def forward(self, x, train=True):
n, c, h, w = x.shape
self.batch_size = n
out_h, out_w = filter_out_size(h, w, self.fh, self.fw, self.stride_h, self.stride_w, self.padding)
col = im2col(x, self.fh, self.fw, self.stride_h, self.stride_w, self.padding)
col = col.reshape(-1, self.fh*self.fw)
out = np.mean(col, axis=1)
out = out.reshape(n, out_h, out_w, c).transpose(0, 3, 1, 2)
return out
def compile(self, input_shape):
if input_shape is None:
assert self.input_shape is not None
else:
self.input_shape = input_shape
assert len(self.input_shape) == 3
c, h, w = input_shape
out_h, out_w = filter_out_size(h, w, self.fh, self.fw, self.stride_h, self.stride_w, self.padding)
return (c, out_h, out_w)
def forward(self, x, train=True):
oc, _, fh, fw = self.w.shape
n, _, h, w = x.shape
out_h, out_w = filter_out_size(h, w, fh, fw, self.stride_h, self.stride_w, self.padding)
col = im2col(x, fh, fw, self.stride_h, self.stride_w, self.padding)
col_w = self.w.reshape(oc, -1).T
out = np.dot(col, col_w) + self.b
out = out.reshape(n, out_h, out_w, -1).transpose(0, 3, 1, 2)
self.x = x
self.col = col
self.col_w = col_w
return out
def compile(self, input_shape):
if input_shape is None:
assert self.input_shape is not None
else:
self.input_shape = input_shape
assert len(self.input_shape) == 3
ic, h, w = self.input_shape
oc, fw, fh = self.out_channel, self.fw, self.fh
out_h, out_w = filter_out_size(h, w, fh, fw, self.stride_h, self.stride_w, self.padding)
self.w = self.weight_initializer(self.input_shape, (oc, ic, fh, fw))
self.b = np.zeros(oc, skml_config.config.f_type)
self.params = [self.w, self.b]
self.grads = [np.zeros_like(self.w, skml_config.config.f_type), np.zeros_like(self.b, skml_config.config.f_type)]
return (oc, out_h, out_w)
