Source code for yolort.v5.models.common

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
"""
Common modules
"""

import logging
import math
from copy import copy
from pathlib import Path
from typing import List

import numpy as np
import pandas as pd
import requests
import torch
from PIL import Image
from torch import nn, Tensor
from torch.cuda import amp
from yolort.v5.utils.general import (
    colorstr,
    increment_path,
    is_ascii,
    make_divisible,
    non_max_suppression,
    scale_coords,
    xyxy2xywh,
)
from yolort.v5.utils.plots import Annotator, colors, save_one_box
from yolort.v5.utils.torch_utils import copy_attr, time_sync

LOGGER = logging.getLogger(__name__)


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


[docs]class Conv(nn.Module): """ Standard convolution Args: c1 (int): ch_in c2 (int): ch_out k (int): kernel s (int): stride p (Optional[int]): padding g (int): groups act (bool or nn.Module): determine the activation function version (str): Module version released by ultralytics. Possible values are ["r3.1", "r4.0"]. Default: "r4.0". """ def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, version="r4.0"): super().__init__() self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) self.bn = nn.BatchNorm2d(c2) if version == "r4.0": self.act = nn.SiLU() if act else (act if isinstance(act, nn.Module) else nn.Identity()) elif version == "r3.1": self.act = nn.Hardswish() if act else (act if isinstance(act, nn.Module) else nn.Identity()) else: raise NotImplementedError(f"Currently doesn't support version {version}.") def forward(self, x: Tensor) -> Tensor: return self.act(self.bn(self.conv(x)))
[docs] def fuseforward(self, x): return self.act(self.conv(x))
[docs]class DWConv(Conv): """ Depth-wise convolution class. Args: c1 (int): ch_in c2 (int): ch_out k (int): kernel s (int): stride act (bool or nn.Module): determine the activation function version (str): Module version released by ultralytics. Possible values are ["r3.1", "r4.0"]. Default: "r4.0". """ def __init__(self, c1, c2, k=1, s=1, act=True, version="r4.0"): super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), act=act, version=version)
[docs]class Bottleneck(nn.Module): """ Standard bottleneck Args: c1 (int): ch_in c2 (int): ch_out shortcut (bool): shortcut g (int): groups e (float): expansion version (str): Module version released by ultralytics. Possible values are ["r3.1", "r4.0"]. Default: "r4.0". """ def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, version="r4.0"): super().__init__() c_ = int(c2 * e) # hidden channels self.cv1 = Conv(c1, c_, 1, 1, version=version) self.cv2 = Conv(c_, c2, 3, 1, g=g, version=version) 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))
[docs]class BottleneckCSP(nn.Module): """ CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks Args: c1 (int): ch_in c2 (int): ch_out n (int): number shortcut (bool): shortcut g (int): groups e (float): expansion """ def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): super().__init__() c_ = int(c2 * e) # hidden channels self.cv1 = Conv(c1, c_, 1, 1, version="r3.1") 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, version="r3.1") 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, version="r3.1") 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))))
[docs]class C3(nn.Module): """ CSP Bottleneck with 3 convolutions Args: c1 (int): ch_in c2 (int): ch_out n (int): number shortcut (bool): shortcut g (int): groups e (float): expansion version (str): Module version released by ultralytics. Possible values are ["r4.0"]. Default: "r4.0". """ def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, version="r4.0"): super().__init__() c_ = int(c2 * e) # hidden channels self.cv1 = Conv(c1, c_, 1, 1, version=version) self.cv2 = Conv(c1, c_, 1, 1, version=version) self.cv3 = Conv(2 * c_, c2, 1, version=version) # act=FReLU(c2) self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0, version=version) for _ in range(n)]) def forward(self, x): return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))
[docs]class SPP(nn.Module): # Spatial pyramid pooling layer used in YOLOv3-SPP def __init__(self, c1, c2, k=(5, 9, 13), version="r4.0"): super().__init__() c_ = c1 // 2 # hidden channels self.cv1 = Conv(c1, c_, 1, 1, version=version) self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1, version=version) 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))
[docs]class SPPF(nn.Module): """ Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher """ def __init__(self, c1, c2, k=5, version="r4.0"): # Equivalent to SPP(k=(5, 9, 13)) when k=5 super().__init__() c_ = c1 // 2 # hidden channels self.cv1 = Conv(c1, c_, 1, 1, version=version) self.cv2 = Conv(c_ * 4, c2, 1, 1, version=version) self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2) def forward(self, x): x = self.cv1(x) y1 = self.m(x) y2 = self.m(y1) return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))
[docs]class Focus(nn.Module): """ Focus wh information into c-space Args: c1 (int): ch_in c2 (int): ch_out k (int): kernel s (int): stride p (Optional[int]): padding g (int): groups act (bool or nn.Module): determine the activation function version (str): Module version released by ultralytics. Possible values are ["r3.1", "r4.0"]. Default: "r4.0". """ def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, version="r4.0"): super().__init__() self.conv = Conv(c1 * 4, c2, k, s, p, g, act, version=version) def forward(self, x: Tensor) -> Tensor: y = focus_transform(x) out = self.conv(y) return out
[docs]def focus_transform(x: Tensor) -> Tensor: """x(b,c,w,h) -> y(b,4c,w/2,h/2)""" y = torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1) return y
[docs]def space_to_depth(x: Tensor) -> Tensor: """x(b,c,w,h) -> y(b,4c,w/2,h/2)""" N, C, H, W = x.size() x = x.reshape(N, C, H // 2, 2, W // 2, 2) x = x.permute(0, 5, 3, 1, 2, 4) y = x.reshape(N, C * 4, H // 2, W // 2) return y
[docs]class Concat(nn.Module): # Concatenate a list of tensors along dimension def __init__(self, dimension: int = 1): super().__init__() self.d = dimension # torchscript does not yet support *args, so we overload method # allowing it to take either a List[Tensor] or single Tensor def forward(self, x: List[Tensor]) -> Tensor: if isinstance(x, Tensor): prev_features = [x] else: prev_features = x return torch.cat(prev_features, self.d)
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 TransformerLayer(nn.Module): """ Transformer layer <https://arxiv.org/abs/2010.11929>. Remove the LayerNorm layers for better performance Args: c (int): number of channels num_heads: number of heads """ 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>. Args: c1 (int): number of input channels c2 (int): number of output channels num_heads: number of heads num_layers: number of layers """ def __init__(self, c1, c2, num_heads, num_layers): super().__init__() self.conv = None if c1 != c2: self.conv = Conv(c1, c2, version="r4.0") 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).unsqueeze(0).transpose(0, 3).squeeze(3) return self.tr(p + self.linear(p)).unsqueeze(3).transpose(0, 3).reshape(b, self.c2, w, h)
[docs]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, version="r4.0") c_ = int(c2 * e) self.m = TransformerBlock(c_, c_, 4, n)
class C3SPP(C3): # C3 module with SPP() def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5): super().__init__(c1, c2, n, shortcut, g, e) c_ = int(c2 * e) self.m = SPP(c_, c_, k) 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)])
[docs]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): super().__init__() c_ = c2 // 2 # hidden channels self.cv1 = Conv(c1, c_, k, s, None, g, act) self.cv2 = Conv(c_, c_, 5, 1, None, c_, act) def forward(self, x): y = self.cv1(x) return torch.cat([y, self.cv2(y)], 1)
[docs]class GhostBottleneck(nn.Module): # Ghost Bottleneck https://github.com/huawei-noah/ghostnet def __init__(self, c1, c2, k=3, s=1): 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)
[docs]class Contract(nn.Module): # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40) def __init__(self, gain=2): super().__init__() self.gain = gain def forward(self, x): b, c, h, w = x.size() # assert (h / s == 0) and (W / s == 0), 'Indivisible gain' s = self.gain x = x.view(b, c, h // s, s, w // s, s) # x(1,64,40,2,40,2) x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40) return x.view(b, c * s * s, h // s, w // s) # x(1,256,40,40)
[docs]class Expand(nn.Module): # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160) def __init__(self, gain=2): super().__init__() self.gain = gain def forward(self, x): b, c, h, w = x.size() # assert C / s ** 2 == 0, 'Indivisible gain' s = self.gain x = x.view(b, s, s, c // s ** 2, h, w) # x(1,2,2,16,80,80) x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2) return x.view(b, c // s ** 2, h * s, w * s) # x(1,16,160,160)
[docs]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 # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs classes = None multi_label = False # NMS multiple labels per box max_det = 1000 # maximum number of detections per image def __init__(self, model): super().__init__() LOGGER.info("Adding AutoShape... ") # copy attributes copy_attr(self, model, include=("yaml", "nc", "hyp", "names", "stride", "abc"), exclude=()) self.model = model.eval() def _apply(self, fn): """ Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers """ self = super()._apply(fn) m = 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
[docs] @torch.no_grad() def forward(self, imgs, size=640, augment=False, profile=False): """ Inference from various sources. For height=640, width=1280, RGB images example inputs are: - file: imgs = '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 """ from yolort.v5.utils.augmentations import letterbox from yolort.v5.utils.datasets import exif_transpose t = [time_sync()] p = next(self.model.parameters()) # for device and type if isinstance(imgs, torch.Tensor): # torch with amp.autocast(enabled=p.device.type != "cpu"): return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference # Pre-process # number of images, list of images n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # image and inference shapes, filenames shape0, shape1, files = [], [], [] for i, im in enumerate(imgs): 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(exif_transpose(im)) elif isinstance(im, Image.Image): # PIL Image im, f = np.asarray(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 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 if im.data.contiguous else np.ascontiguousarray(im) # update # inference shape shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] 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 t.append(time_sync()) with amp.autocast(enabled=p.device.type != "cpu"): # Inference y = self.model(x, augment, profile)[0] # forward t.append(time_sync()) # Post-process y = non_max_suppression( y, self.conf, iou_thres=self.iou, classes=self.classes, multi_label=self.multi_label, max_det=self.max_det, ) for i in range(n): scale_coords(shape1, y[i][:, :4], shape0[i]) t.append(time_sync()) return Detections(imgs, y, files, t, self.names, x.shape)
class Detections: # YOLOv5 detections class for inference results def __init__(self, imgs, pred, files, times=None, names=None, shape=None): super().__init__() d = pred[0].device # device # normalizations gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1.0, 1.0], device=d) for im in imgs] 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.ascii = is_ascii(names) # names are ascii (use PIL for UTF-8) self.files = files # image filenames 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((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms) self.s = shape # inference BCHW shape def display( self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path(""), ): crops = [] for i, (im, pred) in enumerate(zip(self.imgs, self.pred)): str = f"image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} " if pred.shape[0]: for c in pred[:, -1].unique(): n = (pred[:, -1] == c).sum() # detections per class str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string if show or save or render or crop: annotator = Annotator(im, pil=not self.ascii) 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, color=colors(cls)) im = annotator.im else: str += "(no detections)" im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np if pprint: LOGGER.info(str.rstrip(", ")) 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.imgs[i] = np.asarray(im) if crop: if save: LOGGER.info(f"Saved results to {save_dir}\n") return crops def print(self): self.display(pprint=True) # print results LOGGER.info( f"Speed: {self.t[0]:.1f}ms pre-process, {self.t[1]:.1f}ms inference, " f"{self.t[2]:.1f}ms NMS per image at shape {tuple(self.s)}" ) def show(self): self.display(show=True) # show results def save(self, save_dir="runs/detect/exp"): # increment save_dir save_dir = increment_path(save_dir, exist_ok=save_dir != "runs/detect/exp", mkdir=True) self.display(save=True, save_dir=save_dir) # save results def crop(self, save=True, save_dir="runs/detect/exp"): save_dir = ( increment_path(save_dir, exist_ok=save_dir != "runs/detect/exp", mkdir=True) if save else None ) return self.display(crop=True, save=save, save_dir=save_dir) # crop results def render(self): self.display(render=True) # render results return self.imgs def pandas(self): # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0]) new = copy(self) # return copy # xyxy columns ca = ("xmin", "ymin", "xmax", "ymax", "confidence", "class", "name") # xywh columns cb = ("xcenter", "ycenter", "width", "height", "confidence", "class", "name") for k, c in zip(["xyxy", "xyxyn", "xywh", "xywhn"], [ca, ca, cb, cb]): # update a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] 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():' x = [Detections([self.imgs[i]], [self.pred[i]], self.names, self.s) 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 def __len__(self): return self.n 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().__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 = nn.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)