[Fluid Clean] remove nn.topk, nn.ctc_greedy_decoder, nn.im2sequence,...

[Fluid Clean] remove nn.topk, nn.ctc_greedy_decoder, nn.im2sequence, nn.multiplex, nn.smooth_l1 (#48289)

paddle/phi/kernels/funcs/transpose_functor.cu.h

@@ -475,7 +475,7 @@ void SwapDim1And2InNarrow(const phi::GPUContext& d,
         CeilOrFloor<int, false>(input_long_edge, proposed_tile_long_edge) *
             proposed_tile_long_edge;
 
-    int num_full_tiles = 
+    int num_full_tiles =
         CeilOrFloor<int, false>(input_long_edge, proposed_tile_long_edge);
 
     float cost = num_wasted_threads;

python/paddle/fluid/layers/detection.py

@@ -1688,7 +1688,8 @@ def ssd_loss(
     location = __reshape_to_2d(location)
     target_bbox = __reshape_to_2d(target_bbox)
 
-    loc_loss = nn.smooth_l1(location, target_bbox)
+    smooth_l1_loss = paddle.nn.loss.SmoothL1Loss()
+    loc_loss = smooth_l1_loss(location, target_bbox)
     target_loc_weight = __reshape_to_2d(target_loc_weight)
     loc_loss = loc_loss * target_loc_weight
 

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

@@ -1833,8 +1833,8 @@ def fast_decode(
             )
             logits = paddle.reshape(logits, (-1, trg_vocab_size))
 
-            topk_scores, topk_indices = layers.topk(
-                input=paddle.nn.functional.softmax(logits), k=beam_size
+            topk_scores, topk_indices = paddle.topk(
+                x=paddle.nn.functional.softmax(logits), k=beam_size
             )
             accu_scores = layers.elementwise_add(
                 x=paddle.log(topk_scores),

python/paddle/fluid/tests/unittests/dygraph_to_static/seq2seq_dygraph_model.py

@@ -459,9 +459,7 @@ class BaseModel(fluid.dygraph.Layer):
             scores = paddle.reshape(
                 log_probs, [-1, self.beam_size * self.tar_vocab_size]
             )
-            topk_scores, topk_indices = fluid.layers.topk(
-                input=scores, k=self.beam_size
-            )
+            topk_scores, topk_indices = paddle.topk(x=scores, k=self.beam_size)
 
             beam_indices = paddle.floor_divide(topk_indices, vocab_size_tensor)
             token_indices = paddle.remainder(topk_indices, vocab_size_tensor)

python/paddle/fluid/tests/unittests/dygraph_to_static/transformer_dygraph_model.py

@@ -853,9 +853,7 @@ class Transformer(Layer):
                 log_probs, [-1, beam_size * self.trg_vocab_size]
             )
             scores = log_probs
-            topk_scores, topk_indices = fluid.layers.topk(
-                input=scores, k=beam_size
-            )
+            topk_scores, topk_indices = paddle.topk(x=scores, k=beam_size)
             beam_indices = paddle.floor_divide(topk_indices, vocab_size_tensor)
             token_indices = paddle.remainder(topk_indices, vocab_size_tensor)
 

python/paddle/fluid/tests/unittests/ipu/test_topk_op_ipu.py

@@ -31,7 +31,7 @@ class TestTopKOp(IPUOpTest):
         self.set_op_attrs()
 
     def set_test_op(self):
-        self.op = paddle.fluid.layers.topk
+        self.op = paddle.topk
 
     def set_data_feed(self):
         data = np.random.uniform(size=[3, 5])

python/paddle/fluid/tests/unittests/npu/test_smooth_l1_loss_op_npu.py

@@ -138,22 +138,5 @@ class TestSmoothL1LossOp2(OpTest):
         )
 
 
-class TestSmoothL1LossOpError(unittest.TestCase):
-    def test_errors(self):
-        with fluid.program_guard(fluid.Program(), fluid.Program()):
-            # The input type of accuracy_op must be Variable.
-            x1 = fluid.create_lod_tensor(
-                np.array([[-1]]), [[1]], fluid.NPUPlace(0)
-            )
-            y1 = fluid.create_lod_tensor(
-                np.array([[-1]]), [[1]], fluid.NPUPlace(0)
-            )
-            self.assertRaises(TypeError, fluid.layers.smooth_l1, x1, y1)
-            # The input dtype of accuracy_op must be float32 or float64.
-            x2 = fluid.layers.data(name='x2', shape=[4], dtype="int32")
-            y2 = fluid.layers.data(name='x2', shape=[4], dtype="int32")
-            self.assertRaises(TypeError, fluid.layers.smooth_l1, x2, y2)
-
-
 if __name__ == '__main__':
     unittest.main()

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

@@ -312,7 +312,7 @@ class TestBeamSearchOpError(unittest.TestCase):
                 name='pre_scores', shape=[1], lod_level=2, dtype='float32'
             )
             probs = fluid.data(name='probs', shape=[10000], dtype='float32')
-            topk_scores, topk_indices = fluid.layers.topk(probs, k=4)
+            topk_scores, topk_indices = paddle.topk(probs, k=4)
             accu_scores = fluid.layers.elementwise_add(
                 x=paddle.log(x=topk_scores),
                 y=paddle.reshape(pre_scores, shape=[-1]),

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

@@ -18,7 +18,6 @@ import numpy as np
 from op_test import OpTest
 
 import paddle
-import paddle.fluid as fluid
 
 
 def CTCAlign(input, lod, blank, merge_repeated, padding=0, input_length=None):
@@ -226,50 +225,6 @@ class TestCTCAlignOpCase5(TestCTCAlignPaddingOp):
         )
 
 
-class TestCTCAlignOpApi(unittest.TestCase):
-    def test_api(self):
-        x = fluid.layers.data('x', shape=[4], dtype='float32')
-        y = fluid.layers.ctc_greedy_decoder(x, blank=0)
-
-        x_pad = fluid.layers.data('x_pad', shape=[4, 4], dtype='float32')
-        x_pad_len = fluid.layers.data('x_pad_len', shape=[1], dtype='int64')
-        y_pad, y_pad_len = fluid.layers.ctc_greedy_decoder(
-            x_pad, blank=0, input_length=x_pad_len
-        )
-
-        place = fluid.CPUPlace()
-        x_tensor = fluid.create_lod_tensor(
-            np.random.rand(8, 4).astype("float32"), [[4, 4]], place
-        )
-
-        x_pad_tensor = np.random.rand(2, 4, 4).astype("float32")
-        x_pad_len_tensor = np.array([[4], [4]]).reshape([2, 1]).astype("int64")
-
-        exe = fluid.Executor(place)
-
-        exe.run(fluid.default_startup_program())
-        ret = exe.run(
-            feed={
-                'x': x_tensor,
-                'x_pad': x_pad_tensor,
-                'x_pad_len': x_pad_len_tensor,
-            },
-            fetch_list=[y, y_pad, y_pad_len],
-            return_numpy=False,
-        )
-
-
-class BadInputTestCTCAlignr(unittest.TestCase):
-    def test_error(self):
-        with fluid.program_guard(fluid.Program()):
-
-            def test_bad_x():
-                x = fluid.layers.data(name='x', shape=[8], dtype='int64')
-                cost = fluid.layers.ctc_greedy_decoder(input=x, blank=0)
-
-            self.assertRaises(TypeError, test_bad_x)
-
-
 if __name__ == "__main__":
     paddle.enable_static()
     unittest.main()

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

@@ -1519,8 +1519,8 @@ class TestLayer(LayerTest):
         with self.dynamic_graph():
             with _test_eager_guard():
                 input = fluid.dygraph.to_variable(np.random.random((13, 11)))
-                top5_values1, top5_indices1 = layers.topk(input, k=5)
-                top5_values2, top5_indices2 = layers.topk(
+                top5_values1, top5_indices1 = paddle.topk(input, k=5)
+                top5_values2, top5_indices2 = paddle.topk(
                     input, k=fluid.dygraph.to_variable(np.array([5]))
                 )
                 np.testing.assert_array_equal(
@@ -1531,8 +1531,8 @@ class TestLayer(LayerTest):
                 )
 
             input = fluid.dygraph.to_variable(np.random.random((13, 11)))
-            top5_values1, top5_indices1 = layers.topk(input, k=5)
-            top5_values2, top5_indices2 = layers.topk(
+            top5_values1, top5_indices1 = paddle.topk(input, k=5)
+            top5_values2, top5_indices2 = paddle.topk(
                 input, k=fluid.dygraph.to_variable(np.array([5]))
             )
             np.testing.assert_array_equal(
@@ -3104,7 +3104,7 @@ class TestBook(LayerTest):
             x1 = self._get_data(name='x1', shape=[4], dtype='float32')
             x2 = self._get_data(name='x2', shape=[4], dtype='float32')
             index = self._get_data(name='index', shape=[1], dtype='int32')
-            out = layers.multiplex(inputs=[x1, x2], index=index)
+            out = paddle.multiplex(inputs=[x1, x2], index=index)
             return out
 
     def make_softmax_with_cross_entropy(self):
@@ -3144,15 +3144,6 @@ class TestBook(LayerTest):
             self.assertIsNotNone(loss4)
             return loss4
 
-    def make_smooth_l1(self):
-        with program_guard(
-            fluid.default_main_program(), fluid.default_startup_program()
-        ):
-            x = self._get_data(name='x', shape=[4], dtype='float32')
-            y = self._get_data(name='label', shape=[4], dtype='float32')
-            loss = layers.smooth_l1(x, y)
-            return loss
-
     def make_scatter(self):
         with program_guard(
             fluid.default_main_program(), fluid.default_startup_program()
@@ -3192,7 +3183,7 @@ class TestBook(LayerTest):
             fluid.default_main_program(), fluid.default_startup_program()
         ):
             data = self._get_data(name="label", shape=[200], dtype="float32")
-            values, indices = layers.topk(data, k=5)
+            values, indices = paddle.topk(data, k=5)
             return values
             return indices
 
@@ -3559,20 +3550,6 @@ class TestBook(LayerTest):
                 )
             )
 
-    def test_im2sequence(self):
-        # TODO(minqiyang): dygraph do not support lod now
-        with self.static_graph():
-            x = layers.data(name='x', shape=[3, 128, 128], dtype='float32')
-            y = layers.data(name='y', shape=[], dtype='float32')
-            output = layers.im2sequence(
-                input=x,
-                input_image_size=y,
-                stride=[1, 1],
-                filter_size=[2, 2],
-                out_stride=[1, 1],
-            )
-            return output
-
     def test_lod_reset(self):
         # TODO(minqiyang): dygraph do not support lod now
         with self.static_graph():

python/paddle/fluid/tests/unittests/test_smooth_l1_loss_op.py

@@ -17,8 +17,6 @@ import unittest
 import numpy as np
 from op_test import OpTest
 
-import paddle.fluid as fluid
-
 
 def smooth_l1_loss_forward(val, sigma2):
     abs_val = abs(val)
@@ -124,22 +122,5 @@ class TestSmoothL1LossOp2(OpTest):
         )
 
 
-class TestSmoothL1LossOpError(unittest.TestCase):
-    def test_errors(self):
-        with fluid.program_guard(fluid.Program(), fluid.Program()):
-            # The input type of accuracy_op must be Variable.
-            x1 = fluid.create_lod_tensor(
-                np.array([[-1]]), [[1]], fluid.CPUPlace()
-            )
-            y1 = fluid.create_lod_tensor(
-                np.array([[-1]]), [[1]], fluid.CPUPlace()
-            )
-            self.assertRaises(TypeError, fluid.layers.smooth_l1, x1, y1)
-            # The input dtype of accuracy_op must be float32 or float64.
-            x2 = fluid.layers.data(name='x2', shape=[4], dtype="int32")
-            y2 = fluid.layers.data(name='x2', shape=[4], dtype="int32")
-            self.assertRaises(TypeError, fluid.layers.smooth_l1, x2, y2)
-
-
 if __name__ == '__main__':
     unittest.main()