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import mxnet as mx
from nowcasting.ops import *
from nowcasting.operators.common import identity, grid_generator, group_add
from nowcasting.operators.base_rnn import MyBaseRNNCell
import numpy as np
class BaseConvRNN(MyBaseRNNCell):
def __init__(self, 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):
super(BaseConvRNN, self).__init__(prefix=prefix + "_", params=params)
self._num_filter = num_filter
self._h2h_kernel = h2h_kernel
assert (self._h2h_kernel[0] % 2 == 1) and (self._h2h_kernel[1] % 2 == 1), \
"Only support odd number, get h2h_kernel= %s" % str(h2h_kernel)
self._h2h_pad = (h2h_dilate[0] * (h2h_kernel[0] - 1) // 2,
h2h_dilate[1] * (h2h_kernel[1] - 1) // 2)
self._h2h_dilate = h2h_dilate
self._i2h_kernel = i2h_kernel
self._i2h_stride = i2h_stride
self._i2h_pad = i2h_pad
self._i2h_dilate = i2h_dilate
self._act_type = act_type
assert len(b_h_w) == 3
i2h_dilate_ksize_h = 1 + (self._i2h_kernel[0] - 1) * self._i2h_dilate[0]
i2h_dilate_ksize_w = 1 + (self._i2h_kernel[1] - 1) * self._i2h_dilate[1]
self._batch_size, self._height, self._width = b_h_w
self._state_height = (self._height + 2 * self._i2h_pad[0] - i2h_dilate_ksize_h)\
// self._i2h_stride[0] + 1
self._state_width = (self._width + 2 * self._i2h_pad[1] - i2h_dilate_ksize_w) \
// self._i2h_stride[1] + 1
print(self._prefix, self._state_height, self._state_width)
self._curr_states = None
self._counter = 0
class ConvRNN(BaseConvRNN):
def __init__(self, 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):
super(ConvRNN, self).__init__(num_filter=num_filter,
b_h_w=b_h_w,
h2h_kernel=h2h_kernel,
h2h_dilate=h2h_dilate,
i2h_kernel=i2h_kernel,
i2h_pad=i2h_pad,
i2h_dilate=i2h_dilate,
act_type=act_type,
prefix=prefix,
params=params)
self._layer_norm = layer_norm
self.i2h_weight = self.params.get('i2h_weight')
self.i2h_bias = self.params.get('i2h_bias')
self.h2h_weight = self.params.get('h2h_weight')
self.h2h_bias = self.params.get('h2h_bias', init=mx.init.Normal())
@property
def state_info(self):
return [{'shape': (self._batch_size, self._num_filter,
self._state_height, self._state_width),
'__layout__': "NCHW"}]
def __call__(self, inputs, states=None, is_initial=False, ret_mid=False):
name = '%s_t%d' % (self._prefix, self._counter)
self._counter += 1
states = self.begin_state()[0] if is_initial else states[0]
assert states is not None
if inputs is not None:
i2h = mx.sym.Convolution(data=inputs,
weight=self.i2h_weight,
bias=self.i2h_bias,
kernel=self._i2h_kernel,
stride=self._i2h_stride,
dilate=self._i2h_dilate,
pad=self._i2h_pad,
num_filter=self._num_filter,
name="%s_i2h" % name)
else:
i2h = None
h2h = mx.sym.Convolution(data=states,
weight=self.h2h_weight,
bias=self.h2h_bias,
kernel=self._h2h_kernel,
stride=(1, 1),
dilate=self._h2h_dilate,
pad=self._h2h_pad,
num_filter=self._num_filter,
name="%s_h2h" % name)
if i2h is not None:
if self._layer_norm:
next_h = activation(layer_normalization(i2h + h2h,
num_filters=self._num_filter,
name=self._prefix + "ln"),
act_type=self._act_type, name=name + "_state")
else:
next_h = activation(i2h + h2h,
act_type=self._act_type, name=name + "_state")
else:
if self._layer_norm:
next_h = activation(layer_normalization(h2h,
num_filters=self._num_filter,
name=self._prefix + "ln"),
act_type=self._act_type, name=name + "_state")
else:
next_h = activation(h2h, act_type=self._act_type, name=name + "_state")
# next_h = identity(next_h, name=name + "_state", input_debug=True, grad_debug=True)
self._curr_states = [next_h]
if not ret_mid:
return next_h, [next_h]
else:
return next_h, [next_h], [i2h, h2h]
class ConvGRU(BaseConvRNN):
def __init__(self, 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):
"""Initializing a ConvGRU/DeconvGRU
r_t = \sigma(W_r \ast x_t + R_r \ast h_{t-1} + b_{W_r} + b_{R_r})
u_t = \sigma(W_u \ast x_t + R_u \ast h_{t-1} + b_{W_u} + b_{R_u})
h^\prime_t = tanh(W_h \ast x_t + r_t \circ (R_h \ast h_{t-1} + b_{R_h}) + b_{W_h})
h_t = (1 - u_t) \circ h^\prime_t + u_t \circ h_{t-1}
Parameters: (reset_gate, update_gate, new_mem)
W_{i2h} = [W_r, W_u, W_h]
b_{i2h} = [b_{W_r}, b_{W_u}, b_{W_h}]
W_{h2h} = [R_r, R_u, R_h]
b_{h2h} = [b_{R_r}, b_{R_u}, b_{R_h}]
Parameters
----------
num_hidden : int
hidden_act_type : str
name : str
"""
super(ConvGRU, self).__init__(num_filter=num_filter,
b_h_w=b_h_w,
h2h_kernel=h2h_kernel,
h2h_dilate=h2h_dilate,
i2h_kernel=i2h_kernel,
i2h_pad=i2h_pad,
i2h_stride=i2h_stride,
i2h_dilate=i2h_dilate,
act_type=act_type,
prefix=prefix)
self._no_i2h_bias = no_i2h_bias
self._i2h_adj = i2h_adj
self._use_deconv = use_deconv
if self._no_i2h_bias:
assert use_deconv
self._zoneout = zoneout
self.i2h_weight = self.params.get("i2h_weight", lr_mult=lr_mult)
self.i2h_bias = self.params.get("i2h_bias", lr_mult=lr_mult)
self.h2h_weight = self.params.get("h2h_weight", lr_mult=lr_mult)
self.h2h_bias = self.params.get("h2h_bias", lr_mult=lr_mult)
@property
def state_postfix(self):
return ['h']
@property
def state_info(self):
return [{'shape': (self._batch_size, self._num_filter,
self._state_height, self._state_width),
'__layout__': "NCHW"}]
def __call__(self, inputs, states=None, is_initial=False, ret_mid=False):
name = '%s_t%d' % (self._prefix, self._counter)
self._counter += 1
if is_initial:
states = self.begin_state()[0]
else:
states = states[0]
assert states is not None
if inputs is not None:
if self._use_deconv:
if self._no_i2h_bias:
i2h = mx.sym.Deconvolution(data=inputs,
weight=self.i2h_weight,
kernel=self._i2h_kernel,
stride=self._i2h_stride,
pad=self._i2h_pad,
adj=self._i2h_adj,
no_bias=True,
num_filter=self._num_filter * 3,
name="%s_i2h" % name)
else:
i2h = mx.sym.Deconvolution(data=inputs,
weight=self.i2h_weight,
bias=self.i2h_bias,
kernel=self._i2h_kernel,
stride=self._i2h_stride,
pad=self._i2h_pad,
adj=self._i2h_adj,
num_filter=self._num_filter * 3,
name="%s_i2h" % name)
else:
i2h = mx.sym.Convolution(data=inputs,
weight=self.i2h_weight,
bias=self.i2h_bias,
kernel=self._i2h_kernel,
stride=self._i2h_stride,
dilate=self._i2h_dilate,
pad=self._i2h_pad,
num_filter=self._num_filter * 3,
name="%s_i2h" % name)
i2h_slice = mx.sym.SliceChannel(i2h, num_outputs=3, axis=1)
else:
i2h_slice = None
prev_h = states
print("h2h_dilate=", self._h2h_dilate)
h2h = mx.sym.Convolution(data=prev_h,
weight=self.h2h_weight,
bias=self.h2h_bias,
no_bias=False,
kernel=self._h2h_kernel,
stride=(1, 1),
dilate=self._h2h_dilate,
pad=self._h2h_pad,
num_filter=self._num_filter * 3,
name="%s_h2h" % name)
h2h_slice = mx.sym.SliceChannel(h2h, num_outputs=3, axis=1)
if i2h_slice is not None:
reset_gate = mx.sym.Activation(i2h_slice[0] + h2h_slice[0], act_type="sigmoid",
name=name + "_r")
update_gate = mx.sym.Activation(i2h_slice[1] + h2h_slice[1], act_type="sigmoid",
name=name + "_u")
new_mem = activation(i2h_slice[2] + reset_gate * h2h_slice[2],
act_type=self._act_type,
name=name + "_h")
else:
reset_gate = mx.sym.Activation(h2h_slice[0], act_type="sigmoid",
name=name + "_r")
update_gate = mx.sym.Activation(h2h_slice[1], act_type="sigmoid",
name=name + "_u")
new_mem = activation(reset_gate * h2h_slice[2],
act_type=self._act_type,
name=name + "_h")
next_h = update_gate * prev_h + (1 - update_gate) * new_mem
if self._zoneout > 0.0:
mask = mx.sym.Dropout(mx.sym.ones_like(prev_h), p=self._zoneout)
next_h = mx.sym.where(mask, next_h, prev_h)
self._curr_states = [next_h]
if not ret_mid:
return next_h, [next_h]
else:
return next_h, [next_h], []
if __name__ == '__main__':
import numpy as np
# Test ConvGRU
data = mx.sym.Variable('data')
data = mx.sym.SliceChannel(data, axis=0, num_outputs=11, squeeze_axis=True)
conv_gru1 = ConvGRU(num_filter=100, b_h_w=(3, 40, 40),
prefix="conv_gru1")
out, states = conv_gru1(inputs=data[0], is_initial=True)
for i in range(1, 11):
out, states = conv_gru1(inputs=data[i], states=states)
conv_gru_forward_backward_time =\
mx.test_utils.check_speed(out,
location={'data': np.random.normal(size=(11, 3, 128, 40, 40))},
N=2)
net = mx.mod.Module(out, data_names=['data',], label_names=None, context=mx.gpu())
net.bind(data_shapes=[('data', (11, 3, 128, 40, 40))],
grad_req='add')
net.init_params()
net.forward(mx.io.DataBatch(data=[mx.random.normal(shape=(11, 3, 128, 40, 40))], label=None), is_train=False)
print(net.get_outputs()[0].asnumpy())
# Test ConvRNN
data = mx.sym.Variable('data')
data = mx.sym.SliceChannel(data, axis=0, num_outputs=11, squeeze_axis=True)
conv_rnn1 = ConvRNN(num_filter=100, b_h_w=(3, 40, 40),
prefix="conv_rnn1")
out, states = conv_rnn1(inputs=data[0], is_initial=True)
for i in range(1, 11):
out, states = conv_rnn1(inputs=data[i], states=states)
conv_rnn_forward_backward_time = \
mx.test_utils.check_speed(out,
location={'data': np.random.normal(size=(11, 3, 128, 40, 40))},
N=2)
net = mx.mod.Module(out, data_names=['data', ], label_names=None, context=mx.gpu())
net.bind(data_shapes=[('data', (11, 3, 128, 40, 40))],
grad_req='add')
net.init_params()
net.forward(mx.io.DataBatch(data=[mx.random.normal(shape=(11, 3, 128, 40, 40))], label=None),
is_train=False)
print(net.get_outputs()[0].asnumpy())
print("ConvGRU Time:", conv_gru_forward_backward_time)
print("ConvRNN Time:", conv_rnn_forward_backward_time)
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