Fix (util): centralizing auto pad utility

Author: i-colbert

src/qonnx/transformation/lower_convs_to_matmul.py

@@ -32,25 +32,7 @@
 
 from qonnx.transformation.base import Transformation
 from qonnx.transformation.extract_conv_bias import ExtractBiasFromConv
-from qonnx.util.basic import get_by_name
-
-
-def _auto_pad_to_explicit_padding(autopad_str, idim_h, idim_w, k_h, k_w, stride_h, stride_w, n_dims):
-    pad_total_h = (stride_h - 1) * idim_h - stride_h + k_h
-    pad_total_w = (stride_w - 1) * idim_w - stride_w + k_w
-    pad_half_small_h = int((pad_total_h / 2))
-    pad_half_small_w = int((pad_total_w / 2))
-    pad_half_large_h = pad_total_h - pad_half_small_h
-    pad_half_large_w = pad_total_w - pad_half_small_w
-    if autopad_str == "VALID":
-        return [0 for i in range(2 * n_dims)]
-    elif autopad_str == "SAME_UPPER":
-        return [pad_half_small_h, pad_half_small_w, pad_half_large_h, pad_half_large_w]
-    elif autopad_str == "SAME_LOWER":
-        return [pad_half_large_h, pad_half_large_w, pad_half_small_h, pad_half_small_w]
-    else:
-        raise Exception("Unsupported auto_pad: " + autopad_str)
-
+from qonnx.util.basic import get_by_name, auto_pad_to_explicit_padding
 
 class LowerConvsToMatMul(Transformation):
     """Replace Conv layers with pairs of Im2Col-MatMul layers, plus Transpose
@@ -100,7 +82,7 @@ def apply(self, model):
                         # use specified padding
                         pad = get_by_name(n.attribute, "pads").ints
                     else:
-                        pad = _auto_pad_to_explicit_padding(
+                        pad = auto_pad_to_explicit_padding(
                             auto_pad,
                             ifm_dim_h,
                             ifm_dim_w,

src/qonnx/transformation/resize_conv_to_deconv.py

@@ -33,7 +33,7 @@
 from qonnx.core.datatype import DataType
 from qonnx.custom_op.general.quant import quant
 from qonnx.transformation.base import Transformation
-from qonnx.util.basic import get_by_name
+from qonnx.util.basic import get_by_name, auto_pad_to_explicit_padding
 
 
 def _weight_convolution(cnv_weights: np.ndarray, scale: int) -> np.ndarray:
@@ -55,23 +55,6 @@ def _weight_convolution(cnv_weights: np.ndarray, scale: int) -> np.ndarray:
     return dcnv_weights
 
 
-def _auto_pad_to_explicit_padding(autopad_str, idim_h, idim_w, k_h, k_w, stride_h, stride_w, n_dims):
-    pad_total_h = (stride_h - 1) * idim_h - stride_h + k_h
-    pad_total_w = (stride_w - 1) * idim_w - stride_w + k_w
-    pad_half_small_h = int((pad_total_h / 2))
-    pad_half_small_w = int((pad_total_w / 2))
-    pad_half_large_h = pad_total_h - pad_half_small_h
-    pad_half_large_w = pad_total_w - pad_half_small_w
-    if autopad_str == "VALID":
-        return [0 for i in range(2 * n_dims)]
-    elif autopad_str == "SAME_UPPER":
-        return [pad_half_small_h, pad_half_small_w, pad_half_large_h, pad_half_large_w]
-    elif autopad_str == "SAME_LOWER":
-        return [pad_half_large_h, pad_half_large_w, pad_half_small_h, pad_half_small_w]
-    else:
-        raise Exception("Unsupported auto_pad: " + autopad_str)
-
-
 class ResizeConvolutionToDeconvolution(Transformation):
     """Replaces resize convolution layers (e.g., nearest neighbor upsample + same-padded convolution)
     with deconvolution layers using the weight convolution algorithm. Currently does not support
@@ -189,7 +172,7 @@ def apply(self, model):
                             # use specified padding
                             pad = get_by_name(conv.attribute, "pads").ints
                         else:
-                            pad = _auto_pad_to_explicit_padding(
+                            pad = auto_pad_to_explicit_padding(
                                 auto_pad,
                                 ifm_dim_h,
                                 ifm_dim_w,

src/qonnx/transformation/subpixel_to_deconv.py

@@ -31,7 +31,7 @@
 from onnx import helper
 
 from qonnx.transformation.base import Transformation
-from qonnx.util.basic import get_by_name
+from qonnx.util.basic import get_by_name, auto_pad_to_explicit_padding
 
 
 def _weight_shuffle(cnv_weights: np.ndarray, block_size: int) -> np.ndarray:
@@ -62,23 +62,6 @@ def _weight_shuffle(cnv_weights: np.ndarray, block_size: int) -> np.ndarray:
     return dcnv_weights
 
 
-def _auto_pad_to_explicit_padding(autopad_str, idim_h, idim_w, k_h, k_w, stride_h, stride_w, n_dims):
-    pad_total_h = (stride_h - 1) * idim_h - stride_h + k_h
-    pad_total_w = (stride_w - 1) * idim_w - stride_w + k_w
-    pad_half_small_h = int((pad_total_h / 2))
-    pad_half_small_w = int((pad_total_w / 2))
-    pad_half_large_h = pad_total_h - pad_half_small_h
-    pad_half_large_w = pad_total_w - pad_half_small_w
-    if autopad_str == "VALID":
-        return [0 for i in range(2 * n_dims)]
-    elif autopad_str == "SAME_UPPER":
-        return [pad_half_small_h, pad_half_small_w, pad_half_large_h, pad_half_large_w]
-    elif autopad_str == "SAME_LOWER":
-        return [pad_half_large_h, pad_half_large_w, pad_half_small_h, pad_half_small_w]
-    else:
-        raise Exception("Unsupported auto_pad: " + autopad_str)
-
-
 class SubPixelToDeconvolution(Transformation):
     """Replaces sub-pixel convolution layers (i.e., same-padded convolution + depth2space)
     with deconvolution layers using the weight shuffle algorithm. Currently does not support
@@ -181,7 +164,7 @@ def apply(self, model):
                             # use specified padding
                             pad = get_by_name(n.attribute, "pads").ints
                         else:
-                            pad = _auto_pad_to_explicit_padding(
+                            pad = auto_pad_to_explicit_padding(
                                 auto_pad,
                                 ifm_dim_h,
                                 ifm_dim_w,

src/qonnx/util/basic.py

@@ -321,3 +321,20 @@ def sanitize_quant_values(model, node_tensors, execution_context, check_values=F
                 )
             )
     return execution_context
+
+
+def auto_pad_to_explicit_padding(autopad_str, idim_h, idim_w, k_h, k_w, stride_h, stride_w, n_dims):
+    pad_total_h = (stride_h - 1) * idim_h - stride_h + k_h
+    pad_total_w = (stride_w - 1) * idim_w - stride_w + k_w
+    pad_half_small_h = int((pad_total_h / 2))
+    pad_half_small_w = int((pad_total_w / 2))
+    pad_half_large_h = pad_total_h - pad_half_small_h
+    pad_half_large_w = pad_total_w - pad_half_small_w
+    if autopad_str == "VALID":
+        return [0 for i in range(2 * n_dims)]
+    elif autopad_str == "SAME_UPPER":
+        return [pad_half_small_h, pad_half_small_w, pad_half_large_h, pad_half_large_w]
+    elif autopad_str == "SAME_LOWER":
+        return [pad_half_large_h, pad_half_large_w, pad_half_small_h, pad_half_small_w]
+    else:
+        raise Exception("Unsupported auto_pad: " + autopad_str)