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`val.py` refactor (#4053) 

* val.py refactor

* cleanup

* cleanup

* cleanup

* cleanup

* save after eval

* opt.imgsz bug fix

* wandb refactor

* dataloader to train_loader

* capitalize global variables

* runs/hub/exp to runs/detect/exp

* refactor wandb logging

* Refactor wandb operations (#4061)

Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
modifyDataloader
Glenn Jocher GitHub 3 년 전
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8개의 변경된 파일과 220개의 추가작업 그리고 203개의 파일을 삭제
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  1. +3
    -3
      detect.py
  2. +19
    -14
      models/common.py
  3. +21
    -22
      models/yolo.py
  4. +32
    -35
      train.py
  5. +17
    -18
      utils/datasets.py
  6. +7
    -7
      utils/torch_utils.py
  7. +46
    -19
      utils/wandb_logging/wandb_utils.py
  8. +75
    -85
      val.py

+ 3
- 3
detect.py 파일 보기

@@ -21,7 +21,7 @@ from utils.datasets import LoadStreams, LoadImages
from utils.general import check_img_size, check_requirements, check_imshow, colorstr, non_max_suppression, \
apply_classifier, scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path, save_one_box
from utils.plots import colors, plot_one_box
from utils.torch_utils import select_device, load_classifier, time_synchronized
from utils.torch_utils import select_device, load_classifier, time_sync


@torch.no_grad()
@@ -100,14 +100,14 @@ def run(weights='yolov5s.pt', # model.pt path(s)
img = img.unsqueeze(0)

# Inference
t1 = time_synchronized()
t1 = time_sync()
pred = model(img,
augment=augment,
visualize=increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False)[0]

# Apply NMS
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
t2 = time_synchronized()
t2 = time_sync()

# Apply Classifier
if classify:

+ 19
- 14
models/common.py 파일 보기

@@ -1,5 +1,6 @@
# YOLOv5 common modules

import logging
from copy import copy
from pathlib import Path, PosixPath

@@ -15,7 +16,9 @@ from torch.cuda import amp
from utils.datasets import exif_transpose, letterbox
from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh, save_one_box
from utils.plots import colors, plot_one_box
from utils.torch_utils import time_synchronized
from utils.torch_utils import time_sync

LOGGER = logging.getLogger(__name__)


def autopad(k, p=None): # kernel, padding
@@ -226,7 +229,7 @@ class AutoShape(nn.Module):
self.model = model.eval()

def autoshape(self):
print('AutoShape already enabled, skipping... ') # model already converted to model.autoshape()
LOGGER.info('AutoShape already enabled, skipping... ') # model already converted to model.autoshape()
return self

@torch.no_grad()
@@ -240,7 +243,7 @@ class AutoShape(nn.Module):
# torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values)
# multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images

t = [time_synchronized()]
t = [time_sync()]
p = next(self.model.parameters()) # for device and type
if isinstance(imgs, torch.Tensor): # torch
with amp.autocast(enabled=p.device.type != 'cpu'):
@@ -270,19 +273,19 @@ class AutoShape(nn.Module):
x = np.stack(x, 0) if n > 1 else x[0][None] # stack
x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW
x = torch.from_numpy(x).to(p.device).type_as(p) / 255. # uint8 to fp16/32
t.append(time_synchronized())
t.append(time_sync())

with amp.autocast(enabled=p.device.type != 'cpu'):
# Inference
y = self.model(x, augment, profile)[0] # forward
t.append(time_synchronized())
t.append(time_sync())

# Post-process
y = non_max_suppression(y, self.conf, iou_thres=self.iou, classes=self.classes, max_det=self.max_det) # NMS
for i in range(n):
scale_coords(shape1, y[i][:, :4], shape0[i])

t.append(time_synchronized())
t.append(time_sync())
return Detections(imgs, y, files, t, self.names, x.shape)


@@ -323,31 +326,33 @@ class Detections:

im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np
if pprint:
print(str.rstrip(', '))
LOGGER.info(str.rstrip(', '))
if show:
im.show(self.files[i]) # show
if save:
f = self.files[i]
im.save(save_dir / f) # save
print(f"{'Saved' * (i == 0)} {f}", end=',' if i < self.n - 1 else f' to {save_dir}\n')
if i == self.n - 1:
LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to '{save_dir}'")
if render:
self.imgs[i] = np.asarray(im)

def print(self):
self.display(pprint=True) # print results
print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % self.t)
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' %
self.t)

def show(self):
self.display(show=True) # show results

def save(self, save_dir='runs/hub/exp'):
save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp', mkdir=True) # increment save_dir
def save(self, save_dir='runs/detect/exp'):
save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) # increment save_dir
self.display(save=True, save_dir=save_dir) # save results

def crop(self, save_dir='runs/hub/exp'):
save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp', mkdir=True) # increment save_dir
def crop(self, save_dir='runs/detect/exp'):
save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) # increment save_dir
self.display(crop=True, save_dir=save_dir) # crop results
print(f'Saved results to {save_dir}\n')
LOGGER.info(f'Saved results to {save_dir}\n')

def render(self):
self.display(render=True) # render results

+ 21
- 22
models/yolo.py 파일 보기

@@ -5,7 +5,6 @@ Usage:
"""

import argparse
import logging
import sys
from copy import deepcopy
from pathlib import Path
@@ -18,7 +17,7 @@ from models.experimental import *
from utils.autoanchor import check_anchor_order
from utils.general import make_divisible, check_file, set_logging
from utils.plots import feature_visualization
from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \
from utils.torch_utils import time_sync, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \
select_device, copy_attr

try:
@@ -26,7 +25,7 @@ try:
except ImportError:
thop = None

logger = logging.getLogger(__name__)
LOGGER = logging.getLogger(__name__)


class Detect(nn.Module):
@@ -90,15 +89,15 @@ class Model(nn.Module):
# 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}")
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}')
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)
# logger.info([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
# LOGGER.info([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])

# Build strides, anchors
m = self.model[-1] # Detect()
@@ -110,12 +109,12 @@ class Model(nn.Module):
check_anchor_order(m)
self.stride = m.stride
self._initialize_biases() # only run once
# logger.info('Strides: %s' % m.stride.tolist())
# LOGGER.info('Strides: %s' % m.stride.tolist())

# Init weights, biases
initialize_weights(self)
self.info()
logger.info('')
LOGGER.info('')

def forward(self, x, augment=False, profile=False, visualize=False):
if augment:
@@ -143,13 +142,13 @@ class Model(nn.Module):

if profile:
o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs
t = time_synchronized()
t = time_sync()
for _ in range(10):
_ = m(x)
dt.append((time_synchronized() - t) * 100)
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}')
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}')

x = m(x) # run
y.append(x if m.i in self.save else None) # save output
@@ -158,7 +157,7 @@ class Model(nn.Module):
feature_visualization(x, m.type, m.i, save_dir=visualize)

if profile:
logger.info('%.1fms total' % sum(dt))
LOGGER.info('%.1fms total' % sum(dt))
return x

def _descale_pred(self, p, flips, scale, img_size):
@@ -192,16 +191,16 @@ class Model(nn.Module):
m = self.model[-1] # Detect() module
for mi in m.m: # from
b = mi.bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85)
logger.info(
LOGGER.info(
('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))

# def _print_weights(self):
# for m in self.model.modules():
# if type(m) is Bottleneck:
# logger.info('%10.3g' % (m.w.detach().sigmoid() * 2)) # shortcut weights
# LOGGER.info('%10.3g' % (m.w.detach().sigmoid() * 2)) # shortcut weights

def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
logger.info('Fusing layers... ')
LOGGER.info('Fusing layers... ')
for m in self.model.modules():
if type(m) is Conv and hasattr(m, 'bn'):
m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
@@ -213,19 +212,19 @@ class Model(nn.Module):
def nms(self, mode=True): # add or remove NMS module
present = type(self.model[-1]) is NMS # last layer is NMS
if mode and not present:
logger.info('Adding NMS... ')
LOGGER.info('Adding NMS... ')
m = NMS() # module
m.f = -1 # from
m.i = self.model[-1].i + 1 # index
self.model.add_module(name='%s' % m.i, module=m) # add
self.eval()
elif not mode and present:
logger.info('Removing NMS... ')
LOGGER.info('Removing NMS... ')
self.model = self.model[:-1] # remove
return self

def autoshape(self): # add AutoShape module
logger.info('Adding AutoShape... ')
LOGGER.info('Adding AutoShape... ')
m = AutoShape(self) # wrap model
copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=()) # copy attributes
return m
@@ -235,7 +234,7 @@ class Model(nn.Module):


def parse_model(d, ch): # model_dict, input_channels(3)
logger.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
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']
na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
@@ -279,7 +278,7 @@ def parse_model(d, ch): # model_dict, input_channels(3)
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('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # 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
layers.append(m_)
if i == 0:
@@ -308,5 +307,5 @@ if __name__ == '__main__':
# Tensorboard (not working https://github.com/ultralytics/yolov5/issues/2898)
# from torch.utils.tensorboard import SummaryWriter
# tb_writer = SummaryWriter('.')
# logger.info("Run 'tensorboard --logdir=models' to view tensorboard at http://localhost:6006/")
# LOGGER.info("Run 'tensorboard --logdir=models' to view tensorboard at http://localhost:6006/")
# tb_writer.add_graph(torch.jit.trace(model, img, strict=False), []) # add model graph

+ 32
- 35
train.py 파일 보기

@@ -47,7 +47,7 @@ from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_di
from utils.wandb_logging.wandb_utils import WandbLogger, check_wandb_resume
from utils.metrics import fitness

logger = logging.getLogger(__name__)
LOGGER = logging.getLogger(__name__)
LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html
RANK = int(os.getenv('RANK', -1))
WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
@@ -73,7 +73,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
if isinstance(hyp, str):
with open(hyp) as f:
hyp = yaml.safe_load(f) # load hyps dict
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
with open(save_dir / 'hyp.yaml', 'w') as f:
@@ -94,7 +94,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
# TensorBoard
if not evolve:
prefix = colorstr('tensorboard: ')
logger.info(f"{prefix}Start with 'tensorboard --logdir {opt.project}', view at http://localhost:6006/")
LOGGER.info(f"{prefix}Start with 'tensorboard --logdir {opt.project}', view at http://localhost:6006/")
loggers['tb'] = SummaryWriter(str(save_dir))

# W&B
@@ -123,7 +123,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
state_dict = ckpt['model'].float().state_dict() # to FP32
state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect
model.load_state_dict(state_dict, strict=False) # load
logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report
LOGGER.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report
else:
model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
with torch_distributed_zero_first(RANK):
@@ -143,7 +143,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
nbs = 64 # nominal batch size
accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing
hyp['weight_decay'] *= batch_size * accumulate / nbs # scale weight_decay
logger.info(f"Scaled weight_decay = {hyp['weight_decay']}")
LOGGER.info(f"Scaled weight_decay = {hyp['weight_decay']}")

pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in model.named_modules():
@@ -161,7 +161,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary

optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
LOGGER.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
del pg0, pg1, pg2

# Scheduler https://arxiv.org/pdf/1812.01187.pdf
@@ -198,7 +198,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
if resume:
assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs)
if epochs < start_epoch:
logger.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' %
LOGGER.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' %
(weights, ckpt['epoch'], epochs))
epochs += ckpt['epoch'] # finetune additional epochs

@@ -207,7 +207,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
# 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, imgsz_val = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples
imgsz = check_img_size(opt.imgsz, gs) # verify imgsz is gs-multiple

# DP mode
if cuda and RANK == -1 and torch.cuda.device_count() > 1:
@@ -219,33 +219,31 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
if opt.sync_bn and cuda and RANK != -1:
raise Exception('can not train with --sync-bn, known issue https://github.com/ultralytics/yolov5/issues/3998')
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
logger.info('Using SyncBatchNorm()')
LOGGER.info('Using SyncBatchNorm()')

# Trainloader
dataloader, dataset = create_dataloader(train_path, imgsz, batch_size // WORLD_SIZE, gs, single_cls,
hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=RANK,
workers=workers,
image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr('train: '))
train_loader, dataset = create_dataloader(train_path, imgsz, batch_size // WORLD_SIZE, gs, single_cls,
hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=RANK,
workers=workers, image_weights=opt.image_weights, quad=opt.quad,
prefix=colorstr('train: '))
mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
nb = len(dataloader) # number of batches
nb = len(train_loader) # number of batches
assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, data, nc - 1)

# Process 0
if RANK in [-1, 0]:
valloader = create_dataloader(val_path, imgsz_val, batch_size // WORLD_SIZE * 2, gs, single_cls,
hyp=hyp, cache=opt.cache_images and not noval, rect=True, rank=-1,
workers=workers,
pad=0.5, prefix=colorstr('val: '))[0]
val_loader = create_dataloader(val_path, imgsz, batch_size // WORLD_SIZE * 2, gs, single_cls,
hyp=hyp, cache=opt.cache_images and not noval, rect=True, rank=-1,
workers=workers, pad=0.5,
prefix=colorstr('val: '))[0]

if not resume:
labels = np.concatenate(dataset.labels, 0)
c = torch.tensor(labels[:, 0]) # classes
# c = torch.tensor(labels[:, 0]) # classes
# cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency
# model._initialize_biases(cf.to(device))
if plots:
plot_labels(labels, names, save_dir, loggers)
if loggers['tb']:
loggers['tb'].add_histogram('classes', c, 0) # TensorBoard

# Anchors
if not opt.noautoanchor:
@@ -277,8 +275,8 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
scheduler.last_epoch = start_epoch - 1 # do not move
scaler = amp.GradScaler(enabled=cuda)
compute_loss = ComputeLoss(model) # init loss class
logger.info(f'Image sizes {imgsz} train, {imgsz_val} val\n'
f'Using {dataloader.num_workers} dataloader workers\n'
LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n'
f'Using {train_loader.num_workers} dataloader workers\n'
f'Logging results to {save_dir}\n'
f'Starting training for {epochs} epochs...')
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
@@ -304,9 +302,9 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary

mloss = torch.zeros(4, device=device) # mean losses
if RANK != -1:
dataloader.sampler.set_epoch(epoch)
pbar = enumerate(dataloader)
logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'labels', 'img_size'))
train_loader.sampler.set_epoch(epoch)
pbar = enumerate(train_loader)
LOGGER.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'labels', 'img_size'))
if RANK in [-1, 0]:
pbar = tqdm(pbar, total=nb) # progress bar
optimizer.zero_grad()
@@ -389,10 +387,10 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
wandb_logger.current_epoch = epoch + 1
results, maps, _ = val.run(data_dict,
batch_size=batch_size // WORLD_SIZE * 2,
imgsz=imgsz_val,
imgsz=imgsz,
model=ema.ema,
single_cls=single_cls,
dataloader=valloader,
dataloader=val_loader,
save_dir=save_dir,
save_json=is_coco and final_epoch,
verbose=nc < 50 and final_epoch,
@@ -444,7 +442,7 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
# end epoch ----------------------------------------------------------------------------------------------------
# end training -----------------------------------------------------------------------------------------------------
if RANK in [-1, 0]:
logger.info(f'{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.\n')
LOGGER.info(f'{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.\n')
if plots:
plot_results(save_dir=save_dir) # save as results.png
if loggers['wandb']:
@@ -457,10 +455,10 @@ def train(hyp, # path/to/hyp.yaml or hyp dictionary
for m in [last, best] if best.exists() else [last]: # speed, mAP tests
results, _, _ = val.run(data_dict,
batch_size=batch_size // WORLD_SIZE * 2,
imgsz=imgsz_val,
imgsz=imgsz,
model=attempt_load(m, device).half(),
single_cls=single_cls,
dataloader=valloader,
dataloader=val_loader,
save_dir=save_dir,
save_json=True,
plots=False)
@@ -487,7 +485,7 @@ def parse_opt(known=False):
parser.add_argument('--hyp', type=str, default='data/hyps/hyp.scratch.yaml', help='hyperparameters path')
parser.add_argument('--epochs', type=int, default=300)
parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs')
parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, val] image sizes')
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('--resume', nargs='?', const=True, default=False, help='resume most recent training')
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
@@ -534,12 +532,11 @@ def main(opt):
with open(Path(ckpt).parent.parent / 'opt.yaml') as f:
opt = argparse.Namespace(**yaml.safe_load(f)) # replace
opt.cfg, opt.weights, opt.resume = '', ckpt, True # reinstate
logger.info('Resuming training from %s' % ckpt)
LOGGER.info(f'Resuming training from {ckpt}')
else:
# opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml')
opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_file(opt.cfg), check_file(opt.hyp) # check files
assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, val)
opt.name = 'evolve' if opt.evolve else opt.name
opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve))

@@ -602,7 +599,7 @@ def main(opt):
# ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices
yaml_file = Path(opt.save_dir) / 'hyp_evolved.yaml' # save best result here
if opt.bucket:
os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists
os.system(f'gsutil cp gs://{opt.bucket}/evolve.txt .') # download evolve.txt if exists

for _ in range(opt.evolve): # generations to evolve
if Path('evolve.txt').exists(): # if evolve.txt exists: select best hyps and mutate

+ 17
- 18
utils/datasets.py 파일 보기

@@ -22,17 +22,16 @@ from PIL import Image, ExifTags
from torch.utils.data import Dataset
from tqdm import tqdm

from utils.augmentations import Albumentations, augment_hsv, copy_paste, letterbox, mixup, random_perspective, cutout
from utils.augmentations import Albumentations, augment_hsv, copy_paste, letterbox, mixup, random_perspective
from utils.general import check_requirements, check_file, check_dataset, xywh2xyxy, xywhn2xyxy, xyxy2xywhn, \
xyn2xy, segments2boxes, clean_str
from utils.torch_utils import torch_distributed_zero_first

# Parameters
help_url = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'
img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng', 'webp', 'mpo'] # acceptable image suffixes
vid_formats = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv'] # acceptable video suffixes
num_threads = min(8, os.cpu_count()) # number of multiprocessing threads
logger = logging.getLogger(__name__)
HELP_URL = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'
IMG_FORMATS = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng', 'webp', 'mpo'] # acceptable image suffixes
VID_FORMATS = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv'] # acceptable video suffixes
NUM_THREADS = min(8, os.cpu_count()) # number of multiprocessing threads

# Get orientation exif tag
for orientation in ExifTags.TAGS.keys():
@@ -164,8 +163,8 @@ class LoadImages: # for inference
else:
raise Exception(f'ERROR: {p} does not exist')

images = [x for x in files if x.split('.')[-1].lower() in img_formats]
videos = [x for x in files if x.split('.')[-1].lower() in vid_formats]
images = [x for x in files if x.split('.')[-1].lower() in IMG_FORMATS]
videos = [x for x in files if x.split('.')[-1].lower() in VID_FORMATS]
ni, nv = len(images), len(videos)

self.img_size = img_size
@@ -179,7 +178,7 @@ class LoadImages: # for inference
else:
self.cap = None
assert self.nf > 0, f'No images or videos found in {p}. ' \
f'Supported formats are:\nimages: {img_formats}\nvideos: {vid_formats}'
f'Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}'

def __iter__(self):
self.count = 0
@@ -389,11 +388,11 @@ class LoadImagesAndLabels(Dataset): # for training/testing
# f += [p.parent / x.lstrip(os.sep) for x in t] # local to global path (pathlib)
else:
raise Exception(f'{prefix}{p} does not exist')
self.img_files = sorted([x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in img_formats])
self.img_files = sorted([x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in IMG_FORMATS])
# self.img_files = sorted([x for x in f if x.suffix[1:].lower() in img_formats]) # pathlib
assert self.img_files, f'{prefix}No images found'
except Exception as e:
raise Exception(f'{prefix}Error loading data from {path}: {e}\nSee {help_url}')
raise Exception(f'{prefix}Error loading data from {path}: {e}\nSee {HELP_URL}')

# Check cache
self.label_files = img2label_paths(self.img_files) # labels
@@ -411,7 +410,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
tqdm(None, desc=prefix + d, total=n, initial=n) # display cache results
if cache['msgs']:
logging.info('\n'.join(cache['msgs'])) # display warnings
assert nf > 0 or not augment, f'{prefix}No labels in {cache_path}. Can not train without labels. See {help_url}'
assert nf > 0 or not augment, f'{prefix}No labels in {cache_path}. Can not train without labels. See {HELP_URL}'

# Read cache
[cache.pop(k) for k in ('hash', 'version', 'msgs')] # remove items
@@ -460,7 +459,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
if cache_images:
gb = 0 # Gigabytes of cached images
self.img_hw0, self.img_hw = [None] * n, [None] * n
results = ThreadPool(num_threads).imap(lambda x: load_image(*x), zip(repeat(self), range(n)))
results = ThreadPool(NUM_THREADS).imap(lambda x: load_image(*x), zip(repeat(self), range(n)))
pbar = tqdm(enumerate(results), total=n)
for i, x in pbar:
self.imgs[i], self.img_hw0[i], self.img_hw[i] = x # img, hw_original, hw_resized = load_image(self, i)
@@ -473,7 +472,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
x = {} # dict
nm, nf, ne, nc, msgs = 0, 0, 0, 0, [] # number missing, found, empty, corrupt, messages
desc = f"{prefix}Scanning '{path.parent / path.stem}' images and labels..."
with Pool(num_threads) as pool:
with Pool(NUM_THREADS) as pool:
pbar = tqdm(pool.imap_unordered(verify_image_label, zip(self.img_files, self.label_files, repeat(prefix))),
desc=desc, total=len(self.img_files))
for im_file, l, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar:
@@ -491,7 +490,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
if msgs:
logging.info('\n'.join(msgs))
if nf == 0:
logging.info(f'{prefix}WARNING: No labels found in {path}. See {help_url}')
logging.info(f'{prefix}WARNING: No labels found in {path}. See {HELP_URL}')
x['hash'] = get_hash(self.label_files + self.img_files)
x['results'] = nf, nm, ne, nc, len(self.img_files)
x['msgs'] = msgs # warnings
@@ -789,7 +788,7 @@ def extract_boxes(path='../datasets/coco128'): # from utils.datasets import *;
files = list(path.rglob('*.*'))
n = len(files) # number of files
for im_file in tqdm(files, total=n):
if im_file.suffix[1:] in img_formats:
if im_file.suffix[1:] in IMG_FORMATS:
# image
im = cv2.imread(str(im_file))[..., ::-1] # BGR to RGB
h, w = im.shape[:2]
@@ -825,7 +824,7 @@ def autosplit(path='../datasets/coco128/images', weights=(0.9, 0.1, 0.0), annota
annotated_only: Only use images with an annotated txt file
"""
path = Path(path) # images dir
files = sum([list(path.rglob(f"*.{img_ext}")) for img_ext in img_formats], []) # image files only
files = sum([list(path.rglob(f"*.{img_ext}")) for img_ext in IMG_FORMATS], []) # image files only
n = len(files) # number of files
random.seed(0) # for reproducibility
indices = random.choices([0, 1, 2], weights=weights, k=n) # assign each image to a split
@@ -850,7 +849,7 @@ def verify_image_label(args):
im.verify() # PIL verify
shape = exif_size(im) # image size
assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels'
assert im.format.lower() in img_formats, f'invalid image format {im.format}'
assert im.format.lower() in IMG_FORMATS, f'invalid image format {im.format}'
if im.format.lower() in ('jpg', 'jpeg'):
with open(im_file, 'rb') as f:
f.seek(-2, 2)

+ 7
- 7
utils/torch_utils.py 파일 보기

@@ -22,7 +22,7 @@ try:
import thop # for FLOPs computation
except ImportError:
thop = None
logger = logging.getLogger(__name__)
LOGGER = logging.getLogger(__name__)


@contextmanager
@@ -85,11 +85,11 @@ def select_device(device='', batch_size=None):
else:
s += 'CPU\n'

logger.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s) # emoji-safe
LOGGER.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s) # emoji-safe
return torch.device('cuda:0' if cuda else 'cpu')


def time_synchronized():
def time_sync():
# pytorch-accurate time
if torch.cuda.is_available():
torch.cuda.synchronize()
@@ -118,12 +118,12 @@ def profile(x, ops, n=100, device=None):
flops = 0

for _ in range(n):
t[0] = time_synchronized()
t[0] = time_sync()
y = m(x)
t[1] = time_synchronized()
t[1] = time_sync()
try:
_ = y.sum().backward()
t[2] = time_synchronized()
t[2] = time_sync()
except: # no backward method
t[2] = float('nan')
dtf += (t[1] - t[0]) * 1000 / n # ms per op forward
@@ -231,7 +231,7 @@ def model_info(model, verbose=False, img_size=640):
except (ImportError, Exception):
fs = ''

logger.info(f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")
LOGGER.info(f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")


def load_classifier(name='resnet101', n=2):

+ 46
- 19
utils/wandb_logging/wandb_utils.py 파일 보기

@@ -98,7 +98,14 @@ class WandbLogger():
def __init__(self, opt, name, run_id, data_dict, job_type='Training'):
# Pre-training routine --
self.job_type = job_type
self.wandb, self.wandb_run, self.data_dict = wandb, None if not wandb else wandb.run, data_dict
self.wandb, self.wandb_run = wandb, None if not wandb else wandb.run
self.val_artifact, self.train_artifact = None, None
self.train_artifact_path, self.val_artifact_path = None, None
self.result_artifact = None
self.val_table, self.result_table = None, None
self.data_dict = data_dict
self.bbox_media_panel_images = []
self.val_table_path_map = None
# It's more elegant to stick to 1 wandb.init call, but useful config data is overwritten in the WandbLogger's wandb.init call
if isinstance(opt.resume, str): # checks resume from artifact
if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
@@ -156,25 +163,27 @@ class WandbLogger():
self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs, \
config.opt['hyp']
data_dict = dict(self.wandb_run.config.data_dict) # eliminates the need for config file to resume
if 'val_artifact' not in self.__dict__: # If --upload_dataset is set, use the existing artifact, don't download
if self.val_artifact is None: # If --upload_dataset is set, use the existing artifact, don't download
self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(data_dict.get('train'),
opt.artifact_alias)
self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(data_dict.get('val'),
opt.artifact_alias)
self.result_artifact, self.result_table, self.val_table, self.weights = None, None, None, None
if self.train_artifact_path is not None:
train_path = Path(self.train_artifact_path) / 'data/images/'
data_dict['train'] = str(train_path)
if self.val_artifact_path is not None:
val_path = Path(self.val_artifact_path) / 'data/images/'
data_dict['val'] = str(val_path)
self.val_table = self.val_artifact.get("val")
self.map_val_table_path()
wandb.log({"validation dataset": self.val_table})
if self.train_artifact_path is not None:
train_path = Path(self.train_artifact_path) / 'data/images/'
data_dict['train'] = str(train_path)
if self.val_artifact_path is not None:
val_path = Path(self.val_artifact_path) / 'data/images/'
data_dict['val'] = str(val_path)


if self.val_artifact is not None:
self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
self.result_table = wandb.Table(["epoch", "id", "ground truth", "prediction", "avg_confidence"])
self.val_table = self.val_artifact.get("val")
if self.val_table_path_map is None:
self.map_val_table_path()
wandb.log({"validation dataset": self.val_table})
if opt.bbox_interval == -1:
self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1
return data_dict
@@ -182,7 +191,7 @@ class WandbLogger():
def download_dataset_artifact(self, path, alias):
if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX):
artifact_path = Path(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias)
dataset_artifact = wandb.use_artifact(artifact_path.as_posix().replace("\\","/"))
dataset_artifact = wandb.use_artifact(artifact_path.as_posix().replace("\\", "/"))
assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'"
datadir = dataset_artifact.download()
return datadir, dataset_artifact
@@ -246,10 +255,10 @@ class WandbLogger():
return path

def map_val_table_path(self):
self.val_table_map = {}
self.val_table_path_map = {}
print("Mapping dataset")
for i, data in enumerate(tqdm(self.val_table.data)):
self.val_table_map[data[3]] = data[0]
self.val_table_path_map[data[3]] = data[0]

def create_dataset_table(self, dataset, class_to_id, name='dataset'):
# TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging
@@ -283,7 +292,6 @@ class WandbLogger():
return artifact

def log_training_progress(self, predn, path, names):
if self.val_table and self.result_table:
class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()])
box_data = []
total_conf = 0
@@ -297,7 +305,7 @@ class WandbLogger():
"domain": "pixel"})
total_conf = total_conf + conf
boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
id = self.val_table_map[Path(path).name]
id = self.val_table_path_map[Path(path).name]
self.result_table.add_data(self.current_epoch,
id,
self.val_table.data[id][1],
@@ -305,6 +313,22 @@ class WandbLogger():
total_conf / max(1, len(box_data))
)

def val_one_image(self, pred, predn, path, names, im):
if self.val_table and self.result_table: # Log Table if Val dataset is uploaded as artifact
self.log_training_progress(predn, path, names)
else: # Default to bbox media panelif Val artifact not found
log_imgs = min(self.log_imgs, 100)
if len(self.bbox_media_panel_images) < log_imgs and self.current_epoch > 0:
if self.current_epoch % self.bbox_interval == 0:
box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
"class_id": int(cls),
"box_caption": "%s %.3f" % (names[cls], conf),
"scores": {"class_score": conf},
"domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
self.bbox_media_panel_images.append(wandb.Image(im, boxes=boxes, caption=path.name))

def log(self, log_dict):
if self.wandb_run:
for key, value in log_dict.items():
@@ -313,13 +337,16 @@ class WandbLogger():
def end_epoch(self, best_result=False):
if self.wandb_run:
with all_logging_disabled():
if self.bbox_media_panel_images:
self.log_dict["Bounding Box Debugger/Images"] = self.bbox_media_panel_images
wandb.log(self.log_dict)
self.log_dict = {}
self.bbox_media_panel_images = []
if self.result_artifact:
self.result_artifact.add(self.result_table, 'result')
wandb.log_artifact(self.result_artifact, aliases=['latest', 'last', 'epoch ' + str(self.current_epoch),
('best' if best_result else '')])
wandb.log({"evaluation": self.result_table})
self.result_table = wandb.Table(["epoch", "id", "ground truth", "prediction", "avg_confidence"])
self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")

+ 75
- 85
val.py 파일 보기

@@ -25,7 +25,52 @@ from utils.general import coco80_to_coco91_class, check_dataset, check_file, che
box_iou, non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, set_logging, increment_path, colorstr
from utils.metrics import ap_per_class, ConfusionMatrix
from utils.plots import plot_images, output_to_target, plot_study_txt
from utils.torch_utils import select_device, time_synchronized
from utils.torch_utils import select_device, time_sync


def save_one_txt(predn, save_conf, shape, file):
# Save one txt result
gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh
for *xyxy, conf, cls in predn.tolist():
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
with open(file, 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')


def save_one_json(predn, jdict, path, class_map):
# Save one JSON result {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
image_id = int(path.stem) if path.stem.isnumeric() else path.stem
box = xyxy2xywh(predn[:, :4]) # xywh
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
for p, b in zip(predn.tolist(), box.tolist()):
jdict.append({'image_id': image_id,
'category_id': class_map[int(p[5])],
'bbox': [round(x, 3) for x in b],
'score': round(p[4], 5)})


def process_batch(predictions, labels, iouv):
# Evaluate 1 batch of predictions
correct = torch.zeros(predictions.shape[0], len(iouv), dtype=torch.bool, device=iouv.device)
detected = [] # label indices
tcls, pcls = labels[:, 0], predictions[:, 5]
nl = labels.shape[0] # number of labels
for cls in torch.unique(tcls):
ti = (cls == tcls).nonzero().view(-1) # label indices
pi = (cls == pcls).nonzero().view(-1) # prediction indices
if pi.shape[0]: # find detections
ious, i = box_iou(predictions[pi, 0:4], labels[ti, 1:5]).max(1) # best ious, indices
detected_set = set()
for j in (ious > iouv[0]).nonzero():
d = ti[i[j]] # detected label
if d.item() not in detected_set:
detected_set.add(d.item())
detected.append(d) # append detections
correct[pi[j]] = ious[j] > iouv # iou_thres is 1xn
if len(detected) == nl: # all labels already located in image
break
return correct


@torch.no_grad()
@@ -43,7 +88,7 @@ def run(data,
save_txt=False, # save results to *.txt
save_hybrid=False, # save label+prediction hybrid results to *.txt
save_conf=False, # save confidences in --save-txt labels
save_json=False, # save a cocoapi-compatible JSON results file
save_json=False, # save a COCO-JSON results file
project='runs/val', # save to project/name
name='exp', # save to project/name
exist_ok=False, # existing project/name ok, do not increment
@@ -93,10 +138,6 @@ def run(data,
iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95
niou = iouv.numel()

# Logging
log_imgs = 0
if wandb_logger and wandb_logger.wandb:
log_imgs = min(wandb_logger.log_imgs, 100)
# Dataloader
if not training:
if device.type != 'cpu':
@@ -108,24 +149,24 @@ def run(data,
seen = 0
confusion_matrix = ConfusionMatrix(nc=nc)
names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}
coco91class = coco80_to_coco91_class()
class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
s = ('%20s' + '%11s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
p, r, f1, mp, mr, map50, map, t0, t1, t2 = 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
loss = torch.zeros(3, device=device)
jdict, stats, ap, ap_class, wandb_images = [], [], [], [], []
jdict, stats, ap, ap_class = [], [], [], []
for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
t_ = time_synchronized()
t_ = time_sync()
img = img.to(device, non_blocking=True)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
targets = targets.to(device)
nb, _, height, width = img.shape # batch size, channels, height, width
t = time_synchronized()
t = time_sync()
t0 += t - t_

# Run model
out, train_out = model(img, augment=augment) # inference and training outputs
t1 += time_synchronized() - t
t1 += time_sync() - t

# Compute loss
if compute_loss:
@@ -134,16 +175,16 @@ def run(data,
# Run NMS
targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device) # to pixels
lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling
t = time_synchronized()
t = time_sync()
out = non_max_suppression(out, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls)
t2 += time_synchronized() - t
t2 += time_sync() - t

# Statistics per image
for si, pred in enumerate(out):
labels = targets[targets[:, 0] == si, 1:]
nl = len(labels)
tcls = labels[:, 0].tolist() if nl else [] # target class
path = Path(paths[si])
path, shape = Path(paths[si]), shapes[si][0]
seen += 1

if len(pred) == 0:
@@ -155,76 +196,27 @@ def run(data,
if single_cls:
pred[:, 5] = 0
predn = pred.clone()
scale_coords(img[si].shape[1:], predn[:, :4], shapes[si][0], shapes[si][1]) # native-space pred
scale_coords(img[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred

# Append to text file
if save_txt:
gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]] # normalization gain whwh
for *xyxy, conf, cls in predn.tolist():
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
with open(save_dir / 'labels' / (path.stem + '.txt'), 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')

# W&B logging - Media Panel plots
if len(wandb_images) < log_imgs and wandb_logger.current_epoch > 0: # Check for test operation
if wandb_logger.current_epoch % wandb_logger.bbox_interval == 0:
box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
"class_id": int(cls),
"box_caption": "%s %.3f" % (names[cls], conf),
"scores": {"class_score": conf},
"domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
wandb_images.append(wandb_logger.wandb.Image(img[si], boxes=boxes, caption=path.name))
wandb_logger.log_training_progress(predn, path, names) if wandb_logger and wandb_logger.wandb_run else None

# Append to pycocotools JSON dictionary
if save_json:
# [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
image_id = int(path.stem) if path.stem.isnumeric() else path.stem
box = xyxy2xywh(predn[:, :4]) # xywh
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
for p, b in zip(pred.tolist(), box.tolist()):
jdict.append({'image_id': image_id,
'category_id': coco91class[int(p[5])] if is_coco else int(p[5]),
'bbox': [round(x, 3) for x in b],
'score': round(p[4], 5)})

# Assign all predictions as incorrect
correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device)
# Evaluate
if nl:
detected = [] # target indices
tcls_tensor = labels[:, 0]

# target boxes
tbox = xywh2xyxy(labels[:, 1:5])
scale_coords(img[si].shape[1:], tbox, shapes[si][0], shapes[si][1]) # native-space labels
tbox = xywh2xyxy(labels[:, 1:5]) # target boxes
scale_coords(img[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels
labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels
correct = process_batch(predn, labelsn, iouv)
if plots:
confusion_matrix.process_batch(predn, torch.cat((labels[:, 0:1], tbox), 1))

# Per target class
for cls in torch.unique(tcls_tensor):
ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1) # target indices
pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(-1) # prediction indices

# Search for detections
if pi.shape[0]:
# Prediction to target ious
ious, i = box_iou(predn[pi, :4], tbox[ti]).max(1) # best ious, indices

# Append detections
detected_set = set()
for j in (ious > iouv[0]).nonzero(as_tuple=False):
d = ti[i[j]] # detected target
if d.item() not in detected_set:
detected_set.add(d.item())
detected.append(d)
correct[pi[j]] = ious[j] > iouv # iou_thres is 1xn
if len(detected) == nl: # all targets already located in image
break

# Append statistics (correct, conf, pcls, tcls)
stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))
confusion_matrix.process_batch(predn, labelsn)
else:
correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool)
stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls)) # (correct, conf, pcls, tcls)

# Save/log
if save_txt:
save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / (path.stem + '.txt'))
if save_json:
save_one_json(predn, jdict, path, class_map) # append to COCO-JSON dictionary
if wandb_logger:
wandb_logger.val_one_image(pred, predn, path, names, img[si])

# Plot images
if plots and batch_i < 3:
@@ -264,15 +256,13 @@ def run(data,
if wandb_logger and wandb_logger.wandb:
val_batches = [wandb_logger.wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('val*.jpg'))]
wandb_logger.log({"Validation": val_batches})
if wandb_images:
wandb_logger.log({"Bounding Box Debugger/Images": wandb_images})

# Save JSON
if save_json and len(jdict):
w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights
anno_json = str(Path(data.get('path', '../coco')) / 'annotations/instances_val2017.json') # annotations json
pred_json = str(save_dir / f"{w}_predictions.json") # predictions json
print('\nEvaluating pycocotools mAP... saving %s...' % pred_json)
print(f'\nEvaluating pycocotools mAP... saving {pred_json}...')
with open(pred_json, 'w') as f:
json.dump(jdict, f)

@@ -320,7 +310,7 @@ def parse_opt():
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt')
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
parser.add_argument('--save-json', action='store_true', help='save a COCO-JSON results file')
parser.add_argument('--project', default='runs/val', 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')

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