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Commit 0936a502 by Ting PAN
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Fix the wrong grads of LSTMCell

1 parent 40e94d24
Inline Side-by-side
Showing with 504 additions and 171 deletions
Dragon/python/dragon/core/tensor.py
...@@ -771,16 +771,6 @@ class Tensor(object): ...@@ -771,16 +771,6 @@ class Tensor(object):
def __hash__(self): def __hash__(self):
return id(self) return id(self)
def __call__(self, *args, **kwargs):
"""Print the expressions.
Returns
-------
None
"""
return self.debug_expressions()
############################################### ###############################################
# # # #
# Theano API # # Theano API #
...@@ -1030,7 +1020,7 @@ class Tensor(object): ...@@ -1030,7 +1020,7 @@ class Tensor(object):
""" """
expressions = dict() expressions = dict()
# 1. Collect inputs # 1) Collect inputs
if not isinstance(inputs, list): inputs = [inputs] if not isinstance(inputs, list): inputs = [inputs]
for input in inputs: for input in inputs:
for op_idx, expr in input.expressions.items(): for op_idx, expr in input.expressions.items():
...@@ -1044,7 +1034,7 @@ class Tensor(object): ...@@ -1044,7 +1034,7 @@ class Tensor(object):
if not op_idx in expressions: if not op_idx in expressions:
expressions[op_idx] = expr expressions[op_idx] = expr
# 2. Generate outputs # 2) Generate outputs
outputs = [] outputs = []
if existing_outputs is None: if existing_outputs is None:
name_scope = _scope.get_default_name_scope() name_scope = _scope.get_default_name_scope()
...@@ -1061,7 +1051,7 @@ class Tensor(object): ...@@ -1061,7 +1051,7 @@ class Tensor(object):
num_outputs = len(outputs) num_outputs = len(outputs)
if not isinstance(outputs, list): outputs = [outputs] if not isinstance(outputs, list): outputs = [outputs]
# 3. Construct OperatorDef # 3) Construct OperatorDef
inputs_name = [input.name for input in inputs] inputs_name = [input.name for input in inputs]
outputs_name = [output.name for output in outputs] outputs_name = [output.name for output in outputs]
op_idx, op_name = _helper.OperatorHelper.get_index_and_name() op_idx, op_name = _helper.OperatorHelper.get_index_and_name()
...@@ -1073,7 +1063,7 @@ class Tensor(object): ...@@ -1073,7 +1063,7 @@ class Tensor(object):
expressions[op_idx] = op_def expressions[op_idx] = op_def
# 4. Add outputs # 4) Add outputs
for idx, output in enumerate(outputs): for idx, output in enumerate(outputs):
# Deliver expressions # Deliver expressions
output.expressions = expressions output.expressions = expressions
...@@ -1085,11 +1075,11 @@ class Tensor(object): ...@@ -1085,11 +1075,11 @@ class Tensor(object):
for input in extra_inputs: for input in extra_inputs:
output.extra_targets.add(input.name) output.extra_targets.add(input.name)
# 5. Refine the shape and data type # 5) Refine the shape and data type
outputs = _helper.OperatorHelper.apply(op_type, outputs = _helper.OperatorHelper.apply(op_type,
arguments=kwargs, inputs=inputs, outputs=outputs) arguments=kwargs, inputs=inputs, outputs=outputs)
# 6. Returns # 6) Returns
if num_outputs > 1: return outputs if num_outputs > 1: return outputs
elif num_outputs == 1: return outputs[0] elif num_outputs == 1: return outputs[0]
else: return None else: return None
......
Dragon/python/dragon/core/workspace.py
...@@ -112,7 +112,7 @@ class Workspace(_C.Workspace): ...@@ -112,7 +112,7 @@ class Workspace(_C.Workspace):
This class is a fusion of *Workspace*, *Pool* and *tf.Graph*. This class is a fusion of *Workspace*, *Pool* and *tf.Graph*.
We find that they work in a similar way while named different. We find that they work in a similar way while named differently.
""" """
def __init__(self, name=''): def __init__(self, name=''):
...@@ -514,8 +514,8 @@ def RunGraph( ...@@ -514,8 +514,8 @@ def RunGraph(
Returns Returns
------- -------
None, NDArray or list of NDArray sequence of numpy.ndarray
The outputs, format as NDArray. The outputs which are copied to numpy array.
See Also See Also
-------- --------
...@@ -551,11 +551,11 @@ def Backward( ...@@ -551,11 +551,11 @@ def Backward(
input_grads=None, input_grads=None,
ignored_grads=None, ignored_grads=None,
): ):
"""Compute the gradients of given input flows. """Compute the gradients of given input operators.
Parameters Parameters
---------- ----------
input_flow : sequence of OperatorDef forward_ops : sequence of OperatorDef
The referring ops to generate gradients. The referring ops to generate gradients.
targets : sequence or str targets : sequence or str
The solving targets. The solving targets.
...@@ -576,10 +576,13 @@ def Backward( ...@@ -576,10 +576,13 @@ def Backward(
options['log_meta_graph']) else False options['log_meta_graph']) else False
get_default_workspace().Backward( get_default_workspace().Backward(
forward_ops, targets, forward_ops,
input_grads if input_grads else [], targets,
ignored_grads if ignored_grads else [], input_grads if input_grads else [],
options['share_grads'], required_logging) ignored_grads if ignored_grads else [],
options['share_grads'],
required_logging,
)
def LogMetaGraph(graph_def): def LogMetaGraph(graph_def):
...@@ -666,6 +669,7 @@ def Snapshot( ...@@ -666,6 +669,7 @@ def Snapshot(
""" """
file_path = prefix + filename + suffix file_path = prefix + filename + suffix
if _mpi.Is_Init(): if _mpi.Is_Init():
if not _mpi.AllowSnapshot(): return if not _mpi.AllowSnapshot(): return
file_path = file_path + '.rank.{}'.format(_mpi.Rank()) file_path = file_path + '.rank.{}'.format(_mpi.Rank())
......
Dragon/python/dragon/operators/contrib/rcnn/ops.py
...@@ -17,11 +17,21 @@ from dragon.operators import * ...@@ -17,11 +17,21 @@ from dragon.operators import *
@OpSchema.Inputs(3, INT_MAX) @OpSchema.Inputs(3, INT_MAX)
def Proposal(inputs, strides, ratios, scales, def Proposal(
pre_nms_top_n=6000, post_nms_top_n=300, inputs,
nms_thresh=0.7, min_size=16, strides,
min_level=2, max_level=5, ratios,
canonical_scale=224, canonical_level=4, **kwargs): scales,
pre_nms_top_n=6000,
post_nms_top_n=300,
nms_thresh=0.7,
min_size=16,
min_level=2,
max_level=5,
canonical_scale=224,
canonical_level=4,
**kwargs
):
"""Generate Regional Proposals, introduced by `[Ren et.al, 2015] <https://arxiv.org/abs/1506.01497>`_. """Generate Regional Proposals, introduced by `[Ren et.al, 2015] <https://arxiv.org/abs/1506.01497>`_.
Multi-Level proposals was introduced by `[Lin et.al, 2017] <https://arxiv.org/abs/1612.03144>`_. Multi-Level proposals was introduced by `[Lin et.al, 2017] <https://arxiv.org/abs/1612.03144>`_.
......
Dragon/python/dragon/operators/misc.py
...@@ -130,7 +130,7 @@ def Accuracy(inputs, top_k=1, axis=1, ignore_labels=(), **kwargs): ...@@ -130,7 +130,7 @@ def Accuracy(inputs, top_k=1, axis=1, ignore_labels=(), **kwargs):
**Type Constraints**: **Type Constraints**:
* logits (*float16*, *float32*) * logits (*float32*)
* labels (*float32*, *int64*) * labels (*float32*, *int64*)
......
Dragon/python/dragon/operators/rnn/rnn_param.py
...@@ -18,9 +18,17 @@ from .. import * ...@@ -18,9 +18,17 @@ from .. import *
@OpSchema.Inputs(2) @OpSchema.Inputs(2)
def RNNParamSet( def RNNParamSet(
inputs, layer_id, param_id, param_type, inputs,
rnn_mode, input_size, hidden_size, layer_id,
num_layers=1, num_directions=1, **kwargs): param_id,
param_type,
rnn_mode,
input_size,
hidden_size,
num_layers=1,
num_directions=1,
**kwargs
):
arguments = ParseArgs(locals()) arguments = ParseArgs(locals())
arguments['inputs'] = inputs[1] arguments['inputs'] = inputs[1]
arguments['existing_outputs'] = inputs[0] arguments['existing_outputs'] = inputs[0]
......
Dragon/python/dragon/operators/rnn/rnn_wrapper.py
...@@ -14,20 +14,28 @@ from __future__ import division ...@@ -14,20 +14,28 @@ from __future__ import division
from __future__ import print_function from __future__ import print_function
import numpy import numpy
import dragon
import warnings import warnings
from dragon.core.tensor import Tensor from dragon import config as _cfg
from dragon.core.tensor_utils import FromShape from dragon.core import workspace as _workspace
from dragon.operators.rnn.rnn_param import RNNParamSet from dragon.core.tensor import Tensor as _Tensor
from dragon.core import tensor_utils as _tensor_utils
from .rnn_param import RNNParamSet
class RNNBase(object): class RNNBase(object):
"""A simple class wrapping general RNN ops.""" """A simple class wrapping general RNN ops."""
def __init__(self, def __init__(self,
mode, input_size, hidden_size, num_layers=1, mode,
bidirectional=False, dropout=0, name=None): input_size,
hidden_size,
num_layers=1,
bidirectional=False,
dropout=0,
name=None,
):
eligible_rnn_modes = ('rnn_tanh', 'rnn_relu', 'lstm', 'gru') eligible_rnn_modes = ('rnn_tanh', 'rnn_relu', 'lstm', 'gru')
if mode.lower() not in eligible_rnn_modes: if mode.lower() not in eligible_rnn_modes:
raise ValueError('Unknown rnn mode: {}.' raise ValueError('Unknown rnn mode: {}.'
...@@ -50,7 +58,7 @@ class RNNBase(object): ...@@ -50,7 +58,7 @@ class RNNBase(object):
if self.mode == 'lstm': gate_size = 4 * self.hidden_size if self.mode == 'lstm': gate_size = 4 * self.hidden_size
elif self.mode == 'gru': gate_size = 3 * self.hidden_size elif self.mode == 'gru': gate_size = 3 * self.hidden_size
else: gate_size = self.hidden_size else: gate_size = self.hidden_size
# 1. Plan weights # 1) Plan weights
self._matrix_shape, self._bias_shape = [], [] self._matrix_shape, self._bias_shape = [], []
for layer in range(self.num_layers): for layer in range(self.num_layers):
for direction in range(self.num_directions): for direction in range(self.num_directions):
...@@ -64,16 +72,18 @@ class RNNBase(object): ...@@ -64,16 +72,18 @@ class RNNBase(object):
# Bw (0 ~ 3), Br (4 ~ 7) # Bw (0 ~ 3), Br (4 ~ 7)
self._bias_shape.extend([b_ih_shape, b_hh_shape]) self._bias_shape.extend([b_ih_shape, b_hh_shape])
# 2. Compute total number of parameters # 2) Compute total number of parameters
self._weights_count = 0 self._weights_count = 0
for shape in self._matrix_shape + self._bias_shape: for shape in self._matrix_shape + self._bias_shape:
self._weights_count += numpy.prod(shape) self._weights_count += numpy.prod(shape)
# 3. Register the packed weights # 3) Register the packed weights
self.weights = FromShape(shape=[self._weights_count], self.weights = _tensor_utils.FromShape(
name=self.name + '/weights' if self.name else None) shape=[self._weights_count],
name=self.name + '/weights' if self.name else None,
)
# 4. Create the initialization grids # 4) Create the initialization grids
if self.mode == 'lstm': num_params_per_layer = 8 if self.mode == 'lstm': num_params_per_layer = 8
elif self.mode == 'gru': num_params_per_layer = 6 elif self.mode == 'gru': num_params_per_layer = 6
else: num_params_per_layer = 2 else: num_params_per_layer = 2
...@@ -121,8 +131,14 @@ class RNNBase(object): ...@@ -121,8 +131,14 @@ class RNNBase(object):
# # # #
############################################## ##############################################
def set_param(self, layer=0, direction=0, param_id=0, def set_param(
type='matrix', initializer=None): self,
layer=0,
direction=0,
param_id=0,
type='matrix',
initializer=None,
):
if type == 'matrix': if type == 'matrix':
self._matrix_init_grids[layer][direction][param_id] = initializer self._matrix_init_grids[layer][direction][param_id] = initializer
elif type == 'bias': elif type == 'bias':
...@@ -130,20 +146,35 @@ class RNNBase(object): ...@@ -130,20 +146,35 @@ class RNNBase(object):
else: else:
raise ValueError('Unknown param type: ' + type) raise ValueError('Unknown param type: ' + type)
def _set_param(self, layer_id, param_id, param_type, param): def _set_param(
self,
layer_id,
param_id,
param_type,
param,
):
if isinstance(param, numpy.ndarray): if isinstance(param, numpy.ndarray):
param_temp = dragon.Tensor.Ref('/tmp/rnn_param') param_temp = _Tensor.Ref('/tmp/rnn_param')
param_temp.set_value(param) param_temp.set_value(param)
param = param_temp param = param_temp
else: raise ValueError('Excepted a numpy array.') else: raise ValueError('Excepted a numpy array.')
self.weights.expressions = dict() # Clear cached expressions self.weights.expressions = dict() # Clear cached expressions
outputs = RNNParamSet([self.weights, param], layer_id, param_id, param_type, outputs = RNNParamSet(
rnn_mode=self.mode, input_size=self.input_size, hidden_size=self.hidden_size, inputs=[self.weights, param],
num_layers=self.num_layers, num_directions=self.num_directions) layer_id=layer_id,
for k, v in outputs.expressions.items(): dragon.workspace.RunOperator(v) param_id=param_id,
param_type=param_type,
rnn_mode=self.mode,
input_size=self.input_size,
hidden_size=self.hidden_size,
num_layers=self.num_layers,
num_directions=self.num_directions,
)
for k, v in outputs.expressions.items():
_workspace.RunOperator(v)
def _reset_params(self): def _reset_params(self):
numpy.random.seed(dragon.config.GetRandomSeed()) numpy.random.seed(_cfg.GetRandomSeed())
if self.mode == 'lstm': num_gates = 4 if self.mode == 'lstm': num_gates = 4
elif self.mode == 'gru': num_gates = 3 elif self.mode == 'gru': num_gates = 3
else: num_gates = 1 else: num_gates = 1
...@@ -162,15 +193,29 @@ class RNNBase(object): ...@@ -162,15 +193,29 @@ class RNNBase(object):
matrix_shape = self._matrix_shape[packed_id][:] matrix_shape = self._matrix_shape[packed_id][:]
bias_shape = self._bias_shape[packed_id][:] bias_shape = self._bias_shape[packed_id][:]
matrix_shape[0] = bias_shape[0] = int(matrix_shape[0] / num_gates) matrix_shape[0] = bias_shape[0] = int(matrix_shape[0] / num_gates)
self._set_param(layer_id=pseudo_layer_id, param_id=param_id, self._set_param(
param_type='matrix', param=matrix_init(matrix_shape)) layer_id=pseudo_layer_id,
self._set_param(layer_id=pseudo_layer_id, param_id=param_id, param_id=param_id,
param_type='bias', param=bias_init(bias_shape)) param_type='matrix',
param=matrix_init(matrix_shape),
)
self._set_param(
layer_id=pseudo_layer_id,
param_id=param_id,
param_type='bias',
param=bias_init(bias_shape),
)
self.weights.expressions = weights_states self.weights.expressions = weights_states
self._init_params = True self._init_params = True
def create(self, x, hx=None, cx=None, def create(
required_hidden=True, required_cell=False): self,
x,
hx=None,
cx=None,
required_hidden=True,
required_cell=False,
):
"""Return outputs of this rnn. """Return outputs of this rnn.
Parameters Parameters
...@@ -187,9 +232,9 @@ class RNNBase(object): ...@@ -187,9 +232,9 @@ class RNNBase(object):
Return ``y``, ``hidden``, ``cell`` if ``True``. Return ``y``, ``hidden``, ``cell`` if ``True``.
""" """
if hx and not isinstance(hx, Tensor): if hx and not isinstance(hx, _Tensor):
raise TypeError('Excepted hx as a Tensor, got {}.'.format(type(hx))) raise TypeError('Excepted hx as a Tensor, got {}.'.format(type(hx)))
if cx and not isinstance(cx, Tensor): if cx and not isinstance(cx, _Tensor):
raise TypeError('Excepted cx as a Tensor, got {}.'.format(type(cx))) raise TypeError('Excepted cx as a Tensor, got {}.'.format(type(cx)))
if not self._init_params: self._reset_params() if not self._init_params: self._reset_params()
...@@ -211,7 +256,8 @@ class RNNBase(object): ...@@ -211,7 +256,8 @@ class RNNBase(object):
elif required_hidden: num_outputs = 2 elif required_hidden: num_outputs = 2
else: num_outputs = 1 else: num_outputs = 1
return Tensor.CreateOperator(num_outputs=num_outputs, **arguments) return _Tensor.CreateOperator(
num_outputs=num_outputs, **arguments)
def __call__(self, *args, **kwargs): def __call__(self, *args, **kwargs):
return self.create(*args, **kwargs) return self.create(*args, **kwargs)
\ No newline at end of file
Dragon/python/dragon/vm/torch/autograd/variable.py
...@@ -60,10 +60,10 @@ def backward(self, gradient=None): ...@@ -60,10 +60,10 @@ def backward(self, gradient=None):
_tensor_utils.FromArray(gradient.numpy(True), self.name + '_grad') _tensor_utils.FromArray(gradient.numpy(True), self.name + '_grad')
input_grads.append(self.name + '_grad') input_grads.append(self.name + '_grad')
# 3. Dispatch the backward ops # 3) Dispatch the backward ops
_backward_impl(forward_ops, targets, input_grads, ignored_grads) _backward_impl(forward_ops, targets, input_grads, ignored_grads)
# 4. Release resources # 4) Release resources
# We should release both the operator handles and tensors # We should release both the operator handles and tensors
for forward_op in forward_ops: for forward_op in forward_ops:
_get_operator_pool().put(forward_op.name) _get_operator_pool().put(forward_op.name)
......
Dragon/python/dragon/vm/torch/nn/__init__.py
...@@ -29,7 +29,7 @@ from .modules.activation import ( ...@@ -29,7 +29,7 @@ from .modules.activation import (
) )
from .modules.loss import ( from .modules.loss import (
BCEWithLogitsLoss, BCEWithLogitsLoss, SCEWithLogitsLoss,
NLLLoss, CrossEntropyLoss, NLLLoss, CrossEntropyLoss,
L1Loss, MSELoss, SmoothL1Loss, L1Loss, MSELoss, SmoothL1Loss,
SigmoidFocalLoss, SoftmaxFocalLoss, SigmoidFocalLoss, SoftmaxFocalLoss,
......
Dragon/python/dragon/vm/torch/nn/init.py
...@@ -24,7 +24,15 @@ from dragon.vm.torch.autograd.grad_mode import no_grad ...@@ -24,7 +24,15 @@ from dragon.vm.torch.autograd.grad_mode import no_grad
def calculate_gain(nonlinearity, param=None): def calculate_gain(nonlinearity, param=None):
linear_fns = ['linear', 'conv1d', 'conv2d', 'conv3d', 'conv_transpose1d', 'conv_transpose2d', 'conv_transpose3d'] linear_fns = [
'linear',
'conv1d',
'conv2d',
'conv3d',
'conv_transpose1d',
'conv_transpose2d',
'conv_transpose3d',
]
if nonlinearity in linear_fns or nonlinearity == 'sigmoid': if nonlinearity in linear_fns or nonlinearity == 'sigmoid':
return 1 return 1
elif nonlinearity == 'tanh': elif nonlinearity == 'tanh':
...@@ -34,7 +42,9 @@ def calculate_gain(nonlinearity, param=None): ...@@ -34,7 +42,9 @@ def calculate_gain(nonlinearity, param=None):
elif nonlinearity == 'leaky_relu': elif nonlinearity == 'leaky_relu':
if param is None: if param is None:
negative_slope = 0.01 negative_slope = 0.01
elif not isinstance(param, bool) and isinstance(param, int) or isinstance(param, float): elif not isinstance(param, bool) and \
isinstance(param, int) or \
isinstance(param, float):
# True/False are instances of int, hence check above # True/False are instances of int, hence check above
negative_slope = param negative_slope = param
else: else:
......
Dragon/python/dragon/vm/torch/nn/modules/affine.py
...@@ -18,8 +18,14 @@ from dragon.vm.torch.ops.builtin import zeros, ones ...@@ -18,8 +18,14 @@ from dragon.vm.torch.ops.builtin import zeros, ones
class Affine(Module): class Affine(Module):
def __init__(self, num_features, bias=True, def __init__(
fix_weight=False, fix_bias=False, inplace=False): self,
num_features,
bias=True,
fix_weight=False,
fix_bias=False,
inplace=False,
):
super(Affine, self).__init__() super(Affine, self).__init__()
self.num_features = num_features self.num_features = num_features
self.inplace = inplace self.inplace = inplace
......
Dragon/python/dragon/vm/torch/nn/modules/batchnorm.py
...@@ -20,8 +20,14 @@ from dragon.vm.torch.module import RunOperator ...@@ -20,8 +20,14 @@ from dragon.vm.torch.module import RunOperator
class _BatchNorm(Module): class _BatchNorm(Module):
def __init__(self, num_features, eps=1e-5, momentum=0.1, def __init__(
affine=True, track_running_stats=True): self,
num_features,
eps=1e-5,
momentum=0.1,
affine=True,
track_running_stats=True,
):
super(_BatchNorm, self).__init__() super(_BatchNorm, self).__init__()
self.num_features = num_features self.num_features = num_features
self.eps = eps self.eps = eps
......
Dragon/python/dragon/vm/torch/nn/modules/container.py
...@@ -27,10 +27,8 @@ from dragon.vm.torch.nn import Module ...@@ -27,10 +27,8 @@ from dragon.vm.torch.nn import Module
class Container(Module): class Container(Module):
def __init__(self, **kwargs): def __init__(self, **kwargs):
super(Container, self).__init__() super(Container, self).__init__()
# DeprecationWarning is ignored by default <sigh>
warnings.warn("nn.Container is deprecated. All of it's functionality " warnings.warn("nn.Container is deprecated. All of it's functionality "
"is now implemented in nn.Module. Subclass that instead.") "is now implemented in nn.Module. Subclass that instead.")
for key, value in kwargs.items(): for key, value in kwargs.items():
...@@ -38,7 +36,7 @@ class Container(Module): ...@@ -38,7 +36,7 @@ class Container(Module):
class Sequential(Module): class Sequential(Module):
r"""A sequential container. """A sequential container.
Modules will be added to it in the order they are passed in the constructor. Modules will be added to it in the order they are passed in the constructor.
Alternatively, an ordered dict of modules can also be passed in. Alternatively, an ordered dict of modules can also be passed in.
...@@ -59,8 +57,8 @@ class Sequential(Module): ...@@ -59,8 +57,8 @@ class Sequential(Module):
('conv2', nn.Conv2d(20,64,5)), ('conv2', nn.Conv2d(20,64,5)),
('relu2', nn.ReLU()) ('relu2', nn.ReLU())
])) ]))
"""
"""
def __init__(self, *args): def __init__(self, *args):
super(Sequential, self).__init__() super(Sequential, self).__init__()
if len(args) == 1 and isinstance(args[0], OrderedDict): if len(args) == 1 and isinstance(args[0], OrderedDict):
...@@ -71,7 +69,7 @@ class Sequential(Module): ...@@ -71,7 +69,7 @@ class Sequential(Module):
self.add_module(str(idx), module) self.add_module(str(idx), module)
def _get_item_by_idx(self, iterator, idx): def _get_item_by_idx(self, iterator, idx):
"""Get the idx-th item of the iterator""" """Get the idx-th item of the iterator."""
size = len(self) size = len(self)
idx = operator.index(idx) idx = operator.index(idx)
if not -size <= idx < size: if not -size <= idx < size:
......
Dragon/python/dragon/vm/torch/nn/modules/conv.py
...@@ -21,8 +21,19 @@ from dragon.vm.torch.nn.modules.utils import _pair ...@@ -21,8 +21,19 @@ from dragon.vm.torch.nn.modules.utils import _pair
class _ConvNd(Module): class _ConvNd(Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, def __init__(
padding, dilation, transposed, output_padding, groups, bias): self,
in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
transposed,
output_padding,
groups,
bias,
):
super(_ConvNd, self).__init__() super(_ConvNd, self).__init__()
if in_channels % groups != 0: if in_channels % groups != 0:
raise ValueError('in_channels must be divisible by groups') raise ValueError('in_channels must be divisible by groups')
...@@ -94,36 +105,61 @@ class _ConvNd(Module): ...@@ -94,36 +105,61 @@ class _ConvNd(Module):
class Conv2d(_ConvNd): class Conv2d(_ConvNd):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, def __init__(
padding=0, dilation=1, groups=1, bias=True): self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=True,
):
kernel_size = _pair(kernel_size) kernel_size = _pair(kernel_size)
stride = _pair(stride) stride = _pair(stride)
padding = _pair(padding) padding = _pair(padding)
dilation = _pair(dilation) dilation = _pair(dilation)
super(Conv2d, self).__init__( super(Conv2d, self).__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation, in_channels, out_channels,
False, _pair(0), groups, bias) kernel_size, stride, padding, dilation,
False, _pair(0), groups, bias,
)
def forward(self, input): def forward(self, input):
inputs = [input, self.weight] + ([self.bias] if self.bias else []) inputs = [input, self.weight] + \
([self.bias] if self.bias else [])
self.unify_devices(inputs) self.unify_devices(inputs)
outputs = [self.register_output()] outputs = [self.register_output()]
return self.run(inputs, outputs) return self.run(inputs, outputs)
class ConvTranspose2d(_ConvNd): class ConvTranspose2d(_ConvNd):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, def __init__(
padding=0, output_padding=0, groups=1, bias=True, dilation=1): self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
output_padding=0,
groups=1,
bias=True,
dilation=1,
):
kernel_size = _pair(kernel_size) kernel_size = _pair(kernel_size)
stride = _pair(stride) stride = _pair(stride)
padding = _pair(padding) padding = _pair(padding)
dilation = _pair(dilation) dilation = _pair(dilation)
super(ConvTranspose2d, self).__init__( super(ConvTranspose2d, self).__init__(
in_channels, out_channels, kernel_size, stride, padding, dilation, in_channels, out_channels,
True, _pair(0), groups, bias) kernel_size, stride, padding, dilation,
True, _pair(0), groups, bias,
)
def forward(self, input): def forward(self, input):
inputs = [input, self.weight] + ([self.bias] if self.bias else []) inputs = [input, self.weight] + \
([self.bias] if self.bias else [])
self.unify_devices(inputs) self.unify_devices(inputs)
outputs = [self.register_output()] outputs = [self.register_output()]
return self.run(inputs, outputs) return self.run(inputs, outputs)
\ No newline at end of file
Dragon/python/dragon/vm/torch/nn/modules/depthwise_conv.py
...@@ -21,8 +21,16 @@ from dragon.vm.torch.nn.modules.utils import _pair ...@@ -21,8 +21,16 @@ from dragon.vm.torch.nn.modules.utils import _pair
class _DepthwiseConvNd(Module): class _DepthwiseConvNd(Module):
def __init__(self, in_channels, out_channels, kernel_size, def __init__(
stride, padding, output_padding, bias): self,
in_channels,
out_channels,
kernel_size,
stride,
padding,
output_padding,
bias,
):
super(_DepthwiseConvNd, self).__init__() super(_DepthwiseConvNd, self).__init__()
if in_channels != out_channels: if in_channels != out_channels:
raise ValueError('in/out channels must be same') raise ValueError('in/out channels must be same')
...@@ -75,17 +83,26 @@ class _DepthwiseConvNd(Module): ...@@ -75,17 +83,26 @@ class _DepthwiseConvNd(Module):
class DepthwiseConv2d(_DepthwiseConvNd): class DepthwiseConv2d(_DepthwiseConvNd):
def __init__(self, in_channels, out_channels, kernel_size, def __init__(
stride=1, padding=0, bias=True): self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
bias=True,
):
kernel_size = _pair(kernel_size) kernel_size = _pair(kernel_size)
stride = _pair(stride) stride = _pair(stride)
padding = _pair(padding) padding = _pair(padding)
super(DepthwiseConv2d, self).__init__( super(DepthwiseConv2d, self).__init__(
in_channels, out_channels, kernel_size, in_channels, out_channels, kernel_size,
stride, padding, _pair(0), bias) stride, padding, _pair(0), bias,
)
def forward(self, input): def forward(self, input):
inputs = [input, self.weight] + ([self.bias] if self.bias else []) inputs = [input, self.weight] + \
([self.bias] if self.bias else [])
self.unify_devices(inputs) self.unify_devices(inputs)
outputs = [self.register_output()] outputs = [self.register_output()]
return self.run(inputs, outputs) return self.run(inputs, outputs)
\ No newline at end of file
Dragon/python/dragon/vm/torch/nn/modules/dropblock.py
...@@ -17,8 +17,14 @@ from dragon.vm.torch.nn import Module ...@@ -17,8 +17,14 @@ from dragon.vm.torch.nn import Module
class DropBlock2d(Module): class DropBlock2d(Module):
def __init__(self, block_size=7, kp=0.9, def __init__(
alpha=1., decrement=0., inplace=False): self,
block_size=7,
kp=0.9,
alpha=1.,
decrement=0.,
inplace=False,
):
super(DropBlock2d, self).__init__() super(DropBlock2d, self).__init__()
self.kp = kp self.kp = kp
self.block_size = block_size self.block_size = block_size
......
Dragon/python/dragon/vm/torch/nn/modules/groupnorm.py
...@@ -19,8 +19,13 @@ from dragon.vm.torch.ops.builtin import zeros, ones ...@@ -19,8 +19,13 @@ from dragon.vm.torch.ops.builtin import zeros, ones
class _GroupNorm(Module): class _GroupNorm(Module):
def __init__(self, num_features, group=32, def __init__(
eps=1e-5, affine=True): self,
num_features,
group=32,
eps=1e-5,
affine=True,
):
super(_GroupNorm, self).__init__() super(_GroupNorm, self).__init__()
self.num_features = num_features self.num_features = num_features
self.group = group self.group = group
......
Dragon/python/dragon/vm/torch/nn/modules/loss.py
...@@ -22,7 +22,12 @@ from dragon.vm.torch.nn.functional import _Reduction ...@@ -22,7 +22,12 @@ from dragon.vm.torch.nn.functional import _Reduction
class _Loss(Module): class _Loss(Module):
def __init__(self, size_average=None, reduce=None, reduction='elementwise_mean'): def __init__(
self,
size_average=None,
reduce=None,
reduction='elementwise_mean',
):
super(_Loss, self).__init__() super(_Loss, self).__init__()
if size_average is not None or reduce is not None: if size_average is not None or reduce is not None:
self.reduction = _Reduction.legacy_get_string(size_average, reduce) self.reduction = _Reduction.legacy_get_string(size_average, reduce)
...@@ -31,17 +36,31 @@ class _Loss(Module): ...@@ -31,17 +36,31 @@ class _Loss(Module):
class _WeightedLoss(_Loss): class _WeightedLoss(_Loss):
def __init__(self, weight=None, size_average=None, reduce=None, reduction='elementwise_mean'): def __init__(
self,
weight=None,
size_average=None,
reduce=None,
reduction='elementwise_mean',
):
super(_WeightedLoss, self).__init__(size_average, reduce, reduction) super(_WeightedLoss, self).__init__(size_average, reduce, reduction)
self.weight = weight self.weight = weight
if weight is not None: if weight is not None:
raise NotImplementedError('WeightedLoss has been not implemented yet.') raise NotImplementedError(
'WeightedLoss has been not implemented yet.')
class NLLLoss(_WeightedLoss): class NLLLoss(_WeightedLoss):
def __init__(self, weight=None, size_average=None, ignore_index=None, def __init__(
reduce=None, reduction='elementwise_mean'): self,
super(NLLLoss, self).__init__(weight, size_average, reduce, reduction) weight=None,
size_average=None,
ignore_index=None,
reduce=None,
reduction='elementwise_mean',
):
super(NLLLoss, self).__init__(
weight, size_average, reduce, reduction)
self.ignore_index = ignore_index self.ignore_index = ignore_index
self.normalization = { self.normalization = {
'elementwise_mean': 'VALID', 'elementwise_mean': 'VALID',
...@@ -60,17 +79,26 @@ class NLLLoss(_WeightedLoss): ...@@ -60,17 +79,26 @@ class NLLLoss(_WeightedLoss):
} }
def forward(self, input, target): def forward(self, input, target):
inputs = [input, target]; self.unify_devices(inputs) inputs = [input, target]
self.unify_devices(inputs)
outputs = [self.register_output()] outputs = [self.register_output()]
return self.run(inputs, outputs) return self.run(inputs, outputs)
class BCEWithLogitsLoss(_WeightedLoss): class BCEWithLogitsLoss(_WeightedLoss):
def __init__(self, weight=None, size_average=None, reduce=None, def __init__(
reduction='elementwise_mean', pos_weight=None): self,
super(BCEWithLogitsLoss, self).__init__(weight, size_average, reduce, reduction) weight=None,
size_average=None,
reduce=None,
reduction='elementwise_mean',
pos_weight=None,
):
super(BCEWithLogitsLoss, self).__init__(
weight, size_average, reduce, reduction)
if pos_weight is not None: if pos_weight is not None:
raise NotImplementedError('Positive weight has been not implemented yet.') raise NotImplementedError(
'Positive weight has been not implemented yet.')
self.normalization = { self.normalization = {
'elementwise_mean': 'VALID', 'elementwise_mean': 'VALID',
'sum': 'None', 'sum': 'None',
...@@ -86,15 +114,59 @@ class BCEWithLogitsLoss(_WeightedLoss): ...@@ -86,15 +114,59 @@ class BCEWithLogitsLoss(_WeightedLoss):
} }
def forward(self, input, target): def forward(self, input, target):
inputs = [input, target]; self.unify_devices(inputs) inputs = [input, target]
self.unify_devices(inputs)
outputs = [self.register_output()]
return self.run(inputs, outputs)
class SCEWithLogitsLoss(_WeightedLoss):
def __init__(
self,
weight=None,
size_average=None,
reduce=None,
reduction='elementwise_mean',
pos_weight=None,
):
super(SCEWithLogitsLoss, self).__init__(
weight, size_average, reduce, reduction)
if pos_weight is not None:
raise NotImplementedError(
'Positive weight has been not implemented yet.')
self.normalization = {
'elementwise_mean': 'VALID',
'sum': 'None',
'none': 'UNIT'}[self.reduction]
self.register_op()
def register_op(self):
self.op_meta = {
'op_type': 'SoftmaxCrossEntropy',
'arguments': {
'axis': 1,
'normalization': self.normalization,
},
}
def forward(self, input, target):
inputs = [input, target]
self.unify_devices(inputs)
outputs = [self.register_output()] outputs = [self.register_output()]
return self.run(inputs, outputs) return self.run(inputs, outputs)
class CrossEntropyLoss(_WeightedLoss): class CrossEntropyLoss(_WeightedLoss):
def __init__(self, weight=None, size_average=None, ignore_index=None, def __init__(
reduce=None, reduction='elementwise_mean'): self,
super(CrossEntropyLoss, self).__init__(weight, size_average, reduce, reduction) weight=None,
size_average=None,
ignore_index=None,
reduce=None,
reduction='elementwise_mean',
):
super(CrossEntropyLoss, self).__init__(
weight, size_average, reduce, reduction)
self.ignore_index = ignore_index self.ignore_index = ignore_index
self.normalization = { self.normalization = {
'elementwise_mean': 'VALID', 'elementwise_mean': 'VALID',
...@@ -113,13 +185,19 @@ class CrossEntropyLoss(_WeightedLoss): ...@@ -113,13 +185,19 @@ class CrossEntropyLoss(_WeightedLoss):
} }
def forward(self, input, target): def forward(self, input, target):
inputs = [input, target]; self.unify_devices(inputs) inputs = [input, target]
self.unify_devices(inputs)
outputs = [self.register_output()] outputs = [self.register_output()]
return self.run(inputs, outputs) return self.run(inputs, outputs)
class L1Loss(_Loss): class L1Loss(_Loss):
def __init__(self, size_average=None, reduce=None, reduction='elementwise_mean'): def __init__(
self,
size_average=None,
reduce=None,
reduction='elementwise_mean',
):
super(L1Loss, self).__init__(size_average, reduce, reduction) super(L1Loss, self).__init__(size_average, reduce, reduction)
self.normalization = { self.normalization = {
'elementwise_mean': 'BATCH_SIZE', 'elementwise_mean': 'BATCH_SIZE',
...@@ -135,13 +213,19 @@ class L1Loss(_Loss): ...@@ -135,13 +213,19 @@ class L1Loss(_Loss):
} }
def forward(self, input, target): def forward(self, input, target):
inputs = [input, target]; self.unify_devices(inputs) inputs = [input, target]
self.unify_devices(inputs)
outputs = [self.register_output()] outputs = [self.register_output()]
return self.run(inputs, outputs) return self.run(inputs, outputs)
class MSELoss(_Loss): class MSELoss(_Loss):
def __init__(self, size_average=None, reduce=None, reduction='elementwise_mean'): def __init__(
self,
size_average=None,
reduce=None,
reduction='elementwise_mean',
):
super(MSELoss, self).__init__(size_average, reduce, reduction) super(MSELoss, self).__init__(size_average, reduce, reduction)
self.normalization = { self.normalization = {
'elementwise_mean': 'BATCH_SIZE', 'elementwise_mean': 'BATCH_SIZE',
...@@ -158,14 +242,20 @@ class MSELoss(_Loss): ...@@ -158,14 +242,20 @@ class MSELoss(_Loss):
} }
def forward(self, input, target): def forward(self, input, target):
inputs = [input, target]; self.unify_devices(inputs) inputs = [input, target]
self.unify_devices(inputs)
outputs = [self.register_output()] outputs = [self.register_output()]
return self.run(inputs, outputs) return self.run(inputs, outputs)
class SmoothL1Loss(_Loss): class SmoothL1Loss(_Loss):
def __init__(self, size_average=None, beta=1.0, def __init__(
reduce=None, reduction='elementwise_mean'): self,
size_average=None,
beta=1.0,
reduce=None,
reduction='elementwise_mean',
):
super(SmoothL1Loss, self).__init__(size_average, reduce, reduction) super(SmoothL1Loss, self).__init__(size_average, reduce, reduction)
self.normalization = { self.normalization = {
'elementwise_mean': 'BATCH_SIZE', 'elementwise_mean': 'BATCH_SIZE',
...@@ -192,10 +282,19 @@ class SmoothL1Loss(_Loss): ...@@ -192,10 +282,19 @@ class SmoothL1Loss(_Loss):
class SigmoidFocalLoss(_WeightedLoss): class SigmoidFocalLoss(_WeightedLoss):
def __init__(self, weight=None, size_average=None, def __init__(
alpha=0.25, gamma=2.0, neg_id=0, ignore_index=None, self,
reduce=None, reduction='elementwise_mean'): weight=None,
super(SigmoidFocalLoss, self).__init__(weight, size_average, reduce, reduction) size_average=None,
alpha=0.25,
gamma=2.0,
neg_id=0,
ignore_index=None,
reduce=None,
reduction='elementwise_mean',
):
super(SigmoidFocalLoss, self).__init__(
weight, size_average, reduce, reduction)
self.alpha, self.gamma, self.neg_id = alpha, gamma, neg_id self.alpha, self.gamma, self.neg_id = alpha, gamma, neg_id
self.ignore_index = ignore_index self.ignore_index = ignore_index
self.normalization = { self.normalization = {
...@@ -218,16 +317,26 @@ class SigmoidFocalLoss(_WeightedLoss): ...@@ -218,16 +317,26 @@ class SigmoidFocalLoss(_WeightedLoss):
} }
def forward(self, input, target): def forward(self, input, target):
inputs = [input, target]; self.unify_devices(inputs) inputs = [input, target]
self.unify_devices(inputs)
outputs = [self.register_output()] outputs = [self.register_output()]
return self.run(inputs, outputs) return self.run(inputs, outputs)
class SoftmaxFocalLoss(_WeightedLoss): class SoftmaxFocalLoss(_WeightedLoss):
def __init__(self, weight=None, size_average=None, def __init__(
alpha=0.25, gamma=2.0, neg_id=0, ignore_index=None, self,
reduce=None, reduction='elementwise_mean'): weight=None,
super(SoftmaxFocalLoss, self).__init__(weight, size_average, reduce, reduction) size_average=None,
alpha=0.25,
gamma=2.0,
neg_id=0,
ignore_index=None,
reduce=None,
reduction='elementwise_mean',
):
super(SoftmaxFocalLoss, self).__init__(
weight, size_average, reduce, reduction)
self.alpha, self.gamma, self.neg_id = alpha, gamma, neg_id self.alpha, self.gamma, self.neg_id = alpha, gamma, neg_id
self.ignore_index = ignore_index self.ignore_index = ignore_index
self.normalization = { self.normalization = {
...@@ -250,6 +359,7 @@ class SoftmaxFocalLoss(_WeightedLoss): ...@@ -250,6 +359,7 @@ class SoftmaxFocalLoss(_WeightedLoss):
} }
def forward(self, input, target): def forward(self, input, target):
inputs = [input, target]; self.unify_devices(inputs) inputs = [input, target]
self.unify_devices(inputs)
outputs = [self.register_output()] outputs = [self.register_output()]
return self.run(inputs, outputs) return self.run(inputs, outputs)
\ No newline at end of file
Dragon/python/dragon/vm/torch/nn/modules/pooling.py
...@@ -18,8 +18,15 @@ from dragon.vm.torch.nn.modules.utils import _pair ...@@ -18,8 +18,15 @@ from dragon.vm.torch.nn.modules.utils import _pair
class _PoolNd(Module): class _PoolNd(Module):
def __init__(self, kernel_size, stride=None, padding=0, dilation=1, def __init__(
return_indices=False, ceil_mode=False): self,
kernel_size,
stride=None,
padding=0,
dilation=1,
return_indices=False,
ceil_mode=False,
):
super(_PoolNd, self).__init__() super(_PoolNd, self).__init__()
self.kernel_size = kernel_size self.kernel_size = kernel_size
self.stride = stride or kernel_size self.stride = stride or kernel_size
......
Dragon/python/dragon/vm/torch/nn/modules/rnn.py
...@@ -31,9 +31,17 @@ from dragon.vm.torch.ops.builtin import zeros as Zeros, xw_plus_b ...@@ -31,9 +31,17 @@ from dragon.vm.torch.ops.builtin import zeros as Zeros, xw_plus_b
class RNNBase(Module): class RNNBase(Module):
def __init__(self, mode, input_size, hidden_size, def __init__(
num_layers=1, bias=True, batch_first=False, self,
dropout=0, bidirectional=False): mode,
input_size,
hidden_size,
num_layers=1,
bias=True,
batch_first=False,
dropout=0,
bidirectional=False,
):
super(RNNBase, self).__init__() super(RNNBase, self).__init__()
self.mode = mode self.mode = mode
self.input_size = input_size self.input_size = input_size
...@@ -256,14 +264,23 @@ class RNN(RNNBase): ...@@ -256,14 +264,23 @@ class RNN(RNNBase):
Examples Examples
-------- --------
>>> import dragon.vm.torch as torch
>>> rnn = RNN(32, 64, num_layers=1, bidirectional=True) >>> rnn = RNN(32, 64, num_layers=1, bidirectional=True)
>>> x = torch.ones(8, 32, 256) >>> x = torch.ones(8, 32, 256)
>>> outputs, hidden = rnn(x) >>> outputs, hidden = rnn(x)
""" """
def __init__(self, input_size, hidden_size, nonlinearity='relu', def __init__(
num_layers=1, bias=True, batch_first=False, self,
dropout=0, bidirectional=False): input_size,
hidden_size,
nonlinearity='relu',
num_layers=1,
bias=True,
batch_first=False,
dropout=0,
bidirectional=False,
):
"""Construct a RNN module. """Construct a RNN module.
Parameters Parameters
...@@ -303,14 +320,22 @@ class LSTM(RNNBase): ...@@ -303,14 +320,22 @@ class LSTM(RNNBase):
Examples Examples
-------- --------
>>> import dragon.vm.torch as torch
>>> rnn = LSTM(32, 64, num_layers=2, bidirectional=True, dropout=0.5) >>> rnn = LSTM(32, 64, num_layers=2, bidirectional=True, dropout=0.5)
>>> x = torch.ones(8, 32, 256) >>> x = torch.ones(8, 32, 256)
>>> outputs, hidden = rnn(x) >>> outputs, hidden = rnn(x)
""" """
def __init__(self, input_size, hidden_size, def __init__(
num_layers=1, bias=True, batch_first=False, self,
dropout=0, bidirectional=False): input_size,
hidden_size,
num_layers=1,
bias=True,
batch_first=False,
dropout=0,
bidirectional=False,
):
"""Construct a LSTM module. """Construct a LSTM module.
Parameters Parameters
...@@ -347,14 +372,22 @@ class GRU(RNNBase): ...@@ -347,14 +372,22 @@ class GRU(RNNBase):
Examples Examples
-------- --------
>>> import dragon.vm.torch as torch
>>> rnn = GRU(32, 64, num_layers=2, bidirectional=False) >>> rnn = GRU(32, 64, num_layers=2, bidirectional=False)
>>> x = torch.ones(8, 32, 256) >>> x = torch.ones(8, 32, 256)
>>> outputs, hidden = rnn(x) >>> outputs, hidden = rnn(x)
""" """
def __init__(self, input_size, hidden_size, def __init__(
num_layers=1, bias=True, batch_first=False, self,
dropout=0, bidirectional=False): input_size,
hidden_size,
num_layers=1,
bias=True,
batch_first=False,
dropout=0,
bidirectional=False,
):
"""Construct a GRU module. """Construct a GRU module.
Parameters Parameters
...@@ -413,15 +446,12 @@ class RNNCellBase(Module): ...@@ -413,15 +446,12 @@ class RNNCellBase(Module):
for weight in self.parameters(): for weight in self.parameters():
weight.data.uniform_(-stdv, stdv) weight.data.uniform_(-stdv, stdv)
from .activation import Tanh, Sigmoid
class LSTMCell(RNNCellBase): class LSTMCell(RNNCellBase):
def __init__(self, input_size, hidden_size, bias=True): def __init__(self, input_size, hidden_size, bias=True):
super(LSTMCell, self).__init__( super(LSTMCell, self).__init__(
input_size, hidden_size, bias, num_chunks=4) input_size, hidden_size, bias, num_chunks=4)
self.register_op() self.register_op()
self.tanh = Tanh()
self.sigmoid = Sigmoid()
def register_op(self): def register_op(self):
self.op_meta = {'op_type': 'LSTMCell', 'arguments': {}} self.op_meta = {'op_type': 'LSTMCell', 'arguments': {}}
......
Dragon/python/dragon/vm/torch/onnx/utils.py
...@@ -27,9 +27,13 @@ from dragon.vm.onnx import export_from_graph_def ...@@ -27,9 +27,13 @@ from dragon.vm.onnx import export_from_graph_def
def export( def export(
model, args, f, verbose=False, model,
input_names=None, output_names=None, args,
opset_version=None, f,
verbose=False,
input_names=None,
output_names=None,
opset_version=None,
): ):
"""Export a model into ONNX format. """Export a model into ONNX format.
......
Dragon/python/dragon/vm/torch/ops/builtin.py
...@@ -1128,8 +1128,14 @@ def _allreduce(grads): ...@@ -1128,8 +1128,14 @@ def _allreduce(grads):
return module.forward(grads) return module.forward(grads)
def _update(param, grad, op_type, slot, def _update(
lr_mult=1.0, decay_mult=1.0): param,
grad,
op_type,
slot,
lr_mult=1.0,
decay_mult=1.0,
):
dev = MakeDevice(inputs=[param]) dev = MakeDevice(inputs=[param])
key = '{}/{}/{}/{}'.format(op_type, dev, slot, param.name) key = '{}/{}/{}/{}'.format(op_type, dev, slot, param.name)
module = get_module(Update, key, dev, op_type=op_type, module = get_module(Update, key, dev, op_type=op_type,
...@@ -1169,26 +1175,55 @@ def bilinear_resize(input, dsize, fx=-1.0, fy=-1.0): ...@@ -1169,26 +1175,55 @@ def bilinear_resize(input, dsize, fx=-1.0, fy=-1.0):
return _resize_2d(input, 'BilinearResize', dsize, fx, fy) return _resize_2d(input, 'BilinearResize', dsize, fx, fy)
def roi_pool(feature, rois, pooled_h, pooled_w, spatial_scale): def roi_pool(
feature,
rois,
pooled_h,
pooled_w,
spatial_scale,
):
dev = MakeDevice(inputs=[feature]) dev = MakeDevice(inputs=[feature])
key = 'RoIPool/{}/pool_h:{}/pool_w:{}/spatial_scale:{}'.format( key = 'RoIPool/{}' \
dev, pooled_h, pooled_w, spatial_scale) '/pool_h:{}' \
'/pool_w:{}' \
'/spatial_scale:{}' \
.format(dev,
pooled_h,
pooled_w,
spatial_scale)
module = get_module( module = get_module(
RoIPool, key, dev, RoIPool, key, dev,
pooled_h=pooled_h, pooled_w=pooled_w, pooled_h=pooled_h,
spatial_scale=spatial_scale) pooled_w=pooled_w,
spatial_scale=spatial_scale,
)
return module.forward(feature, rois) return module.forward(feature, rois)
def roi_align(feature, rois, pooled_h, pooled_w, def roi_align(
spatial_scale, sampling_ratio=2): feature,
rois,
pooled_h,
pooled_w,
spatial_scale,
sampling_ratio=2,
):
dev = MakeDevice(inputs=[feature]) dev = MakeDevice(inputs=[feature])
key = 'RoIAlign/{}/pool_h:{}/pool_w:{}/' \ key = 'RoIAlign/{}' \
'spatial_scale:{}/sampling_ratio:{}'.format( '/pool_h:{}' \
dev, pooled_h, pooled_w, spatial_scale, sampling_ratio) '/pool_w:{}' \
'/spatial_scale:{}' \
'/sampling_ratio:{}' \
.format(dev,
pooled_h,
pooled_w,
spatial_scale,
sampling_ratio)
module = get_module( module = get_module(
RoIAlign, key, dev, RoIAlign, key, dev,
pooled_h=pooled_h, pooled_w=pooled_w, pooled_h=pooled_h,
spatial_scale=spatial_scale, pooled_w=pooled_w,
sampling_ratio=sampling_ratio) spatial_scale=spatial_scale,
sampling_ratio=sampling_ratio,
)
return module.forward(feature, rois) return module.forward(feature, rois)
\ No newline at end of file
Dragon/src/operators/recurrent/lstm_cell_op.cc
...@@ -37,7 +37,7 @@ void LSTMCellGradientOp<Context>::RunWithType() { ...@@ -37,7 +37,7 @@ void LSTMCellGradientOp<Context>::RunWithType() {
auto* Xdata = Input(0).template data<T, Context>(); auto* Xdata = Input(0).template data<T, Context>();
auto* HXdata = Input(1).template data<T, Context>(); auto* HXdata = Input(1).template data<T, Context>();
auto* Cdata = Input(2).template data<T, Context>(); auto* Cdata = Input(2).template data<T, Context>();
auto* dHdata = Input(-2).template data<T, Context>(); auto* dHdata = Input(3).template data<T, Context>();
auto* dCdata = Input(4).template mutable_data<T, Context>(); auto* dCdata = Input(4).template mutable_data<T, Context>();
auto* dXdata = Output(0)->template mutable_data<T, Context>(); auto* dXdata = Output(0)->template mutable_data<T, Context>();
auto* dHXdata = Output(1)->template mutable_data<T, Context>(); auto* dHXdata = Output(1)->template mutable_data<T, Context>();
...@@ -81,7 +81,7 @@ class GetLSTMCellGradient final : public GradientMakerBase { ...@@ -81,7 +81,7 @@ class GetLSTMCellGradient final : public GradientMakerBase {
GRADIENT_MAKER_CTOR(GetLSTMCellGradient); GRADIENT_MAKER_CTOR(GetLSTMCellGradient);
vector<OperatorDef> MakeDefs() override{ vector<OperatorDef> MakeDefs() override{
return SingleDef(def.type() + "Gradient", "", return SingleDef(def.type() + "Gradient", "",
vector<string>({ I(0), I(1), O(0), GO(0), GO(1) }), vector<string>({ I(0), I(1), O(1), GO(0), GO(1) }),
vector<string>({ GI(0), GI(1) })); vector<string>({ GI(0), GI(1) }));
} }
// Fill zero for dCNext // Fill zero for dCNext
......
Dragon/src/operators/vision/cudnn_conv2d_transpose_op.cc
...@@ -163,7 +163,7 @@ void CuDNNConvTranspose2dOp<Context>::RunOnDevice() { ...@@ -163,7 +163,7 @@ void CuDNNConvTranspose2dOp<Context>::RunOnDevice() {
#if CUDNN_VERSION_MAX(6, 0, 0) #if CUDNN_VERSION_MAX(6, 0, 0)
for (int i = 0; i < dilation.size(); i++) for (int i = 0; i < dilation.size(); i++)
if (dilation[i] != 1) if (dilation[i] != 1)
return Conv2dTransposeOp<Context>::RunOnDevice(); return ConvTranspose2dOp<Context>::RunOnDevice();
#endif #endif
ConvTranspose2dOp<Context>::Reshape(); ConvTranspose2dOp<Context>::Reshape();
...@@ -355,7 +355,7 @@ void CuDNNConvTranspose2dGradientOp<Context>::RunOnDevice() { ...@@ -355,7 +355,7 @@ void CuDNNConvTranspose2dGradientOp<Context>::RunOnDevice() {
#if CUDNN_VERSION_MAX(6, 0, 0) #if CUDNN_VERSION_MAX(6, 0, 0)
for (int i = 0; i < dilation.size(); i++) for (int i = 0; i < dilation.size(); i++)
if (dilation[i] != 1) if (dilation[i] != 1)
return Conv2dTransposeGradientOp<Context>::RunOnDevice(); return ConvTranspose2dGradientOp<Context>::RunOnDevice();
#endif #endif
ConvTranspose2dGradientOp<Context>::GradientReshape(); ConvTranspose2dGradientOp<Context>::GradientReshape();
......
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