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