def conv(x,
in_planes,
out_planes,
kernel_size,
padding,
stride=1,
init_method=None):
Kw = kernel_size
Kh = kernel_size
_C = in_planes
Kc = out_planes
N, C, H, W = x.shape
assert C == _C
if init_method == None:
w = jt.make_var(
[Kc, _C, Kh, Kw],
init=lambda *a: init.relu_invariant_gauss(*a, mode="fan_out"))
else:
w = jt.make_var([Kc, _C, Kh, Kw], init=init_method)
xx = x.reindex(
[
N, Kc, C, (H + padding * 2 - kernel_size) // stride + 1,
(W + padding * 2 - kernel_size) // stride + 1, Kh, Kw
],
[
'i0', # Nid
'i2', # Cid
f'i3*{stride}-{padding}+i5', # Hid+Khid
f'i4*{stride}-{padding}+i6', # Wid+KWid
])
ww = w.broadcast(xx.shape, [0, 3, 4])
yy = xx * ww
y = yy.sum([2, 5, 6]) # C, Kh, Kw
return y
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True):
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size if isinstance(
kernel_size, tuple) else (kernel_size, kernel_size)
self.stride = stride if isinstance(stride, tuple) else (stride, stride)
self.padding = padding if isinstance(padding, tuple) else (padding,
padding)
self.dilation = dilation if isinstance(dilation, tuple) else (dilation,
dilation)
self.groups = groups
assert in_channels % groups == 0, 'in_channels must be divisible by groups'
assert out_channels % groups == 0, 'out_channels must be divisible by groups'
Kh, Kw = self.kernel_size
self.weight = init.relu_invariant_gauss(
[out_channels, in_channels // groups, Kh, Kw],
dtype="float",
mode="fan_out")
if bias:
self.bias = init.uniform([out_channels],
dtype="float",
low=-1,
high=1)
else:
self.bias = None
def __init__(self, in_channels, out_channels, kernel_size, stride=1, \
padding=0, output_padding=0, groups=1, bias=True, dilation=1):
self.in_channels = in_channels
self.out_channels = out_channels
# added
self.dilation = dilation
self.group = groups
assert groups == 1, "Group conv not supported yet."
self.kernel_size = kernel_size if isinstance(
kernel_size, tuple) else (kernel_size, kernel_size)
self.stride = stride if isinstance(stride, tuple) else (stride, stride)
self.dilation = dilation if isinstance(dilation, tuple) else (dilation,
dilation)
# added
self.padding = padding if isinstance(padding, tuple) else (padding,
padding)
self.real_padding = (self.dilation[0] * (self.kernel_size[0] - 1) -
self.padding[0], self.dilation[1] *
(self.kernel_size[1] - 1) - self.padding[1])
self.output_padding = output_padding if isinstance(
output_padding, tuple) else (output_padding, output_padding)
self.weight = init.relu_invariant_gauss(
(in_channels, out_channels) + self.kernel_size,
dtype="float",
mode="fan_out")
if bias:
self.bias = init.uniform([out_channels],
dtype="float",
low=-1,
high=1)
else:
self.bias = None
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True):
assert groups == 1
self.in_channels = in_channels
self.out_channels = out_channels
self.kernel_size = kernel_size if isinstance(
kernel_size, tuple) else (kernel_size, kernel_size)
self.stride = stride if isinstance(stride, tuple) else (stride, stride)
self.padding = padding if isinstance(padding, tuple) else (padding,
padding)
self.dilation = dilation if isinstance(dilation, tuple) else (dilation,
dilation)
Kh, Kw = self.kernel_size
assert groups == 1, "Group conv not supported yet."
self.weight = init.relu_invariant_gauss(
[out_channels, in_channels, Kh, Kw], dtype="float", mode="fan_out")
if bias:
self.bias = init.uniform([out_channels],
dtype="float",
low=-1,
high=1)
else:
self.bias = None
