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import mxnet as mx
import mxnet.ndarray as nd
import nowcasting.config as cfg
from nowcasting.ops import reset_regs
from nowcasting.operators.common import grid_generator
from nowcasting.operators import *
from nowcasting.ops import *
from nowcasting.prediction_base_factory import PredictionBaseFactory
from nowcasting.operators.transformations import DFN
from nowcasting.my_module import MyModule
def get_encoder_forecaster_rnn_blocks(batch_size):
encoder_rnn_blocks = []
forecaster_rnn_blocks = []
gan_rnn_blocks = []
CONFIG = cfg.MODEL.ENCODER_FORECASTER.RNN_BLOCKS
for vec, block_prefix in [(encoder_rnn_blocks, "ebrnn"),
(forecaster_rnn_blocks, "fbrnn"),
(gan_rnn_blocks, "dbrnn")]:
for i in range(len(CONFIG.NUM_FILTER)):
name = "%s%d" % (block_prefix, i + 1)
if CONFIG.LAYER_TYPE[i] == "ConvGRU":
rnn_block = BaseStackRNN(base_rnn_class=ConvGRU,
stack_num=CONFIG.STACK_NUM[i],
name=name,
residual_connection=CONFIG.RES_CONNECTION,
num_filter=CONFIG.NUM_FILTER[i],
b_h_w=(batch_size,
cfg.MODEL.ENCODER_FORECASTER.FEATMAP_SIZE[i],
cfg.MODEL.ENCODER_FORECASTER.FEATMAP_SIZE[i]),
h2h_kernel=CONFIG.H2H_KERNEL[i],
h2h_dilate=CONFIG.H2H_DILATE[i],
i2h_kernel=CONFIG.I2H_KERNEL[i],
i2h_pad=CONFIG.I2H_PAD[i],
act_type=cfg.MODEL.RNN_ACT_TYPE)
elif CONFIG.LAYER_TYPE[i] == "TrajGRU":
rnn_block = BaseStackRNN(base_rnn_class=TrajGRU,
stack_num=CONFIG.STACK_NUM[i],
name=name,
L=CONFIG.L[i],
residual_connection=CONFIG.RES_CONNECTION,
num_filter=CONFIG.NUM_FILTER[i],
b_h_w=(batch_size,
cfg.MODEL.ENCODER_FORECASTER.FEATMAP_SIZE[i],
cfg.MODEL.ENCODER_FORECASTER.FEATMAP_SIZE[i]),
h2h_kernel=CONFIG.H2H_KERNEL[i],
h2h_dilate=CONFIG.H2H_DILATE[i],
i2h_kernel=CONFIG.I2H_KERNEL[i],
i2h_pad=CONFIG.I2H_PAD[i],
act_type=cfg.MODEL.RNN_ACT_TYPE)
else:
raise NotImplementedError
vec.append(rnn_block)
return encoder_rnn_blocks, forecaster_rnn_blocks, gan_rnn_blocks
class EncoderForecasterBaseFactory(PredictionBaseFactory):
def __init__(self,
batch_size,
in_seq_len,
out_seq_len,
height,
width,
ctx_num=1,
name="encoder_forecaster"):
super(EncoderForecasterBaseFactory, self).__init__(batch_size=batch_size,
in_seq_len=in_seq_len,
out_seq_len=out_seq_len,
height=height,
width=width,
name=name)
self._ctx_num = ctx_num
def _init_rnn(self):
self._encoder_rnn_blocks, self._forecaster_rnn_blocks, self._gan_rnn_blocks =\
get_encoder_forecaster_rnn_blocks(batch_size=self._batch_size)
return self._encoder_rnn_blocks + self._forecaster_rnn_blocks + self._gan_rnn_blocks
@property
def init_encoder_state_info(self):
init_state_info = []
for block in self._encoder_rnn_blocks:
for state in block.init_state_vars():
init_state_info.append({'name': state.name,
'shape': state.attr('__shape__'),
'__layout__': state.list_attr()['__layout__']})
return init_state_info
@property
def init_forecaster_state_info(self):
init_state_info = []
for block in self._forecaster_rnn_blocks:
for state in block.init_state_vars():
init_state_info.append({'name': state.name,
'shape': state.attr('__shape__'),
'__layout__': state.list_attr()['__layout__']})
return init_state_info
@property
def init_gan_state_info(self):
init_gan_state_info = []
for block in self._gan_rnn_blocks:
for state in block.init_state_vars():
init_gan_state_info.append({'name': state.name,
'shape': state.attr('__shape__'),
'__layout__': state.list_attr()['__layout__']})
return init_gan_state_info
def stack_rnn_encode(self, data):
CONFIG = cfg.MODEL.ENCODER_FORECASTER
pre_encoded_data = self._pre_encode_frame(frame_data=data, seqlen=self._in_seq_len)
reshape_data = mx.sym.Reshape(pre_encoded_data, shape=(-1, 0, 0, 0), reverse=True)
# Encoder Part
conv1 = conv2d_act(data=reshape_data,
num_filter=CONFIG.FIRST_CONV[0],
kernel=(CONFIG.FIRST_CONV[1], CONFIG.FIRST_CONV[1]),
stride=(CONFIG.FIRST_CONV[2], CONFIG.FIRST_CONV[2]),
pad=(CONFIG.FIRST_CONV[3], CONFIG.FIRST_CONV[3]),
act_type=cfg.MODEL.CNN_ACT_TYPE,
name="econv1")
rnn_block_num = len(CONFIG.RNN_BLOCKS.NUM_FILTER)
encoder_rnn_block_states = []
for i in range(rnn_block_num):
if i == 0:
inputs = conv1
else:
inputs = downsample
rnn_out, states = self._encoder_rnn_blocks[i].unroll(
length=self._in_seq_len,
inputs=inputs,
begin_states=None,
ret_mid=False)
encoder_rnn_block_states.append(states)
if i < rnn_block_num - 1:
downsample = downsample_module(data=rnn_out[-1],
num_filter=CONFIG.RNN_BLOCKS.NUM_FILTER[i + 1],
kernel=(CONFIG.DOWNSAMPLE[i][0],
CONFIG.DOWNSAMPLE[i][0]),
stride=(CONFIG.DOWNSAMPLE[i][1],
CONFIG.DOWNSAMPLE[i][1]),
pad=(CONFIG.DOWNSAMPLE[i][2],
CONFIG.DOWNSAMPLE[i][2]),
b_h_w=(self._batch_size,
CONFIG.FEATMAP_SIZE[i + 1],
CONFIG.FEATMAP_SIZE[i + 1]),
name="edown%d" %(i + 1))
return encoder_rnn_block_states
def stack_rnn_forecast(self, block_state_list, last_frame):
CONFIG = cfg.MODEL.ENCODER_FORECASTER
block_state_list = [self._forecaster_rnn_blocks[i].to_split(block_state_list[i])
for i in range(len(self._forecaster_rnn_blocks))]
rnn_block_num = len(CONFIG.RNN_BLOCKS.NUM_FILTER)
rnn_block_outputs = []
# RNN Forecaster Part
curr_inputs = None
for i in range(rnn_block_num - 1, -1, -1):
rnn_out, rnn_state = self._forecaster_rnn_blocks[i].unroll(
length=self._out_seq_len, inputs=curr_inputs,
begin_states=block_state_list[i][::-1], # Reverse the order of states for the forecaster
ret_mid=False)
rnn_block_outputs.append(rnn_out)
if i > 0:
upsample = upsample_module(data=rnn_out[-1],
num_filter=CONFIG.RNN_BLOCKS.NUM_FILTER[i],
kernel=(CONFIG.UPSAMPLE[i - 1][0],
CONFIG.UPSAMPLE[i - 1][0]),
stride=(CONFIG.UPSAMPLE[i - 1][1],
CONFIG.UPSAMPLE[i - 1][1]),
pad=(CONFIG.UPSAMPLE[i - 1][2],
CONFIG.UPSAMPLE[i - 1][2]),
b_h_w=(self._batch_size, CONFIG.FEATMAP_SIZE[i - 1]),
name="fup%d" %i)
curr_inputs = upsample
# Output
if cfg.MODEL.OUT_TYPE == "DFN":
concat_fbrnn1_out = mx.sym.concat(*rnn_out[-1], dim=0)
dynamic_filter = deconv2d(data=concat_fbrnn1_out,
num_filter=121,
kernel=(CONFIG.LAST_DECONV[1], CONFIG.LAST_DECONV[1]),
stride=(CONFIG.LAST_DECONV[2], CONFIG.LAST_DECONV[2]),
pad=(CONFIG.LAST_DECONV[3], CONFIG.LAST_DECONV[3]))
flow = dynamic_filter
dynamic_filter = mx.sym.SliceChannel(dynamic_filter, axis=0, num_outputs=self._out_seq_len)
prev_frame = last_frame
preds = []
for i in range(self._out_seq_len):
pred_ele = DFN(data=prev_frame, local_kernels=dynamic_filter[i], K=11, batch_size=self._batch_size)
preds.append(pred_ele)
prev_frame = pred_ele
pred = mx.sym.concat(*preds, dim=0)
elif cfg.MODEL.OUT_TYPE == "direct":
flow = None
deconv1 = deconv2d_act(data=mx.sym.concat(*rnn_out[-1], dim=0),
num_filter=CONFIG.LAST_DECONV[0],
kernel=(CONFIG.LAST_DECONV[1], CONFIG.LAST_DECONV[1]),
stride=(CONFIG.LAST_DECONV[2], CONFIG.LAST_DECONV[2]),
pad=(CONFIG.LAST_DECONV[3], CONFIG.LAST_DECONV[3]),
act_type=cfg.MODEL.CNN_ACT_TYPE,
name="fdeconv1")
conv_final = conv2d_act(data=deconv1,
num_filter=CONFIG.LAST_DECONV[0],
kernel=(3, 3), stride=(1, 1), pad=(1, 1),
act_type=cfg.MODEL.CNN_ACT_TYPE, name="conv_final")
pred = conv2d(data=conv_final,
num_filter=1, kernel=(1, 1), name="out")
else:
raise NotImplementedError
pred = mx.sym.Reshape(pred,
shape=(self._out_seq_len, self._batch_size,
1, self._height, self._width),
__layout__="TNCHW")
return pred, flow
def encoder_sym(self):
self.reset_all()
data = mx.sym.Variable('data') # Shape: (in_seq_len, batch_size, C, H, W)
block_state_list = self.stack_rnn_encode(data=data)
states = []
for i, rnn_block in enumerate(self._encoder_rnn_blocks):
states.extend(rnn_block.flatten_add_layout(block_state_list[i]))
return mx.sym.Group(states)
def encoder_data_desc(self):
ret = list()
ret.append(mx.io.DataDesc(name='data',
shape=(self._in_seq_len,
self._batch_size * self._ctx_num,
1,
self._height,
self._width),
layout="TNCHW"))
for info in self.init_encoder_state_info:
state_shape = safe_eval(info['shape'])
assert info['__layout__'].find('N') == 0,\
"Layout=%s is not supported!" %info["__layout__"]
state_shape = (state_shape[0] * self._ctx_num, ) + state_shape[1:]
ret.append(mx.io.DataDesc(name=info['name'],
shape=state_shape,
layout=info['__layout__']))
return ret
def forecaster_sym(self):
self.reset_all()
block_state_list = []
for block in self._forecaster_rnn_blocks:
block_state_list.append(block.init_state_vars())
if cfg.MODEL.OUT_TYPE == "direct":
pred, _ = self.stack_rnn_forecast(block_state_list=block_state_list,
last_frame=None)
return mx.sym.Group([pred])
else:
last_frame = mx.sym.Variable('last_frame') # Shape: (batch_size, C, H, W)
pred, flow = self.stack_rnn_forecast(block_state_list=block_state_list,
last_frame=last_frame)
return mx.sym.Group([pred, mx.sym.BlockGrad(flow)])
def forecaster_data_desc(self):
ret = list()
for info in self.init_forecaster_state_info:
state_shape = safe_eval(info['shape'])
assert info['__layout__'].find('N') == 0, \
"Layout=%s is not supported!" % info["__layout__"]
state_shape = (state_shape[0] * self._ctx_num,) + state_shape[1:]
ret.append(mx.io.DataDesc(name=info['name'],
shape=state_shape,
layout=info['__layout__']))
if cfg.MODEL.OUT_TYPE != "direct":
ret.append(mx.io.DataDesc(name="last_frame",
shape=(self._ctx_num * self._batch_size,
1, self._height, self._width),
layout="NCHW"))
return ret
def loss_sym(self):
raise NotImplementedError
def loss_data_desc(self):
ret = list()
ret.append(mx.io.DataDesc(name='pred',
shape=(self._out_seq_len,
self._ctx_num * self._batch_size,
1,
self._height,
self._width),
layout="TNCHW"))
return ret
def loss_label_desc(self):
ret = list()
ret.append(mx.io.DataDesc(name='target',
shape=(self._out_seq_len,
self._ctx_num * self._batch_size,
1,
self._height,
self._width),
layout="TNCHW"))
if cfg.MODEL.ENCODER_FORECASTER.HAS_MASK:
ret.append(mx.io.DataDesc(name='mask',
shape=(self._out_seq_len,
self._ctx_num * self._batch_size,
1,
self._height,
self._width),
layout="TNCHW"))
return ret
def init_optimizer_using_cfg(net, for_finetune):
if not for_finetune:
lr_scheduler = mx.lr_scheduler.FactorScheduler(step=cfg.MODEL.TRAIN.LR_DECAY_ITER,
factor=cfg.MODEL.TRAIN.LR_DECAY_FACTOR,
stop_factor_lr=cfg.MODEL.TRAIN.MIN_LR)
if cfg.MODEL.TRAIN.OPTIMIZER.lower() == "adam":
net.init_optimizer(optimizer="adam",
optimizer_params={'learning_rate': cfg.MODEL.TRAIN.LR,
'beta1': cfg.MODEL.TRAIN.BETA1,
'rescale_grad': 1.0,
'epsilon': cfg.MODEL.TRAIN.EPS,
'lr_scheduler': lr_scheduler,
'wd': cfg.MODEL.TRAIN.WD})
elif cfg.MODEL.TRAIN.OPTIMIZER.lower() == "rmsprop":
net.init_optimizer(optimizer="rmsprop",
optimizer_params={'learning_rate': cfg.MODEL.TRAIN.LR,
'gamma1': cfg.MODEL.TRAIN.GAMMA1,
'rescale_grad': 1.0,
'epsilon': cfg.MODEL.TRAIN.EPS,
'lr_scheduler': lr_scheduler,
'wd': cfg.MODEL.TRAIN.WD})
elif cfg.MODEL.TRAIN.OPTIMIZER.lower() == "sgd":
net.init_optimizer(optimizer="sgd",
optimizer_params={'learning_rate': cfg.MODEL.TRAIN.LR,
'momentum': 0.0,
'rescale_grad': 1.0,
'lr_scheduler': lr_scheduler,
'wd': cfg.MODEL.TRAIN.WD})
elif cfg.MODEL.TRAIN.OPTIMIZER.lower() == "adagrad":
net.init_optimizer(optimizer="adagrad",
optimizer_params={'learning_rate': cfg.MODEL.TRAIN.LR,
'eps': cfg.MODEL.TRAIN.EPS,
'rescale_grad': 1.0,
'wd': cfg.MODEL.TRAIN.WD})
else:
raise NotImplementedError
else:
if cfg.MODEL.TEST.ONLINE.OPTIMIZER.lower() == "adam":
net.init_optimizer(optimizer="adam",
optimizer_params={'learning_rate': cfg.MODEL.TEST.ONLINE.LR,
'beta1': cfg.MODEL.TEST.ONLINE.BETA1,
'rescale_grad': 1.0,
'epsilon': cfg.MODEL.TEST.ONLINE.EPS,
'wd': cfg.MODEL.TEST.ONLINE.WD})
elif cfg.MODEL.TEST.ONLINE.OPTIMIZER.lower() == "rmsprop":
net.init_optimizer(optimizer="rmsprop",
optimizer_params={'learning_rate': cfg.MODEL.TEST.ONLINE.LR,
'gamma1': cfg.MODEL.TEST.ONLINE.GAMMA1,
'rescale_grad': 1.0,
'epsilon': cfg.MODEL.TEST.ONLINE.EPS,
'wd': cfg.MODEL.TEST.ONLINE.WD})
elif cfg.MODEL.TEST.ONLINE.OPTIMIZER.lower() == "sgd":
net.init_optimizer(optimizer="sgd",
optimizer_params={'learning_rate': cfg.MODEL.TEST.ONLINE.LR,
'momentum': 0.0,
'rescale_grad': 1.0,
'wd': cfg.MODEL.TEST.ONLINE.WD})
elif cfg.MODEL.TEST.ONLINE.OPTIMIZER.lower() == "adagrad":
net.init_optimizer(optimizer="adagrad",
optimizer_params={'learning_rate': cfg.MODEL.TEST.ONLINE.LR,
'eps': cfg.MODEL.TRAIN.EPS,
'rescale_grad': 1.0,
'wd': cfg.MODEL.TEST.ONLINE.WD})
return net
def encoder_forecaster_build_networks(factory, context,
shared_encoder_net=None,
shared_forecaster_net=None,
shared_loss_net=None,
for_finetune=False):
"""
Parameters
----------
factory : EncoderForecasterBaseFactory
context : list
shared_encoder_net : MyModule or None
shared_forecaster_net : MyModule or None
shared_loss_net : MyModule or None
for_finetune : bool
Returns
-------
"""
encoder_net = MyModule(factory.encoder_sym(),
data_names=[ele.name for ele in factory.encoder_data_desc()],
label_names=[],
context=context,
name="encoder_net")
encoder_net.bind(data_shapes=factory.encoder_data_desc(),
label_shapes=None,
inputs_need_grad=True,
shared_module=shared_encoder_net)
if shared_encoder_net is None:
encoder_net.init_params(mx.init.MSRAPrelu(slope=0.2))
init_optimizer_using_cfg(encoder_net, for_finetune=for_finetune)
forecaster_net = MyModule(factory.forecaster_sym(),
data_names=[ele.name for ele in
factory.forecaster_data_desc()],
label_names=[],
context=context,
name="forecaster_net")
forecaster_net.bind(data_shapes=factory.forecaster_data_desc(),
label_shapes=None,
inputs_need_grad=True,
shared_module=shared_forecaster_net)
if shared_forecaster_net is None:
forecaster_net.init_params(mx.init.MSRAPrelu(slope=0.2))
init_optimizer_using_cfg(forecaster_net, for_finetune=for_finetune)
loss_net = MyModule(factory.loss_sym(),
data_names=[ele.name for ele in
factory.loss_data_desc()],
label_names=[ele.name for ele in
factory.loss_label_desc()],
context=context,
name="loss_net")
loss_net.bind(data_shapes=factory.loss_data_desc(),
label_shapes=factory.loss_label_desc(),
inputs_need_grad=True,
shared_module=shared_loss_net)
if shared_loss_net is None:
loss_net.init_params()
return encoder_net, forecaster_net, loss_net
class EncoderForecasterStates(object):
def __init__(self, factory, ctx):
self._factory = factory
self._ctx = ctx
self._encoder_state_info = factory.init_encoder_state_info
self._forecaster_state_info = factory.init_forecaster_state_info
self._states_nd = []
for info in self._encoder_state_info:
state_shape = safe_eval(info['shape'])
state_shape = (state_shape[0] * factory._ctx_num, ) + state_shape[1:]
self._states_nd.append(mx.nd.zeros(shape=state_shape, ctx=ctx))
def reset_all(self):
for ele, info in zip(self._states_nd, self._encoder_state_info):
ele[:] = 0
def reset_batch(self, batch_id):
for ele, info in zip(self._states_nd, self._encoder_state_info):
ele[batch_id][:] = 0
def update(self, states_nd):
for target, src in zip(self._states_nd, states_nd):
target[:] = src
def get_encoder_states(self):
return self._states_nd
def get_forecaster_state(self):
return self._states_nd
def train_step(batch_size, encoder_net, forecaster_net,
loss_net, init_states,
data_nd, gt_nd, mask_nd, iter_id=None):
"""Finetune the encoder, forecaster and GAN for one step
Parameters
----------
batch_size : int
encoder_net : MyModule
forecaster_net : MyModule
loss_net : MyModule
init_states : EncoderForecasterStates
data_nd : mx.nd.ndarray
gt_nd : mx.nd.ndarray
mask_nd : mx.nd.ndarray
iter_id : int
Returns
-------
init_states: EncoderForecasterStates
loss_dict: dict
"""
# Forward Encoder
encoder_net.forward(is_train=True,
data_batch=mx.io.DataBatch(data=[data_nd] + init_states.get_encoder_states()))
encoder_states_nd = encoder_net.get_outputs()
init_states.update(encoder_states_nd)
# Forward Forecaster
if cfg.MODEL.OUT_TYPE == "direct":
forecaster_net.forward(is_train=True,
data_batch=mx.io.DataBatch(data=init_states.get_forecaster_state()))
else:
last_frame_nd = data_nd[data_nd.shape[0] - 1]
forecaster_net.forward(is_train=True,
data_batch=mx.io.DataBatch(data=init_states.get_forecaster_state() +
[last_frame_nd]))
forecaster_outputs = forecaster_net.get_outputs()
pred_nd = forecaster_outputs[0]
# Calculate the gradient of the loss functions
if cfg.MODEL.ENCODER_FORECASTER.HAS_MASK:
loss_net.forward_backward(data_batch=mx.io.DataBatch(data=[pred_nd],
label=[gt_nd, mask_nd]))
else:
loss_net.forward_backward(data_batch=mx.io.DataBatch(data=[pred_nd],
label=[gt_nd]))
pred_grad = loss_net.get_input_grads()[0]
loss_dict = loss_net.get_output_dict()
for k in loss_dict:
loss_dict[k] = nd.mean(loss_dict[k]).asscalar()
# Backward Forecaster
forecaster_net.backward(out_grads=[pred_grad])
if cfg.MODEL.OUT_TYPE == "direct":
encoder_states_grad_nd = forecaster_net.get_input_grads()
else:
encoder_states_grad_nd = forecaster_net.get_input_grads()[:-1]
# Backward Encoder
encoder_net.backward(encoder_states_grad_nd)
# Update forecaster and encoder
forecaster_grad_norm = forecaster_net.clip_by_global_norm(max_norm=cfg.MODEL.TRAIN.GRAD_CLIP)
encoder_grad_norm = encoder_net.clip_by_global_norm(max_norm=cfg.MODEL.TRAIN.GRAD_CLIP)
forecaster_net.update()
encoder_net.update()
loss_str = ", ".join(["%s=%g" %(k, v) for k, v in loss_dict.items()])
if iter_id is not None:
logging.info("Iter:%d, %s, e_gnorm=%g, f_gnorm=%g"
% (iter_id, loss_str, encoder_grad_norm, forecaster_grad_norm))
return init_states, loss_dict
def load_encoder_forecaster_params(load_dir, load_iter, encoder_net, forecaster_net):
logging.info("Loading parameters from {}, Iter = {}"
.format(os.path.realpath(load_dir), load_iter))
encoder_arg_params, encoder_aux_params = load_params(prefix=os.path.join(load_dir,
"encoder_net"),
epoch=load_iter)
encoder_net.init_params(arg_params=encoder_arg_params, aux_params=encoder_aux_params,
allow_missing=False, force_init=True)
forecaster_arg_params, forecaster_aux_params = load_params(prefix=os.path.join(load_dir,
"forecaster_net"),
epoch=load_iter)
forecaster_net.init_params(arg_params=forecaster_arg_params,
aux_params=forecaster_aux_params,
allow_missing=False,
force_init=True)
logging.info("Loading Complete!")
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