batch norm support matrix input (#5980)

* batch norm support matrix input

* update gpu code

* format code

paddle/operators/batch_norm_op.cc

@@ -62,13 +62,14 @@ class BatchNormOp : public framework::OperatorWithKernel {
     const auto x_dims = ctx->GetInputDim("X");
     const TensorFormat tensor_format =
         StringToTensorFormat(ctx->Attrs().Get<std::string>("tensor_format"));
+
+    PADDLE_ENFORCE(x_dims.size() >= 2 && x_dims.size() <= 5,
+                   "Input X must have 2 to 5 dimensions.");
+
     const int C =
         (tensor_format == TensorFormat::NCHW ? x_dims[1]
                                              : x_dims[x_dims.size() - 1]);
 
-    PADDLE_ENFORCE(x_dims.size() >= 3 && x_dims.size() <= 5,
-                   "Input X must have 3 to 5 dimensions.");
-
     PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale").size(), 1UL);
     PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale")[0], C);
     PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias").size(), 1UL);
@@ -146,8 +147,8 @@ class BatchNormKernel<platform::CPUPlace, T> : public framework::OpKernel<T> {
 
     const auto *x = ctx.Input<Tensor>("X");
     const auto &x_dims = x->dims();
-    PADDLE_ENFORCE(x_dims.size() >= 3 && x_dims.size() <= 5,
-                   "The Input dim size should be between 3 and 5");
+    PADDLE_ENFORCE(x_dims.size() >= 2 && x_dims.size() <= 5,
+                   "The Input dim size should be between 2 and 5");
     const int N = x_dims[0];
     const int C =
         (tensor_format == TensorFormat::NCHW ? x_dims[1]
@@ -339,8 +340,8 @@ class BatchNormGradKernel<platform::CPUPlace, T>
     // Get the size for each dimension.
     // NCHW [batch_size, in_channels, in_height, in_width]
     const auto &x_dims = x->dims();
-    PADDLE_ENFORCE(x_dims.size() >= 3 && x_dims.size() <= 5,
-                   "The Input dim size should be between 3 and 5");
+    PADDLE_ENFORCE(x_dims.size() >= 2 && x_dims.size() <= 5,
+                   "The Input dim size should be between 2 and 5");
     const int N = x_dims[0];
     const int C =
         (tensor_format == TensorFormat::NCHW ? x_dims[1]

paddle/operators/batch_norm_op.cu.cc

@@ -29,6 +29,12 @@ void ExtractNCWHD(const framework::DDim &dims,
                   const TensorFormat &tensor_format, int *N, int *C, int *H,
                   int *W, int *D) {
   *N = dims[0];
+  if (dims.size() == 2) {
+    *C = dims[1];
+    *H = 1;
+    *W = 1;
+    *D = 1;
+  } else {
     *C = tensor_format == TensorFormat::NCHW ? dims[1] : dims[dims.size() - 1];
     *H = tensor_format == TensorFormat::NCHW ? dims[2] : dims[1];
     *W = dims.size() > 3
@@ -37,6 +43,7 @@ void ExtractNCWHD(const framework::DDim &dims,
     *D = dims.size() > 4
              ? (tensor_format == TensorFormat::NCHW ? dims[4] : dims[3])
              : 1;
+  }
 }
 
 template <typename T>
@@ -56,8 +63,8 @@ class BatchNormKernel<platform::GPUPlace, T> : public framework::OpKernel<T> {
     // NCHW [batch_size, in_channels, in_height, in_width]
     const auto *x = ctx.Input<Tensor>("X");
     const auto &x_dims = x->dims();
-    PADDLE_ENFORCE(x_dims.size() >= 3 && x_dims.size() <= 5,
-                   "The Input dim size should be between 3 and 5");
+    PADDLE_ENFORCE(x_dims.size() >= 2 && x_dims.size() <= 5,
+                   "The Input dim size should be between 2 and 5");
     int N, C, H, W, D;
     ExtractNCWHD(x_dims, tensor_format, &N, &C, &H, &W, &D);
 
@@ -180,8 +187,8 @@ class BatchNormGradKernel<platform::GPUPlace, T>
 
     const auto &x_dims = x->dims();
 
-    PADDLE_ENFORCE(x_dims.size() >= 3 && x_dims.size() <= 5,
-                   "The Input dim size should be between 3 and 5");
+    PADDLE_ENFORCE(x_dims.size() >= 2 && x_dims.size() <= 5,
+                   "The Input dim size should be between 2 and 5");
     int N, C, H, W, D;
     ExtractNCWHD(x_dims, tensor_format, &N, &C, &H, &W, &D);
 

python/paddle/v2/fluid/tests/book/test_image_classification_train.py

@@ -69,8 +69,7 @@ def vgg16_bn_drop(input):
 
     drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5)
     fc1 = fluid.layers.fc(input=drop, size=512, act=None)
-    reshape1 = fluid.layers.reshape(x=fc1, shape=list(fc1.shape + (1, 1)))
-    bn = fluid.layers.batch_norm(input=reshape1, act='relu')
+    bn = fluid.layers.batch_norm(input=fc1, act='relu')
     drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5)
     fc2 = fluid.layers.fc(input=drop2, size=512, act=None)
     return fc2

python/paddle/v2/fluid/tests/test_batch_norm_op.py

@@ -21,6 +21,13 @@ def get_backward_op(scope, op, no_grad_set):
 
 
 def _reference_training(x, scale, offset, epsilon, data_format):
+    x_shape = x.shape
+    if len(x_shape) == 2:
+        if data_format == "NCHW":
+            x = np.reshape(x, (x.shape[0], x.shape[1], 1, 1))
+        else:
+            x = np.reshape(x, (x.shape[0], 1, 1, x.shape[1]))
+
     if data_format == "NCHW":
         n, c, h, w = x.shape
         x_square = x * x
@@ -39,6 +46,8 @@ def _reference_training(x, scale, offset, epsilon, data_format):
         offset_tile = np.reshape(offset, (1, c, 1, 1))
         offset_tile = np.reshape(offset_tile, (1, c, 1, 1))
         y = normalized * scale_tile + offset_tile
+        if len(x_shape) == 2:
+            y = np.reshape(y, (y.shape[0], y.shape[1]))
         return y, mean, var
     elif data_format == "NHWC":
         x_square = x * x
@@ -48,7 +57,10 @@ def _reference_training(x, scale, offset, epsilon, data_format):
         mean = x_sum / element_count
         var = x_square_sum / element_count - mean * mean
         normalized = (x - mean) / np.sqrt(var + epsilon)
-        return (normalized * scale + offset), mean, var
+        y = normalized * scale + offset
+        if len(x_shape) == 2:
+            y = np.reshape(y, x_shape)
+        return y, mean, var
     else:
         raise ValueError("Unknown data order.")
 
@@ -65,6 +77,18 @@ def _reference_grad(x, grad_y, scale, mean, var, epsilon, data_format):
     #   (x - mean) * sum(grad_y * (x - mean)) / (var + epsilon))
 
     # transfer from (N, C, H, W) to (N, H, W, C) to simplify computation
+    x_shape = x.shape
+
+    if len(x_shape) == 2:
+        if data_format == "NCHW":
+            x = np.reshape(x, (x.shape[0], x.shape[1], 1, 1))
+            grad_y = np.reshape(grad_y,
+                                (grad_y.shape[0], grad_y.shape[1], 1, 1))
+        else:
+            x = np.reshape(x, (x.shape[0], 1, 1, x.shape[1]))
+            grad_y = np.reshape(grad_y,
+                                (grad_y.shape[0], 1, 1, grad_y.shape[1]))
+
     if data_format == "NCHW":
         x = np.transpose(x, (0, 2, 3, 1))
         grad_y = np.transpose(grad_y, (0, 2, 3, 1))
@@ -83,6 +107,9 @@ def _reference_grad(x, grad_y, scale, mean, var, epsilon, data_format):
         grad_x = np.transpose(grad_x, (0, 3, 1, 2))
         x = np.transpose(x, (0, 3, 1, 2))
         grad_y = np.transpose(grad_y, (0, 3, 1, 2))
+
+    if len(x_shape) == 2:
+        grad_x = np.reshape(grad_x, x_shape)
     return grad_x, grad_scale, grad_offset
 
 
@@ -127,7 +154,7 @@ class TestBatchNormOp(OpTest):
         momentum = 0.9
 
         # N, H, W, C: 2, 3, 4, 2
-        n, h, w, c = 2, 3, 4, 2
+        n, h, w, c = 2, 3, 4, 5
         x_shape = [n, h, w, c]
         scale_shape = [c]
 
@@ -184,14 +211,17 @@ class TestBatchNormOp(OpTest):
         print 'python: NHWC, NCHW, backward checking passed'
 
     def test_forward_backward(self):
-        def test_with_place(place, tensor_format):
+        def test_with_place(place, tensor_format, shape):
             # attr
             epsilon = 0.00001
             momentum = 0.9
 
-            # N, H, W, C: 12, 3, 4, 2
-            n, h, w, c = 2, 3, 4, 2
-
+            if len(shape) == 2:
+                x_shape = shape
+                c = shape[1]
+            else:
+                # n, h, w, c = 2, 3, 4, 2
+                n, h, w, c = shape[0], shape[1], shape[2], shape[3]
                 if data_format == "NHWC":
                     x_shape = [n, h, w, c]
                 elif data_format == "NCHW":
@@ -219,6 +249,9 @@ class TestBatchNormOp(OpTest):
             #  for gradient test
             # y_grad = np.ones(x_shape).astype(np.float32)
             y_grad = np.zeros(x_shape).astype(np.float32)
+            if len(y_grad.shape) == 2:
+                y_grad[0, 0] = 1.
+            else:
                 y_grad[0, 0, 0, 0] = 1.
             # y_grad = np.random.random_sample(x_shape).astype(np.float32)
             x_grad_ref, scale_grad_ref, bias_grad_ref = _reference_grad(
@@ -313,7 +346,8 @@ class TestBatchNormOp(OpTest):
             places.append(core.GPUPlace(0))
         for place in places:
             for data_format in ["NCHW", "NHWC"]:
-                test_with_place(place, data_format)
+                test_with_place(place, data_format, [2, 3, 4, 5])
+                test_with_place(place, data_format, [2, 3])
 
 
 if __name__ == '__main__':

python/paddle/v2/fluid/tests/test_image_classification_layer.py

 import unittest
 
-import paddle.v2.fluid.layers as layers
+import paddle.v2.fluid as fluid
 import paddle.v2.fluid.nets as nets
 from paddle.v2.fluid.framework import Program
 
@@ -29,27 +29,35 @@ class TestLayer(unittest.TestCase):
     def test_batch_norm_layer(self):
         main_program = Program()
         startup_program = Program()
-        images = layers.data(
+        images = fluid.layers.data(
             name='pixel',
             shape=[3, 48, 48],
             dtype='float32',
             main_program=main_program)
-        layers.batch_norm(
+        hidden1 = fluid.layers.batch_norm(
             input=images,
             main_program=main_program,
             startup_program=startup_program)
+        hidden2 = fluid.layers.fc(input=hidden1,
+                                  size=128,
+                                  act='relu',
+                                  main_program=main_program)
+        hidden3 = fluid.layers.batch_norm(
+            input=hidden2,
+            main_program=main_program,
+            startup_program=startup_program)
 
-        # print str(main_program)
+        print str(main_program)
 
     def test_dropout_layer(self):
         main_program = Program()
         startup_program = Program()
-        images = layers.data(
+        images = fluid.layers.data(
             name='pixel',
             shape=[3, 48, 48],
             dtype='float32',
             main_program=main_program)
-        layers.dropout(
+        fluid.layers.dropout(
             x=images,
             dropout_prob=0.5,
             main_program=main_program,
@@ -61,7 +69,7 @@ class TestLayer(unittest.TestCase):
         main_program = Program()
         startup_program = Program()
 
-        images = layers.data(
+        images = fluid.layers.data(
             name='pixel',
             shape=[3, 48, 48],
             dtype='float32',
@@ -77,19 +85,19 @@ class TestLayer(unittest.TestCase):
     def test_elementwise_add_with_act(self):
         main_program = Program()
         startup_program = Program()
-        image1 = layers.data(
+        image1 = fluid.layers.data(
             name='pixel1',
             shape=[3, 48, 48],
             dtype='float32',
             main_program=main_program,
             startup_program=startup_program)
-        image2 = layers.data(
+        image2 = fluid.layers.data(
             name='pixel2',
             shape=[3, 48, 48],
             dtype='float32',
             main_program=main_program,
             startup_program=startup_program)
-        out = layers.elementwise_add(
+        out = fluid.layers.elementwise_add(
             x=image1,
             y=image2,
             act='relu',