.nn.pad(data=inexpr, pad_width=((0, 0), (0, 0),
+                                                 (d_pad[0], d_pad[1]),
+                                                 (h_pad[0], h_pad[1]),
+                                                 (w_pad[0], w_pad[1])))
+    else:
+        out = _op.nn.pad(data=inexpr, pad_width=((0, 0),
+                                                 (d_pad[0], d_pad[1]),
+                                                 (h_pad[0], h_pad[1]),
+                                                 (w_pad[0], w_pad[1]),
+                                                 (0, 0)))
+    return out
 
 def _convert_concat(inexpr, keras_layer, etab):
     _check_data_format(keras_layer)
@@ -858,7 +892,8 @@ _convert_map = {
     'AveragePooling3D'         : _convert_pooling3d,
     # 'GlobalMaxPooling3D'     : _convert_pooling3d,
     # 'GlobalAveragePooling3D' : _convert_pooling3d,
-    # 'UpSampling3D'           : _convert_upsample3d,
+    'UpSampling3D'             : _convert_upsample3d,
+    'ZeroPadding3D'            : _convert_padding3d,
 
     'SimpleRNN'                : _convert_simple_rnn,
     'LSTM'                     : _convert_lstm,

diff --git a/tests/python/frontend/keras/test_forward.py b/tests/python/frontend/keras/test_forward.py
index 5f4bcb1..c2caf91 100644 (file)
--- a/tests/python/frontend/keras/test_forward.py
+++ b/tests/python/frontend/keras/test_forward.py
@@ -443,6 +443,28 @@ class TestKeras:
             keras_model = keras.models.Model(data, x)
             verify_keras_frontend(keras_model, layout='NDHWC')
 
+    def test_forward_upsample3d(self, keras):
+        data = keras.layers.Input(shape=(32, 32, 32, 3))
+        x = keras.layers.UpSampling3D(size=(2, 3, 4))(data)
+        keras_model = keras.models.Model(data, x)
+        verify_keras_frontend(keras_model, layout='NDHWC')
+
+    def test_forward_zero_padding3d(self, keras):
+        data = keras.layers.Input(shape=(32, 32, 32, 3))
+        pad_funcs = [# Integer
+                     keras.layers.ZeroPadding3D(padding=2),
+                     # tuple of 3 ints
+                     keras.layers.ZeroPadding3D(padding=(1, 2, 3)),
+                     # tuple of 3 tuples of 2 ints
+                     keras.layers.ZeroPadding3D(padding=((1,1), (2,2), (2,2))),
+                     # tuple of 3 tuples of 2 ints different values
+                     keras.layers.ZeroPadding3D(padding=((1,2), (2,3), (3,2))),
+                    ]
+        for pad_func in pad_funcs:
+            x = pad_func(data)
+            keras_model = keras.models.Model(data, x)
+            verify_keras_frontend(keras_model, layout='NDHWC')
+
 if __name__ == '__main__':
     for k in [keras, tf_keras]:
         sut = TestKeras()
@@ -472,3 +494,6 @@ if __name__ == '__main__':
         sut.test_forward_mobilenet(keras=k, layout='NHWC')
         sut.test_forward_conv3d(keras=k)
         sut.test_forward_pool3d(keras=k)
+        sut.test_forward_upsample3d(keras=k)
+        sut.test_forward_zero_padding3d(keras=k)
+