From c2f07f5bfb0b99387934fbe44400353a9afa367a Mon Sep 17 00:00:00 2001
From: Vvsmile <450864116@qq.com>
Date: Fri, 25 Nov 2022 17:35:39 +0800
Subject: [PATCH] Remove API: random_crop (#47962)

remove random_crop which is not used in Paddle 2.0
---
 python/paddle/fluid/layers/nn.py              | 58 -------------------
 .../tests/unittests/test_random_crop_op.py    | 28 ---------
 2 files changed, 86 deletions(-)

diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py
index 25428cce876..aae6233acc8 100644
--- a/python/paddle/fluid/layers/nn.py
+++ b/python/paddle/fluid/layers/nn.py
@@ -106,7 +106,6 @@ __all__ = [
     'resize_trilinear',
     'resize_nearest',
     'gather_nd',
-    'random_crop',
     'relu',
     'log',
     'crop_tensor',
@@ -6463,63 +6462,6 @@ def gather_nd(input, index, name=None):
     return output
 
 
-@templatedoc()
-def random_crop(x, shape, seed=None):
-    """
-    ${comment}
-
-    Args:
-        x(${x_type}): ${x_comment}
-        shape(${shape_type}): ${shape_comment}
-        seed(int|${seed_type}|None): ${seed_comment} By default, the seed will
-            get from `random.randint(-65536, 65535)`.
-
-    Returns:
-        ${out_comment}
-
-    Examples:
-        .. code-block:: python
-
-            import paddle.fluid as fluid
-            img = fluid.data("img", [None, 3, 256, 256])
-            # cropped_img is [-1, 3, 224, 224]
-            cropped_img = fluid.layers.random_crop(img, shape=[3, 224, 224])
-
-            # cropped_img2 shape: [-1, 2, 224, 224]
-            # cropped_img2 = fluid.layers.random_crop(img, shape=[2, 224, 224])
-
-            # cropped_img3 shape: [-1, 3, 128, 224]
-            # cropped_img3 = fluid.layers.random_crop(img, shape=[128, 224])
-
-    """
-    helper = LayerHelper("random_crop", **locals())
-    check_variable_and_dtype(
-        x, 'x', ['float32', 'float64', 'uint8', 'int16', 'int32'], 'random_crop'
-    )
-    check_type(shape, 'shape', (list, Variable), 'random_crop')
-    dtype = x.dtype
-    out = helper.create_variable_for_type_inference(dtype)
-    if seed is None:
-        seed = np.random.randint(-65536, 65536)
-    op_attrs = {"shape": shape}
-    if isinstance(seed, int):
-        op_attrs["startup_seed"] = seed
-        seed = helper.create_variable(
-            name=unique_name.generate("random_crop_seed"),
-            dtype="int64",
-            persistable=True,
-        )
-    elif not isinstance(seed, Variable):
-        raise ValueError("'seed' must be a Variable or an int.")
-    helper.append_op(
-        type="random_crop",
-        inputs={"X": x, "Seed": seed},
-        outputs={"Out": out, "SeedOut": seed},
-        attrs=op_attrs,
-    )
-    return out
-
-
 def log(x, name=None):
     r"""
     Calculates the natural log of the given input tensor, element-wise.
diff --git a/python/paddle/fluid/tests/unittests/test_random_crop_op.py b/python/paddle/fluid/tests/unittests/test_random_crop_op.py
index ae474618733..abad6d4cb9d 100644
--- a/python/paddle/fluid/tests/unittests/test_random_crop_op.py
+++ b/python/paddle/fluid/tests/unittests/test_random_crop_op.py
@@ -15,7 +15,6 @@
 import unittest
 import numpy as np
 from op_test import OpTest
-import paddle.fluid as fluid
 
 
 class TestRandomCropOp(OpTest):
@@ -44,32 +43,5 @@ class TestRandomCropOp(OpTest):
             self.assertIn(True, is_equal)
 
 
-class TestRandomCropOpError(unittest.TestCase):
-    def test_errors(self):
-        with fluid.program_guard(fluid.Program()):
-
-            def test_x_type():
-                input_data = np.random.random(2, 3, 256, 256).astype("float32")
-                fluid.layers.random_crop(input_data)
-
-            self.assertRaises(TypeError, test_x_type)
-
-            def test_x_dtype():
-                x2 = fluid.layers.data(
-                    name='x2', shape=[None, 3, 256, 256], dtype='float16'
-                )
-                fluid.layers.random_crop(x2)
-
-            self.assertRaises(TypeError, test_x_dtype)
-
-            def test_shape_type():
-                x3 = fluid.layers.data(
-                    name='x3', shape=[None, 3, 256, 256], dtype='float32'
-                )
-                fluid.layers.random_crop(x3, shape=1)
-
-            self.assertRaises(TypeError, test_shape_type)
-
-
 if __name__ == "__main__":
     unittest.main()
-- 
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