From cfda1fdea721196b08c99b81dddb7622d6aff249 Mon Sep 17 00:00:00 2001 From: dengkaipeng Date: Mon, 25 Mar 2019 06:28:05 +0000 Subject: [PATCH] add attr scale. test=develop --- paddle/fluid/operators/interpolate_op.cc | 15 ++++- paddle/fluid/operators/interpolate_op.cu | 38 +++++++---- paddle/fluid/operators/interpolate_op.h | 35 +++++++--- python/paddle/fluid/layers/nn.py | 47 +++++++------ .../unittests/test_bilinear_interp_op.py | 67 ++++++++++++++++++- .../tests/unittests/test_nearest_interp_op.py | 65 +++++++++++++++++- 6 files changed, 218 insertions(+), 49 deletions(-) diff --git a/paddle/fluid/operators/interpolate_op.cc b/paddle/fluid/operators/interpolate_op.cc index 10d01af982d..b18c10b550e 100644 --- a/paddle/fluid/operators/interpolate_op.cc +++ b/paddle/fluid/operators/interpolate_op.cc @@ -36,10 +36,19 @@ class InterpolateOp : public framework::OperatorWithKernel { "Interpolation method can only be \"bilinear\" or \"nearest\"."); auto dim_x = ctx->GetInputDim("X"); // NCHW format - int out_h = ctx->Attrs().Get("out_h"); - int out_w = ctx->Attrs().Get("out_w"); PADDLE_ENFORCE_EQ(dim_x.size(), 4, "X's dimension must be 4"); + // priority: OutSize > scale > out_h/out_w + int out_h, out_w; + float scale = ctx->Attrs().Get("scale"); + if (scale > 0) { + out_h = dim_x[2] * scale; + out_w = dim_x[3] * scale; + } else { + out_h = ctx->Attrs().Get("out_h"); + out_w = ctx->Attrs().Get("out_w"); + } + if (ctx->HasInput("OutSize") && ctx->IsRuntime()) { auto out_size_dim = ctx->GetInputDim("OutSize"); PADDLE_ENFORCE_EQ(out_size_dim.size(), 1, @@ -76,6 +85,8 @@ class InterpolateOpMaker : public framework::OpProtoAndCheckerMaker { AddAttr("out_h", "output height of interpolate op."); AddAttr("out_w", "output width of interpolate op."); + AddAttr("scale", "scale factor of interpolate op.") + .SetDefault(0.); AddAttr("interp_method", "(string, default \"bilinear\"), interpolation " "method, can be \"bilinear\" for " diff --git a/paddle/fluid/operators/interpolate_op.cu b/paddle/fluid/operators/interpolate_op.cu index b887878ea22..745e52b48d5 100644 --- a/paddle/fluid/operators/interpolate_op.cu +++ b/paddle/fluid/operators/interpolate_op.cu @@ -192,9 +192,21 @@ class InterpolateOpCUDAKernel : public framework::OpKernel { auto* output = ctx.Output("Out"); auto* input_data = input->data(); + int n = input->dims()[0]; + int c = input->dims()[1]; + int in_h = input->dims()[2]; + int in_w = input->dims()[3]; + auto interp_method = ctx.Attr("interp_method"); int out_h = ctx.Attr("out_h"); int out_w = ctx.Attr("out_w"); + + float scale = ctx.Attr("scale"); + if (scale > 0) { + out_h = in_h * scale; + out_w = in_w * scale; + } + auto out_size = ctx.Input("OutSize"); if (out_size != nullptr) { Tensor sizes; @@ -207,11 +219,6 @@ class InterpolateOpCUDAKernel : public framework::OpKernel { bool align_corners = ctx.Attr("align_corners"); int align_mode = ctx.Attr("align_mode"); - int n = input->dims()[0]; - int c = input->dims()[1]; - int in_h = input->dims()[2]; - int in_w = input->dims()[3]; - auto* output_data = output->mutable_data({n, c, out_h, out_w}, ctx.GetPlace()); @@ -268,14 +275,20 @@ class InterpolateGradOpCUDAKernel : public framework::OpKernel { math::SetConstant zero; zero(device_ctx, input_grad, static_cast(0.0)); + int n = input_grad->dims()[0]; + int c = input_grad->dims()[1]; + int in_h = input_grad->dims()[2]; + int in_w = input_grad->dims()[3]; + auto interp_method = ctx.Attr("interp_method"); int out_h = ctx.Attr("out_h"); int out_w = ctx.Attr("out_w"); + float scale = ctx.Attr("scale"); + if (scale > 0) { + out_h = in_h * scale; + out_w - in_w * scale; + } auto out_size = ctx.Input("OutSize"); - - bool align_corners = ctx.Attr("align_corners"); - int align_mode = ctx.Attr("align_mode"); - if (out_size != nullptr) { Tensor sizes; framework::TensorCopy(*out_size, platform::CPUPlace(), &sizes); @@ -284,10 +297,9 @@ class InterpolateGradOpCUDAKernel : public framework::OpKernel { out_w = size_data[1]; } - int n = input_grad->dims()[0]; - int c = input_grad->dims()[1]; - int in_h = input_grad->dims()[2]; - int in_w = input_grad->dims()[3]; + bool align_corners = ctx.Attr("align_corners"); + int align_mode = ctx.Attr("align_mode"); + int in_hw = in_h * in_w; int out_hw = out_h * out_w; diff --git a/paddle/fluid/operators/interpolate_op.h b/paddle/fluid/operators/interpolate_op.h index c631ad1dd15..dab62697565 100644 --- a/paddle/fluid/operators/interpolate_op.h +++ b/paddle/fluid/operators/interpolate_op.h @@ -163,9 +163,21 @@ class InterpolateKernel : public framework::OpKernel { auto* input = ctx.Input("X"); auto* output = ctx.Output("Out"); + const int n = input->dims()[0]; + const int c = input->dims()[1]; + const int in_h = input->dims()[2]; + const int in_w = input->dims()[3]; + std::string interp_method = ctx.Attr("interp_method"); int out_h = ctx.Attr("out_h"); int out_w = ctx.Attr("out_w"); + + float scale = ctx.Attr("scale"); + if (scale > 0) { + out_h = in_h * scale; + out_w = in_w * scale; + } + auto out_size = ctx.Input("OutSize"); if (out_size != nullptr) { auto out_size_data = out_size->data(); @@ -175,11 +187,6 @@ class InterpolateKernel : public framework::OpKernel { bool align_corners = ctx.Attr("align_corners"); int align_mode = ctx.Attr("align_mode"); - const int n = input->dims()[0]; - const int c = input->dims()[1]; - const int in_h = input->dims()[2]; - const int in_w = input->dims()[3]; - output->mutable_data({n, c, out_h, out_w}, ctx.GetPlace()); auto& device_ctx = ctx.template device_context(); @@ -221,23 +228,31 @@ class InterpolateGradKernel : public framework::OpKernel { auto* input_grad = ctx.Output(framework::GradVarName("X")); auto* output_grad = ctx.Input(framework::GradVarName("Out")); + const int n = input->dims()[0]; + const int c = input->dims()[1]; + const int in_h = input->dims()[2]; + const int in_w = input->dims()[3]; + std::string interp_method = ctx.Attr("interp_method"); int out_h = ctx.Attr("out_h"); int out_w = ctx.Attr("out_w"); + + float scale = ctx.Attr("scale"); + if (scale > 0) { + out_h = in_h * scale; + out_w = in_w * scale; + } + auto out_size = ctx.Input("OutSize"); if (out_size != nullptr) { auto out_size_data = out_size->data(); out_h = out_size_data[0]; out_w = out_size_data[1]; } + bool align_corners = ctx.Attr("align_corners"); int align_mode = ctx.Attr("align_mode"); - const int n = input->dims()[0]; - const int c = input->dims()[1]; - const int in_h = input->dims()[2]; - const int in_w = input->dims()[3]; - input_grad->mutable_data({n, c, in_h, in_w}, ctx.GetPlace()); auto& device_ctx = ctx.template device_context(); diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index e2c8be613fb..2a6c99e2376 100644 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -7056,10 +7056,10 @@ def image_resize(input, out_shape(list|tuple|Variable|None): Output shape of image resize layer, the shape is (out_h, out_w). Default: None - scale(float|None): The multiplier for the input height or width. - At least one of out_shape or scale must be set. - And out_shape has a higher priority than scale. - Default: None + scale(float|None): The multiplier for the input height or width. At + least one of :attr:`out_shape` or :attr:`scale`` must be set. + And :attr:`scale` has a higher priority than :attr:`out_shape`. + Default: None. name(str|None): A name for this layer(optional). If set None, the layer will be named automatically. resample(str): The resample method. It supports 'BILINEAR' and 'NEAREST' @@ -7093,10 +7093,12 @@ def image_resize(input, Raises: TypeError: out_shape should be a list or tuple or Variable. TypeError: actual_shape should either be Variable or None. + TypeError: scale should either be Variable or None. ValueError: The 'resample' of image_resize can only be 'BILINEAR' or 'NEAREST' currently. ValueError: One of out_shape and scale must not be None. ValueError: out_shape length should be 2. + ValueError: scale should be greater than zero. TypeError: align_corners shoule be a bool value ValueError: align_mode can only be '0' or '1' @@ -7128,9 +7130,15 @@ def image_resize(input, def _is_list_or_turple_(data): return (isinstance(data, list) or isinstance(data, tuple)) + inputs = {"X": input} + attrs={ + "interp_method": resample_type, + "align_corners": align_corners, + "align_mode": align_mode + } + out_h = 0 out_w = 0 - inputs = {"X": input} if out_shape is not None: if isinstance(out_shape, Variable): warnings.warn("out_shape as Variable type is deprecated, \ @@ -7143,11 +7151,14 @@ def image_resize(input, raise ValueError("out_shape length should be 2.") out_shape = list(map(int, out_shape)) - out_h = out_shape[0] - out_w = out_shape[1] + attrs['out_h'] = out_shape[0] + attrs['out_w'] = out_shape[1] else: - out_h = int(input.shape[2] * scale) - out_w = int(input.shape[3] * scale) + if not isinstance(scale, float): + raise TypeError("scale should either be Variable or None.") + if scale <= 0: + raise ValueError("scale should be greater than zero.") + attrs['scale'] = scale if isinstance(actual_shape, Variable): inputs["OutSize"] = actual_shape @@ -7159,13 +7170,7 @@ def image_resize(input, type='{}_interp'.format(resample_type), inputs=inputs, outputs={"Out": out}, - attrs={ - "out_h": out_h, - "out_w": out_w, - "interp_method": resample_type, - "align_corners": align_corners, - "align_mode": align_mode - }) + attrs=attrs) return out @@ -7236,8 +7241,9 @@ def resize_bilinear(input, out_shape(${out_size_type}): ${out_size_comment}. scale(float|None): The multiplier for the input height or width. At - least one of out_shape or scale must be set. And out_shape has - a higher priority than scale. Default: None. + least one of :attr:`out_shape` or :attr:`scale`` must be set. + And :attr:`scale` has a higher priority than :attr:`out_shape`. + Default: None. name(str|None): The output variable name. actual_shape(Variable): An optional input to specify output shape @@ -7327,8 +7333,9 @@ def resize_nearest(input, out_shape(${out_size_type}): ${out_size_comment}. scale(float|None): The multiplier for the input height or width. At - least one of out_shape or scale must be set. And out_shape has - a higher priority than scale. Default: None. + least one of :attr:`out_shape` or :attr:`scale`` must be set. + And :attr:`scale` has a higher priority than :attr:`out_shape`. + Default: None. name(str|None): The output variable name. actual_shape(Variable): An optional input to specify output shape diff --git a/python/paddle/fluid/tests/unittests/test_bilinear_interp_op.py b/python/paddle/fluid/tests/unittests/test_bilinear_interp_op.py index f60ed1d79ae..97e9ea02d44 100644 --- a/python/paddle/fluid/tests/unittests/test_bilinear_interp_op.py +++ b/python/paddle/fluid/tests/unittests/test_bilinear_interp_op.py @@ -91,7 +91,14 @@ class TestBilinearInterpOp(OpTest): self.op_type = "bilinear_interp" input_np = np.random.random(self.input_shape).astype("float32") - output_np = bilinear_interp_np(input_np, self.out_h, self.out_w, + if self.scale > 0: + out_h = int(self.input_shape[2] * self.scale) + out_w = int(self.input_shape[3] * self.scale) + else: + out_h = self.out_h + out_w = self.out_w + + output_np = bilinear_interp_np(input_np, out_h, out_w, self.out_size, self.actual_shape, self.align_corners, self.align_mode) self.inputs = {'X': input_np} @@ -99,9 +106,11 @@ class TestBilinearInterpOp(OpTest): self.inputs['OutSize'] = self.out_size if self.actual_shape is not None: self.inputs['OutSize'] = self.actual_shape + self.attrs = { 'out_h': self.out_h, 'out_w': self.out_w, + 'scale': self.scale, 'interp_method': self.interp_method, 'align_corners': self.align_corners, 'align_mode': self.align_mode @@ -119,6 +128,7 @@ class TestBilinearInterpOp(OpTest): self.input_shape = [2, 3, 4, 4] self.out_h = 2 self.out_w = 2 + self.scale = 0. self.out_size = np.array([3, 3]).astype("int32") self.align_corners = True self.align_mode = 1 @@ -130,6 +140,7 @@ class TestBilinearInterpCase1(TestBilinearInterpOp): self.input_shape = [4, 1, 7, 8] self.out_h = 1 self.out_w = 1 + self.scale = 0. self.align_corners = True self.align_mode = 1 @@ -140,6 +151,7 @@ class TestBilinearInterpCase2(TestBilinearInterpOp): self.input_shape = [3, 3, 9, 6] self.out_h = 12 self.out_w = 12 + self.scale = 0. self.align_corners = True self.align_mode = 1 @@ -150,6 +162,7 @@ class TestBilinearInterpCase3(TestBilinearInterpOp): self.input_shape = [1, 1, 128, 64] self.out_h = 64 self.out_w = 128 + self.scale = 0. self.align_corners = True self.align_mode = 1 @@ -160,6 +173,7 @@ class TestBilinearInterpCase4(TestBilinearInterpOp): self.input_shape = [4, 1, 7, 8] self.out_h = 1 self.out_w = 1 + self.scale = 0. self.out_size = np.array([2, 2]).astype("int32") self.align_corners = True self.align_mode = 1 @@ -171,6 +185,7 @@ class TestBilinearInterpCase5(TestBilinearInterpOp): self.input_shape = [3, 3, 9, 6] self.out_h = 12 self.out_w = 12 + self.scale = 0. self.out_size = np.array([11, 11]).astype("int32") self.align_corners = True self.align_mode = 1 @@ -182,6 +197,7 @@ class TestBilinearInterpCase6(TestBilinearInterpOp): self.input_shape = [1, 1, 128, 64] self.out_h = 64 self.out_w = 128 + self.scale = 0. self.out_size = np.array([65, 129]).astype("int32") self.align_corners = True self.align_mode = 1 @@ -193,6 +209,7 @@ class TestBilinearInterpActualShape(TestBilinearInterpOp): self.input_shape = [3, 2, 32, 16] self.out_h = 64 self.out_w = 32 + self.scale = 0. self.out_size = np.array([66, 40]).astype("int32") self.align_corners = True self.align_mode = 1 @@ -206,15 +223,25 @@ class TestBilinearInterpOpUint8(OpTest): self.op_type = "bilinear_interp" input_np = np.random.randint( low=0, high=256, size=self.input_shape).astype("uint8") - output_np = bilinear_interp_np(input_np, self.out_h, self.out_w, + + if self.scale > 0: + out_h = int(self.input_shape[2] * self.scale) + out_w = int(self.input_shape[3] * self.scale) + else: + out_h = self.out_h + out_w = self.out_w + + output_np = bilinear_interp_np(input_np, out_h, out_w, self.out_size, self.actual_shape, self.align_corners, self.align_mode) self.inputs = {'X': input_np} if self.out_size is not None: self.inputs['OutSize'] = self.out_size + self.attrs = { 'out_h': self.out_h, 'out_w': self.out_w, + 'scale': self.scale, 'interp_method': self.interp_method, 'align_corners': self.align_corners, 'align_mode': self.align_mode @@ -229,6 +256,7 @@ class TestBilinearInterpOpUint8(OpTest): self.input_shape = [1, 3, 9, 6] self.out_h = 10 self.out_w = 9 + self.scale = 0. self.align_corners = True self.align_mode = 1 @@ -239,6 +267,7 @@ class TestBilinearInterpCase1Uint8(TestBilinearInterpOpUint8): self.input_shape = [2, 3, 128, 64] self.out_h = 120 self.out_w = 50 + self.scale = 0. self.align_corners = True self.align_mode = 1 @@ -249,6 +278,7 @@ class TestBilinearInterpCase2Uint8(TestBilinearInterpOpUint8): self.input_shape = [4, 1, 7, 8] self.out_h = 5 self.out_w = 13 + self.scale = 0. self.out_size = np.array([6, 15]).astype("int32") self.align_corners = True self.align_mode = 1 @@ -272,5 +302,38 @@ class TestBilinearInterpWithMethod3(TestBilinearInterpOp): self.align_mode = 0 +class TestBilinearInterpScale1(TestBilinearInterpOp): + def init_test_case(self): + self.interp_method = 'bilinear' + self.input_shape = [2, 3, 16, 32] + self.out_h = 60 + self.out_w = 25 + self.scale = 2. + self.align_corners = True + self.align_mode = 1 + + +class TestBilinearInterpScale2(TestBilinearInterpOp): + def init_test_case(self): + self.interp_method = 'bilinear' + self.input_shape = [2, 3, 16, 32] + self.out_h = 60 + self.out_w = 25 + self.scale = 1. + self.align_corners = True + self.align_mode = 1 + + +class TestBilinearInterpScale3(TestBilinearInterpOp): + def init_test_case(self): + self.interp_method = 'bilinear' + self.input_shape = [2, 3, 16, 32] + self.out_h = 60 + self.out_w = 25 + self.scale = 1.5 + self.align_corners = True + self.align_mode = 1 + + if __name__ == "__main__": unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_nearest_interp_op.py b/python/paddle/fluid/tests/unittests/test_nearest_interp_op.py index 5bb2260ef7a..eb82af75e4a 100644 --- a/python/paddle/fluid/tests/unittests/test_nearest_interp_op.py +++ b/python/paddle/fluid/tests/unittests/test_nearest_interp_op.py @@ -73,7 +73,14 @@ class TestNearestInterpOp(OpTest): self.op_type = "nearest_interp" input_np = np.random.random(self.input_shape).astype("float32") - output_np = nearest_neighbor_interp_np(input_np, self.out_h, self.out_w, + if self.scale > 0: + out_h = int(self.input_shape[2] * self.scale) + out_w = int(self.input_shape[3] * self.scale) + else: + out_h = self.out_h + out_w = self.out_w + + output_np = nearest_neighbor_interp_np(input_np, out_h, out_w, self.out_size, self.actual_shape, self.align_corners) self.inputs = {'X': input_np} @@ -84,6 +91,7 @@ class TestNearestInterpOp(OpTest): self.attrs = { 'out_h': self.out_h, 'out_w': self.out_w, + 'scale': self.scale, 'interp_method': self.interp_method, 'align_corners': self.align_corners, } @@ -100,6 +108,7 @@ class TestNearestInterpOp(OpTest): self.input_shape = [2, 3, 4, 4] self.out_h = 2 self.out_w = 2 + self.scale = 0. self.out_size = np.array([3, 3]).astype("int32") self.align_corners = True @@ -110,6 +119,7 @@ class TestNearestNeighborInterpCase1(TestNearestInterpOp): self.input_shape = [4, 1, 7, 8] self.out_h = 1 self.out_w = 1 + self.scale = 0. self.align_corners = True @@ -119,6 +129,7 @@ class TestNearestNeighborInterpCase2(TestNearestInterpOp): self.input_shape = [3, 3, 9, 6] self.out_h = 12 self.out_w = 12 + self.scale = 0. self.align_corners = True @@ -128,6 +139,7 @@ class TestNearestNeighborInterpCase3(TestNearestInterpOp): self.input_shape = [1, 1, 128, 64] self.out_h = 64 self.out_w = 128 + self.scale = 0. self.align_corners = True @@ -137,6 +149,7 @@ class TestNearestNeighborInterpCase4(TestNearestInterpOp): self.input_shape = [4, 1, 7, 8] self.out_h = 1 self.out_w = 1 + self.scale = 0. self.out_size = np.array([2, 2]).astype("int32") self.align_corners = True @@ -147,6 +160,7 @@ class TestNearestNeighborInterpCase5(TestNearestInterpOp): self.input_shape = [3, 3, 9, 6] self.out_h = 12 self.out_w = 12 + self.scale = 0. self.out_size = np.array([11, 11]).astype("int32") self.align_corners = True @@ -157,6 +171,7 @@ class TestNearestNeighborInterpCase6(TestNearestInterpOp): self.input_shape = [1, 1, 128, 64] self.out_h = 64 self.out_w = 128 + self.scale = 0. self.out_size = np.array([65, 129]).astype("int32") self.align_corners = True @@ -167,6 +182,7 @@ class TestNearestNeighborInterpActualShape(TestNearestInterpOp): self.input_shape = [3, 2, 32, 16] self.out_h = 64 self.out_w = 32 + self.scale = 0. self.out_size = np.array([66, 40]).astype("int32") self.align_corners = True @@ -179,7 +195,15 @@ class TestNearestInterpOpUint8(OpTest): self.op_type = "nearest_interp" input_np = np.random.randint( low=0, high=256, size=self.input_shape).astype("uint8") - output_np = nearest_neighbor_interp_np(input_np, self.out_h, self.out_w, + + if self.scale > 0: + out_h = int(self.input_shape[2] * self.scale) + out_w = int(self.input_shape[3] * self.scale) + else: + out_h = self.out_h + out_w = self.out_w + + output_np = nearest_neighbor_interp_np(input_np, out_h, out_w, self.out_size, self.actual_shape, self.align_corners) self.inputs = {'X': input_np} @@ -188,6 +212,7 @@ class TestNearestInterpOpUint8(OpTest): self.attrs = { 'out_h': self.out_h, 'out_w': self.out_w, + 'scale': self.scale, 'interp_method': self.interp_method, 'align_corners': self.align_corners } @@ -201,6 +226,7 @@ class TestNearestInterpOpUint8(OpTest): self.input_shape = [1, 3, 9, 6] self.out_h = 10 self.out_w = 9 + self.scale = 0. self.align_corners = True @@ -210,6 +236,7 @@ class TestNearestNeighborInterpCase1Uint8(TestNearestInterpOpUint8): self.input_shape = [2, 3, 128, 64] self.out_h = 120 self.out_w = 50 + self.scale = 0. self.align_corners = True @@ -219,6 +246,7 @@ class TestNearestNeighborInterpCase2Uint8(TestNearestInterpOpUint8): self.input_shape = [4, 1, 7, 8] self.out_h = 5 self.out_w = 13 + self.scale = 0. self.out_size = np.array([6, 15]).astype("int32") self.align_corners = True @@ -228,5 +256,38 @@ class TestNearestInterpWithoutCorners(TestNearestInterpOp): self.align_corners = False +class TestNearestNeighborInterpScale1(TestNearestInterpOp): + def init_test_case(self): + self.interp_method = 'nearest' + self.input_shape = [3, 2, 32, 16] + self.out_h = 64 + self.out_w = 32 + self.scale = 2. + self.out_size = np.array([66, 40]).astype("int32") + self.align_corners = True + + +class TestNearestNeighborInterpScale2(TestNearestInterpOp): + def init_test_case(self): + self.interp_method = 'nearest' + self.input_shape = [3, 2, 32, 16] + self.out_h = 64 + self.out_w = 32 + self.scale = 1.5 + self.out_size = np.array([66, 40]).astype("int32") + self.align_corners = True + + +class TestNearestNeighborInterpScale3(TestNearestInterpOp): + def init_test_case(self): + self.interp_method = 'nearest' + self.input_shape = [3, 2, 32, 16] + self.out_h = 64 + self.out_w = 32 + self.scale = 1. + self.out_size = np.array([66, 40]).astype("int32") + self.align_corners = True + + if __name__ == "__main__": unittest.main() -- GitLab