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neural_network_vision_model.py
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neural_network_vision_model.py
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import torch
import torch.nn as nn
# # # # https://www.researchgate.net/figure/A-comparison-between-ResNet-v1-and-ResNet-v2-on-residual-blocks-23_fig2_342334669
# class Residual_block(nn.Module): #V1
# def __init__(self, num_channels, stride=1):
# super().__init__()
# activation= nn.LeakyReLU() #, nn.LeakyReLU(), nn.GELU, nn.ReLU(), nn.ELU
# convolution = torch.nn.Conv2d(num_channels, num_channels,
# kernel_size=3, stride=stride,
# padding=1, bias=False)
# batch_norm = torch.nn.BatchNorm2d(num_channels)
# self.activation = nn.ReLU()
# first_layer_sequence = [
# convolution,
# batch_norm,
# self.activation
# ]
# recursive_layer_sequence = [
# convolution,
# batch_norm
# ]
# sequence = first_layer_sequence + (recursive_layer_sequence*1)
# self.sequential_container = nn.Sequential(*tuple(sequence)) # # # combine layers
# self.last_layer = activation # # # last layer
# def forward(self, state):
# x = self.sequential_container(state)
# x = x + state
# return self.last_layer(x)
# # # https://www.researchgate.net/figure/A-comparison-between-ResNet-v1-and-ResNet-v2-on-residual-blocks-23_fig2_342334669
class Residual_block(nn.Module): #V2
def __init__(self, num_channels, stride=1):
super().__init__()
activation= nn.LeakyReLU() #, nn.LeakyReLU(), nn.GELU, nn.ReLU(), nn.ELU
convolution_3 = torch.nn.Conv2d(num_channels, num_channels,
kernel_size=3, stride=stride,
padding=1, bias=False)
convolution_1 = torch.nn.Conv2d(num_channels, num_channels,
kernel_size=3, stride=stride,
padding=1, bias=False)
batch_norm = torch.nn.BatchNorm2d(num_channels)
activation = nn.ReLU()
dropout = nn.Dropout2d(p=0.5)
first_layer_sequence = [
batch_norm,
activation,
dropout,
convolution_1
]
recursive_layer_sequence = [
batch_norm,
activation,
dropout,
convolution_3,
batch_norm,
activation,
dropout,
convolution_1
]
sequence = first_layer_sequence + (recursive_layer_sequence*1)
self.sequential_container = nn.Sequential(*tuple(sequence)) # # # combine layers
self.last_layer = activation # # # last layer
def forward(self, state):
x = self.sequential_container(state)
x = x + state
return x
class Down_sample(torch.nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
convolution_in = torch.nn.Conv2d(in_channels, out_channels // 2,
kernel_size=3, stride=2,
padding=1, bias=False,)
convolution_out = torch.nn.Conv2d(out_channels // 2, out_channels,
kernel_size=3, stride=2,
padding=1, bias=False,)
res_blocks_in = Residual_block(out_channels // 2)
pooling = torch.nn.AvgPool2d(kernel_size=3, stride=2, padding=1)
res_blocks_out = Residual_block(out_channels)
sequence = [
convolution_in,
res_blocks_in,
res_blocks_in,
convolution_out,
res_blocks_out,
res_blocks_out,
pooling,
res_blocks_out,
res_blocks_out,
res_blocks_out,
pooling
]
self.sequential_container = nn.Sequential(*tuple(sequence)) # # # combine layers
def forward(self, x):
x = self.sequential_container(x)
return x
class Representation_function(torch.nn.Module):
def __init__(self,
observation_space_dimensions,
state_dimension,
action_dimension,
hidden_layer_dimensions,
number_of_hidden_layer,
num_channels = 3 ,
stacked_observations = 1,
down_sampling = True ):
super().__init__()
self.action_space = action_dimension
self.down_sampling = down_sampling
stack_observation = observation_space_dimensions[-1]
downsample_net = Down_sample( stack_observation, num_channels)
convolution = torch.nn.Conv2d(stack_observation, num_channels, kernel_size=3, stride=1, padding=1, bias=False)#3x3
batchnorm = torch.nn.BatchNorm2d(num_channels)
activation = torch.nn.ReLU()
resblock = Residual_block(num_channels)
sequence_down_samp = [
downsample_net
] + [resblock]
sequence_conv_norm = [
convolution,
batchnorm,
activation,
] + [resblock]
self.sequential_downsampler = nn.Sequential(*tuple(sequence_down_samp))
self.sequential_convolution_activation = nn.Sequential(*tuple(sequence_conv_norm))
def forward(self, state):
state_normalize = self.sequential_downsampler(state) if self.down_sampling else self.sequential_convolution_activation(state)
return state_normalize
class Dynamics_function(torch.nn.Module):
def __init__(self,
state_dimension,
action_dimension,
observation_space_dimensions,
hidden_layer_dimensions,
number_of_hidden_layer,
num_channels = 3,
reduced_channels_reward = 1,
down_sampling=True):
super().__init__()
block_output_size_reward = ( (reduced_channels_reward * \
int(observation_space_dimensions[0]/14) * \
int(observation_space_dimensions[1]/14) * \
observation_space_dimensions[2])
if down_sampling
else (reduced_channels_reward * \
observation_space_dimensions[0] * \
observation_space_dimensions[1] * \
observation_space_dimensions[2])
)
self.action_space = action_dimension
dropout1d = nn.Dropout(p=0.5)
convolution = torch.nn.Conv2d(num_channels + 1, num_channels , kernel_size=3, stride=1, padding=1, bias=False) # 3x3
batchnorm = torch.nn.BatchNorm2d(num_channels )
resblock = Residual_block(num_channels )
convolution_reward = torch.nn.Conv2d(num_channels + 1 , num_channels, 1)#1x1 # reduced_channels_reward for second arg
activation=torch.nn.ReLU()
sequence_layer_init = [nn.Linear(block_output_size_reward, hidden_layer_dimensions),
activation]
sequence_layer_recursive = [nn.Linear(hidden_layer_dimensions, hidden_layer_dimensions),
activation] * number_of_hidden_layer
sequence_layer_out = [nn.Linear(hidden_layer_dimensions, state_dimension)]
multilayer_perceptron_reward = nn.Sequential(*tuple(sequence_layer_init + \
sequence_layer_recursive + \
sequence_layer_out) )
flatten = torch.nn.Flatten(1,-1) # or x.view(-1, self.block_output_size_reward)
sequence = [
convolution,
batchnorm,
activation,
] + ([resblock] * number_of_hidden_layer)+ \
[activation]
sequence_reward = [
convolution_reward,
flatten,
multilayer_perceptron_reward
]
self.sequential_container = nn.Sequential(*tuple(sequence))
self.sequential_reward = nn.Sequential(*tuple(sequence_reward))
def forward(self, state_normalized, action):
x = torch.cat([state_normalized, action],dim=1)
next_state_normalize = self.sequential_container(x)
reward = self.sequential_reward(x)
return reward , next_state_normalize
class Prediction_function(torch.nn.Module):
def __init__(self,
state_dimension,
action_dimension,
observation_space_dimensions,
hidden_layer_dimensions,
number_of_hidden_layer,
down_sampling = True,
reduced_channels_value = 1,
reduced_channels_policy = 1,
num_channels = 3):
super().__init__()
block_output_size_value = ((reduced_channels_value * observation_space_dimensions[2] * int(observation_space_dimensions[1]/14) * int(observation_space_dimensions[0]/14))
if down_sampling
else (reduced_channels_value * observation_space_dimensions[2] * observation_space_dimensions[1] * observation_space_dimensions[0]) )
block_output_size_policy = ((reduced_channels_policy * observation_space_dimensions[2] * int(observation_space_dimensions[1]/14) * int(observation_space_dimensions[0]/14))
if down_sampling
else (reduced_channels_policy * observation_space_dimensions[2] * observation_space_dimensions[1] * observation_space_dimensions[0]))
resblock = Residual_block(num_channels)
convolution_value = torch.nn.Conv2d(num_channels, num_channels, 1) #1x1 # reduced_channels_value for second arg
convolution_policy = torch.nn.Conv2d(num_channels, num_channels, 1) #1x1 # reduced_channels_policy for second arg
flatten = torch.nn.Flatten(1,-1)
activation = torch.nn.ReLU()
dropout1d = nn.Dropout(p=0.5)
sequence_layer_init = [nn.Linear(block_output_size_value, hidden_layer_dimensions),
activation]
sequence_layer_recursive = [nn.Linear(hidden_layer_dimensions, hidden_layer_dimensions),
activation] * number_of_hidden_layer
sequence_layer_out = [nn.Linear(hidden_layer_dimensions, state_dimension)]
multilayer_perceptron_value = nn.Sequential(*tuple(sequence_layer_init + \
sequence_layer_recursive + \
sequence_layer_out) )
# self.fc_policy = mlp(self.block_output_size_policy,fc_policy_layers,action_space_size,)
sequence_layer_init = [nn.Linear(block_output_size_policy, hidden_layer_dimensions),
activation]
sequence_layer_recursive = [nn.Linear(hidden_layer_dimensions, hidden_layer_dimensions),
activation] * number_of_hidden_layer
sequence_layer_out = [nn.Linear(hidden_layer_dimensions, action_dimension)]
multilayer_perceptron_policy = nn.Sequential(*tuple(sequence_layer_init + \
sequence_layer_recursive + \
sequence_layer_out) )
sequence_1 = [ resblock ] * number_of_hidden_layer
sequence_2 = [convolution_value,
flatten,
multilayer_perceptron_value]
sequence_3 = [convolution_policy,
flatten,
multilayer_perceptron_policy]
self.resnet = nn.Sequential(*tuple(sequence_1))
self.nn_value = nn.Sequential(*tuple(sequence_2))
self.nn_policy = nn.Sequential(*tuple(sequence_3))
def forward(self, state_normalize):
resnet_state_normalize = self.resnet(state_normalize)
value = self.nn_value(resnet_state_normalize)
policy = self.nn_policy(resnet_state_normalize)
return policy , value