mirror of
https://github.com/comfyanonymous/ComfyUI.git
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236 lines
9.6 KiB
Python
236 lines
9.6 KiB
Python
import math
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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 yaml # for torch hub
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from torch import nn
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from custom_nodes.facerestore.facelib.detection.yolov5face.models.common import (
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C3,
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NMS,
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SPP,
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AutoShape,
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Bottleneck,
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BottleneckCSP,
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Concat,
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Conv,
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DWConv,
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Focus,
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ShuffleV2Block,
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StemBlock,
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)
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from custom_nodes.facerestore.facelib.detection.yolov5face.models.experimental import CrossConv, MixConv2d
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from custom_nodes.facerestore.facelib.detection.yolov5face.utils.autoanchor import check_anchor_order
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from custom_nodes.facerestore.facelib.detection.yolov5face.utils.general import make_divisible
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from custom_nodes.facerestore.facelib.detection.yolov5face.utils.torch_utils import copy_attr, fuse_conv_and_bn
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class Detect(nn.Module):
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stride = None # strides computed during build
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export = False # onnx export
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def __init__(self, nc=80, anchors=(), ch=()): # detection layer
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super().__init__()
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self.nc = nc # number of classes
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self.no = nc + 5 + 10 # 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.zeros(1)] * self.nl # init grid
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a = torch.tensor(anchors).float().view(self.nl, -1, 2)
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self.register_buffer("anchors", a) # shape(nl,na,2)
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self.register_buffer("anchor_grid", a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2)
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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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def forward(self, x):
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z = [] # inference output
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if self.export:
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for i in range(self.nl):
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x[i] = self.m[i](x[i])
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return x
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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.grid[i].shape[2:4] != x[i].shape[2:4]:
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self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
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y = torch.full_like(x[i], 0)
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y[..., [0, 1, 2, 3, 4, 15]] = x[i][..., [0, 1, 2, 3, 4, 15]].sigmoid()
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y[..., 5:15] = x[i][..., 5:15]
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y[..., 0:2] = (y[..., 0:2] * 2.0 - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i] # xy
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y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
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y[..., 5:7] = (
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y[..., 5:7] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
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) # landmark x1 y1
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y[..., 7:9] = (
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y[..., 7:9] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
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) # landmark x2 y2
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y[..., 9:11] = (
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y[..., 9:11] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
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) # landmark x3 y3
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y[..., 11:13] = (
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y[..., 11:13] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
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) # landmark x4 y4
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y[..., 13:15] = (
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y[..., 13:15] * self.anchor_grid[i] + self.grid[i].to(x[i].device) * self.stride[i]
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) # landmark x5 y5
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z.append(y.view(bs, -1, self.no))
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return x if self.training else (torch.cat(z, 1), x)
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@staticmethod
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def _make_grid(nx=20, ny=20):
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# yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)], indexing="ij") # for pytorch>=1.10
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yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
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return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()
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class Model(nn.Module):
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def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None): # model, input channels, number of classes
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super().__init__()
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self.yaml_file = Path(cfg).name
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with Path(cfg).open(encoding="utf8") 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
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if nc and nc != self.yaml["nc"]:
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self.yaml["nc"] = nc # 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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# Build strides, anchors
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m = self.model[-1] # Detect()
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if isinstance(m, Detect):
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s = 128 # 2x min stride
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m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward
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m.anchors /= m.stride.view(-1, 1, 1)
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check_anchor_order(m)
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self.stride = m.stride
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self._initialize_biases() # only run once
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def forward(self, x):
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return self.forward_once(x) # single-scale inference, train
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def forward_once(self, x):
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y = [] # 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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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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return x
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def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency
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# https://arxiv.org/abs/1708.02002 section 3.3
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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:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum()) # cls
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mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
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def _print_biases(self):
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m = self.model[-1] # Detect() module
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for mi in m.m: # from
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b = mi.bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85)
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print(("%6g Conv2d.bias:" + "%10.3g" * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
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def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
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print("Fusing layers... ")
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for m in self.model.modules():
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if isinstance(m, Conv) 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.fuseforward # update forward
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elif type(m) is nn.Upsample:
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m.recompute_scale_factor = None # torch 1.11.0 compatibility
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return self
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def nms(self, mode=True): # add or remove NMS module
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present = isinstance(self.model[-1], NMS) # last layer is NMS
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if mode and not present:
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print("Adding NMS... ")
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m = NMS() # module
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m.f = -1 # from
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m.i = self.model[-1].i + 1 # index
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self.model.add_module(name=str(m.i), module=m) # add
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self.eval()
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elif not mode and present:
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print("Removing NMS... ")
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self.model = self.model[:-1] # remove
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return self
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def autoshape(self): # add autoShape module
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print("Adding autoShape... ")
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m = AutoShape(self) # wrap model
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copy_attr(m, self, include=("yaml", "nc", "hyp", "names", "stride"), exclude=()) # copy attributes
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return m
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def parse_model(d, ch): # model_dict, input_channels(3)
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anchors, nc, gd, gw = d["anchors"], d["nc"], d["depth_multiple"], d["width_multiple"]
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na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
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no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
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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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try:
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args[j] = eval(a) if isinstance(a, str) else a # eval strings
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except:
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pass
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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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Bottleneck,
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SPP,
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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,
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ShuffleV2Block,
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StemBlock,
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]:
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c1, c2 = ch[f], args[0]
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c2 = make_divisible(c2 * gw, 8) if c2 != no else c2
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args = [c1, c2, *args[1:]]
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if m in [BottleneckCSP, C3]:
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args.insert(2, n)
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n = 1
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elif m is nn.BatchNorm2d:
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args = [ch[f]]
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elif m is Concat:
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c2 = sum(ch[-1 if x == -1 else x + 1] for x in f)
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elif m is Detect:
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args.append([ch[x + 1] for x in f])
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if isinstance(args[1], int): # number of anchors
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args[1] = [list(range(args[1] * 2))] * len(f)
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else:
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c2 = ch[f]
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m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module
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t = str(m)[8:-2].replace("__main__.", "") # module type
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np = sum(x.numel() for x in m_.parameters()) # number params
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m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params
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save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
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layers.append(m_)
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ch.append(c2)
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return nn.Sequential(*layers), sorted(save)
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