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Add autoShape() speed profiling (#2459)
* Add autoShape() speed profiling * Update common.py * Create README.md * Update hubconf.py * cleanuip
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Original file line number | Diff line number | Diff line change |
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@@ -12,6 +12,7 @@ | |
from utils.datasets import letterbox | ||
from utils.general import non_max_suppression, make_divisible, scale_coords, xyxy2xywh | ||
from utils.plots import color_list, plot_one_box | ||
from utils.torch_utils import time_synchronized | ||
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def autopad(k, p=None): # kernel, padding | ||
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@@ -190,6 +191,7 @@ def forward(self, imgs, size=640, augment=False, profile=False): | |
# torch: = torch.zeros(16,3,720,1280) # BCHW | ||
# multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images | ||
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t = [time_synchronized()] | ||
p = next(self.model.parameters()) # for device and type | ||
if isinstance(imgs, torch.Tensor): # torch | ||
return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference | ||
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@@ -216,22 +218,25 @@ def forward(self, imgs, size=640, augment=False, profile=False): | |
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()) | ||
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# Inference | ||
with torch.no_grad(): | ||
y = self.model(x, augment, profile)[0] # forward | ||
y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS | ||
t.append(time_synchronized()) | ||
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# Post-process | ||
y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS | ||
for i in range(n): | ||
scale_coords(shape1, y[i][:, :4], shape0[i]) | ||
t.append(time_synchronized()) | ||
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return Detections(imgs, y, files, self.names) | ||
return Detections(imgs, y, files, t, self.names, x.shape) | ||
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class Detections: | ||
# detections class for YOLOv5 inference results | ||
def __init__(self, imgs, pred, files, names=None): | ||
def __init__(self, imgs, pred, files, times, names=None, shape=None): | ||
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glenn-jocher
Author
Member
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super(Detections, self).__init__() | ||
d = pred[0].device # device | ||
gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs] # normalizations | ||
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@@ -244,6 +249,8 @@ def __init__(self, imgs, pred, files, names=None): | |
self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized | ||
self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized | ||
self.n = len(self.pred) | ||
self.t = ((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms) | ||
self.s = shape # inference BCHW shape | ||
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def display(self, pprint=False, show=False, save=False, render=False, save_dir=''): | ||
colors = color_list() | ||
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@@ -271,6 +278,7 @@ def display(self, pprint=False, show=False, save=False, render=False, save_dir=' | |
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def print(self): | ||
self.display(pprint=True) # print results | ||
print(f'Speed: %.1f/%.1f/%.1f ms pre-process/inference/NMS per image at shape {tuple(self.s)}' % tuple(self.t)) | ||
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def show(self): | ||
self.display(show=True) # show results | ||
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This change breaks the function Detections.tolist(). The expected argument 'times' is not given. This can be fixed by assigning the default 'None' to attribute 'times'.
I propose:
def __init__(self, imgs, pred, files, times=None, names=None, shape=None):