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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 models.common import Conv |
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from utils.downloads import attempt_download |
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class Sum(nn.Module): |
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def __init__(self, n, weight=False): |
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super().__init__() |
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self.weight = weight |
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self.iter = range(n - 1) |
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if weight: |
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self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True) |
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def forward(self, x): |
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y = x[0] |
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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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def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): |
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super().__init__() |
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n = len(k) |
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if equal_ch: |
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i = torch.linspace(0, n - 1E-6, c2).floor() |
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c_ = [(i == g).sum() for g in range(n)] |
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else: |
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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() |
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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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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.cat(y, 1) |
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return y, None |
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def attempt_load(weights, device=None, inplace=True, fuse=True): |
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from 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)) |
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ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float() |
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model.append(ckpt.fuse().eval() if fuse else ckpt.eval()) |
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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 |
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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 Conv: |
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m._non_persistent_buffers_set = set() |
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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 |
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if len(model) == 1: |
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return model[-1] |
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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 |
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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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