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58 lines
2.4 KiB
58 lines
2.4 KiB
# The MIT License (MIT)
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#
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# Copyright (c) 2021, NVIDIA CORPORATION.
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy of
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# this software and associated documentation files (the "Software"), to deal in
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# the Software without restriction, including without limitation the rights to
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# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
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# the Software, and to permit persons to whom the Software is furnished to do so,
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# subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
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# FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
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# COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
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# IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
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# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
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import numpy as np
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# This is the bridge between an argparse based approach and a non-argparse one
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def setparam(args, param, paramstr):
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argsparam = getattr(args, paramstr, None)
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if param is not None or argsparam is None:
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return param
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else:
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return argsparam
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class BaseTracer(object):
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"""Virtual base class for tracer"""
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def __init__(self,
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args = None,
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camera_clamp : list = None,
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step_size : float = None,
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grad_method : str = None,
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num_steps : int = None, # samples for raymaching, iterations for sphere trace
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min_dis : float = None):
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self.args = args
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self.camera_clamp = setparam(args, camera_clamp, 'camera_clamp')
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self.step_size = setparam(args, step_size, 'step_size')
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self.grad_method = setparam(args, grad_method, 'grad_method')
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self.num_steps = setparam(args, num_steps, 'num_steps')
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self.min_dis = setparam(args, min_dis, 'min_dis')
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self.inv_num_steps = 1.0 / self.num_steps
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self.diagonal = np.sqrt(3) * 2.0
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def __call__(self, *args, **kwargs):
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return self.forward(*args, **kwargs)
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def forward(self, net, ray_o, ray_d):
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"""Base implementation for forward"""
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raise NotImplementedError
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