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import torch
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import numpy as np
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import yaml
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import os
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from torch.utils.data import Sampler
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def load_yaml_with_includes(yaml_file):
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def loader_with_include(loader, node):
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include_path = os.path.join(os.path.dirname(yaml_file), loader.construct_scalar(node))
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with open(include_path, 'r') as f:
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return yaml.load(f, Loader=yaml.FullLoader)
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yaml.add_constructor('!include', loader_with_include, Loader=yaml.FullLoader)
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with open(yaml_file, 'r') as f:
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return yaml.load(f, Loader=yaml.FullLoader)
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def customized_lr_scheduler(optimizer, warmup_steps=10000, decay_steps=1e6, end_factor=1e-4):
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from torch.optim.lr_scheduler import LinearLR, SequentialLR
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warmup_scheduler = LinearLR(optimizer,
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start_factor=min(1 / warmup_steps, 1),
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end_factor=1.0, total_iters=warmup_steps)
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decay_scheduler = LinearLR(optimizer,
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start_factor=1.0,
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end_factor=end_factor,
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total_iters=decay_steps)
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scheduler = SequentialLR(optimizer, schedulers=[warmup_scheduler, decay_scheduler],
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milestones=[warmup_steps])
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return scheduler
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def get_lr_scheduler(optimizer, name, **kwargs):
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if name == 'customized':
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return customized_lr_scheduler(optimizer, **kwargs)
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elif name == 'cosine':
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from torch.optim.lr_scheduler import CosineAnnealingLR
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return CosineAnnealingLR(optimizer, **kwargs)
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else:
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raise NotImplementedError(name)
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class ConcatDatasetBatchSampler(Sampler):
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def __init__(self, samplers, batch_sizes, epoch=0):
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self.batch_sizes = batch_sizes
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self.samplers = samplers
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self.offsets = [0] + np.cumsum([len(x) for x in self.samplers]).tolist()[:-1]
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self.epoch = epoch
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self.set_epoch(self.epoch)
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def _iter_one_dataset(self, c_batch_size, c_sampler, c_offset):
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batch = []
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for idx in c_sampler:
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batch.append(c_offset + idx)
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if len(batch) == c_batch_size:
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yield batch
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def set_epoch(self, epoch):
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if hasattr(self.samplers[0], "epoch"):
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for s in self.samplers:
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s.set_epoch(epoch)
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def __iter__(self):
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iterators = [iter(i) for i in self.samplers]
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tot_batch = []
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for b_num in range(len(self)):
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for samp_idx in range(len(self.samplers)):
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c_batch = []
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while len(c_batch) < self.batch_sizes[samp_idx]:
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c_batch.append(self.offsets[samp_idx] + next(iterators[samp_idx]))
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tot_batch.extend(c_batch)
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yield tot_batch
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tot_batch = []
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def __len__(self):
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min_len = float("inf")
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for idx, sampler in enumerate(self.samplers):
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c_len = (len(sampler)) // self.batch_sizes[idx]
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min_len = min(c_len, min_len)
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return min_len |
