Python time.perf_counter方法代码示例

# 需要导入模块: import time [as 别名]
# 或者: from time import perf_counter [as 别名]
def _setup_query_latency_tracking(self):
        engine = self._engine.sync_engine

        @event.listens_for(engine, "before_cursor_execute")
        def before_cursor_execute(
            conn, cursor, statement, parameters, context, executemany
        ):
            conn.info["query_start_time"] = perf_counter()

        @event.listens_for(engine, "after_cursor_execute")
        def after_cursor_execute(
            conn, cursor, statement, parameters, context, executemany
        ):
            conn.info["query_latency"] = perf_counter() - conn.info.pop(
                "query_start_time"
            ) 

# 需要导入模块: import time [as 别名]
# 或者: from time import perf_counter [as 别名]
def setUp(self):
        self.num_bboxes1 = 200
        self.num_bboxes2 = 20000
        self.image_width = 1024
        self.image_height = 1024

        self.bboxes1 = np.zeros([self.num_bboxes1, 4])
        self.bboxes1[:, [0, 2]] = np.sort(np.random.rand(self.num_bboxes1, 2) * (self.image_width - 1))
        self.bboxes1[:, [1, 3]] = np.sort(np.random.rand(self.num_bboxes1, 2) * (self.image_height - 1))

        self.bboxes2 = np.zeros([self.num_bboxes2, 4])
        self.bboxes2[:, [0, 2]] = np.sort(np.random.rand(self.num_bboxes2, 2) * (self.image_width - 1))
        self.bboxes2[:, [1, 3]] = np.sort(np.random.rand(self.num_bboxes2, 2) * (self.image_height - 1))

        self.bboxes1_tensor = torch.from_numpy(self.bboxes1)
        self.bboxes2_tensor = torch.from_numpy(self.bboxes2)

        start = time.perf_counter()
        self.ious = bbox_overlaps(self.bboxes1_tensor, self.bboxes2_tensor).numpy()
        elapsed = (time.perf_counter() - start)
        print('bbox_overlaps time: ', elapsed) 

# 需要导入模块: import time [as 别名]
# 或者: from time import perf_counter [as 别名]
def bench(f_):
    timings_fwd = []
    timings_bck = []
    for _ in range(100):

        with f_ as f:
            tic = time.perf_counter()
            f.forward()
            torch.cuda.synchronize()
            toc = time.perf_counter()
            timings_fwd.append(toc - tic)

            tic = time.perf_counter()
            f.backward()
            torch.cuda.synchronize()
            toc = time.perf_counter()
            timings_bck.append(toc - tic)

    return (np.percentile(timings_fwd, [25, 50, 75]),
            np.percentile(timings_bck, [25, 50, 75])) 

# 需要导入模块: import time [as 别名]
# 或者: from time import perf_counter [as 别名]
def __init__(self, *args, **kwargs):
        ws = kwargs.pop('ws', None)
        interval = kwargs.pop('interval', None)
        shard_id = kwargs.pop('shard_id', None)
        threading.Thread.__init__(self, *args, **kwargs)
        self.ws = ws
        self._main_thread_id = ws.thread_id
        self.interval = interval
        self.daemon = True
        self.shard_id = shard_id
        self.msg = 'Keeping websocket alive with sequence %s.'
        self.block_msg = 'Heartbeat blocked for more than %s seconds.'
        self.behind_msg = 'Can\'t keep up, websocket is %.1fs behind.'
        self._stop_ev = threading.Event()
        self._last_ack = time.perf_counter()
        self._last_send = time.perf_counter()
        self.latency = float('inf')
        self.heartbeat_timeout = ws._max_heartbeat_timeout 

# 需要导入模块: import time [as 别名]
# 或者: from time import perf_counter [as 别名]
def youtube_daemon_worker(id, youtube_queue, social_context_queue, youtube_module_communication, youtube_oauth_credentials_folder):
    while True:
        if social_context_queue.qsize() > 50:
            time.sleep(10.0)
        else:
            url_counter, url, upper_timestamp = youtube_queue.get()

            try:
                # start_time = time.perf_counter()
                social_context = youtube_social_context.collect(url, youtube_module_communication + "_" + str(id), youtube_oauth_credentials_folder)
                # elapsed_time = time.perf_counter() - start_time
                # if social_context is not None:
                #     social_context["elapsed_time"] = elapsed_time
            except KeyError:
                social_context = None

            social_context_queue.put((url_counter, social_context))
            youtube_queue.task_done() 

# 需要导入模块: import time [as 别名]
# 或者: from time import perf_counter [as 别名]
def reddit_daemon_worker(id, reddit_queue, social_context_queue, reddit_oauth_credentials_path):
    while True:
        if social_context_queue.qsize() > 50:
            time.sleep(10.0)
        else:
            url_counter, url, upper_timestamp = reddit_queue.get()

            try:
                # start_time = time.perf_counter()
                social_context = reddit_social_context.collect(url, reddit_oauth_credentials_path)
                # elapsed_time = time.perf_counter() - start_time
                # if social_context is not None:
                #     social_context["elapsed_time"] = elapsed_time
            except KeyError:
                social_context = None

            social_context_queue.put((url_counter, social_context))
            reddit_queue.task_done() 

# 需要导入模块: import time [as 别名]
# 或者: from time import perf_counter [as 别名]
def form_graphs(social_context_generator, assessment_timestamp):
    # fp = open("/home/georgerizos/Documents/fetch_times/build_graph_time" + ".txt", "a")
    for social_context_dict in social_context_generator:
        # start_time = time.perf_counter()
        snapshots,\
        targets,\
        title = get_snapshot_graphs(social_context_dict["social_context"],
                                    # social_context_dict["tweet_timestamp"],
                                    assessment_timestamp,
                                    social_context_dict["platform_name"])
        # elapsed_time = time.perf_counter() - start_time
        # fp.write(repr(elapsed_time) + "\n")
        if snapshots is None:
            continue

        if len(snapshots) > 1:
            graph_dict = social_context_dict
            graph_dict["snapshots"] = snapshots
            graph_dict["targets"] = targets
            graph_dict["title"] = title
            yield graph_dict 

# 需要导入模块: import time [as 别名]
# 或者: from time import perf_counter [as 别名]
def train_runtime(model, data, epochs, device):
    optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
    model = model.to(device)
    data = data.to(device)
    model.train()
    mask = data.train_mask if 'train_mask' in data else data.train_idx
    y = data.y[mask] if 'train_mask' in data else data.train_y

    if torch.cuda.is_available():
        torch.cuda.synchronize()
    t_start = time.perf_counter()

    for epoch in range(epochs):
        optimizer.zero_grad()
        out = model(data)
        loss = F.nll_loss(out[mask], y)
        loss.backward()
        optimizer.step()

    if torch.cuda.is_available():
        torch.cuda.synchronize()
    t_end = time.perf_counter()

    return t_end - t_start 

# 需要导入模块: import time [as 别名]
# 或者: from time import perf_counter [as 别名]
def _onepop_expgrowth(
        engine_id, out_dir, seed, N0=5000, N1=500, T=1000, **sim_kwargs):
    growth_rate = - np.log(N1 / N0) / T
    species = stdpopsim.get_species("DroMel")
    contig = species.get_contig("chr2R", length_multiplier=0.01)  # ~250 kb
    contig = irradiate(contig)
    model = _PiecewiseSize(N0, growth_rate, (T, N1, 0))
    model.generation_time = species.generation_time
    samples = model.get_samples(100)
    engine = stdpopsim.get_engine(engine_id)
    t0 = time.perf_counter()
    ts = engine.simulate(model, contig, samples, seed=seed, **sim_kwargs)
    t1 = time.perf_counter()
    out_file = out_dir / f"{seed}.trees"
    ts.dump(out_file)
    return out_file, t1 - t0 

# 需要导入模块: import time [as 别名]
# 或者: from time import perf_counter [as 别名]
def _twopop_IM(
        engine_id, out_dir, seed,
        NA=1000, N1=500, N2=5000, T=1000, M12=0, M21=0, pulse=None, samples=None,
        **sim_kwargs):
    species = stdpopsim.get_species("AraTha")
    contig = species.get_contig("chr5", length_multiplier=0.01)  # ~270 kb
    contig = irradiate(contig)
    model = stdpopsim.IsolationWithMigration(
            NA=NA, N1=N1, N2=N2, T=T, M12=M12, M21=M21)
    if pulse is not None:
        model.demographic_events.append(pulse)
        model.demographic_events.sort(key=lambda x: x.time)
    # XXX: AraTha has species.generation_time == 1, but there is the potential
    # for this to mask bugs related to generation_time scaling, so we use 3 here.
    model.generation_time = 3
    if samples is None:
        samples = model.get_samples(50, 50, 0)
    engine = stdpopsim.get_engine(engine_id)
    t0 = time.perf_counter()
    ts = engine.simulate(model, contig, samples, seed=seed, **sim_kwargs)
    t1 = time.perf_counter()
    out_file = out_dir / f"{seed}.trees"
    ts.dump(out_file)
    return out_file, t1 - t0 

# 需要导入模块: import time [as 别名]
# 或者: from time import perf_counter [as 别名]
def _run_synchronous(self, progress):
        a = np.zeros(self.num_sites, dtype=np.int8)
        for t, focal_sites in self.descriptors:
            before = time.perf_counter()
            s, e = self.ancestor_builder.make_ancestor(focal_sites, a)
            duration = time.perf_counter() - before
            logger.debug(
                "Made ancestor in {:.2f}s at timepoint {} (epoch {}) "
                "from {} to {} (len={}) with {} focal sites ({})".format(
                    duration,
                    t,
                    self.timepoint_to_epoch[t],
                    s,
                    e,
                    e - s,
                    focal_sites.shape[0],
                    focal_sites,
                )
            )
            self.ancestor_data.add_ancestor(
                start=s, end=e, time=t, focal_sites=focal_sites, haplotype=a[s:e]
            )
            progress.update() 

# 需要导入模块: import time [as 别名]
# 或者: from time import perf_counter [as 别名]
def metrics(self):
        if self._run_t is None:
            activity_ratio = 0
        else:
            total_t = perf_counter() - self._run_t
            activity_ratio = self.t_active / (total_t)
        own_computations = { c.name for c in self.computations(include_technical=True)}
        m = {
            'count_ext_msg': {k: v
                              for k, v in self._messaging.count_ext_msg.items()
                              if k in own_computations},
            'size_ext_msg': {k: v
                             for k, v in self._messaging.size_ext_msg.items()
                             if k in own_computations},
            # 'last_msg_time': self._messaging.last_msg_time,
            'activity_ratio': activity_ratio,
            'cycles': {c.name: c.cycle_count for c in self.computations()}
        }
        return m 

# 需要导入模块: import time [as 别名]
# 或者: from time import perf_counter [as 别名]
def main(arguments):
    global verbose
    (ids, names, result) = check_args(arguments)
    if arguments.v:
        verbose = True
    if not result:
        return 0
    acc_time = 0
    len_name = len(names)
    table = []
    for i in range(0, len_name + 1):
        table.append([])
    table[0].append("")
    for i in range(0, len_name):
        table[0].append(names[i])
    for i in range(0, len_name):
        table[i+1].append(names[i])
        for j in range(0, len_name):
            if i != j:
                start = time.perf_counter()
                table[i+1].append(compute_files(names[i], names[j], ids, args.fd, args.r))
                end = time.perf_counter()
                if table[i+1][-1] != -1.0:
                    acc_time += end-start
            else:
                table[i+1].append("")
    # check table
    for i in range(0, len_name + 1):
        for j in range(0, len_name + 1):
            if i != j:
                if table[i][j] != table[j][i]:
                    if table[i][j] > table[j][i]:
                        table[j][i] = table[i][j]
                    else:
                        table[i][j] = table[j][i]
    pprint_table(table)
    return acc_time 

# 需要导入模块: import time [as 别名]
# 或者: from time import perf_counter [as 别名]
def pingt(self,ctx):
        """pseudo-ping time"""
        channel = ctx.message.channel
        t1 = time.perf_counter()
        await self.bot.send_typing(channel)
        t2 = time.perf_counter()
        await self.bot.say("pseudo-ping: {}ms".format(round((t2-t1)*1000))) 

# 需要导入模块: import time [as 别名]
# 或者: from time import perf_counter [as 别名]
def perf_counter() :
        return ticks_ms() / 1000

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