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Commit 17bc28d5 authored by sunxx1's avatar sunxx1
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Merge branch 'main' into 'main' 

yolov5增加了mpi单机多卡和多机多卡启动方式，其readme文件进行了更新，对maskrcnn的debug输出日志进行了删除，并更新了该模型的readme文件

See merge request dcutoolkit/deeplearing/dlexamples_new!46
parents 7143f128 5a567950
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PyTorch/Compute-Vision/Objection/yolov5/hubconf.py
View file @ 17bc28d5
...@@ -5,7 +5,6 @@ PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/ ...@@ -5,7 +5,6 @@ PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/
Usage: Usage:
import torch import torch
model = torch.hub.load('ultralytics/yolov5', 'yolov5s') model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
model = torch.hub.load('ultralytics/yolov5:master', 'custom', 'path/to/yolov5s.onnx') # file from branch
""" """
import torch import torch
...@@ -28,35 +27,36 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo ...@@ -28,35 +27,36 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
""" """
from pathlib import Path from pathlib import Path
from models.common import AutoShape, DetectMultiBackend
from models.yolo import Model from models.yolo import Model
from models.experimental import attempt_load
from utils.general import check_requirements, set_logging
from utils.downloads import attempt_download from utils.downloads import attempt_download
from utils.general import check_requirements, intersect_dicts, set_logging
from utils.torch_utils import select_device from utils.torch_utils import select_device
file = Path(__file__).resolve()
check_requirements(exclude=('tensorboard', 'thop', 'opencv-python')) check_requirements(exclude=('tensorboard', 'thop', 'opencv-python'))
set_logging(verbose=verbose) set_logging(verbose=verbose)
name = Path(name) save_dir = Path('') if str(name).endswith('.pt') else file.parent
path = name.with_suffix('.pt') if name.suffix == '' else name # checkpoint path path = (save_dir / name).with_suffix('.pt') # checkpoint path
try: try:
device = select_device(('0' if torch.cuda.is_available() else 'cpu') if device is None else device) device = select_device(('0' if torch.cuda.is_available() else 'cpu') if device is None else device)
if pretrained and channels == 3 and classes == 80: if pretrained and channels == 3 and classes == 80:
model = DetectMultiBackend(path, device=device) # download/load FP32 model model = attempt_load(path, map_location=device) # download/load FP32 model
# model = models.experimental.attempt_load(path, map_location=device) # download/load FP32 model
else: else:
cfg = list((Path(__file__).parent / 'models').rglob(f'{path.stem}.yaml'))[0] # model.yaml path cfg = list((Path(__file__).parent / 'models').rglob(f'{name}.yaml'))[0] # model.yaml path
model = Model(cfg, channels, classes) # create model model = Model(cfg, channels, classes) # create model
if pretrained: if pretrained:
ckpt = torch.load(attempt_download(path), map_location=device) # load ckpt = torch.load(attempt_download(path), map_location=device) # load
msd = model.state_dict() # model state_dict
csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32 csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32
csd = intersect_dicts(csd, model.state_dict(), exclude=['anchors']) # intersect csd = {k: v for k, v in csd.items() if msd[k].shape == v.shape} # filter
model.load_state_dict(csd, strict=False) # load model.load_state_dict(csd, strict=False) # load
if len(ckpt['model'].names) == classes: if len(ckpt['model'].names) == classes:
model.names = ckpt['model'].names # set class names attribute model.names = ckpt['model'].names # set class names attribute
if autoshape: if autoshape:
model = AutoShape(model) # for file/URI/PIL/cv2/np inputs and NMS model = model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS
return model.to(device) return model.to(device)
except Exception as e: except Exception as e:
...@@ -125,11 +125,10 @@ if __name__ == '__main__': ...@@ -125,11 +125,10 @@ if __name__ == '__main__':
# model = custom(path='path/to/model.pt') # custom # model = custom(path='path/to/model.pt') # custom
# Verify inference # Verify inference
from pathlib import Path
import cv2 import cv2
import numpy as np import numpy as np
from PIL import Image from PIL import Image
from pathlib import Path
imgs = ['data/images/zidane.jpg', # filename imgs = ['data/images/zidane.jpg', # filename
Path('data/images/zidane.jpg'), # Path Path('data/images/zidane.jpg'), # Path
...@@ -138,6 +137,6 @@ if __name__ == '__main__': ...@@ -138,6 +137,6 @@ if __name__ == '__main__':
Image.open('data/images/bus.jpg'), # PIL Image.open('data/images/bus.jpg'), # PIL
np.zeros((320, 640, 3))] # numpy np.zeros((320, 640, 3))] # numpy
results = model(imgs, size=320) # batched inference results = model(imgs) # batched inference
results.print() results.print()
results.save() results.save()
PyTorch/Compute-Vision/Objection/yolov5/models/experimental.py
View file @ 17bc28d5
...@@ -2,7 +2,6 @@ ...@@ -2,7 +2,6 @@
""" """
Experimental modules Experimental modules
""" """
import math
import numpy as np import numpy as np
import torch import torch
...@@ -33,7 +32,7 @@ class Sum(nn.Module): ...@@ -33,7 +32,7 @@ class Sum(nn.Module):
self.weight = weight # apply weights boolean self.weight = weight # apply weights boolean
self.iter = range(n - 1) # iter object self.iter = range(n - 1) # iter object
if weight: if weight:
self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True) # layer weights self.w = nn.Parameter(-torch.arange(1., n) / 2, requires_grad=True) # layer weights
def forward(self, x): def forward(self, x):
y = x[0] # no weight y = x[0] # no weight
...@@ -49,27 +48,26 @@ class Sum(nn.Module): ...@@ -49,27 +48,26 @@ class Sum(nn.Module):
class MixConv2d(nn.Module): class MixConv2d(nn.Module):
# Mixed Depth-wise Conv https://arxiv.org/abs/1907.09595 # Mixed Depth-wise Conv https://arxiv.org/abs/1907.09595
def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): # ch_in, ch_out, kernel, stride, ch_strategy def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
super().__init__() super().__init__()
n = len(k) # number of convolutions groups = len(k)
if equal_ch: # equal c_ per group if equal_ch: # equal c_ per group
i = torch.linspace(0, n - 1E-6, c2).floor() # c2 indices i = torch.linspace(0, groups - 1E-6, c2).floor() # c2 indices
c_ = [(i == g).sum() for g in range(n)] # intermediate channels c_ = [(i == g).sum() for g in range(groups)] # intermediate channels
else: # equal weight.numel() per group else: # equal weight.numel() per group
b = [c2] + [0] * n b = [c2] + [0] * groups
a = np.eye(n + 1, n, k=-1) a = np.eye(groups + 1, groups, k=-1)
a -= np.roll(a, 1, axis=1) a -= np.roll(a, 1, axis=1)
a *= np.array(k) ** 2 a *= np.array(k) ** 2
a[0] = 1 a[0] = 1
c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b
self.m = nn.ModuleList( self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)])
[nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)])
self.bn = nn.BatchNorm2d(c2) self.bn = nn.BatchNorm2d(c2)
self.act = nn.SiLU() self.act = nn.LeakyReLU(0.1, inplace=True)
def forward(self, x): def forward(self, x):
return self.act(self.bn(torch.cat([m(x) for m in self.m], 1))) return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
class Ensemble(nn.ModuleList): class Ensemble(nn.ModuleList):
...@@ -99,6 +97,7 @@ def attempt_load(weights, map_location=None, inplace=True, fuse=True): ...@@ -99,6 +97,7 @@ def attempt_load(weights, map_location=None, inplace=True, fuse=True):
else: else:
model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().eval()) # without layer fuse model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().eval()) # without layer fuse
# Compatibility updates # Compatibility updates
for m in model.modules(): for m in model.modules():
if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model]: if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model]:
......
PyTorch/Compute-Vision/Objection/yolov5/models/hub/yolov5-bifpn.yaml
View file @ 17bc28d5
...@@ -9,22 +9,22 @@ anchors: ...@@ -9,22 +9,22 @@ anchors:
- [30,61, 62,45, 59,119] # P4/16 - [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32 - [116,90, 156,198, 373,326] # P5/32
# YOLOv5 v6.0 backbone # YOLOv5 backbone
backbone: backbone:
# [from, number, module, args] # [from, number, module, args]
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [[-1, 1, Focus, [64, 3]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]], [-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 6, C3, [256]], [-1, 9, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]], [-1, 9, C3, [512]]
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 3, C3, [1024]], [-1, 1, SPP, [1024, [5, 9, 13]]],
[-1, 1, SPPF, [1024, 5]], # 9 [-1, 3, C3, [1024, False]], # 9
] ]
# YOLOv5 v6.0 BiFPN head # YOLOv5 BiFPN head
head: head:
[[-1, 1, Conv, [512, 1, 1]], [[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [-1, 1, nn.Upsample, [None, 2, 'nearest']],
...@@ -37,7 +37,7 @@ head: ...@@ -37,7 +37,7 @@ head:
[-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 3, C3, [256, False]], # 17 (P3/8-small)
[-1, 1, Conv, [256, 3, 2]], [-1, 1, Conv, [256, 3, 2]],
[[-1, 14, 6], 1, Concat, [1]], # cat P4 <--- BiFPN change [[-1, 14, 6], 1, Concat, [1]], # cat P4
[-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 3, C3, [512, False]], # 20 (P4/16-medium)
[-1, 1, Conv, [512, 3, 2]], [-1, 1, Conv, [512, 3, 2]],
......
PyTorch/Compute-Vision/Objection/yolov5/models/hub/yolov5-fpn.yaml
View file @ 17bc28d5
...@@ -9,34 +9,34 @@ anchors: ...@@ -9,34 +9,34 @@ anchors:
- [30,61, 62,45, 59,119] # P4/16 - [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32 - [116,90, 156,198, 373,326] # P5/32
# YOLOv5 v6.0 backbone # YOLOv5 backbone
backbone: backbone:
# [from, number, module, args] # [from, number, module, args]
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [[-1, 1, Focus, [64, 3]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]], [-1, 3, Bottleneck, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 6, C3, [256]], [-1, 9, BottleneckCSP, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]], [-1, 9, BottleneckCSP, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 3, C3, [1024]], [-1, 1, SPP, [1024, [5, 9, 13]]],
[-1, 1, SPPF, [1024, 5]], # 9 [-1, 6, BottleneckCSP, [1024]], # 9
] ]
# YOLOv5 v6.0 FPN head # YOLOv5 FPN head
head: head:
[[-1, 3, C3, [1024, False]], # 10 (P5/32-large) [[-1, 3, BottleneckCSP, [1024, False]], # 10 (P5/32-large)
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4 [[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 1, Conv, [512, 1, 1]], [-1, 1, Conv, [512, 1, 1]],
[-1, 3, C3, [512, False]], # 14 (P4/16-medium) [-1, 3, BottleneckCSP, [512, False]], # 14 (P4/16-medium)
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3 [[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 1, Conv, [256, 1, 1]], [-1, 1, Conv, [256, 1, 1]],
[-1, 3, C3, [256, False]], # 18 (P3/8-small) [-1, 3, BottleneckCSP, [256, False]], # 18 (P3/8-small)
[[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) [[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
] ]
PyTorch/Compute-Vision/Objection/yolov5/models/hub/yolov5-p2.yaml
View file @ 17bc28d5
...@@ -4,24 +4,24 @@ ...@@ -4,24 +4,24 @@
nc: 80 # number of classes nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple width_multiple: 1.0 # layer channel multiple
anchors: 3 # AutoAnchor evolves 3 anchors per P output layer anchors: 3
# YOLOv5 v6.0 backbone # YOLOv5 backbone
backbone: backbone:
# [from, number, module, args] # [from, number, module, args]
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [[-1, 1, Focus, [64, 3]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]], [-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 6, C3, [256]], [-1, 9, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]], [-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 3, C3, [1024]], [-1, 1, SPP, [1024, [5, 9, 13]]],
[-1, 1, SPPF, [1024, 5]], # 9 [-1, 3, C3, [1024, False]], # 9
] ]
# YOLOv5 v6.0 head with (P2, P3, P4, P5) outputs # YOLOv5 head
head: head:
[[-1, 1, Conv, [512, 1, 1]], [[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [-1, 1, nn.Upsample, [None, 2, 'nearest']],
......
PyTorch/Compute-Vision/Objection/yolov5/models/hub/yolov5-p34.yaml deleted 100644 → 0
View file @ 7143f128
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Parameters
nc: 80 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # layer channel multiple
anchors: 3 # AutoAnchor evolves 3 anchors per P output layer
# YOLOv5 v6.0 backbone
backbone:
# [from, number, module, args]
[ [ -1, 1, Conv, [ 64, 6, 2, 2 ] ], # 0-P1/2
[ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4
[ -1, 3, C3, [ 128 ] ],
[ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8
[ -1, 6, C3, [ 256 ] ],
[ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16
[ -1, 9, C3, [ 512 ] ],
[ -1, 1, Conv, [ 1024, 3, 2 ] ], # 7-P5/32
[ -1, 3, C3, [ 1024 ] ],
[ -1, 1, SPPF, [ 1024, 5 ] ], # 9
]
# YOLOv5 v6.0 head with (P3, P4) outputs
head:
[ [ -1, 1, Conv, [ 512, 1, 1 ] ],
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4
[ -1, 3, C3, [ 512, False ] ], # 13
[ -1, 1, Conv, [ 256, 1, 1 ] ],
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3
[ -1, 3, C3, [ 256, False ] ], # 17 (P3/8-small)
[ -1, 1, Conv, [ 256, 3, 2 ] ],
[ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P4
[ -1, 3, C3, [ 512, False ] ], # 20 (P4/16-medium)
[ [ 17, 20 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4)
]
PyTorch/Compute-Vision/Objection/yolov5/models/hub/yolov5-p6.yaml
View file @ 17bc28d5
...@@ -4,26 +4,26 @@ ...@@ -4,26 +4,26 @@
nc: 80 # number of classes nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple width_multiple: 1.0 # layer channel multiple
anchors: 3 # AutoAnchor evolves 3 anchors per P output layer anchors: 3
# YOLOv5 v6.0 backbone # YOLOv5 backbone
backbone: backbone:
# [from, number, module, args] # [from, number, module, args]
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [[-1, 1, Focus, [64, 3]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]], [-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 6, C3, [256]], [-1, 9, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]], [-1, 9, C3, [512]],
[-1, 1, Conv, [768, 3, 2]], # 7-P5/32 [-1, 1, Conv, [768, 3, 2]], # 7-P5/32
[-1, 3, C3, [768]], [-1, 3, C3, [768]],
[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
[-1, 3, C3, [1024]], [-1, 1, SPP, [1024, [3, 5, 7]]],
[-1, 1, SPPF, [1024, 5]], # 11 [-1, 3, C3, [1024, False]], # 11
] ]
# YOLOv5 v6.0 head with (P3, P4, P5, P6) outputs # YOLOv5 head
head: head:
[[-1, 1, Conv, [768, 1, 1]], [[-1, 1, Conv, [768, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [-1, 1, nn.Upsample, [None, 2, 'nearest']],
...@@ -50,7 +50,7 @@ head: ...@@ -50,7 +50,7 @@ head:
[-1, 1, Conv, [768, 3, 2]], [-1, 1, Conv, [768, 3, 2]],
[[-1, 12], 1, Concat, [1]], # cat head P6 [[-1, 12], 1, Concat, [1]], # cat head P6
[-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) [-1, 3, C3, [1024, False]], # 32 (P5/64-xlarge)
[[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
] ]
PyTorch/Compute-Vision/Objection/yolov5/models/hub/yolov5-p7.yaml
View file @ 17bc28d5
...@@ -4,16 +4,16 @@ ...@@ -4,16 +4,16 @@
nc: 80 # number of classes nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple width_multiple: 1.0 # layer channel multiple
anchors: 3 # AutoAnchor evolves 3 anchors per P output layer anchors: 3
# YOLOv5 v6.0 backbone # YOLOv5 backbone
backbone: backbone:
# [from, number, module, args] # [from, number, module, args]
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [[-1, 1, Focus, [64, 3]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]], [-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 6, C3, [256]], [-1, 9, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]], [-1, 9, C3, [512]],
[-1, 1, Conv, [768, 3, 2]], # 7-P5/32 [-1, 1, Conv, [768, 3, 2]], # 7-P5/32
...@@ -21,11 +21,11 @@ backbone: ...@@ -21,11 +21,11 @@ backbone:
[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
[-1, 3, C3, [1024]], [-1, 3, C3, [1024]],
[-1, 1, Conv, [1280, 3, 2]], # 11-P7/128 [-1, 1, Conv, [1280, 3, 2]], # 11-P7/128
[-1, 3, C3, [1280]], [-1, 1, SPP, [1280, [3, 5]]],
[-1, 1, SPPF, [1280, 5]], # 13 [-1, 3, C3, [1280, False]], # 13
] ]
# YOLOv5 v6.0 head with (P3, P4, P5, P6, P7) outputs # YOLOv5 head
head: head:
[[-1, 1, Conv, [1024, 1, 1]], [[-1, 1, Conv, [1024, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [-1, 1, nn.Upsample, [None, 2, 'nearest']],
......
PyTorch/Compute-Vision/Objection/yolov5/models/hub/yolov5-panet.yaml
View file @ 17bc28d5
...@@ -9,40 +9,40 @@ anchors: ...@@ -9,40 +9,40 @@ anchors:
- [30,61, 62,45, 59,119] # P4/16 - [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32 - [116,90, 156,198, 373,326] # P5/32
# YOLOv5 v6.0 backbone # YOLOv5 backbone
backbone: backbone:
# [from, number, module, args] # [from, number, module, args]
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [[-1, 1, Focus, [64, 3]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]], [-1, 3, BottleneckCSP, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 6, C3, [256]], [-1, 9, BottleneckCSP, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]], [-1, 9, BottleneckCSP, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 3, C3, [1024]], [-1, 1, SPP, [1024, [5, 9, 13]]],
[-1, 1, SPPF, [1024, 5]], # 9 [-1, 3, BottleneckCSP, [1024, False]], # 9
] ]
# YOLOv5 v6.0 PANet head # YOLOv5 PANet head
head: head:
[[-1, 1, Conv, [512, 1, 1]], [[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4 [[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, C3, [512, False]], # 13 [-1, 3, BottleneckCSP, [512, False]], # 13
[-1, 1, Conv, [256, 1, 1]], [-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3 [[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, C3, [256, False]], # 17 (P3/8-small) [-1, 3, BottleneckCSP, [256, False]], # 17 (P3/8-small)
[-1, 1, Conv, [256, 3, 2]], [-1, 1, Conv, [256, 3, 2]],
[[-1, 14], 1, Concat, [1]], # cat head P4 [[-1, 14], 1, Concat, [1]], # cat head P4
[-1, 3, C3, [512, False]], # 20 (P4/16-medium) [-1, 3, BottleneckCSP, [512, False]], # 20 (P4/16-medium)
[-1, 1, Conv, [512, 3, 2]], [-1, 1, Conv, [512, 3, 2]],
[[-1, 10], 1, Concat, [1]], # cat head P5 [[-1, 10], 1, Concat, [1]], # cat head P5
[-1, 3, C3, [1024, False]], # 23 (P5/32-large) [-1, 3, BottleneckCSP, [1024, False]], # 23 (P5/32-large)
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
] ]
PyTorch/Compute-Vision/Objection/yolov5/models/hub/yolov5s-ghost.yaml
View file @ 17bc28d5
...@@ -9,22 +9,22 @@ anchors: ...@@ -9,22 +9,22 @@ anchors:
- [30,61, 62,45, 59,119] # P4/16 - [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32 - [116,90, 156,198, 373,326] # P5/32
# YOLOv5 v6.0 backbone # YOLOv5 backbone
backbone: backbone:
# [from, number, module, args] # [from, number, module, args]
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [[-1, 1, Focus, [64, 3]], # 0-P1/2
[-1, 1, GhostConv, [128, 3, 2]], # 1-P2/4 [-1, 1, GhostConv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3Ghost, [128]], [-1, 3, C3Ghost, [128]],
[-1, 1, GhostConv, [256, 3, 2]], # 3-P3/8 [-1, 1, GhostConv, [256, 3, 2]], # 3-P3/8
[-1, 6, C3Ghost, [256]], [-1, 9, C3Ghost, [256]],
[-1, 1, GhostConv, [512, 3, 2]], # 5-P4/16 [-1, 1, GhostConv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3Ghost, [512]], [-1, 9, C3Ghost, [512]],
[-1, 1, GhostConv, [1024, 3, 2]], # 7-P5/32 [-1, 1, GhostConv, [1024, 3, 2]], # 7-P5/32
[-1, 3, C3Ghost, [1024]], [-1, 1, SPP, [1024, [5, 9, 13]]],
[-1, 1, SPPF, [1024, 5]], # 9 [-1, 3, C3Ghost, [1024, False]], # 9
] ]
# YOLOv5 v6.0 head # YOLOv5 head
head: head:
[[-1, 1, GhostConv, [512, 1, 1]], [[-1, 1, GhostConv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [-1, 1, nn.Upsample, [None, 2, 'nearest']],
......
PyTorch/Compute-Vision/Objection/yolov5/models/hub/yolov5s-transformer.yaml
View file @ 17bc28d5
...@@ -9,22 +9,22 @@ anchors: ...@@ -9,22 +9,22 @@ anchors:
- [30,61, 62,45, 59,119] # P4/16 - [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32 - [116,90, 156,198, 373,326] # P5/32
# YOLOv5 v6.0 backbone # YOLOv5 backbone
backbone: backbone:
# [from, number, module, args] # [from, number, module, args]
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [[-1, 1, Focus, [64, 3]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]], [-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 6, C3, [256]], [-1, 9, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]], [-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 3, C3TR, [1024]], # 9 <--- C3TR() Transformer module [-1, 1, SPP, [1024, [5, 9, 13]]],
[-1, 1, SPPF, [1024, 5]], # 9 [-1, 3, C3TR, [1024, False]], # 9 <-------- C3TR() Transformer module
] ]
# YOLOv5 v6.0 head # YOLOv5 head
head: head:
[[-1, 1, Conv, [512, 1, 1]], [[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [-1, 1, nn.Upsample, [None, 2, 'nearest']],
......
PyTorch/Compute-Vision/Objection/yolov5/models/tf.py
View file @ 17bc28d5
...@@ -11,6 +11,7 @@ Export: ...@@ -11,6 +11,7 @@ Export:
""" """
import argparse import argparse
import logging
import sys import sys
from copy import deepcopy from copy import deepcopy
from pathlib import Path from pathlib import Path
...@@ -27,17 +28,19 @@ import torch ...@@ -27,17 +28,19 @@ import torch
import torch.nn as nn import torch.nn as nn
from tensorflow import keras from tensorflow import keras
from models.common import C3, SPP, SPPF, Bottleneck, BottleneckCSP, Concat, Conv, DWConv, Focus, autopad from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, autopad, C3
from models.experimental import CrossConv, MixConv2d, attempt_load from models.experimental import CrossConv, MixConv2d, attempt_load
from models.yolo import Detect from models.yolo import Detect
from utils.general import make_divisible, print_args, set_logging
from utils.activations import SiLU from utils.activations import SiLU
from utils.general import LOGGER, make_divisible, print_args
LOGGER = logging.getLogger(__name__)
class TFBN(keras.layers.Layer): class TFBN(keras.layers.Layer):
# TensorFlow BatchNormalization wrapper # TensorFlow BatchNormalization wrapper
def __init__(self, w=None): def __init__(self, w=None):
super().__init__() super(TFBN, self).__init__()
self.bn = keras.layers.BatchNormalization( self.bn = keras.layers.BatchNormalization(
beta_initializer=keras.initializers.Constant(w.bias.numpy()), beta_initializer=keras.initializers.Constant(w.bias.numpy()),
gamma_initializer=keras.initializers.Constant(w.weight.numpy()), gamma_initializer=keras.initializers.Constant(w.weight.numpy()),
...@@ -51,7 +54,7 @@ class TFBN(keras.layers.Layer): ...@@ -51,7 +54,7 @@ class TFBN(keras.layers.Layer):
class TFPad(keras.layers.Layer): class TFPad(keras.layers.Layer):
def __init__(self, pad): def __init__(self, pad):
super().__init__() super(TFPad, self).__init__()
self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]]) self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]])
def call(self, inputs): def call(self, inputs):
...@@ -62,7 +65,7 @@ class TFConv(keras.layers.Layer): ...@@ -62,7 +65,7 @@ class TFConv(keras.layers.Layer):
# Standard convolution # Standard convolution
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
# ch_in, ch_out, weights, kernel, stride, padding, groups # ch_in, ch_out, weights, kernel, stride, padding, groups
super().__init__() super(TFConv, self).__init__()
assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
assert isinstance(k, int), "Convolution with multiple kernels are not allowed." assert isinstance(k, int), "Convolution with multiple kernels are not allowed."
# TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding) # TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding)
...@@ -93,11 +96,11 @@ class TFFocus(keras.layers.Layer): ...@@ -93,11 +96,11 @@ class TFFocus(keras.layers.Layer):
# Focus wh information into c-space # Focus wh information into c-space
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
# ch_in, ch_out, kernel, stride, padding, groups # ch_in, ch_out, kernel, stride, padding, groups
super().__init__() super(TFFocus, self).__init__()
self.conv = TFConv(c1 * 4, c2, k, s, p, g, act, w.conv) self.conv = TFConv(c1 * 4, c2, k, s, p, g, act, w.conv)
def call(self, inputs): # x(b,w,h,c) -> y(b,w/2,h/2,4c) def call(self, inputs): # x(b,w,h,c) -> y(b,w/2,h/2,4c)
# inputs = inputs / 255 # normalize 0-255 to 0-1 # inputs = inputs / 255. # normalize 0-255 to 0-1
return self.conv(tf.concat([inputs[:, ::2, ::2, :], return self.conv(tf.concat([inputs[:, ::2, ::2, :],
inputs[:, 1::2, ::2, :], inputs[:, 1::2, ::2, :],
inputs[:, ::2, 1::2, :], inputs[:, ::2, 1::2, :],
...@@ -107,7 +110,7 @@ class TFFocus(keras.layers.Layer): ...@@ -107,7 +110,7 @@ class TFFocus(keras.layers.Layer):
class TFBottleneck(keras.layers.Layer): class TFBottleneck(keras.layers.Layer):
# Standard bottleneck # Standard bottleneck
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None): # ch_in, ch_out, shortcut, groups, expansion def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None): # ch_in, ch_out, shortcut, groups, expansion
super().__init__() super(TFBottleneck, self).__init__()
c_ = int(c2 * e) # hidden channels c_ = int(c2 * e) # hidden channels
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
self.cv2 = TFConv(c_, c2, 3, 1, g=g, w=w.cv2) self.cv2 = TFConv(c_, c2, 3, 1, g=g, w=w.cv2)
...@@ -120,7 +123,7 @@ class TFBottleneck(keras.layers.Layer): ...@@ -120,7 +123,7 @@ class TFBottleneck(keras.layers.Layer):
class TFConv2d(keras.layers.Layer): class TFConv2d(keras.layers.Layer):
# Substitution for PyTorch nn.Conv2D # Substitution for PyTorch nn.Conv2D
def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None): def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None):
super().__init__() super(TFConv2d, self).__init__()
assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
self.conv = keras.layers.Conv2D( self.conv = keras.layers.Conv2D(
c2, k, s, 'VALID', use_bias=bias, c2, k, s, 'VALID', use_bias=bias,
...@@ -135,7 +138,7 @@ class TFBottleneckCSP(keras.layers.Layer): ...@@ -135,7 +138,7 @@ class TFBottleneckCSP(keras.layers.Layer):
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
# ch_in, ch_out, number, shortcut, groups, expansion # ch_in, ch_out, number, shortcut, groups, expansion
super().__init__() super(TFBottleneckCSP, self).__init__()
c_ = int(c2 * e) # hidden channels c_ = int(c2 * e) # hidden channels
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
self.cv2 = TFConv2d(c1, c_, 1, 1, bias=False, w=w.cv2) self.cv2 = TFConv2d(c1, c_, 1, 1, bias=False, w=w.cv2)
...@@ -155,7 +158,7 @@ class TFC3(keras.layers.Layer): ...@@ -155,7 +158,7 @@ class TFC3(keras.layers.Layer):
# CSP Bottleneck with 3 convolutions # CSP Bottleneck with 3 convolutions
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
# ch_in, ch_out, number, shortcut, groups, expansion # ch_in, ch_out, number, shortcut, groups, expansion
super().__init__() super(TFC3, self).__init__()
c_ = int(c2 * e) # hidden channels c_ = int(c2 * e) # hidden channels
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2) self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2)
...@@ -169,7 +172,7 @@ class TFC3(keras.layers.Layer): ...@@ -169,7 +172,7 @@ class TFC3(keras.layers.Layer):
class TFSPP(keras.layers.Layer): class TFSPP(keras.layers.Layer):
# Spatial pyramid pooling layer used in YOLOv3-SPP # Spatial pyramid pooling layer used in YOLOv3-SPP
def __init__(self, c1, c2, k=(5, 9, 13), w=None): def __init__(self, c1, c2, k=(5, 9, 13), w=None):
super().__init__() super(TFSPP, self).__init__()
c_ = c1 // 2 # hidden channels c_ = c1 // 2 # hidden channels
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
self.cv2 = TFConv(c_ * (len(k) + 1), c2, 1, 1, w=w.cv2) self.cv2 = TFConv(c_ * (len(k) + 1), c2, 1, 1, w=w.cv2)
...@@ -180,25 +183,9 @@ class TFSPP(keras.layers.Layer): ...@@ -180,25 +183,9 @@ class TFSPP(keras.layers.Layer):
return self.cv2(tf.concat([x] + [m(x) for m in self.m], 3)) return self.cv2(tf.concat([x] + [m(x) for m in self.m], 3))
class TFSPPF(keras.layers.Layer):
# Spatial pyramid pooling-Fast layer
def __init__(self, c1, c2, k=5, w=None):
super().__init__()
c_ = c1 // 2 # hidden channels
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
self.cv2 = TFConv(c_ * 4, c2, 1, 1, w=w.cv2)
self.m = keras.layers.MaxPool2D(pool_size=k, strides=1, padding='SAME')
def call(self, inputs):
x = self.cv1(inputs)
y1 = self.m(x)
y2 = self.m(y1)
return self.cv2(tf.concat([x, y1, y2, self.m(y2)], 3))
class TFDetect(keras.layers.Layer): class TFDetect(keras.layers.Layer):
def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None): # detection layer def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None): # detection layer
super().__init__() super(TFDetect, self).__init__()
self.stride = tf.convert_to_tensor(w.stride.numpy(), dtype=tf.float32) self.stride = tf.convert_to_tensor(w.stride.numpy(), dtype=tf.float32)
self.nc = nc # number of classes self.nc = nc # number of classes
self.no = nc + 5 # number of outputs per anchor self.no = nc + 5 # number of outputs per anchor
...@@ -226,13 +213,13 @@ class TFDetect(keras.layers.Layer): ...@@ -226,13 +213,13 @@ class TFDetect(keras.layers.Layer):
if not self.training: # inference if not self.training: # inference
y = tf.sigmoid(x[i]) y = tf.sigmoid(x[i])
xy = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i] # xy xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy
wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]
# Normalize xywh to 0-1 to reduce calibration error # Normalize xywh to 0-1 to reduce calibration error
xy /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32) xy /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32)
wh /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32) wh /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32)
y = tf.concat([xy, wh, y[..., 4:]], -1) y = tf.concat([xy, wh, y[..., 4:]], -1)
z.append(tf.reshape(y, [-1, self.na * ny * nx, self.no])) z.append(tf.reshape(y, [-1, 3 * ny * nx, self.no]))
return x if self.training else (tf.concat(z, 1), x) return x if self.training else (tf.concat(z, 1), x)
...@@ -246,7 +233,7 @@ class TFDetect(keras.layers.Layer): ...@@ -246,7 +233,7 @@ class TFDetect(keras.layers.Layer):
class TFUpsample(keras.layers.Layer): class TFUpsample(keras.layers.Layer):
def __init__(self, size, scale_factor, mode, w=None): # warning: all arguments needed including 'w' def __init__(self, size, scale_factor, mode, w=None): # warning: all arguments needed including 'w'
super().__init__() super(TFUpsample, self).__init__()
assert scale_factor == 2, "scale_factor must be 2" assert scale_factor == 2, "scale_factor must be 2"
self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * 2, x.shape[2] * 2), method=mode) self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * 2, x.shape[2] * 2), method=mode)
# self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode) # self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode)
...@@ -260,7 +247,7 @@ class TFUpsample(keras.layers.Layer): ...@@ -260,7 +247,7 @@ class TFUpsample(keras.layers.Layer):
class TFConcat(keras.layers.Layer): class TFConcat(keras.layers.Layer):
def __init__(self, dimension=1, w=None): def __init__(self, dimension=1, w=None):
super().__init__() super(TFConcat, self).__init__()
assert dimension == 1, "convert only NCHW to NHWC concat" assert dimension == 1, "convert only NCHW to NHWC concat"
self.d = 3 self.d = 3
...@@ -269,7 +256,7 @@ class TFConcat(keras.layers.Layer): ...@@ -269,7 +256,7 @@ class TFConcat(keras.layers.Layer):
def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3) def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3)
LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}") LOGGER.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'] anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
no = na * (nc + 5) # number of outputs = anchors * (classes + 5) no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
...@@ -285,7 +272,7 @@ def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3) ...@@ -285,7 +272,7 @@ def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3)
pass pass
n = max(round(n * gd), 1) if n > 1 else n # depth gain n = max(round(n * gd), 1) if n > 1 else n # depth gain
if m in [nn.Conv2d, Conv, Bottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3]: if m in [nn.Conv2d, Conv, Bottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3]:
c1, c2 = ch[f], args[0] c1, c2 = ch[f], args[0]
c2 = make_divisible(c2 * gw, 8) if c2 != no else c2 c2 = make_divisible(c2 * gw, 8) if c2 != no else c2
...@@ -296,7 +283,7 @@ def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3) ...@@ -296,7 +283,7 @@ def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3)
elif m is nn.BatchNorm2d: elif m is nn.BatchNorm2d:
args = [ch[f]] args = [ch[f]]
elif m is Concat: elif m is Concat:
c2 = sum(ch[-1 if x == -1 else x + 1] for x in f) c2 = sum([ch[-1 if x == -1 else x + 1] for x in f])
elif m is Detect: elif m is Detect:
args.append([ch[x + 1] for x in f]) args.append([ch[x + 1] for x in f])
if isinstance(args[1], int): # number of anchors if isinstance(args[1], int): # number of anchors
...@@ -309,11 +296,11 @@ def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3) ...@@ -309,11 +296,11 @@ def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3)
m_ = keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)]) if n > 1 \ m_ = keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)]) if n > 1 \
else tf_m(*args, w=model.model[i]) # module else tf_m(*args, w=model.model[i]) # module
torch_m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module torch_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 t = str(m)[8:-2].replace('__main__.', '') # module type
np = sum(x.numel() for x in torch_m_.parameters()) # number params np = sum([x.numel() for x in torch_m_.parameters()]) # number params
m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, 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}{str(n):>3}{np:>10} {t:<40}{str(args):<30}') # print LOGGER.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
layers.append(m_) layers.append(m_)
ch.append(c2) ch.append(c2)
...@@ -322,7 +309,7 @@ def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3) ...@@ -322,7 +309,7 @@ def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3)
class TFModel: class TFModel:
def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None, imgsz=(640, 640)): # model, channels, classes def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None, imgsz=(640, 640)): # model, channels, classes
super().__init__() super(TFModel, self).__init__()
if isinstance(cfg, dict): if isinstance(cfg, dict):
self.yaml = cfg # model dict self.yaml = cfg # model dict
else: # is *.yaml else: # is *.yaml
...@@ -333,7 +320,7 @@ class TFModel: ...@@ -333,7 +320,7 @@ class TFModel:
# Define model # Define model
if nc and nc != self.yaml['nc']: if nc and nc != self.yaml['nc']:
LOGGER.info(f"Overriding {cfg} nc={self.yaml['nc']} with nc={nc}") print('Overriding %s nc=%g with nc=%g' % (cfg, self.yaml['nc'], nc))
self.yaml['nc'] = nc # override yaml value self.yaml['nc'] = nc # override yaml value
self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model, imgsz=imgsz) self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model, imgsz=imgsz)
...@@ -410,10 +397,10 @@ class AgnosticNMS(keras.layers.Layer): ...@@ -410,10 +397,10 @@ class AgnosticNMS(keras.layers.Layer):
def representative_dataset_gen(dataset, ncalib=100): def representative_dataset_gen(dataset, ncalib=100):
# Representative dataset generator for use with converter.representative_dataset, returns a generator of np arrays # Representative dataset generator for use with converter.representative_dataset, returns a generator of np arrays
for n, (path, img, im0s, vid_cap, string) in enumerate(dataset): for n, (path, img, im0s, vid_cap) in enumerate(dataset):
input = np.transpose(img, [1, 2, 0]) input = np.transpose(img, [1, 2, 0])
input = np.expand_dims(input, axis=0).astype(np.float32) input = np.expand_dims(input, axis=0).astype(np.float32)
input /= 255 input /= 255.0
yield [input] yield [input]
if n >= ncalib: if n >= ncalib:
break break
...@@ -440,8 +427,6 @@ def run(weights=ROOT / 'yolov5s.pt', # weights path ...@@ -440,8 +427,6 @@ def run(weights=ROOT / 'yolov5s.pt', # weights path
keras_model = keras.Model(inputs=im, outputs=tf_model.predict(im)) keras_model = keras.Model(inputs=im, outputs=tf_model.predict(im))
keras_model.summary() keras_model.summary()
LOGGER.info('PyTorch, TensorFlow and Keras models successfully verified.\nUse export.py for TF model export.')
def parse_opt(): def parse_opt():
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
...@@ -456,6 +441,7 @@ def parse_opt(): ...@@ -456,6 +441,7 @@ def parse_opt():
def main(opt): def main(opt):
set_logging()
run(**vars(opt)) run(**vars(opt))
......
PyTorch/Compute-Vision/Objection/yolov5/models/yolo.py
View file @ 17bc28d5
...@@ -20,15 +20,18 @@ if str(ROOT) not in sys.path: ...@@ -20,15 +20,18 @@ if str(ROOT) not in sys.path:
from models.common import * from models.common import *
from models.experimental import * from models.experimental import *
from utils.autoanchor import check_anchor_order from utils.autoanchor import check_anchor_order
from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args from utils.general import check_yaml, make_divisible, print_args, set_logging
from utils.plots import feature_visualization from utils.plots import feature_visualization
from utils.torch_utils import fuse_conv_and_bn, initialize_weights, model_info, scale_img, select_device, time_sync from utils.torch_utils import copy_attr, fuse_conv_and_bn, initialize_weights, model_info, scale_img, \
select_device, time_sync
try: try:
import thop # for FLOPs computation import thop # for FLOPs computation
except ImportError: except ImportError:
thop = None thop = None
LOGGER = logging.getLogger(__name__)
class Detect(nn.Module): class Detect(nn.Module):
stride = None # strides computed during build stride = None # strides computed during build
...@@ -54,15 +57,15 @@ class Detect(nn.Module): ...@@ -54,15 +57,15 @@ class Detect(nn.Module):
x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() 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 not self.training: # inference
if self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]: if self.grid[i].shape[2:4] != x[i].shape[2:4] or self.onnx_dynamic:
self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i) self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)
y = x[i].sigmoid() y = x[i].sigmoid()
if self.inplace: if self.inplace:
y[..., 0:2] = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i] # xy y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy
y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
else: # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953 else: # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
xy = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i] # xy xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy
wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
y = torch.cat((xy, wh, y[..., 4:]), -1) y = torch.cat((xy, wh, y[..., 4:]), -1)
z.append(y.view(bs, -1, self.no)) z.append(y.view(bs, -1, self.no))
...@@ -71,10 +74,7 @@ class Detect(nn.Module): ...@@ -71,10 +74,7 @@ class Detect(nn.Module):
def _make_grid(self, nx=20, ny=20, i=0): def _make_grid(self, nx=20, ny=20, i=0):
d = self.anchors[i].device d = self.anchors[i].device
if check_version(torch.__version__, '1.10.0'): # torch>=1.10.0 meshgrid workaround for torch>=0.7 compatibility yv, xv = torch.meshgrid([torch.arange(ny).to(d), torch.arange(nx).to(d)])
yv, xv = torch.meshgrid([torch.arange(ny, device=d), torch.arange(nx, device=d)], indexing='ij')
else:
yv, xv = torch.meshgrid([torch.arange(ny, device=d), torch.arange(nx, device=d)])
grid = torch.stack((xv, yv), 2).expand((1, self.na, ny, nx, 2)).float() grid = torch.stack((xv, yv), 2).expand((1, self.na, ny, nx, 2)).float()
anchor_grid = (self.anchors[i].clone() * self.stride[i]) \ anchor_grid = (self.anchors[i].clone() * self.stride[i]) \
.view((1, self.na, 1, 1, 2)).expand((1, self.na, ny, nx, 2)).float() .view((1, self.na, 1, 1, 2)).expand((1, self.na, ny, nx, 2)).float()
...@@ -89,7 +89,7 @@ class Model(nn.Module): ...@@ -89,7 +89,7 @@ class Model(nn.Module):
else: # is *.yaml else: # is *.yaml
import yaml # for torch hub import yaml # for torch hub
self.yaml_file = Path(cfg).name self.yaml_file = Path(cfg).name
with open(cfg, encoding='ascii', errors='ignore') as f: with open(cfg, errors='ignore') as f:
self.yaml = yaml.safe_load(f) # model dict self.yaml = yaml.safe_load(f) # model dict
# Define model # Define model
...@@ -200,7 +200,7 @@ class Model(nn.Module): ...@@ -200,7 +200,7 @@ class Model(nn.Module):
for mi, s in zip(m.m, m.stride): # from for mi, s in zip(m.m, m.stride): # from
b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85) 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[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image)
b.data[:, 5:] += math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum()) # cls b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum()) # cls
mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
def _print_biases(self): def _print_biases(self):
...@@ -225,6 +225,12 @@ class Model(nn.Module): ...@@ -225,6 +225,12 @@ class Model(nn.Module):
self.info() self.info()
return self return self
def autoshape(self): # add AutoShape module
LOGGER.info('Adding AutoShape... ')
m = AutoShape(self) # wrap model
copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=()) # copy attributes
return m
def info(self, verbose=False, img_size=640): # print model information def info(self, verbose=False, img_size=640): # print model information
model_info(self, verbose, img_size) model_info(self, verbose, img_size)
...@@ -241,7 +247,7 @@ class Model(nn.Module): ...@@ -241,7 +247,7 @@ class Model(nn.Module):
def parse_model(d, ch): # model_dict, input_channels(3) def parse_model(d, ch): # model_dict, input_channels(3)
LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}") LOGGER.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'] anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
no = na * (nc + 5) # number of outputs = anchors * (classes + 5) no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
...@@ -269,7 +275,7 @@ def parse_model(d, ch): # model_dict, input_channels(3) ...@@ -269,7 +275,7 @@ def parse_model(d, ch): # model_dict, input_channels(3)
elif m is nn.BatchNorm2d: elif m is nn.BatchNorm2d:
args = [ch[f]] args = [ch[f]]
elif m is Concat: elif m is Concat:
c2 = sum(ch[x] for x in f) c2 = sum([ch[x] for x in f])
elif m is Detect: elif m is Detect:
args.append([ch[x] for x in f]) args.append([ch[x] for x in f])
if isinstance(args[1], int): # number of anchors if isinstance(args[1], int): # number of anchors
...@@ -281,11 +287,11 @@ def parse_model(d, ch): # model_dict, input_channels(3) ...@@ -281,11 +287,11 @@ def parse_model(d, ch): # model_dict, input_channels(3)
else: else:
c2 = ch[f] c2 = ch[f]
m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module 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 t = str(m)[8:-2].replace('__main__.', '') # module type
np = sum(x.numel() for x in m_.parameters()) # number params 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 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 LOGGER.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n_, np, t, args)) # print
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
layers.append(m_) layers.append(m_)
if i == 0: if i == 0:
...@@ -299,10 +305,10 @@ if __name__ == '__main__': ...@@ -299,10 +305,10 @@ if __name__ == '__main__':
parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml') parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') 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('--profile', action='store_true', help='profile model speed')
parser.add_argument('--test', action='store_true', help='test all yolo*.yaml')
opt = parser.parse_args() opt = parser.parse_args()
opt.cfg = check_yaml(opt.cfg) # check YAML opt.cfg = check_yaml(opt.cfg) # check YAML
print_args(FILE.stem, opt) print_args(FILE.stem, opt)
set_logging()
device = select_device(opt.device) device = select_device(opt.device)
# Create model # Create model
...@@ -314,14 +320,6 @@ if __name__ == '__main__': ...@@ -314,14 +320,6 @@ if __name__ == '__main__':
img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device) img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device)
y = model(img, profile=True) y = model(img, profile=True)
# Test all models
if opt.test:
for cfg in Path(ROOT / 'models').rglob('yolo*.yaml'):
try:
_ = Model(cfg)
except Exception as e:
print(f'Error in {cfg}: {e}')
# Tensorboard (not working https://github.com/ultralytics/yolov5/issues/2898) # Tensorboard (not working https://github.com/ultralytics/yolov5/issues/2898)
# from torch.utils.tensorboard import SummaryWriter # from torch.utils.tensorboard import SummaryWriter
# tb_writer = SummaryWriter('.') # tb_writer = SummaryWriter('.')
......
PyTorch/Compute-Vision/Objection/yolov5/requirements.txt
View file @ 17bc28d5
...@@ -27,7 +27,6 @@ seaborn>=0.11.0 ...@@ -27,7 +27,6 @@ seaborn>=0.11.0
# scikit-learn==0.19.2 # CoreML quantization # scikit-learn==0.19.2 # CoreML quantization
# tensorflow>=2.4.1 # TFLite export # tensorflow>=2.4.1 # TFLite export
# tensorflowjs>=3.9.0 # TF.js export # tensorflowjs>=3.9.0 # TF.js export
# openvino-dev # OpenVINO export
# Extras -------------------------------------- # Extras --------------------------------------
# albumentations>=1.0.3 # albumentations>=1.0.3
......
PyTorch/Compute-Vision/Objection/yolov5/setup.cfg deleted 100644 → 0
View file @ 7143f128
# Project-wide configuration file, can be used for package metadata and other toll configurations
# Example usage: global configuration for PEP8 (via flake8) setting or default pytest arguments
[metadata]
license_file = LICENSE
description-file = README.md
[tool:pytest]
norecursedirs =
.git
dist
build
addopts =
--doctest-modules
--durations=25
--color=yes
[flake8]
max-line-length = 120
exclude = .tox,*.egg,build,temp
select = E,W,F
doctests = True
verbose = 2
# https://pep8.readthedocs.io/en/latest/intro.html#error-codes
format = pylint
# see: https://www.flake8rules.com/
ignore =
E731 # Do not assign a lambda expression, use a def
F405
E402
F841
E741
F821
E722
F401
W504
E127
W504
E231
E501
F403
E302
F541
[isort]
# https://pycqa.github.io/isort/docs/configuration/options.html
line_length = 120
multi_line_output = 0
PyTorch/Compute-Vision/Objection/yolov5/single_process.sh 0 → 100644
View file @ 17bc28d5
#!/bin/bash
export MIOPEN_DEBUG_DISABLE_FIND_DB=1
export NCCL_SOCKET_IFNAME=ib0
export HSA_USERPTR_FOR_PAGED_MEM=0
module rm compiler/dtk/21.10
module load compiler/dtk/22.04.2
lrank=$OMPI_COMM_WORLD_LOCAL_RANK
comm_rank=$OMPI_COMM_WORLD_RANK
comm_size=$OMPI_COMM_WORLD_SIZE
echo $lrank
echo $comm_rank
echo $comm_size
APP="python3 `pwd`/train_multi.py --batch 128 --dist-url tcp://${1}:34567 --dist-backend nccl --world-size=${comm_size} --rank=${comm_rank} --local_rank=${lrank} --data coco.yaml --weight yolov5m.pt --project yolov5m/train --hyp data/hyps/hyp.scratch-high.yaml --cfg yolov5m.yaml --epochs 5000"
case ${lrank} in
[0])
export HIP_VISIBLE_DEVICES=0,1,2,3
export UCX_NET_DEVICES=mlx5_0:1
export UCX_IB_PCI_BW=mlx5_0:50Gbs
echo NCCL_SOCKET_IFNAME=ib0 numactl --cpunodebind=0 --membind=0 ${APP}
NCCL_SOCKET_IFNAME=ib0 numactl --cpunodebind=0 --membind=0 ${APP}
#echo GLOO_SOCKET_IFNAME=ib0 numactl --cpunodebind=0 --membind=0 ${APP}
#GLOO_SOCKET_IFNAME=ib0 numactl --cpunodebind=0 --membind=0 ${APP}
;;
[1])
export HIP_VISIBLE_DEVICES=0,1,2,3
export UCX_NET_DEVICES=mlx5_1:1
export UCX_IB_PCI_BW=mlx5_1:50Gbs
echo NCCL_SOCKET_IFNAME=ib0 numactl --cpunodebind=1 --membind=1 ${APP}
NCCL_SOCKET_IFNAME=ib0 numactl --cpunodebind=1 --membind=1 ${APP}
;;
[2])
export HIP_VISIBLE_DEVICES=0,1,2,3
export UCX_NET_DEVICES=mlx5_2:1
export UCX_IB_PCI_BW=mlx5_2:50Gbs
echo NCCL_SOCKET_IFNAME=ib0 numactl --cpunodebind=2 --membind=2 ${APP}
NCCL_SOCKET_IFNAME=ib0 numactl --cpunodebind=2 --membind=2 ${APP}
;;
[3])
export HIP_VISIBLE_DEVICES=0,1,2,3
export UCX_NET_DEVICES=mlx5_3:1
export UCX_IB_PCI_BW=mlx5_3:50Gbs
echo NCCL_SOCKET_IFNAME=ib0 numactl --cpunodebind=3 --membind=3 ${APP}
NCCL_SOCKET_IFNAME=ib0 numactl --cpunodebind=3 --membind=3 ${APP}
;;
esac
PyTorch/Compute-Vision/Objection/yolov5/train.py
View file @ 17bc28d5
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license # YOLOv5 🚀 by Ultralytics, GPL-3.0 license
""" """
Train a YOLOv5 model on a custom dataset. Train a YOLOv5 model on a custom dataset
Models and datasets download automatically from the latest YOLOv5 release.
Models: https://github.com/ultralytics/yolov5/tree/master/models
Datasets: https://github.com/ultralytics/yolov5/tree/master/data
Tutorial: https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data
Usage: Usage:
$ python path/to/train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (RECOMMENDED) $ python path/to/train.py --data coco128.yaml --weights yolov5s.pt --img 640
$ python path/to/train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640 # from scratch
""" """
import argparse import argparse
import logging
import math import math
import os import os
import random import random
import sys import sys
import time import time
from copy import deepcopy from copy import deepcopy
from datetime import datetime
from pathlib import Path from pathlib import Path
import numpy as np import numpy as np
...@@ -29,7 +23,7 @@ import torch.nn as nn ...@@ -29,7 +23,7 @@ import torch.nn as nn
import yaml import yaml
from torch.cuda import amp from torch.cuda import amp
from torch.nn.parallel import DistributedDataParallel as DDP from torch.nn.parallel import DistributedDataParallel as DDP
from torch.optim import SGD, Adam, AdamW, lr_scheduler from torch.optim import Adam, SGD, lr_scheduler
from tqdm import tqdm from tqdm import tqdm
FILE = Path(__file__).resolve() FILE = Path(__file__).resolve()
...@@ -42,21 +36,21 @@ import val # for end-of-epoch mAP ...@@ -42,21 +36,21 @@ import val # for end-of-epoch mAP
from models.experimental import attempt_load from models.experimental import attempt_load
from models.yolo import Model from models.yolo import Model
from utils.autoanchor import check_anchors from utils.autoanchor import check_anchors
from utils.autobatch import check_train_batch_size
from utils.callbacks import Callbacks
from utils.datasets import create_dataloader from utils.datasets import create_dataloader
from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \
strip_optimizer, get_latest_run, check_dataset, check_git_status, check_img_size, check_requirements, \
check_file, check_yaml, check_suffix, print_args, print_mutation, set_logging, one_cycle, colorstr, methods
from utils.downloads import attempt_download from utils.downloads import attempt_download
from utils.general import (LOGGER, check_dataset, check_file, check_git_status, check_img_size, check_requirements,
check_suffix, check_yaml, colorstr, get_latest_run, increment_path, init_seeds,
intersect_dicts, labels_to_class_weights, labels_to_image_weights, methods, one_cycle,
print_args, print_mutation, strip_optimizer)
from utils.loggers import Loggers
from utils.loggers.wandb.wandb_utils import check_wandb_resume
from utils.loss import ComputeLoss from utils.loss import ComputeLoss
from utils.plots import plot_labels, plot_evolve
from utils.torch_utils import EarlyStopping, ModelEMA, de_parallel, intersect_dicts, select_device, \
torch_distributed_zero_first
from utils.loggers.wandb.wandb_utils import check_wandb_resume
from utils.metrics import fitness from utils.metrics import fitness
from utils.plots import plot_evolve, plot_labels from utils.loggers import Loggers
from utils.torch_utils import EarlyStopping, ModelEMA, de_parallel, select_device, torch_distributed_zero_first from utils.callbacks import Callbacks
LOGGER = logging.getLogger(__name__)
LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html
RANK = int(os.getenv('RANK', -1)) RANK = int(os.getenv('RANK', -1))
WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1)) WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
...@@ -67,7 +61,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary ...@@ -67,7 +61,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
device, device,
callbacks callbacks
): ):
save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = \ save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze, = \
Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \ Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \
opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze
...@@ -83,14 +77,13 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary ...@@ -83,14 +77,13 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items())) LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
# Save run settings # Save run settings
if not evolve: with open(save_dir / 'hyp.yaml', 'w') as f:
with open(save_dir / 'hyp.yaml', 'w') as f: yaml.safe_dump(hyp, f, sort_keys=False)
yaml.safe_dump(hyp, f, sort_keys=False) with open(save_dir / 'opt.yaml', 'w') as f:
with open(save_dir / 'opt.yaml', 'w') as f: yaml.safe_dump(vars(opt), f, sort_keys=False)
yaml.safe_dump(vars(opt), f, sort_keys=False) data_dict = None
# Loggers # Loggers
data_dict = None
if RANK in [-1, 0]: if RANK in [-1, 0]:
loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance
if loggers.wandb: if loggers.wandb:
...@@ -112,7 +105,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary ...@@ -112,7 +105,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
nc = 1 if single_cls else int(data_dict['nc']) # number of classes nc = 1 if single_cls else int(data_dict['nc']) # number of classes
names = ['item'] if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names names = ['item'] if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names
assert len(names) == nc, f'{len(names)} names found for nc={nc} dataset in {data}' # check assert len(names) == nc, f'{len(names)} names found for nc={nc} dataset in {data}' # check
is_coco = isinstance(val_path, str) and val_path.endswith('coco/val2017.txt') # COCO dataset is_coco = data.endswith('coco.yaml') and nc == 80 # COCO dataset
# Model # Model
check_suffix(weights, '.pt') # check weights check_suffix(weights, '.pt') # check weights
...@@ -131,22 +124,13 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary ...@@ -131,22 +124,13 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
# Freeze # Freeze
freeze = [f'model.{x}.' for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze freeze = [f'model.{x}.' for x in range(freeze)] # layers to freeze
for k, v in model.named_parameters(): for k, v in model.named_parameters():
v.requires_grad = True # train all layers v.requires_grad = True # train all layers
if any(x in k for x in freeze): if any(x in k for x in freeze):
LOGGER.info(f'freezing {k}') print(f'freezing {k}')
v.requires_grad = False v.requires_grad = False
# Image size
gs = max(int(model.stride.max()), 32) # grid size (max stride)
imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple
# Batch size
if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size
batch_size = check_train_batch_size(model, imgsz)
loggers.on_params_update({"batch_size": batch_size})
# Optimizer # Optimizer
nbs = 64 # nominal batch size nbs = 64 # nominal batch size
accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing
...@@ -162,10 +146,8 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary ...@@ -162,10 +146,8 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter): # weight (with decay) elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter): # weight (with decay)
g1.append(v.weight) g1.append(v.weight)
if opt.optimizer == 'Adam': if opt.adam:
optimizer = Adam(g0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum optimizer = Adam(g0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum
elif opt.optimizer == 'AdamW':
optimizer = AdamW(g0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum
else: else:
optimizer = SGD(g0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True) optimizer = SGD(g0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
...@@ -208,10 +190,15 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary ...@@ -208,10 +190,15 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
del ckpt, csd del ckpt, csd
# Image sizes
gs = max(int(model.stride.max()), 32) # grid size (max stride)
nl = model.model[-1].nl # number of detection layers (used for scaling hyp['obj'])
imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple
# DP mode # DP mode
if cuda and RANK == -1 and torch.cuda.device_count() > 1: if cuda and RANK == -1 and torch.cuda.device_count() > 1:
LOGGER.warning('WARNING: DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n' logging.warning('DP not recommended, instead use torch.distributed.run for best DDP Multi-GPU results.\n'
'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.') 'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.')
model = torch.nn.DataParallel(model) model = torch.nn.DataParallel(model)
# SyncBatchNorm # SyncBatchNorm
...@@ -223,7 +210,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary ...@@ -223,7 +210,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
train_loader, dataset = create_dataloader(train_path, imgsz, batch_size // WORLD_SIZE, gs, single_cls, train_loader, dataset = create_dataloader(train_path, imgsz, batch_size // WORLD_SIZE, gs, single_cls,
hyp=hyp, augment=True, cache=opt.cache, rect=opt.rect, rank=LOCAL_RANK, hyp=hyp, augment=True, cache=opt.cache, rect=opt.rect, rank=LOCAL_RANK,
workers=workers, image_weights=opt.image_weights, quad=opt.quad, workers=workers, image_weights=opt.image_weights, quad=opt.quad,
prefix=colorstr('train: '), shuffle=True) prefix=colorstr('train: '))
mlc = int(np.concatenate(dataset.labels, 0)[:, 0].max()) # max label class mlc = int(np.concatenate(dataset.labels, 0)[:, 0].max()) # max label class
nb = len(train_loader) # number of batches nb = len(train_loader) # number of batches
assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}' assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}'
...@@ -254,11 +241,10 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary ...@@ -254,11 +241,10 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
if cuda and RANK != -1: if cuda and RANK != -1:
model = DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK) model = DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
# Model attributes # Model parameters
nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps) hyp['box'] *= 3. / nl # scale to layers
hyp['box'] *= 3 / nl # scale to layers hyp['cls'] *= nc / 80. * 3. / nl # scale to classes and layers
hyp['cls'] *= nc / 80 * 3 / nl # scale to classes and layers hyp['obj'] *= (imgsz / 640) ** 2 * 3. / nl # scale to image size and layers
hyp['obj'] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers
hyp['label_smoothing'] = opt.label_smoothing hyp['label_smoothing'] = opt.label_smoothing
model.nc = nc # attach number of classes to model model.nc = nc # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model model.hyp = hyp # attach hyperparameters to model
...@@ -277,7 +263,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary ...@@ -277,7 +263,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
stopper = EarlyStopping(patience=opt.patience) stopper = EarlyStopping(patience=opt.patience)
compute_loss = ComputeLoss(model) # init loss class compute_loss = ComputeLoss(model) # init loss class
LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n' LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n'
f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n' f'Using {train_loader.num_workers} dataloader workers\n'
f"Logging results to {colorstr('bold', save_dir)}\n" f"Logging results to {colorstr('bold', save_dir)}\n"
f'Starting training for {epochs} epochs...') f'Starting training for {epochs} epochs...')
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
...@@ -299,11 +285,11 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary ...@@ -299,11 +285,11 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
pbar = enumerate(train_loader) pbar = enumerate(train_loader)
LOGGER.info(('\n' + '%10s' * 7) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'labels', 'img_size')) LOGGER.info(('\n' + '%10s' * 7) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'labels', 'img_size'))
if RANK in [-1, 0]: if RANK in [-1, 0]:
pbar = tqdm(pbar, total=nb, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar pbar = tqdm(pbar, total=nb) # progress bar
optimizer.zero_grad() optimizer.zero_grad()
for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start) ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0 imgs = imgs.to(device, non_blocking=True).float() / 255.0 # uint8 to float32, 0-255 to 0.0-1.0
# Warmup # Warmup
if ni <= nw: if ni <= nw:
...@@ -390,8 +376,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary ...@@ -390,8 +376,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
'ema': deepcopy(ema.ema).half(), 'ema': deepcopy(ema.ema).half(),
'updates': ema.updates, 'updates': ema.updates,
'optimizer': optimizer.state_dict(), 'optimizer': optimizer.state_dict(),
'wandb_id': loggers.wandb.wandb_run.id if loggers.wandb else None, 'wandb_id': loggers.wandb.wandb_run.id if loggers.wandb else None}
'date': datetime.now().isoformat()}
# Save last, best and delete # Save last, best and delete
torch.save(ckpt, last) torch.save(ckpt, last)
...@@ -438,10 +423,8 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary ...@@ -438,10 +423,8 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
plots=True, plots=True,
callbacks=callbacks, callbacks=callbacks,
compute_loss=compute_loss) # val best model with plots compute_loss=compute_loss) # val best model with plots
if is_coco:
callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi)
callbacks.run('on_train_end', last, best, plots, epoch, results) callbacks.run('on_train_end', last, best, plots, epoch)
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}") LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
torch.cuda.empty_cache() torch.cuda.empty_cache()
...@@ -455,13 +438,13 @@ def parse_opt(known=False): ...@@ -455,13 +438,13 @@ def parse_opt(known=False):
parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch.yaml', help='hyperparameters path') parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch.yaml', help='hyperparameters path')
parser.add_argument('--epochs', type=int, default=300) parser.add_argument('--epochs', type=int, default=300)
parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch') parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs')
parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)') parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)')
parser.add_argument('--rect', action='store_true', help='rectangular training') parser.add_argument('--rect', action='store_true', help='rectangular training')
parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
parser.add_argument('--noval', action='store_true', help='only validate final epoch') parser.add_argument('--noval', action='store_true', help='only validate final epoch')
parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor') parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations') parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')
parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"') parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"')
...@@ -469,9 +452,9 @@ def parse_opt(known=False): ...@@ -469,9 +452,9 @@ def parse_opt(known=False):
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')
parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer') parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer')
parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers')
parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name') parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name')
parser.add_argument('--name', default='exp', help='save to project/name') parser.add_argument('--name', default='exp', help='save to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
...@@ -479,13 +462,13 @@ def parse_opt(known=False): ...@@ -479,13 +462,13 @@ def parse_opt(known=False):
parser.add_argument('--linear-lr', action='store_true', help='linear LR') parser.add_argument('--linear-lr', action='store_true', help='linear LR')
parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')
parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)') parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)')
parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2') parser.add_argument('--freeze', type=int, default=0, help='Number of layers to freeze. backbone=10, all=24')
parser.add_argument('--save-period', type=int, default=1, help='Save checkpoint every x epochs (disabled if < 1)') parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)')
parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify') parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
# Weights & Biases arguments # Weights & Biases arguments
parser.add_argument('--entity', default=None, help='W&B: Entity') parser.add_argument('--entity', default=None, help='W&B: Entity')
parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='W&B: Upload data, "val" option') parser.add_argument('--upload_dataset', action='store_true', help='W&B: Upload dataset as artifact table')
parser.add_argument('--bbox_interval', type=int, default=-1, help='W&B: Set bounding-box image logging interval') parser.add_argument('--bbox_interval', type=int, default=-1, help='W&B: Set bounding-box image logging interval')
parser.add_argument('--artifact_alias', type=str, default='latest', help='W&B: Version of dataset artifact to use') parser.add_argument('--artifact_alias', type=str, default='latest', help='W&B: Version of dataset artifact to use')
...@@ -495,6 +478,7 @@ def parse_opt(known=False): ...@@ -495,6 +478,7 @@ def parse_opt(known=False):
def main(opt, callbacks=Callbacks()): def main(opt, callbacks=Callbacks()):
# Checks # Checks
set_logging(RANK)
if RANK in [-1, 0]: if RANK in [-1, 0]:
print_args(FILE.stem, opt) print_args(FILE.stem, opt)
check_git_status() check_git_status()
...@@ -618,9 +602,9 @@ def main(opt, callbacks=Callbacks()): ...@@ -618,9 +602,9 @@ def main(opt, callbacks=Callbacks()):
# Plot results # Plot results
plot_evolve(evolve_csv) plot_evolve(evolve_csv)
LOGGER.info(f'Hyperparameter evolution finished\n' print(f'Hyperparameter evolution finished\n'
f"Results saved to {colorstr('bold', save_dir)}\n" f"Results saved to {colorstr('bold', save_dir)}\n"
f'Use best hyperparameters example: $ python train.py --hyp {evolve_yaml}') f'Use best hyperparameters example: $ python train.py --hyp {evolve_yaml}')
def run(**kwargs): def run(**kwargs):
......
PyTorch/Compute-Vision/Objection/yolov5/train_multi.py 0 → 100644
View file @ 17bc28d5
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