swirlspy.qpf.dl.operators package¶
Submodules¶
swirlspy.qpf.dl.operators.base_rnn module¶
-
class
swirlspy.qpf.dl.operators.base_rnn.
BaseStackRNN
(base_rnn_class, stack_num=1, name='BaseStackRNN', residual_connection=True, **kwargs)¶ Bases:
object
-
check_concat
(states)¶
-
concat_to_split
(concat_states)¶
-
flatten_add_layout
(states, blocked=False)¶ - Parameters
states (list of list or list) –
- Returns
ret
- Return type
list
-
init_state_vars
()¶ Initial state variable for this cell.
- Returns
state_vars – starting states for first RNN step
- Return type
nested list of Symbol
-
reset
()¶
-
split_to_concat
(split_states)¶
-
property
state_info
¶
-
property
state_postfix
¶
-
to_concat
(states)¶
-
to_split
(states)¶
-
unroll
(length, inputs=None, begin_states=None, ret_mid=False)¶
-
-
class
swirlspy.qpf.dl.operators.base_rnn.
MyBaseRNNCell
(prefix='MyBaseRNNCell', params=None)¶ Bases:
mxnet.rnn.rnn_cell.BaseRNNCell
-
get_current_states
()¶
-
reset
()¶ Reset before re-using the cell for another graph.
-
unroll
(length, inputs=None, begin_state=None, ret_mid=False, input_prefix='', layout='TC', merge_outputs=False)¶ Unroll an RNN cell across time steps.
- Parameters
length (int) – number of steps to unroll
inputs (Symbol, list of Symbol, or None) –
if inputs is a single Symbol (usually the output of Embedding symbol), it should have shape (batch_size, length, …) if layout == ‘NTC’, or (length, batch_size, …) if layout == ‘TNC’.
If inputs is a list of symbols (usually output of previous unroll), they should all have shape (batch_size, …).
If inputs is None, Placeholder variables are automatically created.
begin_state (nested list of Symbol) – input states. Created by begin_state() or output state of another cell. Created from begin_state() if None.
input_prefix (str) – prefix for automatically created input placehodlers.
layout (str) – layout of input symbol. Only used if inputs is a single Symbol.
merge_outputs (bool) – if False, return outputs as a list of Symbols. If True, concatenate output across time steps and return a single symbol with shape (batch_size, length, …) if layout == ‘NTC’, or (length, batch_size, …) if layout == ‘TNC’.
- Returns
outputs (list of Symbol) – output symbols.
states (Symbol or nested list of Symbol) – has the same structure as begin_state()
mid_info (list of Symbol)
-
-
class
swirlspy.qpf.dl.operators.base_rnn.
MyGRU
(num_hidden, zoneout=0.0, act_type='tanh', prefix='gru_', params=None)¶ Bases:
swirlspy.qpf.dl.operators.base_rnn.MyBaseRNNCell
GRU cell.
- Parameters
num_hidden (int) – number of units in output symbol
prefix (str, default ‘rnn_’) – prefix for name of layers (and name of weight if params is None)
params (RNNParams or None) – container for weight sharing between cells. created if None.
-
property
state_info
¶ shape(s) of states
swirlspy.qpf.dl.operators.common module¶
-
class
swirlspy.qpf.dl.operators.common.
ConstantOp
(data)¶ Bases:
mxnet.operator.CustomOp
Implementation of mask on minibatch layer.
-
backward
(req, out_grad, in_data, out_data, in_grad, aux)¶ Backward interface. Can override when creating new operators.
- Parameters
req (list of str) – how to assign to in_grad. can be ‘null’, ‘write’, or ‘add’. You can optionally use self.assign(dst, req, src) to handle this.
out_grad (list of NDArrays) – input and output for backward. See document for corresponding arguments of Operator::Backward
in_data (list of NDArrays) – input and output for backward. See document for corresponding arguments of Operator::Backward
out_data (list of NDArrays) – input and output for backward. See document for corresponding arguments of Operator::Backward
in_grad (list of NDArrays) – input and output for backward. See document for corresponding arguments of Operator::Backward
aux (list of NDArrays) – input and output for backward. See document for corresponding arguments of Operator::Backward
-
forward
(is_train, req, in_data, out_data, aux)¶ Forward interface. Can override when creating new operators.
- Parameters
is_train (bool) – whether this is for training
req (list of str) – how to assign to out_data. can be ‘null’, ‘write’, or ‘add’. You can optionally use self.assign(dst, req, src) to handle this.
in_data (list of NDArrays) – input, output, and auxiliary states for forward. See document for corresponding arguments of Operator::Forward
out_data (list of NDArrays) – input, output, and auxiliary states for forward. See document for corresponding arguments of Operator::Forward
aux (list of NDArrays) – input, output, and auxiliary states for forward. See document for corresponding arguments of Operator::Forward
-
-
class
swirlspy.qpf.dl.operators.common.
ConstantOpProp
(pkl_data)¶ Bases:
mxnet.operator.CustomOpProp
-
create_operator
(ctx, shapes, dtypes)¶ Create an operator that carries out the real computation given the context, input shapes, and input data types.
-
infer_shape
(in_shape)¶ infer_shape interface. Can override when creating new operators.
- Parameters
in_shape (list) – List of argument shapes in the same order as declared in list_arguments.
- Returns
in_shape (list) – List of argument shapes. Can be modified from in_shape.
out_shape (list) – List of output shapes calculated from in_shape, in the same order as declared in list_outputs.
aux_shape (Optional, list) – List of aux shapes calculated from in_shape, in the same order as declared in list_auxiliary_states.
-
list_arguments
()¶ list_arguments interface. Can override when creating new operators.
- Returns
arguments – List of argument blob names.
- Return type
list
-
list_outputs
()¶ list_outputs interface. Can override when creating new operators.
- Returns
outputs – List of output blob names.
- Return type
list
-
-
class
swirlspy.qpf.dl.operators.common.
EntropyMultinomialDist
¶ Bases:
mxnet.operator.CustomOp
-
backward
(req, out_grad, in_data, out_data, in_grad, aux)¶ Backward interface. Can override when creating new operators.
- Parameters
req (list of str) – how to assign to in_grad. can be ‘null’, ‘write’, or ‘add’. You can optionally use self.assign(dst, req, src) to handle this.
out_grad (list of NDArrays) – input and output for backward. See document for corresponding arguments of Operator::Backward
in_data (list of NDArrays) – input and output for backward. See document for corresponding arguments of Operator::Backward
out_data (list of NDArrays) – input and output for backward. See document for corresponding arguments of Operator::Backward
in_grad (list of NDArrays) – input and output for backward. See document for corresponding arguments of Operator::Backward
aux (list of NDArrays) – input and output for backward. See document for corresponding arguments of Operator::Backward
-
forward
(is_train, req, in_data, out_data, aux)¶ Forward interface. Can override when creating new operators.
- Parameters
is_train (bool) – whether this is for training
req (list of str) – how to assign to out_data. can be ‘null’, ‘write’, or ‘add’. You can optionally use self.assign(dst, req, src) to handle this.
in_data (list of NDArrays) – input, output, and auxiliary states for forward. See document for corresponding arguments of Operator::Forward
out_data (list of NDArrays) – input, output, and auxiliary states for forward. See document for corresponding arguments of Operator::Forward
aux (list of NDArrays) – input, output, and auxiliary states for forward. See document for corresponding arguments of Operator::Forward
-
-
class
swirlspy.qpf.dl.operators.common.
EntropyMultinomialDistProp
¶ Bases:
mxnet.operator.CustomOpProp
-
create_operator
(ctx, shapes, dtypes)¶ Create an operator that carries out the real computation given the context, input shapes, and input data types.
-
infer_shape
(in_shape)¶ infer_shape interface. Can override when creating new operators.
- Parameters
in_shape (list) – List of argument shapes in the same order as declared in list_arguments.
- Returns
in_shape (list) – List of argument shapes. Can be modified from in_shape.
out_shape (list) – List of output shapes calculated from in_shape, in the same order as declared in list_outputs.
aux_shape (Optional, list) – List of aux shapes calculated from in_shape, in the same order as declared in list_auxiliary_states.
-
list_arguments
()¶ list_arguments interface. Can override when creating new operators.
- Returns
arguments – List of argument blob names.
- Return type
list
-
list_outputs
()¶ list_outputs interface. Can override when creating new operators.
- Returns
outputs – List of output blob names.
- Return type
list
-
-
class
swirlspy.qpf.dl.operators.common.
IdentityOp
(logging_prefix='identity', input_debug=False, grad_debug=False)¶ Bases:
mxnet.operator.CustomOp
-
backward
(req, out_grad, in_data, out_data, in_grad, aux)¶ Backward interface. Can override when creating new operators.
- Parameters
req (list of str) – how to assign to in_grad. can be ‘null’, ‘write’, or ‘add’. You can optionally use self.assign(dst, req, src) to handle this.
out_grad (list of NDArrays) – input and output for backward. See document for corresponding arguments of Operator::Backward
in_data (list of NDArrays) – input and output for backward. See document for corresponding arguments of Operator::Backward
out_data (list of NDArrays) – input and output for backward. See document for corresponding arguments of Operator::Backward
in_grad (list of NDArrays) – input and output for backward. See document for corresponding arguments of Operator::Backward
aux (list of NDArrays) – input and output for backward. See document for corresponding arguments of Operator::Backward
-
forward
(is_train, req, in_data, out_data, aux)¶ Forward interface. Can override when creating new operators.
- Parameters
is_train (bool) – whether this is for training
req (list of str) – how to assign to out_data. can be ‘null’, ‘write’, or ‘add’. You can optionally use self.assign(dst, req, src) to handle this.
in_data (list of NDArrays) – input, output, and auxiliary states for forward. See document for corresponding arguments of Operator::Forward
out_data (list of NDArrays) – input, output, and auxiliary states for forward. See document for corresponding arguments of Operator::Forward
aux (list of NDArrays) – input, output, and auxiliary states for forward. See document for corresponding arguments of Operator::Forward
-
-
class
swirlspy.qpf.dl.operators.common.
IdentityOpProp
(logging_prefix='identity', input_debug=False, grad_debug=False)¶ Bases:
mxnet.operator.CustomOpProp
-
create_operator
(ctx, shapes, dtypes)¶ Create an operator that carries out the real computation given the context, input shapes, and input data types.
-
infer_shape
(in_shape)¶ infer_shape interface. Can override when creating new operators.
- Parameters
in_shape (list) – List of argument shapes in the same order as declared in list_arguments.
- Returns
in_shape (list) – List of argument shapes. Can be modified from in_shape.
out_shape (list) – List of output shapes calculated from in_shape, in the same order as declared in list_outputs.
aux_shape (Optional, list) – List of aux shapes calculated from in_shape, in the same order as declared in list_auxiliary_states.
-
list_arguments
()¶ list_arguments interface. Can override when creating new operators.
- Returns
arguments – List of argument blob names.
- Return type
list
-
list_outputs
()¶ list_outputs interface. Can override when creating new operators.
- Returns
outputs – List of output blob names.
- Return type
list
-
-
class
swirlspy.qpf.dl.operators.common.
LogisticRegressionMaskOutput
(ignore_label)¶ Bases:
mxnet.operator.CustomOp
-
backward
(req, out_grad, in_data, out_data, in_grad, aux)¶ Backward interface. Can override when creating new operators.
- Parameters
req (list of str) – how to assign to in_grad. can be ‘null’, ‘write’, or ‘add’. You can optionally use self.assign(dst, req, src) to handle this.
out_grad (list of NDArrays) – input and output for backward. See document for corresponding arguments of Operator::Backward
in_data (list of NDArrays) – input and output for backward. See document for corresponding arguments of Operator::Backward
out_data (list of NDArrays) – input and output for backward. See document for corresponding arguments of Operator::Backward
in_grad (list of NDArrays) – input and output for backward. See document for corresponding arguments of Operator::Backward
aux (list of NDArrays) – input and output for backward. See document for corresponding arguments of Operator::Backward
-
forward
(is_train, req, in_data, out_data, aux)¶ Forward interface. Can override when creating new operators.
- Parameters
is_train (bool) – whether this is for training
req (list of str) – how to assign to out_data. can be ‘null’, ‘write’, or ‘add’. You can optionally use self.assign(dst, req, src) to handle this.
in_data (list of NDArrays) – input, output, and auxiliary states for forward. See document for corresponding arguments of Operator::Forward
out_data (list of NDArrays) – input, output, and auxiliary states for forward. See document for corresponding arguments of Operator::Forward
aux (list of NDArrays) – input, output, and auxiliary states for forward. See document for corresponding arguments of Operator::Forward
-
-
class
swirlspy.qpf.dl.operators.common.
LogisticRegressionMaskOutputProp
(ignore_label)¶ Bases:
mxnet.operator.CustomOpProp
-
create_operator
(ctx, shapes, dtypes)¶ Create an operator that carries out the real computation given the context, input shapes, and input data types.
-
infer_shape
(in_shape)¶ infer_shape interface. Can override when creating new operators.
- Parameters
in_shape (list) – List of argument shapes in the same order as declared in list_arguments.
- Returns
in_shape (list) – List of argument shapes. Can be modified from in_shape.
out_shape (list) – List of output shapes calculated from in_shape, in the same order as declared in list_outputs.
aux_shape (Optional, list) – List of aux shapes calculated from in_shape, in the same order as declared in list_auxiliary_states.
-
list_arguments
()¶ list_arguments interface. Can override when creating new operators.
- Returns
arguments – List of argument blob names.
- Return type
list
-
list_outputs
()¶ list_outputs interface. Can override when creating new operators.
- Returns
outputs – List of output blob names.
- Return type
list
-
-
class
swirlspy.qpf.dl.operators.common.
MyConstant
(value)¶ Bases:
mxnet.initializer.Initializer
-
class
swirlspy.qpf.dl.operators.common.
SaveNpyOp
(save_name='op', save_dir=None)¶ Bases:
mxnet.operator.CustomOp
-
backward
(req, out_grad, in_data, out_data, in_grad, aux)¶ Backward interface. Can override when creating new operators.
- Parameters
req (list of str) – how to assign to in_grad. can be ‘null’, ‘write’, or ‘add’. You can optionally use self.assign(dst, req, src) to handle this.
out_grad (list of NDArrays) – input and output for backward. See document for corresponding arguments of Operator::Backward
in_data (list of NDArrays) – input and output for backward. See document for corresponding arguments of Operator::Backward
out_data (list of NDArrays) – input and output for backward. See document for corresponding arguments of Operator::Backward
in_grad (list of NDArrays) – input and output for backward. See document for corresponding arguments of Operator::Backward
aux (list of NDArrays) – input and output for backward. See document for corresponding arguments of Operator::Backward
-
forward
(is_train, req, in_data, out_data, aux)¶ Forward interface. Can override when creating new operators.
- Parameters
is_train (bool) – whether this is for training
req (list of str) – how to assign to out_data. can be ‘null’, ‘write’, or ‘add’. You can optionally use self.assign(dst, req, src) to handle this.
in_data (list of NDArrays) – input, output, and auxiliary states for forward. See document for corresponding arguments of Operator::Forward
out_data (list of NDArrays) – input, output, and auxiliary states for forward. See document for corresponding arguments of Operator::Forward
aux (list of NDArrays) – input, output, and auxiliary states for forward. See document for corresponding arguments of Operator::Forward
-
-
class
swirlspy.qpf.dl.operators.common.
SaveNpyOpProp
(save_name='op', save_dir='.')¶ Bases:
mxnet.operator.CustomOpProp
-
create_operator
(ctx, shapes, dtypes)¶ Create an operator that carries out the real computation given the context, input shapes, and input data types.
-
infer_shape
(in_shape)¶ infer_shape interface. Can override when creating new operators.
- Parameters
in_shape (list) – List of argument shapes in the same order as declared in list_arguments.
- Returns
in_shape (list) – List of argument shapes. Can be modified from in_shape.
out_shape (list) – List of output shapes calculated from in_shape, in the same order as declared in list_outputs.
aux_shape (Optional, list) – List of aux shapes calculated from in_shape, in the same order as declared in list_auxiliary_states.
-
list_arguments
()¶ list_arguments interface. Can override when creating new operators.
- Returns
arguments – List of argument blob names.
- Return type
list
-
list_outputs
()¶ list_outputs interface. Can override when creating new operators.
- Returns
outputs – List of output blob names.
- Return type
list
-
-
swirlspy.qpf.dl.operators.common.
calculate_derivative
(data, deriv_variable, step_size, batch_size)¶ Calculate derivatives of input data tensor.
- Parameters
data (mx.sym.Symbol : data to take derivative over) – Shape : (seq_len, batch_size, 1, H, W)
deriv_variable (str : 'x' or 'y' or 't') –
step_size (float : step size, e.g. dx, dy or dt (e.g. dt=6min)) –
batch_size (int) –
Returns –
-------- –
deriv (mx.sym.Symbol : tensor of (approximate) derivatives) – Shape : (seq_len, batch_size, 1, H, W)
-
swirlspy.qpf.dl.operators.common.
calculate_lagrangian_derivative
(data, motion_vector_field, step_sizes, batch_size)¶ Calculate lagrangian derivative (in 2 dimensions) of input data tensor.
- Parameters
data (mx.sym.Symbol : data to take derivative over) – Shape : (seq_len, batch_size, 1, H, W)
motion_vector_field (list of mx.sym.Symbol : velocity fields [u, v]) –
step_sizes (list of float : step sizes [dx, dy, dt]) –
batch_size (int) –
Returns –
-------- –
lagragian_deriv (mx.sym.Symbol :) – Shape : (seq_len, batch_size, 1, H, W)
-
swirlspy.qpf.dl.operators.common.
constant
(data, name='constant')¶
-
swirlspy.qpf.dl.operators.common.
entropy_multinomial
(data, name='entropy')¶
-
swirlspy.qpf.dl.operators.common.
grid_generator
(batch_size, height, width, normalize=True)¶ Generate the grid based on width and height
- Parameters
batch_size (int) –
width (int) –
height (int) –
normalize (bool) – Whether to normalize the grid elements into [-1, 1]
- Returns
ret – Shape : (batch_size, 2, height, width), the channel contains (x, y)
- Return type
mx.sym.Symbol
-
swirlspy.qpf.dl.operators.common.
group_add
(lhs, rhs)¶ - Parameters
lhs (list of mx.sym.Symbol) –
rhs (list of mx.sym.Symbol) –
- Returns
ret
- Return type
list of mx.sym.Symbol
-
swirlspy.qpf.dl.operators.common.
identity
(data, name='identity', logging_prefix=None, input_debug=False, grad_debug=False)¶
-
swirlspy.qpf.dl.operators.common.
logistic_regression_mask_output
(data, label, ignore_label, name=None)¶
-
swirlspy.qpf.dl.operators.common.
masked_gdl_loss
(pred, gt, mask)¶ - Parameters
pred (mx.sym.Symbol) – Shape: (seq_len, batch_size, 1, H, W)
gt (mx.sym.Symbol) – Shape: (seq_len, batch_size, 1, H, W)
mask (mx.sym.Symbol) – Shape: (seq_len, batch_size, 1, H, W)
- Returns
gdl – Shape: (seq_len, batch_size)
- Return type
mx.sym.Symbol
-
swirlspy.qpf.dl.operators.common.
masked_hit_miss_counts
(pred, gt, mask, thresholds)¶ - Parameters
pred (mx.sym.Symbol) – Shape: (seq_len, batch_size, 1, H, W)
gt (mx.sym.Symbol) – Shape: (seq_len, batch_size, 1, H, W)
mask (mx.sym.Symbol) – Shape: (seq_len, batch_size, 1, H, W)
thresholds (list) –
- Returns
hits (mx.nd.NDArray) – Shape: (seq_len, batch_size, len(thresholds))
misses (mx.nd.NDArray) – Shape: (seq_len, batch_size, len(thresholds))
false_alarms (mx.nd.NDArray) – Shape: (seq_len, batch_size, len(thresholds))
correct_negatives (mx.nd.NDArray) – Shape: (seq_len, batch_size, len(thresholds))
-
swirlspy.qpf.dl.operators.common.
multi_segment_slice_axis
(data, axis, segment_lengths)¶ Split the data to multiple segments
- Parameters
data (mx.sym.Symbol) –
axis (int) –
segment_lengths (list or tuple) – Get the segment_lengths
- Returns
ret
- Return type
list
-
swirlspy.qpf.dl.operators.common.
normalize_grid
(un_norm_grid, width, height)¶ Normalize the grid to [-1, 1]
- Parameters
un_norm_grid (mx.sym.Symbol) – Shape : (batch_size, 2, height, width)
width (int) –
height (int) –
- Returns
ret
- Return type
mx.sym.Symbol
-
swirlspy.qpf.dl.operators.common.
one_step_diff
(dat, axis)¶ - Parameters
dat (mx.sym.Symbol) –
axes (tuple) –
-
swirlspy.qpf.dl.operators.common.
save_npy
(data, save_name='op', save_dir='.')¶
-
swirlspy.qpf.dl.operators.common.
weighted_fss
(pred, gt, weight, fss_kernel_HW, batch_size)¶ - Parameters
pred (mx.sym.Symbol) – Shape: (seq_len, batch_size, 1, H, W)
gt (mx.sym.Symbol) – Shape: (seq_len, batch_size, 1, H, W)
fss_kernel_HW (tuple) – Shape: (1, 1)
weight (mx.sym.Symbol) – Shape: (seq_len, batch_size, 1, H, W)
batch_size (int) –
- Returns
loss_fss – Shape: (seq_len, batch_size)
- Return type
mx.sym.Symbol
-
swirlspy.qpf.dl.operators.common.
weighted_l1
(pred, gt, weight)¶
-
swirlspy.qpf.dl.operators.common.
weighted_l2
(pred, gt, weight)¶ - Parameters
pred (mx.sym.Symbol) – Shape: (seq_len, batch_size, 1, H, W)
gt (mx.sym.Symbol) – Shape: (seq_len, batch_size, 1, H, W)
weight (mx.sym.Symbol) – Shape: (seq_len, batch_size, 1, H, W)
- Returns
l2 – Shape: (seq_len, batch_size)
- Return type
mx.nd.NDArray
-
swirlspy.qpf.dl.operators.common.
weighted_mae
(pred, gt, weight)¶
-
swirlspy.qpf.dl.operators.common.
weighted_mse
(pred, gt, weight)¶
-
swirlspy.qpf.dl.operators.common.
weighted_physical_loss
(pred, source, motion_vector_field, step_sizes, weight, batch_size)¶ - Parameters
pred (mx.sym.Symbol) – Shape : (seq_len, batch_size, 1, H, W)
source (mx.sym.Symbol : source term in conservation law) – Shape : (seq_len, batch_size, 1, H, W)
motion_vector_field (list of mx.sym.Symbol : velocity fields [u, v]) – Shape : [(seq_len, batch_size, 1, H, W), (seq_len, bach_size, 1, H, W)]
step_sizes (list of float : step sizes [dx, dy, dt]) –
weight (mx.sym.Symbol) – Shape : (seq_len, batch_size, 1, H, W)
batch_size (int) –
- Returns
loss – Shape: (seq_len, batch_size)
- Return type
mx.sym.Symbol
swirlspy.qpf.dl.operators.conv_rnn module¶
-
class
swirlspy.qpf.dl.operators.conv_rnn.
BaseConvRNN
(num_filter, b_h_w, h2h_kernel=(3, 3), h2h_dilate=(1, 1), i2h_kernel=(3, 3), i2h_stride=(1, 1), i2h_pad=(1, 1), i2h_dilate=(1, 1), act_type='tanh', prefix='ConvRNN', params=None)¶
-
class
swirlspy.qpf.dl.operators.conv_rnn.
ConvGRU
(num_filter, b_h_w, zoneout=0.0, h2h_kernel=(3, 3), h2h_dilate=(1, 1), i2h_kernel=(3, 3), i2h_stride=(1, 1), i2h_pad=(1, 1), i2h_dilate=(1, 1), i2h_adj=(0, 0), no_i2h_bias=False, use_deconv=False, act_type='leaky', prefix='ConvGRU', lr_mult=1.0)¶ Bases:
swirlspy.qpf.dl.operators.conv_rnn.BaseConvRNN
-
property
state_info
¶ shape and layout information of states
-
property
state_postfix
¶
-
property
-
class
swirlspy.qpf.dl.operators.conv_rnn.
ConvRNN
(num_filter, b_h_w, h2h_kernel=(3, 3), h2h_dilate=(1, 1), i2h_kernel=(3, 3), i2h_stride=(1, 1), i2h_pad=(1, 1), i2h_dilate=(1, 1), act_type='leaky', layer_norm=False, prefix='ConvRNN', params=None)¶ Bases:
swirlspy.qpf.dl.operators.conv_rnn.BaseConvRNN
-
property
state_info
¶ shape and layout information of states
-
property
swirlspy.qpf.dl.operators.traj_rnn module¶
-
class
swirlspy.qpf.dl.operators.traj_rnn.
TrajGRU
(b_h_w, num_filter, zoneout=0.0, L=5, i2h_kernel=(3, 3), i2h_stride=(1, 1), i2h_pad=(1, 1), h2h_kernel=(5, 5), h2h_dilate=(1, 1), act_type='leaky', prefix='TrajGRU', lr_mult=1.0)¶ Bases:
swirlspy.qpf.dl.operators.conv_rnn.BaseConvRNN
-
property
state_info
¶ shape and layout information of states
-
property
state_postfix
¶
-
property
-
swirlspy.qpf.dl.operators.traj_rnn.
flow_conv
(data, num_filter, flows, weight, bias, name)¶
swirlspy.qpf.dl.operators.transformations module¶
-
swirlspy.qpf.dl.operators.transformations.
CDNA
(data, kernels, mask, batch_size, num_filter, kernel_size)¶ We assume that the kernels and masks are the output of an identity activation
- Parameters
data (mx.sym.symbol) – Shape: (batch_size, C, H, W)
kernels (mx.sym.symbol) – Shape: (batch_size, M, K, K)
mask (mx.sym.symbol) – Shape: (batch_size, M, H, W)
batch_size (int) –
num_filter (int) – M
kernel_size (int) – K
- Returns
ret – Shape: (batch_size, C, H, W)
- Return type
mx.sym.symbol
-
swirlspy.qpf.dl.operators.transformations.
DFN
(data, local_kernels, K, batch_size)¶ [NIPS2016] Dynamic Filter Network
- Parameters
data (mx.sym.symbol) – Shape: (batch_size, C, H, W)
local_kernels (mx.sym.symbol) – Shape: (batch_size, K*K, H, W)
K (int) – size of the local convolutional kernel
batch_size (int) – size of the minibatch
-
swirlspy.qpf.dl.operators.transformations.
STP
(data, affine_transform_matrices, mask, num_filter, kernel_size)¶ Spatial Transformer Predictor
- Parameters
data (mx.sym.symbol) –
affine_transform_matrices –
mask –