[NNVM][KERAS] Add cropping support (#1636)

nnvm/python/nnvm/frontend/keras.py

@@ -311,6 +311,21 @@ def _convert_upsample(insym, keras_layer, _):
     return _sym.upsampling(insym, **params)
 
 
+def _convert_cropping(insym, keras_layer, _):
+    _check_data_format(keras_layer)
+    crop_type = type(keras_layer).__name__
+    if crop_type == "Cropping1D":
+        raise NotImplementedError("Cropping1D not implemented")
+    elif crop_type == "Cropping2D":
+        (_, in_h, in_w, _) = keras_layer.input_shape
+        ((crop_t, crop_b), (crop_l, crop_r)) = keras_layer.cropping
+    else:
+        raise TypeError("Unrecognized cropping type : {}".format(crop_type))
+    int32_max = np.iinfo(np.int32).max
+    return _sym.strided_slice(insym, begin=[0, 0, crop_t, crop_l],
+                              end=[int32_max, int32_max, in_h-crop_b, in_w-crop_r])
+
+
 def _convert_batchnorm(insym, keras_layer, symtab):
     params = {'scale': False,
               'center': False,
@@ -409,6 +424,7 @@ _convert_map = {
     'Multiply'                 : _convert_merge,
     'ZeroPadding2D'            : _convert_padding,
     'UpSampling2D'             : _convert_upsample,
+    'Cropping2D'               : _convert_cropping,
 
     # 'ZeroPadding1D'          : _convert_padding,
     # 'AveragePooling1D'       : _convert_pooling,
@@ -416,7 +432,6 @@ _convert_map = {
     # 'GlobalAveragePooling1D' : _convert_pooling,
     # 'GlobalMaxPooling1D'     : _convert_pooling,
     # 'Cropping1D'             : _convert_cropping,
-    # 'Cropping2D'             : _convert_cropping,
     # 'UpSampling1D'           : _convert_upsample,
     # 'UpSampling3D'           : _convert_upsample,
     # 'Conv1D'                 : _convert_convolution1d,

nnvm/tests/python/frontend/keras/test_forward.py

@@ -110,6 +110,20 @@ def test_forward_reshape():
     verify_keras_frontend(keras_model)
 
 
+def test_forward_crop():
+    data = keras.layers.Input(shape=(32,32,3))
+    x = keras.layers.Cropping2D(cropping=((1, 1), (1, 1)))(data)
+    x = keras.layers.Cropping2D(cropping=(1, 1))(x)
+    x = keras.layers.Cropping2D(cropping=1)(x)
+    x = keras.layers.Cropping2D(cropping=((0, 1), (1, 0)))(x)
+    x = keras.layers.Cropping2D(cropping=(1, 0))(x)
+    x = keras.layers.Cropping2D(cropping=0)(x)
+    x = keras.layers.Add()([x, x])
+    x = keras.layers.GlobalAveragePooling2D()(x)
+    keras_model = keras.models.Model(data, x)
+    verify_keras_frontend(keras_model)
+
+
 def test_forward_vgg16():
     keras_model = keras.applications.vgg16.VGG16(include_top=True, weights=None,
         input_shape=(224,224,3), classes=1000)
@@ -196,6 +210,7 @@ if __name__ == '__main__':
     test_forward_separable_conv()
     test_forward_upsample()
     test_forward_reshape()
+    test_forward_crop()
     test_forward_vgg16()
     test_forward_xception()
     test_forward_resnet50()