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model.py
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model.py
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import torch
import torch.nn as nn
from torch.nn import functional as F
import torchvision.models as models
import numpy as np
from utils.distributions import Categorical, DiagGaussian
from utils.model import get_grid, ChannelPool, Flatten, NNBase
# Global Policy model code
class Global_Policy(NNBase):
def __init__(self, input_shape, recurrent=False, hidden_size=512,
downscaling=1):
super(Global_Policy, self).__init__(recurrent, hidden_size,
hidden_size)
out_size = int(input_shape[1] / 16. * input_shape[2] / 16.)
self.main = nn.Sequential(
nn.MaxPool2d(2),
nn.Conv2d(8, 32, 3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(32, 64, 3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(64, 128, 3, stride=1, padding=1),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Conv2d(128, 64, 3, stride=1, padding=1),
nn.ReLU(),
nn.Conv2d(64, 32, 3, stride=1, padding=1),
nn.ReLU(),
Flatten()
)
self.linear1 = nn.Linear(out_size * 32 + 8, hidden_size)
self.linear2 = nn.Linear(hidden_size, 256)
self.critic_linear = nn.Linear(256, 1)
self.orientation_emb = nn.Embedding(72, 8)
self.train()
def forward(self, inputs, rnn_hxs, masks, extras):
x = self.main(inputs)
orientation_emb = self.orientation_emb(extras).squeeze(1)
x = torch.cat((x, orientation_emb), 1)
x = nn.ReLU()(self.linear1(x))
if self.is_recurrent:
x, rnn_hxs = self._forward_gru(x, rnn_hxs, masks)
x = nn.ReLU()(self.linear2(x))
return self.critic_linear(x).squeeze(-1), x, rnn_hxs
# Neural SLAM Module code
class Neural_SLAM_Module(nn.Module):
"""
"""
def __init__(self, args):
super(Neural_SLAM_Module, self).__init__()
self.device = args.device
self.screen_h = args.frame_height
self.screen_w = args.frame_width
self.resolution = args.map_resolution
self.map_size_cm = args.map_size_cm // args.global_downscaling
self.n_channels = 3
self.vision_range = args.vision_range
self.dropout = 0.5
self.use_pe = args.use_pose_estimation
# Visual Encoding
resnet = models.resnet18(pretrained=args.pretrained_resnet)
self.resnet_l5 = nn.Sequential(*list(resnet.children())[0:8])
self.conv = nn.Sequential(*filter(bool, [
nn.Conv2d(512, 64, (1, 1), stride=(1, 1)),
nn.ReLU()
]))
# convolution output size
input_test = torch.randn(1,
self.n_channels,
self.screen_h,
self.screen_w)
conv_output = self.conv(self.resnet_l5(input_test))
self.pool = ChannelPool(1)
self.conv_output_size = conv_output.view(-1).size(0)
# projection layer
self.proj1 = nn.Linear(self.conv_output_size, 1024)
self.proj2 = nn.Linear(1024, 4096)
if self.dropout > 0:
self.dropout1 = nn.Dropout(self.dropout)
self.dropout2 = nn.Dropout(self.dropout)
# Deconv layers to predict map
self.deconv = nn.Sequential(*filter(bool, [
nn.ConvTranspose2d(64, 32, (4, 4), stride=(2, 2), padding=(1, 1)),
nn.ReLU(),
nn.ConvTranspose2d(32, 16, (4, 4), stride=(2, 2), padding=(1, 1)),
nn.ReLU(),
nn.ConvTranspose2d(16, 2, (4, 4), stride=(2, 2), padding=(1, 1)),
]))
# Pose Estimator
self.pose_conv = nn.Sequential(*filter(bool, [
nn.Conv2d(4, 64, (4, 4), stride=(2, 2)),
nn.ReLU(),
nn.Conv2d(64, 32, (4, 4), stride=(2, 2)),
nn.ReLU(),
nn.Conv2d(32, 16, (3, 3), stride=(1, 1)),
nn.ReLU()
]))
pose_conv_output = self.pose_conv(torch.randn(1, 4,
self.vision_range,
self.vision_range))
self.pose_conv_output_size = pose_conv_output.view(-1).size(0)
# projection layer
self.pose_proj1 = nn.Linear(self.pose_conv_output_size, 1024)
self.pose_proj2_x = nn.Linear(1024, 128)
self.pose_proj2_y = nn.Linear(1024, 128)
self.pose_proj2_o = nn.Linear(1024, 128)
self.pose_proj3_x = nn.Linear(128, 1)
self.pose_proj3_y = nn.Linear(128, 1)
self.pose_proj3_o = nn.Linear(128, 1)
if self.dropout > 0:
self.pose_dropout1 = nn.Dropout(self.dropout)
self.st_poses_eval = torch.zeros(args.num_processes,
3).to(self.device)
self.st_poses_train = torch.zeros(args.slam_batch_size,
3).to(self.device)
grid_size = self.vision_range * 2
self.grid_map_eval = torch.zeros(args.num_processes, 2,
grid_size, grid_size
).float().to(self.device)
self.grid_map_train = torch.zeros(args.slam_batch_size, 2,
grid_size, grid_size
).float().to(self.device)
self.agent_view = torch.zeros(args.num_processes, 2,
self.map_size_cm // self.resolution,
self.map_size_cm // self.resolution
).float().to(self.device)
def forward(self, obs_last, obs, poses, maps, explored, current_poses,
build_maps=True):
# Get egocentric map prediction for the current obs
bs, c, h, w = obs.size()
resnet_output = self.resnet_l5(obs[:, :3, :, :])
conv_output = self.conv(resnet_output)
proj1 = nn.ReLU()(self.proj1(
conv_output.view(-1, self.conv_output_size)))
if self.dropout > 0:
proj1 = self.dropout1(proj1)
proj3 = nn.ReLU()(self.proj2(proj1))
deconv_input = proj3.view(bs, 64, 8, 8)
deconv_output = self.deconv(deconv_input)
pred = torch.sigmoid(deconv_output)
proj_pred = pred[:, :1, :, :]
fp_exp_pred = pred[:, 1:, :, :]
with torch.no_grad():
# Get egocentric map prediction for the last obs
bs, c, h, w = obs_last.size()
resnet_output = self.resnet_l5(obs_last[:, :3, :, :])
conv_output = self.conv(resnet_output)
proj1 = nn.ReLU()(self.proj1(
conv_output.view(-1, self.conv_output_size)))
if self.dropout > 0:
proj1 = self.dropout1(proj1)
proj3 = nn.ReLU()(self.proj2(proj1))
deconv_input = proj3.view(bs, 64, 8, 8)
deconv_output = self.deconv(deconv_input)
pred_last = torch.sigmoid(deconv_output)
# ST of proj
vr = self.vision_range
grid_size = vr * 2
if build_maps:
st_poses = self.st_poses_eval.detach_()
grid_map = self.grid_map_eval.detach_()
else:
st_poses = self.st_poses_train.detach_()
grid_map = self.grid_map_train.detach_()
st_poses.fill_(0.)
st_poses[:, 0] = poses[:, 1] * 200. / self.resolution / grid_size
st_poses[:, 1] = poses[:, 0] * 200. / self.resolution / grid_size
st_poses[:, 2] = poses[:, 2] * 57.29577951308232
rot_mat, trans_mat = get_grid(st_poses,
(bs, 2, grid_size, grid_size),
self.device)
grid_map.fill_(0.)
grid_map[:, :, vr:, int(vr / 2):int(vr / 2 + vr)] = pred_last
translated = F.grid_sample(grid_map, trans_mat)
rotated = F.grid_sample(translated, rot_mat)
rotated = rotated[:, :, vr:, int(vr / 2):int(vr / 2 + vr)]
pred_last_st = rotated
# Pose estimator
pose_est_input = torch.cat((pred.detach(), pred_last_st.detach()),
dim=1)
pose_conv_output = self.pose_conv(pose_est_input)
pose_conv_output = pose_conv_output.view(-1,
self.pose_conv_output_size)
proj1 = nn.ReLU()(self.pose_proj1(pose_conv_output))
if self.dropout > 0:
proj1 = self.pose_dropout1(proj1)
proj2_x = nn.ReLU()(self.pose_proj2_x(proj1))
pred_dx = self.pose_proj3_x(proj2_x)
proj2_y = nn.ReLU()(self.pose_proj2_y(proj1))
pred_dy = self.pose_proj3_y(proj2_y)
proj2_o = nn.ReLU()(self.pose_proj2_o(proj1))
pred_do = self.pose_proj3_o(proj2_o)
pose_pred = torch.cat((pred_dx, pred_dy, pred_do), dim=1)
if self.use_pe == 0:
pose_pred = pose_pred * self.use_pe
if build_maps:
# Aggregate egocentric map prediction in the geocentric map
# using the predicted pose
with torch.no_grad():
agent_view = self.agent_view.detach_()
agent_view.fill_(0.)
x1 = self.map_size_cm // (self.resolution * 2) \
- self.vision_range // 2
x2 = x1 + self.vision_range
y1 = self.map_size_cm // (self.resolution * 2)
y2 = y1 + self.vision_range
agent_view[:, :, y1:y2, x1:x2] = pred
corrected_pose = poses + pose_pred
def get_new_pose_batch(pose, rel_pose_change):
pose[:, 1] += rel_pose_change[:, 0] * \
torch.sin(pose[:, 2] / 57.29577951308232) \
+ rel_pose_change[:, 1] * \
torch.cos(pose[:, 2] / 57.29577951308232)
pose[:, 0] += rel_pose_change[:, 0] * \
torch.cos(pose[:, 2] / 57.29577951308232) \
- rel_pose_change[:, 1] * \
torch.sin(pose[:, 2] / 57.29577951308232)
pose[:, 2] += rel_pose_change[:, 2] * 57.29577951308232
pose[:, 2] = torch.fmod(pose[:, 2] - 180.0, 360.0) + 180.0
pose[:, 2] = torch.fmod(pose[:, 2] + 180.0, 360.0) - 180.0
return pose
current_poses = get_new_pose_batch(current_poses,
corrected_pose)
st_pose = current_poses.clone().detach()
st_pose[:, :2] = - (st_pose[:, :2] * 100.0 / self.resolution
- self.map_size_cm \
// (self.resolution * 2)) \
/ (self.map_size_cm // (self.resolution * 2))
st_pose[:, 2] = 90. - (st_pose[:, 2])
rot_mat, trans_mat = get_grid(st_pose, agent_view.size(),
self.device)
rotated = F.grid_sample(agent_view, rot_mat)
translated = F.grid_sample(rotated, trans_mat)
maps2 = torch.cat((maps.unsqueeze(1),
translated[:, :1, :, :]), 1)
explored2 = torch.cat((explored.unsqueeze(1),
translated[:, 1:, :, :]), 1)
map_pred = self.pool(maps2).squeeze(1)
exp_pred = self.pool(explored2).squeeze(1)
else:
map_pred = None
exp_pred = None
current_poses = None
return proj_pred, fp_exp_pred, map_pred, exp_pred,\
pose_pred, current_poses
# Local Policy model code
class Local_IL_Policy(NNBase):
def __init__(self, input_shape, num_actions, recurrent=False,
hidden_size=512, deterministic=False):
super(Local_IL_Policy, self).__init__(recurrent, hidden_size,
hidden_size)
self.deterministic = deterministic
self.dropout = 0.5
resnet = models.resnet18(pretrained=True)
self.resnet_l5 = nn.Sequential(*list(resnet.children())[0:8])
# Extra convolution layer
self.conv = nn.Sequential(*filter(bool, [
nn.Conv2d(512, 64, (1, 1), stride=(1, 1)),
nn.ReLU()
]))
# convolution output size
input_test = torch.randn(1, 3, input_shape[1], input_shape[2])
conv_output = self.conv(self.resnet_l5(input_test))
self.conv_output_size = conv_output.view(-1).size(0)
# projection layers
self.proj1 = nn.Linear(self.conv_output_size, hidden_size - 16)
if self.dropout > 0:
self.dropout1 = nn.Dropout(self.dropout)
self.linear = nn.Linear(hidden_size, hidden_size)
# Short-term goal embedding layers
self.embedding_angle = nn.Embedding(72, 8)
self.embedding_dist = nn.Embedding(24, 8)
# Policy linear layer
self.policy_linear = nn.Linear(hidden_size, num_actions)
self.train()
def forward(self, rgb, rnn_hxs, masks, extras):
if self.deterministic:
x = torch.zeros(extras.size(0), 3)
for i, stg in enumerate(extras):
if stg[0] < 3 or stg[0] > 68:
x[i] = torch.tensor([0.0, 0.0, 1.0])
elif stg[0] < 36:
x[i] = torch.tensor([0.0, 1.0, 0.0])
else:
x[i] = torch.tensor([1.0, 0.0, 0.0])
else:
resnet_output = self.resnet_l5(rgb[:, :3, :, :])
conv_output = self.conv(resnet_output)
proj1 = nn.ReLU()(self.proj1(conv_output.view(
-1, self.conv_output_size)))
if self.dropout > 0:
proj1 = self.dropout1(proj1)
angle_emb = self.embedding_angle(extras[:, 0]).view(-1, 8)
dist_emb = self.embedding_dist(extras[:, 1]).view(-1, 8)
x = torch.cat((proj1, angle_emb, dist_emb), 1)
x = nn.ReLU()(self.linear(x))
if self.is_recurrent:
x, rnn_hxs = self._forward_gru(x, rnn_hxs, masks)
x = nn.Softmax(dim=1)(self.policy_linear(x))
action = torch.argmax(x, dim=1)
return action, x, rnn_hxs
# https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail/blob/master/a2c_ppo_acktr/model.py#L15
class RL_Policy(nn.Module):
def __init__(self, obs_shape, action_space, model_type=0,
base_kwargs=None):
super(RL_Policy, self).__init__()
if base_kwargs is None:
base_kwargs = {}
if model_type == 0:
self.network = Global_Policy(obs_shape, **base_kwargs)
else:
raise NotImplementedError
if action_space.__class__.__name__ == "Discrete":
num_outputs = action_space.n
self.dist = Categorical(self.network.output_size, num_outputs)
elif action_space.__class__.__name__ == "Box":
num_outputs = action_space.shape[0]
self.dist = DiagGaussian(self.network.output_size, num_outputs)
else:
raise NotImplementedError
self.model_type = model_type
@property
def is_recurrent(self):
return self.network.is_recurrent
@property
def rec_state_size(self):
"""Size of rnn_hx."""
return self.network.rec_state_size
def forward(self, inputs, rnn_hxs, masks, extras):
if extras is None:
return self.network(inputs, rnn_hxs, masks)
else:
return self.network(inputs, rnn_hxs, masks, extras)
def act(self, inputs, rnn_hxs, masks, extras=None, deterministic=False):
value, actor_features, rnn_hxs = self(inputs, rnn_hxs, masks, extras)
dist = self.dist(actor_features)
if deterministic:
action = dist.mode()
else:
action = dist.sample()
action_log_probs = dist.log_probs(action)
return value, action, action_log_probs, rnn_hxs
def get_value(self, inputs, rnn_hxs, masks, extras=None):
value, _, _ = self(inputs, rnn_hxs, masks, extras)
return value
def evaluate_actions(self, inputs, rnn_hxs, masks, action, extras=None):
value, actor_features, rnn_hxs = self(inputs, rnn_hxs, masks, extras)
dist = self.dist(actor_features)
action_log_probs = dist.log_probs(action)
dist_entropy = dist.entropy().mean()
return value, action_log_probs, dist_entropy, rnn_hxs