Improving Code Quality of the Project #20
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| result = self.interpolator(points[not a]) | ||
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| condition = ((points[:, 0] < 0) + (points[:, 0] >= self.data.dwi.shape[0]) + # OR |
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Please add a comment explaining the condition, i.e. say that it is checking whether we are within the domain of our dwi data
| out_memmap[idx:(idx + lengths[i])] = out.numpy() | ||
| idx = idx + lengths[i] | ||
| assert len(self) == len(lengths) | ||
| for i, ((inp, out), length) in enumerate(zip(self, lengths)): |
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please add a comment that explains what you are doing here
| def __len__(self): | ||
| return len(self.sl_lengths) | ||
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| def __getitem__(self, index): |
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add documentation to this function, i.e. what are you returning, dimensions, etc.
| self.feature_shapes = (torch.prod(input_shape).item(), torch.prod(output_shape).item()) | ||
| return self.feature_shapes | ||
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| def cuda(self, device=None, non_blocking=False, memory_format=torch.preserve_format): |
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explain why you are implementing the cuda function here + when to use it :)
| self.device = dwi.device | ||
| return self | ||
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| def cpu(self, memory_format=torch.preserve_format): |
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explain why you are using a cpu function + when to use it
| import dfibert.envs.tractography as RLTe | ||
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| def train(path, max_steps=3000000, replay_memory_size=20000, eps_annealing_steps=100000, agent_history_length=1, evaluate_every=20000, eval_runs=5, network_update_every=10000, max_episode_length=200, learning_rate=0.0000625): |
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documentation missing but would be really helpful. @phanfeld might probably provide more insights in the trainign algorithm etc
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | ||
| print("Init environment..") | ||
| env = RLTe.RLtractEnvironment(device = 'cpu') | ||
| env = RLTe.EnvTractography(device = 'cpu') |
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why do you actually set the device to cpu?
This reverts commit 7c097f8.
3 participants
Went through the whole codebase and fixed sensible code quality issues highlighted by pylint.
Closes #15.