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| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| from models.common import Conv, DWConv | |
| from utils.google_utils import attempt_download | |
| class CrossConv(nn.Module): | |
| # Cross Convolution Downsample | |
| def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False): | |
| # ch_in, ch_out, kernel, stride, groups, expansion, shortcut | |
| super(CrossConv, self).__init__() | |
| c_ = int(c2 * e) # hidden channels | |
| self.cv1 = Conv(c1, c_, (1, k), (1, s)) | |
| self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g) | |
| self.add = shortcut and c1 == c2 | |
| def forward(self, x): | |
| return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) | |
| 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(Sum, self).__init__() | |
| self.weight = weight # apply weights boolean | |
| self.iter = range(n - 1) # iter object | |
| if weight: | |
| self.w = nn.Parameter(-torch.arange(1., 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 Depthwise Conv https://arxiv.org/abs/1907.09595 | |
| def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): | |
| super(MixConv2d, self).__init__() | |
| groups = len(k) | |
| if equal_ch: # equal c_ per group | |
| i = torch.linspace(0, groups - 1E-6, c2).floor() # c2 indices | |
| c_ = [(i == g).sum() for g in range(groups)] # intermediate channels | |
| else: # equal weight.numel() per group | |
| b = [c2] + [0] * groups | |
| a = np.eye(groups + 1, groups, 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_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)]) | |
| self.bn = nn.BatchNorm2d(c2) | |
| self.act = nn.LeakyReLU(0.1, inplace=True) | |
| def forward(self, x): | |
| return x + 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(Ensemble, self).__init__() | |
| def forward(self, x, augment=False): | |
| y = [] | |
| for module in self: | |
| y.append(module(x, augment)[0]) | |
| # 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, map_location=None): | |
| # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a | |
| model = Ensemble() | |
| for w in weights if isinstance(weights, list) else [weights]: | |
| attempt_download(w) | |
| ckpt = torch.load(w, map_location=map_location) # load | |
| model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval()) # FP32 model | |
| # Compatibility updates | |
| for m in model.modules(): | |
| if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]: | |
| m.inplace = True # pytorch 1.7.0 compatibility | |
| elif type(m) is nn.Upsample: | |
| m.recompute_scale_factor = None # torch 1.11.0 compatibility | |
| elif type(m) is Conv: | |
| m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility | |
| if len(model) == 1: | |
| return model[-1] # return model | |
| else: | |
| print('Ensemble created with %s\n' % weights) | |
| for k in ['names', 'stride']: | |
| setattr(model, k, getattr(model[-1], k)) | |
| return model # return ensemble | |