many thanks Sven 
so here is the code:
import gym
from gym import spaces
import numpy as np
import torch
from torch import nn
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.agents.ppo import ppo
import ray
from ray.rllib.models import ModelCatalog
class dummyvisoinenv(gym.Env):
def __init__(self,config):
self.length = length
self.width = width
self.channels = channels
self.action_space = spaces.Tuple([spaces.Discrete(4),spaces.Discrete(4),spaces.Discrete(3)])
self.observation_space = spaces.Box(low=-1 * 10 ** 10, high=10 ** 10, shape=(self.length,self.width,self.channels), dtype=np.float32)
self.episode_len = episode_len
def reset(self):
self.timestamp = 0
self.done = False
observation = np.random.rand(self.length,self.width,self.channels)
return observation
def step(self,action):
a1, a2, a3 = action
self.timestamp = self.timestamp + 1
if self.timestamp == self.episode_len:
self.done = True
reward = np.random.randint(0,5)
observation = np.random.rand(self.length,self.width,self.channels)
info = {}
return observation, reward, self.done, info
#resnet model based on pytorch official
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, groups=groups, bias=False, dilation=dilation)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
# import torch
# from torch import nn
# from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None):
# from torch import nn
nn.Module.__init__(self)
if norm_layer is None:
norm_layer = nn.BatchNorm2d
if groups != 1 or base_width != 64:
raise ValueError('BasicBlock only supports groups=1 and base_width=64')
if dilation > 1:
raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
# Both self.conv1 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = norm_layer(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = norm_layer(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class Bottleneck(nn.Module):
# Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
# while original implementation places the stride at the first 1x1 convolution(self.conv1)
# according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
# This variant is also known as ResNet V1.5 and improves accuracy according to
# https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None):
super(Bottleneck, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
width = int(planes * (base_width / 64.)) * groups
# Both self.conv2 and self.downsample layers downsample the input when stride != 1
self.conv1 = conv1x1(inplanes, width)
self.bn1 = norm_layer(width)
self.conv2 = conv3x3(width, width, stride, groups, dilation)
self.bn2 = norm_layer(width)
self.conv3 = conv1x1(width, planes * self.expansion)
self.bn3 = norm_layer(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class ResNet(TorchModelV2,nn.Module):
def __init__(self, obs_space, action_space, num_outputs, model_config,
name,block , layers = [2, 2, 2, 2], zero_init_residual=False,
groups=1, width_per_group=64, replace_stride_with_dilation=None,
norm_layer=None):
TorchModelV2.__init__(self, obs_space, action_space, num_outputs,
model_config, name)
nn.Module.__init__(self)
if norm_layer is None:
norm_layer = nn.BatchNorm2d
self._norm_layer = norm_layer
self.inplanes = 64
self.dilation = 1
if replace_stride_with_dilation is None:
# each element in the tuple indicates if we should replace
# the 2x2 stride with a dilated convolution instead
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError("replace_stride_with_dilation should be None "
"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
self.groups = groups
self.base_width = width_per_group
self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = norm_layer(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
dilate=replace_stride_with_dilation[0])
self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
dilate=replace_stride_with_dilation[1])
self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
dilate=replace_stride_with_dilation[2])
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(512 * block.expansion, num_outputs)
self.vfc = nn.Linear(512 * block.expansion, 1)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0)
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0)
def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
norm_layer = self._norm_layer
downsample = None
previous_dilation = self.dilation
if dilate:
self.dilation *= stride
stride = 1
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
conv1x1(self.inplanes, planes * block.expansion, stride),
norm_layer(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
self.base_width, previous_dilation, norm_layer))
self.inplanes = planes * block.expansion
for _ in range(1, blocks):
layers.append(block(self.inplanes, planes, groups=self.groups,
base_width=self.base_width, dilation=self.dilation,
norm_layer=norm_layer))
return nn.Sequential(*layers)
def _forward_impl(self, x):
# print(x)
# print(x.size())
x = torch.permute(x,(0,3,1,2))
# See note [TorchScript super()]
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
last_x = torch.flatten(x, 1)
x = self.fc(last_x)
self.value = self.vfc(last_x)
return x
def forward(
self,
input ,
state,
seq_lens):
x = input["obs"].float()
return (self._forward_impl(x), state)
def value_function(self):
assert self.value is not None, "must call forward() first"
# print("1stvalue",self.value)
self.value = torch.reshape(self.value , [-1])
# print('secondvalue',self.value)
return self.value
#configs
length = 200
width = 300
channels = 3
episode_len = 100
env_config = {"length" : length , 'width': width , 'channels': channels,'episode_len' : episode_len}
env_instance = dummyvisoinenv(env_config)
model = ppo
model_config = model.DEFAULT_CONFIG.copy()
model_config["env"] = env_instance
model_config['train_batch_size'] = 50
model_config["framework"]="torch"
model_config['sgd_minibatch_size'] = 10
model_config["model"] = {
"custom_model": 'resnet18torch',
'custom_model_config': {
'block':BasicBlock ,
'layers':[2, 2, 2, 2],
'zero_init_residual': False ,
'groups' : 1,
'width_per_group' : 64 ,
'replace_stride_with_dilation' : None ,
'norm_layer' : None
}
}
#
ray.shutdown()
ray.init(ignore_reinit_error=True)
ModelCatalog.register_custom_model('resnet18torch', ResNet)
total_episodes = 100
trainer = model.PPOTrainer(env= dummyvisoinenv, config=model_config)
for i in range(total_episodes):
result = trainer.train()
print('done: ',i)
thanks for helping 
