HI Im not sure what happen but when i try to run my code this error keep pop up:RuntimeError: size mismatch, m1: [2 x 21504], m2: [25088 x 512] at C:\Users\builder\AppData\Local\Temp\pip-req-build-r5jc5joa\aten\src\TH/generic/THTensorMath.cpp:41
Here is my code can anyone check what went wrong
class Bottleneck(nn.Module):
def init(self, inp, oup, stride, expansion):
super(Bottleneck, self).init()
self.connect = stride == 1 and inp == oup
#
self.conv = nn.Sequential(
# pw
nn.Conv2d(inp, inp * expansion, 1, 1, 0, bias=False),
nn.BatchNorm2d(inp * expansion),
nn.PReLU(inp * expansion),
# nn.ReLU(inplace=True),
# dw
nn.Conv2d(inp * expansion, inp * expansion, 3, stride, 1, groups=inp * expansion, bias=False),
nn.BatchNorm2d(inp * expansion),
nn.PReLU(inp * expansion),
# nn.ReLU(inplace=True),
# pw-linear
nn.Conv2d(inp * expansion, oup, 1, 1, 0, bias=False),
nn.BatchNorm2d(oup),
)
def forward(self, x):
if self.connect:
return x + self.conv(x)
else:
return self.conv(x)
class ConvBlock(nn.Module):
def init(self, inp, oup, k, s, p, dw=False, linear=False):
super(ConvBlock, self).init()
self.linear = linear
if dw:
self.conv = nn.Conv2d(inp, oup, k, s, p, groups=inp, bias=False)
else:
self.conv = nn.Conv2d(inp, oup, k, s, p, bias=False)
self.bn = nn.BatchNorm2d(oup)
if not linear:
self.prelu = nn.PReLU(oup)
def forward(self, x):
x = self.conv(x)
x = self.bn(x)
if self.linear:
return x
else:
return self.prelu(x)
MobiFace_bottleneck_setting = [
# t, c , n, s
[2, 64, 1, 2],
[2, 64, 2, 1],
[4, 128, 1, 2],
[2, 128, 3, 1],
[4, 256, 1, 2],
[2, 256, 6, 1]
]
class MobiFace(nn.Module):
def init(self, bottleneck_setting=MobiFace_bottleneck_setting, final_linear=False):
super(MobiFace, self).init()
self.final_linear = final_linear
self.conv1 = ConvBlock(3, 64, 3, 2, 1)
self.dw_conv1 = ConvBlock(64, 64, 3, 1, 1, dw=True)
self.inplanes = 64
block = Bottleneck
self.blocks = self._make_layer(block, bottleneck_setting)
self.conv2 = ConvBlock(256, 512, 1, 1, 0, linear=True)
self.linear1 = nn.Linear(7*7*512, 512)
self.prelu1 = nn.PReLU()
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, setting):
layers = []
for t, c, n, s in setting:
for i in range(n):
if i == 0:
layers.append(block(self.inplanes, c, s, t))
else:
layers.append(block(self.inplanes, c, 1, t))
self.inplanes = c
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.dw_conv1(x)
x = self.blocks(x)
x = self.conv2(x)
x = x.view(x.size(0), -1)
x = self.linear1(x)
if self.final_linear is False:
x = self.prelu1(x)
return x
class ArcMarginProduct(nn.Module):
def init(self, in_features=128, out_features=200, s=32.0, m=0.50, easy_margin=False):
super(ArcMarginProduct, self).init()
self.in_features = in_features
self.out_features = out_features
self.s = s
self.m = m
self.weight = Parameter(torch.Tensor(out_features, in_features))
nn.init.xavier_uniform_(self.weight)
# init.kaiming_uniform_()
# self.weight.data.normal_(std=0.001)
self.easy_margin = easy_margin
self.cos_m = math.cos(m)
self.sin_m = math.sin(m)
# make the function cos(theta+m) monotonic decreasing while theta in [0°,180°]
self.th = math.cos(math.pi - m)
self.mm = math.sin(math.pi - m) * m
def forward(self, x, label):
cosine = F.linear(F.normalize(x), F.normalize(self.weight))
sine = torch.sqrt(1.0 - torch.pow(cosine, 2))
phi = cosine * self.cos_m - sine * self.sin_m
if self.easy_margin:
phi = torch.where(cosine > 0, phi, cosine)
else:
phi = torch.where((cosine - self.th) > 0, phi, cosine - self.mm)
one_hot = torch.zeros(cosine.size(), device='cuda')
one_hot.scatter_(1, label.view(-1, 1).long(), 1)
output = (one_hot * phi) + ((1.0 - one_hot) * cosine)
output *= self.s
return output
if name == “main”:
input = Variable(torch.FloatTensor(2, 3, 112, 96))
net = MobiFace()
print(net)
x = net(input)
print(x.shape)
