Merge pull request apache#50 from heliqi/paddle_frontend

SAME of padding_algorithm
wjj19950828 · Sep 24, 2021 · f893670 · f893670
2 parents 2bc59f5 + 552ee2a
commit f893670
Showing 1 changed file with 28 additions and 6 deletions.
diff --git a/python/tvm/relay/frontend/paddlepaddle.py b/python/tvm/relay/frontend/paddlepaddle.py
@@ -526,8 +526,9 @@ def convert_conv2d(g, op, block):
             pad_w = _get_pad_size(0, (k_w - 1) * dilations[1] + 1, strides[1])
         else:
             input_shape = shape_of(input_x)
+            h_w = _op.strided_slice(input_shape, [2], [4])
             try:
-                _, _, in_h, in_w = infer_value(input_shape, g.get_params()).numpy().tolist()
+                in_h, in_w = infer_value(h_w, g.get_params()).numpy().tolist()
             except Exception as e:
                 msg = "The SAME padding algorithm of Conv not support dynamic shape"
                 raise tvm.error.OpAttributeInvalid(msg) from e
@@ -572,9 +573,19 @@ def convert_conv2d_transpose(g, op, block):
     if padding_algorithm == "VALID":
         paddings = [0, 0]
     elif padding_algorithm == "SAME":
-        in_h, in_w = infer_shape(input_x)[2:]
-        pad_h = _get_pad_size(in_h, (k_h - 1) * dilations[0] + 1, strides[0])
-        pad_w = _get_pad_size(in_w, (k_w - 1) * dilations[1] + 1, strides[1])
+        if strides[0] == 1 and strides[1] == 1:
+            pad_h = _get_pad_size(0, (k_h - 1) * dilations[0] + 1, strides[0])
+            pad_w = _get_pad_size(0, (k_w - 1) * dilations[1] + 1, strides[1])
+        else:
+            input_shape = shape_of(input_x)
+            h_w = _op.strided_slice(input_shape, [2], [4])
+            try:
+                in_h, in_w = infer_value(h_w, g.get_params()).numpy().tolist()
+            except Exception as e:
+                msg = "The SAME padding algorithm of Conv_Transpose not support dynamic shape"
+                raise tvm.error.OpAttributeInvalid(msg) from e
+            pad_h = _get_pad_size(in_h, (k_h - 1) * dilations[0] + 1, strides[0])
+            pad_w = _get_pad_size(in_w, (k_w - 1) * dilations[1] + 1, strides[1])
         paddings = [pad_h[0], pad_w[0], pad_h[1], pad_w[1]]
     elif padding_algorithm == "EXPLICIT":
         if len(paddings) == 2:
@@ -1377,8 +1388,19 @@ def convert_pool2d(g, op, block):
     if padding_algorithm == "VALID":
         paddings = [0, 0]
     elif padding_algorithm == "SAME":
-        pad_h = _get_pad_size(in_h, ksize[0], strides[0])
-        pad_w = _get_pad_size(in_w, ksize[1], strides[1])
+        if strides[0] == 1 and strides[1] == 1:
+            pad_h = _get_pad_size(0, ksize[0], strides[0])
+            pad_w = _get_pad_size(0, ksize[1], strides[1])
+        else:
+            input_shape = shape_of(input_x)
+            h_w = _op.strided_slice(input_shape, [2], [4])
+            try:
+                in_h, in_w = infer_value(h_w, g.get_params()).numpy().tolist()
+            except Exception as e:
+                msg = "The SAME padding algorithm of Conv not support dynamic shape"
+                raise tvm.error.OpAttributeInvalid(msg) from e
+            pad_h = _get_pad_size(in_h, ksize[0], strides[0])
+            pad_w = _get_pad_size(in_w, ksize[1], strides[1])
         paddings = [pad_h[0], pad_w[0], pad_h[1], pad_w[1]]
     elif padding_algorithm == "EXPLICIT":
         if len(paddings) == 2: