def test_ReduceL2(tmpdir, dtype):
with C.default_options(dtype=dtype):
data = np.array(
[[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]],
dtype=dtype)
model = C.reduce_l2(data, 0)
verify_no_input(model, tmpdir, 'ReduceL2_0')
def gaussian_mdn_phi(target, mu, sigma, ndim: int):
"""
Calculates phi between the target tensor and the network prediction
Does not assumes independence between components of target.
Arguments:
target: target tensor with shape (ndim, )
mu: means of gaussian mdn with shape (nmix, ndim)
sigma: sigma of gaussian mdn
nmix (int): number of mixtures
ndim (int): number of dimensions in gaussian
Returns:
:class:`~cntk.ops.functions.Function`
"""
if not len(mu.shape) == 2:
raise ValueError("mu {0} must have shape (nmix, ndim)".format(mu.shape))
t = C.expand_dims(target, axis=0)
exp_term = C.exp(C.negate(C.square(C.reduce_l2(t - mu, axis=-1)) / (2 * C.square(sigma))))
factor = C.reciprocal((2 * pi) ** (ndim / 2) * C.pow(sigma, ndim))
return factor * exp_term
def test_ReduceL2(tmpdir):
data = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]], [[9, 10], [11, 12]]],
dtype=np.float32)
model = C.reduce_l2(data, 0)
verify_no_input(model, tmpdir, 'ReduceL2_0')
def test_ReduceL2(tmpdir, dtype):
with C.default_options(dtype = dtype):
data = np.array([[[1,2], [3,4]],[[5,6], [7,8]],[[9,10], [11,12]]], dtype=dtype)
model = C.reduce_l2(data, 0)
verify_no_input(model, tmpdir, 'ReduceL2_0')
C_step_loss = 0
for _ in range(n_critic):
z_data = np.random.normal(size=(minibatch_size,
z_dim)).astype("float32")
x_data = train_reader.next_minibatch(minibatch_size,
input_map=input_map)
batch_input = {x: x_data[x].data, z: z_data}
#
# compute gradient penalty
#
gradient = C_real.grad(
{C_real.arguments[0]: interpolate.eval(batch_input)})
gp_norm = np.square(
C.reduce_l2(gradient, axis=(1, 2, 3)).eval() - 1)
batch_input[gradient_penalty] = gp_norm
C_trainer.train_minibatch(batch_input)
C_step_loss += C_trainer.previous_minibatch_loss_average
C_step_loss /= n_critic
#
# generator
#
z_data = np.random.normal(size=(minibatch_size,
z_dim)).astype("float32")
batch_input = {z: z_data}
output = G_trainer.train_minibatch(batch_input, outputs=[G_fake])
def main():
show_image = False
if show_image:
bs = 1
ci = 3
co = 3
cg = co * (ci + 1)
gd = 8
gh = 64
gw = 64
h = 256
w = 256
else:
bs = 1
ci = 3
co = 3
cg = co * (ci + 1)
gd = 8
gh = 64
gw = 64
h = 1024
w = 1024
im = C.input_variable([bs, ci, h, w], needs_gradient=True, dynamic_axes=[])
guide = C.input_variable([bs, h, w], needs_gradient=True, dynamic_axes=[])
guide_no_grad = C.input_variable([bs, h, w],
needs_gradient=False,
dynamic_axes=[])
grid = C.input_variable([bs, cg, gd, gh, gw],
needs_gradient=True,
dynamic_axes=[])
# Create indices
xx = np.arange(0, w).reshape(1, -1).repeat(h, 0).astype(np.float32)
yy = np.arange(0, h).reshape(-1, 1).repeat(w, 1).astype(np.float32)
xx = C.Constant(xx, xx.shape)
yy = C.Constant(yy, yy.shape)
gx = ((xx + 0.5) / w) * gw
gy = ((yy + 0.5) / h) * gh
gz = C.clip(guide, 0.0, 1.0) * gd
gz_no_grad = C.clip(guide_no_grad, 0.0, 1.0) * gd
fx = C.element_max(C.floor(gx - 0.5), 0.0)
fy = C.element_max(C.floor(gy - 0.5), 0.0)
fz = C.element_max(C.floor(gz - 0.5), 0.0)
fz_no_grad = C.element_max(C.floor(gz_no_grad - 0.5), 0.0)
wx = gx - 0.5 - fx
wy = gy - 0.5 - fy
wx = C.expand_dims(C.expand_dims(wx, -1 - len(wx.shape)),
-1 - len(wx.shape))
wy = C.expand_dims(C.expand_dims(wy, -1 - len(wy.shape)),
-1 - len(wy.shape))
wz = C.abs(gz - 0.5 - fz)
wz = C.expand_dims(wz, 0)
fx = C.expand_dims(C.expand_dims(fx, -1 - len(fx.shape)),
-1 - len(fx.shape))
fy = C.expand_dims(C.expand_dims(fy, -1 - len(fy.shape)),
-1 - len(fy.shape))
cx = C.element_min(fx + 1, gw - 1)
cy = C.element_min(fy + 1, gh - 1)
cz = C.element_min(fz_no_grad + 1, gd - 1)
batch_idx = np.arange(bs).reshape(bs, 1, 1, 1).astype(np.float32)
batch_idx = C.Constant(batch_idx, batch_idx.shape)
out = []
flat_grid = C.reshape(grid, [-1])
for c_ in range(co):
c_idx = np.arange((ci + 1) * c_,
(ci + 1) * (c_ + 1)).reshape(1, ci + 1, 1,
1).astype(np.float32)
c_idx = C.Constant(c_idx, c_idx.shape)
def flatten_and_gather(x, y, z):
linear_idx = x + gw * y + gw * gh * z + c_idx * gw * gh * gd + batch_idx * gw * gh * gd * cg
flat_linear_idx = C.reshape(linear_idx, [-1])
return C.reshape(C.gather(flat_grid, flat_linear_idx),
linear_idx.shape)
gather_fff = flatten_and_gather(fx, fy, fz_no_grad)
gather_ffc = flatten_and_gather(fx, fy, cz)
gather_fcf = flatten_and_gather(fx, cy, fz_no_grad)
gather_fcc = flatten_and_gather(fx, cy, cz)
gather_cff = flatten_and_gather(cx, fy, fz_no_grad)
gather_cfc = flatten_and_gather(cx, fy, cz)
gather_ccf = flatten_and_gather(cx, cy, fz_no_grad)
gather_ccc = flatten_and_gather(cx, cy, cz)
a = gather_fff*(1-wx)*(1-wy)*(1-wz) + \
gather_ffc*(1-wx)*(1-wy)*( wz) + \
gather_fcf*(1-wx)*( wy)*(1-wz) + \
gather_fcc*(1-wx)*( wy)*( wz) + \
gather_cff*( wx)*(1-wy)*(1-wz) + \
gather_cfc*( wx)*(1-wy)*( wz) + \
gather_ccf*( wx)*( wy)*(1-wz) + \
gather_ccc*( wx)*( wy)*( wz)
o = C.reduce_sum(a[:, :-1, ...] * im, 1) + a[:, -1, ...]
print(o.shape)
out.append(C.expand_dims(o, 0))
out = C.splice(*out, axis=1)
loss = C.reduce_l2(out)
grid_val = np.random.rand(bs, cg, gd, gh, gw).astype(np.float32)
if show_image:
guide_val = skio.imread("/data/rgb.png").mean(2)[:h, :w].astype(
np.float32)
guide_val = np.expand_dims(guide_val / 255.0, 0)
im_val = np.tile(np.expand_dims(guide_val, 1), [1, 3, 1, 1])
out_val = out.eval({
im: im_val,
guide: guide_val,
guide_no_grad: guide_val,
grid: grid_val
})
out_val = np.clip(np.transpose(np.squeeze(out_val), [1, 2, 0]), 0, 1)
skio.imsave("/output/imout.png", out_val)
else:
im_val = np.random.randn(bs, ci, h, w)
guide_val = np.random.rand(bs, h, w).astype(np.float32)
# burning iteration
for it in range(5):
print('burning (', it, ')')
g = loss.grad({
im: im_val,
guide: guide_val,
guide_no_grad: guide_val,
grid: grid_val
})
# actual iterations
start = time.time()
for it in range(50):
print('profiling (', it, ')')
g = loss.grad({
im: im_val,
guide: guide_val,
guide_no_grad: guide_val,
grid: grid_val
})
end = time.time()
runtime = (end - start) * 1000.0 / 50.0
print('Runtime:', runtime)
def main():
bs = 4
c = 64
h = 512
w = 512
im = C.input_variable([bs, c, h, w], needs_gradient=True, dynamic_axes=[])
warp = C.input_variable([bs, 2, h, w],
needs_gradient=True,
dynamic_axes=[])
warp_ng = C.input_variable([bs, 2, h, w],
needs_gradient=False,
dynamic_axes=[])
# Create indices
dx = 0.5 * (warp[:, 0, :, :] + 1.0)
dy = 0.5 * (warp[:, 1, :, :] + 1.0)
new_x = C.clip(dx * w, 0, w)
new_y = C.clip(dy * h, 0, h)
fx = C.clip(C.floor(new_x), 0, w - 2)
fy = C.clip(C.floor(new_y), 0, h - 2)
wx = new_x - fx
wy = new_y - fy
dx_ng = 0.5 * (warp_ng[:, 0, :, :] + 1.0)
dy_ng = 0.5 * (warp_ng[:, 1, :, :] + 1.0)
new_x_ng = C.clip(dx_ng * w, 0, w)
new_y_ng = C.clip(dy_ng * h, 0, h)
fx_ng = C.clip(C.floor(new_x_ng), 0, w - 2)
fy_ng = C.clip(C.floor(new_y_ng), 0, h - 2)
chan_idx = np.arange(c).reshape(1, c, 1, 1)
chan_idx = C.Constant(chan_idx, chan_idx.shape)
batch_idx = np.arange(bs).reshape(bs, 1, 1, 1)
batch_idx = C.Constant(batch_idx, batch_idx.shape)
flat_im = C.reshape(im, [-1])
def flatten_and_gather(x, y):
linear_idx = x + w * y + w * h * chan_idx + w * h * c * batch_idx
flat_linear_idx = C.reshape(linear_idx, [-1])
return C.reshape(C.gather(flat_im, flat_linear_idx), linear_idx.shape)
gather_ff = flatten_and_gather(fx_ng, fy_ng)
gather_fc = flatten_and_gather(fx_ng, fy_ng + 1)
gather_cf = flatten_and_gather(fx_ng + 1, fy_ng)
gather_cc = flatten_and_gather(fx_ng + 1, fy_ng + 1)
out = gather_ff*(1-wx)*(1-wy) + \
gather_fc*(1-wx)*( wy) + \
gather_cf*( wx)*(1-wy) + \
gather_cc*( wx)*( wy)
loss = C.reduce_l2(out)
im_val = np.random.randn(bs, c, h, w).astype(np.float32)
warp_val = np.random.rand(bs, 2, h, w).astype(np.float32)
# burning iteration
for it in range(5):
print('burning (', it, ')')
g = loss.grad({im: im_val, warp: warp_val, warp_ng: warp_val})
# actual iterations
start = time.time()
for it in range(50):
print('profiling (', it, ')')
g = loss.grad({im: im_val, warp: warp_val, warp_ng: warp_val})
end = time.time()
runtime = (end - start) * 1000.0 / 50.0
print('Runtime:', runtime)
def _build_model(self):
hidden_size = self.hidden_size
output_size = self.output_size
num_layers = self.num_layers
keep_prob = self.keep_prob
inputs = cntk.sequence.input_variable((output_size), name='inputs')
target = cntk.input_variable((output_size), name='target')
def lstm_cell():
_cell_creator = cntk.layers.Recurrence(cntk.layers.LSTM(
hidden_size, use_peepholes=self.params.use_peephole),
name='basic_lstm')
if self.params.use_dropout:
print(" ** using dropout for LSTM ** ")
_cell_creator = cntk.layers.Dropout(
keep_prob=keep_prob)(_cell_creator)
return _cell_creator
def gru_cell():
_cell_creator = cntk.layers.Recurrence(
cntk.layers.GRU(hidden_size), name='gru')
if self.params.use_dropout:
print(" ** using dropout for LSTM ** ")
_cell_creator = cntk.layers.Dropout(
keep_prob=keep_prob)(_cell_creator)
return _cell_creator
def cifg_cell():
_cell_creator = cntk.layers.Recurrence(CIFG_LSTM(
hidden_size, use_peepholes=self.params.use_peephole),
name='cifg_lstm')
if self.params.use_dropout:
print(" ** using dropout for LSTM ** ")
_cell_creator = cntk.layers.Dropout(
keep_prob=keep_prob)(_cell_creator)
return _cell_creator
if self.config.cell == 'gru':
_cell_creator = gru_cell
elif self.config.cell == 'lstm':
_cell_creator = lstm_cell
elif self.config.cell == 'cifg_lstm':
_cell_creator = cifg_cell
else:
raise ValueError(
"Unsupported cell type, choose from {'lstm', 'gru', 'cifg_lstm'}."
)
if self.params.use_residual:
print(" ** using residual ** ")
_output = inputs
for _ in range(num_layers):
_output = self.params.resWeight * _cell_creator()(
_output) + _output
# _output = _cell_creator()(_output) + _output
else:
cell = cntk.layers.For(range(num_layers), lambda: _cell_creator())
_output = cell(inputs)
_output = cntk.sequence.last(_output)
output = cntk.layers.Dense(output_size)(_output)
self.output = output
self.loss = cntk.squared_error(output, target)
cost_mape = cntk.reduce_mean(cntk.abs(output - target) / target,
axis=cntk.Axis.all_axes(),
name='mape')
cost_mae = cntk.reduce_mean(cntk.abs(output - target),
axis=cntk.Axis.all_axes(),
name='mae')
cost_rmse = cntk.reduce_l2((output - target),
axis=cntk.Axis.all_axes(),
name='rmse')
self.cost = cntk.combine([cost_mape, cost_mae, cost_rmse])
self.criterion = cntk.combine([loss, cost_mape])
def main():
bs = 4
c = 16
h = 512
w = 512
im = C.input_variable([bs, c, h, w], needs_gradient=True, dynamic_axes=[])
affine_mtx = C.input_variable([bs, 2, 3], needs_gradient=True, dynamic_axes=[])
affine_mtx_ng = C.input_variable([bs, 2, 3], needs_gradient=False, dynamic_axes=[])
xx = np.arange(0, w).reshape(1, -1).repeat(h, 0).astype(np.float32)
yy = np.arange(0, h).reshape(-1, 1).repeat(w, 1).astype(np.float32)
xx = C.Constant(xx, xx.shape)
yy = C.Constant(yy, yy.shape)
nrm_x = 2.0 * (xx / w) - 1.0
nrm_y = 2.0 * (yy / h) - 1.0
nrm_x = C.expand_dims(nrm_x, -1 - len(nrm_x.shape))
nrm_y = C.expand_dims(nrm_y, -1 - len(nrm_y.shape))
xformed_x = affine_mtx[:, 0, 0] * nrm_x + \
affine_mtx[:, 0, 1] * nrm_y + \
affine_mtx[:, 0, 2]
xformed_y = affine_mtx[:, 1, 0] * nrm_x + \
affine_mtx[:, 1, 1] * nrm_y + \
affine_mtx[:, 1, 2]
xformed_x = 0.5 * xformed_x + 1.0
xformed_y = 0.5 * xformed_y + 1.0
xformed_x = C.expand_dims(xformed_x, 0)
xformed_y = C.expand_dims(xformed_y, 0)
xformed_x_ng = affine_mtx_ng[:, 0, 0] * nrm_x + \
affine_mtx_ng[:, 0, 1] * nrm_y + \
affine_mtx_ng[:, 0, 2]
xformed_y_ng = affine_mtx_ng[:, 1, 0] * nrm_x + \
affine_mtx_ng[:, 1, 1] * nrm_y + \
affine_mtx_ng[:, 1, 2]
xformed_x_ng = C.expand_dims(xformed_x_ng, 0)
xformed_y_ng = C.expand_dims(xformed_y_ng, 0)
fx = C.clip(w * xformed_x, 0, w-2)
fy = C.clip(h * xformed_y, 0, h-2)
wx = xformed_x - fx
wy = xformed_y - fy
fx_ng = C.clip(w * xformed_x_ng, 0, w-2)
fy_ng = C.clip(h * xformed_y_ng, 0, h-2)
chan_idx = np.arange(c).reshape(1, c, 1, 1)
chan_idx = C.Constant(chan_idx, chan_idx.shape)
batch_idx = np.arange(bs).reshape(bs, 1, 1, 1)
batch_idx = C.Constant(batch_idx, batch_idx.shape)
flat_im = C.reshape(im, [-1])
def flatten_and_gather(x, y):
linear_idx = x + w*y
linear_idx = linear_idx + w*h*chan_idx + w*h*c*batch_idx
flat_linear_idx = C.reshape(linear_idx, [-1])
return C.reshape(C.gather(flat_im, flat_linear_idx),linear_idx.shape)
gather_ff = flatten_and_gather(fx_ng , fy_ng )
gather_fc = flatten_and_gather(fx_ng , fy_ng + 1)
gather_cf = flatten_and_gather(fx_ng + 1, fy_ng )
gather_cc = flatten_and_gather(fx_ng + 1, fy_ng + 1)
out = gather_ff*(1-wx)*(1-wy) + \
gather_fc*(1-wx)*( wy) + \
gather_cf*( wx)*(1-wy) + \
gather_cc*( wx)*( wy)
loss = C.reduce_l2(out)
im_val = np.random.randn(bs, c, h, w).astype(np.float32)
affine_mtx_val = np.zeros([bs, 2, 3], dtype=np.float32)
affine_mtx_val[:, 0, 1] = 1.0
affine_mtx_val[:, 1, 0] = 1.0
# burning iteration
for it in range(5):
print('burning (', it, ')')
g = loss.grad({im : im_val, affine_mtx : affine_mtx_val, affine_mtx_ng : affine_mtx_val})
# actual iterations
start = time.time()
for it in range(50):
print('profiling (', it, ')')
g = loss.grad({im : im_val, affine_mtx : affine_mtx_val, affine_mtx_ng : affine_mtx_val})
end = time.time()
runtime = (end-start)*1000.0/50.0
print('Runtime:', runtime)
