Merge pull request apache#47 from wjj19950828/paddle_frontend

Fixed pool2d bug

python/tvm/relay/frontend/paddlepaddle.py

@@ -1356,6 +1356,7 @@ def convert_pool2d(g, op, block):
         ksize = [1, 1]
 
     input_x = g.get_node(op.input("X")[0])
+    input_shape = infer_shape(input_x)
 
     op_map = {
         "avg": "avg_pool2d",
@@ -1372,7 +1373,7 @@ def convert_pool2d(g, op, block):
     if padding_algorithm == "VALID":
         paddings = [0, 0]
     elif padding_algorithm == "SAME":
-        in_h, in_w = infer_shape(input_x)[2:]
+        in_h, in_w = input_shape[2:]
         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]]
@@ -1385,6 +1386,11 @@ def convert_pool2d(g, op, block):
         msg = 'Value {} in attribute "padding" of operator Pool2d is not "valid."'
         raise tvm.error.OpAttributeInvalid(msg.format(padding_algorithm))
 
+    if input_shape[2] < ksize[0]:
+        ksize[0] = input_shape[2]
+    if input_shape[3] < ksize[1]:
+        ksize[1] = input_shape[3]
+
     if not adaptive:
         out = getattr(_op.nn, op_map[pooling_type])(
             input_x, pool_size=ksize, strides=strides, padding=paddings, ceil_mode=ceil_mode

tests/python/frontend/paddlepaddle/test_forward.py

@@ -1458,6 +1458,13 @@ def pool2d2(inputs):
 
     @paddle.jit.to_static
     def pool2d3(inputs):
+        output = nn.functional.max_pool2d(
+            inputs, kernel_size=2, stride=2, padding=0
+        )
+        return output
+
+    @paddle.jit.to_static
+    def pool2d4(inputs):
         output, max_indices = nn.functional.max_pool2d(
             inputs, kernel_size=2, stride=2, padding=0, return_mask=True
         )
@@ -1466,8 +1473,10 @@ def pool2d3(inputs):
     input_data = paddle.uniform(shape=[1, 2, 32, 32], dtype="float32", min=-1, max=1)
     verify_model(pool2d1, input_data, input_shape=[[-1, 2, 32, 32]])
     verify_model(pool2d2, input_data=input_data)
+    input_data1 = paddle.uniform(shape=[1, 2, 1, 50], dtype="float32", min=-1, max=1)
+    verify_model(pool2d3, input_data=input_data1)
     # need op max_pool2d_with_index
-    verify_model(pool2d3, input_data=input_data)
+    verify_model(pool2d4, input_data=input_data)
 
 
 @tvm.testing.uses_gpu