Fix fold python examples (#38636)

* fix fold python examples, test=develop

* fix size type, test=develop

* fix python example, test=develop

* fix fold shape check

* fix fold dygraph mode, test=develop

paddle/fluid/operators/fold_op.cc

@@ -95,6 +95,17 @@ class FoldOp : public framework::OperatorWithKernel {
                           "but recieved strides_height: %d strides_width: %d.",
                           strides[0], strides[1]));
     // check dilations
+    PADDLE_ENFORCE_GT(output_height, 1,
+                      platform::errors::InvalidArgument(
+                          "The `output_height` should be greater than one, "
+                          "but recieved output_height: %d .",
+                          output_height));
+    PADDLE_ENFORCE_GT(output_width, 1,
+                      platform::errors::InvalidArgument(
+                          "The `output_width` should be greater than one, "
+                          "but recieved output_width: %d .",
+                          output_width));
+    // check output size
     PADDLE_ENFORCE_GT(
         dilation_height, 0,
         platform::errors::InvalidArgument(
@@ -146,7 +157,7 @@ class FoldOp : public framework::OperatorWithKernel {
             output_width));
 
     PADDLE_ENFORCE_EQ(
-        blocks_height * blocks_width, in_dims[1],
+        blocks_height * blocks_width, in_dims[2],
         platform::errors::InvalidArgument(
             "Given input output_size (%d, %d), "
             "kernel_sizes (%d, %d), strides (%d, %d), dilations (%d, %d), "
@@ -156,6 +167,15 @@ class FoldOp : public framework::OperatorWithKernel {
             strides[0], strides[1], dilations[0], dilations[1], blocks_height,
             blocks_width, blocks_height * blocks_width, in_dims[2]));
 
+    PADDLE_ENFORCE_EQ(
+        in_dims[1] % (kernel_sizes[0] * kernel_sizes[1]), 0,
+        platform::errors::InvalidArgument(
+            "Expected size of input's dimension 1 to be divisible by the"
+            "product of kernel_size, but got input.size(1)=%d and "
+            "kernel_size=( %d"
+            ", %d).",
+            in_dims[1], kernel_sizes[0], kernel_sizes[1]));
+
     out_dims.push_back(output_height);
     out_dims.push_back(output_width);
     ctx->SetOutputDim("Y", phi::make_ddim(out_dims));

python/paddle/fluid/tests/unittests/test_fold_op.py

@@ -174,6 +174,15 @@ class TestFoldOpError(unittest.TestCase):
                     x, output_sizes=[6, 6], kernel_sizes=[2, 2],
                     strides=[1, 1])
 
+            def test_output_size_2():
+                # out_size must GT 1
+                x = paddle.randn(shape=[2, 6, 6], dtype="float32")
+                out = fold(
+                    x,
+                    output_sizes=[0.1, 0.2],
+                    kernel_sizes=[2, 2],
+                    strides=[1, 1])
+
             def test_block_h_w():
                 # test_block_h_w GT 0
                 x = paddle.randn(shape=[2, 1, 1], dtype="float32")
@@ -196,6 +205,7 @@ class TestFoldOpError(unittest.TestCase):
             self.assertRaises(AssertionError, test_dilations_shape)
             self.assertRaises(AssertionError, test_strides_shape)
             self.assertRaises(ValueError, test_output_size)
+            self.assertRaises(ValueError, test_output_size_2)
             self.assertRaises(ValueError, test_block_h_w)
             self.assertRaises(ValueError, test_GT_0)
 

python/paddle/nn/functional/common.py

@@ -351,7 +351,6 @@ def interpolate(x,
 
     out_shape = size
     scale = scale_factor
-
     if out_shape is not None and scale is not None:
         raise ValueError("Only one of size or scale_factor should be defined.")
     if out_shape is not None:
@@ -362,6 +361,8 @@ def interpolate(x,
             if in_dynamic_mode():
                 if isinstance(out_shape, Variable):
                     out_shape = list(out_shape.numpy())
+                else:
+                    out_shape = list(out_shape)
                 for i, dim in enumerate(out_shape):
                     if isinstance(dim, Variable):
                         out_shape[i] = dim.numpy()[0]
@@ -1818,7 +1819,6 @@ def fold(x,
     can be calculated as following.
 
     .. math::
-
         H_out &= output_size[0]
         W_out &= output_size[1]
         C_out &= C_in / kernel\_sizes[0] / kernel\_sizes[1]
@@ -1826,21 +1826,21 @@ def fold(x,
     Parameters:
         x(Tensor):                3-D Tensor, input tensor of format [N, C, L],
                                   data type can be float32 or float64
-        output_sizes(list):       The size of output size, should be [output_size_h, output_size_w]
+        output_sizes(int|list|tuple):       The size of output size, should be [output_size_h, output_size_w]
                                   or an interger o treated as [o, o].
-        kernel_sizes(int|list):   The size of convolution kernel, should be [k_h, k_w]
+        kernel_sizes(int|list|tuple):   The size of convolution kernel, should be [k_h, k_w]
                                   or an integer k treated as [k, k].
-        strides(int|list):        The strides, should be [stride_h, stride_w]
+        strides(int|list|tuple):        The strides, should be [stride_h, stride_w]
                                   or an integer stride treated as [sride, stride].
                                   For default, strides will be [1, 1].
-        paddings(int|list):       The paddings of each dimension, should be
+        paddings(int|list|tuple):       The paddings of each dimension, should be
                                   [padding_top, padding_left, padding_bottom, padding_right]
                                   or [padding_h, padding_w] or an integer padding.
                                   If [padding_h, padding_w] was given, it will expanded to
                                   [padding_h, padding_w, padding_h, padding_w]. If an integer
                                   padding was given, [padding, padding, padding, padding] will
                                   be used. For default, paddings will be [0, 0, 0, 0]
-        dilations(int|list):      the dilations of convolution kernel, should be
+        dilations(int|list|tuple):      the dilations of convolution kernel, should be
                                   [dilation_h, dilation_w], or an integer dilation treated as
                                   [dilation, dilation]. For default, it will be [1, 1].
         name(str, optional): The default value is None.
@@ -1859,9 +1859,9 @@ def fold(x,
             import paddle
             import paddle.nn.functional as F
 
-            x = paddle.randn([2,12,9])
-            y = F.fold(x, output_sizes=(4, 4), kernel_sizes=2)
-            # y.shape = [2,3,4,4]
+            x = paddle.randn([2,3*2*2,12])
+            y = F.fold(x, output_sizes=[4, 5], kernel_sizes=2)
+            # y.shape = [2,3,4,5]
 
     """
 
@@ -1872,29 +1872,32 @@ def fold(x,
     assert len(x.shape) == 3, \
             "input should be the format of [N, C, L]"
 
+    def _is_list_or_turple_(data):
+        return (isinstance(data, list) or isinstance(data, tuple))
+
     if isinstance(output_sizes, int):
         output_sizes = [output_sizes, output_sizes]
     else:
-        assert isinstance(output_sizes, list) and (len(output_sizes) == 2), \
-            "output_sizes should either be an integer or a list of two integers"
+        assert _is_list_or_turple_(output_sizes) and (len(output_sizes) == 2), \
+            "output_sizes should either be an integer or a list/tuple of two integers"
 
     if isinstance(kernel_sizes, int):
         kernel_sizes = [kernel_sizes, kernel_sizes]
     else:
-        assert isinstance(kernel_sizes, list) and (len(kernel_sizes) == 2), \
-            "kernel_sizes should either be an integer or a list of two integers"
+        assert _is_list_or_turple_(kernel_sizes) and (len(kernel_sizes) == 2), \
+            "kernel_sizes should either be an integer or a list/tuple of two integers"
 
     if isinstance(strides, int):
         strides = [strides, strides]
     else:
-        assert isinstance(strides, list) and (len(strides) == 2), \
-            "strides should either be an integer or a list of two integers"
+        assert _is_list_or_turple_(strides) and (len(strides) == 2), \
+            "strides should either be an integer or a list/tuple of two integers"
 
     if isinstance(dilations, int):
         dilations = [dilations, dilations]
     else:
-        assert isinstance(dilations, list) and (len(dilations) == 2), \
-            "dilations should either be an integer or a list of two integers"
+        assert _is_list_or_turple_(dilations) and (len(dilations) == 2), \
+            "dilations should either be an integer or a list/tuple of two integers"
 
     if isinstance(paddings, int):
         paddings = [paddings] * 4
@@ -1912,6 +1915,11 @@ def fold(x,
             "Unexpected type of paddings, it should be either an integer or a list"
             "of 2 or 4 integers")
 
+    if in_dynamic_mode():
+        out = _C_ops.fold(x, "output_sizes", output_sizes, "kernel_sizes",
+                          kernel_sizes, "strides", strides, "paddings",
+                          paddings, "dilations", dilations)
+    else:
         out = helper.create_variable_for_type_inference(dtype=x.dtype)
         helper.append_op(
             type="fold",

python/paddle/nn/layer/common.py

@@ -1565,7 +1565,6 @@ class Fold(Layer):
     can be calculated as following.
 
     .. math::
-
         H_out &= output_size[0]
         W_out &= output_size[1]
         C_out &= C_in / kernel\_sizes[0] / kernel\_sizes[1]
@@ -1573,19 +1572,19 @@ class Fold(Layer):
     Parameters:
         output_sizes(list):       The size of output size, should be [output_size_h, output_size_w]
                                   or an interger o treated as [o, o].
-        kernel_sizes(int|list):   The size of convolution kernel, should be [k_h, k_w]
+        kernel_sizes(int|list|tuple):   The size of convolution kernel, should be [k_h, k_w]
                                   or an integer k treated as [k, k].
-        strides(int|list):        The strides, should be [stride_h, stride_w]
+        strides(int|list|tuple):        The strides, should be [stride_h, stride_w]
                                   or an integer stride treated as [sride, stride].
                                   For default, strides will be [1, 1].
-        paddings(int|list):       The paddings of each dimension, should be
+        paddings(int|list|tuple):       The paddings of each dimension, should be
                                   [padding_top, padding_left, padding_bottom, padding_right]
                                   or [padding_h, padding_w] or an integer padding.
                                   If [padding_h, padding_w] was given, it will expanded to
                                   [padding_h, padding_w, padding_h, padding_w]. If an integer
                                   padding was given, [padding, padding, padding, padding] will
                                   be used. For default, paddings will be [0, 0, 0, 0]
-        dilations(int|list):      the dilations of convolution kernel, should be
+        dilations(int|list|tuple):      the dilations of convolution kernel, should be
                                   [dilation_h, dilation_w], or an integer dilation treated as
                                   [dilation, dilation]. For default, it will be [1, 1].
         name(str, optional): The default value is None.
@@ -1604,10 +1603,10 @@ class Fold(Layer):
             import paddle
             import paddle.nn as nn
 
-            x = paddle.randn([2,12,9])
-            fold = nn.Fold(output_sizes=(4, 4), kernel_sizes=2)
+            x = paddle.randn([2,3*2*2,12])
+            fold = nn.Fold(output_sizes=[4, 5], kernel_sizes=2)
             y = fold(x)
-            # y.shape = [2,3,4,4]
+            # y.shape = [2,3,4,5]
    """
 
     def __init__(self,