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PyTorch Hub and autoShape update (#1415)

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* PyTorch Hub and autoShape update

* comment x for imgs

* reduce comment
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Glenn Jocher authored and GitHub committed 2020-11-16 23:09:55 +01:00
1 parent 92c9b72832
commit f5429260ca
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+84 -35
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models/common.py
+71 -22
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@@ -5,9 +5,11 @@ import math
import numpy as np
import torch
import torch.nn as nn
from PIL import Image, ImageDraw
from utils.datasets import letterbox
from utils.general import non_max_suppression, make_divisible, scale_coords
from utils.general import non_max_suppression, make_divisible, scale_coords, xyxy2xywh
from utils.plots import color_list
def autopad(k, p=None): # kernel, padding
@@ -125,47 +127,94 @@ class autoShape(nn.Module):
def __init__(self, model):
super(autoShape, self).__init__()
self.model = model
self.model = model.eval()
def forward(self, x, size=640, augment=False, profile=False):
def forward(self, imgs, size=640, augment=False, profile=False):
# supports inference from various sources. For height=720, width=1280, RGB images example inputs are:
# opencv: x = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(720,1280,3)
# PIL: x = Image.open('image.jpg') # HWC x(720,1280,3)
# numpy: x = np.zeros((720,1280,3)) # HWC
# torch: x = torch.zeros(16,3,720,1280) # BCHW
# multiple: x = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images
# opencv: imgs = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(720,1280,3)
# PIL: imgs = Image.open('image.jpg') # HWC x(720,1280,3)
# numpy: imgs = np.zeros((720,1280,3)) # HWC
# torch: imgs = torch.zeros(16,3,720,1280) # BCHW
# multiple: imgs = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images
p = next(self.model.parameters()) # for device and type
if isinstance(x, torch.Tensor): # torch
return self.model(x.to(p.device).type_as(p), augment, profile) # inference
if isinstance(imgs, torch.Tensor): # torch
return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference
# Pre-process
if not isinstance(x, list):
x = [x]
if not isinstance(imgs, list):
imgs = [imgs]
shape0, shape1 = [], [] # image and inference shapes
batch = range(len(x)) # batch size
batch = range(len(imgs)) # batch size
for i in batch:
x[i] = np.array(x[i]) # to numpy
x[i] = x[i][:, :, :3] if x[i].ndim == 3 else np.tile(x[i][:, :, None], 3) # enforce 3ch input
s = x[i].shape[:2] # HWC
imgs[i] = np.array(imgs[i]) # to numpy
imgs[i] = imgs[i][:, :, :3] if imgs[i].ndim == 3 else np.tile(imgs[i][:, :, None], 3) # enforce 3ch input
s = imgs[i].shape[:2] # HWC
shape0.append(s) # image shape
g = (size / max(s)) # gain
shape1.append([y * g for y in s])
shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape
x = [letterbox(x[i], new_shape=shape1, auto=False)[0] for i in batch] # pad
x = [letterbox(imgs[i], new_shape=shape1, auto=False)[0] for i in batch] # pad
x = np.stack(x, 0) if batch[-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
# Inference
x = self.model(x, augment, profile) # forward
x = non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS
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
# Post-process
for i in batch:
if x[i] is not None:
x[i][:, :4] = scale_coords(shape1, x[i][:, :4], shape0[i])
return x
if y[i] is not None:
y[i][:, :4] = scale_coords(shape1, y[i][:, :4], shape0[i])
return Detections(imgs, y, self.names)
class Detections:
# detections class for YOLOv5 inference results
def __init__(self, imgs, pred, names=None):
super(Detections, self).__init__()
self.imgs = imgs # list of images as numpy arrays
self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
self.names = names # class names
self.xyxy = pred # xyxy pixels
self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels
gn = [torch.Tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.]) for im in imgs] # normalization gains
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
def display(self, pprint=False, show=False, save=False):
colors = color_list()
for i, (img, pred) in enumerate(zip(self.imgs, self.pred)):
str = f'Image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} '
if pred is not None:
for c in pred[:, -1].unique():
n = (pred[:, -1] == c).sum() # detections per class
str += f'{n} {self.names[int(c)]}s, ' # add to string
if show or save:
img = Image.fromarray(img.astype(np.uint8)) if isinstance(img, np.ndarray) else img # from np
for *box, conf, cls in pred: # xyxy, confidence, class
# str += '%s %.2f, ' % (names[int(cls)], conf) # label
ImageDraw.Draw(img).rectangle(box, width=4, outline=colors[int(cls) % 10]) # plot
if save:
f = f'results{i}.jpg'
str += f"saved to '{f}'"
img.save(f) # save
if show:
img.show(f'Image {i}') # show
if pprint:
print(str)
def print(self):
self.display(pprint=True) # print results
def show(self):
self.display(show=True) # show results
def save(self):
self.display(save=True) # save results
class Flatten(nn.Module):