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rtdetr_paddle/ppdet/modeling/transformers/ext_op/README.md

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+# Multi-scale deformable attention自定义OP编译
+该自定义OP是参考[自定义外部算子](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/custom_op/new_cpp_op_cn.html) 。
+
+## 1. 环境依赖
+- Paddle >= 2.3.2
+- gcc 8.2
+
+## 2. 安装
+请在当前路径下进行编译安装
+```
+cd rtdetr_paddle/ppdet/modeling/transformers/ext_op/
+python setup_ms_deformable_attn_op.py install
+```
+
+编译完成后即可使用，以下为`ms_deformable_attn`的使用示例
+```
+# 引入自定义op
+from deformable_detr_ops import ms_deformable_attn
+
+# 构造fake input tensor
+bs, n_heads, c = 2, 8, 8
+query_length, n_levels, n_points = 2, 2, 2
+spatial_shapes = paddle.to_tensor([(6, 4), (3, 2)], dtype=paddle.int64)
+level_start_index = paddle.concat((paddle.to_tensor(
+    [0], dtype=paddle.int64), spatial_shapes.prod(1).cumsum(0)[:-1]))
+value_length = sum([(H * W).item() for H, W in spatial_shapes])
+
+def get_test_tensors(channels):
+    value = paddle.rand(
+        [bs, value_length, n_heads, channels], dtype=paddle.float32) * 0.01
+    sampling_locations = paddle.rand(
+        [bs, query_length, n_heads, n_levels, n_points, 2],
+        dtype=paddle.float32)
+    attention_weights = paddle.rand(
+        [bs, query_length, n_heads, n_levels, n_points],
+        dtype=paddle.float32) + 1e-5
+    attention_weights /= attention_weights.sum(-1, keepdim=True).sum(
+        -2, keepdim=True)
+    return [value, sampling_locations, attention_weights]
+
+value, sampling_locations, attention_weights = get_test_tensors(c)
+
+output = ms_deformable_attn(value,
+                            spatial_shapes,
+                            level_start_index,
+                            sampling_locations,
+                            attention_weights)
+```
+
+## 3. 单元测试
+可以通过执行单元测试来确认自定义算子功能的正确性，执行单元测试的示例如下所示：
+```
+python test_ms_deformable_attn_op.py
+```
+运行成功后，打印如下：
+```
+*True check_forward_equal_with_paddle_float: max_abs_err 6.98e-10 max_rel_err 2.03e-07
+*tensor1 True check_gradient_numerical(D=30)
+*tensor2 True check_gradient_numerical(D=30)
+*tensor3 True check_gradient_numerical(D=30)
+*tensor1 True check_gradient_numerical(D=32)
+*tensor2 True check_gradient_numerical(D=32)
+*tensor3 True check_gradient_numerical(D=32)
+*tensor1 True check_gradient_numerical(D=64)
+*tensor2 True check_gradient_numerical(D=64)
+*tensor3 True check_gradient_numerical(D=64)
+*tensor1 True check_gradient_numerical(D=71)
+*tensor2 True check_gradient_numerical(D=71)
+*tensor3 True check_gradient_numerical(D=71)
+*tensor1 True check_gradient_numerical(D=128)
+*tensor2 True check_gradient_numerical(D=128)
+*tensor3 True check_gradient_numerical(D=128)
+*tensor1 True check_gradient_numerical(D=1024)
+*tensor2 True check_gradient_numerical(D=1024)
+*tensor3 True check_gradient_numerical(D=1024)
+*tensor1 True check_gradient_numerical(D=1025)
+*tensor2 True check_gradient_numerical(D=1025)
+*tensor3 True check_gradient_numerical(D=1025)
+*tensor1 True check_gradient_numerical(D=2048)
+*tensor2 True check_gradient_numerical(D=2048)
+*tensor3 True check_gradient_numerical(D=2048)
+*tensor1 True check_gradient_numerical(D=3096)
+*tensor2 True check_gradient_numerical(D=3096)
+*tensor3 True check_gradient_numerical(D=3096)
+```