def rotate_sample(sample, rotation, reverse=False):
if reverse:
if rotation == 1:
sample = cp.rot90(sample, -2, (0, 1)).copy()
elif rotation == 2:
sample = cp.rot90(sample, -1, (0, 1)).copy()
elif rotation == 3:
sample = cp.rot90(sample, -1, (1, 0)).copy()
elif rotation == 4:
sample = cp.rot90(sample, -1, (2, 0)).copy()
elif rotation == 5:
sample = cp.rot90(sample, -1, (0, 2)).copy()
else:
if rotation == 1:
sample = cp.rot90(sample, 2, (0, 1)).copy()
elif rotation == 2:
sample = cp.rot90(sample, 1, (0, 1)).copy()
elif rotation == 3:
sample = cp.rot90(sample, 1, (1, 0)).copy()
elif rotation == 4:
sample = cp.rot90(sample, 1, (2, 0)).copy()
elif rotation == 5:
sample = cp.rot90(sample, 1, (0, 2)).copy()
return sample
def test_roberts_diagonal2():
"""Roberts' filter on a diagonal edge should be a diagonal line."""
image = cp.rot90(cp.tri(10, 10, 0), 3)
expected = ~cp.rot90(
cp.tri(10, 10, -1).astype(bool)
| cp.tri(10, 10, -2).astype(bool).transpose())
expected = _mask_filter_result(expected, None)
result = filters.roberts(image).astype(bool)
assert_array_almost_equal(result, expected)
def regularize_conv(kernel, input_shape, clip_to, devices=-1):
kernel = cp.transpose(kernel, (3, 2, 1, 0)) # tensor flow format
kernel = cp.rot90(kernel, k=2, axes=(0, 1)) # normalizing the back prop
kernel = cp.transpose(kernel, (0, 1, 3, 2))
kernel = cp_regularize_conv(kernel, input_shape, clip_to, devices)
kernel = cp.transpose(kernel, (0, 1, 3, 2))
kernel = cp.rot90(kernel, k=2, axes=(0, 1))
kernel = cp.transpose(kernel, (3, 2, 1, 0)) # back to chainer format
return kernel
def backward(self, dA):
"""Using numpy stride tricks for the backward propagation implementation.
Args:
dA (np.array): gradient of output values
Returns:
np.array: dX gradient of input values
"""
if len(dA.shape) == 2:
dZ = dA.reshape(dA.shape[1], *self.dim_out[1:]) * self._deriv_relu(
self.Z)
else:
dZ = dA * self._deriv_relu(self.Z)
if dZ.shape[0] != self.dZ_pad.shape[0]:
self.dZ_pad = self._allocate_dZ_pad(dZ.shape[0])
self.dW[:, :, :, :] = 0
self.db[:, :, :, :] = 0
(m, n_H_prev, n_W_prev, n_C_prev) = self.dim_in
(f, f, n_C_prev, n_C) = self.W.shape
stride = self.stride
(m, n_H, n_W, n_C) = dZ.shape
W_rot = np.rot90(self.W, 2)
pad_dZ = self.W.shape[0] - (self.pad + 1)
if pad_dZ == 0:
self.dZ_pad[:, 0::stride, 0::stride] = dZ
else:
self.dZ_pad[:, pad_dZ:-pad_dZ:stride,
pad_dZ:-pad_dZ:stride, :] = dZ
shape = (
self.dZ_pad.shape[0], # m
self.dZ_pad.shape[1] - W_rot.shape[0] + 1, # X_nx
self.dZ_pad.shape[2] - W_rot.shape[1] + 1, # X_ny
self.dZ_pad.shape[3], # dZ_nc
W_rot.shape[0], # f
W_rot.shape[1]) # f
strides = (self.dZ_pad.strides[0], self.dZ_pad.strides[1],
self.dZ_pad.strides[2], self.dZ_pad.strides[3],
self.dZ_pad.strides[1], self.dZ_pad.strides[2])
M = np.lib.stride_tricks.as_strided(
self.dZ_pad, shape=shape, strides=strides) # , writeable=False,)
self.dX = np.tensordot(M, W_rot, axes=((4, 5, 3), (0, 1, 3)))
#self.dX = np.einsum('pqrs,bmnspq->bmnr', W_rot, M)
shape_Z = (f, f, n_C_prev, m, n_H, n_W)
strides_Z = (self.X_pad.strides)[1:] + (self.X_pad.strides)[0:3]
strides_Z = (*strides_Z[:-2], strides_Z[-2] * stride,
strides_Z[-1] * stride)
M = np.lib.stride_tricks.as_strided(
self.X_pad, shape=shape_Z, strides=strides_Z) # , writeable=False)
# self.dW = np.einsum('abcd,pqsabc->pqsd', dZ, M)
self.dW = np.tensordot(M, dZ, axes=((3, 4, 5), (0, 1, 2)))
self.dW += self.lamb / self.dim_in[0] * self.W
# self.db = np.einsum('abcd->d', dZ).reshape(1, 1, 1, n_C)
self.db = np.sum(dZ, axis=(0, 1, 2)).reshape(1, 1, 1, n_C)
return self.dX
def __getitem__(self, index):
idx = index
ID = self.list_IDs[idx][0]
rotation = self.list_IDs[idx][1]
filename = 'data/' + str(self.resolution) + '/' + ID + '.binvox'
with open(filename, 'rb') as f:
sample = libs.binvox_rw.read_as_3d_array(f).data
if rotation == 1:
sample = cp.rot90(sample, 2, (0, 1)).copy()
elif rotation == 2:
sample = cp.rot90(sample, 1, (0, 1)).copy()
elif rotation == 3:
sample = cp.rot90(sample, 1, (1, 0)).copy()
elif rotation == 4:
sample = cp.rot90(sample, 1, (2, 0)).copy()
elif rotation == 5:
sample = cp.rot90(sample, 1, (0, 2)).copy()
if self.data_augmentation:
sample = dataAugmentation(sample).copy()
label = int(os.path.basename(filename).split('_')[0])
if self.output_type == '3d':
sample = cp.expand_dims(sample, axis=3)
sample = torch.from_numpy(sample).float()
sample = 2 * (sample - 0.5)
elif self.output_type == '2d_multiple':
sample = createImgs(sample, self.num_cuts)
elif self.output_type == '2d_single':
sample = createImgs(sample, self.num_cuts)
label = torch.zeros(self.num_cuts, dtype=torch.int64) + label
return sample, label
def rotateStack(origStack, angleInitial=0, angleStep=90, rotateaxes=(0,1), gpu=True):
# pop out a warning
print('Rotating the stack...')
print('This functions assumes the axis=0 as the one storing different views')
# create the
rotatedStack = np.zeros_like(origStack)
if gpu == True:
# perform the rotation
print('Running on the GPU')
for i in range(origStack.shape[0]):
if angleStep == 0:
rotatedStack[i,:,:,:] = origStack[i,:,:,:]
elif angleStep != 90:
tempStack = cp.asarray(origStack[i,:,:,:])
rotatedStack[i,:,:,:] = cupyx.scipy.ndimage.rotate(tempStack, -angleStep*i, axes=rotateaxes, reshape=False).get()
else:
tempStack = cp.asarray(origStack[i,:,:,:])
rotatedStack[i,:,:,:] = cp.rot90(tempStack, -i, axes=rotateaxes).get()
print('Rotating view ' + str(i) + ' - deg = ' + str(-angleStep*i))
else:
# perform the rotation
print('Running on the CPU')
for i in range(origStack.shape[0]):
if angleStep == 0:
rotatedStack[i,:,:,:] = origStack[i,:,:,:]
elif angleStep != 90:
rotatedStack[i,:,:,:] = scipy.ndimage.rotate(origStack[i,:,:,:], -angleStep*i, axes=rotateaxes, reshape=False)
else:
rotatedStack[i,:,:,:] = np.rot90(origStack[i,:,:,:], -i, axes=rotateaxes)
print('Rotating view ' + str(i) + ' - deg = ' + str(-angleStep*i))
return rotatedStack
def rotate_sample24(sample):
# strategy = random.randint(0,3)
#
# if strategy <= 2:
# sample = cp.flip(sample,strategy).copy()
rotation = random.randint(0, 23)
if rotation == 1:
sample = cp.rot90(sample, 1, (1, 2)).copy()
elif rotation == 2:
sample = cp.rot90(sample, 2, (1, 2)).copy()
elif rotation == 3:
sample = cp.rot90(sample, 1, (2, 1)).copy()
elif rotation == 4:
sample = cp.rot90(sample, 1, (0, 1)).copy()
elif rotation == 5:
sample = cp.rot90(sample, 1, (0, 1)).copy()
sample = cp.rot90(sample, 1, (1, 2)).copy()
elif rotation == 6:
sample = cp.rot90(sample, 1, (0, 1)).copy()
sample = cp.rot90(sample, 2, (1, 2)).copy()
elif rotation == 7:
sample = cp.rot90(sample, 1, (0, 1)).copy()
sample = cp.rot90(sample, 1, (2, 1)).copy()
elif rotation == 8:
sample = cp.rot90(sample, 1, (1, 0)).copy()
elif rotation == 9:
sample = cp.rot90(sample, 1, (1, 0)).copy()
sample = cp.rot90(sample, 1, (1, 2)).copy()
elif rotation == 10:
sample = cp.rot90(sample, 1, (1, 0)).copy()
sample = cp.rot90(sample, 2, (1, 2)).copy()
elif rotation == 11:
sample = cp.rot90(sample, 1, (1, 0)).copy()
sample = cp.rot90(sample, 1, (2, 1)).copy()
elif rotation == 12:
sample = cp.rot90(sample, 2, (1, 0)).copy()
elif rotation == 13:
sample = cp.rot90(sample, 2, (1, 0)).copy()
sample = cp.rot90(sample, 1, (1, 2)).copy()
elif rotation == 14:
sample = cp.rot90(sample, 2, (1, 0)).copy()
sample = cp.rot90(sample, 2, (1, 2)).copy()
elif rotation == 15:
sample = cp.rot90(sample, 2, (1, 0)).copy()
sample = cp.rot90(sample, 1, (2, 1)).copy()
elif rotation == 16:
sample = cp.rot90(sample, 1, (0, 2)).copy()
elif rotation == 17:
sample = cp.rot90(sample, 1, (0, 2)).copy()
sample = cp.rot90(sample, 1, (1, 2)).copy()
elif rotation == 18:
sample = cp.rot90(sample, 1, (0, 2)).copy()
sample = cp.rot90(sample, 2, (1, 2)).copy()
elif rotation == 19:
sample = cp.rot90(sample, 1, (0, 2)).copy()
sample = cp.rot90(sample, 1, (2, 1)).copy()
elif rotation == 20:
sample = cp.rot90(sample, 1, (2, 0)).copy()
elif rotation == 21:
sample = cp.rot90(sample, 1, (2, 0)).copy()
sample = cp.rot90(sample, 1, (1, 2)).copy()
elif rotation == 22:
sample = cp.rot90(sample, 1, (2, 0)).copy()
sample = cp.rot90(sample, 2, (1, 2)).copy()
elif rotation == 23:
sample = cp.rot90(sample, 1, (2, 0)).copy()
sample = cp.rot90(sample, 1, (2, 1)).copy()
return sample
i_shift = int(shift)
csv_path = stack[0:stack.rfind(".")] + "_Tij.csv"
if (os.path.isfile(csv_path)):
print("csv_file already exists")
print()
else:
with mrcfile.open(stack, permissive=True) as mrc:
cp_dens = cp.asarray(mrc.data, dtype="float32")
mrc.close
sta_dens = cp_dens.shape[0]
print("number of density images = " + str(sta_dens))
print()
cp_dens_rot = cp.rot90(cp_dens, axes=(1, 2))
cp_dens_rot = cp.rot90(cp_dens_rot, axes=(1, 2))
Tij = cp.zeros((sta_dens, sta_dens))
temp_dens = cp.zeros(cp_dens.shape)
for i in range(sta_dens):
Tij_shift_rot = cp.zeros((2, sta_dens, 2 * i_shift, 2 * i_shift))
temp_dens[:, :, :] = cp_dens[i, :, :]
for x in range(2 * i_shift):
for y in range(2 * i_shift):
if ((y - i_shift == 0) & (x - i_shift == 0)):
cp_dens_shift[:, :, :] = cp_dens[:, :, :]
cp_dens_rot_shift[:, :, :] = cp_dens_rot[:, :, :]
elif ((y - i_shift == 0) & (x - i_shift != 0)):
cp_dens_shift = cp.roll(cp_dens, x - i_shift, axis=1)
cp_dens_rot_shift = cp.roll(cp_dens_rot,
