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ultralytics 8.0.47 Docker and reformat updates (#1153)

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Glenn Jocher 2023-02-25 22:49:19 -08:00 • committed by GitHub
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41 changed files with 224 additions and 201 deletions
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41
ultralytics/nn/tasks.py
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@ -11,7 +11,7 @@ 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_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, colorstr, yaml_load
from ultralytics.yolo.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, colorstr, emojis, yaml_load
from ultralytics.yolo.utils.checks import check_requirements, check_yaml
from ultralytics.yolo.utils.torch_utils import (fuse_conv_and_bn, fuse_deconv_and_bn, initialize_weights,
intersect_dicts, make_divisible, model_info, scale_img, time_sync)
@ -76,7 +76,7 @@ class BaseModel(nn.Module):
None
"""
c = m == self.model[-1] # is final layer, copy input as inplace fix
o = thop.profile(m, inputs=(x.clone() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs
o = thop.profile(m, inputs=[x.clone() if c else x], verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs
t = time_sync()
for _ in range(10):
m(x.clone() if c else x)
@ -339,14 +339,20 @@ def torch_safe_load(weight):
file = attempt_download_asset(weight) # search online if missing locally
try:
return torch.load(file, map_location='cpu'), file # load
except ModuleNotFoundError as e:
if e.name == 'omegaconf': # e.name is missing module name
LOGGER.warning(f'WARNING ⚠️ {weight} requires {e.name}, which is not in ultralytics requirements.'
f'\nAutoInstall will run now for {e.name} but this feature will be removed in the future.'
f'\nRecommend fixes are to train a new model using updated ultralytics package or to '
f'download updated models from https://github.com/ultralytics/assets/releases/tag/v0.0.0')
if e.name != 'models':
check_requirements(e.name) # install missing module
except ModuleNotFoundError as e: # e.name is missing module name
if e.name == 'models':
raise TypeError(
emojis(f'ERROR ❌️ {weight} appears to be an Ultralytics YOLOv5 model originally trained '
f'with https://github.com/ultralytics/yolov5.\nThis model is NOT forwards compatible with '
f'YOLOv8 at https://github.com/ultralytics/ultralytics.'
f"\nRecommend fixes are to train a new model using the latest 'ultralytics' package or to "
f"run a command with an official YOLOv8 model, i.e. 'yolo predict model=yolov8n.pt'")) from e
LOGGER.warning(f"WARNING ⚠️ {weight} appears to require '{e.name}', which is not in ultralytics requirements."
f"\nAutoInstall will run now for '{e.name}' but this feature will be removed in the future."
f"\nRecommend fixes are to train a new model using the latest 'ultralytics' package or to "
f"run a command with an official YOLOv8 model, i.e. 'yolo predict model=yolov8n.pt'")
check_requirements(e.name) # install missing module
return torch.load(file, map_location='cpu'), file # load
@ -437,22 +443,21 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
args[j] = eval(a) if isinstance(a, str) else a # eval strings
n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain
if m in {
Classify, Conv, ConvTranspose, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, Focus,
BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}:
if m in (Classify, Conv, ConvTranspose, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, Focus,
BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x):
c1, c2 = ch[f], args[0]
if c2 != nc: # if c2 not equal to number of classes (i.e. for Classify() output)
c2 = make_divisible(c2 * gw, 8)
args = [c1, c2, *args[1:]]
if m in {BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, C3x}:
if m in (BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, C3x):
args.insert(2, n) # number of repeats
n = 1
elif m is nn.BatchNorm2d:
args = [ch[f]]
elif m is Concat:
c2 = sum(ch[x] for x in f)
elif m in {Detect, Segment}:
elif m in (Detect, Segment):
args.append([ch[x] for x in f])
if m is Segment:
args[2] = make_divisible(args[2] * gw, 8)
@ -490,11 +495,11 @@ def guess_model_task(model):
def cfg2task(cfg):
# Guess from YAML dictionary
m = cfg['head'][-1][-2].lower() # output module name
if m in ['classify', 'classifier', 'cls', 'fc']:
if m in ('classify', 'classifier', 'cls', 'fc'):
return 'classify'
if m in ['detect']:
if m == 'detect':
return 'detect'
if m in ['segment']:
if m == 'segment':
return 'segment'
# Guess from model cfg