like20
/
mindspore2022
forked from huawei/mindspore2022
0
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

modify ResizeNearestNeighborV2D

This commit is contained in:
chang zherui 2020-04-23 15:17:06 +08:00
parent 3c1785a121
commit bd13f9ba33
7 changed files with 35 additions and 47 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
mindspore/ccsrc/kernel/gpu/nn/fused_batch_norm_gpu_kernel.cc
View File

@ -55,7 +55,6 @@ MS_REG_GPU_KERNEL_ONE(BatchNorm,
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32)
.AddOutputAttr(kNumberTypeFloat32),
FusedBatchNormGpuKernel, float)
MS_REG_GPU_KERNEL_ONE(BatchNorm,
@ -69,7 +68,6 @@ MS_REG_GPU_KERNEL_ONE(BatchNorm,
.AddOutputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat16)
.AddOutputAttr(kNumberTypeFloat16),
FusedBatchNormGpuKernel, half)
} // namespace kernel

3
mindspore/ccsrc/kernel/gpu/nn/fused_batch_norm_gpu_kernel.h
View File

@ -156,9 +156,6 @@ class FusedBatchNormGpuKernel : public GpuKernel {
output_size_list_.push_back(para_size); // running variance
output_size_list_.push_back(para_size); // save mean
output_size_list_.push_back(para_size); // save variance
if (!is_train_) {
output_size_list_.push_back(para_size); // reserve
}
return;
}

59
mindspore/ccsrc/transform/op_declare.cc
View File

@ -154,14 +154,14 @@ ATTR_MAP(BatchNorm) = {{"data_format", ATTR_DESC(data_format, AnyTraits<std::str
OUTPUT_MAP(BatchNorm) = {{0, OUTPUT_DESC(y)},
{1, OUTPUT_DESC(batch_mean)},
{2, OUTPUT_DESC(batch_variance)},
{3, OUTPUT_DESC(reserve_space_1)},
{4, OUTPUT_DESC(reserve_space_2)},
{5, OUTPUT_DESC(reserve_space_3)}};
{4, OUTPUT_DESC(reserve_space_2)}};
// BatchNormGrad
INPUT_MAP(BatchNormGrad) = {{1, INPUT_DESC(y_backprop)}, {2, INPUT_DESC(x)},
{3, INPUT_DESC(scale)}, {4, INPUT_DESC(reserve_space_1)},
{5, INPUT_DESC(reserve_space_2)}, {6, INPUT_DESC(reserve_space_3)}};
INPUT_MAP(BatchNormGrad) = {{1, INPUT_DESC(y_backprop)},
{2, INPUT_DESC(x)},
{3, INPUT_DESC(scale)},
{4, INPUT_DESC(reserve_space_1)},
{5, INPUT_DESC(reserve_space_2)}};
ATTR_MAP(BatchNormGrad) = {{"data_format", ATTR_DESC(data_format, AnyTraits<std::string>())},
{"epsilon", ATTR_DESC(epsilon, AnyTraits<float>())},
{"is_training", ATTR_DESC(is_training, AnyTraits<bool>())}};
@ -266,11 +266,6 @@ INPUT_MAP(GatherV2) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(indices)}, {3, INPUT_D
ATTR_MAP(GatherV2) = EMPTY_ATTR_MAP;
OUTPUT_MAP(GatherV2) = {{0, OUTPUT_DESC(y)}};
// ReduceSum
INPUT_MAP(ReduceSum) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(axes)}};
ATTR_MAP(ReduceSum) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
OUTPUT_MAP(ReduceSum) = {{0, OUTPUT_DESC(y)}};
// ReduceSumD
INPUT_MAP(ReduceSumD) = {{1, INPUT_DESC(x)}};
INPUT_ATTR_MAP(ReduceSumD) = {
@ -451,17 +446,17 @@ INPUT_MAP(Iou) = {{1, INPUT_DESC(bboxes)}, {2, INPUT_DESC(gtboxes)}};
ATTR_MAP(Iou) = {{"mode", ATTR_DESC(mode, AnyTraits<std::string>())}};
OUTPUT_MAP(Iou) = {{0, OUTPUT_DESC(overlap)}};
// ResizeNearestNeighborD
INPUT_MAP(ResizeNearestNeighborD) = {{1, INPUT_DESC(x)}};
ATTR_MAP(ResizeNearestNeighborD) = {
// ResizeNearestNeighborV2D
INPUT_MAP(ResizeNearestNeighborV2D) = {{1, INPUT_DESC(x)}};
ATTR_MAP(ResizeNearestNeighborV2D) = {
{"size", ATTR_DESC(size, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
{"align_corners", ATTR_DESC(align_corners, AnyTraits<bool>())}};
OUTPUT_MAP(ResizeNearestNeighborD) = {{0, OUTPUT_DESC(y)}};
OUTPUT_MAP(ResizeNearestNeighborV2D) = {{0, OUTPUT_DESC(y)}};
// ResizeNearestNeighborGrad
INPUT_MAP(ResizeNearestNeighborGrad) = {{1, INPUT_DESC(grads)}, {2, INPUT_DESC(size)}};
ATTR_MAP(ResizeNearestNeighborGrad) = {{"align_corners", ATTR_DESC(align_corners, AnyTraits<bool>())}};
OUTPUT_MAP(ResizeNearestNeighborGrad) = {{0, OUTPUT_DESC(y)}};
// ResizeNearestNeighborV2Grad
INPUT_MAP(ResizeNearestNeighborV2Grad) = {{1, INPUT_DESC(grads)}, {2, INPUT_DESC(size)}};
ATTR_MAP(ResizeNearestNeighborV2Grad) = {{"align_corners", ATTR_DESC(align_corners, AnyTraits<bool>())}};
OUTPUT_MAP(ResizeNearestNeighborV2Grad) = {{0, OUTPUT_DESC(y)}};
// ApplyAdam
INPUT_MAP(ApplyAdam) = {{1, INPUT_DESC(var)}, {2, INPUT_DESC(m)}, {3, INPUT_DESC(v)},
@ -486,17 +481,17 @@ INPUT_MAP(Relu6Grad) = {{1, INPUT_DESC(gradients)}, {2, INPUT_DESC(features)}};
ATTR_MAP(Relu6Grad) = EMPTY_ATTR_MAP;
OUTPUT_MAP(Relu6Grad) = {{0, OUTPUT_DESC(backprops)}};
// ResizeBilinearGrad
INPUT_MAP(ResizeBilinearGrad) = {{1, INPUT_DESC(grads)}, {2, INPUT_DESC(original_image)}};
ATTR_MAP(ResizeBilinearGrad) = {{"align_corners", ATTR_DESC(align_corners, AnyTraits<bool>())}};
OUTPUT_MAP(ResizeBilinearGrad) = {{0, OUTPUT_DESC(y)}};
// ResizeBilinearV2Grad
INPUT_MAP(ResizeBilinearV2Grad) = {{1, INPUT_DESC(grads)}, {2, INPUT_DESC(original_image)}};
ATTR_MAP(ResizeBilinearV2Grad) = {{"align_corners", ATTR_DESC(align_corners, AnyTraits<bool>())}};
OUTPUT_MAP(ResizeBilinearV2Grad) = {{0, OUTPUT_DESC(y)}};
// ResizeBilinearD
INPUT_MAP(ResizeBilinearD) = {{1, INPUT_DESC(x)}};
ATTR_MAP(ResizeBilinearD) = {
// ResizeBilinearV2D
INPUT_MAP(ResizeBilinearV2D) = {{1, INPUT_DESC(x)}};
ATTR_MAP(ResizeBilinearV2D) = {
{"size", ATTR_DESC(size, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())},
{"align_corners", ATTR_DESC(align_corners, AnyTraits<bool>())}};
OUTPUT_MAP(ResizeBilinearD) = {{0, OUTPUT_DESC(y)}};
OUTPUT_MAP(ResizeBilinearV2D) = {{0, OUTPUT_DESC(y)}};
// ZerosLike
INPUT_MAP(ZerosLike) = {{1, INPUT_DESC(x)}};
@ -609,10 +604,12 @@ ATTR_MAP(ArgMinWithValue) = {{"axis", ATTR_DESC(dimension, AnyTraits<int>())},
{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
OUTPUT_MAP(ArgMinWithValue) = {{0, OUTPUT_DESC(indice)}, {1, OUTPUT_DESC(values)}};
// ReduceAll
INPUT_MAP(ReduceAll) = {{1, INPUT_DESC(x)}, {2, INPUT_DESC(axes)}};
ATTR_MAP(ReduceAll) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
OUTPUT_MAP(ReduceAll) = {{0, OUTPUT_DESC(y)}}
// ReduceAllD
INPUT_MAP(ReduceAllD) = {{1, INPUT_DESC(x)}};
INPUT_ATTR_MAP(ReduceAllD) = {
{2, ATTR_DESC(axis, AnyTraits<std::vector<int64_t>>(), AnyTraits<std::vector<int64_t>>())}};
ATTR_MAP(ReduceAllD) = {{"keep_dims", ATTR_DESC(keep_dims, AnyTraits<bool>())}};
OUTPUT_MAP(ReduceAllD) = {{0, OUTPUT_DESC(y)}};
// ReduceMeanD
INPUT_MAP(ReduceMeanD) = {{1, INPUT_DESC(x)}};

4
mindspore/ops/_grad/grad_nn_ops.py
View File

@ -356,12 +356,10 @@ def get_bprop_batch_norm(self):
if is_training:
saved_reserve_1 = out[3]
saved_reserve_2 = out[4]
saved_reserve_3 = out[5]
else:
saved_reserve_1 = mean
saved_reserve_2 = variance
saved_reserve_3 = variance
out = input_grad(dout[0], x, scale, saved_reserve_1, saved_reserve_2, saved_reserve_3)
out = input_grad(dout[0], x, scale, saved_reserve_1, saved_reserve_2)
dx = out[0]
dscale = out[1]
dbias = out[2]

4
mindspore/ops/operations/_grad_ops.py
View File

@ -69,11 +69,11 @@ class BatchNormGrad(PrimitiveWithInfer):
self.epsilon = validator.check_number_range('epsilon', epsilon, 0, 1, Rel.INC_RIGHT)
self.add_prim_attr('data_format', "NCHW")
def infer_shape(self, y_backprop_shape, x_shape, scale_shape, reserve_1_shape, reserve_2_shape, reserve_3_shape):
def infer_shape(self, y_backprop_shape, x_shape, scale_shape, reserve_1_shape, reserve_2_shape):
validator.check("BatchNorm y_backprop_shape", y_backprop_shape, "BatchNorm x_shape", x_shape)
return (x_shape, scale_shape, scale_shape, reserve_1_shape, reserve_2_shape)
def infer_dtype(self, y_backprop_type, x_type, scale_type, reserve_1_type, reserve_2_type, reserve_3_type):
def infer_dtype(self, y_backprop_type, x_type, scale_type, reserve_1_type, reserve_2_type):
return (x_type, scale_type, scale_type, reserve_1_type, reserve_2_type)

8
mindspore/ops/operations/nn_ops.py
View File

@ -537,7 +537,6 @@ class BatchNorm(PrimitiveWithInfer):
- **updated_bias** (Tensor) - Tensor of shape :math:`(C,)`.
- **reserve_space_1** (Tensor) - Tensor of shape :math:`(C,)`.
- **reserve_space_2** (Tensor) - Tensor of shape :math:`(C,)`.
- **reserve_space_3** (Tensor) - Tensor of shape :math:`(C,)`.
"""
@prim_attr_register
@ -546,8 +545,7 @@ class BatchNorm(PrimitiveWithInfer):
validator.check_number_range('epsilon', epsilon, 0, 1, Rel.INC_RIGHT, self.name)
self.add_prim_attr('data_format', "NCHW")
self.init_prim_io_names(inputs=['x', 'scale', 'offset', 'mean', 'variance'],
outputs=['y', 'batch_mean', 'batch_variance', 'reserve_space_1', 'reserve_space_2',
'reserve_space_3'])
outputs=['y', 'batch_mean', 'batch_variance', 'reserve_space_1', 'reserve_space_2'])
def infer_shape(self, input_x, scale, bias, mean, variance):
validator.check_integer("scale rank", len(scale), 1, Rel.EQ, self.name)
@ -557,7 +555,7 @@ class BatchNorm(PrimitiveWithInfer):
validator.check_integer("mean rank", len(mean), 1, Rel.EQ, self.name)
validator.check("mean shape", mean, "variance shape", variance, Rel.EQ, self.name)
validator.check("mean shape", mean, "scale shape", scale, Rel.EQ, self.name)
return (input_x, scale, scale, scale, scale, scale)
return (input_x, scale, scale, scale, scale)
def infer_dtype(self, input_x, scale, bias, mean, variance):
validator.check_tensor_type_same({"input_x": input_x}, [mstype.float16, mstype.float32], self.name)
@ -570,7 +568,7 @@ class BatchNorm(PrimitiveWithInfer):
else:
args_moving = {"mean": mean, "variance": variance}
validator.check_tensor_type_same(args_moving, [mstype.float16, mstype.float32], self.name)
return (input_x, scale, bias, input_x, input_x, input_x)
return (input_x, scale, bias, input_x, input_x)
class Conv2D(PrimitiveWithInfer):

2
tests/ut/python/ops/test_ops.py
View File

@ -671,7 +671,7 @@ test_case_nn_ops = [
'skip': []}),
('BatchNormGrad', {
'block': G.BatchNormGrad(),
'desc_inputs': [[128, 64, 32, 32], [128, 64, 32, 32], [64], [64], [64], [64]],
'desc_inputs': [[128, 64, 32, 32], [128, 64, 32, 32], [64], [64], [64]],
'desc_bprop': [[128, 64, 32, 32], [64], [64], [64], [64]],
'skip': ['backward']}),
('ApplyMomentum', {