82 lines
2.6 KiB
Python
82 lines
2.6 KiB
Python
#!/usr/bin/env python
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# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from collections import deque
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import torch
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from torch import nn
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def populate_queues(queues, batch):
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for key in batch:
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# Ignore keys not in the queues already (leaving the responsibility to the caller to make sure the
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# queues have the keys they want).
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if key not in queues:
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continue
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if len(queues[key]) != queues[key].maxlen:
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# initialize by copying the first observation several times until the queue is full
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while len(queues[key]) != queues[key].maxlen:
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queues[key].append(batch[key])
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else:
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# add latest observation to the queue
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queues[key].append(batch[key])
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return queues
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def get_device_from_parameters(module: nn.Module) -> torch.device:
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"""Get a module's device by checking one of its parameters.
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Note: assumes that all parameters have the same device
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"""
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return next(iter(module.parameters())).device
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def get_dtype_from_parameters(module: nn.Module) -> torch.dtype:
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"""Get a module's parameter dtype by checking one of its parameters.
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Note: assumes that all parameters have the same dtype.
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"""
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return next(iter(module.parameters())).dtype
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class TemporalQueue:
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def __init__(self, maxlen):
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# TODO(rcadene): set proper maxlen
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self.items = deque(maxlen=maxlen)
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self.timestamps = deque(maxlen=maxlen)
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def add(self, item, timestamp):
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self.items.append(item)
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self.timestamps.append(timestamp)
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def get_latest(self):
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return self.items[-1], self.timestamps[-1]
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def get(self, timestamp):
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import numpy as np
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timestamps = np.array(list(self.timestamps))
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distances = np.abs(timestamps - timestamp)
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nearest_idx = distances.argmin()
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# print(float(distances[nearest_idx]))
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if float(distances[nearest_idx]) > 1 / 5:
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raise ValueError()
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return self.items[nearest_idx], self.timestamps[nearest_idx]
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def __len__(self):
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return len(self.items)
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