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Running
on
Zero
Running
on
Zero
File size: 2,025 Bytes
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# This file includes code derived from the SiT project (https://github.com/willisma/SiT),
# which is licensed under the MIT License.
#
# MIT License
#
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import torch as th
class EasyDict:
def __init__(self, sub_dict):
for k, v in sub_dict.items():
setattr(self, k, v)
def __getitem__(self, key):
return getattr(self, key)
def mean_flat(x):
"""
Take the mean over all non-batch dimensions.
"""
return th.mean(x, dim=list(range(1, len(x.size()))))
def log_state(state):
result = []
sorted_state = dict(sorted(state.items()))
for key, value in sorted_state.items():
# Check if the value is an instance of a class
if "<object" in str(value) or "object at" in str(value):
result.append(f"{key}: [{value.__class__.__name__}]")
else:
result.append(f"{key}: {value}")
return '\n'.join(result)
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