You cannot select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
496 lines
20 KiB
Python
496 lines
20 KiB
Python
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
|
|
"""
|
|
YOLO-specific modules.
|
|
|
|
Usage:
|
|
$ python models/yolo.py --cfg yolov5s.yaml
|
|
"""
|
|
|
|
import argparse
|
|
import contextlib
|
|
import math
|
|
import os
|
|
import platform
|
|
import sys
|
|
from copy import deepcopy
|
|
from pathlib import Path
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
FILE = Path(__file__).resolve()
|
|
ROOT = FILE.parents[1] # YOLOv5 root directory
|
|
if str(ROOT) not in sys.path:
|
|
sys.path.append(str(ROOT)) # add ROOT to PATH
|
|
if platform.system() != "Windows":
|
|
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
|
|
|
from models.common import (
|
|
C3,
|
|
C3SPP,
|
|
C3TR,
|
|
SPP,
|
|
SPPF,
|
|
Bottleneck,
|
|
BottleneckCSP,
|
|
C3Ghost,
|
|
C3x,
|
|
Classify,
|
|
Concat,
|
|
Contract,
|
|
Conv,
|
|
CrossConv,
|
|
DetectMultiBackend,
|
|
DWConv,
|
|
DWConvTranspose2d,
|
|
Expand,
|
|
Focus,
|
|
GhostBottleneck,
|
|
GhostConv,
|
|
Proto,
|
|
)
|
|
from models.experimental import MixConv2d
|
|
from utils.autoanchor import check_anchor_order
|
|
from utils.general import LOGGER, check_version, check_yaml, colorstr, make_divisible, print_args
|
|
from utils.plots import feature_visualization
|
|
from utils.torch_utils import (
|
|
fuse_conv_and_bn,
|
|
initialize_weights,
|
|
model_info,
|
|
profile,
|
|
scale_img,
|
|
select_device,
|
|
time_sync,
|
|
)
|
|
|
|
try:
|
|
import thop # for FLOPs computation
|
|
except ImportError:
|
|
thop = None
|
|
|
|
|
|
class Detect(nn.Module):
|
|
"""YOLOv5 Detect head for processing input tensors and generating detection outputs in object detection models."""
|
|
|
|
stride = None # strides computed during build
|
|
dynamic = False # force grid reconstruction
|
|
export = False # export mode
|
|
|
|
def __init__(self, nc=80, anchors=(), ch=(), inplace=True):
|
|
"""Initializes YOLOv5 detection layer with specified classes, anchors, channels, and inplace operations."""
|
|
super().__init__()
|
|
self.nc = nc # number of classes
|
|
self.no = nc + 5 # number of outputs per anchor
|
|
self.nl = len(anchors) # number of detection layers
|
|
self.na = len(anchors[0]) // 2 # number of anchors
|
|
self.grid = [torch.empty(0) for _ in range(self.nl)] # init grid
|
|
self.anchor_grid = [torch.empty(0) for _ in range(self.nl)] # init anchor grid
|
|
self.register_buffer("anchors", torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2)
|
|
self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
|
|
self.inplace = inplace # use inplace ops (e.g. slice assignment)
|
|
|
|
def forward(self, x):
|
|
"""Processes input through YOLOv5 layers, altering shape for detection: `x(bs, 3, ny, nx, 85)`."""
|
|
z = [] # inference output
|
|
for i in range(self.nl):
|
|
x[i] = self.m[i](x[i]) # conv
|
|
bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85)
|
|
x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
|
|
|
|
if not self.training: # inference
|
|
if self.dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
|
|
self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)
|
|
|
|
if isinstance(self, Segment): # (boxes + masks)
|
|
xy, wh, conf, mask = x[i].split((2, 2, self.nc + 1, self.no - self.nc - 5), 4)
|
|
xy = (xy.sigmoid() * 2 + self.grid[i]) * self.stride[i] # xy
|
|
wh = (wh.sigmoid() * 2) ** 2 * self.anchor_grid[i] # wh
|
|
y = torch.cat((xy, wh, conf.sigmoid(), mask), 4)
|
|
else: # Detect (boxes only)
|
|
xy, wh, conf = x[i].sigmoid().split((2, 2, self.nc + 1), 4)
|
|
xy = (xy * 2 + self.grid[i]) * self.stride[i] # xy
|
|
wh = (wh * 2) ** 2 * self.anchor_grid[i] # wh
|
|
y = torch.cat((xy, wh, conf), 4)
|
|
z.append(y.view(bs, self.na * nx * ny, self.no))
|
|
|
|
return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x)
|
|
|
|
def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, "1.10.0")):
|
|
"""Generates a mesh grid for anchor boxes with optional compatibility for torch versions < 1.10."""
|
|
d = self.anchors[i].device
|
|
t = self.anchors[i].dtype
|
|
shape = 1, self.na, ny, nx, 2 # grid shape
|
|
y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t)
|
|
yv, xv = torch.meshgrid(y, x, indexing="ij") if torch_1_10 else torch.meshgrid(y, x) # torch>=0.7 compatibility
|
|
grid = torch.stack((xv, yv), 2).expand(shape) - 0.5 # add grid offset, i.e. y = 2.0 * x - 0.5
|
|
anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape)
|
|
return grid, anchor_grid
|
|
|
|
|
|
class Segment(Detect):
|
|
"""YOLOv5 Segment head for segmentation models, extending Detect with mask and prototype layers."""
|
|
|
|
def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), inplace=True):
|
|
"""Initializes YOLOv5 Segment head with options for mask count, protos, and channel adjustments."""
|
|
super().__init__(nc, anchors, ch, inplace)
|
|
self.nm = nm # number of masks
|
|
self.npr = npr # number of protos
|
|
self.no = 5 + nc + self.nm # number of outputs per anchor
|
|
self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
|
|
self.proto = Proto(ch[0], self.npr, self.nm) # protos
|
|
self.detect = Detect.forward
|
|
|
|
def forward(self, x):
|
|
"""Processes input through the network, returning detections and prototypes; adjusts output based on
|
|
training/export mode.
|
|
"""
|
|
p = self.proto(x[0])
|
|
x = self.detect(self, x)
|
|
return (x, p) if self.training else (x[0], p) if self.export else (x[0], p, x[1])
|
|
|
|
|
|
class BaseModel(nn.Module):
|
|
"""YOLOv5 base model."""
|
|
|
|
def forward(self, x, profile=False, visualize=False):
|
|
"""Executes a single-scale inference or training pass on the YOLOv5 base model, with options for profiling and
|
|
visualization.
|
|
"""
|
|
return self._forward_once(x, profile, visualize) # single-scale inference, train
|
|
|
|
def _forward_once(self, x, profile=False, visualize=False):
|
|
"""Performs a forward pass on the YOLOv5 model, enabling profiling and feature visualization options."""
|
|
y, dt = [], [] # outputs
|
|
for m in self.model:
|
|
if m.f != -1: # if not from previous layer
|
|
x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
|
|
if profile:
|
|
self._profile_one_layer(m, x, dt)
|
|
x = m(x) # run
|
|
y.append(x if m.i in self.save else None) # save output
|
|
if visualize:
|
|
feature_visualization(x, m.type, m.i, save_dir=visualize)
|
|
return x
|
|
|
|
def _profile_one_layer(self, m, x, dt):
|
|
"""Profiles a single layer's performance by computing GFLOPs, execution time, and parameters."""
|
|
c = m == self.model[-1] # is final layer, copy input as inplace fix
|
|
o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1e9 * 2 if thop else 0 # FLOPs
|
|
t = time_sync()
|
|
for _ in range(10):
|
|
m(x.copy() if c else x)
|
|
dt.append((time_sync() - t) * 100)
|
|
if m == self.model[0]:
|
|
LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} module")
|
|
LOGGER.info(f"{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}")
|
|
if c:
|
|
LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total")
|
|
|
|
def fuse(self):
|
|
"""Fuses Conv2d() and BatchNorm2d() layers in the model to improve inference speed."""
|
|
LOGGER.info("Fusing layers... ")
|
|
for m in self.model.modules():
|
|
if isinstance(m, (Conv, DWConv)) and hasattr(m, "bn"):
|
|
m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
|
|
delattr(m, "bn") # remove batchnorm
|
|
m.forward = m.forward_fuse # update forward
|
|
self.info()
|
|
return self
|
|
|
|
def info(self, verbose=False, img_size=640):
|
|
"""Prints model information given verbosity and image size, e.g., `info(verbose=True, img_size=640)`."""
|
|
model_info(self, verbose, img_size)
|
|
|
|
def _apply(self, fn):
|
|
"""Applies transformations like to(), cpu(), cuda(), half() to model tensors excluding parameters or registered
|
|
buffers.
|
|
"""
|
|
self = super()._apply(fn)
|
|
m = self.model[-1] # Detect()
|
|
if isinstance(m, (Detect, Segment)):
|
|
m.stride = fn(m.stride)
|
|
m.grid = list(map(fn, m.grid))
|
|
if isinstance(m.anchor_grid, list):
|
|
m.anchor_grid = list(map(fn, m.anchor_grid))
|
|
return self
|
|
|
|
|
|
class DetectionModel(BaseModel):
|
|
"""YOLOv5 detection model class for object detection tasks, supporting custom configurations and anchors."""
|
|
|
|
def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None, anchors=None):
|
|
"""Initializes YOLOv5 model with configuration file, input channels, number of classes, and custom anchors."""
|
|
super().__init__()
|
|
if isinstance(cfg, dict):
|
|
self.yaml = cfg # model dict
|
|
else: # is *.yaml
|
|
import yaml # for torch hub
|
|
|
|
self.yaml_file = Path(cfg).name
|
|
with open(cfg, encoding="ascii", errors="ignore") as f:
|
|
self.yaml = yaml.safe_load(f) # model dict
|
|
|
|
# Define model
|
|
ch = self.yaml["ch"] = self.yaml.get("ch", ch) # input channels
|
|
if nc and nc != self.yaml["nc"]:
|
|
LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
|
|
self.yaml["nc"] = nc # override yaml value
|
|
if anchors:
|
|
LOGGER.info(f"Overriding model.yaml anchors with anchors={anchors}")
|
|
self.yaml["anchors"] = round(anchors) # override yaml value
|
|
self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist
|
|
self.names = [str(i) for i in range(self.yaml["nc"])] # default names
|
|
self.inplace = self.yaml.get("inplace", True)
|
|
|
|
# Build strides, anchors
|
|
m = self.model[-1] # Detect()
|
|
if isinstance(m, (Detect, Segment)):
|
|
|
|
def _forward(x):
|
|
"""Passes the input 'x' through the model and returns the processed output."""
|
|
return self.forward(x)[0] if isinstance(m, Segment) else self.forward(x)
|
|
|
|
s = 256 # 2x min stride
|
|
m.inplace = self.inplace
|
|
m.stride = torch.tensor([s / x.shape[-2] for x in _forward(torch.zeros(1, ch, s, s))]) # forward
|
|
check_anchor_order(m)
|
|
m.anchors /= m.stride.view(-1, 1, 1)
|
|
self.stride = m.stride
|
|
self._initialize_biases() # only run once
|
|
|
|
# Init weights, biases
|
|
initialize_weights(self)
|
|
self.info()
|
|
LOGGER.info("")
|
|
|
|
def forward(self, x, augment=False, profile=False, visualize=False):
|
|
"""Performs single-scale or augmented inference and may include profiling or visualization."""
|
|
if augment:
|
|
return self._forward_augment(x) # augmented inference, None
|
|
return self._forward_once(x, profile, visualize) # single-scale inference, train
|
|
|
|
def _forward_augment(self, x):
|
|
"""Performs augmented inference across different scales and flips, returning combined detections."""
|
|
img_size = x.shape[-2:] # height, width
|
|
s = [1, 0.83, 0.67] # scales
|
|
f = [None, 3, None] # flips (2-ud, 3-lr)
|
|
y = [] # outputs
|
|
for si, fi in zip(s, f):
|
|
xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
|
|
yi = self._forward_once(xi)[0] # forward
|
|
# cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save
|
|
yi = self._descale_pred(yi, fi, si, img_size)
|
|
y.append(yi)
|
|
y = self._clip_augmented(y) # clip augmented tails
|
|
return torch.cat(y, 1), None # augmented inference, train
|
|
|
|
def _descale_pred(self, p, flips, scale, img_size):
|
|
"""De-scales predictions from augmented inference, adjusting for flips and image size."""
|
|
if self.inplace:
|
|
p[..., :4] /= scale # de-scale
|
|
if flips == 2:
|
|
p[..., 1] = img_size[0] - p[..., 1] # de-flip ud
|
|
elif flips == 3:
|
|
p[..., 0] = img_size[1] - p[..., 0] # de-flip lr
|
|
else:
|
|
x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale # de-scale
|
|
if flips == 2:
|
|
y = img_size[0] - y # de-flip ud
|
|
elif flips == 3:
|
|
x = img_size[1] - x # de-flip lr
|
|
p = torch.cat((x, y, wh, p[..., 4:]), -1)
|
|
return p
|
|
|
|
def _clip_augmented(self, y):
|
|
"""Clips augmented inference tails for YOLOv5 models, affecting first and last tensors based on grid points and
|
|
layer counts.
|
|
"""
|
|
nl = self.model[-1].nl # number of detection layers (P3-P5)
|
|
g = sum(4**x for x in range(nl)) # grid points
|
|
e = 1 # exclude layer count
|
|
i = (y[0].shape[1] // g) * sum(4**x for x in range(e)) # indices
|
|
y[0] = y[0][:, :-i] # large
|
|
i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e)) # indices
|
|
y[-1] = y[-1][:, i:] # small
|
|
return y
|
|
|
|
def _initialize_biases(self, cf=None):
|
|
"""
|
|
Initializes biases for YOLOv5's Detect() module, optionally using class frequencies (cf).
|
|
|
|
For details see https://arxiv.org/abs/1708.02002 section 3.3.
|
|
"""
|
|
# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
|
|
m = self.model[-1] # Detect() module
|
|
for mi, s in zip(m.m, m.stride): # from
|
|
b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85)
|
|
b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image)
|
|
b.data[:, 5 : 5 + m.nc] += (
|
|
math.log(0.6 / (m.nc - 0.99999)) if cf is None else torch.log(cf / cf.sum())
|
|
) # cls
|
|
mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
|
|
|
|
|
|
Model = DetectionModel # retain YOLOv5 'Model' class for backwards compatibility
|
|
|
|
|
|
class SegmentationModel(DetectionModel):
|
|
"""YOLOv5 segmentation model for object detection and segmentation tasks with configurable parameters."""
|
|
|
|
def __init__(self, cfg="yolov5s-seg.yaml", ch=3, nc=None, anchors=None):
|
|
"""Initializes a YOLOv5 segmentation model with configurable params: cfg (str) for configuration, ch (int) for channels, nc (int) for num classes, anchors (list)."""
|
|
super().__init__(cfg, ch, nc, anchors)
|
|
|
|
|
|
class ClassificationModel(BaseModel):
|
|
"""YOLOv5 classification model for image classification tasks, initialized with a config file or detection model."""
|
|
|
|
def __init__(self, cfg=None, model=None, nc=1000, cutoff=10):
|
|
"""Initializes YOLOv5 model with config file `cfg`, input channels `ch`, number of classes `nc`, and `cuttoff`
|
|
index.
|
|
"""
|
|
super().__init__()
|
|
self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg)
|
|
|
|
def _from_detection_model(self, model, nc=1000, cutoff=10):
|
|
"""Creates a classification model from a YOLOv5 detection model, slicing at `cutoff` and adding a classification
|
|
layer.
|
|
"""
|
|
if isinstance(model, DetectMultiBackend):
|
|
model = model.model # unwrap DetectMultiBackend
|
|
model.model = model.model[:cutoff] # backbone
|
|
m = model.model[-1] # last layer
|
|
ch = m.conv.in_channels if hasattr(m, "conv") else m.cv1.conv.in_channels # ch into module
|
|
c = Classify(ch, nc) # Classify()
|
|
c.i, c.f, c.type = m.i, m.f, "models.common.Classify" # index, from, type
|
|
model.model[-1] = c # replace
|
|
self.model = model.model
|
|
self.stride = model.stride
|
|
self.save = []
|
|
self.nc = nc
|
|
|
|
def _from_yaml(self, cfg):
|
|
"""Creates a YOLOv5 classification model from a specified *.yaml configuration file."""
|
|
self.model = None
|
|
|
|
|
|
def parse_model(d, ch):
|
|
"""Parses a YOLOv5 model from a dict `d`, configuring layers based on input channels `ch` and model architecture."""
|
|
LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}")
|
|
anchors, nc, gd, gw, act, ch_mul = (
|
|
d["anchors"],
|
|
d["nc"],
|
|
d["depth_multiple"],
|
|
d["width_multiple"],
|
|
d.get("activation"),
|
|
d.get("channel_multiple"),
|
|
)
|
|
if act:
|
|
Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU()
|
|
LOGGER.info(f"{colorstr('activation:')} {act}") # print
|
|
if not ch_mul:
|
|
ch_mul = 8
|
|
na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
|
|
no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
|
|
|
|
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
|
|
for j, a in enumerate(args):
|
|
with contextlib.suppress(NameError):
|
|
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 {
|
|
Conv,
|
|
GhostConv,
|
|
Bottleneck,
|
|
GhostBottleneck,
|
|
SPP,
|
|
SPPF,
|
|
DWConv,
|
|
MixConv2d,
|
|
Focus,
|
|
CrossConv,
|
|
BottleneckCSP,
|
|
C3,
|
|
C3TR,
|
|
C3SPP,
|
|
C3Ghost,
|
|
nn.ConvTranspose2d,
|
|
DWConvTranspose2d,
|
|
C3x,
|
|
}:
|
|
c1, c2 = ch[f], args[0]
|
|
if c2 != no: # if not output
|
|
c2 = make_divisible(c2 * gw, ch_mul)
|
|
|
|
args = [c1, c2, *args[1:]]
|
|
if m in {BottleneckCSP, 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)
|
|
# TODO: channel, gw, gd
|
|
elif m in {Detect, Segment}:
|
|
args.append([ch[x] for x in f])
|
|
if isinstance(args[1], int): # number of anchors
|
|
args[1] = [list(range(args[1] * 2))] * len(f)
|
|
if m is Segment:
|
|
args[3] = make_divisible(args[3] * gw, ch_mul)
|
|
elif m is Contract:
|
|
c2 = ch[f] * args[0] ** 2
|
|
elif m is Expand:
|
|
c2 = ch[f] // args[0] ** 2
|
|
else:
|
|
c2 = ch[f]
|
|
|
|
m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
|
|
t = str(m)[8:-2].replace("__main__.", "") # module type
|
|
np = sum(x.numel() for x in m_.parameters()) # number params
|
|
m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params
|
|
LOGGER.info(f"{i:>3}{str(f):>18}{n_:>3}{np:10.0f} {t:<40}{str(args):<30}") # print
|
|
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
|
|
layers.append(m_)
|
|
if i == 0:
|
|
ch = []
|
|
ch.append(c2)
|
|
return nn.Sequential(*layers), sorted(save)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("--cfg", type=str, default="yolov5s.yaml", help="model.yaml")
|
|
parser.add_argument("--batch-size", type=int, default=1, help="total batch size for all GPUs")
|
|
parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
|
|
parser.add_argument("--profile", action="store_true", help="profile model speed")
|
|
parser.add_argument("--line-profile", action="store_true", help="profile model speed layer by layer")
|
|
parser.add_argument("--test", action="store_true", help="test all yolo*.yaml")
|
|
opt = parser.parse_args()
|
|
opt.cfg = check_yaml(opt.cfg) # check YAML
|
|
print_args(vars(opt))
|
|
device = select_device(opt.device)
|
|
|
|
# Create model
|
|
im = torch.rand(opt.batch_size, 3, 640, 640).to(device)
|
|
model = Model(opt.cfg).to(device)
|
|
|
|
# Options
|
|
if opt.line_profile: # profile layer by layer
|
|
model(im, profile=True)
|
|
|
|
elif opt.profile: # profile forward-backward
|
|
results = profile(input=im, ops=[model], n=3)
|
|
|
|
elif opt.test: # test all models
|
|
for cfg in Path(ROOT / "models").rglob("yolo*.yaml"):
|
|
try:
|
|
_ = Model(cfg)
|
|
except Exception as e:
|
|
print(f"Error in {cfg}: {e}")
|
|
|
|
else: # report fused model summary
|
|
model.fuse()
|