# Modified by Microsoft Corporation. # Licensed under the MIT license. import json import operator import os import pickle import subprocess import sys import time from collections import deque from contextlib import contextmanager from datetime import datetime from importlib import reload from pprint import pformat import numpy as np import pandas as pd import pydash as ps import regex as re import torch import torch.multiprocessing as mp import ujson import yaml from convlab import ROOT_DIR, EVAL_MODES NUM_CPUS = mp.cpu_count() FILE_TS_FORMAT = '%Y_%m_%d_%H%M%S' RE_FILE_TS = re.compile(r'(\d{4}_\d{2}_\d{2}_\d{6})') class LabJsonEncoder(json.JSONEncoder): def default(self, obj): if isinstance(obj, np.integer): return int(obj) elif isinstance(obj, np.floating): return float(obj) elif isinstance(obj, (np.ndarray, pd.Series)): return obj.tolist() else: return str(obj) def batch_get(arr, idxs): '''Get multi-idxs from an array depending if it's a python list or np.array''' if isinstance(arr, (list, deque)): return np.array(operator.itemgetter(*idxs)(arr)) else: return arr[idxs] def calc_srs_mean_std(sr_list): '''Given a list of series, calculate their mean and std''' cat_df = pd.DataFrame(dict(enumerate(sr_list))) mean_sr = cat_df.mean(axis=1) std_sr = cat_df.std(axis=1) return mean_sr, std_sr def calc_ts_diff(ts2, ts1): ''' Calculate the time from tss ts1 to ts2 @param {str} ts2 Later ts in the FILE_TS_FORMAT @param {str} ts1 Earlier ts in the FILE_TS_FORMAT @returns {str} delta_t in %H:%M:%S format @example ts1 = '2017_10_17_084739' ts2 = '2017_10_17_084740' ts_diff = util.calc_ts_diff(ts2, ts1) # => '0:00:01' ''' delta_t = datetime.strptime(ts2, FILE_TS_FORMAT) - datetime.strptime(ts1, FILE_TS_FORMAT) return str(delta_t) def cast_df(val): '''missing pydash method to cast value as DataFrame''' if isinstance(val, pd.DataFrame): return val return pd.DataFrame(val) def cast_list(val): '''missing pydash method to cast value as list''' if ps.is_list(val): return val else: return [val] def clear_periodic_ckpt(prepath): '''Clear periodic (with -epi) ckpt files in prepath''' if '-epi' in prepath: run_cmd(f'rm {prepath}*') def concat_batches(batches): ''' Concat batch objects from body.memory.sample() into one batch, when all bodies experience similar envs Also concat any nested epi sub-batches into flat batch {k: arr1} + {k: arr2} = {k: arr1 + arr2} ''' # if is nested, then is episodic is_episodic = isinstance(batches[0]['dones'][0], (list, np.ndarray)) concat_batch = {} for k in batches[0]: datas = [] for batch in batches: data = batch[k] if is_episodic: # make into plain batch instead of nested data = np.concatenate(data) datas.append(data) concat_batch[k] = np.concatenate(datas) return concat_batch def downcast_float32(df): '''Downcast any float64 col to float32 to allow safer pandas comparison''' for col in df.columns: if df[col].dtype == 'float': df[col] = df[col].astype('float32') return df def epi_done(done): ''' General method to check if episode is done for both single and vectorized env Only return True for singleton done since vectorized env does not have a natural episode boundary ''' return np.isscalar(done) and done def find_ckpt(prepath): '''Find the ckpt-lorem-ipsum in a string and return lorem-ipsum''' if 'ckpt' in prepath: ckpt_str = ps.find(prepath.split('_'), lambda s: s.startswith('ckpt')) ckpt = ckpt_str.replace('ckpt-', '') else: ckpt = None return ckpt def frame_mod(frame, frequency, num_envs): ''' Generic mod for (frame % frequency == 0) for when num_envs is 1 or more, since frame will increase multiple ticks for vector env, use the remainder''' remainder = num_envs or 1 return (frame % frequency < remainder) def flatten_dict(obj, delim='.'): '''Missing pydash method to flatten dict''' nobj = {} for key, val in obj.items(): if ps.is_dict(val) and not ps.is_empty(val): strip = flatten_dict(val, delim) for k, v in strip.items(): nobj[key + delim + k] = v elif ps.is_list(val) and not ps.is_empty(val) and ps.is_dict(val[0]): for idx, v in enumerate(val): nobj[key + delim + str(idx)] = v if ps.is_object(v): nobj = flatten_dict(nobj, delim) else: nobj[key] = val return nobj def get_class_name(obj, lower=False): '''Get the class name of an object''' class_name = obj.__class__.__name__ if lower: class_name = class_name.lower() return class_name def get_class_attr(obj): '''Get the class attr of an object as dict''' attr_dict = {} for k, v in obj.__dict__.items(): if hasattr(v, '__dict__') or ps.is_tuple(v): val = str(v) else: val = v attr_dict[k] = val return attr_dict def get_file_ext(data_path): '''get the `.ext` of file.ext''' return os.path.splitext(data_path)[-1] def get_fn_list(a_cls): ''' Get the callable, non-private functions of a class @returns {[*str]} A list of strings of fn names ''' fn_list = ps.filter_(dir(a_cls), lambda fn: not fn.endswith('__') and callable(getattr(a_cls, fn))) return fn_list def get_git_sha(): return subprocess.check_output(['git', 'rev-parse', 'HEAD'], close_fds=True, cwd=ROOT_DIR).decode().strip() def get_lab_mode(): return os.environ.get('lab_mode') def get_prepath(spec, unit='experiment'): spec_name = spec['name'] meta_spec = spec['meta'] predir = f'output/{spec_name}_{meta_spec["experiment_ts"]}' prename = f'{spec_name}' trial_index = meta_spec['trial'] session_index = meta_spec['session'] t_str = '' if trial_index is None else f'_t{trial_index}' s_str = '' if session_index is None else f'_s{session_index}' if unit == 'trial': prename += t_str elif unit == 'session': prename += f'{t_str}{s_str}' ckpt = meta_spec['ckpt'] if ckpt is not None: prename += f'_ckpt-{ckpt}' prepath = f'{predir}/{prename}' return prepath def get_ts(pattern=FILE_TS_FORMAT): ''' Get current ts, defaults to format used for filename @param {str} pattern To format the ts @returns {str} ts @example util.get_ts() # => '2017_10_17_084739' ''' ts_obj = datetime.now() ts = ts_obj.strftime(pattern) assert RE_FILE_TS.search(ts) return ts def insert_folder(prepath, folder): '''Insert a folder into prepath''' split_path = prepath.split('/') prename = split_path.pop() split_path += [folder, prename] return '/'.join(split_path) def in_eval_lab_modes(): '''Check if lab_mode is one of EVAL_MODES''' return get_lab_mode() in EVAL_MODES def is_jupyter(): '''Check if process is in Jupyter kernel''' try: get_ipython().config return True except NameError: return False return False @contextmanager def ctx_lab_mode(lab_mode): ''' Creates context to run method with a specific lab_mode @example with util.ctx_lab_mode('eval'): foo() @util.ctx_lab_mode('eval') def foo(): ... ''' prev_lab_mode = os.environ.get('lab_mode') os.environ['lab_mode'] = lab_mode yield if prev_lab_mode is None: del os.environ['lab_mode'] else: os.environ['lab_mode'] = prev_lab_mode def monkey_patch(base_cls, extend_cls): '''Monkey patch a base class with methods from extend_cls''' ext_fn_list = get_fn_list(extend_cls) for fn in ext_fn_list: setattr(base_cls, fn, getattr(extend_cls, fn)) def parallelize(fn, args, num_cpus=NUM_CPUS): ''' Parallelize a method fn, args and return results with order preserved per args. args should be a list of tuples. @returns {list} results Order preserved output from fn. ''' pool = mp.Pool(num_cpus, maxtasksperchild=1) results = pool.starmap(fn, args) pool.close() pool.join() return results def prepath_split(prepath): ''' Split prepath into useful names. Works with predir (prename will be None) prepath: output/dqn_pong_2018_12_02_082510/dqn_pong_t0_s0 predir: output/dqn_pong_2018_12_02_082510 prefolder: dqn_pong_2018_12_02_082510 prename: dqn_pong_t0_s0 spec_name: dqn_pong experiment_ts: 2018_12_02_082510 ckpt: ckpt-best of dqn_pong_t0_s0_ckpt-best if available ''' prepath = prepath.strip('_') tail = prepath.split('output/')[-1] ckpt = find_ckpt(tail) if ckpt is not None: # separate ckpt tail = tail.replace(f'_ckpt-{ckpt}', '') if '/' in tail: # tail = prefolder/prename prefolder, prename = tail.split('/', 1) else: prefolder, prename = tail, None predir = f'output/{prefolder}' spec_name = RE_FILE_TS.sub('', prefolder).strip('_') experiment_ts = RE_FILE_TS.findall(prefolder)[0] return predir, prefolder, prename, spec_name, experiment_ts, ckpt def prepath_to_idxs(prepath): '''Extract trial index and session index from prepath if available''' _, _, prename, spec_name, _, _ = prepath_split(prepath) idxs_tail = prename.replace(spec_name, '').strip('_') idxs_strs = ps.compact(idxs_tail.split('_')[:2]) if ps.is_empty(idxs_strs): return None, None tidx = idxs_strs[0] assert tidx.startswith('t') trial_index = int(tidx.strip('t')) if len(idxs_strs) == 1: # has session session_index = None else: sidx = idxs_strs[1] assert sidx.startswith('s') session_index = int(sidx.strip('s')) return trial_index, session_index def prepath_to_spec(prepath): ''' Given a prepath, read the correct spec recover the meta_spec that will return the same prepath for eval lab modes example: output/a2c_cartpole_2018_06_13_220436/a2c_cartpole_t0_s0 ''' predir, _, prename, _, experiment_ts, ckpt = prepath_split(prepath) sidx_res = re.search('_s\d+', prename) if sidx_res: # replace the _s0 if any prename = prename.replace(sidx_res[0], '') spec_path = f'{predir}/{prename}_spec.json' # read the spec of prepath spec = read(spec_path) # recover meta_spec trial_index, session_index = prepath_to_idxs(prepath) meta_spec = spec['meta'] meta_spec['experiment_ts'] = experiment_ts meta_spec['ckpt'] = ckpt meta_spec['experiment'] = 0 meta_spec['trial'] = trial_index meta_spec['session'] = session_index check_prepath = get_prepath(spec, unit='session') assert check_prepath in prepath, f'{check_prepath}, {prepath}' return spec def read(data_path, **kwargs): ''' Universal data reading method with smart data parsing - {.csv} to DataFrame - {.json} to dict, list - {.yml} to dict - {*} to str @param {str} data_path The data path to read from @returns {data} The read data in sensible format @example data_df = util.read('test/fixture/lib/util/test_df.csv') # => <DataFrame> data_dict = util.read('test/fixture/lib/util/test_dict.json') data_dict = util.read('test/fixture/lib/util/test_dict.yml') # => <dict> data_list = util.read('test/fixture/lib/util/test_list.json') # => <list> data_str = util.read('test/fixture/lib/util/test_str.txt') # => <str> ''' data_path = smart_path(data_path) try: assert os.path.isfile(data_path) except AssertionError: raise FileNotFoundError(data_path) ext = get_file_ext(data_path) if ext == '.csv': data = read_as_df(data_path, **kwargs) elif ext == '.pkl': data = read_as_pickle(data_path, **kwargs) else: data = read_as_plain(data_path, **kwargs) return data def read_as_df(data_path, **kwargs): '''Submethod to read data as DataFrame''' ext = get_file_ext(data_path) data = pd.read_csv(data_path, **kwargs) return data def read_as_pickle(data_path, **kwargs): '''Submethod to read data as pickle''' with open(data_path, 'rb') as f: data = pickle.load(f) return data def read_as_plain(data_path, **kwargs): '''Submethod to read data as plain type''' open_file = open(data_path, 'r') ext = get_file_ext(data_path) if ext == '.json': data = ujson.load(open_file, **kwargs) elif ext == '.yml': data = yaml.load(open_file, **kwargs) else: data = open_file.read() open_file.close() return data def run_cmd(cmd): '''Run shell command''' print(f'+ {cmd}') proc = subprocess.Popen(cmd, cwd=ROOT_DIR, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, close_fds=True) return proc def run_cmd_wait(proc): '''Wait on a running process created by util.run_cmd and print its stdout''' for line in proc.stdout: print(line.decode(), end='') output = proc.communicate()[0] if proc.returncode != 0: raise subprocess.CalledProcessError(proc.args, proc.returncode, output) else: return output def self_desc(cls): '''Method to get self description, used at init.''' desc_list = [f'{get_class_name(cls)}:'] for k, v in get_class_attr(cls).items(): if k == 'spec': desc_v = v['name'] elif ps.is_dict(v) or ps.is_dict(ps.head(v)): desc_v = pformat(v) else: desc_v = v desc_list.append(f'- {k} = {desc_v}') desc = '\n'.join(desc_list) return desc def set_attr(obj, attr_dict, keys=None): '''Set attribute of an object from a dict''' if keys is not None: attr_dict = ps.pick(attr_dict, keys) for attr, val in attr_dict.items(): setattr(obj, attr, val) return obj def set_cuda_id(spec): '''Use trial and session id to hash and modulo cuda device count for a cuda_id to maximize device usage. Sets the net_spec for the base Net class to pick up.''' # Don't trigger any cuda call if not using GPU. Otherwise will break multiprocessing on machines with CUDA. # see issues https://github.com/pytorch/pytorch/issues/334 https://github.com/pytorch/pytorch/issues/3491 https://github.com/pytorch/pytorch/issues/9996 for agent_spec in spec['agent']: if 'net' not in agent_spec or not agent_spec['net'].get('gpu'): return meta_spec = spec['meta'] trial_idx = meta_spec['trial'] or 0 session_idx = meta_spec['session'] or 0 if meta_spec['distributed'] == 'shared': # shared hogwild uses only global networks, offset them to idx 0 session_idx = 0 job_idx = trial_idx * meta_spec['max_session'] + session_idx job_idx += meta_spec['cuda_offset'] device_count = torch.cuda.device_count() cuda_id = None if not device_count else job_idx % device_count for agent_spec in spec['agent']: agent_spec['net']['cuda_id'] = cuda_id def set_logger(spec, logger, unit=None): '''Set the logger for a lab unit give its spec''' os.environ['LOG_PREPATH'] = insert_folder(get_prepath(spec, unit=unit), 'log') reload(logger) # to set session-specific logger def set_random_seed(spec): '''Generate and set random seed for relevant modules, and record it in spec.meta.random_seed''' torch.set_num_threads(1) # prevent multithread slowdown, set again for hogwild trial = spec['meta']['trial'] session = spec['meta']['session'] random_seed = int(1e5 * (trial or 0) + 1e3 * (session or 0) + time.time()) torch.cuda.manual_seed_all(random_seed) torch.manual_seed(random_seed) np.random.seed(random_seed) spec['meta']['random_seed'] = random_seed return random_seed def _sizeof(obj, seen=None): '''Recursively finds size of objects''' size = sys.getsizeof(obj) if seen is None: seen = set() obj_id = id(obj) if obj_id in seen: return 0 # Important mark as seen *before* entering recursion to gracefully handle # self-referential objects seen.add(obj_id) if isinstance(obj, dict): size += sum([_sizeof(v, seen) for v in obj.values()]) size += sum([_sizeof(k, seen) for k in obj.keys()]) elif hasattr(obj, '__dict__'): size += _sizeof(obj.__dict__, seen) elif hasattr(obj, '__iter__') and not isinstance(obj, (str, bytes, bytearray)): size += sum([_sizeof(i, seen) for i in obj]) return size def sizeof(obj, divisor=1e6): '''Return the size of object, in MB by default''' return _sizeof(obj) / divisor def smart_path(data_path, as_dir=False): ''' Resolve data_path into abspath with fallback to join from ROOT_DIR @param {str} data_path The input data path to resolve @param {bool} as_dir Whether to return as dirname @returns {str} The normalized absolute data_path @example util.smart_path('convlab/lib') # => '/Users/ANON/Documents/convlab/convlab/lib' util.smart_path('/tmp') # => '/tmp' ''' if not os.path.isabs(data_path): abs_path = os.path.abspath(data_path) if os.path.exists(abs_path): data_path = abs_path else: data_path = os.path.join(ROOT_DIR, data_path) if as_dir: data_path = os.path.dirname(data_path) return os.path.normpath(data_path) def split_minibatch(batch, mb_size): '''Split a batch into minibatches of mb_size or smaller, without replacement''' size = len(batch['rewards']) assert mb_size < size, f'Minibatch size {mb_size} must be < batch size {size}' idxs = np.arange(size) np.random.shuffle(idxs) chunks = int(size / mb_size) nested_idxs = np.array_split(idxs, chunks) mini_batches = [] for minibatch_idxs in nested_idxs: minibatch = {k: v[minibatch_idxs] for k, v in batch.items()} mini_batches.append(minibatch) return mini_batches def to_json(d, indent=2): '''Shorthand method for stringify JSON with indent''' return json.dumps(d, indent=indent, cls=LabJsonEncoder) def to_render(): return get_lab_mode() in ('dev', 'enjoy') and os.environ.get('RENDER', 'true') == 'true' def to_torch_batch(batch, device, is_episodic): '''Mutate a batch (dict) to make its values from numpy into PyTorch tensor''' for k in batch: if is_episodic: # for episodic format batch[k] = np.concatenate(batch[k]) elif ps.is_list(batch[k]): batch[k] = np.array(batch[k]) batch[k] = torch.from_numpy(batch[k].astype(np.float32)).to(device) return batch def write(data, data_path): ''' Universal data writing method with smart data parsing - {.csv} from DataFrame - {.json} from dict, list - {.yml} from dict - {*} from str(*) @param {*} data The data to write @param {str} data_path The data path to write to @returns {data_path} The data path written to @example data_path = util.write(data_df, 'test/fixture/lib/util/test_df.csv') data_path = util.write(data_dict, 'test/fixture/lib/util/test_dict.json') data_path = util.write(data_dict, 'test/fixture/lib/util/test_dict.yml') data_path = util.write(data_list, 'test/fixture/lib/util/test_list.json') data_path = util.write(data_str, 'test/fixture/lib/util/test_str.txt') ''' data_path = smart_path(data_path) data_dir = os.path.dirname(data_path) os.makedirs(data_dir, exist_ok=True) ext = get_file_ext(data_path) if ext == '.csv': write_as_df(data, data_path) elif ext == '.pkl': write_as_pickle(data, data_path) else: write_as_plain(data, data_path) return data_path def write_as_df(data, data_path): '''Submethod to write data as DataFrame''' df = cast_df(data) ext = get_file_ext(data_path) df.to_csv(data_path, index=False) return data_path def write_as_pickle(data, data_path): '''Submethod to write data as pickle''' with open(data_path, 'wb') as f: pickle.dump(data, f) return data_path def write_as_plain(data, data_path): '''Submethod to write data as plain type''' open_file = open(data_path, 'w') ext = get_file_ext(data_path) if ext == '.json': json.dump(data, open_file, indent=2, cls=LabJsonEncoder) elif ext == '.yml': yaml.dump(data, open_file) else: open_file.write(str(data)) open_file.close() return data_path