165 lines
7.1 KiB
Python
165 lines
7.1 KiB
Python
import argparse
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from copy import deepcopy
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import torch
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#from experimental import *
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from .common import *
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#from .common_v3 import *
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from pathlib import Path
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import math
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import yaml
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class Detect(nn.Module):
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def __init__(self, nc=80, anchors=(), ch=()): # detection layer
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super(Detect, self).__init__()
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self.stride = None # strides computed during build
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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.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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self.export = False # onnx export
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def forward(self, x):
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# x = x.copy() # for profiling
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z = [] # inference output
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self.training |= self.export
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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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x[i] = x[i].sigmoid()
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# return x if self.training else (torch.cat(z, 1), x)
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return 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)])
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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(Model, self).__init__()
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with open(cfg) as f:
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self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict
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# Define model
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if nc and nc != self.yaml['nc']:
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print('Overriding %s nc=%g with nc=%g' % (cfg, self.yaml['nc'], 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, ch_out
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# print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
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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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# FocusNoSliceCat
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m.stride = torch.tensor([8.0,16.0,32.0]) # 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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# print('Strides: %s' % m.stride.tolist())
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# Init weights, biases
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initialize_weights(self)
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def forward(self, x, augment=False, profile=False):
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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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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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# 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[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image)
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b[:, 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 make_divisible(x, divisor):
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# Returns x evenly divisble by divisor
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return math.ceil(x / divisor) * divisor
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def check_anchor_order(m):
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# Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary
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a = m.anchor_grid.prod(-1).view(-1) # anchor area
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da = a[-1] - a[0] # delta a
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ds = m.stride[-1] - m.stride[0] # delta s
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if da.sign() != ds.sign(): # same order
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print('Reversing anchor order')
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m.anchors[:] = m.anchors.flip(0)
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m.anchor_grid[:] = m.anchor_grid.flip(0)
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def initialize_weights(model):
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for m in model.modules():
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t = type(m)
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if t is nn.Conv2d:
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pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
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elif t is nn.BatchNorm2d:
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m.eps = 1e-3
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m.momentum = 0.03
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elif t in [nn.LeakyReLU, nn.ReLU, nn.ReLU6]:
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m.inplace = True
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def parse_model(d, ch): # model_dict, input_channels(3) #original
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#print('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
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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 [nn.Conv2d, Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP]:
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#print('*m',m)
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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]:
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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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#print('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print
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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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