134 lines
4.9 KiB
Python
134 lines
4.9 KiB
Python
# This file contains experimental modules
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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, DWConv
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from utils.google_utils import attempt_download
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class CrossConv(nn.Module):
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# Cross Convolution Downsample
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def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
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# ch_in, ch_out, kernel, stride, groups, expansion, shortcut
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super(CrossConv, self).__init__()
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c_ = int(c2 * e) # hidden channels
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self.cv1 = Conv(c1, c_, (1, k), (1, s))
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self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
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self.add = shortcut and c1 == c2
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def forward(self, x):
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return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
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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(Sum, self).__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., 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 GhostConv(nn.Module):
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# Ghost Convolution https://github.com/huawei-noah/ghostnet
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def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups
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super(GhostConv, self).__init__()
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c_ = c2 // 2 # hidden channels
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self.cv1 = Conv(c1, c_, k, s, None, g, act)
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self.cv2 = Conv(c_, c_, 5, 1, None, c_, act)
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def forward(self, x):
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y = self.cv1(x)
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return torch.cat([y, self.cv2(y)], 1)
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class GhostBottleneck(nn.Module):
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# Ghost Bottleneck https://github.com/huawei-noah/ghostnet
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def __init__(self, c1, c2, k, s):
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super(GhostBottleneck, self).__init__()
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c_ = c2 // 2
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self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw
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DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw
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GhostConv(c_, c2, 1, 1, act=False)) # pw-linear
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self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False),
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Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()
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def forward(self, x):
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return self.conv(x) + self.shortcut(x)
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class MixConv2d(nn.Module):
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# Mixed Depthwise Conv https://arxiv.org/abs/1907.09595
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def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True):
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super(MixConv2d, self).__init__()
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groups = len(k)
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if equal_ch: # equal c_ per group
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i = torch.linspace(0, groups - 1E-6, c2).floor() # c2 indices
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c_ = [(i == g).sum() for g in range(groups)] # intermediate channels
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else: # equal weight.numel() per group
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b = [c2] + [0] * groups
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a = np.eye(groups + 1, groups, 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([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)])
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self.bn = nn.BatchNorm2d(c2)
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self.act = nn.LeakyReLU(0.1, inplace=True)
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def forward(self, x):
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return x + 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(Ensemble, self).__init__()
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def forward(self, x, augment=False):
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y = []
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for module in self:
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y.append(module(x, augment)[0])
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# y = torch.stack(y).max(0)[0] # max ensemble
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# y = torch.cat(y, 1) # nms ensemble
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y = torch.stack(y).mean(0) # mean ensemble
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return y, None # inference, train output
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def attempt_load(weights, map_location=None):
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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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model = Ensemble()
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for w in weights if isinstance(weights, list) else [weights]:
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attempt_download(w)
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model.append(torch.load(w, map_location=map_location)['model'].float().fuse().eval()) # load FP32 model
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# Compatibility updates
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for m in model.modules():
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if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6]:
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m.inplace = True # pytorch 1.7.0 compatibility
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elif type(m) is Conv:
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m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
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if len(model) == 1:
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return model[-1] # return model
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else:
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print('Ensemble created with %s\n' % weights)
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for k in ['names', 'stride']:
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setattr(model, k, getattr(model[-1], k))
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return model # return ensemble
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