112 lines
4.2 KiB
Python
112 lines
4.2 KiB
Python
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Experimental modules
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"""
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import math
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import numpy as np
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import torch
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import torch.nn as nn
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from app.yolov5.utils.downloads import attempt_download
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class Sum(nn.Module):
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# Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
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def __init__(self, n, weight=False): # n: number of inputs
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super().__init__()
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self.weight = weight # apply weights boolean
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self.iter = range(n - 1) # iter object
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if weight:
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self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True) # layer weights
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def forward(self, x):
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y = x[0] # no weight
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if self.weight:
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w = torch.sigmoid(self.w) * 2
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for i in self.iter:
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y = y + x[i + 1] * w[i]
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else:
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for i in self.iter:
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y = y + x[i + 1]
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return y
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class MixConv2d(nn.Module):
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# Mixed Depth-wise Conv https://arxiv.org/abs/1907.09595
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def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): # ch_in, ch_out, kernel, stride, ch_strategy
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super().__init__()
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n = len(k) # number of convolutions
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if equal_ch: # equal c_ per group
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i = torch.linspace(0, n - 1E-6, c2).floor() # c2 indices
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c_ = [(i == g).sum() for g in range(n)] # intermediate channels
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else: # equal weight.numel() per group
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b = [c2] + [0] * n
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a = np.eye(n + 1, n, k=-1)
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a -= np.roll(a, 1, axis=1)
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a *= np.array(k) ** 2
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a[0] = 1
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c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b
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self.m = nn.ModuleList([
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nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)])
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self.bn = nn.BatchNorm2d(c2)
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self.act = nn.SiLU()
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def forward(self, x):
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return self.act(self.bn(torch.cat([m(x) for m in self.m], 1)))
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class Ensemble(nn.ModuleList):
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# Ensemble of models
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def __init__(self):
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super().__init__()
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def forward(self, x, augment=False, profile=False, visualize=False):
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y = [module(x, augment, profile, visualize)[0] for module in self]
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# y = torch.stack(y).max(0)[0] # max ensemble
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# y = torch.stack(y).mean(0) # mean ensemble
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y = torch.cat(y, 1) # nms ensemble
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return y, None # inference, train output
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def attempt_load(weights, device=None, inplace=True, fuse=True):
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# Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
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from app.yolov5.models.yolo import Detect, Model
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model = Ensemble()
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for w in weights if isinstance(weights, list) else [weights]:
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ckpt = torch.load(attempt_download(w), map_location='cpu') # load
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ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float() # FP32 model
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# Model compatibility updates
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if not hasattr(ckpt, 'stride'):
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ckpt.stride = torch.tensor([32.])
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if hasattr(ckpt, 'names') and isinstance(ckpt.names, (list, tuple)):
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ckpt.names = dict(enumerate(ckpt.names)) # convert to dict
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model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, 'fuse') else ckpt.eval()) # model in eval mode
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# Module compatibility updates
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for m in model.modules():
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t = type(m)
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if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model):
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m.inplace = inplace # torch 1.7.0 compatibility
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if t is Detect and not isinstance(m.anchor_grid, list):
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delattr(m, 'anchor_grid')
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setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl)
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elif t is nn.Upsample and not hasattr(m, 'recompute_scale_factor'):
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m.recompute_scale_factor = None # torch 1.11.0 compatibility
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# Return model
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if len(model) == 1:
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return model[-1]
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# Return detection ensemble
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print(f'Ensemble created with {weights}\n')
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for k in 'names', 'nc', 'yaml':
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setattr(model, k, getattr(model[0], k))
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model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride
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assert all(model[0].nc == m.nc for m in model), f'Models have different class counts: {[m.nc for m in model]}'
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return model
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