def resample2d_numpy(image,size,timer=None):
start_timer(timer,"resample2d bilinear numpy")
(N,M) = image.shape[-2:];
(n,m) = size;
xs = np.linspace(1,M-1,m)[None,:];
ys = np.linspace(1,N-1,n)[:,None];
yminus,iminus= np.modf(ys-0.5);
yplus, iplus = np.modf(ys+0.5);
xminus,jminus= np.modf(xs-0.5);
xplus, jplus = np.modf(xs+0.5);
I = to_np(image.reshape((-1,M*N)))
LD = (iminus*M + jminus).astype(np.uint64); # x-,y-
LU = (iplus *M + jminus).astype(np.uint64); # x-,y+
RD = (iminus*M + jplus).astype(np.uint64); # x+,y-
RU = (iplus *M + jplus).astype(np.uint64); # x+,y+
I_bilin = (1-xminus)*(1-yminus)*I[:,LD] \
+(1-xminus)*yplus *I[:,LU] \
+xplus *yplus *I[:,RD] \
+xplus*(1-yminus) *I[:,RU];
new_shape = image.shape[:-2]+(n,m)
end_timer(timer)
return I_bilin.reshape(new_shape)
def downscale(Ft,nx,timer=None):
start_timer(timer,"downscale")
ct=dct(Ft,norm='ortho')[:nx];
result = idct(ct,norm='ortho');
end_timer(timer)
return result
def upscale(ft,nX,timer=None):
start_timer(timer,"upscale")
ct=dct(ft,norm='ortho');
result = idct(ct,n=nX,norm='ortho');
end_timer(timer)
return result
def resample2d_sequence(sequence, size, timer=None):
"""Resample a squence of 2d-arrays to size"""
start_timer(timer,"resample2d_sequence")
scaled_sequence = resample2d(sequence,size,timer)
end_timer(timer)
return scaled_sequence
def input_map(image, operations, timer=None):
start_timer(timer,"input_map")
features = []
for F in operations:
start_timer(timer,F['type'])
features.append(apply_input_map(image,F).reshape(-1))
end_timer(timer)
end_timer(timer)
return features
def forward(self, image, state):
start_timer(self.timer,"forward")
self.check_dtypes(image, state)
input_stack = self.input_map(to_np(image)) # np
x_input = to_bh(bh.concatenate(input_stack)) # np -> bh
x_state = self.state_map(to_bh(state)) # bh
start_timer(self.timer,"tanh")
new_state = bh.tanh(x_input+x_state) # bh
end_timer(self.timer) # /tanh
end_timer(self.timer) # /forward
return new_state
def state_map(self, state):
start_timer(self.timer,"state_map")
new_state = self.weight_hh.sparse_dense_mv(state)
end_timer(self.timer)
return new_state
def resample2d_skt(image, size, timer=None):
start_timer(timer,"resample2d skimage")
res = skt.resize(image, size, mode="reflect", anti_aliasing=True).astype(image.dtype)
end_timer(timer)
return res
def lstsq(A,B,timer=None):
start_timer(timer,"lstsq")
X,b,c,s = sp.linalg.lstsq(A,B)
end_timer(timer)
return X,b,c,s
def eigh(A, timer=None):
start_timer(timer,"eigh")
lambdas, U = sp.linalg.eigh(A)
end_timer(timer)
return lambdas, U
def svd(A,timer=None):
start_timer(timer,"svd")
U, s, Vh = sp.linalg.svd(A)
end_timer(timer)
return U, s, Vh