mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-06-13 00:15:35 +08:00
68 lines
2.3 KiB
Python
68 lines
2.3 KiB
Python
# TODO: implement BatchNorm2d and Swish
|
|
# aka batch_norm, pad, swish, dropout
|
|
# https://github.com/lukemelas/EfficientNet-PyTorch/releases/download/1.0/efficientnet-b0-355c32eb.pth
|
|
# a rough copy of
|
|
# https://github.com/lukemelas/EfficientNet-PyTorch/blob/master/efficientnet_pytorch/model.py
|
|
|
|
class BatchNorm2D:
|
|
def __init__(self, sz):
|
|
self.weight = Tensor.zeros(sz)
|
|
self.bias = Tensor.zeros(sz)
|
|
# TODO: need running_mean and running_var
|
|
|
|
def __call__(self, x):
|
|
# this work at inference?
|
|
return x * self.weight + self.bias
|
|
|
|
class MBConvBlock:
|
|
def __init__(self, input_filters, expand_ratio, se_ratio, output_filters):
|
|
oup = expand_ratio * input_filters
|
|
if expand_ratio != 1:
|
|
self._expand_conv = Tensor.zeros(oup, input_filters, 1, 1)
|
|
self._bn0 = BatchNorm2D(oup)
|
|
self._depthwise_conv = Tensor.zeros(oup, 1, 3, 3)
|
|
self._bn1 = BatchNorm2D(oup)
|
|
|
|
num_squeezed_channels = max(1, int(input_filters * se_ratio))
|
|
self._se_reduce = Tensor.zeros(num_squeezed_channels, oup, 1, 1)
|
|
self._se_reduce_bias = Tensor.zeros(num_squeezed_channels)
|
|
self._se_expand = Tensor.zeros(oup, num_squeezed_channels, 1, 1)
|
|
self._se_expand_bias = Tensor.zeros(oup)
|
|
|
|
self._project_conv = Tensor.zeros(output_filters, oup, 1, 1)
|
|
self._bn2 = BatchNorm2D(output_filters)
|
|
|
|
def __call__(self, x):
|
|
x = self._bn0(x.conv2d(self._expand_conv)).swish()
|
|
x = self._bn1(x.conv2d(self._depthwise_conv)).swish() # TODO: repeat on axis 1
|
|
|
|
# has_se
|
|
x_squeezed = x.avg_pool2d()
|
|
x_squeezed = (x_squeezed.conv2d(self._se_reduce) + self._se_reduce_bias).swish()
|
|
x_squeezed = x_squeezed.conv2d(self._se_expand) + self._se_expand_bias
|
|
x = x * x_squeezed.sigmoid()
|
|
|
|
x = self._bn2(x.conv2d(self._project_conv))
|
|
return x.swish()
|
|
|
|
class EfficientNet:
|
|
def __init__(self):
|
|
self._conv_stem = Tensor.zeros(32, 3, 3, 3)
|
|
self._bn0 = BatchNorm2D(32)
|
|
self._blocks = []
|
|
# TODO: create blocks
|
|
|
|
self._conv_head = Tensor.zeros(1280, 320, 1, 1)
|
|
self._bn1 = BatchNorm2D(1280)
|
|
self._fc = Tensor.zeros(1280, 1000)
|
|
|
|
def forward(x):
|
|
x = self._bn0(x.pad(0,1,0,1).conv2d(self._conv_stem, stride=2))
|
|
for b in self._blocks:
|
|
x = b(x)
|
|
x = self._bn1(x.conv2d(self._conv_head))
|
|
x = x.avg_pool2d() # wrong?
|
|
x = x.dropout(0.2)
|
|
return x.dot(self_fc).swish()
|
|
|