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ultralytics 8.0.12 - Hydra removal (#506)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Pronoy Mandal <lukex9442@gmail.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
This commit is contained in:
Glenn Jocher
2023-01-21 00:45:19 +01:00
committed by GitHub
parent 6eec39162a
commit c5fccc3fc4
37 changed files with 395 additions and 469 deletions
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12
ultralytics/nn/tasks.py
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@ -10,7 +10,7 @@ import torch.nn as nn
from ultralytics.nn.modules import (C1, C2, C3, C3TR, SPP, SPPF, Bottleneck, BottleneckCSP, C2f, C3Ghost, C3x, Classify,
Concat, Conv, ConvTranspose, Detect, DWConv, DWConvTranspose2d, Ensemble, Focus,
GhostBottleneck, GhostConv, Segment)
from ultralytics.yolo.utils import DEFAULT_CONFIG_DICT, DEFAULT_CONFIG_KEYS, LOGGER, colorstr, yaml_load
from ultralytics.yolo.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, colorstr, yaml_load
from ultralytics.yolo.utils.checks import check_yaml
from ultralytics.yolo.utils.torch_utils import (fuse_conv_and_bn, initialize_weights, intersect_dicts, make_divisible,
model_info, scale_img, time_sync)
@ -113,7 +113,7 @@ class BaseModel(nn.Module):
thresh (int, optional): The threshold number of BatchNorm layers. Default is 10.
Returns:
bool: True if the number of BatchNorm layers in the model is less than the threshold, False otherwise.
(bool): True if the number of BatchNorm layers in the model is less than the threshold, False otherwise.
"""
bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k) # normalization layers, i.e. BatchNorm2d()
return sum(isinstance(v, bn) for v in self.modules()) < thresh # True if < 'thresh' BatchNorm layers in model
@ -321,11 +321,11 @@ def attempt_load_weights(weights, device=None, inplace=True, fuse=False):
model = Ensemble()
for w in weights if isinstance(weights, list) else [weights]:
ckpt = torch.load(attempt_download(w), map_location='cpu') # load
args = {**DEFAULT_CONFIG_DICT, **ckpt['train_args']} # combine model and default args, preferring model args
args = {**DEFAULT_CFG_DICT, **ckpt['train_args']} # combine model and default args, preferring model args
ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float() # FP32 model
# Model compatibility updates
ckpt.args = {k: v for k, v in args.items() if k in DEFAULT_CONFIG_KEYS} # attach args to model
ckpt.args = {k: v for k, v in args.items() if k in DEFAULT_CFG_KEYS} # attach args to model
ckpt.pt_path = weights # attach *.pt file path to model
if not hasattr(ckpt, 'stride'):
ckpt.stride = torch.tensor([32.])
@ -359,11 +359,11 @@ def attempt_load_one_weight(weight, device=None, inplace=True, fuse=False):
from ultralytics.yolo.utils.downloads import attempt_download
ckpt = torch.load(attempt_download(weight), map_location='cpu') # load
args = {**DEFAULT_CONFIG_DICT, **ckpt['train_args']} # combine model and default args, preferring model args
args = {**DEFAULT_CFG_DICT, **ckpt['train_args']} # combine model and default args, preferring model args
model = (ckpt.get('ema') or ckpt['model']).to(device).float() # FP32 model
# Model compatibility updates
model.args = {k: v for k, v in args.items() if k in DEFAULT_CONFIG_KEYS} # attach args to model
model.args = {k: v for k, v in args.items() if k in DEFAULT_CFG_KEYS} # attach args to model
model.pt_path = weight # attach *.pt file path to model
if not hasattr(model, 'stride'):
model.stride = torch.tensor([32.])