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features.py
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features.py
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import collections
from absl import logging
import random
import time
import enum
import numpy as np
import six
import torch
import copy
import random
from concurrent.futures import ThreadPoolExecutor
from distar.pysc2.lib import actions
from distar.pysc2.lib import point, colors, static_data
from distar.pysc2.lib import named_array
from distar.pysc2.lib import stopwatch
from distar.pysc2.lib.static_data import BUFFS_REORDER, BUFFS_REORDER_ARRAY, UPGRADES_REORDER_ARRAY, ADDON_REORDER_ARRAY, \
UNIT_TYPES_REORDER_ARRAY, NUM_UNIT_TYPES, NUM_UPGRADES
from s2clientprotocol import raw_pb2 as sc_raw
from distar.pysc2.lib import unit_controls
from s2clientprotocol import sc2api_pb2 as sc_pb
from torch import int8, uint8, int16, int32, float32, float16, int64
from .actions import ACTIONS, ABILITY_TO_QUEUE_ACTION, BEGINNING_ORDER_ACTIONS, CUMULATIVE_STAT_ACTIONS, FUNC_ID_TO_ACTION_TYPE_DICT, UNIT_ABILITY_REORDER, NUM_UNIT_MIX_ABILITIES
from collections import defaultdict
sw = stopwatch.sw
SPATIAL_SIZE = [152, 160] # y, x
BUFF_LENGTH = 3
UPGRADE_LENGTH = 20
MAX_DELAY = 127
BEGINNING_ORDER_LENGTH = 20
MAX_SELECTED_UNITS_NUM = 64
MAX_ENTITY_NUM = 512
EFFECT_LEN = 100
SPATIAL_INFO = [('height_map', uint8), ('visibility_map', uint8), ('creep', uint8), ('player_relative', uint8),
('alerts', uint8), ('pathable', uint8), ('buildable', uint8), ('effect_PsiStorm', int16),
('effect_NukeDot', int16), ('effect_LiberatorDefenderZone', int16), ('effect_BlindingCloud', int16),
('effect_CorrosiveBile', int16), ('effect_LurkerSpines', int16)]
# (name, dtype, size)
SCALAR_INFO = [('home_race', uint8, ()), ('away_race', uint8, ()), ('upgrades', int16, (NUM_UPGRADES,)),
('time', float32, ()), ('unit_counts_bow', uint8, (NUM_UNIT_TYPES,)),
('agent_statistics', float32, (10, )),
('cumulative_stat', uint8, (len(CUMULATIVE_STAT_ACTIONS), )),
('beginning_order', int16, (BEGINNING_ORDER_LENGTH, )), ('last_queued', int16, ()),
('last_delay', int16, ()), ('last_action_type', int16, ()),
('bo_location', int16, (BEGINNING_ORDER_LENGTH, )),
('unit_order_type', uint8, (NUM_UNIT_MIX_ABILITIES,)), ('unit_type_bool', uint8, (NUM_UNIT_TYPES,)),
('enemy_unit_type_bool', uint8, (NUM_UNIT_TYPES,))]
ENTITY_INFO = [('unit_type', int16), ('alliance', uint8), ('cargo_space_taken', uint8),
('build_progress', float16), ('health_ratio', float16), ('shield_ratio', float16),
('energy_ratio', float16), ('display_type', uint8), ('x', uint8), ('y', uint8),
('cloak', uint8), ('is_blip', uint8), ('is_powered', uint8), ('mineral_contents', float16),
('vespene_contents', float16), ('cargo_space_max', uint8), ('assigned_harvesters', uint8),
('weapon_cooldown', uint8), ('order_length', uint8), ('order_id_0', int16),
('order_id_1', int16), ('is_hallucination', uint8), ('buff_id_0', uint8), ('buff_id_1', uint8),
('addon_unit_type', uint8), ('is_active', uint8), ('order_progress_0', float16),
('order_progress_1', float16), ('order_id_2', int16), ('order_id_3', int16),
('is_in_cargo', uint8), ('attack_upgrade_level', uint8), ('armor_upgrade_level', uint8),
('shield_upgrade_level', uint8), ('last_selected_units', int8), ('last_targeted_unit', int8)]
ACTION_INFO = {'action_type': torch.tensor(0, dtype=torch.long), 'delay': torch.tensor(0, dtype=torch.long),
'queued': torch.tensor(0, dtype=torch.long), 'selected_units': torch.zeros((MAX_SELECTED_UNITS_NUM,), dtype=torch.long),
'target_unit': torch.tensor(0, dtype=torch.long),
'target_location': torch.tensor(0, dtype=torch.long)}
ACTION_LOGP = {'action_type': torch.tensor(0, dtype=torch.float), 'delay': torch.tensor(0, dtype=torch.float),
'queued': torch.tensor(0, dtype=torch.float), 'selected_units': torch.zeros((MAX_SELECTED_UNITS_NUM,), dtype=torch.float),
'target_unit': torch.tensor(0, dtype=torch.float),
'target_location': torch.tensor(0, dtype=torch.float)}
ACTION_LOGIT = {'action_type': torch.zeros(len(ACTIONS), dtype=torch.float), 'delay': torch.zeros(MAX_DELAY + 1, dtype=torch.float),
'queued': torch.zeros(2, dtype=torch.float), 'selected_units': torch.zeros((MAX_SELECTED_UNITS_NUM, MAX_ENTITY_NUM + 1), dtype=torch.float),
'target_unit': torch.zeros(MAX_ENTITY_NUM, dtype=torch.float),
'target_location': torch.zeros(SPATIAL_SIZE[0] * SPATIAL_SIZE[1], dtype=torch.float)}
def recursive_to_share_memory(data, batch_size):
if isinstance(data, torch.Tensor):
if batch_size is not None:
data_shape_len = len(data.shape)
data = data.repeat(batch_size, *([1] * data_shape_len))
return data.share_memory_()
elif isinstance(data, dict):
return {k: recursive_to_share_memory(v, batch_size) for k, v in data.items()}
def fake_step_data(share_memory=False, batch_size=None, train=True, hidden_size=None, hidden_layer=None):
spatial_info = {}
scalar_info = {}
entity_info = {}
for k, dtype in SPATIAL_INFO:
if 'effect' in k:
spatial_info[k] = torch.zeros(EFFECT_LEN, dtype=dtype)
else:
spatial_info[k] = torch.zeros(size=SPATIAL_SIZE, dtype=dtype)
for k, dtype, size in SCALAR_INFO:
scalar_info[k] = torch.zeros(size=size, dtype=dtype)
for k, dtype in ENTITY_INFO:
entity_info[k] = torch.zeros(size=(MAX_ENTITY_NUM, ), dtype=dtype)
action_mask = {'action_type': torch.tensor(1, dtype=torch.bool), 'delay': torch.tensor(1, dtype=torch.bool),
'queued': torch.tensor(1, dtype=torch.bool), 'selected_units': torch.tensor(1, dtype=torch.bool),
'target_unit': torch.tensor(1, dtype=torch.bool),
'target_location': torch.tensor(1, dtype=torch.bool)}
ret = {
'spatial_info': spatial_info,
'scalar_info': scalar_info,
'entity_info': entity_info,
'entity_num': torch.randint(0, MAX_ENTITY_NUM, size=(), dtype=torch.long),
}
if train:
ret.update({'action_info': copy.deepcopy(ACTION_INFO),
'action_mask': action_mask,
'selected_units_num': torch.randint(0, MAX_SELECTED_UNITS_NUM, size=(), dtype=torch.long)})
if share_memory:
ret = recursive_to_share_memory(ret, batch_size)
if hidden_size is not None:
ret['hidden_state'] = [(torch.zeros(batch_size, hidden_size).share_memory_(),
torch.zeros(batch_size, hidden_size).share_memory_()) for _ in range(hidden_layer)]
return ret
def fake_model_output(batch_size, hidden_size, hidden_layer, teacher=False):
ret = {
'logit': copy.deepcopy(ACTION_LOGIT),
'entity_num': torch.randint(0, MAX_ENTITY_NUM, size=(), dtype=torch.long),
'selected_units_num': torch.randint(0, MAX_SELECTED_UNITS_NUM, size=(), dtype=torch.long)
}
if not teacher:
ret.update({
'action_info': copy.deepcopy(ACTION_INFO),
'action_logp': copy.deepcopy(ACTION_LOGP),
'extra_units': torch.zeros(MAX_ENTITY_NUM + 1),
})
ret = recursive_to_share_memory(ret, batch_size)
ret['hidden_state'] = [(torch.zeros(batch_size, hidden_size).share_memory_(),
torch.zeros(batch_size, hidden_size).share_memory_()) for _ in range(hidden_layer)]
return ret
class FeatureType(enum.Enum):
SCALAR = 1
CATEGORICAL = 2
class PlayerRelative(enum.IntEnum):
"""The values for the `player_relative` feature layers."""
NONE = 0
SELF = 1
ALLY = 2
NEUTRAL = 3
ENEMY = 4
class Effects(enum.IntEnum):
"""Values for the `effects` feature layer."""
# pylint: disable=invalid-name
none = 0
PsiStorm = 1
GuardianShield = 2
TemporalFieldGrowing = 3
TemporalField = 4
ThermalLance = 5
ScannerSweep = 6
NukeDot = 7
LiberatorDefenderZoneSetup = 8
LiberatorDefenderZone = 9
BlindingCloud = 10
CorrosiveBile = 11
LurkerSpines = 12
# pylint: enable=invalid-name
class ScoreCategories(enum.IntEnum):
"""Indices for the `score_by_category` observation's second dimension."""
none = 0
army = 1
economy = 2
technology = 3
upgrade = 4
class Player(enum.IntEnum):
"""Indices into the `player` observation."""
player_id = 0
minerals = 1
vespene = 2
food_used = 3
food_cap = 4
food_army = 5
food_workers = 6
idle_worker_count = 7
army_count = 8
warp_gate_count = 9
larva_count = 10
class FeatureUnit(enum.IntEnum):
"""Indices for the `feature_unit` observations."""
unit_type = 0
alliance = 1
cargo_space_taken = 2
build_progress = 3
health_max = 4
shield_max = 5
energy_max = 6
display_type = 7
owner = 8
x = 9
y = 10
cloak = 11
is_blip = 12
is_powered = 13
mineral_contents = 14
vespene_contents = 15
cargo_space_max = 16
assigned_harvesters = 17
weapon_cooldown = 18
order_length = 19 # If zero, the unit is idle.
order_id_0 = 20
order_id_1 = 21
# tag = 22 # Unique identifier for a unit (only populated for raw units).
is_hallucination = 22
buff_id_0 = 23
buff_id_1 = 24
addon_unit_type = 25
is_active = 26
order_progress_0 = 27
order_progress_1 = 28
order_id_2 = 29
order_id_3 = 30
is_in_cargo = 31
attack_upgrade_level = 32
armor_upgrade_level = 33
shield_upgrade_level = 34
health = 35
shield = 36
energy = 37
class EffectPos(enum.IntEnum):
"""Positions of the active effects."""
effect = 0
alliance = 1
owner = 2
radius = 3
x = 4
y = 5
class Feature(collections.namedtuple(
"Feature", ["index", "name", "layer_set", "full_name", "scale", "type",
"palette", "clip"])):
"""Define properties of a feature layer.
Attributes:
index: Index of this layer into the set of layers.
name: The name of the layer within the set.
layer_set: Which set of feature layers to look at in the observation proto.
full_name: The full name including for visualization.
scale: Max value (+1) of this layer, used to scale the values.
type: A FeatureType for scalar vs categorical.
palette: A color palette for rendering.
clip: Whether to clip the values for coloring.
"""
__slots__ = ()
dtypes = {
1: np.uint8,
8: np.uint8,
16: np.uint16,
32: np.int32,
}
def unpack(self, obs):
"""Return a correctly shaped numpy array for this feature."""
planes = getattr(obs.feature_layer_data, self.layer_set)
plane = getattr(planes, self.name)
return self.unpack_layer(plane)
@staticmethod
@sw.decorate
def unpack_layer(plane):
"""Return a correctly shaped numpy array given the feature layer bytes."""
size = point.Point.build(plane.size)
if size == (0, 0):
# New layer that isn't implemented in this SC2 version.
return None
data = np.frombuffer(plane.data, dtype=Feature.dtypes[plane.bits_per_pixel])
if plane.bits_per_pixel == 1:
data = np.unpackbits(data)
if data.shape[0] != size.x * size.y:
# This could happen if the correct length isn't a multiple of 8, leading
# to some padding bits at the end of the string which are incorrectly
# interpreted as data.
data = data[:size.x * size.y]
return data.reshape(size.y, size.x)
@staticmethod
@sw.decorate
def unpack_rgb_image(plane):
"""Return a correctly shaped numpy array given the image bytes."""
assert plane.bits_per_pixel == 24, "{} != 24".format(plane.bits_per_pixel)
size = point.Point.build(plane.size)
data = np.frombuffer(plane.data, dtype=np.uint8)
return data.reshape(size.y, size.x, 3)
@sw.decorate
def color(self, plane):
if self.clip:
plane = np.clip(plane, 0, self.scale - 1)
return self.palette[plane]
class MinimapFeatures(collections.namedtuple("MinimapFeatures", [
"height_map", "visibility_map", "creep", "player_relative", "alerts", "pathable", "buildable"])):
"""The set of minimap feature layers."""
__slots__ = ()
def __new__(cls, **kwargs):
feats = {}
for name, (scale, type_, palette) in six.iteritems(kwargs):
feats[name] = Feature(
index=MinimapFeatures._fields.index(name),
name=name,
layer_set="minimap_renders",
full_name="minimap " + name,
scale=scale,
type=type_,
palette=palette(scale) if callable(palette) else palette,
clip=False)
return super(MinimapFeatures, cls).__new__(cls, **feats) # pytype: disable=missing-parameter
MINIMAP_FEATURES = MinimapFeatures(
height_map=(256, FeatureType.SCALAR, colors.height_map),
visibility_map=(4, FeatureType.CATEGORICAL, colors.VISIBILITY_PALETTE),
creep=(2, FeatureType.CATEGORICAL, colors.CREEP_PALETTE),
player_relative=(5, FeatureType.CATEGORICAL,
colors.PLAYER_RELATIVE_PALETTE),
alerts=(2, FeatureType.CATEGORICAL, colors.winter),
pathable=(2, FeatureType.CATEGORICAL, colors.winter),
buildable=(2, FeatureType.CATEGORICAL, colors.winter),
)
def compute_battle_score(obs):
if obs is None:
return 0.
score_details = obs.observation.score.score_details
killed_mineral, killed_vespene = 0., 0.
for s in ScoreCategories:
killed_mineral += getattr(score_details.killed_minerals, s.name)
killed_vespene += getattr(score_details.killed_vespene, s.name)
battle_score = killed_mineral + 1.5 * killed_vespene
return battle_score
class Features(object):
def __init__(self, game_info, raw_ob, cfg={}):
self._map_size = game_info.start_raw.map_size
self._requested_races = {
info.player_id: info.race_requested for info in game_info.player_info
if info.type != sc_pb.Observer}
self._map_name = game_info.map_name
self._start_location = game_info.start_raw.start_locations[0]
self._whole_cfg = cfg
self._cfg = cfg.get('feature', {})
self._bo_zergling_num = self._cfg.get('bo_zergling_num', 8)
self._beginning_order_flag = random.random() < self._cfg.get('beginning_order_prob', 1.)
self._cumulative_stat_flag = random.random() < self._cfg.get('cumulative_stat_prob', 1.)
self._zero_z_value = self._cfg.get('zero_z_value', 1.)
self._filter_spine = self._cfg.get('filter_spine', True)
self._init_born_location(game_info, raw_ob)
def _init_born_location(self, game_info, raw_ob):
location = []
for i in raw_ob.observation.raw_data.units:
if i.unit_type == 59 or i.unit_type == 18 or i.unit_type == 86:
location.append([i.pos.x, i.pos.y])
assert len(location) == 1, 'this replay is corrupt, no fog of war, check replays from this game version'
born_location = location[0]
self._born_location = int(born_location[0]) + int(self.map_size.y - born_location[1]) * SPATIAL_SIZE[1]
away_born_location = game_info.start_raw.start_locations[0]
self._away_born_location = int(away_born_location.x) + int(self.map_size.y - away_born_location.y) * SPATIAL_SIZE[1]
@property
def home_born_location(self):
return self._born_location
@property
def away_born_location(self):
return self._away_born_location
@property
def start_location(self):
return self._start_location
@property
def map_name(self):
return self._map_name
@property
def map_size(self):
return self._map_size
@property
def requested_races(self):
return self._requested_races
def get_z(self, traj_data):
zergling_count = 0
beginning_order = []
bo_location = []
cumulative_stat = torch.zeros(len(CUMULATIVE_STAT_ACTIONS), dtype=torch.int8)
own_x = self.home_born_location % SPATIAL_SIZE[1]
own_y = self.home_born_location // SPATIAL_SIZE[1]
away_x = self.away_born_location % SPATIAL_SIZE[1]
away_y = self.away_born_location // SPATIAL_SIZE[1]
strategy_flag = 0.
for step_data in traj_data:
action_type = step_data['action_info']['action_type'].item()
if action_type == 322:
zergling_count += 1
if zergling_count > self._bo_zergling_num:
continue
if action_type in BEGINNING_ORDER_ACTIONS:
location = step_data['action_info']['target_location'].item()
if self._filter_spine and action_type == 54:
x, y = location % SPATIAL_SIZE[1], location // SPATIAL_SIZE[1]
own_distance = (own_x - x) ** 2 + (own_y - y) ** 2
away_distance = (away_x - x) ** 2 + (away_y - y) ** 2
if own_distance < away_distance:
continue
beginning_order.append(BEGINNING_ORDER_ACTIONS.index(action_type))
bo_location.append(location)
if action_type in CUMULATIVE_STAT_ACTIONS:
cumulative_stat[CUMULATIVE_STAT_ACTIONS.index(action_type)] = 1
bo_len = len(beginning_order)
if bo_len < BEGINNING_ORDER_LENGTH:
beginning_order += [0] * (BEGINNING_ORDER_LENGTH - bo_len)
bo_location += [0] * (BEGINNING_ORDER_LENGTH - bo_len)
else:
beginning_order = beginning_order[:BEGINNING_ORDER_LENGTH]
bo_location = bo_location[:BEGINNING_ORDER_LENGTH]
beginning_order = torch.as_tensor(beginning_order, dtype=torch.short)
bo_location = torch.as_tensor(bo_location, dtype=torch.short)
beginning_order = self._beginning_order_flag * beginning_order
bo_location = self._beginning_order_flag * bo_location
if not self._cumulative_stat_flag:
cumulative_stat = 0 * cumulative_stat + self._zero_z_value
return beginning_order, cumulative_stat, bo_len, bo_location
@sw.decorate
def transform_obs(self, obs, padding_spatial=False, opponent_obs=None):
spatial_info = defaultdict(list)
scalar_info = {}
entity_info = dict()
game_info = {}
raw = obs.observation.raw_data
# spatial info
for f in MINIMAP_FEATURES:
d = f.unpack(obs.observation).copy()
d = torch.from_numpy(d)
padding_y = SPATIAL_SIZE[0] - d.shape[0]
padding_x = SPATIAL_SIZE[1] - d.shape[1]
if (padding_y != 0 or padding_x != 0) and padding_spatial:
d = torch.nn.functional.pad(d, (0, padding_x, 0, padding_y), 'constant', 0)
spatial_info[f.name] = d
for e in raw.effects:
name = Effects(e.effect_id).name
if name in ['LiberatorDefenderZone', 'LurkerSpines'] and e.owner == 1:
continue
for p in e.pos:
location = int(p.x) + int(self.map_size.y - p.y) * SPATIAL_SIZE[1]
spatial_info['effect_' + name].append(location)
for k, _ in SPATIAL_INFO:
if 'effect' in k:
padding_num = EFFECT_LEN - len(spatial_info[k])
if padding_num > 0:
spatial_info[k] += [0] * padding_num
else:
spatial_info[k] = spatial_info[k][:EFFECT_LEN]
spatial_info[k] = torch.as_tensor(spatial_info[k], dtype=int16)
# entity info
tag_types = {} # Only populate the cache if it's needed.
def get_addon_type(tag):
if not tag_types:
for u in raw.units:
tag_types[u.tag] = u.unit_type
return tag_types.get(tag, 0)
tags = []
units = []
for u in raw.units:
tags.append(u.tag)
units.append([
u.unit_type,
u.alliance, # Self = 1, Ally = 2, Neutral = 3, Enemy = 4
u.cargo_space_taken,
u.build_progress,
u.health_max,
u.shield_max,
u.energy_max,
u.display_type, # Visible = 1, Snapshot = 2, Hidden = 3
u.owner, # 1-15, 16 = neutral
u.pos.x,
u.pos.y,
u.cloak, # Cloaked = 1, CloakedDetected = 2, NotCloaked = 3
u.is_blip,
u.is_powered,
u.mineral_contents,
u.vespene_contents,
# Not populated for enemies or neutral
u.cargo_space_max,
u.assigned_harvesters,
u.weapon_cooldown,
len(u.orders),
u.orders[0].ability_id if len(u.orders) > 0 else 0,
u.orders[1].ability_id if len(u.orders) > 1 else 0,
u.is_hallucination,
u.buff_ids[0] if len(u.buff_ids) >= 1 else 0,
u.buff_ids[1] if len(u.buff_ids) >= 2 else 0,
get_addon_type(u.add_on_tag) if u.add_on_tag else 0,
u.is_active,
u.orders[0].progress if len(u.orders) >= 1 else 0,
u.orders[1].progress if len(u.orders) >= 2 else 0,
u.orders[2].ability_id if len(u.orders) > 2 else 0,
u.orders[3].ability_id if len(u.orders) > 3 else 0,
0,
u.attack_upgrade_level,
u.armor_upgrade_level,
u.shield_upgrade_level,
u.health,
u.shield,
u.energy,
])
for v in u.passengers:
tags.append(v.tag)
units.append([
v.unit_type,
u.alliance, # Self = 1, Ally = 2, Neutral = 3, Enemy = 4
0,
0,
v.health_max,
v.shield_max,
v.energy_max,
0, # Visible = 1, Snapshot = 2, Hidden = 3
u.owner, # 1-15, 16 = neutral
u.pos.x,
u.pos.y,
0, # Cloaked = 1, CloakedDetected = 2, NotCloaked = 3
0,
0,
0,
0,
# Not populated for enemies or neutral
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
0,
1,
0,
0,
0,
v.health,
v.shield,
v.energy,
])
units = units[:MAX_ENTITY_NUM]
tags = tags[:MAX_ENTITY_NUM]
raw_entity_info = named_array.NamedNumpyArray(units, [None, FeatureUnit], dtype=np.float32)
for k, dtype in ENTITY_INFO:
if 'last' in k:
pass
elif k == 'unit_type':
entity_info[k] = UNIT_TYPES_REORDER_ARRAY[raw_entity_info[:, 'unit_type']].short()
elif 'order_id' in k:
order_idx = int(k.split('_')[-1])
if order_idx == 0:
entity_info[k] = UNIT_ABILITY_REORDER[raw_entity_info[:, k]].short()
invalid_actions = entity_info[k] == -1
if invalid_actions.any():
print('[ERROR] invalid unit ability', raw_entity_info[invalid_actions, k])
else:
entity_info[k] = ABILITY_TO_QUEUE_ACTION[raw_entity_info[:, k]].short()
invalid_actions = entity_info[k] == -1
if invalid_actions.any():
print('[ERROR] invalid queue ability', raw_entity_info[invalid_actions, k])
elif 'buff_id' in k:
entity_info[k] = BUFFS_REORDER_ARRAY[raw_entity_info[:, k]].short()
elif k == 'addon_unit_type':
entity_info[k] = ADDON_REORDER_ARRAY[raw_entity_info[:, k]].short()
elif k == 'cargo_space_taken':
entity_info[k] = torch.as_tensor(raw_entity_info[:, 'cargo_space_taken'], dtype=dtype).clamp_(min=0, max=8)
elif k == 'cargo_space_max':
entity_info[k] = torch.as_tensor(raw_entity_info[:, 'cargo_space_max'], dtype=dtype).clamp_(min=0, max=8)
elif k == 'health_ratio':
entity_info[k] = torch.as_tensor(raw_entity_info[:, 'health'], dtype=dtype) / (torch.as_tensor(raw_entity_info[:, 'health_max'], dtype=dtype) + 1e-6)
elif k == 'shield_ratio':
entity_info[k] = torch.as_tensor(raw_entity_info[:, 'shield'], dtype=dtype) / (torch.as_tensor(raw_entity_info[:, 'shield_max'], dtype=dtype) + 1e-6)
elif k == 'energy_ratio':
entity_info[k] = torch.as_tensor(raw_entity_info[:, 'energy'], dtype=dtype) / (torch.as_tensor(raw_entity_info[:, 'energy_max'], dtype=dtype) + 1e-6)
elif k == 'mineral_contents':
entity_info[k] = torch.as_tensor(raw_entity_info[:, 'mineral_contents'], dtype=dtype) / 1800
elif k == 'vespene_contents':
entity_info[k] = torch.as_tensor(raw_entity_info[:, 'vespene_contents'], dtype=dtype) / 2500
elif k == 'y':
entity_info[k] = torch.as_tensor(self.map_size.y - raw_entity_info[:, 'y'], dtype=dtype)
else:
entity_info[k] = torch.as_tensor(raw_entity_info[:, k], dtype=dtype)
# scalar info
scalar_info['time'] = torch.tensor(obs.observation.game_loop, dtype=torch.float)
player = obs.observation.player_common
scalar_info['agent_statistics'] = torch.tensor([
player.minerals,
player.vespene,
player.food_used,
player.food_cap,
player.food_army,
player.food_workers,
player.idle_worker_count,
player.army_count,
player.warp_gate_count,
player.larva_count], dtype=torch.float)
scalar_info['agent_statistics'] = torch.log(scalar_info['agent_statistics'] + 1)
scalar_info["home_race"] = torch.tensor(
self._requested_races[player.player_id], dtype=torch.uint8)
for player_id, race in self._requested_races.items():
if player_id != player.player_id:
scalar_info["away_race"] = torch.tensor(race, dtype=torch.uint8)
upgrades = torch.zeros(NUM_UPGRADES, dtype=torch.uint8)
raw_upgrades = UPGRADES_REORDER_ARRAY[raw.player.upgrade_ids[:UPGRADE_LENGTH]]
# for u in raw.player.upgrade_ids:
#if UPGRADES_REORDER_ARRAY[u] == -1:
# print('[ERROR]', u)
upgrades.scatter_(dim=0, index=raw_upgrades, value=1.)
scalar_info["upgrades"] = upgrades
unit_counts_bow = torch.zeros(NUM_UNIT_TYPES, dtype=torch.uint8)
scalar_info['unit_type_bool'] = torch.zeros(NUM_UNIT_TYPES, dtype=uint8)
own_unit_types = entity_info['unit_type'][entity_info['alliance'] == 1]
scalar_info['unit_counts_bow'] = torch.scatter_add(unit_counts_bow, dim=0, index=own_unit_types.long(), src=torch.ones_like(own_unit_types, dtype=torch.uint8))
scalar_info['unit_type_bool'] = (scalar_info['unit_counts_bow'] > 0).to(uint8)
scalar_info['unit_order_type'] = torch.zeros(NUM_UNIT_MIX_ABILITIES, dtype=uint8)
own_unit_orders = entity_info['order_id_0'][entity_info['alliance'] == 1]
scalar_info['unit_order_type'].scatter_(0, own_unit_orders.long(), torch.ones_like(own_unit_orders, dtype=uint8))
enemy_unit_types = entity_info['unit_type'][entity_info['alliance'] == 4]
enemy_unit_type_bool = torch.zeros(NUM_UNIT_TYPES, dtype=torch.uint8)
scalar_info['enemy_unit_type_bool'] = torch.scatter(enemy_unit_type_bool, dim=0, index=enemy_unit_types.long(), src=torch.ones_like(enemy_unit_types, dtype=torch.uint8))
# game info
game_info['map_name'] = self._map_name
game_info['action_result'] = [o.result for o in obs.action_errors]
game_info['game_loop'] = obs.observation.game_loop
game_info['tags'] = tags
game_info['battle_score'] = compute_battle_score(obs)
game_info['opponent_battle_score'] = 0.
ret = {
'spatial_info': spatial_info, 'scalar_info': scalar_info, 'entity_num': torch.tensor(len(entity_info['unit_type']), dtype=torch.long),
'entity_info': entity_info, 'game_info': game_info,
}
# value feature
if opponent_obs:
raw = opponent_obs.observation.raw_data
enemy_unit_counts_bow = torch.zeros(NUM_UNIT_TYPES, dtype=torch.uint8)
enemy_x = []
enemy_y = []
enemy_unit_type = []
unit_alliance = []
for u in raw.units:
if u.alliance == 1:
enemy_x.append(u.pos.x)
enemy_y.append(u.pos.y)
enemy_unit_type.append(u.unit_type)
unit_alliance.append(1)
enemy_unit_type = UNIT_TYPES_REORDER_ARRAY[enemy_unit_type].short()
enemy_unit_counts_bow = torch.scatter_add(enemy_unit_counts_bow, dim=0, index=enemy_unit_type.long(),
src=torch.ones_like(enemy_unit_type, dtype=torch.uint8))
enemy_unit_type_bool = (enemy_unit_counts_bow > 0).to(uint8)
unit_type = torch.cat([enemy_unit_type, own_unit_types], dim=0)
enemy_x = torch.as_tensor(enemy_x, dtype=uint8)
unit_x = torch.cat([enemy_x, entity_info['x'][entity_info['alliance'] == 1]], dim=0)
enemy_y = torch.as_tensor(enemy_y, dtype=float32)
enemy_y = torch.as_tensor(self.map_size.y - enemy_y, dtype=uint8)
unit_y = torch.cat([enemy_y, entity_info['y'][entity_info['alliance'] == 1]], dim=0)
total_unit_count = len(unit_y)
unit_alliance += [0] * (total_unit_count - len(unit_alliance))
unit_alliance = torch.as_tensor(unit_alliance, dtype=torch.bool)
padding_num = MAX_ENTITY_NUM - total_unit_count
if padding_num > 0:
unit_x = torch.nn.functional.pad(unit_x, (0, padding_num), 'constant', 0)
unit_y = torch.nn.functional.pad(unit_y, (0, padding_num), 'constant', 0)
unit_type = torch.nn.functional.pad(unit_type, (0, padding_num), 'constant', 0)
unit_alliance = torch.nn.functional.pad(unit_alliance, (0, padding_num), 'constant', 0)
else:
unit_x = unit_x[:MAX_ENTITY_NUM]
unit_y = unit_y[:MAX_ENTITY_NUM]
unit_type = unit_type[:MAX_ENTITY_NUM]
unit_alliance = unit_alliance[:MAX_ENTITY_NUM]
total_unit_count = torch.tensor(total_unit_count, dtype=torch.long)
player = opponent_obs.observation.player_common
enemy_agent_statistics = torch.tensor([
player.minerals,
player.vespene,
player.food_used,
player.food_cap,
player.food_army,
player.food_workers,
player.idle_worker_count,
player.army_count,
player.warp_gate_count,
player.larva_count], dtype=torch.float)
enemy_agent_statistics = torch.log(enemy_agent_statistics + 1)
enemy_raw_upgrades = UPGRADES_REORDER_ARRAY[raw.player.upgrade_ids[:UPGRADE_LENGTH]]
enemy_upgrades = torch.zeros(NUM_UPGRADES, dtype=torch.uint8)
enemy_upgrades.scatter_(dim=0, index=enemy_raw_upgrades, value=1.)
d = MINIMAP_FEATURES.player_relative.unpack(opponent_obs.observation).copy()
d = torch.from_numpy(d)
padding_y = SPATIAL_SIZE[0] - d.shape[0]
padding_x = SPATIAL_SIZE[1] - d.shape[1]
if (padding_y != 0 or padding_x != 0) and padding_spatial:
d = torch.nn.functional.pad(d, (0, padding_x, 0, padding_y), 'constant', 0)
enemy_units_spatial = d == 1
own_units_spatial = ret['spatial_info']['player_relative'] == 1
value_feature = {'unit_type': unit_type, 'enemy_unit_counts_bow': enemy_unit_counts_bow,
'enemy_unit_type_bool': enemy_unit_type_bool, 'unit_x': unit_x, 'unit_y': unit_y,
'unit_alliance': unit_alliance, 'total_unit_count': total_unit_count,
'enemy_agent_statistics': enemy_agent_statistics, 'enemy_upgrades': enemy_upgrades,
'own_units_spatial': own_units_spatial.unsqueeze(dim=0), 'enemy_units_spatial': enemy_units_spatial.unsqueeze(dim=0)}
ret['value_feature'] = value_feature
game_info['opponent_battle_score'] = compute_battle_score(opponent_obs)
return ret
@sw.decorate
def transform_action(self, func_call):
"""Transform an agent-style action to one that SC2 can consume.
Args:
obs: a `sc_pb.Observation` from the previous frame.
func_call: a `FunctionCall` to be turned into a `sc_pb.Action`.
skip_available: If True, assume the action is available. This should only
be used for testing or if you expect to make actions that weren't
valid at the last observation.
Returns:
a corresponding `sc_pb.Action`.
Raises:
ValueError: if the action doesn't pass validation.
"""
# Ignore sc_pb.Action's to make the env more flexible, eg raw actions.
if isinstance(func_call, sc_pb.Action):
return func_call
func_id = func_call.function
raw = True # TODO(nyz) self._raw
try:
if raw:
func = actions.RAW_FUNCTIONS[func_id]
else:
func = actions.FUNCTIONS[func_id]
except KeyError:
raise ValueError("Invalid function id: %s." % func_id)
# Right number of args?
if len(func_call.arguments) != len(func.args):
raise ValueError(
"Wrong number of arguments for function: %s, got: %s" % (
func, func_call.arguments))
# Args are valid?
aif = self._agent_interface_format
for t, arg in zip(func.args, func_call.arguments):
if t.count:
if 1 <= len(arg) <= t.count:
continue
else:
raise ValueError(
"Wrong number of values for argument of %s, got: %s" % (
func, func_call.arguments))
if t.name in ("screen", "screen2"):
sizes = aif.action_dimensions.screen
elif t.name == "minimap":
sizes = aif.action_dimensions.minimap
elif t.name == "world":
sizes = aif.raw_resolution
else:
sizes = t.sizes
if len(sizes) != len(arg):
raise ValueError(
"Wrong number of values for argument of %s, got: %s" % (
func, func_call.arguments))
for s, a in zip(sizes, arg):
if not np.all(0 <= a) and np.all(a < s):
raise ValueError("Argument is out of range for %s, got: %s" % (
func, func_call.arguments))
# Convert them to python types.
kwargs = {type_.name: type_.fn(a)
for type_, a in zip(func.args, func_call.arguments)}
# Call the right callback to get an SC2 action proto.
sc2_action = sc_pb.Action()
kwargs["action"] = sc2_action
if func.ability_id:
kwargs["ability_id"] = func.ability_id
if raw:
actions.RAW_FUNCTIONS[func_id].function_type(**kwargs)
else:
kwargs["action_space"] = aif.action_space
actions.FUNCTIONS[func_id].function_type(**kwargs)
return sc2_action
@sw.decorate
def reverse_raw_action(self, action, raw_tags):
action_ret = {'action_type': None, 'delay': torch.tensor(0, dtype=torch.long), 'queued': None, 'selected_units': None, 'target_unit': None, 'target_location': None}
last_selected_unit_tags = None
last_target_unit_tag = None
invalid_action_flag = False
units = []
tags = []
def transfer_action_type(ability_id, cmd_type):
cancel_slot = {313, 1039, 305, 307, 309, 1832, 1834, 3672}
unload_unit = {410, 415, 397, 1440, 2373, 1409, 914, 3670}
frivolous = {6, 7} # Dance and Cheer
if ability_id in frivolous:
return None
elif ability_id in unload_unit:
ability_id = 3664 # unload all
elif ability_id in cancel_slot:
ability_id = 3671 # cancel_slot to cancel_quick
general_id = next(iter(actions.RAW_ABILITY_IDS[ability_id])).general_id
if general_id:
ability_id = general_id
for func in actions.RAW_ABILITY_IDS[ability_id]:
if func.function_type is cmd_type:
action_type = FUNC_ID_TO_ACTION_TYPE_DICT[func.id]
return action_type
print('[ERROR] invalid action ability', ability_id)
return None
raw_act = action.action_raw
if raw_act.HasField("unit_command"):
uc = raw_act.unit_command
ability_id = uc.ability_id
queue_command = uc.queue_command
action_ret['queued'] = torch.tensor(queue_command, dtype=torch.long)
for t in uc.unit_tags:
try:
unit_index = raw_tags.index(t)
units.append(unit_index)
tags.append(t)
except ValueError:
pass
if uc.HasField("target_unit_tag"):
try:
action_ret['target_unit'] = torch.tensor(raw_tags.index(uc.target_unit_tag), dtype=torch.long)
last_target_unit_tag = uc.target_unit_tag
except ValueError:
invalid_action_flag = True
action_ret['action_type'] = transfer_action_type(ability_id, actions.raw_cmd_unit)
elif uc.HasField("target_world_space_pos"):
x = min(int(uc.target_world_space_pos.x), self.map_size.x - 1)
y = min(self.map_size.y - int(uc.target_world_space_pos.y), self.map_size.y - 1)
label = y * SPATIAL_SIZE[1] + x
action_ret['target_location'] = torch.tensor(label, dtype=torch.long)
action_ret['action_type'] = transfer_action_type(ability_id, actions.raw_cmd_pt)
else:
action_ret['action_type'] = transfer_action_type(ability_id, actions.raw_cmd)
if raw_act.HasField("toggle_autocast"):
uc = raw_act.toggle_autocast
ability_id = uc.ability_id
action_ret['action_type'] = transfer_action_type(ability_id, actions.raw_autocast)
for t in uc.unit_tags:
try:
unit_index = raw_tags.index(t)
units.append(unit_index)
tags.append(t)
except ValueError:
pass
if action_ret['action_type'] is not None:
action_ret['action_type'] = torch.tensor(action_ret['action_type'], dtype=torch.long)
else:
invalid_action_flag = True
if len(units) and not invalid_action_flag:
last_selected_unit_tags = tags
units.append(len(raw_tags)) # add end flag
action_ret['selected_units'] = torch.tensor(units, dtype=torch.long)
selected_units_num = torch.tensor(len(action_ret['selected_units']), dtype=torch.long)
else:
invalid_action_flag = True
selected_units_num = torch.tensor(0, dtype=torch.long)
action_mask = {}
for k, v in action_ret.items():
if v is None:
action_mask[k] = torch.tensor(0, dtype=torch.bool)
if k == 'selected_units':
action_ret[k] = torch.tensor([0], dtype=torch.long)
else:
action_ret[k] = ACTION_INFO[k]
else:
action_mask[k] = torch.tensor(1, dtype=torch.bool)
action_ret['selected_units'] = action_ret['selected_units'][:MAX_SELECTED_UNITS_NUM]
selected_units_num.clamp_(max=MAX_SELECTED_UNITS_NUM)
return action_ret, action_mask, selected_units_num, last_selected_unit_tags, last_target_unit_tag, invalid_action_flag