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Add Hub results.pandas() method #2725

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merged 7 commits into from
Apr 7, 2021
Merged

Add Hub results.pandas() method #2725

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bfba802
Add Hub results.pandas() method
glenn-jocher Apr 6, 2021
ccbe7de
Update comments
glenn-jocher Apr 7, 2021
2ff0dfb
apply decorators
glenn-jocher Apr 7, 2021
dd710dd
PEP8
glenn-jocher Apr 7, 2021
49563c7
Update common.py
glenn-jocher Apr 7, 2021
af52afa
pd.options.display.max_columns = 10
glenn-jocher Apr 7, 2021
69771d7
Update common.py
glenn-jocher Apr 7, 2021
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apply decorators
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@glenn-jocher
glenn-jocher committed Apr 7, 2021
commit 2ff0dfb54f522597a44fdc8956ff1374ab03c24c
20 changes: 10 additions & 10 deletions models/common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,6 @@
import torch
import torch.nn as nn
from PIL import Image
from torch.cuda import amp

from utils.datasets import letterbox
from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh
Expand Down Expand Up @@ -237,14 +236,16 @@ def autoshape(self):
print('autoShape already enabled, skipping... ') # model already converted to model.autoshape()
return self

@torch.no_grad()
@torch.cuda.amp.autocast()
def forward(self, imgs, size=640, augment=False, profile=False):
# Inference from various sources. For height=640, width=1280, RGB images example inputs are:
# filename: imgs = 'data/samples/zidane.jpg'
# URI: = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg'
# OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3)
# PIL: = Image.open('image.jpg') # HWC x(640,1280,3)
# numpy: = np.zeros((640,1280,3)) # HWC
# torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640)
# 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()]
Expand Down Expand Up @@ -277,15 +278,14 @@ def forward(self, imgs, size=640, augment=False, profile=False):
x = torch.from_numpy(x).to(p.device).type_as(p) / 255. # uint8 to fp16/32
t.append(time_synchronized())

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

# 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])
# 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())
return Detections(imgs, y, files, t, self.names, x.shape)
Expand Down