import numpy as np
from mindspore.common.initializer import initializer, Orthogonal, Constant
from xuance.common import Sequence, Optional, Callable
from xuance.mindspore import Module, Tensor
from xuance.mindspore.utils import ms, nn, cnn_block, mlp_block, ModuleType
# process the input observations with stacks of CNN layers
[文档]class Basic_CNN(Module):
def __init__(self,
input_shape: Sequence[int],
kernels: Sequence[int],
strides: Sequence[int],
filters: Sequence[int],
normalize: Optional[ModuleType] = None,
initialize: Optional[Callable[..., Tensor]] = None,
activation: Optional[ModuleType] = None,
**kwargs):
super(Basic_CNN, self).__init__()
self.input_shape = (input_shape[2], input_shape[0], input_shape[1]) # Channels x Height x Width
self.kernels = kernels
self.strides = strides
self.filters = filters
self.normalize = normalize
self.initialize = initialize
self.activation = activation
self.output_shapes = {'state': (filters[-1],)}
self._transpose = ms.ops.Transpose()
self.model = self._create_network()
def _create_network(self):
layers = []
input_shape = self.input_shape
for k, s, f in zip(self.kernels, self.strides, self.filters):
cnn, input_shape = cnn_block(input_shape, f, k, s, self.normalize, self.activation, self.initialize)
layers.extend(cnn)
layers.append(nn.AdaptiveMaxPool2d((1, 1)))
layers.append(nn.Flatten())
return nn.SequentialCell(*layers)
[文档] def construct(self, observations: Tensor):
observations = observations / 255.0
tensor_observation = observations.transpose([0, 3, 1, 2])
return self.model(tensor_observation)
[文档]class AC_CNN_Atari(Module):
def __init__(self,
input_shape: Sequence[int],
kernels: Sequence[int],
strides: Sequence[int],
filters: Sequence[int],
normalize: Optional[ModuleType] = None,
initialize: Optional[Callable[..., Tensor]] = None,
activation: Optional[ModuleType] = None,
fc_hidden_sizes: Sequence[int] = (),
**kwargs):
super(AC_CNN_Atari, self).__init__()
self.input_shape = (input_shape[2], input_shape[0], input_shape[1]) # Channels x Height x Width
self.kernels = kernels
self.strides = strides
self.filters = filters
self.normalize = normalize
self.initialize = initialize
self.activation = activation
self.fc_hidden_sizes = fc_hidden_sizes
self.output_shapes = {'state': (fc_hidden_sizes[-1],)}
self.model = self._create_network()
def _init_layer(self, layer, gain=np.sqrt(2), bias=0.0):
layer.weight.assign_value(initializer(Orthogonal(gain=gain), shape=layer.weight.shape, dtype=ms.float32))
if layer.bias is not None:
layer.bias.assign_value(initializer(Constant(bias), shape=layer.bias.shape, dtype=ms.float32))
return layer
def _create_network(self):
layers = []
input_shape = self.input_shape
for k, s, f in zip(self.kernels, self.strides, self.filters):
cnn, input_shape = cnn_block(input_shape, f, k, s, None, self.activation, None)
cnn[0] = self._init_layer(cnn[0])
layers.extend(cnn)
layers.append(nn.Flatten())
input_shape = (np.prod(input_shape, dtype=np.int32), )
for h in self.fc_hidden_sizes:
mlp, input_shape = mlp_block(input_shape[0], h, None, self.activation, None)
mlp[0] = self._init_layer(mlp[0])
layers.extend(mlp)
return nn.SequentialCell(*layers)
[文档] def construct(self, observations: Tensor):
observations = observations / 255.0
tensor_observation = observations.transpose([0, 3, 1, 2])
return self.model(tensor_observation)