Temporary moved vision_actor_critic back to network_builder.

rl_games/algos_torch/model_builder.py

@@ -1,7 +1,7 @@
 from rl_games.common import object_factory
 import rl_games.algos_torch
-from rl_games.algos_torch import models, network_builder
-from rl_games.networks import vision_networks
+from rl_games.algos_torch import network_builder, models
+
 
 NETWORK_REGISTRY = {}
 MODEL_REGISTRY = {}
@@ -21,9 +21,9 @@ def __init__(self):
         self.network_factory.register_builder('resnet_actor_critic',
                                             lambda **kwargs: network_builder.A2CResnetBuilder())
         self.network_factory.register_builder('vision_actor_critic',
-                                            lambda **kwargs: vision_networks.A2CVisionBuilder())
-        self.network_factory.register_builder('e2e_vision_actor_critic',
-                                            lambda **kwargs: vision_networks.A2CVisionBackboneBuilder())
+                                            lambda **kwargs: network_builder.A2CVisionBuilder())
+        # self.network_factory.register_builder('e2e_vision_actor_critic',
+        #                                     lambda **kwargs: vision_networks.A2CVisionBackboneBuilder())
 
         self.network_factory.register_builder('rnd_curiosity', lambda **kwargs: network_builder.RNDCuriosityBuilder())
         self.network_factory.register_builder('soft_actor_critic', lambda **kwargs: network_builder.SACBuilder())

rl_games/algos_torch/models.py

@@ -10,6 +10,7 @@
 from rl_games.algos_torch.running_mean_std import RunningMeanStd, RunningMeanStdObs
 from rl_games.algos_torch.moving_mean_std import GeneralizedMovingStats
 
+
 class BaseModel():
     def __init__(self, model_class):
         self.model_class = model_class
@@ -25,12 +26,14 @@ def get_value_layer(self):
 
     def build(self, config):
         obs_shape = config['input_shape']
+        print(f"obs_shape: {obs_shape}")
         normalize_value = config.get('normalize_value', False)
         normalize_input = config.get('normalize_input', False)
         value_size = config.get('value_size', 1)
         return self.Network(self.network_builder.build(self.model_class, **config), obs_shape=obs_shape,
             normalize_value=normalize_value, normalize_input=normalize_input, value_size=value_size)
 
+
 class BaseModelNetwork(nn.Module):
     def __init__(self, obs_shape, normalize_value, normalize_input, value_size):
         nn.Module.__init__(self)
@@ -366,5 +369,3 @@ def forward(self, input_dict):
             dist = SquashedNormal(mu, sigma)
             return dist
 
-
-

rl_games/algos_torch/network_builder.py

@@ -488,19 +488,19 @@ def get_default_rnn_state(self):
                 rnn_units = self.rnn_units
             if self.rnn_name == 'lstm':
                 if self.separate:
-                    return (torch.zeros((num_layers, self.num_seqs, rnn_units)), 
+                    return (torch.zeros((num_layers, self.num_seqs, rnn_units)),
+                            torch.zeros((num_layers, self.num_seqs, rnn_units)),
                             torch.zeros((num_layers, self.num_seqs, rnn_units)),
-                            torch.zeros((num_layers, self.num_seqs, rnn_units)), 
                             torch.zeros((num_layers, self.num_seqs, rnn_units)))
                 else:
-                    return (torch.zeros((num_layers, self.num_seqs, rnn_units)), 
+                    return (torch.zeros((num_layers, self.num_seqs, rnn_units)),
                             torch.zeros((num_layers, self.num_seqs, rnn_units)))
             else:
                 if self.separate:
-                    return (torch.zeros((num_layers, self.num_seqs, rnn_units)), 
+                    return (torch.zeros((num_layers, self.num_seqs, rnn_units)),
                             torch.zeros((num_layers, self.num_seqs, rnn_units)))
                 else:
-                    return (torch.zeros((num_layers, self.num_seqs, rnn_units)),)                
+                    return (torch.zeros((num_layers, self.num_seqs, rnn_units)),)
 
         def load(self, params):
             self.separate = params.get('separate', False)
@@ -849,6 +849,209 @@ def build(self, name, **kwargs):
         return net
 
 
+class A2CVisionBuilder(NetworkBuilder):
+    def __init__(self, **kwargs):
+        NetworkBuilder.__init__(self)
+
+    def load(self, params):
+        self.params = params
+
+    class Network(NetworkBuilder.BaseNetwork):
+        def __init__(self, params, **kwargs):
+            self.actions_num = actions_num = kwargs.pop('actions_num')
+            input_shape = kwargs.pop('input_shape')
+            print('input_shape:', input_shape)
+            if type(input_shape) is dict:
+                input_shape = input_shape['camera']
+                proprio_shape = input_shape['proprio']
+
+            self.num_seqs = kwargs.pop('num_seqs', 1)
+            self.value_size = kwargs.pop('value_size', 1)
+
+            NetworkBuilder.BaseNetwork.__init__(self)
+            self.load(params)
+            if self.permute_input:
+                input_shape = torch_ext.shape_whc_to_cwh(input_shape)
+
+            self.cnn = self._build_impala(input_shape, self.conv_depths)
+            cnn_output_size = self._calc_input_size(input_shape, self.cnn)
+            proprio_size = proprio_shape[0] # Number of proprioceptive features
+
+            mlp_input_size = cnn_output_size + proprio_size
+            if len(self.units) == 0:
+                out_size = cnn_output_size
+            else:
+                out_size = self.units[-1]
+
+            if self.has_rnn:
+                if not self.is_rnn_before_mlp:
+                    rnn_in_size = out_size
+                    out_size = self.rnn_units
+                else:
+                    rnn_in_size =  mlp_input_size
+                    mlp_input_size = self.rnn_units
+
+                self.rnn = self._build_rnn(self.rnn_name, rnn_in_size, self.rnn_units, self.rnn_layers)
+                self.layer_norm = torch.nn.LayerNorm(self.rnn_units)
+
+            mlp_args = {
+                'input_size' : mlp_input_size,
+                'units' :self.units,
+                'activation' : self.activation,
+                'norm_func_name' : self.normalization,
+                'dense_func' : torch.nn.Linear
+            }
+
+            self.mlp = self._build_mlp(**mlp_args)
+
+            self.value = self._build_value_layer(out_size, self.value_size)
+            self.value_act = self.activations_factory.create(self.value_activation)
+            self.flatten_act = self.activations_factory.create(self.activation)
+
+            if self.is_discrete:
+                self.logits = torch.nn.Linear(out_size, actions_num)
+            if self.is_continuous:
+                self.mu = torch.nn.Linear(out_size, actions_num)
+                self.mu_act = self.activations_factory.create(self.space_config['mu_activation'])
+                mu_init = self.init_factory.create(**self.space_config['mu_init'])
+                self.sigma_act = self.activations_factory.create(self.space_config['sigma_activation'])
+                sigma_init = self.init_factory.create(**self.space_config['sigma_init'])
+
+                if self.fixed_sigma:
+                    self.sigma = nn.Parameter(torch.zeros(actions_num, requires_grad=True, dtype=torch.float32), requires_grad=True)
+                else:
+                    self.sigma = torch.nn.Linear(out_size, actions_num)
+
+            mlp_init = self.init_factory.create(**self.initializer)
+
+            for m in self.modules():
+                if isinstance(m, nn.Conv2d):
+                    nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+            for m in self.mlp:
+                if isinstance(m, nn.Linear):
+                    mlp_init(m.weight)
+
+            if self.is_discrete:
+                mlp_init(self.logits.weight)
+            if self.is_continuous:
+                mu_init(self.mu.weight)
+                if self.fixed_sigma:
+                    sigma_init(self.sigma)
+                else:
+                    sigma_init(self.sigma.weight)
+
+            mlp_init(self.value.weight)
+
+        def forward(self, obs_dict):
+            # for key in obs_dict:
+            #     print(key)
+            obs = obs_dict['camera']
+            proprio = obs_dict['proprio']
+            if self.permute_input:
+                obs = obs.permute((0, 3, 1, 2))
+
+            dones = obs_dict.get('dones', None)
+            bptt_len = obs_dict.get('bptt_len', 0)
+            states = obs_dict.get('rnn_states', None)
+
+            out = obs
+            out = self.cnn(out)
+            out = out.flatten(1)
+            out = self.flatten_act(out)
+
+            out = torch.cat([out, proprio], dim=1)
+
+            if self.has_rnn:
+                seq_length = obs_dict['seq_length']
+
+                out_in = out
+                if not self.is_rnn_before_mlp:
+                    out_in = out
+                    out = self.mlp(out)
+
+                batch_size = out.size()[0]
+                num_seqs = batch_size // seq_length
+                out = out.reshape(num_seqs, seq_length, -1)
+
+                if len(states) == 1:
+                    states = states[0]
+
+                out = out.transpose(0, 1)
+                if dones is not None:
+                    dones = dones.reshape(num_seqs, seq_length, -1)
+                    dones = dones.transpose(0, 1)
+                out, states = self.rnn(out, states, dones, bptt_len)
+                out = out.transpose(0, 1)
+                out = out.contiguous().reshape(out.size()[0] * out.size()[1], -1)
+
+                if self.rnn_ln:
+                    out = self.layer_norm(out)
+                if self.is_rnn_before_mlp:
+                    out = self.mlp(out)
+                if type(states) is not tuple:
+                    states = (states,)
+            else:
+                out = self.mlp(out)
+
+            value = self.value_act(self.value(out))
+
+            if self.is_discrete:
+                logits = self.logits(out)
+                return logits, value, states
+
+            if self.is_continuous:
+                mu = self.mu_act(self.mu(out))
+                if self.fixed_sigma:
+                    sigma = self.sigma_act(self.sigma)
+                else:
+                    sigma = self.sigma_act(self.sigma(out))
+                return mu, mu*0 + sigma, value, states
+
+        def load(self, params):
+            self.separate = False
+            self.units = params['mlp']['units']
+            self.activation = params['mlp']['activation']
+            self.initializer = params['mlp']['initializer']
+            self.is_discrete = 'discrete' in params['space']
+            self.is_continuous = 'continuous' in params['space']
+            self.is_multi_discrete = 'multi_discrete'in params['space']
+            self.value_activation = params.get('value_activation', 'None')
+            self.normalization = params.get('normalization', None)
+
+            if self.is_continuous:
+                self.space_config = params['space']['continuous']
+                self.fixed_sigma = self.space_config['fixed_sigma']
+            elif self.is_discrete:
+                self.space_config = params['sA2CVisionBuildernv_depths']
+            self.require_rewards = params.get('require_rewards')
+            self.require_last_actions = params.get('require_last_actions')
+
+        def _build_impala(self, input_shape, depths):
+            in_channels = input_shape[0]
+            layers = nn.ModuleList()
+            for d in depths:
+                layers.append(ImpalaSequential(in_channels, d))
+                in_channels = d
+            return nn.Sequential(*layers)
+
+        def is_separate_critic(self):
+            return False
+
+        def is_rnn(self):
+            return self.has_rnn
+
+        def get_default_rnn_state(self):
+            num_layers = self.rnn_layers
+            if self.rnn_name == 'lstm':
+                return (torch.zeros((num_layers, self.num_seqs, self.rnn_units)),
+                            torch.zeros((num_layers, self.num_seqs, self.rnn_units)))
+            else:
+                return (torch.zeros((num_layers, self.num_seqs, self.rnn_units)))
+
+    def build(self, name, **kwargs):
+        net = A2CVisionBuilder.Network(self.params, **kwargs)
+        return net
+
 class DiagGaussianActor(NetworkBuilder.BaseNetwork):
     """torch.distributions implementation of an diagonal Gaussian policy."""
     def __init__(self, output_dim, log_std_bounds, **mlp_args):

rl_games/common/a2c_common.py

@@ -10,7 +10,7 @@
 from rl_games.common.experience import ExperienceBuffer
 from rl_games.common.interval_summary_writer import IntervalSummaryWriter
 from rl_games.common.diagnostics import DefaultDiagnostics, PpoDiagnostics
-from rl_games.algos_torch import  model_builder
+from rl_games.algos_torch import model_builder
 from rl_games.interfaces.base_algorithm import  BaseAlgorithm
 import numpy as np
 import time
@@ -356,7 +356,7 @@ def trancate_gradients_and_step(self):
     def load_networks(self, params):
         builder = model_builder.ModelBuilder()
         self.config['network'] = builder.load(params)
-        has_central_value_net = self.config.get('central_value_config') is not  None
+        has_central_value_net = self.config.get('central_value_config') is not None
         if has_central_value_net:
             print('Adding Central Value Network')
             if 'model' not in params['config']['central_value_config']:

rl_games/networks/__init__.py

@@ -1,4 +1,7 @@
 from rl_games.networks.tcnn_mlp import TcnnNetBuilder
+#from rl_games.networks.vision_networks import A2CVisionBuilder, A2CVisionBackboneBuilder
 from rl_games.algos_torch import model_builder
 
-model_builder.register_network('tcnnnet', TcnnNetBuilder)
+model_builder.register_network('tcnnnet', TcnnNetBuilder)
+# model_builder.register_network('vision_actor_critic', A2CVisionBuilder)
+# model_builder.register_network('e2e_vision_actor_critic', A2CVisionBackboneBuilder)

rl_games/networks/vision_networks.py

@@ -2,7 +2,7 @@
 from torch import nn
 from torchvision import models
 import torch.nn.functional as F
-import torch_ext
+from rl_games.algos_torch import torch_ext
 from rl_games.algos_torch.network_builder import NetworkBuilder, ImpalaSequential