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ultralytics 8.0.42 DDP fix and Docs updates (#1065)

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Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: Noobtoss <96134731+Noobtoss@users.noreply.github.com>
Co-authored-by: Laughing-q <1185102784@qq.com>
This commit is contained in:
Glenn Jocher
2023-02-20 12:56:20 +01:00
committed by GitHub
parent f6e393c1d2
commit f2a7a29e53
33 changed files with 196 additions and 93 deletions
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4
ultralytics/yolo/utils/metrics.py
View File

@ -11,7 +11,7 @@ import numpy as np
import torch
import torch.nn as nn
from ultralytics.yolo.utils import TryExcept
from ultralytics.yolo.utils import LOGGER, TryExcept
# boxes
@ -260,7 +260,7 @@ class ConfusionMatrix:
def print(self):
for i in range(self.nc + 1):
print(' '.join(map(str, self.matrix[i])))
LOGGER.info(' '.join(map(str, self.matrix[i])))
def smooth(y, f=0.05):

4
ultralytics/yolo/utils/plotting.py
View File

@ -12,7 +12,7 @@ import torch
from PIL import Image, ImageDraw, ImageFont
from PIL import __version__ as pil_version
from ultralytics.yolo.utils import threaded
from ultralytics.yolo.utils import LOGGER, threaded
from .checks import check_font, check_version, is_ascii
from .files import increment_path
@ -300,7 +300,7 @@ def plot_results(file='path/to/results.csv', dir='', segment=False):
# if j in [8, 9, 10]: # share train and val loss y axes
# ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
except Exception as e:
print(f'Warning: Plotting error for {f}: {e}')
LOGGER.warning(f'WARNING: Plotting error for {f}: {e}')
ax[1].legend()
fig.savefig(save_dir / 'results.png', dpi=200)
plt.close()

15
ultralytics/yolo/utils/torch_utils.py
View File

@ -167,11 +167,12 @@ def model_info(model, verbose=False, imgsz=640):
n_p = get_num_params(model)
n_g = get_num_gradients(model) # number gradients
if verbose:
print(f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}")
LOGGER.info(
f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}")
for i, (name, p) in enumerate(model.named_parameters()):
name = name.replace('module_list.', '')
print('%5g %40s %9s %12g %20s %10.3g %10.3g' %
(i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
LOGGER.info('%5g %40s %9s %12g %20s %10.3g %10.3g' %
(i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
flops = get_flops(model, imgsz)
fused = ' (fused)' if model.is_fused() else ''
@ -362,8 +363,8 @@ def profile(input, ops, n=10, device=None):
results = []
if not isinstance(device, torch.device):
device = select_device(device)
print(f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}"
f"{'input':>24s}{'output':>24s}")
LOGGER.info(f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}"
f"{'input':>24s}{'output':>24s}")
for x in input if isinstance(input, list) else [input]:
x = x.to(device)
@ -393,10 +394,10 @@ def profile(input, ops, n=10, device=None):
mem = torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0 # (GB)
s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' for x in (x, y)) # shapes
p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0 # parameters
print(f'{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}')
LOGGER.info(f'{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}')
results.append([p, flops, mem, tf, tb, s_in, s_out])
except Exception as e:
print(e)
LOGGER.info(e)
results.append(None)
torch.cuda.empty_cache()
return results