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Zero
Running
on
Zero
File size: 3,243 Bytes
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# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os
import random
import subprocess
from urllib.parse import urlparse
import numpy as np
import torch
from torch import nn
logger = logging.getLogger("dinov2")
def load_pretrained_weights(model, pretrained_weights, checkpoint_key):
if urlparse(pretrained_weights).scheme: # If it looks like an URL
state_dict = torch.hub.load_state_dict_from_url(pretrained_weights, map_location="cpu")
else:
state_dict = torch.load(pretrained_weights, map_location="cpu")
if checkpoint_key is not None and checkpoint_key in state_dict:
logger.info(f"Take key {checkpoint_key} in provided checkpoint dict")
state_dict = state_dict[checkpoint_key]
# remove `module.` prefix
state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()}
# remove `backbone.` prefix induced by multicrop wrapper
state_dict = {k.replace("backbone.", ""): v for k, v in state_dict.items()}
msg = model.load_state_dict(state_dict, strict=False)
logger.info("Pretrained weights found at {} and loaded with msg: {}".format(pretrained_weights, msg))
def fix_random_seeds(seed=31):
"""
Fix random seeds.
"""
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
def get_sha():
cwd = os.path.dirname(os.path.abspath(__file__))
def _run(command):
return subprocess.check_output(command, cwd=cwd).decode("ascii").strip()
sha = "N/A"
diff = "clean"
branch = "N/A"
try:
sha = _run(["git", "rev-parse", "HEAD"])
subprocess.check_output(["git", "diff"], cwd=cwd)
diff = _run(["git", "diff-index", "HEAD"])
diff = "has uncommitted changes" if diff else "clean"
branch = _run(["git", "rev-parse", "--abbrev-ref", "HEAD"])
except Exception:
pass
message = f"sha: {sha}, status: {diff}, branch: {branch}"
return message
class CosineScheduler(object):
def __init__(self, base_value, final_value, total_iters, warmup_iters=0, start_warmup_value=0, freeze_iters=0):
super().__init__()
self.final_value = final_value
self.total_iters = total_iters
freeze_schedule = np.zeros((freeze_iters))
warmup_schedule = np.linspace(start_warmup_value, base_value, warmup_iters)
iters = np.arange(total_iters - warmup_iters - freeze_iters)
schedule = final_value + 0.5 * (base_value - final_value) * (1 + np.cos(np.pi * iters / len(iters)))
self.schedule = np.concatenate((freeze_schedule, warmup_schedule, schedule))
assert len(self.schedule) == self.total_iters
def __getitem__(self, it):
if it >= self.total_iters:
return self.final_value
else:
return self.schedule[it]
def has_batchnorms(model):
bn_types = (nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d, nn.SyncBatchNorm)
for name, module in model.named_modules():
if isinstance(module, bn_types):
return True
return False
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