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# Ultralytics YOLO 🚀, GPL-3.0 license | |
""" | |
Common modules | |
""" | |
import math | |
import warnings | |
from copy import copy | |
from pathlib import Path | |
import cv2 | |
import numpy as np | |
import pandas as pd | |
import requests | |
import torch | |
import torch.nn as nn | |
from PIL import Image, ImageOps | |
from torch.cuda import amp | |
from ultralytics.nn.autobackend import AutoBackend | |
from ultralytics.yolo.data.augment import LetterBox | |
from ultralytics.yolo.utils import LOGGER, colorstr | |
from ultralytics.yolo.utils.files import increment_path | |
from ultralytics.yolo.utils.ops import Profile, make_divisible, non_max_suppression, scale_boxes, xyxy2xywh | |
from ultralytics.yolo.utils.plotting import Annotator, colors, save_one_box | |
from ultralytics.yolo.utils.tal import dist2bbox, make_anchors | |
from ultralytics.yolo.utils.torch_utils import copy_attr, smart_inference_mode | |
# from utils.plots import feature_visualization TODO | |
def autopad(k, p=None, d=1): # kernel, padding, dilation | |
# Pad to 'same' shape outputs | |
if d > 1: | |
k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k] # actual kernel-size | |
if p is None: | |
p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad | |
return p | |
class Conv(nn.Module): | |
# Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation) | |
default_act = nn.SiLU() # default activation | |
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True): | |
super().__init__() | |
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False) | |
self.bn = nn.BatchNorm2d(c2) | |
self.act = self.default_act 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 forward_fuse(self, x): | |
return self.act(self.conv(x)) | |
class DWConv(Conv): | |
# Depth-wise convolution | |
def __init__(self, c1, c2, k=1, s=1, d=1, act=True): # ch_in, ch_out, kernel, stride, dilation, activation | |
super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act) | |
class DWConvTranspose2d(nn.ConvTranspose2d): | |
# Depth-wise transpose convolution | |
def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0): # ch_in, ch_out, kernel, stride, padding, padding_out | |
super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2)) | |
class ConvTranspose(nn.Module): | |
# Convolution transpose 2d layer | |
default_act = nn.SiLU() # default activation | |
def __init__(self, c1, c2, k=2, s=2, p=0, bn=True, act=True): | |
super().__init__() | |
self.conv_transpose = nn.ConvTranspose2d(c1, c2, k, s, p, bias=not bn) | |
self.bn = nn.BatchNorm2d(c2) if bn else nn.Identity() | |
self.act = self.default_act 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_transpose(x))) | |
class DFL(nn.Module): | |
# DFL module | |
def __init__(self, c1=16): | |
super().__init__() | |
self.conv = nn.Conv2d(c1, 1, 1, bias=False).requires_grad_(False) | |
x = torch.arange(c1, dtype=torch.float) | |
self.conv.weight.data[:] = nn.Parameter(x.view(1, c1, 1, 1)) | |
self.c1 = c1 | |
def forward(self, x): | |
b, c, a = x.shape # batch, channels, anchors | |
return self.conv(x.view(b, 4, self.c1, a).transpose(2, 1).softmax(1)).view(b, 4, a) | |
# return self.conv(x.view(b, self.c1, 4, a).softmax(1)).view(b, 4, a) | |
class TransformerLayer(nn.Module): | |
# Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance) | |
def __init__(self, c, num_heads): | |
super().__init__() | |
self.q = nn.Linear(c, c, bias=False) | |
self.k = nn.Linear(c, c, bias=False) | |
self.v = nn.Linear(c, c, bias=False) | |
self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads) | |
self.fc1 = nn.Linear(c, c, bias=False) | |
self.fc2 = nn.Linear(c, c, bias=False) | |
def forward(self, x): | |
x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x | |
x = self.fc2(self.fc1(x)) + x | |
return x | |
class TransformerBlock(nn.Module): | |
# Vision Transformer https://arxiv.org/abs/2010.11929 | |
def __init__(self, c1, c2, num_heads, num_layers): | |
super().__init__() | |
self.conv = None | |
if c1 != c2: | |
self.conv = Conv(c1, c2) | |
self.linear = nn.Linear(c2, c2) # learnable position embedding | |
self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers))) | |
self.c2 = c2 | |
def forward(self, x): | |
if self.conv is not None: | |
x = self.conv(x) | |
b, _, w, h = x.shape | |
p = x.flatten(2).permute(2, 0, 1) | |
return self.tr(p + self.linear(p)).permute(1, 2, 0).reshape(b, self.c2, w, h) | |
class Bottleneck(nn.Module): | |
# Standard bottleneck | |
def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5): # ch_in, ch_out, shortcut, kernels, groups, expand | |
super().__init__() | |
c_ = int(c2 * e) # hidden channels | |
self.cv1 = Conv(c1, c_, k[0], 1) | |
self.cv2 = Conv(c_, c2, k[1], 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().__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.SiLU() | |
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), 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().__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) # optional act=FReLU(c2) | |
self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) | |
def forward(self, x): | |
return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1)) | |
class C2(nn.Module): | |
# CSP Bottleneck with 2 convolutions | |
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__() | |
self.c = int(c2 * e) # hidden channels | |
self.cv1 = Conv(c1, 2 * self.c, 1, 1) | |
self.cv2 = Conv(2 * self.c, c2, 1) # optional act=FReLU(c2) | |
# self.attention = ChannelAttention(2 * self.c) # or SpatialAttention() | |
self.m = nn.Sequential(*(Bottleneck(self.c, self.c, shortcut, g, k=((3, 3), (3, 3)), e=1.0) for _ in range(n))) | |
def forward(self, x): | |
a, b = self.cv1(x).split((self.c, self.c), 1) | |
return self.cv2(torch.cat((self.m(a), b), 1)) | |
class C2f(nn.Module): | |
# CSP Bottleneck with 2 convolutions | |
def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__() | |
self.c = int(c2 * e) # hidden channels | |
self.cv1 = Conv(c1, 2 * self.c, 1, 1) | |
self.cv2 = Conv((2 + n) * self.c, c2, 1) # optional act=FReLU(c2) | |
self.m = nn.ModuleList(Bottleneck(self.c, self.c, shortcut, g, k=((3, 3), (3, 3)), e=1.0) for _ in range(n)) | |
def forward(self, x): | |
y = list(self.cv1(x).split((self.c, self.c), 1)) | |
y.extend(m(y[-1]) for m in self.m) | |
return self.cv2(torch.cat(y, 1)) | |
class ChannelAttention(nn.Module): | |
# Channel-attention module https://github.com/open-mmlab/mmdetection/tree/v3.0.0rc1/configs/rtmdet | |
def __init__(self, channels: int) -> None: | |
super().__init__() | |
self.pool = nn.AdaptiveAvgPool2d(1) | |
self.fc = nn.Conv2d(channels, channels, 1, 1, 0, bias=True) | |
self.act = nn.Sigmoid() | |
def forward(self, x: torch.Tensor) -> torch.Tensor: | |
return x * self.act(self.fc(self.pool(x))) | |
class SpatialAttention(nn.Module): | |
# Spatial-attention module | |
def __init__(self, kernel_size=7): | |
super().__init__() | |
assert kernel_size in (3, 7), 'kernel size must be 3 or 7' | |
padding = 3 if kernel_size == 7 else 1 | |
self.cv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False) | |
self.act = nn.Sigmoid() | |
def forward(self, x): | |
return x * self.act(self.cv1(torch.cat([torch.mean(x, 1, keepdim=True), torch.max(x, 1, keepdim=True)[0]], 1))) | |
class CBAM(nn.Module): | |
# CSP Bottleneck with 3 convolutions | |
def __init__(self, c1, ratio=16, kernel_size=7): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__() | |
self.channel_attention = ChannelAttention(c1) | |
self.spatial_attention = SpatialAttention(kernel_size) | |
def forward(self, x): | |
return self.spatial_attention(self.channel_attention(x)) | |
class C1(nn.Module): | |
# CSP Bottleneck with 3 convolutions | |
def __init__(self, c1, c2, n=1): # ch_in, ch_out, number, shortcut, groups, expansion | |
super().__init__() | |
self.cv1 = Conv(c1, c2, 1, 1) | |
self.m = nn.Sequential(*(Conv(c2, c2, 3) for _ in range(n))) | |
def forward(self, x): | |
y = self.cv1(x) | |
return self.m(y) + y | |
class C3x(C3): | |
# C3 module with cross-convolutions | |
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): | |
super().__init__(c1, c2, n, shortcut, g, e) | |
self.c_ = int(c2 * e) | |
self.m = nn.Sequential(*(Bottleneck(self.c_, self.c_, shortcut, g, k=((1, 3), (3, 1)), e=1) for _ in range(n))) | |
class C3TR(C3): | |
# C3 module with TransformerBlock() | |
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): | |
super().__init__(c1, c2, n, shortcut, g, e) | |
c_ = int(c2 * e) | |
self.m = TransformerBlock(c_, c_, 4, n) | |
class C3Ghost(C3): | |
# C3 module with GhostBottleneck() | |
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): | |
super().__init__(c1, c2, n, shortcut, g, e) | |
c_ = int(c2 * e) # hidden channels | |
self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n))) | |
class SPP(nn.Module): | |
# Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729 | |
def __init__(self, c1, c2, k=(5, 9, 13)): | |
super().__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) | |
with warnings.catch_warnings(): | |
warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning | |
return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) | |
class SPPF(nn.Module): | |
# Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher | |
def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13)) | |
super().__init__() | |
c_ = c1 // 2 # hidden channels | |
self.cv1 = Conv(c1, c_, 1, 1) | |
self.cv2 = Conv(c_ * 4, c2, 1, 1) | |
self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2) | |
def forward(self, x): | |
x = self.cv1(x) | |
with warnings.catch_warnings(): | |
warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning | |
y1 = self.m(x) | |
y2 = self.m(y1) | |
return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 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().__init__() | |
self.conv = Conv(c1 * 4, c2, k, s, p, g, act=act) | |
# self.contract = Contract(gain=2) | |
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)) | |
# return self.conv(self.contract(x)) | |
class GhostConv(nn.Module): | |
# Ghost Convolution https://github.com/huawei-noah/ghostnet | |
def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups | |
super().__init__() | |
c_ = c2 // 2 # hidden channels | |
self.cv1 = Conv(c1, c_, k, s, None, g, act=act) | |
self.cv2 = Conv(c_, c_, 5, 1, None, c_, act=act) | |
def forward(self, x): | |
y = self.cv1(x) | |
return torch.cat((y, self.cv2(y)), 1) | |
class GhostBottleneck(nn.Module): | |
# Ghost Bottleneck https://github.com/huawei-noah/ghostnet | |
def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride | |
super().__init__() | |
c_ = c2 // 2 | |
self.conv = nn.Sequential( | |
GhostConv(c1, c_, 1, 1), # pw | |
DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw | |
GhostConv(c_, c2, 1, 1, act=False)) # pw-linear | |
self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1, | |
act=False)) if s == 2 else nn.Identity() | |
def forward(self, x): | |
return self.conv(x) + self.shortcut(x) | |
class Concat(nn.Module): | |
# Concatenate a list of tensors along dimension | |
def __init__(self, dimension=1): | |
super().__init__() | |
self.d = dimension | |
def forward(self, x): | |
return torch.cat(x, self.d) | |
class AutoShape(nn.Module): | |
# YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS | |
conf = 0.25 # NMS confidence threshold | |
iou = 0.45 # NMS IoU threshold | |
agnostic = False # NMS class-agnostic | |
multi_label = False # NMS multiple labels per box | |
classes = None # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs | |
max_det = 1000 # maximum number of detections per image | |
amp = False # Automatic Mixed Precision (AMP) inference | |
def __init__(self, model, verbose=True): | |
super().__init__() | |
if verbose: | |
LOGGER.info('Adding AutoShape... ') | |
copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=()) # copy attributes | |
self.dmb = isinstance(model, AutoBackend) # DetectMultiBackend() instance | |
self.pt = not self.dmb or model.pt # PyTorch model | |
self.model = model.eval() | |
if self.pt: | |
m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect() | |
m.inplace = False # Detect.inplace=False for safe multithread inference | |
m.export = True # do not output loss values | |
def _apply(self, fn): | |
# Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers | |
self = super()._apply(fn) | |
if self.pt: | |
m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect() | |
m.stride = fn(m.stride) | |
m.grid = list(map(fn, m.grid)) | |
if isinstance(m.anchor_grid, list): | |
m.anchor_grid = list(map(fn, m.anchor_grid)) | |
return self | |
def forward(self, ims, size=640, augment=False, profile=False): | |
# Inference from various sources. For size(height=640, width=1280), RGB images example inputs are: | |
# file: ims = 'data/images/zidane.jpg' # str or PosixPath | |
# URI: = 'https://ultralytics.com/images/zidane.jpg' | |
# OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3) | |
# PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3) | |
# numpy: = np.zeros((640,1280,3)) # HWC | |
# 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 | |
dt = (Profile(), Profile(), Profile()) | |
with dt[0]: | |
if isinstance(size, int): # expand | |
size = (size, size) | |
p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device) # param | |
autocast = self.amp and (p.device.type != 'cpu') # Automatic Mixed Precision (AMP) inference | |
if isinstance(ims, torch.Tensor): # torch | |
with amp.autocast(autocast): | |
return self.model(ims.to(p.device).type_as(p), augment=augment) # inference | |
# Pre-process | |
n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims]) # number, list of images | |
shape0, shape1, files = [], [], [] # image and inference shapes, filenames | |
for i, im in enumerate(ims): | |
f = f'image{i}' # filename | |
if isinstance(im, (str, Path)): # filename or uri | |
im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im | |
im = np.asarray(ImageOps.exif_transpose(im)) | |
elif isinstance(im, Image.Image): # PIL Image | |
im, f = np.asarray(ImageOps.exif_transpose(im)), getattr(im, 'filename', f) or f | |
files.append(Path(f).with_suffix('.jpg').name) | |
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 cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) # enforce 3ch input | |
s = im.shape[:2] # HWC | |
shape0.append(s) # image shape | |
g = max(size) / max(s) # gain | |
shape1.append([y * g for y in s]) | |
ims[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update | |
shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)] if self.pt else size # inf shape | |
x = [LetterBox(shape1, auto=False)(image=im)["img"] for im in ims] # pad | |
x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2))) # stack and BHWC to BCHW | |
x = torch.from_numpy(x).to(p.device).type_as(p) / 255 # uint8 to fp16/32 | |
with amp.autocast(autocast): | |
# Inference | |
with dt[1]: | |
y = self.model(x, augment=augment) # forward | |
# Post-process | |
with dt[2]: | |
y = non_max_suppression(y if self.dmb else y[0], | |
self.conf, | |
self.iou, | |
self.classes, | |
self.agnostic, | |
self.multi_label, | |
max_det=self.max_det) # NMS | |
for i in range(n): | |
scale_boxes(shape1, y[i][:, :4], shape0[i]) | |
return Detections(ims, y, files, dt, self.names, x.shape) | |
class Detections: | |
# YOLOv5 detections class for inference results | |
def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None): | |
super().__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 ims] # normalizations | |
self.ims = ims # list of images as numpy arrays | |
self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) | |
self.names = names # class names | |
self.files = files # image filenames | |
self.times = times # profiling times | |
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) # number of images (batch size) | |
self.t = tuple(x.t / self.n * 1E3 for x in times) # timestamps (ms) | |
self.s = tuple(shape) # inference BCHW shape | |
def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')): | |
s, crops = '', [] | |
for i, (im, pred) in enumerate(zip(self.ims, self.pred)): | |
s += f'\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' # string | |
if pred.shape[0]: | |
for c in pred[:, -1].unique(): | |
n = (pred[:, -1] == c).sum() # detections per class | |
s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string | |
s = s.rstrip(', ') | |
if show or save or render or crop: | |
annotator = Annotator(im, example=str(self.names)) | |
for *box, conf, cls in reversed(pred): # xyxy, confidence, class | |
label = f'{self.names[int(cls)]} {conf:.2f}' | |
if crop: | |
file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None | |
crops.append({ | |
'box': box, | |
'conf': conf, | |
'cls': cls, | |
'label': label, | |
'im': save_one_box(box, im, file=file, save=save)}) | |
else: # all others | |
annotator.box_label(box, label if labels else '', color=colors(cls)) | |
im = annotator.im | |
else: | |
s += '(no detections)' | |
im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np | |
if show: | |
im.show(self.files[i]) # show | |
if save: | |
f = self.files[i] | |
im.save(save_dir / f) # save | |
if i == self.n - 1: | |
LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}") | |
if render: | |
self.ims[i] = np.asarray(im) | |
if pprint: | |
s = s.lstrip('\n') | |
return f'{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}' % self.t | |
if crop: | |
if save: | |
LOGGER.info(f'Saved results to {save_dir}\n') | |
return crops | |
def show(self, labels=True): | |
self._run(show=True, labels=labels) # show results | |
def save(self, labels=True, save_dir='runs/detect/exp', exist_ok=False): | |
save_dir = increment_path(save_dir, exist_ok, mkdir=True) # increment save_dir | |
self._run(save=True, labels=labels, save_dir=save_dir) # save results | |
def crop(self, save=True, save_dir='runs/detect/exp', exist_ok=False): | |
save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None | |
return self._run(crop=True, save=save, save_dir=save_dir) # crop results | |
def render(self, labels=True): | |
self._run(render=True, labels=labels) # render results | |
return self.ims | |
def pandas(self): | |
# return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0]) | |
new = copy(self) # return copy | |
ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns | |
cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns | |
for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]): | |
a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update | |
setattr(new, k, [pd.DataFrame(x, columns=c) for x in a]) | |
return new | |
def tolist(self): | |
# return a list of Detections objects, i.e. 'for result in results.tolist():' | |
r = range(self.n) # iterable | |
x = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r] | |
# for d in x: | |
# for k in ['ims', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']: | |
# setattr(d, k, getattr(d, k)[0]) # pop out of list | |
return x | |
def print(self): | |
LOGGER.info(self.__str__()) | |
def __len__(self): # override len(results) | |
return self.n | |
def __str__(self): # override print(results) | |
return self._run(pprint=True) # print results | |
def __repr__(self): | |
return f'YOLOv5 {self.__class__} instance\n' + self.__str__() | |
class Proto(nn.Module): | |
# YOLOv8 mask Proto module for segmentation models | |
def __init__(self, c1, c_=256, c2=32): # ch_in, number of protos, number of masks | |
super().__init__() | |
self.cv1 = Conv(c1, c_, k=3) | |
self.upsample = nn.ConvTranspose2d(c_, c_, 2, 2, 0, bias=True) # nn.Upsample(scale_factor=2, mode='nearest') | |
self.cv2 = Conv(c_, c_, k=3) | |
self.cv3 = Conv(c_, c2) | |
def forward(self, x): | |
return self.cv3(self.cv2(self.upsample(self.cv1(x)))) | |
class Ensemble(nn.ModuleList): | |
# Ensemble of models | |
def __init__(self): | |
super().__init__() | |
def forward(self, x, augment=False, profile=False, visualize=False): | |
y = [module(x, augment, profile, visualize)[0] for module in self] | |
# y = torch.stack(y).max(0)[0] # max ensemble | |
# y = torch.stack(y).mean(0) # mean ensemble | |
y = torch.cat(y, 1) # nms ensemble | |
return y, None # inference, train output | |
# heads | |
class Detect(nn.Module): | |
# YOLOv5 Detect head for detection models | |
dynamic = False # force grid reconstruction | |
export = False # export mode | |
shape = None | |
anchors = torch.empty(0) # init | |
strides = torch.empty(0) # init | |
def __init__(self, nc=80, ch=()): # detection layer | |
super().__init__() | |
self.nc = nc # number of classes | |
self.nl = len(ch) # number of detection layers | |
self.reg_max = 16 # DFL channels (ch[0] // 16 to scale 4/8/12/16/20 for n/s/m/l/x) | |
self.no = nc + self.reg_max * 4 # number of outputs per anchor | |
self.stride = torch.zeros(self.nl) # strides computed during build | |
c2, c3 = max((16, ch[0] // 4, self.reg_max * 4)), max(ch[0], self.nc) # channels | |
self.cv2 = nn.ModuleList( | |
nn.Sequential(Conv(x, c2, 3), Conv(c2, c2, 3), nn.Conv2d(c2, 4 * self.reg_max, 1)) for x in ch) | |
self.cv3 = nn.ModuleList(nn.Sequential(Conv(x, c3, 3), Conv(c3, c3, 3), nn.Conv2d(c3, self.nc, 1)) for x in ch) | |
self.dfl = DFL(self.reg_max) if self.reg_max > 1 else nn.Identity() | |
def forward(self, x): | |
shape = x[0].shape # BCHW | |
for i in range(self.nl): | |
x[i] = torch.cat((self.cv2[i](x[i]), self.cv3[i](x[i])), 1) | |
if self.training: | |
return x | |
elif self.dynamic or self.shape != shape: | |
self.anchors, self.strides = (x.transpose(0, 1) for x in make_anchors(x, self.stride, 0.5)) | |
self.shape = shape | |
box, cls = torch.cat([xi.view(shape[0], self.no, -1) for xi in x], 2).split((self.reg_max * 4, self.nc), 1) | |
dbox = dist2bbox(self.dfl(box), self.anchors.unsqueeze(0), xywh=True, dim=1) * self.strides | |
y = torch.cat((dbox, cls.sigmoid()), 1) | |
return y if self.export else (y, x) | |
def bias_init(self): | |
# Initialize Detect() biases, WARNING: requires stride availability | |
m = self # self.model[-1] # Detect() module | |
# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1 | |
# ncf = math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum()) # nominal class frequency | |
for a, b, s in zip(m.cv2, m.cv3, m.stride): # from | |
a[-1].bias.data[:] = 1.0 # box | |
b[-1].bias.data[:m.nc] = math.log(5 / m.nc / (640 / s) ** 2) # cls (.01 objects, 80 classes, 640 img) | |
class Segment(Detect): | |
# YOLOv5 Segment head for segmentation models | |
def __init__(self, nc=80, nm=32, npr=256, ch=()): | |
super().__init__(nc, ch) | |
self.nm = nm # number of masks | |
self.npr = npr # number of protos | |
self.proto = Proto(ch[0], self.npr, self.nm) # protos | |
self.detect = Detect.forward | |
c4 = max(ch[0] // 4, self.nm) | |
self.cv4 = nn.ModuleList(nn.Sequential(Conv(x, c4, 3), Conv(c4, c4, 3), nn.Conv2d(c4, self.nm, 1)) for x in ch) | |
def forward(self, x): | |
p = self.proto(x[0]) # mask protos | |
bs = p.shape[0] # batch size | |
mc = torch.cat([self.cv4[i](x[i]).view(bs, self.nm, -1) for i in range(self.nl)], 2) # mask coefficients | |
x = self.detect(self, x) | |
if self.training: | |
return x, mc, p | |
return (torch.cat([x, mc], 1), p) if self.export else (torch.cat([x[0], mc], 1), (x[1], mc, p)) | |
class Classify(nn.Module): | |
# YOLOv5 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().__init__() | |
c_ = 1280 # efficientnet_b0 size | |
self.conv = Conv(c1, c_, k, s, autopad(k, p), g) | |
self.pool = nn.AdaptiveAvgPool2d(1) # to x(b,c_,1,1) | |
self.drop = nn.Dropout(p=0.0, inplace=True) | |
self.linear = nn.Linear(c_, c2) # to x(b,c2) | |
def forward(self, x): | |
if isinstance(x, list): | |
x = torch.cat(x, 1) | |
return self.linear(self.drop(self.pool(self.conv(x)).flatten(1))) | |