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Running
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Zero
import argparse | |
import os | |
import platform | |
import sys | |
from copy import deepcopy | |
from pathlib import Path | |
FILE = Path(__file__).resolve() | |
ROOT = FILE.parents[1] # YOLO 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 * | |
from models.experimental import * | |
from utils.general import LOGGER, check_version, check_yaml, 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) | |
from utils.tal.anchor_generator import make_anchors, dist2bbox | |
from models.attention.blocks import * | |
try: | |
import thop # for FLOPs computation | |
except ImportError: | |
thop = None | |
class Detect(nn.Module): | |
# YOLO Detect head for detection models | |
dynamic = False # force grid reconstruction | |
export = False # export mode | |
shape = None | |
anchors = torch.empty(0) # init | |
strides = torch.empty(0) # init | |
def __init__(self, nc=80, ch=(), inplace=True): # detection layer | |
super().__init__() | |
self.nc = nc # number of classes | |
self.nl = len(ch) # number of detection layers | |
self.reg_max = 16 | |
self.no = nc + self.reg_max * 4 # number of outputs per anchor | |
self.inplace = inplace # use inplace ops (e.g. slice assignment) | |
self.stride = torch.zeros(self.nl) # strides computed during build | |
c2, c3 = max((ch[0] // 4, self.reg_max * 4, 16)), max((ch[0], min((self.nc * 2, 128)))) # channels | |
self.cv2 = nn.ModuleList( | |
nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3), nn.Conv2d(c2, 4 * self.reg_max, 1)) for x in ch) | |
self.cv3 = nn.ModuleList( | |
nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch) | |
self.dfl = DFL(self.reg_max) if self.reg_max > 1 else nn.Identity() | |
def forward(self, x): | |
shape = x[0].shape # BCHW | |
for i in range(self.nl): | |
x[i] = torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1) | |
if self.training: | |
return x | |
elif self.dynamic or self.shape != shape: | |
self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5)) | |
self.shape = shape | |
box, cls = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2).split((self.reg_max * 4, self.nc), 1) | |
dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides | |
y = torch.cat((dbox, cls.sigmoid()), 1) | |
return y if self.export else (y, x) | |
def bias_init(self): | |
# Initialize Detect() biases, WARNING: requires stride availability | |
m = self # self.model[-1] # Detect() module | |
# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1 | |
# ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum()) # nominal class frequency | |
for a, b, s in zip(m.cv2, m.cv3, m.stride): # from | |
a[-1].bias.data[:] = 1.0 # box | |
b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2) # cls (5 objects and 80 classes per 640 image) | |
class DDetect(nn.Module): | |
# YOLO Detect head for detection models | |
dynamic = False # force grid reconstruction | |
export = False # export mode | |
shape = None | |
anchors = torch.empty(0) # init | |
strides = torch.empty(0) # init | |
def __init__(self, nc=80, ch=(), inplace=True): # detection layer | |
super().__init__() | |
self.nc = nc # number of classes | |
self.nl = len(ch) # number of detection layers | |
self.reg_max = 16 | |
self.no = nc + self.reg_max * 4 # number of outputs per anchor | |
self.inplace = inplace # use inplace ops (e.g. slice assignment) | |
self.stride = torch.zeros(self.nl) # strides computed during build | |
c2, c3 = make_divisible(max((ch[0] // 4, self.reg_max * 4, 16)), 4), max((ch[0], min((self.nc * 2, 128)))) # channels | |
self.cv2 = nn.ModuleList( | |
nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3, g=4), nn.Conv2d(c2, 4 * self.reg_max, 1, groups=4)) for x in ch) | |
self.cv3 = nn.ModuleList( | |
nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch) | |
self.dfl = DFL(self.reg_max) if self.reg_max > 1 else nn.Identity() | |
def forward(self, x): | |
shape = x[0].shape # BCHW | |
for i in range(self.nl): | |
x[i] = torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1) | |
if self.training: | |
return x | |
elif self.dynamic or self.shape != shape: | |
self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5)) | |
self.shape = shape | |
box, cls = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2).split((self.reg_max * 4, self.nc), 1) | |
dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides | |
y = torch.cat((dbox, cls.sigmoid()), 1) | |
return y if self.export else (y, x) | |
def bias_init(self): | |
# Initialize Detect() biases, WARNING: requires stride availability | |
m = self # self.model[-1] # Detect() module | |
# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1 | |
# ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum()) # nominal class frequency | |
for a, b, s in zip(m.cv2, m.cv3, m.stride): # from | |
a[-1].bias.data[:] = 1.0 # box | |
b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2) # cls (5 objects and 80 classes per 640 image) | |
class DualDetect(nn.Module): | |
# YOLO Detect head for detection models | |
dynamic = False # force grid reconstruction | |
export = False # export mode | |
shape = None | |
anchors = torch.empty(0) # init | |
strides = torch.empty(0) # init | |
def __init__(self, nc=80, ch=(), inplace=True): # detection layer | |
super().__init__() | |
self.nc = nc # number of classes | |
self.nl = len(ch) // 2 # number of detection layers | |
self.reg_max = 16 | |
self.no = nc + self.reg_max * 4 # number of outputs per anchor | |
self.inplace = inplace # use inplace ops (e.g. slice assignment) | |
self.stride = torch.zeros(self.nl) # strides computed during build | |
c2, c3 = max((ch[0] // 4, self.reg_max * 4, 16)), max((ch[0], min((self.nc * 2, 128)))) # channels | |
c4, c5 = max((ch[self.nl] // 4, self.reg_max * 4, 16)), max((ch[self.nl], min((self.nc * 2, 128)))) # channels | |
self.cv2 = nn.ModuleList( | |
nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3), nn.Conv2d(c2, 4 * self.reg_max, 1)) for x in ch[:self.nl]) | |
self.cv3 = nn.ModuleList( | |
nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch[:self.nl]) | |
self.cv4 = nn.ModuleList( | |
nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, 4 * self.reg_max, 1)) for x in ch[self.nl:]) | |
self.cv5 = nn.ModuleList( | |
nn.Sequential(Conv(x, c5, 3), Conv(c5, c5, 3), nn.Conv2d(c5, self.nc, 1)) for x in ch[self.nl:]) | |
self.dfl = DFL(self.reg_max) | |
self.dfl2 = DFL(self.reg_max) | |
def forward(self, x): | |
shape = x[0].shape # BCHW | |
d1 = [] | |
d2 = [] | |
for i in range(self.nl): | |
d1.append(torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1)) | |
d2.append(torch.cat((self.cv4[i](x[self.nl+i]), self.cv5[i](x[self.nl+i])), 1)) | |
if self.training: | |
return [d1, d2] | |
elif self.dynamic or self.shape != shape: | |
self.anchors, self.strides = (d1.transpose(0, 1) for d1 in make_anchors(d1, self.stride, 0.5)) | |
self.shape = shape | |
box, cls = torch.cat([di.view(shape[0], self.no, -1) for di in d1], 2).split((self.reg_max * 4, self.nc), 1) | |
dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides | |
box2, cls2 = torch.cat([di.view(shape[0], self.no, -1) for di in d2], 2).split((self.reg_max * 4, self.nc), 1) | |
dbox2 = dist2bbox(self.dfl2(box2), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides | |
y = [torch.cat((dbox, cls.sigmoid()), 1), torch.cat((dbox2, cls2.sigmoid()), 1)] | |
return y if self.export else (y, [d1, d2]) | |
def bias_init(self): | |
# Initialize Detect() biases, WARNING: requires stride availability | |
m = self # self.model[-1] # Detect() module | |
# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1 | |
# ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum()) # nominal class frequency | |
for a, b, s in zip(m.cv2, m.cv3, m.stride): # from | |
a[-1].bias.data[:] = 1.0 # box | |
b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2) # cls (5 objects and 80 classes per 640 image) | |
for a, b, s in zip(m.cv4, m.cv5, m.stride): # from | |
a[-1].bias.data[:] = 1.0 # box | |
b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2) # cls (5 objects and 80 classes per 640 image) | |
class DualDDetect(nn.Module): | |
# YOLO Detect head for detection models | |
dynamic = False # force grid reconstruction | |
export = False # export mode | |
shape = None | |
anchors = torch.empty(0) # init | |
strides = torch.empty(0) # init | |
def __init__(self, nc=80, ch=(), inplace=True): # detection layer | |
super().__init__() | |
self.nc = nc # number of classes | |
self.nl = len(ch) // 2 # number of detection layers | |
self.reg_max = 16 | |
self.no = nc + self.reg_max * 4 # number of outputs per anchor | |
self.inplace = inplace # use inplace ops (e.g. slice assignment) | |
self.stride = torch.zeros(self.nl) # strides computed during build | |
c2, c3 = make_divisible(max((ch[0] // 4, self.reg_max * 4, 16)), 4), max((ch[0], min((self.nc * 2, 128)))) # channels | |
c4, c5 = make_divisible(max((ch[self.nl] // 4, self.reg_max * 4, 16)), 4), max((ch[self.nl], min((self.nc * 2, 128)))) # channels | |
self.cv2 = nn.ModuleList( | |
nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3, g=4), nn.Conv2d(c2, 4 * self.reg_max, 1, groups=4)) for x in ch[:self.nl]) | |
self.cv3 = nn.ModuleList( | |
nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch[:self.nl]) | |
self.cv4 = nn.ModuleList( | |
nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3, g=4), nn.Conv2d(c4, 4 * self.reg_max, 1, groups=4)) for x in ch[self.nl:]) | |
self.cv5 = nn.ModuleList( | |
nn.Sequential(Conv(x, c5, 3), Conv(c5, c5, 3), nn.Conv2d(c5, self.nc, 1)) for x in ch[self.nl:]) | |
self.dfl = DFL(self.reg_max) | |
self.dfl2 = DFL(self.reg_max) | |
def forward(self, x): | |
shape = x[0].shape # BCHW | |
d1 = [] | |
d2 = [] | |
for i in range(self.nl): | |
d1.append(torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1)) | |
d2.append(torch.cat((self.cv4[i](x[self.nl+i]), self.cv5[i](x[self.nl+i])), 1)) | |
if self.training: | |
return [d1, d2] | |
elif self.dynamic or self.shape != shape: | |
self.anchors, self.strides = (d1.transpose(0, 1) for d1 in make_anchors(d1, self.stride, 0.5)) | |
self.shape = shape | |
box, cls = torch.cat([di.view(shape[0], self.no, -1) for di in d1], 2).split((self.reg_max * 4, self.nc), 1) | |
dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides | |
box2, cls2 = torch.cat([di.view(shape[0], self.no, -1) for di in d2], 2).split((self.reg_max * 4, self.nc), 1) | |
dbox2 = dist2bbox(self.dfl2(box2), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides | |
y = [torch.cat((dbox, cls.sigmoid()), 1), torch.cat((dbox2, cls2.sigmoid()), 1)] | |
return y if self.export else (y, [d1, d2]) | |
#y = torch.cat((dbox2, cls2.sigmoid()), 1) | |
#return y if self.export else (y, d2) | |
#y1 = torch.cat((dbox, cls.sigmoid()), 1) | |
#y2 = torch.cat((dbox2, cls2.sigmoid()), 1) | |
#return [y1, y2] if self.export else [(y1, d1), (y2, d2)] | |
#return [y1, y2] if self.export else [(y1, y2), (d1, d2)] | |
def bias_init(self): | |
# Initialize Detect() biases, WARNING: requires stride availability | |
m = self # self.model[-1] # Detect() module | |
# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1 | |
# ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum()) # nominal class frequency | |
for a, b, s in zip(m.cv2, m.cv3, m.stride): # from | |
a[-1].bias.data[:] = 1.0 # box | |
b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2) # cls (5 objects and 80 classes per 640 image) | |
for a, b, s in zip(m.cv4, m.cv5, m.stride): # from | |
a[-1].bias.data[:] = 1.0 # box | |
b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2) # cls (5 objects and 80 classes per 640 image) | |
class IDualDDetect(nn.Module): | |
# YOLO Detect head for detection models | |
dynamic = False # force grid reconstruction | |
export = False # export mode | |
shape = None | |
anchors = torch.empty(0) # init | |
strides = torch.empty(0) # init | |
def __init__(self, nc=80, ch=(), inplace=True): # detection layer | |
super().__init__() | |
self.nc = nc # number of classes | |
self.nl = len(ch) // 2 # number of detection layers | |
self.reg_max = 16 | |
self.no = nc + self.reg_max * 4 # number of outputs per anchor | |
self.inplace = inplace # use inplace ops (e.g. slice assignment) | |
self.stride = torch.zeros(self.nl) # strides computed during build | |
c2, c3 = make_divisible(max((ch[0] // 4, self.reg_max * 4, 16)), 4), max((ch[0], min((self.nc * 2, 128)))) # channels | |
c4, c5 = make_divisible(max((ch[self.nl] // 4, self.reg_max * 4, 16)), 4), max((ch[self.nl], min((self.nc * 2, 128)))) # channels | |
self.cv2 = nn.ModuleList( | |
nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3, g=4), nn.Conv2d(c2, 4 * self.reg_max, 1, groups=4)) for x in ch[:self.nl]) | |
self.cv3 = nn.ModuleList( | |
nn.Sequential(Convb(x, c3, 1), Conv(c3, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch[:self.nl]) | |
self.cv4 = nn.ModuleList( | |
nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3, g=4), nn.Conv2d(c4, 4 * self.reg_max, 1, groups=4)) for x in ch[self.nl:]) | |
self.cv5 = nn.ModuleList( | |
nn.Sequential(Convb(x, c5, 1), Conv(c5, c5, 3), Conv(c5, c5, 3), nn.Conv2d(c5, self.nc, 1)) for x in ch[self.nl:]) | |
self.dfl = DFL(self.reg_max) | |
self.dfl2 = DFL(self.reg_max) | |
# Define ImplicitA and ImplicitM modules | |
self.implicita = nn.ModuleList(ImplicitA(x) for x in ch) | |
self.implicitm = nn.ModuleList(ImplicitM(self.nc) for _ in ch) | |
def fuse(self): | |
# Fuse weights with implicit knowledge | |
for i in range(self.nl): | |
# Fuse ImplicitA with Convolution | |
c1, c2, _, _ = self.cv3[i][0].conv.weight.shape | |
c1_, c2_, _, _ = self.implicita[i].implicit.shape | |
self.cv3[i][0].conv.bias.data += torch.matmul(self.cv3[i][0].conv.weight.reshape(c1, c2), self.implicita[i].implicit.reshape(c2_, c1_)).squeeze(1) | |
c1, c2, _, _ = self.cv5[i][0].conv.weight.shape | |
self.cv5[i][0].conv.bias.data += torch.matmul(self.cv5[i][0].conv.weight.reshape(c1, c2), self.implicita[i].implicit.reshape(c2_, c1_)).squeeze(1) | |
# Fuse ImplicitM with Convolution | |
c1,c2, _,_ = self.implicitm[i].implicit.shape | |
self.cv3[i][-1].weight.data = torch.mul(self.cv3[i][-1].weight, self.implicitm[i].implicit.transpose(0,1)) | |
self.cv3[i][-1].bias.data = torch.matmul(self.cv3[i][-1].bias, self.implicitm[i].implicit.reshape(c2)) | |
self.cv5[i][-1].weight.data = torch.mul(self.cv5[i][-1].weight, self.implicitm[i].implicit.transpose(0,1)) | |
self.cv5[i][-1].bias.data = torch.matmul(self.cv5[i][-1].bias, self.implicitm[i].implicit.reshape(c2)) | |
def forward(self, x): | |
shape = x[0].shape # BCHW | |
d1 = [] | |
d2 = [] | |
for i in range(self.nl): | |
d1.append(torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1)) | |
d2.append(torch.cat((self.cv4[i](x[self.nl+i]),self.cv5[i](x[self.nl+i])), 1)) | |
if self.training: | |
return [d1, d2] | |
elif self.dynamic or self.shape != shape: | |
self.anchors, self.strides = (d1.transpose(0, 1) for d1 in make_anchors(d1, self.stride, 0.5)) | |
self.shape = shape | |
box, cls = torch.cat([di.view(shape[0], self.no, -1) for di in d1], 2).split((self.reg_max * 4, self.nc), 1) | |
dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides | |
box2, cls2 = torch.cat([di.view(shape[0], self.no, -1) for di in d2], 2).split((self.reg_max * 4, self.nc), 1) | |
dbox2 = dist2bbox(self.dfl2(box2), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides | |
y = [torch.cat((dbox, cls.sigmoid()), 1), torch.cat((dbox2, cls2.sigmoid()), 1)] | |
return y if self.export else (y, [d1, d2]) | |
def bias_init(self): | |
# Initialize Detect() biases, WARNING: requires stride availability | |
m = self # self.model[-1] # Detect() module | |
# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1 | |
# ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum()) # nominal class frequency | |
for a, b, s in zip(m.cv2, m.cv3, m.stride): # from | |
a[-1].bias.data[:] = 1.0 # box | |
b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2) # cls (5 objects and 80 classes per 640 image) | |
for a, b, s in zip(m.cv4, m.cv5, m.stride): # from | |
a[-1].bias.data[:] = 1.0 # box | |
b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2) # cls (5 objects and 80 classes per 640 image) | |
class TripleDetect(nn.Module): | |
# YOLO Detect head for detection models | |
dynamic = False # force grid reconstruction | |
export = False # export mode | |
shape = None | |
anchors = torch.empty(0) # init | |
strides = torch.empty(0) # init | |
def __init__(self, nc=80, ch=(), inplace=True): # detection layer | |
super().__init__() | |
self.nc = nc # number of classes | |
self.nl = len(ch) // 3 # number of detection layers | |
self.reg_max = 16 | |
self.no = nc + self.reg_max * 4 # number of outputs per anchor | |
self.inplace = inplace # use inplace ops (e.g. slice assignment) | |
self.stride = torch.zeros(self.nl) # strides computed during build | |
c2, c3 = max((ch[0] // 4, self.reg_max * 4, 16)), max((ch[0], min((self.nc * 2, 128)))) # channels | |
c4, c5 = max((ch[self.nl] // 4, self.reg_max * 4, 16)), max((ch[self.nl], min((self.nc * 2, 128)))) # channels | |
c6, c7 = max((ch[self.nl * 2] // 4, self.reg_max * 4, 16)), max((ch[self.nl * 2], min((self.nc * 2, 128)))) # channels | |
self.cv2 = nn.ModuleList( | |
nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3), nn.Conv2d(c2, 4 * self.reg_max, 1)) for x in ch[:self.nl]) | |
self.cv3 = nn.ModuleList( | |
nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch[:self.nl]) | |
self.cv4 = nn.ModuleList( | |
nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, 4 * self.reg_max, 1)) for x in ch[self.nl:self.nl*2]) | |
self.cv5 = nn.ModuleList( | |
nn.Sequential(Conv(x, c5, 3), Conv(c5, c5, 3), nn.Conv2d(c5, self.nc, 1)) for x in ch[self.nl:self.nl*2]) | |
self.cv6 = nn.ModuleList( | |
nn.Sequential(Conv(x, c6, 3), Conv(c6, c6, 3), nn.Conv2d(c6, 4 * self.reg_max, 1)) for x in ch[self.nl*2:self.nl*3]) | |
self.cv7 = nn.ModuleList( | |
nn.Sequential(Conv(x, c7, 3), Conv(c7, c7, 3), nn.Conv2d(c7, self.nc, 1)) for x in ch[self.nl*2:self.nl*3]) | |
self.dfl = DFL(self.reg_max) | |
self.dfl2 = DFL(self.reg_max) | |
self.dfl3 = DFL(self.reg_max) | |
def forward(self, x): | |
shape = x[0].shape # BCHW | |
d1 = [] | |
d2 = [] | |
d3 = [] | |
for i in range(self.nl): | |
d1.append(torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1)) | |
d2.append(torch.cat((self.cv4[i](x[self.nl+i]), self.cv5[i](x[self.nl+i])), 1)) | |
d3.append(torch.cat((self.cv6[i](x[self.nl*2+i]), self.cv7[i](x[self.nl*2+i])), 1)) | |
if self.training: | |
return [d1, d2, d3] | |
elif self.dynamic or self.shape != shape: | |
self.anchors, self.strides = (d1.transpose(0, 1) for d1 in make_anchors(d1, self.stride, 0.5)) | |
self.shape = shape | |
box, cls = torch.cat([di.view(shape[0], self.no, -1) for di in d1], 2).split((self.reg_max * 4, self.nc), 1) | |
dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides | |
box2, cls2 = torch.cat([di.view(shape[0], self.no, -1) for di in d2], 2).split((self.reg_max * 4, self.nc), 1) | |
dbox2 = dist2bbox(self.dfl2(box2), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides | |
box3, cls3 = torch.cat([di.view(shape[0], self.no, -1) for di in d3], 2).split((self.reg_max * 4, self.nc), 1) | |
dbox3 = dist2bbox(self.dfl3(box3), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides | |
y = [torch.cat((dbox, cls.sigmoid()), 1), torch.cat((dbox2, cls2.sigmoid()), 1), torch.cat((dbox3, cls3.sigmoid()), 1)] | |
return y if self.export else (y, [d1, d2, d3]) | |
def bias_init(self): | |
# Initialize Detect() biases, WARNING: requires stride availability | |
m = self # self.model[-1] # Detect() module | |
# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1 | |
# ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum()) # nominal class frequency | |
for a, b, s in zip(m.cv2, m.cv3, m.stride): # from | |
a[-1].bias.data[:] = 1.0 # box | |
b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2) # cls (5 objects and 80 classes per 640 image) | |
for a, b, s in zip(m.cv4, m.cv5, m.stride): # from | |
a[-1].bias.data[:] = 1.0 # box | |
b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2) # cls (5 objects and 80 classes per 640 image) | |
for a, b, s in zip(m.cv6, m.cv7, m.stride): # from | |
a[-1].bias.data[:] = 1.0 # box | |
b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2) # cls (5 objects and 80 classes per 640 image) | |
class TripleDDetect(nn.Module): | |
# YOLO Detect head for detection models | |
dynamic = False # force grid reconstruction | |
export = False # export mode | |
shape = None | |
anchors = torch.empty(0) # init | |
strides = torch.empty(0) # init | |
def __init__(self, nc=80, ch=(), inplace=True): # detection layer | |
super().__init__() | |
self.nc = nc # number of classes | |
self.nl = len(ch) // 3 # number of detection layers | |
self.reg_max = 16 | |
self.no = nc + self.reg_max * 4 # number of outputs per anchor | |
self.inplace = inplace # use inplace ops (e.g. slice assignment) | |
self.stride = torch.zeros(self.nl) # strides computed during build | |
c2, c3 = make_divisible(max((ch[0] // 4, self.reg_max * 4, 16)), 4), \ | |
max((ch[0], min((self.nc * 2, 128)))) # channels | |
c4, c5 = make_divisible(max((ch[self.nl] // 4, self.reg_max * 4, 16)), 4), \ | |
max((ch[self.nl], min((self.nc * 2, 128)))) # channels | |
c6, c7 = make_divisible(max((ch[self.nl * 2] // 4, self.reg_max * 4, 16)), 4), \ | |
max((ch[self.nl * 2], min((self.nc * 2, 128)))) # channels | |
self.cv2 = nn.ModuleList( | |
nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3, g=4), | |
nn.Conv2d(c2, 4 * self.reg_max, 1, groups=4)) for x in ch[:self.nl]) | |
self.cv3 = nn.ModuleList( | |
nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch[:self.nl]) | |
self.cv4 = nn.ModuleList( | |
nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3, g=4), | |
nn.Conv2d(c4, 4 * self.reg_max, 1, groups=4)) for x in ch[self.nl:self.nl*2]) | |
self.cv5 = nn.ModuleList( | |
nn.Sequential(Conv(x, c5, 3), Conv(c5, c5, 3), nn.Conv2d(c5, self.nc, 1)) for x in ch[self.nl:self.nl*2]) | |
self.cv6 = nn.ModuleList( | |
nn.Sequential(Conv(x, c6, 3), Conv(c6, c6, 3, g=4), | |
nn.Conv2d(c6, 4 * self.reg_max, 1, groups=4)) for x in ch[self.nl*2:self.nl*3]) | |
self.cv7 = nn.ModuleList( | |
nn.Sequential(Conv(x, c7, 3), Conv(c7, c7, 3), nn.Conv2d(c7, self.nc, 1)) for x in ch[self.nl*2:self.nl*3]) | |
self.dfl = DFL(self.reg_max) | |
self.dfl2 = DFL(self.reg_max) | |
self.dfl3 = DFL(self.reg_max) | |
def forward(self, x): | |
shape = x[0].shape # BCHW | |
d1 = [] | |
d2 = [] | |
d3 = [] | |
for i in range(self.nl): | |
d1.append(torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1)) | |
d2.append(torch.cat((self.cv4[i](x[self.nl+i]), self.cv5[i](x[self.nl+i])), 1)) | |
d3.append(torch.cat((self.cv6[i](x[self.nl*2+i]), self.cv7[i](x[self.nl*2+i])), 1)) | |
if self.training: | |
return [d1, d2, d3] | |
elif self.dynamic or self.shape != shape: | |
self.anchors, self.strides = (d1.transpose(0, 1) for d1 in make_anchors(d1, self.stride, 0.5)) | |
self.shape = shape | |
box, cls = torch.cat([di.view(shape[0], self.no, -1) for di in d1], 2).split((self.reg_max * 4, self.nc), 1) | |
dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides | |
box2, cls2 = torch.cat([di.view(shape[0], self.no, -1) for di in d2], 2).split((self.reg_max * 4, self.nc), 1) | |
dbox2 = dist2bbox(self.dfl2(box2), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides | |
box3, cls3 = torch.cat([di.view(shape[0], self.no, -1) for di in d3], 2).split((self.reg_max * 4, self.nc), 1) | |
dbox3 = dist2bbox(self.dfl3(box3), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides | |
#y = [torch.cat((dbox, cls.sigmoid()), 1), torch.cat((dbox2, cls2.sigmoid()), 1), torch.cat((dbox3, cls3.sigmoid()), 1)] | |
#return y if self.export else (y, [d1, d2, d3]) | |
y = torch.cat((dbox3, cls3.sigmoid()), 1) | |
return y if self.export else (y, d3) | |
def bias_init(self): | |
# Initialize Detect() biases, WARNING: requires stride availability | |
m = self # self.model[-1] # Detect() module | |
# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1 | |
# ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum()) # nominal class frequency | |
for a, b, s in zip(m.cv2, m.cv3, m.stride): # from | |
a[-1].bias.data[:] = 1.0 # box | |
b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2) # cls (5 objects and 80 classes per 640 image) | |
for a, b, s in zip(m.cv4, m.cv5, m.stride): # from | |
a[-1].bias.data[:] = 1.0 # box | |
b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2) # cls (5 objects and 80 classes per 640 image) | |
for a, b, s in zip(m.cv6, m.cv7, m.stride): # from | |
a[-1].bias.data[:] = 1.0 # box | |
b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2) # cls (5 objects and 80 classes per 640 image) | |
class Segment(Detect): | |
# YOLO Segment head for segmentation models | |
def __init__(self, nc=80, nm=32, npr=256, ch=(), inplace=True): | |
super().__init__(nc, ch, inplace) | |
self.nm = nm # number of masks | |
self.npr = npr # number of protos | |
self.proto = Proto(ch[0], self.npr, self.nm) # protos | |
self.detect = Detect.forward | |
c4 = max(ch[0] // 4, self.nm) | |
self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.nm, 1)) for x in ch) | |
def forward(self, x): | |
p = self.proto(x[0]) | |
bs = p.shape[0] | |
mc = torch.cat([self.cv4[i](x[i]).view(bs, self.nm, -1) for i in range(self.nl)], 2) # mask coefficients | |
x = self.detect(self, x) | |
if self.training: | |
return x, mc, p | |
return (torch.cat([x, mc], 1), p) if self.export else (torch.cat([x[0], mc], 1), (x[1], mc, p)) | |
class Panoptic(Detect): | |
# YOLO Panoptic head for panoptic segmentation models | |
def __init__(self, nc=80, sem_nc=93, nm=32, npr=256, ch=(), inplace=True): | |
super().__init__(nc, ch, inplace) | |
self.sem_nc = sem_nc | |
self.nm = nm # number of masks | |
self.npr = npr # number of protos | |
self.proto = Proto(ch[0], self.npr, self.nm) # protos | |
self.uconv = UConv(ch[0], ch[0]//4, self.sem_nc+self.nc) | |
self.detect = Detect.forward | |
c4 = max(ch[0] // 4, self.nm) | |
self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.nm, 1)) for x in ch) | |
def forward(self, x): | |
p = self.proto(x[0]) | |
s = self.uconv(x[0]) | |
bs = p.shape[0] | |
mc = torch.cat([self.cv4[i](x[i]).view(bs, self.nm, -1) for i in range(self.nl)], 2) # mask coefficients | |
x = self.detect(self, x) | |
if self.training: | |
return x, mc, p, s | |
return (torch.cat([x, mc], 1), p, s) if self.export else (torch.cat([x[0], mc], 1), (x[1], mc, p, s)) | |
class BaseModel(nn.Module): | |
# YOLO base model | |
def forward(self, x, profile=False, visualize=False): | |
return self._forward_once(x, profile, visualize) # single-scale inference, train | |
def _forward_once(self, x, profile=False, visualize=False): | |
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): | |
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): # fuse model Conv2d() + BatchNorm2d() layers | |
LOGGER.info('Fusing layers... ') | |
for m in self.model.modules(): | |
if isinstance(m, (RepConvN)) and hasattr(m, 'fuse_convs'): | |
m.fuse_convs() | |
m.forward = m.forward_fuse # update forward | |
if isinstance(m, (Conv, Convb, 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 | |
elif isinstance(m, (IDualDDetect)): | |
m.fuse() | |
self.info() | |
return self | |
def info(self, verbose=False, img_size=640): # print model information | |
model_info(self, verbose, img_size) | |
def _apply(self, fn): | |
# Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers | |
self = super()._apply(fn) | |
m = self.model[-1] # Detect() | |
if isinstance(m, (Detect, DualDetect, TripleDetect, DDetect, DualDDetect, IDualDDetect, TripleDDetect, Segment)): | |
m.stride = fn(m.stride) | |
m.anchors = fn(m.anchors) | |
m.strides = fn(m.strides) | |
# m.grid = list(map(fn, m.grid)) | |
return self | |
class DetectionModel(BaseModel): | |
# YOLO detection model | |
def __init__(self, cfg='yolo.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes | |
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, DDetect, Segment)): | |
s = 256 # 2x min stride | |
m.inplace = self.inplace | |
forward = lambda x: self.forward(x)[0] if isinstance(m, (Segment)) else self.forward(x) | |
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 | |
m.bias_init() # only run once | |
if isinstance(m, (DualDetect, TripleDetect, DualDDetect, IDualDDetect, TripleDDetect)): | |
s = 256 # 2x min stride | |
m.inplace = self.inplace | |
#forward = lambda x: self.forward(x)[0][0] if isinstance(m, (DualSegment)) else self.forward(x)[0] | |
forward = lambda x: self.forward(x)[0] | |
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 | |
m.bias_init() # only run once | |
# Init weights, biases | |
initialize_weights(self) | |
self.info() | |
LOGGER.info('') | |
def forward(self, x, augment=False, profile=False, visualize=False): | |
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): | |
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-scale predictions following augmented inference (inverse operation) | |
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): | |
# Clip YOLO augmented inference tails | |
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 | |
Model = DetectionModel # retain YOLO 'Model' class for backwards compatibility | |
class SegmentationModel(DetectionModel): | |
# YOLO segmentation model | |
def __init__(self, cfg='yolo-seg.yaml', ch=3, nc=None, anchors=None): | |
super().__init__(cfg, ch, nc, anchors) | |
class ClassificationModel(BaseModel): | |
# YOLO classification model | |
def __init__(self, cfg=None, model=None, nc=1000, cutoff=10): # yaml, model, number of classes, cutoff 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): | |
# Create a YOLO classification model from a YOLO detection model | |
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): | |
# Create a YOLO classification model from a *.yaml file | |
self.model = None | |
def parse_model(d, ch): # model_dict, input_channels(3) | |
# Parse a YOLO model.yaml dictionary | |
LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}") | |
anchors, nc, gd, gw, act = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation') | |
if act: | |
Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU() | |
RepConvN.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU() | |
LOGGER.info(f"{colorstr('activation:')} {act}") # print | |
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, Convb, AConv, ConvTranspose, | |
Bottleneck, SPP, SPPF, DWConv, BottleneckCSP, nn.ConvTranspose2d, DWConvTranspose2d, SPPCSPC, ADown, | |
RepNCSPELAN4, SPPELAN, CBAMC4, RepNCBAMELAN4, SOCA, RepNSAELAN4, SABottleneck, LSKBottleneck, RepNLSKELAN4, | |
RepNECALAN4, C2f_DCNv2, RepDCNv2LEAN4}: | |
c1, c2 = ch[f], args[0] | |
if c2 != no: # if not output | |
c2 = make_divisible(c2 * gw, 8) | |
args = [c1, c2, *args[1:]] | |
if m in {BottleneckCSP, SPPCSPC}: | |
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 is Shortcut: | |
c2 = ch[f[0]] | |
elif m is ReOrg: | |
c2 = ch[f] * 4 | |
elif m is CBLinear: | |
c2 = args[0] | |
c1 = ch[f] | |
args = [c1, c2, *args[1:]] | |
elif m is CBFuse: | |
c2 = ch[f[-1]] | |
# TODO: channel, gw, gd | |
elif m in {Detect, DualDetect, TripleDetect, DDetect, DualDDetect, IDualDDetect, TripleDDetect, 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 in {Segment}: | |
args[2] = make_divisible(args[2] * gw, 8) | |
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='./detect/attention/yolov9-e-repnlskelan4.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) | |
model.eval() | |
# 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() | |