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ultralytics 8.0.50 AMP check and YOLOv5u YAMLs (#1263)

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Glenn Jocher 2023-03-06 11:39:26 +01:00 • committed by GitHub
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commit f0d8e4718b
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12
ultralytics/nn/tasks.py
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@ -8,9 +8,7 @@ import thop
import torch
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.nn.modules import * # noqa: F403
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,
@ -87,7 +85,7 @@ class BaseModel(nn.Module):
if c:
LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total")
def fuse(self):
def fuse(self, verbose=True):
"""
Fuse the `Conv2d()` and `BatchNorm2d()` layers of the model into a single layer, in order to improve the
computation efficiency.
@ -105,7 +103,7 @@ class BaseModel(nn.Module):
m.conv_transpose = fuse_deconv_and_bn(m.conv_transpose, m.bn)
delattr(m, 'bn') # remove batchnorm
m.forward = m.forward_fuse # update forward
self.info()
self.info(verbose=verbose)
return self
@ -130,7 +128,7 @@ class BaseModel(nn.Module):
verbose (bool): if True, prints out the model information. Defaults to False
imgsz (int): the size of the image that the model will be trained on. Defaults to 640
"""
model_info(self, verbose, imgsz)
model_info(self, verbose=verbose, imgsz=imgsz)
def _apply(self, fn):
"""
@ -437,7 +435,7 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
ch = [ch]
layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args
m = eval(m) if isinstance(m, str) else m # eval strings
m = getattr(torch.nn, m[3:]) if 'nn.' in m else globals()[m] # get module
for j, a in enumerate(args):
# TODO: re-implement with eval() removal if possible
# args[j] = (locals()[a] if a in locals() else ast.literal_eval(a)) if isinstance(a, str) else a