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Fix 6 Flake8 issues (#6541) 

* F541

* F821

* F841

* E741

* E302

* E722

* Apply suggestions from code review

* Update general.py

* Update datasets.py

* Update export.py

* Update plots.py

* Update plots.py

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Jirka Borovec 2022-02-05 15:22:59 +01:00 committed by GitHub
parent e1a6a0b100
commit cba4303d32
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GPG Key ID: 4AEE18F83AFDEB23
10 changed files with 55 additions and 56 deletions
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14
export.py
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@ -244,7 +244,7 @@ def export_saved_model(model, im, file, dynamic,
tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz) tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
im = tf.zeros((batch_size, *imgsz, 3)) # BHWC order for TensorFlow im = tf.zeros((batch_size, *imgsz, 3)) # BHWC order for TensorFlow
y = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres) _ = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
inputs = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size) inputs = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size)
outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres) outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
keras_model = keras.Model(inputs=inputs, outputs=outputs) keras_model = keras.Model(inputs=inputs, outputs=outputs)
@ -407,16 +407,17 @@ def run(data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path'
tf_exports = list(x in include for x in ('saved_model', 'pb', 'tflite', 'edgetpu', 'tfjs')) # TensorFlow exports tf_exports = list(x in include for x in ('saved_model', 'pb', 'tflite', 'edgetpu', 'tfjs')) # TensorFlow exports
file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights) file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights)
# Checks
imgsz *= 2 if len(imgsz) == 1 else 1 # expand
opset = 12 if ('openvino' in include) else opset # OpenVINO requires opset <= 12
# Load PyTorch model # Load PyTorch model
device = select_device(device) device = select_device(device)
assert not (device.type == 'cpu' and half), '--half only compatible with GPU export, i.e. use --device 0' assert not (device.type == 'cpu' and half), '--half only compatible with GPU export, i.e. use --device 0'
model = attempt_load(weights, map_location=device, inplace=True, fuse=True) # load FP32 model model = attempt_load(weights, map_location=device, inplace=True, fuse=True) # load FP32 model
nc, names = model.nc, model.names # number of classes, class names nc, names = model.nc, model.names # number of classes, class names
# Checks
imgsz *= 2 if len(imgsz) == 1 else 1 # expand
opset = 12 if ('openvino' in include) else opset # OpenVINO requires opset <= 12
assert nc == len(names), f'Model class count {nc} != len(names) {len(names)}'
# Input # Input
gs = int(max(model.stride)) # grid size (max stride) gs = int(max(model.stride)) # grid size (max stride)
imgsz = [check_img_size(x, gs) for x in imgsz] # verify img_size are gs-multiples imgsz = [check_img_size(x, gs) for x in imgsz] # verify img_size are gs-multiples
@ -438,7 +439,8 @@ def run(data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path'
for _ in range(2): for _ in range(2):
y = model(im) # dry runs y = model(im) # dry runs
LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} ({file_size(file):.1f} MB)") shape = tuple(y[0].shape) # model output shape
LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)")
# Exports # Exports
f = [''] * 10 # exported filenames f = [''] * 10 # exported filenames

4
models/tf.py
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@ -427,13 +427,13 @@ def run(weights=ROOT / 'yolov5s.pt', # weights path
# PyTorch model # PyTorch model
im = torch.zeros((batch_size, 3, *imgsz)) # BCHW image im = torch.zeros((batch_size, 3, *imgsz)) # BCHW image
model = attempt_load(weights, map_location=torch.device('cpu'), inplace=True, fuse=False) model = attempt_load(weights, map_location=torch.device('cpu'), inplace=True, fuse=False)
y = model(im) # inference _ = model(im) # inference
model.info() model.info()
# TensorFlow model # TensorFlow model
im = tf.zeros((batch_size, *imgsz, 3)) # BHWC image im = tf.zeros((batch_size, *imgsz, 3)) # BHWC image
tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz) tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
y = tf_model.predict(im) # inference _ = tf_model.predict(im) # inference
# Keras model # Keras model
im = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size) im = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size)

6
setup.cfg
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@ -30,10 +30,6 @@ ignore =
E731 # Do not assign a lambda expression, use a def E731 # Do not assign a lambda expression, use a def
F405 # name may be undefined, or defined from star imports: module F405 # name may be undefined, or defined from star imports: module
E402 # module level import not at top of file E402 # module level import not at top of file
F841 # local variable name is assigned to but never used
E741 # do not use variables named ‘l’, ‘O’, or ‘I’
F821 # undefined name name
E722 # do not use bare except, specify exception instead
F401 # module imported but unused F401 # module imported but unused
W504 # line break after binary operator W504 # line break after binary operator
E127 # continuation line over-indented for visual indent E127 # continuation line over-indented for visual indent
@ -41,8 +37,6 @@ ignore =
E231 # missing whitespace after ‘,’, ‘;’, or ‘:’ E231 # missing whitespace after ‘,’, ‘;’, or ‘:’
E501 # line too long E501 # line too long
F403 # ‘from module import *’ used; unable to detect undefined names F403 # ‘from module import *’ used; unable to detect undefined names
E302 # expected 2 blank lines, found 0
F541 # f-string without any placeholders
[isort] [isort]

53
utils/datasets.py
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@ -59,7 +59,7 @@ def exif_size(img):
s = (s[1], s[0]) s = (s[1], s[0])
elif rotation == 8: # rotation 90 elif rotation == 8: # rotation 90
s = (s[1], s[0]) s = (s[1], s[0])
except: except Exception:
pass pass
return s return s
@ -420,7 +420,7 @@ class LoadImagesAndLabels(Dataset):
cache, exists = np.load(cache_path, allow_pickle=True).item(), True # load dict cache, exists = np.load(cache_path, allow_pickle=True).item(), True # load dict
assert cache['version'] == self.cache_version # same version assert cache['version'] == self.cache_version # same version
assert cache['hash'] == get_hash(self.label_files + self.img_files) # same hash assert cache['hash'] == get_hash(self.label_files + self.img_files) # same hash
except: except Exception:
cache, exists = self.cache_labels(cache_path, prefix), False # cache cache, exists = self.cache_labels(cache_path, prefix), False # cache
# Display cache # Display cache
@ -514,13 +514,13 @@ class LoadImagesAndLabels(Dataset):
with Pool(NUM_THREADS) as pool: with Pool(NUM_THREADS) as pool:
pbar = tqdm(pool.imap(verify_image_label, zip(self.img_files, self.label_files, repeat(prefix))), pbar = tqdm(pool.imap(verify_image_label, zip(self.img_files, self.label_files, repeat(prefix))),
desc=desc, total=len(self.img_files)) desc=desc, total=len(self.img_files))
for im_file, l, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar: for im_file, lb, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar:
nm += nm_f nm += nm_f
nf += nf_f nf += nf_f
ne += ne_f ne += ne_f
nc += nc_f nc += nc_f
if im_file: if im_file:
x[im_file] = [l, shape, segments] x[im_file] = [lb, shape, segments]
if msg: if msg:
msgs.append(msg) msgs.append(msg)
pbar.desc = f"{desc}{nf} found, {nm} missing, {ne} empty, {nc} corrupt" pbar.desc = f"{desc}{nf} found, {nm} missing, {ne} empty, {nc} corrupt"
@ -627,8 +627,8 @@ class LoadImagesAndLabels(Dataset):
@staticmethod @staticmethod
def collate_fn(batch): def collate_fn(batch):
img, label, path, shapes = zip(*batch) # transposed img, label, path, shapes = zip(*batch) # transposed
for i, l in enumerate(label): for i, lb in enumerate(label):
l[:, 0] = i # add target image index for build_targets() lb[:, 0] = i # add target image index for build_targets()
return torch.stack(img, 0), torch.cat(label, 0), path, shapes return torch.stack(img, 0), torch.cat(label, 0), path, shapes
@staticmethod @staticmethod
@ -645,15 +645,15 @@ class LoadImagesAndLabels(Dataset):
if random.random() < 0.5: if random.random() < 0.5:
im = F.interpolate(img[i].unsqueeze(0).float(), scale_factor=2.0, mode='bilinear', align_corners=False)[ im = F.interpolate(img[i].unsqueeze(0).float(), scale_factor=2.0, mode='bilinear', align_corners=False)[
0].type(img[i].type()) 0].type(img[i].type())
l = label[i] lb = label[i]
else: else:
im = torch.cat((torch.cat((img[i], img[i + 1]), 1), torch.cat((img[i + 2], img[i + 3]), 1)), 2) im = torch.cat((torch.cat((img[i], img[i + 1]), 1), torch.cat((img[i + 2], img[i + 3]), 1)), 2)
l = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s lb = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s
img4.append(im) img4.append(im)
label4.append(l) label4.append(lb)
for i, l in enumerate(label4): for i, lb in enumerate(label4):
l[:, 0] = i # add target image index for build_targets() lb[:, 0] = i # add target image index for build_targets()
return torch.stack(img4, 0), torch.cat(label4, 0), path4, shapes4 return torch.stack(img4, 0), torch.cat(label4, 0), path4, shapes4
@ -743,6 +743,7 @@ def load_mosaic9(self, index):
s = self.img_size s = self.img_size
indices = [index] + random.choices(self.indices, k=8) # 8 additional image indices indices = [index] + random.choices(self.indices, k=8) # 8 additional image indices
random.shuffle(indices) random.shuffle(indices)
hp, wp = -1, -1 # height, width previous
for i, index in enumerate(indices): for i, index in enumerate(indices):
# Load image # Load image
img, _, (h, w) = load_image(self, index) img, _, (h, w) = load_image(self, index)
@ -906,30 +907,30 @@ def verify_image_label(args):
if os.path.isfile(lb_file): if os.path.isfile(lb_file):
nf = 1 # label found nf = 1 # label found
with open(lb_file) as f: with open(lb_file) as f:
l = [x.split() for x in f.read().strip().splitlines() if len(x)] lb = [x.split() for x in f.read().strip().splitlines() if len(x)]
if any([len(x) > 8 for x in l]): # is segment if any([len(x) > 8 for x in lb]): # is segment
classes = np.array([x[0] for x in l], dtype=np.float32) classes = np.array([x[0] for x in lb], dtype=np.float32)
segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in l] # (cls, xy1...) segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb] # (cls, xy1...)
l = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh) lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh)
l = np.array(l, dtype=np.float32) lb = np.array(lb, dtype=np.float32)
nl = len(l) nl = len(lb)
if nl: if nl:
assert l.shape[1] == 5, f'labels require 5 columns, {l.shape[1]} columns detected' assert lb.shape[1] == 5, f'labels require 5 columns, {lb.shape[1]} columns detected'
assert (l >= 0).all(), f'negative label values {l[l < 0]}' assert (lb >= 0).all(), f'negative label values {lb[lb < 0]}'
assert (l[:, 1:] <= 1).all(), f'non-normalized or out of bounds coordinates {l[:, 1:][l[:, 1:] > 1]}' assert (lb[:, 1:] <= 1).all(), f'non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}'
_, i = np.unique(l, axis=0, return_index=True) _, i = np.unique(lb, axis=0, return_index=True)
if len(i) < nl: # duplicate row check if len(i) < nl: # duplicate row check
l = l[i] # remove duplicates lb = lb[i] # remove duplicates
if segments: if segments:
segments = segments[i] segments = segments[i]
msg = f'{prefix}WARNING: {im_file}: {nl - len(i)} duplicate labels removed' msg = f'{prefix}WARNING: {im_file}: {nl - len(i)} duplicate labels removed'
else: else:
ne = 1 # label empty ne = 1 # label empty
l = np.zeros((0, 5), dtype=np.float32) lb = np.zeros((0, 5), dtype=np.float32)
else: else:
nm = 1 # label missing nm = 1 # label missing
l = np.zeros((0, 5), dtype=np.float32) lb = np.zeros((0, 5), dtype=np.float32)
return im_file, l, shape, segments, nm, nf, ne, nc, msg return im_file, lb, shape, segments, nm, nf, ne, nc, msg
except Exception as e: except Exception as e:
nc = 1 nc = 1
msg = f'{prefix}WARNING: {im_file}: ignoring corrupt image/label: {e}' msg = f'{prefix}WARNING: {im_file}: ignoring corrupt image/label: {e}'

4
utils/downloads.py
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@ -62,12 +62,12 @@ def attempt_download(file, repo='ultralytics/yolov5'): # from utils.downloads i
response = requests.get(f'https://api.github.com/repos/{repo}/releases/latest').json() # github api response = requests.get(f'https://api.github.com/repos/{repo}/releases/latest').json() # github api
assets = [x['name'] for x in response['assets']] # release assets, i.e. ['yolov5s.pt', 'yolov5m.pt', ...] assets = [x['name'] for x in response['assets']] # release assets, i.e. ['yolov5s.pt', 'yolov5m.pt', ...]
tag = response['tag_name'] # i.e. 'v1.0' tag = response['tag_name'] # i.e. 'v1.0'
except: # fallback plan except Exception: # fallback plan
assets = ['yolov5n.pt', 'yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt', assets = ['yolov5n.pt', 'yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt',
'yolov5n6.pt', 'yolov5s6.pt', 'yolov5m6.pt', 'yolov5l6.pt', 'yolov5x6.pt'] 'yolov5n6.pt', 'yolov5s6.pt', 'yolov5m6.pt', 'yolov5l6.pt', 'yolov5x6.pt']
try: try:
tag = subprocess.check_output('git tag', shell=True, stderr=subprocess.STDOUT).decode().split()[-1] tag = subprocess.check_output('git tag', shell=True, stderr=subprocess.STDOUT).decode().split()[-1]
except: except Exception:
tag = 'v6.0' # current release tag = 'v6.0' # current release
if name in assets: if name in assets:

15
utils/general.py
View File

@ -295,7 +295,7 @@ def check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), insta
for r in requirements: for r in requirements:
try: try:
pkg.require(r) pkg.require(r)
except Exception as e: # DistributionNotFound or VersionConflict if requirements not met except Exception: # DistributionNotFound or VersionConflict if requirements not met
s = f"{prefix} {r} not found and is required by YOLOv5" s = f"{prefix} {r} not found and is required by YOLOv5"
if install: if install:
LOGGER.info(f"{s}, attempting auto-update...") LOGGER.info(f"{s}, attempting auto-update...")
@ -699,16 +699,16 @@ def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=Non
output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0] output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0]
for xi, x in enumerate(prediction): # image index, image inference for xi, x in enumerate(prediction): # image index, image inference
# Apply constraints # Apply constraints
# x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height
x = x[xc[xi]] # confidence x = x[xc[xi]] # confidence
# Cat apriori labels if autolabelling # Cat apriori labels if autolabelling
if labels and len(labels[xi]): if labels and len(labels[xi]):
l = labels[xi] lb = labels[xi]
v = torch.zeros((len(l), nc + 5), device=x.device) v = torch.zeros((len(lb), nc + 5), device=x.device)
v[:, :4] = l[:, 1:5] # box v[:, :4] = lb[:, 1:5] # box
v[:, 4] = 1.0 # conf v[:, 4] = 1.0 # conf
v[range(len(l)), l[:, 0].long() + 5] = 1.0 # cls v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls
x = torch.cat((x, v), 0) x = torch.cat((x, v), 0)
# If none remain process next image # If none remain process next image
@ -783,7 +783,8 @@ def strip_optimizer(f='best.pt', s=''): # from utils.general import *; strip_op
def print_mutation(results, hyp, save_dir, bucket): def print_mutation(results, hyp, save_dir, bucket):
evolve_csv, results_csv, evolve_yaml = save_dir / 'evolve.csv', save_dir / 'results.csv', save_dir / 'hyp_evolve.yaml' evolve_csv = save_dir / 'evolve.csv'
evolve_yaml = save_dir / 'hyp_evolve.yaml'
keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
'val/box_loss', 'val/obj_loss', 'val/cls_loss') + tuple(hyp.keys()) # [results + hyps] 'val/box_loss', 'val/obj_loss', 'val/cls_loss') + tuple(hyp.keys()) # [results + hyps]
keys = tuple(x.strip() for x in keys) keys = tuple(x.strip() for x in keys)

2
utils/loggers/wandb/wandb_utils.py
View File

@ -288,7 +288,7 @@ class WandbLogger():
model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest") model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest")
assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist' assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist'
modeldir = model_artifact.download() modeldir = model_artifact.download()
epochs_trained = model_artifact.metadata.get('epochs_trained') # epochs_trained = model_artifact.metadata.get('epochs_trained')
total_epochs = model_artifact.metadata.get('total_epochs') total_epochs = model_artifact.metadata.get('total_epochs')
is_finished = total_epochs is None is_finished = total_epochs is None
assert not is_finished, 'training is finished, can only resume incomplete runs.' assert not is_finished, 'training is finished, can only resume incomplete runs.'

1
utils/metrics.py
View File

@ -239,6 +239,7 @@ def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=
return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf
return iou # IoU return iou # IoU
def box_iou(box1, box2): def box_iou(box1, box2):
# https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
""" """

4
utils/plots.py
View File

@ -54,7 +54,7 @@ def check_pil_font(font=FONT, size=10):
font = font if font.exists() else (CONFIG_DIR / font.name) font = font if font.exists() else (CONFIG_DIR / font.name)
try: try:
return ImageFont.truetype(str(font) if font.exists() else font.name, size) return ImageFont.truetype(str(font) if font.exists() else font.name, size)
except Exception as e: # download if missing except Exception: # download if missing
check_font(font) check_font(font)
try: try:
return ImageFont.truetype(str(font), size) return ImageFont.truetype(str(font), size)
@ -340,7 +340,7 @@ def plot_labels(labels, names=(), save_dir=Path('')):
matplotlib.use('svg') # faster matplotlib.use('svg') # faster
ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel() ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8) y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
# [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # update colors bug #3195 [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # update colors bug #3195
ax[0].set_ylabel('instances') ax[0].set_ylabel('instances')
if 0 < len(names) < 30: if 0 < len(names) < 30:
ax[0].set_xticks(range(len(names))) ax[0].set_xticks(range(len(names)))

8
utils/torch_utils.py
View File

@ -49,7 +49,7 @@ def git_describe(path=Path(__file__).parent): # path must be a directory
s = f'git -C {path} describe --tags --long --always' s = f'git -C {path} describe --tags --long --always'
try: try:
return subprocess.check_output(s, shell=True, stderr=subprocess.STDOUT).decode()[:-1] return subprocess.check_output(s, shell=True, stderr=subprocess.STDOUT).decode()[:-1]
except subprocess.CalledProcessError as e: except subprocess.CalledProcessError:
return '' # not a git repository return '' # not a git repository
@ -59,7 +59,7 @@ def device_count():
try: try:
cmd = 'nvidia-smi -L | wc -l' cmd = 'nvidia-smi -L | wc -l'
return int(subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]) return int(subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1])
except Exception as e: except Exception:
return 0 return 0
@ -124,7 +124,7 @@ def profile(input, ops, n=10, device=None):
tf, tb, t = 0, 0, [0, 0, 0] # dt forward, backward tf, tb, t = 0, 0, [0, 0, 0] # dt forward, backward
try: try:
flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # GFLOPs flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # GFLOPs
except: except Exception:
flops = 0 flops = 0
try: try:
@ -135,7 +135,7 @@ def profile(input, ops, n=10, device=None):
try: try:
_ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward() _ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward()
t[2] = time_sync() t[2] = time_sync()
except Exception as e: # no backward method except Exception: # no backward method
# print(e) # for debug # print(e) # for debug
t[2] = float('nan') t[2] = float('nan')
tf += (t[1] - t[0]) * 1000 / n # ms per op forward tf += (t[1] - t[0]) * 1000 / n # ms per op forward