274 lines
11 KiB
Python
274 lines
11 KiB
Python
|
# This file contains modules common to various models
|
||
|
|
|
||
|
|
import math
|
||
|
|
import numpy as np
|
||
|
|
import requests
|
||
|
|
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, xyxy2xywh
|
||
|
|
from utils.plots import color_list
|
||
|
|
|
||
|
|
|
||
|
|
def autopad(k, p=None): # kernel, padding
|
||
|
|
# Pad to 'same'
|
||
|
|
if p is None:
|
||
|
|
p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
|
||
|
|
return p
|
||
|
|
|
||
|
|
|
||
|
|
def DWConv(c1, c2, k=1, s=1, act=True):
|
||
|
|
# Depthwise convolution
|
||
|
|
return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
|
||
|
|
|
||
|
|
|
||
|
|
class Conv(nn.Module):
|
||
|
|
# Standard convolution
|
||
|
|
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
|
||
|
|
super(Conv, self).__init__()
|
||
|
|
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
|
||
|
|
self.bn = nn.BatchNorm2d(c2)
|
||
|
|
self.act = nn.Hardswish() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
return self.act(self.bn(self.conv(x)))
|
||
|
|
|
||
|
|
def fuseforward(self, x):
|
||
|
|
return self.act(self.conv(x))
|
||
|
|
|
||
|
|
|
||
|
|
class Bottleneck(nn.Module):
|
||
|
|
# Standard bottleneck
|
||
|
|
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion
|
||
|
|
super(Bottleneck, self).__init__()
|
||
|
|
c_ = int(c2 * e) # hidden channels
|
||
|
|
self.cv1 = Conv(c1, c_, 1, 1)
|
||
|
|
self.cv2 = Conv(c_, c2, 3, 1, g=g)
|
||
|
|
self.add = shortcut and c1 == c2
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
|
||
|
|
|
||
|
|
|
||
|
|
class BottleneckCSP(nn.Module):
|
||
|
|
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
|
||
|
|
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
|
||
|
|
super(BottleneckCSP, self).__init__()
|
||
|
|
c_ = int(c2 * e) # hidden channels
|
||
|
|
self.cv1 = Conv(c1, c_, 1, 1)
|
||
|
|
self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
|
||
|
|
self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
|
||
|
|
self.cv4 = Conv(2 * c_, c2, 1, 1)
|
||
|
|
self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3)
|
||
|
|
self.act = nn.LeakyReLU(0.1, inplace=True)
|
||
|
|
self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
y1 = self.cv3(self.m(self.cv1(x)))
|
||
|
|
y2 = self.cv2(x)
|
||
|
|
return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
|
||
|
|
|
||
|
|
|
||
|
|
class C3(nn.Module):
|
||
|
|
# CSP Bottleneck with 3 convolutions
|
||
|
|
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
|
||
|
|
super(C3, self).__init__()
|
||
|
|
c_ = int(c2 * e) # hidden channels
|
||
|
|
self.cv1 = Conv(c1, c_, 1, 1)
|
||
|
|
self.cv2 = Conv(c1, c_, 1, 1)
|
||
|
|
self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2)
|
||
|
|
self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
|
||
|
|
# self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))
|
||
|
|
|
||
|
|
|
||
|
|
class SPP(nn.Module):
|
||
|
|
# Spatial pyramid pooling layer used in YOLOv3-SPP
|
||
|
|
def __init__(self, c1, c2, k=(5, 9, 13)):
|
||
|
|
super(SPP, self).__init__()
|
||
|
|
c_ = c1 // 2 # hidden channels
|
||
|
|
self.cv1 = Conv(c1, c_, 1, 1)
|
||
|
|
self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
|
||
|
|
self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
x = self.cv1(x)
|
||
|
|
return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
|
||
|
|
|
||
|
|
|
||
|
|
class Focus(nn.Module):
|
||
|
|
# Focus wh information into c-space
|
||
|
|
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
|
||
|
|
super(Focus, self).__init__()
|
||
|
|
self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
|
||
|
|
|
||
|
|
def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
|
||
|
|
return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
|
||
|
|
|
||
|
|
|
||
|
|
class Concat(nn.Module):
|
||
|
|
# Concatenate a list of tensors along dimension
|
||
|
|
def __init__(self, dimension=1):
|
||
|
|
super(Concat, self).__init__()
|
||
|
|
self.d = dimension
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
return torch.cat(x, self.d)
|
||
|
|
|
||
|
|
|
||
|
|
class NMS(nn.Module):
|
||
|
|
# Non-Maximum Suppression (NMS) module
|
||
|
|
conf = 0.25 # confidence threshold
|
||
|
|
iou = 0.45 # IoU threshold
|
||
|
|
classes = None # (optional list) filter by class
|
||
|
|
|
||
|
|
def __init__(self):
|
||
|
|
super(NMS, self).__init__()
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes)
|
||
|
|
|
||
|
|
|
||
|
|
class autoShape(nn.Module):
|
||
|
|
# input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
|
||
|
|
img_size = 640 # inference size (pixels)
|
||
|
|
conf = 0.25 # NMS confidence threshold
|
||
|
|
iou = 0.45 # NMS IoU threshold
|
||
|
|
classes = None # (optional list) filter by class
|
||
|
|
|
||
|
|
def __init__(self, model):
|
||
|
|
super(autoShape, self).__init__()
|
||
|
|
self.model = model.eval()
|
||
|
|
|
||
|
|
def autoshape(self):
|
||
|
|
print('autoShape already enabled, skipping... ') # model already converted to model.autoshape()
|
||
|
|
return self
|
||
|
|
|
||
|
|
def forward(self, imgs, size=640, augment=False, profile=False):
|
||
|
|
# Inference from various sources. For height=720, 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(720,1280,3)
|
||
|
|
# PIL: = Image.open('image.jpg') # HWC x(720,1280,3)
|
||
|
|
# numpy: = np.zeros((720,1280,3)) # HWC
|
||
|
|
# torch: = torch.zeros(16,3,720,1280) # BCHW
|
||
|
|
# multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images
|
||
|
|
|
||
|
|
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
|
||
|
|
|
||
|
|
# Pre-process
|
||
|
|
n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images
|
||
|
|
shape0, shape1 = [], [] # image and inference shapes
|
||
|
|
for i, im in enumerate(imgs):
|
||
|
|
if isinstance(im, str): # filename or uri
|
||
|
|
im = Image.open(requests.get(im, stream=True).raw if im.startswith('http') else im) # open
|
||
|
|
im = np.array(im) # to numpy
|
||
|
|
if im.shape[0] < 5: # image in CHW
|
||
|
|
im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1)
|
||
|
|
im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3) # enforce 3ch input
|
||
|
|
s = im.shape[:2] # HWC
|
||
|
|
shape0.append(s) # image shape
|
||
|
|
g = (size / max(s)) # gain
|
||
|
|
shape1.append([y * g for y in s])
|
||
|
|
imgs[i] = im # update
|
||
|
|
shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape
|
||
|
|
x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad
|
||
|
|
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
|
||
|
|
|
||
|
|
# 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
|
||
|
|
|
||
|
|
# Post-process
|
||
|
|
for i in range(n):
|
||
|
|
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__()
|
||
|
|
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
|
||
|
|
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
|
||
|
|
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)
|
||
|
|
|
||
|
|
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
|
||
|
|
|
||
|
|
def __len__(self):
|
||
|
|
return self.n
|
||
|
|
|
||
|
|
def tolist(self):
|
||
|
|
# return a list of Detections objects, i.e. 'for result in results.tolist():'
|
||
|
|
x = [Detections([self.imgs[i]], [self.pred[i]], self.names) for i in range(self.n)]
|
||
|
|
for d in x:
|
||
|
|
for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
|
||
|
|
setattr(d, k, getattr(d, k)[0]) # pop out of list
|
||
|
|
return x
|
||
|
|
|
||
|
|
|
||
|
|
class Flatten(nn.Module):
|
||
|
|
# Use after nn.AdaptiveAvgPool2d(1) to remove last 2 dimensions
|
||
|
|
@staticmethod
|
||
|
|
def forward(x):
|
||
|
|
return x.view(x.size(0), -1)
|
||
|
|
|
||
|
|
|
||
|
|
class Classify(nn.Module):
|
||
|
|
# Classification head, i.e. x(b,c1,20,20) to x(b,c2)
|
||
|
|
def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups
|
||
|
|
super(Classify, self).__init__()
|
||
|
|
self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1)
|
||
|
|
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1)
|
||
|
|
self.flat = Flatten()
|
||
|
|
|
||
|
|
def forward(self, x):
|
||
|
|
z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list
|
||
|
|
return self.flat(self.conv(z)) # flatten to x(b,c2)
|