mirror of
https://github.com/iperov/DeepFaceLive.git
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75 lines
2.6 KiB
Python
75 lines
2.6 KiB
Python
import numpy as np
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from typing import List
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from ..AShape import AShape
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from ..backend import Kernel
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from ..HArgs import HArgs
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from ..HKernel import HKernel
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from ..HType import HType
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from ..info import StackInfo
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from ..SCacheton import SCacheton
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from ..Tensor import Tensor
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def stack(tensor_list : List[Tensor], axis, dtype=None, output_t=None, is_add_to_output=False):
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"""
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Stack operator.
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arguments:
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tensor_list List of Tensors
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axis Int
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output_t compute result to this Tensor.
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Tensor may be with different shape, but should match total size.
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gradfn will not be set.
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is_add_to_output add result to output_t if output_t is set.
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"""
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HArgs.check_zero_get_length(tensor_list)
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HArgs.check_all_tensors(tensor_list)
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device = HArgs.check_get_same_device(tensor_list)
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shape_list, dtype_list, _ = HArgs.decompose(tensor_list)
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op = SCacheton.get(_StackOp, shape_list, dtype_list, int(axis), dtype, False if output_t is None else is_add_to_output)
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if output_t is None:
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output_t = Tensor (op.info.o_shape, op.o_dtype, device=device)
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elif output_t.shape.size != op.info.o_shape.size:
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raise ValueError(f'output_t must have size {op.info.o_shape.size}')
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for i, krn in enumerate(op.forward_krns):
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device.run_kernel(krn, output_t.get_buffer(), tensor_list[i].get_buffer(), np.int64(i) )
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return output_t
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class _StackOp:
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def __init__(self, shape_list : List[AShape], dtype_list : List[np.dtype], axis, o_dtype, is_add_to_output):
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self.stack_count = stack_count = len(shape_list)
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i_shape = shape_list[0]
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if not all (s == i_shape for s in shape_list):
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raise ValueError('All shapes must be the same')
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self.o_dtype = o_dtype = o_dtype if o_dtype is not None else HType.get_most_weighted_dtype (dtype_list)
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self.info = info = StackInfo(i_shape, axis, stack_count)
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self.forward_krns = forward_krns = []
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for i_dtype in dtype_list:
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forward_krns.append( Kernel(global_shape=(i_shape.size,), kernel_text=f"""
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{HKernel.define_tensor('O', info.o_shape, o_dtype )}
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{HKernel.define_tensor('I', i_shape, i_dtype )}
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__kernel void impl(__global O_PTR_TYPE* O_PTR_NAME, __global const I_PTR_TYPE* I_PTR_NAME, long i_new_idx)
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{{
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size_t gid = get_global_id(0);
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{HKernel.decompose_idx_to_axes_idxs('gid', 'I', i_shape.ndim)}
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{'O_STORE_ADD' if is_add_to_output else 'O_GLOBAL_STORE'}( O_IDX({HKernel.axes_seq_enum('I', i_shape.ndim, new_axis=('i_new_idx', info.axis))}), I_GLOBAL_LOAD(gid) );
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}}
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"""))
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