Use resampling in both RandomResizedCrop and Resize #642
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| attr_output.Resize(Dims{2}); | ||
| int *t = attr_output.mutable_data<int>(); | ||
| t[0] = out_shape[0]; |
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I think this was a bug. GPU variant uses input size, which seems more reasonable.
mzient
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| @@ -170,6 +171,18 @@ view_as_tensor(const TensorList<Backend> &data) { | |||
| return { data.template data<U>(), tensor_shape<ndim>(data) }; | |||
| } | |||
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| template <int ndim> | |||
| void to_dims_vec(std::vector<Dims> &dims_vec, const kernels::TensorListShape<ndim> &tls) { | |||
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why not returning the vector ?
And if an output variable, I think that it'd be more intuitive to have the outputs at the end of the arguments
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I don't want to have a dynamic allocation in steady code path. As for the argument order, I always follow memcpy convention.
| DALI_INTERP_LINEAR) | ||
| .AddOptionalArg("mag_filter", "Filter used when scaling up", |
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if several operators are using those resampling related arguments you could register a schema ResamplingAttr (just the schema, no operator) and just write here AddParent("ResamplingAttr").
You can look at what I did with CropAttr or RandomCropAttr.
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OK, I'll see how it works out.
| @@ -52,8 +61,8 @@ void ResizeBase::GetResamplingFilters(kernels::FilterDesc &min_filter, | |||
| else if (spec.HasArgument("interp_type")) | |||
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why don't you rely on default arguments instead of initializing to default and then checking if HasArgument?
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I have to know if the argument has been specified or not. If it has, it overrides the value defined by interp_type. It's different than relying on default value, in which case I wouldn't know if the value was specified, but was assigned the default value explicitly, or was not defined at all (in which case interp_type should be used, if defined). The logic is a bit convoluted, but it preserves the old behavior if interp_type is set.
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Refactor RandomResizedCrop and Resize to have common base worker class. ResizeBase uses resampling kernels for CPU and GPU. Signed-off-by: Michal Zientkiewicz <michalz@nvidia.com>
Signed-off-by: Michal Zientkiewicz <michalz@nvidia.com>
Signed-off-by: Michal Zientkiewicz <michalz@nvidia.com>
Add hint for GPU scratch memory. Signed-off-by: Michal Zientkiewicz <michalz@nvidia.com>
* Add min_filter and mag_filter parameters to RandomResizedCrop schema. * Use resampling in RandomResizedCrop. * Refactor RandomResizedCrop and Resize to have common base worker class. * ResizeBase uses resampling kernels for CPU and GPU. * Add ResamplingFilterAttr, remove duplicate attributes from operators. * Set output layout in Resize. * Changed downscaling to linear for the sake of presized datasets. * Add hint for GPU scratch memory. Signed-off-by: Michal Zientkiewicz <michalz@nvidia.com>
* Add min_filter and mag_filter parameters to RandomResizedCrop schema. * Use resampling in RandomResizedCrop. * Refactor RandomResizedCrop and Resize to have common base worker class. * ResizeBase uses resampling kernels for CPU and GPU. * Add ResamplingFilterAttr, remove duplicate attributes from operators. * Set output layout in Resize. * Changed downscaling to linear for the sake of presized datasets. * Add hint for GPU scratch memory. Signed-off-by: Michal Zientkiewicz <michalz@nvidia.com>
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