worker.py

"""
MIT License from https://github.com/marmotlab/CAtNIPP/

Copyright (c) 2022 MARMot Lab @ NUS-ME

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""

import copy
import os
from sklearn.metrics import explained_variance_score

import imageio
import numpy as np
import torch
from env import Env
from attention_net import AttentionNet
from parameters import *
import scipy.signal as signal

def discount(x, gamma):
    return signal.lfilter([1], [1, -gamma], x[::-1], axis=0)[::-1]

class Worker:
    def __init__(self, metaAgentID, localNetwork, global_step, budget_range, sample_length=None, num_plants=0, device='cuda', greedy=False, save_image=False):
        self.device = device
        self.greedy = greedy
        self.metaAgentID = metaAgentID
        self.global_step = global_step
        self.save_image = save_image
        self.sample_length = sample_length
        self.num_plants = num_plants

        start = np.array([0.0, 0.0, 0.0])
        dest = np.array([1.0, 1.0, 1.0])
        self.env = Env(global_step, self.num_plants, K_SIZE, budget_range, start, dest, self.save_image)
        self.sample_size = len(self.env.node_coords)*4 - 2
        self.local_net = localNetwork
        self.experience = None

    def run_episode(self, currEpisode):
        reward_seq = []
        util_seq = []
        gp_seq = []

        value_list = []

        path = logs_path + '/log.txt'

        episode_buffer = []
        perf_metrics = dict()
        for i in range(13):
            episode_buffer.append([])

        done = False
        node_coords, _, graph, node_utils, node_std, budget = self.env.reset()
        self.sample_size = len(self.env.node_coords)*4 - 2
        tree_binary = self.env.tree_binary[:len(self.env.action_coords)]

        n_nodes = node_coords.shape[0]
        node_util_inputs = node_utils.reshape((n_nodes, 1))
        node_std_inputs = node_std.reshape((n_nodes,1))
        tree_inputs = tree_binary.reshape((n_nodes, 1))
        budget_inputs = self.calc_estimate_budget(budget, current_idx=0)
        if TREE_BINARY:
            node_inputs = np.concatenate((node_coords, node_util_inputs, node_std_inputs, tree_inputs), axis=1)
        else:
            node_inputs = np.concatenate((node_coords, node_util_inputs, node_std_inputs), axis=1)
        node_inputs = torch.FloatTensor(node_inputs).unsqueeze(0).to(self.device)
        budget_inputs = torch.FloatTensor(budget_inputs).unsqueeze(0).to(self.device)
        
        graph = list(graph.values())
        edge_inputs = []
        for node in graph:
            node_edges = list(map(int, node))
            edge_inputs.append(node_edges)

        pos_encoding = self.calculate_position_embedding(edge_inputs)
        pos_encoding = torch.from_numpy(pos_encoding).float().unsqueeze(0).to(self.device)

        edge_inputs = torch.tensor(edge_inputs).unsqueeze(0).to(self.device)

        current_index = torch.tensor([self.env.current_node_index]).unsqueeze(0).unsqueeze(0).to(self.device)
        route = [current_index.item()]

        LSTM_h = torch.zeros((1,1,EMBEDDING_DIM)).to(self.device)
        LSTM_c = torch.zeros((1,1,EMBEDDING_DIM)).to(self.device)

        mask = torch.zeros((1, self.sample_size+2, K_SIZE*len(FACING_ACTIONS)), dtype=torch.int64).to(self.device)
        for i in range(256):
            episode_buffer[9] += LSTM_h
            episode_buffer[10] += LSTM_c
            episode_buffer[11] += mask
            episode_buffer[12] += pos_encoding

            with torch.no_grad():
                logp_list, value, LSTM_h, LSTM_c = self.local_net(node_inputs, edge_inputs, budget_inputs, current_index, LSTM_h, LSTM_c, pos_encoding, mask)

            if self.greedy:
                action_index = torch.argmax(logp_list, dim=1).long()
            else:
                action_index = torch.multinomial(logp_list.exp(), 1).long().squeeze(1)

            value_list.append(value.squeeze(0).squeeze(0).item())

            episode_buffer[0] += node_inputs
            episode_buffer[1] += edge_inputs
            episode_buffer[2] += current_index
            episode_buffer[3] += action_index.unsqueeze(0).unsqueeze(0)
            episode_buffer[4] += value
            episode_buffer[8] += budget_inputs 

            next_node_index = edge_inputs[:, current_index.item(), action_index.item()]
            route.append(next_node_index.item())

            reward, done, node_utils, node_std, remain_budget, utility, gp_pred, tree_binary = self.env.step_sample(next_node_index.item(), save_img=self.save_image)
            graph, node_coords = self.env.graph, self.env.action_coords
            self.sample_size = len(self.env.node_coords)*4 - 2

            graph = list(graph.values())
            edge_inputs = []
            for node in graph:
                node_edges = list(map(int, node))
                edge_inputs.append(node_edges)

            pos_encoding = self.calculate_position_embedding(edge_inputs)
            pos_encoding = torch.from_numpy(pos_encoding).float().unsqueeze(0).to(self.device)

            edge_inputs = torch.tensor(edge_inputs).unsqueeze(0).to(self.device)
            n_nodes = node_coords.shape[0]

            reward_seq.append(reward) # Logging
            util_seq.append(utility) # Logging
            gp_seq.append(float(gp_pred)) # Logging

            episode_buffer[5] += torch.FloatTensor([[[reward]]]).to(self.device)

            current_index = next_node_index.unsqueeze(0).unsqueeze(0)
            node_info_inputs = node_utils.reshape(n_nodes, 1)
            node_std_inputs = node_std.reshape(n_nodes, 1)
            tree_inputs = tree_binary.reshape((n_nodes, 1))
            budget_inputs = self.calc_estimate_budget(remain_budget, current_idx=current_index.item())
            if TREE_BINARY:
                node_inputs = np.concatenate((node_coords, node_info_inputs, node_std_inputs, tree_inputs), axis=1)
            else:
                node_inputs = np.concatenate((node_coords, node_info_inputs, node_std_inputs), axis=1)
            node_inputs = torch.FloatTensor(node_inputs).unsqueeze(0).to(self.device)
            budget_inputs = torch.FloatTensor(budget_inputs).unsqueeze(0).to(self.device)
            
            mask = torch.zeros((1, self.sample_size+2, K_SIZE*len(FACING_ACTIONS)), dtype=torch.int64).to(self.device)

            if done:
                episode_buffer[6] = episode_buffer[4][1:]
                episode_buffer[6].append(torch.FloatTensor([[0]]).to(self.device))
                perf_metrics['remain_budget'] = remain_budget / budget
                perf_metrics['RMSE'] = self.env.RMSE
                perf_metrics['F1Score'] = self.env.F1score
                perf_metrics['delta_cov_trace'] = self.env.cov_trace0 - self.env.cov_trace
                perf_metrics['node_utils'] = self.env.prev_utility_avg
                perf_metrics['MI'] = self.env.MI
                perf_metrics['cov_trace'] = self.env.cov_trace
                perf_metrics['entropy'] = self.env.entropy
                perf_metrics['success_rate'] = True
                perf_metrics['detection_rate'] = self.env.detected_fruits
                print('{} Goodbye world! We did it!'.format(i))

                # Write logs for debugging
                f = open(path, "a")
                f.write("Episode {} Success!\n".format(self.currEpisode))
                f.write("Total fruits - {}, Detected - {}, percentage - {}\n".format(self.env.total_fruits, self.env.total_fruits*self.env.detected_fruits, self.env.detected_fruits))
                f.write("Route - {}\n".format(self.env.route))
                f.write("Reward Sequence - {}\n".format(reward_seq))
                f.write("Utility Sequence - {}\n".format(util_seq))
                f.write("GP Prediction Sequence - {}\n".format(gp_seq))
                f.write("Final cov_tr - {}\n".format(self.env.cov_trace))
                f.write("---------------------------------\n")
                f.close()
                break
        if not done:
            episode_buffer[6] = episode_buffer[4][1:]
            with torch.no_grad():
                 _, value, LSTM_h, LSTM_c = self.local_net(node_inputs, edge_inputs, budget_inputs, current_index, LSTM_h, LSTM_c, pos_encoding, mask)
            episode_buffer[6].append(value.squeeze(0))
            perf_metrics['remain_budget'] = remain_budget / budget
            perf_metrics['RMSE'] = self.env.RMSE
            perf_metrics['F1Score'] = self.env.F1score
            perf_metrics['delta_cov_trace'] =  self.env.cov_trace0 - self.env.cov_trace
            perf_metrics['node_utils'] = self.env.prev_utility_avg
            perf_metrics['MI'] = self.env.MI
            perf_metrics['cov_trace'] = self.env.cov_trace
            perf_metrics['entropy'] = self.env.entropy
            perf_metrics['success_rate'] = False
            perf_metrics['detection_rate'] = self.env.detected_fruits
            f = open(path, "a")
            f.write("Episode {} FAILED!\n".format(self.currEpisode))
            f.write("Total fruits - {}, Detected - {}\n".format(self.env.total_fruits, self.env.total_fruits*self.env.detected_fruits))
            f.write("Route - {}\n".format(self.env.route))
            f.write("Reward Sequence - {}\n".format(reward_seq))
            f.write("Utility Sequence - {}\n".format(util_seq))
            f.write("GP Prediction Sequence - {}\n".format(gp_seq))
            f.write("Final cov_tr - {}\n".format(self.env.cov_trace))
            f.write("---------------------------------\n")
            f.close()

        print('route is ', route)
        reward = copy.deepcopy(episode_buffer[5])
        reward.append(episode_buffer[6][-1])
        for i in range(len(reward)):
            reward[i] = reward[i].cpu().numpy()
        reward_plus = np.array(reward,dtype=object).reshape(-1)
        discounted_rewards = discount(reward_plus, GAMMA)[:-1]
        discounted_rewards = discounted_rewards.tolist()
        target_v = torch.FloatTensor(discounted_rewards).unsqueeze(1).unsqueeze(1).to(self.device)
        target_value = np.array(discounted_rewards)
        perf_metrics['variance score'] = explained_variance_score(target_value, np.array(value_list))
        perf_metrics['residual var'] = np.var(target_value - np.array(value_list)) / np.var(target_value)
        perf_metrics['Ep len'] = len(self.env.route)
        for i in range(target_v.size()[0]):
            episode_buffer[7].append(target_v[i,:,:])

        if self.save_image:
            path = gifs_path
            self.make_gif(path, currEpisode)

        self.experience = episode_buffer
        return perf_metrics

    def work(self, currEpisode):
        '''
        Interacts with the environment. The agent gets either gradients or experience buffer
        '''
        self.currEpisode = currEpisode
        self.perf_metrics = self.run_episode(currEpisode)

    def calc_estimate_budget(self, budget, current_idx):
        all_budget = []
        current_coord = self.env.action_coords[current_idx]
        end_coord = self.env.action_coords[0]
        for i, point_coord in enumerate(self.env.action_coords):
            dist_current2point = self.env.controller.calcDistance(current_coord, point_coord)
            dist_point2end = self.env.controller.calcDistance(point_coord, end_coord)
            estimate_budget = (budget - dist_current2point - dist_point2end) / 10
            all_budget.append(estimate_budget)
        return np.asarray(all_budget).reshape(i+1, 1)
    
    def calculate_position_embedding(self, edge_inputs):
        A_matrix = np.zeros((self.sample_size+2, self.sample_size+2))
        D_matrix = np.zeros((self.sample_size+2, self.sample_size+2))
        for i in range(self.sample_size+2):
            for j in range(self.sample_size+2):
                if j in edge_inputs[i] and i != j:
                    A_matrix[i][j] = 1.0
        for i in range(self.sample_size+2):
            D_matrix[i][i] = 1/np.sqrt(len(edge_inputs[i])-1)
        L = np.eye(self.sample_size+2) - np.matmul(D_matrix, A_matrix, D_matrix)
        eigen_values, eigen_vector = np.linalg.eig(L)
        idx = eigen_values.argsort()
        eigen_values, eigen_vector = eigen_values[idx], np.real(eigen_vector[:, idx])
        eigen_vector = eigen_vector[:,1:32+1]
        return eigen_vector
    
    def make_gif(self, path, n):
        with imageio.get_writer('{}/{}_cov_trace_{:.4g}.gif'.format(path, n, self.env.cov_trace), mode='I', duration=0.5) as writer:
            for frame in self.env.frame_files:
                image = imageio.imread(frame)
                writer.append_data(image)
        print('gif complete\n')

        # Remove files
        for filename in self.env.frame_files[:-1]:
            os.remove(filename)




if __name__=='__main__':
    device = torch.device('cuda')
    localNetwork = AttentionNet(INPUT_DIM, EMBEDDING_DIM).cuda()
    worker = Worker(1, localNetwork, 0, budget_range=(4, 6), save_image=False, sample_length=0.05)
    worker.run_episode(0)