MLBEDSW-7997: [MLCE] Extended stride support for TRANSPOSE CONV

- Support for stride WxH 1x1
- Support for stride WxH 2x1 when IFM and KERNEL
  is 1D shape with height 1
- Added test to supported operators
- Updated SUPPORTED_OPS.md

Change-Id: Ic1abead8399a5e14a78d962f8aded0d3b3dbfcc4
Signed-off-by: Johan Alfven <johan.alfven@arm.com>X

1 files changed, 20 insertions, 9 deletions

diff --git a/ethosu/vela/tflite_graph_optimiser.py b/ethosu/vela/tflite_graph_optimiser.py
index 9b98b8fa..51078718 100644
--- a/ethosu/vela/tflite_graph_optimiser.py
+++ b/ethosu/vela/tflite_graph_optimiser.py
@@ -243,13 +243,15 @@ def calc_padding_and_skirt(padding_type, kernel, input_shape, explicit_padding):
     return padding, skirt
 
 
-def calc_upscaled_padding_and_skirt(padding_type, kernel_size, stride, input_shape, upscaling_factor):
+def calc_upscaled_padding_and_skirt(
+    padding_type, kernel_size, stride, input_shape, upscaling_factor_y, upscaling_factor_x
+):
     kernel_height, kernel_width = kernel_size[0], kernel_size[1]
     if padding_type == Padding.SAME:
-        ypad = needed_total_padding(int(input_shape.height) * upscaling_factor, int(stride[1]), int(kernel_height))
-        xpad = needed_total_padding(int(input_shape.width) * upscaling_factor, int(stride[2]), int(kernel_width))
-        right_pad = max(((xpad + 1) // upscaling_factor) - 1, 0)
-        bottom_pad = max(((ypad + 1) // upscaling_factor) - 1, 0)
+        ypad = needed_total_padding(int(input_shape.height) * upscaling_factor_y, int(stride[1]), int(kernel_height))
+        xpad = needed_total_padding(int(input_shape.width) * upscaling_factor_x, int(stride[2]), int(kernel_width))
+        right_pad = max(((xpad + 1) // upscaling_factor_x) - 1, 0)
+        bottom_pad = max(((ypad + 1) // upscaling_factor_y) - 1, 0)
         left_pad = max(kernel_width - 1 - right_pad, 0)
         top_pad = max(kernel_height - 1 - bottom_pad, 0)
     elif padding_type == Padding.VALID:
@@ -269,7 +271,11 @@ def fixup_conv2d_backprop(op: Operation, arch, nng) -> Operation:
         # flip the inputs
         op.inputs[0], op.inputs[2] = op.inputs[2], op.inputs[0]
         op.type = Op.Conv2DBackpropInputSwitchedBias
-        op.ifm_resampling_mode = resampling_mode.TRANSPOSE
+        stride_w = op.kernel.stride.x
+        stride_h = op.kernel.stride.y
+        if stride_w > 1 or stride_h > 1:
+            # Transpose conv2d with upscaling
+            op.ifm_resampling_mode = resampling_mode.TRANSPOSE
 
         # Update strides
         op.attrs.update({"stride_w": 1, "stride_h": 1, "strides": (1, 1, 1, 1)})
@@ -924,10 +930,15 @@ def add_padding_fields(op, arch, nng):
             else:
                 raise UnsupportedFeatureError(f"Unknown operation that uses padding: {optype_to_builtintype(op.type)}")
 
-            if op.type == Op.Conv2DBackpropInputSwitchedBias:
-                upscaling_factor = output_shape.height // input_shape.height
+            if op.type == Op.Conv2DBackpropInputSwitchedBias and op.ifm_resampling_mode == resampling_mode.TRANSPOSE:
+                # Transpose with upscale
                 padding, skirt = calc_upscaled_padding_and_skirt(
-                    op.attrs["padding"], kernel_size, op.attrs["strides"], input_shape, upscaling_factor
+                    op.attrs["padding"],
+                    kernel_size,
+                    op.attrs["strides"],
+                    input_shape,
+                    output_shape.height // input_shape.height,
+                    output_shape.width // input_shape.width,
                 )
             else:
                 padding, skirt = calc_padding_and_skirt(