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58 lines
1.9 KiB
Python
58 lines
1.9 KiB
Python
# TODO: implement BatchNorm2d and Swish
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# aka batch_norm, pad, swish, dropout
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# https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth
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# a rough copy of
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# https://github.com/lukemelas/EfficientNet-PyTorch/blob/master/efficientnet_pytorch/model.py
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class BatchNorm2D:
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def __init__(self, sz):
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self.weight = Tensor.zeros(sz)
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self.bias = Tensor.zeros(sz)
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# TODO: need running_mean and running_var
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def __call__(self, x):
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# this work at inference?
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return x * self.weight + self.bias
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class MBConvBlock:
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def __init__(self, d0, d1, d2, d3):
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self._expand_conv = Tensor.zeros(d1, d0, 1, 1)
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self._bn0 = BatchNorm2D(d1)
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self._depthwise_conv = Tensor.zeros(d1, 1, 3, 3)
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self._bn1 = BatchNorm2D(d1)
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self._se_reduce = Tensor.zeros(d2, d1, 1, 1)
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self._se_reduce_bias = Tensor.zeros(d2)
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self._se_expand = Tensor.zeros(d1, d2, 1, 1)
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self._se_expand_bias = Tensor.zeros(d1)
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self._project_conv = Tensor.zeros(d3, d2, 1, 1)
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self._bn2 = BatchNorm2D(d3)
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def __call__(self, x):
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x = self._bn0(x.conv2d(self._expand_conv))
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x = self._bn1(x.conv2d(self._depthwise_conv)) # TODO: repeat on axis 1
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x = x.conv2d(self._se_reduce) + self._se_reduce_bias
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x = x.conv2d(self._se_expand) + self._se_expand_bias
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x = self._bn2(x.conv2d(self._project_conv))
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return x.swish()
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class EfficientNet:
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def __init__(self):
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self._conv_stem = Tensor.zeros(32, 3, 3, 3)
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self._bn0 = BatchNorm2D(32)
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self._blocks = []
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# TODO: create blocks
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self._conv_head = Tensor.zeros(1280, 320, 1, 1)
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self._bn1 = BatchNorm2D(1280)
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self._fc = Tensor.zeros(1280, 1000)
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def forward(x):
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x = self._bn0(x.pad(0,1,0,1).conv2d(self._conv_stem, stride=2))
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for b in self._blocks:
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x = b(x)
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x = self._bn1(x.conv2d(self._conv_head))
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x = x.avg_pool2d() # wrong
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x = x.dropout(0.2)
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return x.dot(self_fc).swish()
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