add (N,C,*) input support for GroupNorm

paddle/fluid/operators/group_norm_op.cc

@@ -37,6 +37,13 @@ class GroupNormOp : public framework::OperatorWithKernel {
                    "GroupNorm");
 
     auto x_dim = ctx->GetInputDim("X");
+    PADDLE_ENFORCE_GE(
+        x_dim.size(), 2,
+        platform::errors::InvalidArgument(
+            "The Input(X)'s dimension of Op(group_norm) must be "
+            "greater than 1. But received: %u-D Tensor, which shape is [%s].",
+            x_dim.size(), x_dim));
+
     const std::string data_layout_str =
         ctx->Attrs().Get<std::string>("data_layout");
     const framework::DataLayout data_layout =

paddle/fluid/operators/group_norm_op.cu

@@ -171,9 +171,16 @@ class GroupNormKernel<platform::CUDADeviceContext, T>
     const T* bias_data = nullptr;
     if (bias) bias_data = bias->data<T>();
 
-    int imsize = (data_layout == DataLayout::kNCHW ? x_dims[2] * x_dims[3]
-                                                   : x_dims[1] * x_dims[2]);
-
+    int imsize = 1;
+    if (data_layout == DataLayout::kNCHW) {
+      for (int i = 2; i < x_dims.size(); ++i) {
+        imsize *= x_dims[i];
+      }
+    } else {
+      for (int i = 1; i < x_dims.size() - 1; ++i) {
+        imsize *= x_dims[i];
+      }
+    }
 #ifdef __HIPCC__
     int block_size = std::max(std::min(256, imsize), 64);
 #else
@@ -349,8 +356,16 @@ class GroupNormGradKernel<platform::CUDADeviceContext, T>
     const T* bias_data = nullptr;
     if (bias) bias_data = bias->data<T>();
 
-    int imsize = (data_layout == DataLayout::kNCHW ? x_dims[2] * x_dims[3]
-                                                   : x_dims[1] * x_dims[2]);
+    int imsize = 1;
+    if (data_layout == DataLayout::kNCHW) {
+      for (int i = 2; i < x_dims.size(); ++i) {
+        imsize *= x_dims[i];
+      }
+    } else {
+      for (int i = 1; i < x_dims.size() - 1; ++i) {
+        imsize *= x_dims[i];
+      }
+    }
 
 #ifdef __HIPCC__
     int block_size = std::max(std::min(256, imsize), 64);

paddle/fluid/operators/group_norm_op.h

@@ -68,9 +68,16 @@ class GroupNormKernel : public framework::OpKernel<T> {
     const T* bias_data = nullptr;
     if (bias) bias_data = bias->data<T>();
 
-    int imsize = (data_layout == DataLayout::kNCHW ? x_dims[2] * x_dims[3]
-                                                   : x_dims[1] * x_dims[2]);
-
+    int imsize = 1;
+    if (data_layout == DataLayout::kNCHW) {
+      for (int i = 2; i < x_dims.size(); ++i) {
+        imsize *= x_dims[i];
+      }
+    } else {
+      for (int i = 1; i < x_dims.size() - 1; ++i) {
+        imsize *= x_dims[i];
+      }
+    }
     auto* iter_x_data = x_data;
     auto* iter_y_data = y_data;
     for (int bid = 0; bid < x_dims[0]; bid++) {
@@ -257,8 +264,16 @@ class GroupNormGradKernel : public framework::OpKernel<T> {
     const T* bias_data = nullptr;
     if (bias) bias_data = bias->data<T>();
 
-    int imsize = (data_layout == DataLayout::kNCHW ? x_dims[2] * x_dims[3]
-                                                   : x_dims[1] * x_dims[2]);
+    int imsize = 1;
+    if (data_layout == DataLayout::kNCHW) {
+      for (int i = 2; i < x_dims.size(); ++i) {
+        imsize *= x_dims[i];
+      }
+    } else {
+      for (int i = 1; i < x_dims.size() - 1; ++i) {
+        imsize *= x_dims[i];
+      }
+    }
     auto* iter_x_data = x_data;
     auto* iter_d_x_data = d_x_data;
     auto* iter_y_data = y_data;

python/paddle/fluid/tests/unittests/test_group_norm_op_v2.py

@@ -25,13 +25,29 @@
 import paddle
 
 
+def group_norm_naive_for_general_dimension(x, scale, bias, epsilon, groups):
+    # original version group norm only support 4-D tensor
+    # this function generalizes to support differnt dimensions tensor (>= 2-D)
+    input_shape = x.shape
+    N, C = x.shape[0], x.shape[1]
+    G = groups
+    x = x.reshape((N * G, -1))
+    mean = np.mean(x, axis=1, keepdims=True)
+    var = np.var(x, axis=1, keepdims=True)
+    output = (x - mean) / np.sqrt(var + epsilon)
+    output = output.reshape(input_shape) * scale.reshape(
+        (-1, 1, 1)) + bias.reshape((-1, 1, 1))
+    return output
+
+
 class TestDygraphGroupNormv2(unittest.TestCase):
     def test_dygraph(self):
         places = [fluid.CPUPlace()]
         if core.is_compiled_with_cuda() and core.op_support_gpu("group_norm"):
             places.append(fluid.CUDAPlace(0))
+        shapes = [[2, 2, 2, 2], [2, 2, 4], [4, 2], [4, 2, 6, 6, 2],
+                  [2, 2, 2, 2, 2, 2]]
         for p in places:
-            shape = [2, 2, 2, 2]
 
             def compute_v1(x):
                 with fluid.dygraph.guard(p):
@@ -62,23 +78,26 @@ def attr_data_format():
 
                     self.assertRaises(ValueError, attr_data_format)
 
-            x = np.random.randn(*shape).astype("float32")
-            y1 = compute_v1(x)
-            y2 = compute_v2(x)
-            result = np.allclose(y1, y2, atol=1e-5)
-            if not result:
-                print("y1:", y1, "\ty2:", y2)
-            self.assertTrue(result)
-            test_weight_bias_false()
-            test_nn_exception()
+            for shape in shapes:
+                x = np.random.randn(*shape).astype("float32")
+                y1 = compute_v1(x)
+                y2 = compute_v2(x)
+                result = np.allclose(y1, y2, atol=1e-5)
+                if not result:
+                    print("y1:", y1, "\ty2:", y2)
+                self.assertTrue(result)
+                test_weight_bias_false()
+                test_nn_exception()
 
     def test_static(self):
+        paddle.enable_static()
         places = [fluid.CPUPlace()]
         if core.is_compiled_with_cuda() and core.op_support_gpu("group_norm"):
             places.append(fluid.CUDAPlace(0))
+        shapes = [[2, 6, 2, 2], [2, 6, 4], [4, 6], [4, 6, 6, 6, 2],
+                  [4, 6, 2, 2, 2, 2]]
         for p in places:
             exe = fluid.Executor(p)
-            shape = [2, 6, 2, 2]
 
             def compute_v1(x_np):
                 with program_guard(Program(), Program()):
@@ -98,10 +117,39 @@ def compute_v2(x_np):
                     r = exe.run(feed={'x': x_np}, fetch_list=[y])[0]
                 return r
 
-            x = np.random.randn(*shape).astype("float32")
-            y1 = compute_v1(x)
-            y2 = compute_v2(x)
-            self.assertTrue(np.allclose(y1, y2, atol=1e-5))
+            for shape in shapes:
+                x = np.random.randn(*shape).astype("float32")
+                y1 = compute_v1(x)
+                y2 = compute_v2(x)
+                self.assertTrue(np.allclose(y1, y2, atol=1e-5))
+
+
+class TestGroupNormAPIV2_With_General_Dimensions(unittest.TestCase):
+    def test_numerical_accuracy(self):
+        paddle.disable_static()
+        shapes = [(2, 6), (2, 6, 4), (2, 6, 4, 4), (2, 6, 6, 6, 2), (2, 6, 6, 6,
+                                                                     2, 3)]
+        places = [fluid.CPUPlace()]
+        if core.is_compiled_with_cuda() and core.op_support_gpu("group_norm"):
+            places.append(fluid.CUDAPlace(0))
+
+        for place in places:
+            for shape in shapes:
+                scale = np.array([1]).astype("float32")
+                bias = np.array([0]).astype("float32")
+                data = np.random.random(shape).astype("float32")
+                expect_res1 = group_norm_naive_for_general_dimension(
+                    data, scale, bias, epsilon=1e-5, groups=6)
+                expect_res2 = group_norm_naive_for_general_dimension(
+                    data, scale, bias, epsilon=1e-5, groups=2)
+
+                gn1 = paddle.nn.GroupNorm(num_channels=6, num_groups=6)
+                gn2 = paddle.nn.GroupNorm(num_channels=6, num_groups=2)
+                data_pd = paddle.to_tensor(data)
+                result1 = gn1(data_pd).numpy()
+                result2 = gn2(data_pd).numpy()
+                self.assertTrue(np.allclose(result1, expect_res1, atol=1e-5))
+                self.assertTrue(np.allclose(result2, expect_res2, atol=1e-5))
 
 
 if __name__ == '__main__':

python/paddle/nn/layer/norm.py

@@ -338,8 +338,8 @@ class GroupNorm(Layer):
         name(str, optional): Name for the GroupNorm, default is None. For more information, please refer to :ref:`api_guide_Name`..
 
     Shape:
-        - x: 4-D tensor with shape: (batch, num_features, height, weight).
-        - output: 4-D tensor with same shape as input x.
+        - x: Tensor with shape: (batch, num_features, *).
+        - output: The same shape as input x.
 
     Returns:
         None