494 lines
20 KiB
Python
494 lines
20 KiB
Python
# Ultralytics YOLOv5 🚀, AGPL-3.0 license
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"""
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YOLO-specific modules.
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Usage:
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$ python models/yolo.py --cfg yolov5s.yaml
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"""
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import argparse
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import contextlib
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import math
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import os
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import platform
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import sys
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from copy import deepcopy
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from pathlib import Path
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import torch
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import torch.nn as nn
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FILE = Path(__file__).resolve()
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ROOT = FILE.parents[1] # YOLOv5 root directory
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if str(ROOT) not in sys.path:
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sys.path.append(str(ROOT)) # add ROOT to PATH
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if platform.system() != "Windows":
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ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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from models.common import (
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C3,
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C3SPP,
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C3TR,
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SPP,
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SPPF,
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Bottleneck,
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BottleneckCSP,
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C3Ghost,
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C3x,
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Classify,
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Concat,
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Contract,
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Conv,
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CrossConv,
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DetectMultiBackend,
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DWConv,
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DWConvTranspose2d,
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Expand,
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Focus,
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GhostBottleneck,
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GhostConv,
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Proto,
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)
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from models.experimental import MixConv2d
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from models.lsknet import LSKNet
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from utils.autoanchor import check_anchor_order
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from utils.general import LOGGER, check_version, check_yaml, colorstr, make_divisible, print_args
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from utils.plots import feature_visualization
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from utils.torch_utils import (
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fuse_conv_and_bn,
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initialize_weights,
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model_info,
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profile,
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scale_img,
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select_device,
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time_sync,
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)
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try:
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import thop # for FLOPs computation
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except ImportError:
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thop = None
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class Detect(nn.Module):
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# YOLOv5 Detect head for detection models
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stride = None # strides computed during build
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dynamic = False # force grid reconstruction
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export = False # export mode
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def __init__(self, nc=80, anchors=(), ch=(), inplace=True):
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"""Initializes YOLOv5 detection layer with specified classes, anchors, channels, and inplace operations."""
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super().__init__()
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self.nc = nc # number of classes
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self.no = nc + 5 # number of outputs per anchor
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self.nl = len(anchors) # number of detection layers
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self.na = len(anchors[0]) // 2 # number of anchors
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self.grid = [torch.empty(0) for _ in range(self.nl)] # init grid
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self.anchor_grid = [torch.empty(0) for _ in range(self.nl)] # init anchor grid
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self.register_buffer("anchors", torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2)
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self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
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self.inplace = inplace # use inplace ops (e.g. slice assignment)
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def forward(self, x):
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"""Processes input through YOLOv5 layers, altering shape for detection: `x(bs, 3, ny, nx, 85)`."""
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z = [] # inference output
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for i in range(self.nl):
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x[i] = self.m[i](x[i]) # conv
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bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85)
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x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
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if not self.training: # inference
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if self.dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
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self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)
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if isinstance(self, Segment): # (boxes + masks)
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xy, wh, conf, mask = x[i].split((2, 2, self.nc + 1, self.no - self.nc - 5), 4)
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xy = (xy.sigmoid() * 2 + self.grid[i]) * self.stride[i] # xy
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wh = (wh.sigmoid() * 2) ** 2 * self.anchor_grid[i] # wh
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y = torch.cat((xy, wh, conf.sigmoid(), mask), 4)
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else: # Detect (boxes only)
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xy, wh, conf = x[i].sigmoid().split((2, 2, self.nc + 1), 4)
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xy = (xy * 2 + self.grid[i]) * self.stride[i] # xy
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wh = (wh * 2) ** 2 * self.anchor_grid[i] # wh
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y = torch.cat((xy, wh, conf), 4)
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z.append(y.view(bs, self.na * nx * ny, self.no))
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return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x)
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def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, "1.10.0")):
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"""Generates a mesh grid for anchor boxes with optional compatibility for torch versions < 1.10."""
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d = self.anchors[i].device
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t = self.anchors[i].dtype
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shape = 1, self.na, ny, nx, 2 # grid shape
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y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t)
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yv, xv = torch.meshgrid(y, x, indexing="ij") if torch_1_10 else torch.meshgrid(y, x) # torch>=0.7 compatibility
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grid = torch.stack((xv, yv), 2).expand(shape) - 0.5 # add grid offset, i.e. y = 2.0 * x - 0.5
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anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape)
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return grid, anchor_grid
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class Segment(Detect):
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# YOLOv5 Segment head for segmentation models
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def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), inplace=True):
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"""Initializes YOLOv5 Segment head with options for mask count, protos, and channel adjustments."""
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super().__init__(nc, anchors, ch, inplace)
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self.nm = nm # number of masks
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self.npr = npr # number of protos
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self.no = 5 + nc + self.nm # number of outputs per anchor
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self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
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self.proto = Proto(ch[0], self.npr, self.nm) # protos
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self.detect = Detect.forward
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def forward(self, x):
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"""Processes input through the network, returning detections and prototypes; adjusts output based on
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training/export mode.
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"""
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p = self.proto(x[0])
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x = self.detect(self, x)
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return (x, p) if self.training else (x[0], p) if self.export else (x[0], p, x[1])
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class BaseModel(nn.Module):
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"""YOLOv5 base model."""
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def forward(self, x, profile=False, visualize=False):
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"""Executes a single-scale inference or training pass on the YOLOv5 base model, with options for profiling and
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visualization.
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"""
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return self._forward_once(x, profile, visualize) # single-scale inference, train
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def _forward_once(self, x, profile=False, visualize=False):
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"""Performs a forward pass on the YOLOv5 model, enabling profiling and feature visualization options."""
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y, dt = [], [] # outputs
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for m in self.model:
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if m.f != -1: # if not from previous layer
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x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
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if profile:
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self._profile_one_layer(m, x, dt)
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x = m(x) # run
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y.append(x if m.i in self.save else None) # save output
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if visualize:
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feature_visualization(x, m.type, m.i, save_dir=visualize)
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return x
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def _profile_one_layer(self, m, x, dt):
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"""Profiles a single layer's performance by computing GFLOPs, execution time, and parameters."""
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c = m == self.model[-1] # is final layer, copy input as inplace fix
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o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1e9 * 2 if thop else 0 # FLOPs
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t = time_sync()
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for _ in range(10):
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m(x.copy() if c else x)
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dt.append((time_sync() - t) * 100)
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if m == self.model[0]:
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LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} module")
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LOGGER.info(f"{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}")
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if c:
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LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total")
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def fuse(self):
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"""Fuses Conv2d() and BatchNorm2d() layers in the model to improve inference speed."""
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LOGGER.info("Fusing layers... ")
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for m in self.model.modules():
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if isinstance(m, (Conv, DWConv)) and hasattr(m, "bn"):
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m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
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delattr(m, "bn") # remove batchnorm
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m.forward = m.forward_fuse # update forward
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self.info()
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return self
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def info(self, verbose=False, img_size=640):
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"""Prints model information given verbosity and image size, e.g., `info(verbose=True, img_size=640)`."""
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model_info(self, verbose, img_size)
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def _apply(self, fn):
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"""Applies transformations like to(), cpu(), cuda(), half() to model tensors excluding parameters or registered
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buffers.
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"""
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self = super()._apply(fn)
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m = self.model[-1] # Detect()
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if isinstance(m, (Detect, Segment)):
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m.stride = fn(m.stride)
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m.grid = list(map(fn, m.grid))
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if isinstance(m.anchor_grid, list):
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m.anchor_grid = list(map(fn, m.anchor_grid))
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return self
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class DetectionModel(BaseModel):
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# YOLOv5 detection model
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def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None, anchors=None):
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"""Initializes YOLOv5 model with configuration file, input channels, number of classes, and custom anchors."""
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super().__init__()
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if isinstance(cfg, dict):
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self.yaml = cfg # model dict
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else: # is *.yaml
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import yaml # for torch hub
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self.yaml_file = Path(cfg).name
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with open(cfg, encoding="ascii", errors="ignore") as f:
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self.yaml = yaml.safe_load(f) # model dict
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# Define model
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ch = self.yaml["ch"] = self.yaml.get("ch", ch) # input channels 3
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if nc and nc != self.yaml["nc"]:
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LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
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self.yaml["nc"] = nc # override yaml value
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if anchors:
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LOGGER.info(f"Overriding model.yaml anchors with anchors={anchors}")
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self.yaml["anchors"] = round(anchors) # override yaml value
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self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist
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self.names = [str(i) for i in range(self.yaml["nc"])] # default names
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self.inplace = self.yaml.get("inplace", True)
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# Build strides, anchors
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m = self.model[-1] # Detect()
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if isinstance(m, (Detect, Segment)):
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def _forward(x):
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"""Passes the input 'x' through the model and returns the processed output."""
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return self.forward(x)[0] if isinstance(m, Segment) else self.forward(x)
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s = 256 # 2x min stride
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m.inplace = self.inplace
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m.stride = torch.tensor([s / x.shape[-2] for x in _forward(torch.zeros(1, ch, s, s))]) # forward
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check_anchor_order(m)
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m.anchors /= m.stride.view(-1, 1, 1)
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self.stride = m.stride
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self._initialize_biases() # only run once
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# Init weights, biases
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initialize_weights(self)
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self.info()
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LOGGER.info("")
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def forward(self, x, augment=False, profile=False, visualize=False):
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"""Performs single-scale or augmented inference and may include profiling or visualization."""
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if augment:
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return self._forward_augment(x) # augmented inference, None
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return self._forward_once(x, profile, visualize) # single-scale inference, train
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def _forward_augment(self, x):
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"""Performs augmented inference across different scales and flips, returning combined detections."""
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img_size = x.shape[-2:] # height, width
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s = [1, 0.83, 0.67] # scales
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f = [None, 3, None] # flips (2-ud, 3-lr)
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y = [] # outputs
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for si, fi in zip(s, f):
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xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
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yi = self._forward_once(xi)[0] # forward
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# cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save
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yi = self._descale_pred(yi, fi, si, img_size)
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y.append(yi)
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y = self._clip_augmented(y) # clip augmented tails
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return torch.cat(y, 1), None # augmented inference, train
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def _descale_pred(self, p, flips, scale, img_size):
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"""De-scales predictions from augmented inference, adjusting for flips and image size."""
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if self.inplace:
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p[..., :4] /= scale # de-scale
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if flips == 2:
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p[..., 1] = img_size[0] - p[..., 1] # de-flip ud
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elif flips == 3:
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p[..., 0] = img_size[1] - p[..., 0] # de-flip lr
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else:
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x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale # de-scale
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if flips == 2:
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y = img_size[0] - y # de-flip ud
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elif flips == 3:
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x = img_size[1] - x # de-flip lr
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p = torch.cat((x, y, wh, p[..., 4:]), -1)
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return p
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def _clip_augmented(self, y):
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"""Clips augmented inference tails for YOLOv5 models, affecting first and last tensors based on grid points and
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layer counts.
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"""
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nl = self.model[-1].nl # number of detection layers (P3-P5)
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g = sum(4**x for x in range(nl)) # grid points
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e = 1 # exclude layer count
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i = (y[0].shape[1] // g) * sum(4**x for x in range(e)) # indices
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y[0] = y[0][:, :-i] # large
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i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e)) # indices
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y[-1] = y[-1][:, i:] # small
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return y
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def _initialize_biases(self, cf=None):
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"""
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Initializes biases for YOLOv5's Detect() module, optionally using class frequencies (cf).
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For details see https://arxiv.org/abs/1708.02002 section 3.3.
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"""
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# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
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m = self.model[-1] # Detect() module
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for mi, s in zip(m.m, m.stride): # from
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b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85)
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b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image)
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b.data[:, 5 : 5 + m.nc] += (
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math.log(0.6 / (m.nc - 0.99999)) if cf is None else torch.log(cf / cf.sum())
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) # cls
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mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
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Model = DetectionModel # retain YOLOv5 'Model' class for backwards compatibility
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class SegmentationModel(DetectionModel):
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# YOLOv5 segmentation model
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def __init__(self, cfg="yolov5s-seg.yaml", ch=3, nc=None, anchors=None):
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"""Initializes a YOLOv5 segmentation model with configurable params: cfg (str) for configuration, ch (int) for channels, nc (int) for num classes, anchors (list)."""
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super().__init__(cfg, ch, nc, anchors)
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class ClassificationModel(BaseModel):
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# YOLOv5 classification model
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def __init__(self, cfg=None, model=None, nc=1000, cutoff=10):
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"""Initializes YOLOv5 model with config file `cfg`, input channels `ch`, number of classes `nc`, and `cuttoff`
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index.
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"""
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super().__init__()
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self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg)
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def _from_detection_model(self, model, nc=1000, cutoff=10):
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"""Creates a classification model from a YOLOv5 detection model, slicing at `cutoff` and adding a classification
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layer.
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"""
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if isinstance(model, DetectMultiBackend):
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model = model.model # unwrap DetectMultiBackend
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model.model = model.model[:cutoff] # backbone
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m = model.model[-1] # last layer
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ch = m.conv.in_channels if hasattr(m, "conv") else m.cv1.conv.in_channels # ch into module
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c = Classify(ch, nc) # Classify()
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c.i, c.f, c.type = m.i, m.f, "models.common.Classify" # index, from, type
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model.model[-1] = c # replace
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self.model = model.model
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self.stride = model.stride
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self.save = []
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self.nc = nc
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def _from_yaml(self, cfg):
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"""Creates a YOLOv5 classification model from a specified *.yaml configuration file."""
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self.model = None
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def parse_model(d, ch):
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"""Parses a YOLOv5 model from a dict `d`, configuring layers based on input channels `ch` and model architecture."""
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LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}")
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anchors, nc, gd, gw, act, ch_mul = (
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d["anchors"], # 配置文件中的anchors
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d["nc"], # 80
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d["depth_multiple"],
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d["width_multiple"],
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d.get("activation"), # None
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d.get("channel_multiple"), # None
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)
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if act:
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Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU()
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LOGGER.info(f"{colorstr('activation:')} {act}") # print
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if not ch_mul: # True
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ch_mul = 8
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na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors 3
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no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
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# c2: 3
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layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out
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for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]): # from, number, module, args
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m = eval(m) if isinstance(m, str) else m # eval strings
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for j, a in enumerate(args):
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with contextlib.suppress(NameError):
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args[j] = eval(a) if isinstance(a, str) else a # eval strings
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n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain
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if m in {
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Conv,
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GhostConv,
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Bottleneck,
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GhostBottleneck,
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SPP,
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SPPF,
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DWConv,
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MixConv2d,
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Focus,
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CrossConv,
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BottleneckCSP,
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C3,
|
||
C3TR,
|
||
C3SPP,
|
||
C3Ghost,
|
||
nn.ConvTranspose2d,
|
||
DWConvTranspose2d,
|
||
C3x,
|
||
}:
|
||
c1, c2 = ch[f], args[0] # 3, 64
|
||
if c2 != no: # if not output no: anchors * (classes + 5)
|
||
c2 = make_divisible(c2 * gw, ch_mul) # 16
|
||
|
||
args = [c1, c2, *args[1:]]
|
||
if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x}:
|
||
args.insert(2, n) # number of repeats
|
||
n = 1
|
||
elif m is nn.BatchNorm2d:
|
||
args = [ch[f]]
|
||
elif m is Concat:
|
||
c2 = sum(ch[x] for x in f)
|
||
# TODO: channel, gw, gd
|
||
elif m in {Detect, Segment}:
|
||
args.append([ch[x] for x in f])
|
||
if isinstance(args[1], int): # number of anchors
|
||
args[1] = [list(range(args[1] * 2))] * len(f)
|
||
if m is Segment:
|
||
args[3] = make_divisible(args[3] * gw, ch_mul)
|
||
elif m is Contract:
|
||
c2 = ch[f] * args[0] ** 2
|
||
elif m is Expand:
|
||
c2 = ch[f] // args[0] ** 2
|
||
elif m is LSKNet:
|
||
c2 = 16
|
||
else:
|
||
c2 = ch[f]
|
||
|
||
m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
|
||
t = str(m)[8:-2].replace("__main__.", "") # module type, "<class 'models.common.Conv'>"
|
||
np = sum(x.numel() for x in m_.parameters()) # number params
|
||
m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params
|
||
LOGGER.info(f"{i:>3}{str(f):>18}{n_:>3}{np:10.0f} {t:<40}{str(args):<30}") # print
|
||
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
|
||
layers.append(m_)
|
||
if i == 0:
|
||
ch = []
|
||
ch.append(c2) # 增加输出的channels：[16, ]
|
||
return nn.Sequential(*layers), sorted(save)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
parser = argparse.ArgumentParser()
|
||
parser.add_argument("--cfg", type=str, default="yolov5s.yaml", help="model.yaml")
|
||
parser.add_argument("--batch-size", type=int, default=1, help="total batch size for all GPUs")
|
||
parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
|
||
parser.add_argument("--profile", action="store_true", help="profile model speed")
|
||
parser.add_argument("--line-profile", action="store_true", help="profile model speed layer by layer")
|
||
parser.add_argument("--test", action="store_true", help="test all yolo*.yaml")
|
||
opt = parser.parse_args()
|
||
opt.cfg = check_yaml(opt.cfg) # check YAML
|
||
print_args(vars(opt))
|
||
device = select_device(opt.device)
|
||
|
||
# Create model
|
||
im = torch.rand(opt.batch_size, 3, 640, 640).to(device)
|
||
model = Model(opt.cfg).to(device)
|
||
|
||
# Options
|
||
if opt.line_profile: # profile layer by layer
|
||
model(im, profile=True)
|
||
|
||
elif opt.profile: # profile forward-backward
|
||
results = profile(input=im, ops=[model], n=3)
|
||
|
||
elif opt.test: # test all models
|
||
for cfg in Path(ROOT / "models").rglob("yolo*.yaml"):
|
||
try:
|
||
_ = Model(cfg)
|
||
except Exception as e:
|
||
print(f"Error in {cfg}: {e}")
|
||
|
||
else: # report fused model summary
|
||
model.fuse()
|