From 82d4f90325803ea6426c53d1a1d7e6c7b453224a Mon Sep 17 00:00:00 2001 From: dengkaipeng Date: Sat, 9 Mar 2019 16:37:49 +0800 Subject: [PATCH] fix format. test=develop --- paddle/fluid/operators/temporal_shift_op.cc | 38 +++--- paddle/fluid/operators/temporal_shift_op.cu | 114 +++++++++--------- paddle/fluid/operators/temporal_shift_op.h | 15 ++- python/paddle/fluid/layers/nn.py | 6 +- .../tests/unittests/test_temporal_shift_op.py | 13 +- 5 files changed, 97 insertions(+), 89 deletions(-) diff --git a/paddle/fluid/operators/temporal_shift_op.cc b/paddle/fluid/operators/temporal_shift_op.cc index 735237058e..7690942334 100644 --- a/paddle/fluid/operators/temporal_shift_op.cc +++ b/paddle/fluid/operators/temporal_shift_op.cc @@ -17,7 +17,7 @@ namespace operators { using framework::Tensor; -class TemporalShiftOp: public framework::OperatorWithKernel { +class TemporalShiftOp : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; @@ -29,23 +29,23 @@ class TemporalShiftOp: public framework::OperatorWithKernel { "Output(Out) of TemporalShiftOp should not be null."); auto dim_x = ctx->GetInputDim("X"); - PADDLE_ENFORCE_EQ(dim_x.size(), 4, - "Input(X) rank should be 4 in shape of [N*T, C, H, W]."); + PADDLE_ENFORCE_EQ(dim_x.size(), 4, + "Input(X) rank should be 4 in shape of [N*T, C, H, W]."); int seg_num = ctx->Attrs().Get("seg_num"); float shift_ratio = ctx->Attrs().Get("shift_ratio"); - PADDLE_ENFORCE_GT(seg_num, 0, - "Attr(seg_num) should be greater than 0."); + PADDLE_ENFORCE_GT(seg_num, 0, "Attr(seg_num) should be greater than 0."); PADDLE_ENFORCE(shift_ratio > 0 || shift_ratio < .5, "Attr(shift_ratio) should be greater than 0 and less " "than 0.5."); if (ctx->IsRuntime()) { - PADDLE_ENFORCE_EQ(dim_x[0] % seg_num, 0, - "Input(X) dims[0] should be divided exactly by Attr(seg_num)."); + PADDLE_ENFORCE_EQ( + dim_x[0] % seg_num, 0, + "Input(X) dims[0] should be divided exactly by Attr(seg_num)."); } - ctx->SetOutputDim("Out", dim_x); + ctx->SetOutputDim("Out", dim_x); ctx->ShareLoD("X", "Out"); } @@ -70,14 +70,15 @@ class TemporalShiftOpMaker : public framework::OpProtoAndCheckerMaker { "The output tensor of temporal shift operator. " "This is a 4-D tensor in the same shape with Input(X)."); - AddAttr("seg_num", - "The temporal segment number, this should be a positive " - "interger."); - AddAttr("shift_ratio", - "The shift ratio of the channels, the first shift ratio part " - "of channels will be shifted by -1 along the temporal dimension, " - "and the second shift ratio part of channels will be shifted by " - "1 along the temporal dimension. Default 0.25.") + AddAttr("seg_num", + "The temporal segment number, this should be a positive " + "interger."); + AddAttr( + "shift_ratio", + "The shift ratio of the channels, the first shift ratio part " + "of channels will be shifted by -1 along the temporal dimension, " + "and the second shift ratio part of channels will be shifted by " + "1 along the temporal dimension. Default 0.25.") .SetDefault(0.25); AddComment(R"DOC( @@ -118,7 +119,7 @@ class TemporalShiftOpMaker : public framework::OpProtoAndCheckerMaker { } }; -class TemporalShiftOpGrad: public framework::OperatorWithKernel { +class TemporalShiftOpGrad : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; @@ -144,7 +145,8 @@ class TemporalShiftOpGrad: public framework::OperatorWithKernel { } // namespace paddle namespace ops = paddle::operators; -REGISTER_OPERATOR(temporal_shift, ops::TemporalShiftOp, ops::TemporalShiftOpMaker, +REGISTER_OPERATOR(temporal_shift, ops::TemporalShiftOp, + ops::TemporalShiftOpMaker, paddle::framework::DefaultGradOpDescMaker); REGISTER_OPERATOR(temporal_shift_grad, ops::TemporalShiftOpGrad); REGISTER_OP_CPU_KERNEL(temporal_shift, ops::TemporalShiftKernel, diff --git a/paddle/fluid/operators/temporal_shift_op.cu b/paddle/fluid/operators/temporal_shift_op.cu index 3d9c9ddd5a..24f1f8e178 100644 --- a/paddle/fluid/operators/temporal_shift_op.cu +++ b/paddle/fluid/operators/temporal_shift_op.cu @@ -17,70 +17,72 @@ namespace operators { using framework::Tensor; - template __global__ void KeTemporalShiftFw(const T* input, T* output, const int ntchw, - const int tchw, const int chw, const int hw, const int w, const int t, const int c, - const float shift_ratio) { + const int tchw, const int chw, const int hw, + const int w, const int t, const int c, + const float shift_ratio) { int tid = blockIdx.x * blockDim.x + threadIdx.x; int stride = blockDim.x * gridDim.x; int src_it = 0; for (; tid < ntchw; tid += stride) { - int in = tid / tchw; - int it = (tid % tchw) / chw; - int ic = (tid % chw) / hw; - int ih = (tid % hw) / w; - int iw = tid % w; - - const int c1 = static_cast(c * shift_ratio); - const int c2 = static_cast(c * 2 * shift_ratio); - - if (ic < c1) { - src_it = it - 1; - } else if (ic < c2) { - src_it = it + 1; - } else { - src_it = it; - } - - if (src_it < 0 || src_it >= t) { - output[tid] = 0; - } else { - int src_idx = GetEntryIndex(in, src_it, ic, ih, iw, tchw, chw, hw, w); - output[tid] = input[src_idx]; - } + int in = tid / tchw; + int it = (tid % tchw) / chw; + int ic = (tid % chw) / hw; + int ih = (tid % hw) / w; + int iw = tid % w; + + const int c1 = static_cast(c * shift_ratio); + const int c2 = static_cast(c * 2 * shift_ratio); + + if (ic < c1) { + src_it = it - 1; + } else if (ic < c2) { + src_it = it + 1; + } else { + src_it = it; + } + + if (src_it < 0 || src_it >= t) { + output[tid] = 0; + } else { + int src_idx = GetEntryIndex(in, src_it, ic, ih, iw, tchw, chw, hw, w); + output[tid] = input[src_idx]; + } } } template -__global__ void KeTemporalShiftBw(const T* output_grad, T* input_grad, const int ntchw, - const int tchw, const int chw, const int hw, const int w, const int t, const int c, - const float shift_ratio) { +__global__ void KeTemporalShiftBw(const T* output_grad, T* input_grad, + const int ntchw, const int tchw, + const int chw, const int hw, const int w, + const int t, const int c, + const float shift_ratio) { int tid = blockIdx.x * blockDim.x + threadIdx.x; int stride = blockDim.x * gridDim.x; int src_it = 0; for (; tid < ntchw; tid += stride) { - int in = tid / tchw; - int it = (tid % tchw) / chw; - int ic = (tid % chw) / hw; - int ih = (tid % hw) / w; - int iw = tid % w; - - const int c1 = static_cast(c * shift_ratio); - const int c2 = static_cast(c * 2 * shift_ratio); - - if (ic < c1) { - src_it = it - 1; - } else if (ic < c2) { - src_it = it + 1; - } else { - src_it = it; - } - - if (src_it >= 0 && src_it < t) { - int src_idx = GetEntryIndex(in, src_it, ic, ih, iw, tchw, chw, hw, w); - input_grad[src_idx] = output_grad[tid]; - } + int in = tid / tchw; + int it = (tid % tchw) / chw; + int ic = (tid % chw) / hw; + int ih = (tid % hw) / w; + int iw = tid % w; + + const int c1 = static_cast(c * shift_ratio); + const int c2 = static_cast(c * 2 * shift_ratio); + + if (ic < c1) { + src_it = it - 1; + } else if (ic < c2) { + src_it = it + 1; + } else { + src_it = it; + } + + if (src_it >= 0 && src_it < t) { + int src_idx = GetEntryIndex(in, src_it, ic, ih, iw, tchw, chw, hw, w); + input_grad[src_idx] = output_grad[tid]; + } } } @@ -113,8 +115,8 @@ class TemporalShiftOpCUDAKernel : public framework::OpKernel { grid_dim = grid_dim > 8 ? 8 : grid_dim; KeTemporalShiftFw< - T><<>>( - input_data, output_data, ntchw, tchw, chw, hw, w, t, c, shift_ratio); + T><<>>( + input_data, output_data, ntchw, tchw, chw, hw, w, t, c, shift_ratio); } }; @@ -138,7 +140,8 @@ class TemporalShiftGradOpCUDAKernel : public framework::OpKernel { const int ntchw = nt * chw; const T* output_grad_data = output_grad->data(); - T* input_grad_data = input_grad->mutable_data({nt, c, h, w}, ctx.GetPlace()); + T* input_grad_data = + input_grad->mutable_data({nt, c, h, w}, ctx.GetPlace()); math::SetConstant()( ctx.template device_context(), input_grad, static_cast(0)); @@ -148,8 +151,9 @@ class TemporalShiftGradOpCUDAKernel : public framework::OpKernel { grid_dim = grid_dim > 8 ? 8 : grid_dim; KeTemporalShiftBw< - T><<>>( - output_grad_data, input_grad_data, ntchw, tchw, chw, hw, w, t, c, shift_ratio); + T><<>>( + output_grad_data, input_grad_data, ntchw, tchw, chw, hw, w, t, c, + shift_ratio); } }; diff --git a/paddle/fluid/operators/temporal_shift_op.h b/paddle/fluid/operators/temporal_shift_op.h index 6b8001596c..4c7eed5af4 100644 --- a/paddle/fluid/operators/temporal_shift_op.h +++ b/paddle/fluid/operators/temporal_shift_op.h @@ -18,13 +18,15 @@ namespace operators { using Tensor = framework::Tensor; -static HOSTDEVICE inline int GetEntryIndex(int in, int it, int ic, int ih, int iw, - const int tchw, const int chw, const int hw, const int w) { +static HOSTDEVICE inline int GetEntryIndex(int in, int it, int ic, int ih, + int iw, const int tchw, + const int chw, const int hw, + const int w) { return in * tchw + it * chw + ic * hw + ih * w + iw; } template -class TemporalShiftKernel: public framework::OpKernel { +class TemporalShiftKernel : public framework::OpKernel { public: void Compute(const framework::ExecutionContext& ctx) const override { auto* input = ctx.Input("X"); @@ -62,7 +64,7 @@ class TemporalShiftKernel: public framework::OpKernel { } else { src_it = it; } - + if (src_it < 0 || src_it >= t) { output_data[i] = 0; } else { @@ -95,7 +97,8 @@ class TemporalShiftGradKernel : public framework::OpKernel { const int tchw = t * chw; const T* output_grad_data = output_grad->data(); - T* input_grad_data = input_grad->mutable_data({nt, c, h, w}, ctx.GetPlace()); + T* input_grad_data = + input_grad->mutable_data({nt, c, h, w}, ctx.GetPlace()); memset(input_grad_data, 0, input_grad->numel() * sizeof(T)); int src_it = 0; @@ -113,7 +116,7 @@ class TemporalShiftGradKernel : public framework::OpKernel { } else { src_it = it; } - + if (src_it >= 0 && src_it < t) { int src_idx = GetEntryIndex(in, src_it, ic, ih, iw, tchw, chw, hw, w); input_grad_data[src_idx] = output_grad_data[i]; diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index 1280baae5d..d6129a4ac0 100644 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -10301,10 +10301,8 @@ def temporal_shift(x, seg_num, shift_ratio=0.25, name=None): type="temporal_shift", inputs={"X": x}, outputs={"Out": out}, - attrs={ - "seg_num": seg_num, - "shift_ratio": shift_ratio - }) + attrs={"seg_num": seg_num, + "shift_ratio": shift_ratio}) return out diff --git a/python/paddle/fluid/tests/unittests/test_temporal_shift_op.py b/python/paddle/fluid/tests/unittests/test_temporal_shift_op.py index dbef184d63..14d3d67522 100644 --- a/python/paddle/fluid/tests/unittests/test_temporal_shift_op.py +++ b/python/paddle/fluid/tests/unittests/test_temporal_shift_op.py @@ -24,15 +24,17 @@ from paddle.fluid import core def temporal_shift(x, seg_num, shift_ratio): shape = x.shape reshape_x = x.reshape((-1, seg_num, shape[1], shape[2], shape[3])) - pad_x = np.pad(reshape_x, ((0, 0), (1, 1), (0, 0), (0, 0), (0, 0)), 'constant') + pad_x = np.pad(reshape_x, ((0, 0), (1, 1), (0, 0), (0, 0), (0, 0)), + 'constant') c1 = int(shape[1] * shift_ratio) c2 = int(shape[1] * 2 * shift_ratio) slice1 = pad_x[:, :seg_num, :c1, :, :] - slice2 = pad_x[:, 2:seg_num+2, c1:c2, :, :] - slice3 = pad_x[:, 1:seg_num+1, c2:, :, :] + slice2 = pad_x[:, 2:seg_num + 2, c1:c2, :, :] + slice3 = pad_x[:, 1:seg_num + 1, c2:, :, :] concat_x = np.concatenate([slice1, slice2, slice3], axis=2) return concat_x.reshape(shape) + class TestTemporalShift(OpTest): def setUp(self): self.initTestCase() @@ -44,9 +46,7 @@ class TestTemporalShift(OpTest): "shift_ratio": self.shift_ratio, } - self.inputs = { - "X": x, - } + self.inputs = {"X": x, } output = temporal_shift(x, self.seg_num, self.shift_ratio) self.outputs = {"Out": output} @@ -62,6 +62,7 @@ class TestTemporalShift(OpTest): self.seg_num = 3 self.shift_ratio = 0.25 + class TestTemporalShift2(TestTemporalShift): def initTestCase(self): self.x_shape = (4, 9, 7, 7) -- GitLab