rlcard-showdown/pve_server/deep.py

import os

import torch
import numpy as np
from collections import Counter

Card2Column = {3: 0, 4: 1, 5: 2, 6: 3, 7: 4, 8: 5, 9: 6, 10: 7,
               11: 8, 12: 9, 13: 10, 14: 11, 17: 12, 20: 13, 30: 14}

NumOnes2Array = {0: np.array([0, 0, 0, 0, 0, 0, 0, 0]),
                 1: np.array([1, 0, 0, 0, 0, 0, 0, 0]),
                 2: np.array([1, 1, 0, 0, 0, 0, 0, 0]),
                 3: np.array([1, 1, 1, 0, 0, 0, 0, 0]),
                 4: np.array([1, 1, 1, 1, 0, 0, 0, 0]),
                 5: np.array([1, 1, 1, 1, 1, 0, 0, 0]),
                 6: np.array([1, 1, 1, 1, 1, 1, 0, 0]),
                 7: np.array([1, 1, 1, 1, 1, 1, 1, 0]),
                 8: np.array([1, 1, 1, 1, 1, 1, 1, 1])}


def _get_one_hot_bomb(bomb_num, use_legacy = False):
    """
    A utility function to encode the number of bombs
    into one-hot representation.
    """
    if use_legacy:
        one_hot = np.zeros(29)
        one_hot[bomb_num[0] + bomb_num[1]] = 1
    else:
        one_hot = np.zeros(56) # 14 + 15 + 27
        one_hot[bomb_num[0]] = 1
        one_hot[14 + bomb_num[1]] = 1
        one_hot[29 + bomb_num[2]] = 1
    return one_hot

def _load_model(position, model_dir, use_onnx):
    if not use_onnx or not os.path.isfile(os.path.join(model_dir, position+'.onnx')) :
        from models import model_dict_new
        model = model_dict_new[position]()
        model_state_dict = model.state_dict()
        model_path = os.path.join(model_dir, position+'.ckpt')
        if torch.cuda.is_available():
            pretrained = torch.load(model_path, map_location='cuda:0')
        else:
            pretrained = torch.load(model_path, map_location='cpu')
        pretrained = {k: v for k, v in pretrained.items() if k in model_state_dict}
        model_state_dict.update(pretrained)
        model.load_state_dict(model_state_dict)
        if torch.cuda.is_available():
            model.cuda()
        model.eval()

        if use_onnx:
            z = torch.randn(1, 5, 162, requires_grad=True)
            if position == 'landlord':
                x = torch.randn(1, 373, requires_grad=True)
            else:
                x = torch.randn(1, 484, requires_grad=True)
            torch.onnx.export(model,
                              (z,x),
                              os.path.join(model_dir, position+'.onnx'),
                              export_params=True,
                              opset_version=10,
                              do_constant_folding=True,
                              input_names = ['z', 'x'],
                              output_names = ['y'],
                              dynamic_axes={'z' : {0 : 'batch_size'},
                                            'x' : {0 : 'batch_size'},
                                            'y' : {0 : 'batch_size'}})

    if use_onnx:
        import onnxruntime
        model = onnxruntime.InferenceSession(os.path.join(model_dir, position+'.onnx'))
    return model

def _process_action_seq(sequence, length=20, new_model=True):
    """
    A utility function encoding historical moves. We
    encode 20 moves. If there is no 20 moves, we pad
    with zeros.
    """
    sequence = sequence[-length:].copy()
    if new_model:
        sequence = sequence[::-1]
    if len(sequence) < length:
        empty_sequence = [[] for _ in range(length - len(sequence))]
        empty_sequence.extend(sequence)
        sequence = empty_sequence
    return sequence

class DeepAgent:

    def __init__(self, position, model_dir, use_onnx=False):
        self.model = _load_model(position, model_dir, use_onnx)
        self.use_onnx = use_onnx

    def cards2array(self, list_cards):
        if len(list_cards) == 0:
            return np.zeros(108, dtype=np.int8)

        matrix = np.zeros([8, 13], dtype=np.int8)
        jokers = np.zeros(4, dtype=np.int8)
        counter = Counter(list_cards)
        for card, num_times in counter.items():
            if card < 20:
                matrix[:, Card2Column[card]] = NumOnes2Array[num_times]
            elif card == 20:
                jokers[0] = 1
                if num_times == 2:
                    jokers[1] = 1
            elif card == 30:
                jokers[2] = 1
                if num_times == 2:
                    jokers[3] = 1
        return np.concatenate((matrix.flatten('F'), jokers))

    def get_one_hot_array(self, num_left_cards, max_num_cards):
        one_hot = np.zeros(max_num_cards, dtype=np.float32)
        one_hot[num_left_cards - 1] = 1

        return one_hot

    def action_seq_list2array(self, action_seq_list, new_model=True):
        """
        A utility function to encode the historical moves.
        We encode the historical 20 actions. If there is
        no 20 actions, we pad the features with 0. Since
        three moves is a round in DouDizhu, we concatenate
        the representations for each consecutive three moves.
        Finally, we obtain a 5x432 matrix, which will be fed
        into LSTM for encoding.
        """

        if new_model:
            # position_map = {"landlord": 0, "landlord_up": 1, "landlord_front": 2, "landlord_down": 3}
            action_seq_array = np.ones((len(action_seq_list), 108)) * -1  # Default Value -1 for not using area
            for row, list_cards in enumerate(action_seq_list):
                if list_cards != []:
                    action_seq_array[row, :108] = self.cards2array(list_cards)
        else:
            action_seq_array = np.zeros((len(action_seq_list), 108))
            for row, list_cards in enumerate(action_seq_list):
                if list_cards != []:
                    action_seq_array[row, :] = self.cards2array(list_cards)
            action_seq_array = action_seq_array.reshape(5, 432)
        return action_seq_array

    def get_obs_general(self, infoset, position, use_legacy = False):
        num_legal_actions = len(infoset.legal_actions)
        my_handcards = self.cards2array(infoset.player_hand_cards)
        my_handcards_batch = np.repeat(my_handcards[np.newaxis, :],
                                    num_legal_actions, axis=0)

        other_handcards = self.cards2array(infoset.other_hand_cards)

        bid_info = np.array(infoset.bid_info).flatten()
        bid_info_batch = np.repeat(bid_info[np.newaxis, :],
                                num_legal_actions, axis=0)

        multiply_info = np.array(infoset.multiply_info)
        multiply_info_batch = np.repeat(multiply_info[np.newaxis, :],
                                        num_legal_actions, axis=0)

        my_action_batch = np.zeros(my_handcards_batch.shape)
        for j, action in enumerate(infoset.legal_actions):
            my_action_batch[j, :] = self.cards2array(action)

        landlord_num_cards_left = self.get_one_hot_array(
            infoset.num_cards_left_dict['landlord'], 33)

        landlord_up_num_cards_left = self.get_one_hot_array(
            infoset.num_cards_left_dict['landlord_up'], 25)

        landlord_front_num_cards_left = self.get_one_hot_array(
            infoset.num_cards_left_dict['landlord_front'], 25)

        landlord_down_num_cards_left = self.get_one_hot_array(
            infoset.num_cards_left_dict['landlord_down'], 25)

        landlord_played_cards = self.cards2array(
            infoset.played_cards[0])

        landlord_up_played_cards = self.cards2array(
            infoset.played_cards[1])

        landlord_front_played_cards = self.cards2array(
            infoset.played_cards[2])

        landlord_down_played_cards = self.cards2array(
            infoset.played_cards[3])

        bomb_num = _get_one_hot_bomb(
            infoset.bomb_num, use_legacy)
        bomb_num_batch = np.repeat(
            bomb_num[np.newaxis, :],
            num_legal_actions, axis=0)
        num_cards_left = np.hstack((
                            landlord_num_cards_left,  # 33
                            landlord_up_num_cards_left,  # 25
                            landlord_front_num_cards_left,  # 25
                            landlord_down_num_cards_left))

        if use_legacy:
            x_batch = np.hstack((
                                bid_info_batch,  # 20
                                multiply_info_batch))  # 4
            x_no_action = np.hstack((
                                    bid_info,
                                    multiply_info))
        else:
            x_batch = np.hstack((
                                bomb_num_batch,  # 56
                                bid_info_batch,  # 20
                                multiply_info_batch))  # 4
            x_no_action = np.hstack((
                                    bomb_num, # 56
                                    bid_info,
                                    multiply_info))
        z =np.vstack((
                    num_cards_left,
                    my_handcards,  # 108
                    other_handcards,  # 108
                    #   three_landlord_cards,  # 108
                    landlord_played_cards,  # 108
                    landlord_up_played_cards,  # 108
                    landlord_front_played_cards,  # 108
                    landlord_down_played_cards,  # 108
                    self.action_seq_list2array(_process_action_seq(infoset.card_play_action_seq, 32))
                    ))

        _z_batch = np.repeat(
            z[np.newaxis, :, :],
            num_legal_actions, axis=0)
        my_action_batch = my_action_batch[:,np.newaxis,:]
        z_batch = np.zeros([len(_z_batch),40,108],int)
        for i in range(0,len(_z_batch)):
            z_batch[i] = np.vstack((my_action_batch[i],_z_batch[i]))
        obs = {
            'position': position,
            'x_batch': x_batch.astype(np.float32),
            'z_batch': z_batch.astype(np.float32),
            'legal_actions': infoset.legal_actions,
            'x_no_action': x_no_action.astype(np.int8),
            'z': z.astype(np.int8),
        }
        return obs

    def act(self, infoset):
        obs = self.get_obs_general(infoset, infoset.player_position)
        z_batch = obs['z_batch']
        x_batch = obs['x_batch']
        if self.use_onnx:
            ort_inputs = {'z': z_batch, 'x': x_batch}
            y_pred = self.model.run(None, ort_inputs)[0]
        elif torch.cuda.is_available():
            y_pred = self.model.forward(torch.from_numpy(z_batch).float().cuda(),
                                        torch.from_numpy(x_batch).float().cuda())
            y_pred = y_pred.cpu().detach().numpy()
        else:
            y_pred = self.model.forward(torch.from_numpy(z_batch).float(),
                                        torch.from_numpy(x_batch).float())['values']
            y_pred = y_pred.detach().numpy()

        y_pred = y_pred.flatten()

        #best_action_index = np.argmax(y_pred, axis=0)[0]
        size = min(3, len(y_pred))
        best_action_index = np.argpartition(y_pred, -size)[-size:]
        best_action_confidence = y_pred[best_action_index]
        best_action = [infoset.legal_actions[index] for index in best_action_index]

        return best_action, best_action_confidence