[Api2.0] add pixel shuffle class

paddle/fluid/operators/pixel_shuffle_op.cc

@@ -28,40 +28,59 @@ class PixelShuffleOp : public framework::OperatorWithKernel {
                           "Output(Out) of PixelShuffleOp should not be null."));
 
     auto input_dims = ctx->GetInputDim("X");
-    PADDLE_ENFORCE_EQ(
-        input_dims.size(), 4,
-        platform::errors::InvalidArgument(
-            "Input should be a 4-D tensor of format [N, C, H, W], but got %u.",
-            input_dims.size()));
+    PADDLE_ENFORCE_EQ(input_dims.size(), 4,
+                      platform::errors::InvalidArgument(
+                          "Input should be a 4-D tensor of format [N, C, H, W] "
+                          "or [N, H, W, C], but got %u.",
+                          input_dims.size()));
 
     auto upscale_factor = ctx->Attrs().Get<int>("upscale_factor");
 
-    PADDLE_ENFORCE_EQ(input_dims[1] % (upscale_factor * upscale_factor), 0,
-                      platform::errors::InvalidArgument(
-                          "The square of upscale_factor[%u] should divide the "
-                          "number of channel[%u]",
-                          input_dims[1], upscale_factor * upscale_factor));
-
+    const std::string data_format =
+        ctx->Attrs().Get<std::string>("data_format");
+    const bool channel_last = (data_format == "NHWC");
+
+    if (!channel_last) {
+      PADDLE_ENFORCE_EQ(
+          input_dims[1] % (upscale_factor * upscale_factor), 0,
+          platform::errors::InvalidArgument(
+              "The square of upscale_factor[%u] should divide the "
+              "number of channel[%u]",
+              input_dims[1], upscale_factor * upscale_factor));
+    } else {
+      PADDLE_ENFORCE_EQ(
+          input_dims[3] % (upscale_factor * upscale_factor), 0,
+          platform::errors::InvalidArgument(
+              "The square of upscale_factor[%u] should divide the "
+              "number of channel[%u]",
+              input_dims[3], upscale_factor * upscale_factor));
+    }
     auto output_dims = input_dims;
     output_dims[0] = input_dims[0];
-    output_dims[1] = input_dims[1] / (upscale_factor * upscale_factor);
-    output_dims[2] = input_dims[2] * upscale_factor;
-    output_dims[3] = input_dims[3] * upscale_factor;
+    if (!channel_last) {
+      output_dims[1] = input_dims[1] / (upscale_factor * upscale_factor);
+      output_dims[2] = input_dims[2] * upscale_factor;
+      output_dims[3] = input_dims[3] * upscale_factor;
+    } else {
+      output_dims[1] = input_dims[1] * upscale_factor;
+      output_dims[2] = input_dims[2] * upscale_factor;
+      output_dims[3] = input_dims[3] / (upscale_factor * upscale_factor);
+    }
     ctx->SetOutputDim("Out", output_dims);
   }
 };
 
 class PixelShuffleOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
-    AddInput(
-        "X",
-        "(Tensor, default Tensor<float>), "
-        "the input feature data of PixelShuffleOp, the layout is [N C H W].");
-    AddOutput(
-        "Out",
-        "(Tensor, default Tensor<float>), the output of "
-        "PixelShuffleOp. The layout is [N,C/factor^2,H*factor,W*factor].");
+    AddInput("X",
+             "(Tensor, default Tensor<float>), "
+             "the input feature data of PixelShuffleOp, the layout is [N, C, "
+             "H, W] or [N, H, W, C].");
+    AddOutput("Out",
+              "(Tensor, default Tensor<float>), the output of "
+              "PixelShuffleOp. The layout is [N, C/factor^2, H*factor, "
+              "W*factor] or [N, H*factor, W*factor, C/factor^2].");
     AddAttr<int>("upscale_factor",
                  "the factor to increase spatial resolution by.")
         .SetDefault(1)
@@ -70,6 +89,12 @@ class PixelShuffleOpMaker : public framework::OpProtoAndCheckerMaker {
                             platform::errors::InvalidArgument(
                                 "upscale_factor should be larger than 0."));
         });
+    AddAttr<std::string>(
+        "data_format",
+        "(string, default NCHW) Only used in "
+        "An optional string from: \"NHWC\", \"NCHW\". "
+        "Defaults to \"NHWC\", Specify the data format of the input data.")
+        .SetDefault("NCHW");
 
     AddComment(R"DOC(
 		Pixel Shuffle operator
@@ -114,19 +139,30 @@ class PixelShuffleGradOp : public framework::OperatorWithKernel {
         platform::errors::NotFound("Output(X@Grad) should not be null"));
 
     auto do_dims = ctx->GetInputDim(framework::GradVarName("Out"));
-    PADDLE_ENFORCE_EQ(
-        do_dims.size(), 4,
-        platform::errors::InvalidArgument(
-            "Input should be a 4-D tensor of format [N, C, H, W], but got %u.",
-            do_dims.size()));
+    PADDLE_ENFORCE_EQ(do_dims.size(), 4,
+                      platform::errors::InvalidArgument(
+                          "Input should be a 4-D tensor of format [N, C, H, W] "
+                          "or [N, H, W, C], but got %u.",
+                          do_dims.size()));
 
     auto upscale_factor = ctx->Attrs().Get<int>("upscale_factor");
 
+    const std::string data_format =
+        ctx->Attrs().Get<std::string>("data_format");
+    const bool channel_last = (data_format == "NHWC");
+
     auto dx_dims = do_dims;
     dx_dims[0] = do_dims[0];
-    dx_dims[1] = do_dims[1] * (upscale_factor * upscale_factor);
-    dx_dims[2] = do_dims[2] / upscale_factor;
-    dx_dims[3] = do_dims[3] / upscale_factor;
+
+    if (!channel_last) {
+      dx_dims[1] = do_dims[1] * (upscale_factor * upscale_factor);
+      dx_dims[2] = do_dims[2] / upscale_factor;
+      dx_dims[3] = do_dims[3] / upscale_factor;
+    } else {
+      dx_dims[1] = do_dims[1] / upscale_factor;
+      dx_dims[2] = do_dims[2] / upscale_factor;
+      dx_dims[3] = do_dims[3] * (upscale_factor * upscale_factor);
+    }
     ctx->SetOutputDim(framework::GradVarName("X"), dx_dims);
   }
 };

paddle/fluid/operators/pixel_shuffle_op.h

@@ -11,6 +11,7 @@ limitations under the License. */
 
 #pragma once
 #include <algorithm>
+#include <string>
 #include <vector>
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/math/math_function.h"
@@ -24,23 +25,33 @@ class PixelShuffleOpKernel : public framework::OpKernel<T> {
   void Compute(const framework::ExecutionContext& ctx) const override {
     auto* in = ctx.Input<framework::Tensor>("X");
     auto* out = ctx.Output<framework::Tensor>("Out");
+
     out->mutable_data<T>(ctx.GetPlace());
 
     int factor = ctx.Attr<int>("upscale_factor");
 
+    std::string data_format = ctx.Attr<std::string>("data_format");
+    bool channel_last = (data_format == "NHWC");
+
     auto in_dims = in->dims();
     auto o_dims = out->dims();
 
     framework::Tensor t;
     t.ShareDataWith(*in);
-    t.Resize({in_dims[0], o_dims[1], factor, factor, in_dims[2], in_dims[3]});
-
+    if (!channel_last) {
+      t.Resize({in_dims[0], o_dims[1], factor, factor, in_dims[2], in_dims[3]});
+    } else {
+      t.Resize({in_dims[0], in_dims[1], in_dims[2], o_dims[3], factor, factor});
+    }
     std::vector<int> axis = {0, 1, 4, 2, 5, 3};
 
     framework::Tensor o;
     o.ShareDataWith(*out);
-    o.Resize({in_dims[0], o_dims[1], in_dims[2], factor, in_dims[3], factor});
-
+    if (!channel_last) {
+      o.Resize({in_dims[0], o_dims[1], in_dims[2], factor, in_dims[3], factor});
+    } else {
+      o.Resize({in_dims[0], in_dims[1], factor, in_dims[2], factor, o_dims[3]});
+    }
     math::Transpose<DeviceContext, T, 6> trans;
     auto& dev_ctx = ctx.template device_context<DeviceContext>();
     trans(dev_ctx, t, &o, axis);
@@ -58,19 +69,32 @@ class PixelShuffleGradOpKernel : public framework::OpKernel<T> {
 
     int factor = ctx.Attr<int>("upscale_factor");
 
+    std::string data_format = ctx.Attr<std::string>("data_format");
+    bool channel_last = (data_format == "NHWC");
+
     auto do_dims = dout->dims();
     auto dx_dims = dx->dims();
 
     framework::Tensor t;
     t.ShareDataWith(*dout);
-    t.Resize({do_dims[0], do_dims[1], dx_dims[2], factor, dx_dims[3], factor});
-
+    if (!channel_last) {
+      t.Resize(
+          {do_dims[0], do_dims[1], dx_dims[2], factor, dx_dims[3], factor});
+    } else {
+      t.Resize(
+          {do_dims[0], dx_dims[1], factor, dx_dims[2], factor, do_dims[3]});
+    }
     std::vector<int> axis = {0, 1, 3, 5, 2, 4};
 
     framework::Tensor o;
     o.ShareDataWith(*dx);
-    o.Resize({do_dims[0], do_dims[1], factor, factor, dx_dims[2], dx_dims[3]});
-
+    if (!channel_last) {
+      o.Resize(
+          {do_dims[0], do_dims[1], factor, factor, dx_dims[2], dx_dims[3]});
+    } else {
+      o.Resize(
+          {do_dims[0], dx_dims[1], dx_dims[2], do_dims[3], factor, factor});
+    }
     math::Transpose<DeviceContext, T, 6> trans;
     auto& dev_ctx = ctx.template device_context<DeviceContext>();
     trans(dev_ctx, t, &o, axis);

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

@@ -17,15 +17,12 @@
 import unittest
 import numpy as np
 from op_test import OpTest
+import paddle
 
 
-class TestPixelShuffle(OpTest):
-    def setUp(self):
-        self.op_type = "pixel_shuffle"
-        n, c, h, w = 2, 9, 4, 4
-        up_factor = 3
-        shape = [n, c, h, w]
-        x = np.random.random(shape).astype("float64")
+def pixel_shuffle_np(x, up_factor, data_format="NCHW"):
+    if data_format == "NCHW":
+        n, c, h, w = x.shape
         new_shape = (n, c // (up_factor * up_factor), up_factor, up_factor, h,
                      w)
         # reshape to (num,output_channel,upscale_factor,upscale_factor,h,w)
@@ -34,10 +31,43 @@ def setUp(self):
         npresult = npresult.transpose(0, 1, 4, 2, 5, 3)
         oshape = [n, c // (up_factor * up_factor), h * up_factor, w * up_factor]
         npresult = np.reshape(npresult, oshape)
+        return npresult
+    else:
+        n, h, w, c = x.shape
+
+        new_shape = (n, h, w, c // (up_factor * up_factor), up_factor,
+                     up_factor)
+        # reshape to (num,h,w,output_channel,upscale_factor,upscale_factor)
+        npresult = np.reshape(x, new_shape)
+        # transpose to (num,h,upscale_factor,w,upscale_factor,output_channel)
+        npresult = npresult.transpose(0, 1, 4, 2, 5, 3)
+        oshape = [n, h * up_factor, w * up_factor, c // (up_factor * up_factor)]
+        npresult = np.reshape(npresult, oshape)
+        return npresult
+
+
+class TestPixelShuffle(OpTest):
+    def setUp(self):
+        self.op_type = "pixel_shuffle"
+        self.init_data_format()
+        n, c, h, w = 2, 9, 4, 4
+
+        if self.format == "NCHW":
+            shape = [n, c, h, w]
+        if self.format == "NHWC":
+            shape = [n, h, w, c]
+
+        up_factor = 3
+
+        x = np.random.random(shape).astype("float64")
+        npresult = pixel_shuffle_np(x, up_factor, self.format)
 
         self.inputs = {'X': x}
         self.outputs = {'Out': npresult}
-        self.attrs = {'upscale_factor': up_factor}
+        self.attrs = {'upscale_factor': up_factor, "data_format": self.format}
+
+    def init_data_format(self):
+        self.format = "NCHW"
 
     def test_check_output(self):
         self.check_output()
@@ -46,5 +76,26 @@ def test_check_grad(self):
         self.check_grad(['X'], 'Out')
 
 
+class TestChannelLast(TestPixelShuffle):
+    def init_data_format(self):
+        self.format = "NHWC"
+
+
+class TestPixelShuffleDygraph(unittest.TestCase):
+    def run_pixel_shuffle(self, up_factor):
+        x = np.random.rand(2, 9, 4, 4).astype(np.float32)
+
+        npresult = pixel_shuffle_np(x, up_factor)
+
+        paddle.disable_static()
+        pixel_shuffle = paddle.nn.PixelShuffle(up_factor)
+        result = pixel_shuffle(paddle.to_variable(x))
+
+        self.assertTrue(np.allclose(result.numpy(), npresult))
+
+    def test_pixel_shuffle(self):
+        self.run_pixel_shuffle(3)
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/nn/__init__.py

@@ -98,7 +98,10 @@
 # from .layer.rnn import LSTMCell        #DEFINE_ALIAS
 from .layer.distance import PairwiseDistance  #DEFINE_ALIAS
 
+from .layer.vision import PixelShuffle
+
 from .layer import loss  #DEFINE_ALIAS
 from .layer import conv  #DEFINE_ALIAS
+from .layer import vision  #DEFINE_ALIAS
 from ..fluid.dygraph.layers import Layer  #DEFINE_ALIAS
 from ..fluid.dygraph.container import LayerList, ParameterList, Sequential  #DEFINE_ALIAS

python/paddle/nn/functional/__init__.py

@@ -158,40 +158,40 @@
 # from .rnn import gru_unit        #DEFINE_ALIAS
 # from .rnn import lstm        #DEFINE_ALIAS
 # from .rnn import lstm_unit        #DEFINE_ALIAS
-from .vision import affine_channel  #DEFINE_ALIAS
-from .vision import affine_grid  #DEFINE_ALIAS
-from .vision import anchor_generator  #DEFINE_ALIAS
-from .vision import bipartite_match  #DEFINE_ALIAS
-from .vision import box_clip  #DEFINE_ALIAS
-from .vision import box_coder  #DEFINE_ALIAS
-from .vision import box_decoder_and_assign  #DEFINE_ALIAS
-from .vision import collect_fpn_proposals  #DEFINE_ALIAS
+#from .vision import affine_channel  #DEFINE_ALIAS
+#from .vision import affine_grid  #DEFINE_ALIAS
+#from .vision import anchor_generator  #DEFINE_ALIAS
+#from .vision import bipartite_match  #DEFINE_ALIAS
+#from .vision import box_clip  #DEFINE_ALIAS
+#from .vision import box_coder  #DEFINE_ALIAS
+#from .vision import box_decoder_and_assign  #DEFINE_ALIAS
+#from .vision import collect_fpn_proposals  #DEFINE_ALIAS
 # from .vision import deformable_conv        #DEFINE_ALIAS
-from .vision import deformable_roi_pooling  #DEFINE_ALIAS
-from .vision import density_prior_box  #DEFINE_ALIAS
-from .vision import detection_output  #DEFINE_ALIAS
-from .vision import distribute_fpn_proposals  #DEFINE_ALIAS
-from .vision import fsp_matrix  #DEFINE_ALIAS
-from .vision import generate_mask_labels  #DEFINE_ALIAS
-from .vision import generate_proposal_labels  #DEFINE_ALIAS
-from .vision import generate_proposals  #DEFINE_ALIAS
-from .vision import grid_sampler  #DEFINE_ALIAS
-from .vision import image_resize  #DEFINE_ALIAS
-from .vision import image_resize_short  #DEFINE_ALIAS
+#from .vision import deformable_roi_pooling  #DEFINE_ALIAS
+#from .vision import density_prior_box  #DEFINE_ALIAS
+#from .vision import detection_output  #DEFINE_ALIAS
+#from .vision import distribute_fpn_proposals  #DEFINE_ALIAS
+#from .vision import fsp_matrix  #DEFINE_ALIAS
+#from .vision import generate_mask_labels  #DEFINE_ALIAS
+#from .vision import generate_proposal_labels  #DEFINE_ALIAS
+#from .vision import generate_proposals  #DEFINE_ALIAS
+#from .vision import grid_sampler  #DEFINE_ALIAS
+#from .vision import image_resize  #DEFINE_ALIAS
+#from .vision import image_resize_short  #DEFINE_ALIAS
 # from .vision import multi_box_head        #DEFINE_ALIAS
 from .vision import pixel_shuffle  #DEFINE_ALIAS
-from .vision import prior_box  #DEFINE_ALIAS
-from .vision import prroi_pool  #DEFINE_ALIAS
-from .vision import psroi_pool  #DEFINE_ALIAS
-from .vision import resize_bilinear  #DEFINE_ALIAS
-from .vision import resize_nearest  #DEFINE_ALIAS
-from .vision import resize_trilinear  #DEFINE_ALIAS
-from .vision import retinanet_detection_output  #DEFINE_ALIAS
-from .vision import retinanet_target_assign  #DEFINE_ALIAS
-from .vision import roi_align  #DEFINE_ALIAS
-from .vision import roi_perspective_transform  #DEFINE_ALIAS
-from .vision import roi_pool  #DEFINE_ALIAS
-from .vision import shuffle_channel  #DEFINE_ALIAS
-from .vision import space_to_depth  #DEFINE_ALIAS
-from .vision import yolo_box  #DEFINE_ALIAS
-from .vision import yolov3_loss  #DEFINE_ALIAS
+#from .vision import prior_box  #DEFINE_ALIAS
+#from .vision import prroi_pool  #DEFINE_ALIAS
+#from .vision import psroi_pool  #DEFINE_ALIAS
+#from .vision import resize_bilinear  #DEFINE_ALIAS
+#from .vision import resize_nearest  #DEFINE_ALIAS
+#from .vision import resize_trilinear  #DEFINE_ALIAS
+#from .vision import retinanet_detection_output  #DEFINE_ALIAS
+#from .vision import retinanet_target_assign  #DEFINE_ALIAS
+#from .vision import roi_align  #DEFINE_ALIAS
+#from .vision import roi_perspective_transform  #DEFINE_ALIAS
+#from .vision import roi_pool  #DEFINE_ALIAS
+#from .vision import shuffle_channel  #DEFINE_ALIAS
+#from .vision import space_to_depth  #DEFINE_ALIAS
+#from .vision import yolo_box  #DEFINE_ALIAS
+#from .vision import yolov3_loss  #DEFINE_ALIAS