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fix bug of max_unpoolnd about setting of parameter output_size

This commit is contained in:
ZhidanLiu 2023-02-02 15:35:36 +08:00
parent 6500d2afd8
commit d66cb58dac
3 changed files with 71 additions and 41 deletions
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3
.jenkins/check/config/whitelizard.txt
View File

@ -30,6 +30,8 @@ mindspore/mindspore/ccsrc/pipeline/jit/resource.cc:mindspore::pipeline::GetMetho
mindspore/mindspore/python/mindspore/ops/operations/array_ops.py:_compute_slicing_shape
mindspore/mindspore/python/mindspore/ops/function/array_func.py:scatter_nd
mindspore/mindspore/python/mindspore/ops/function/nn_func.py:max_unpool3d
mindspore/mindspore/python/mindspore/ops/function/nn_func.py:max_unpool2d
mindspore/mindspore/python/mindspore/ops/function/nn_func.py:max_unpool1d
mindspore/mindspore/python/mindspore/ops/function/nn_func.py:pad
mindspore/mindspore/python/mindspore/ops/function/math_func.py:cov
mindspore/mindspore/python/mindspore/ops/function/math_func.py:norm
@ -267,7 +269,6 @@ mindspore/mindspore/ccsrc/pybind_api/ir/tensor_py.cc:mindspore::tensor::RegMetaT
mindspore/mindspore/ccsrc/plugin/device/cpu/kernel/eltwise_grad_cpu_kernel.cc:mindspore::kernel::EltWiseGradCpuTypeFunc<T>::InitFunc
mindspore/mindspore/lite/tools/converter/quantizer/weight_quantizer.cc:mindspore::lite::quant::WeightQuantizer::LinearQuant
mindspore/mindspore/python/mindspore/ops/function/nn_func.py:conv3d
mindspore/mindspore/python/mindspore/ops/function/nn_func.py:max_unpool3d
mindspore/mindspore/ccsrc/plugin/device/cpu/kernel/nnacl/fp32/matmul_avx512_mask_fp32.c:GemmRowxColMaskKernelFp32
mindspore/mindspore/ccsrc/plugin/device/cpu/kernel/crop_and_resize_cpu_kernel.cc:mindspore::kernel::CropAndResizeCpuKernelMod::LaunchKernel
mindspore/mindspore/ccsrc/plugin/device/cpu/hal/device/cpu_device_address.cc:mindspore::device::cpu::CPUDeviceAddress::SyncHostToDevice

2
mindspore/python/mindspore/ops/function/math_func.py
View File

@ -9975,7 +9975,7 @@ def sum(x, dim=None, keepdim=False, *, dtype=None):
out = reduce_sum(x, dim)
else:
out = reduce_sum(x)
if dtype:
if dtype is not None:
out = out.astype(dtype)
return out

107
mindspore/python/mindspore/ops/function/nn_func.py
View File

@ -735,19 +735,7 @@ def max_unpool1d(x, indices, kernel_size, stride=None, padding=0, output_size=No
[[0, 2, 0, 4, 0, 6, 0, 8]]
"""
if stride is None:
stride = 0
if output_size is None:
output_size = ()
else:
if not isinstance(output_size, tuple):
raise ValueError(f"For max_unpool1d, output_size must be tuple, but type {type(output_size)}.")
if len(output_size) not in [0, 2, 3]:
raise ValueError(f"For max_unpool1d, length of output_size with tuple must be 0, 2, 3, "
f"but got type {len(output_size)}.")
if len(output_size) == 2:
output_size = (1,) + output_size + (1,)
if len(output_size) == 3:
output_size = output_size + (1,)
stride = kernel_size
shape = P.Shape()
x_shape = shape(x)
@ -759,8 +747,37 @@ def max_unpool1d(x, indices, kernel_size, stride=None, padding=0, output_size=No
if x_dim not in (2, 3):
raise ValueError(f"For max_unpool1d, the x shape must have 2 or 3 dims, but got {x_dim}.")
max_unpool_2d = _get_cache_prim(MaxUnpool2D)(ksize=(kernel_size, 1), strides=(stride, 1),
pads=(padding, 0), output_shape=output_size, data_format="NCHW")
if output_size is None:
output_size = ()
else:
if not isinstance(output_size, tuple):
raise ValueError(f"For max_unpool1d, output_size must be tuple, but type {type(output_size)}.")
if len(output_size) not in [0, 1, 2, 3]:
raise ValueError(f"For max_unpool1d, length of output_size with tuple must be 0, 1, 2, 3, "
f"but got type {len(output_size)}.")
if not output_size:
output_size = ()
elif x_dim == 2:
output_size = (1,) + x_shape[:1] + output_size[-1:] + (1,)
else:
output_size = x_shape[:2] + output_size[-1:] + (1,)
if isinstance(kernel_size, tuple):
kernel_size = kernel_size + (1,)
elif isinstance(kernel_size, int):
kernel_size = (kernel_size, 1)
if isinstance(stride, tuple):
stride = stride + (1,)
elif isinstance(stride, int):
stride = (stride, 1)
if isinstance(padding, tuple):
padding = padding + (0,)
elif isinstance(padding, int):
padding = (padding, 0)
max_unpool_2d = _get_cache_prim(MaxUnpool2D)(ksize=kernel_size, strides=stride,
pads=padding, output_shape=output_size, data_format="NCHW")
if x_dim == 2:
x = x.expand_dims(axis=0)
indices = indices.expand_dims(axis=0)
@ -844,17 +861,7 @@ def max_unpool2d(x, indices, kernel_size, stride=None, padding=0, output_size=No
[8. 9.]]]]
"""
if stride is None:
stride = 0
if output_size is None:
output_size = ()
else:
if not isinstance(output_size, tuple):
raise ValueError(f"For max_unpool2d, output_size must be tuple, but type {type(output_size)}.")
if len(output_size) not in [0, 3, 4]:
raise ValueError(f"For max_unpool2d, length of output_size with tuple must be 0, 3, 4, "
f"but got type {len(output_size)}.")
if len(output_size) == 3:
output_size = (1,) + output_size
stride = kernel_size
shape = P.Shape()
x_shape = shape(x)
@ -866,6 +873,21 @@ def max_unpool2d(x, indices, kernel_size, stride=None, padding=0, output_size=No
if x_dim not in (3, 4):
raise ValueError(f"For max_unpool2d, the x shape must have 3 or 4 dims, but got {x_dim}.")
if output_size is None:
output_size = ()
else:
if not isinstance(output_size, tuple):
raise ValueError(f"For max_unpool2d, output_size must be tuple, but type {type(output_size)}.")
if len(output_size) not in [0, 2, 3, 4]:
raise ValueError(f"For max_unpool2d, length of output_size with tuple must be 0, 2, 3, 4, "
f"but got type {len(output_size)}.")
if not output_size:
output_size = ()
elif x_dim == 3:
output_size = (1,) + x_shape[:1] + output_size[-2:]
else:
output_size = x_shape[:2] + output_size[-2:]
max_unpool_2d = MaxUnpool2D(ksize=kernel_size, strides=stride, pads=padding, output_shape=output_size,
data_format="NCHW")
if x_dim == 3:
@ -950,18 +972,8 @@ def max_unpool3d(x, indices, kernel_size, stride=None, padding=0, output_size=No
[0. 0. 0.]]]]]
"""
if stride is None:
stride = 0
if output_size is None:
output_size = ()
elif not isinstance(output_size, tuple):
raise ValueError(f"For max_unpool3d, output_size must be tuple, but type {type(output_size)}.")
elif len(output_size) not in [0, 4, 5]:
raise ValueError(f"For max_unpool3d, length of output_size with tuple must be 0, 4, 5, "
f"but got type {len(output_size)}.")
elif len(output_size) == 4:
output_size = (1,) + output_size
max_unpool_3d = MaxUnpool3D(ksize=kernel_size, strides=stride, pads=padding, output_shape=output_size,
data_format="NCDHW")
stride = kernel_size
x_shape = P.Shape()(x)
indices_shape = P.Shape()(indices)
x_dim = len(x_shape)
@ -970,6 +982,23 @@ def max_unpool3d(x, indices, kernel_size, stride=None, padding=0, output_size=No
f"shape {x_shape} and indices shape {indices_shape}.")
if x_dim not in (4, 5):
raise ValueError(f"For max_unpool3d, the x shape must have 4 or 5 dims, but got {x_dim}.")
if output_size is None:
output_size = ()
elif not isinstance(output_size, tuple):
raise ValueError(f"For max_unpool3d, output_size must be tuple, but type {type(output_size)}.")
elif len(output_size) not in [0, 3, 4, 5]:
raise ValueError(f"For max_unpool3d, length of output_size with tuple must be 0, 3, 4, 5, "
f"but got type {len(output_size)}.")
if not output_size:
output_size = ()
elif x_dim == 5:
output_size = x_shape[:2] + output_size[-3:]
else:
output_size = (1,) + x_shape[:1] + output_size[-3:]
max_unpool_3d = MaxUnpool3D(ksize=kernel_size, strides=stride, pads=padding, output_shape=output_size,
data_format="NCDHW")
if x_dim == 4:
x = x.expand_dims(axis=0)
indices = indices.expand_dims(axis=0)
@ -2225,7 +2254,7 @@ def silu(x):
For more details, please refer to :class:`mindspore.nn.SiLU`.
"""
return sigmoid_(x)*x
return sigmoid_(x) * x
def selu(input_x):