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import numpy as np |
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import io |
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import os |
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import json |
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import logging |
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import random |
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import time |
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from collections import defaultdict, deque |
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import datetime |
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from pathlib import Path |
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from typing import List, Union |
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import torch |
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import torch.distributed as dist |
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from .distributed import is_dist_avail_and_initialized |
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logger = logging.getLogger(__name__) |
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class SmoothedValue(object): |
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"""Track a series of values and provide access to smoothed values over a |
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window or the global series average. |
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""" |
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def __init__(self, window=20, fmt=None): |
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if fmt is None: |
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fmt = "{median:.4f} ({global_avg:.4f})" |
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self.deque = deque(maxlen=window) |
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self.total = 0.0 |
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self.count = 0 |
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self.fmt = fmt |
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def update(self, value, n=1): |
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self.deque.append(value) |
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self.count += n |
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self.total += value * n |
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def synchronize_between_processes(self): |
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""" |
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Warning: does not synchronize the deque! |
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""" |
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if not is_dist_avail_and_initialized(): |
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return |
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t = torch.tensor([self.count, self.total], |
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dtype=torch.float64, device='cuda') |
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dist.barrier() |
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dist.all_reduce(t) |
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t = t.tolist() |
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self.count = int(t[0]) |
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self.total = t[1] |
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@property |
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def median(self): |
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d = torch.tensor(list(self.deque)) |
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return d.median().item() |
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@property |
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def avg(self): |
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d = torch.tensor(list(self.deque), dtype=torch.float32) |
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return d.mean().item() |
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@property |
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def global_avg(self): |
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return self.total / self.count |
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@property |
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def max(self): |
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return max(self.deque) |
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@property |
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def value(self): |
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return self.deque[-1] |
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def __str__(self): |
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return self.fmt.format( |
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median=self.median, |
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avg=self.avg, |
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global_avg=self.global_avg, |
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max=self.max, |
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value=self.value) |
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class MetricLogger(object): |
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def __init__(self, delimiter="\t"): |
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self.meters = defaultdict(SmoothedValue) |
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self.delimiter = delimiter |
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def update(self, **kwargs): |
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for k, v in kwargs.items(): |
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if isinstance(v, torch.Tensor): |
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v = v.item() |
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assert isinstance(v, (float, int)) |
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self.meters[k].update(v) |
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def __getattr__(self, attr): |
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if attr in self.meters: |
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return self.meters[attr] |
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if attr in self.__dict__: |
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return self.__dict__[attr] |
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raise AttributeError("'{}' object has no attribute '{}'".format( |
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type(self).__name__, attr)) |
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def __str__(self): |
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loss_str = [] |
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for name, meter in self.meters.items(): |
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if meter.count == 0: |
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loss_str.append( |
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"{}: {}".format(name, "No data") |
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) |
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else: |
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loss_str.append( |
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"{}: {}".format(name, str(meter)) |
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) |
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return self.delimiter.join(loss_str) |
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def global_avg(self): |
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loss_str = [] |
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for name, meter in self.meters.items(): |
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if meter.count == 0: |
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loss_str.append( |
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"{}: {}".format(name, "No data") |
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) |
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else: |
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loss_str.append( |
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"{}: {:.4f}".format(name, meter.global_avg) |
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) |
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return self.delimiter.join(loss_str) |
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def get_global_avg_dict(self, prefix=""): |
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"""include a separator (e.g., `/`, or "_") at the end of `prefix`""" |
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d = {f"{prefix}{k}": m.global_avg if m.count > 0 else 0. for k, m in self.meters.items()} |
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return d |
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def synchronize_between_processes(self): |
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for meter in self.meters.values(): |
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meter.synchronize_between_processes() |
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def add_meter(self, name, meter): |
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self.meters[name] = meter |
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def log_every(self, iterable, log_freq, header=None): |
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i = 0 |
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if not header: |
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header = '' |
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start_time = time.time() |
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end = time.time() |
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iter_time = SmoothedValue(fmt='{avg:.4f}') |
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data_time = SmoothedValue(fmt='{avg:.4f}') |
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space_fmt = ':' + str(len(str(len(iterable)))) + 'd' |
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log_msg = [ |
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header, |
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'[{0' + space_fmt + '}/{1}]', |
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'eta: {eta}', |
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'{meters}', |
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'time: {time}', |
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'data: {data}' |
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] |
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if torch.cuda.is_available(): |
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log_msg.append('max mem: {memory:.0f} res mem: {res_mem:.0f}') |
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log_msg = self.delimiter.join(log_msg) |
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MB = 1024.0 * 1024.0 |
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for obj in iterable: |
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data_time.update(time.time() - end) |
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yield obj |
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iter_time.update(time.time() - end) |
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if i % log_freq == 0 or i == len(iterable) - 1: |
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eta_seconds = iter_time.global_avg * (len(iterable) - i) |
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eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) |
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if torch.cuda.is_available(): |
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logger.info(log_msg.format( |
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i, len(iterable), eta=eta_string, |
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meters=str(self), |
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time=str(iter_time), data=str(data_time), |
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memory=torch.cuda.max_memory_allocated() / MB, |
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res_mem=torch.cuda.max_memory_reserved() / MB, |
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)) |
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else: |
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logger.info(log_msg.format( |
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i, len(iterable), eta=eta_string, |
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meters=str(self), |
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time=str(iter_time), data=str(data_time))) |
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i += 1 |
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end = time.time() |
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total_time = time.time() - start_time |
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total_time_str = str(datetime.timedelta(seconds=int(total_time))) |
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logger.info('{} Total time: {} ({:.4f} s / it)'.format( |
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header, total_time_str, total_time / len(iterable))) |
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class AttrDict(dict): |
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def __init__(self, *args, **kwargs): |
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super(AttrDict, self).__init__(*args, **kwargs) |
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self.__dict__ = self |
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def compute_acc(logits, label, reduction='mean'): |
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ret = (torch.argmax(logits, dim=1) == label).float() |
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if reduction == 'none': |
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return ret.detach() |
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elif reduction == 'mean': |
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return ret.mean().item() |
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def compute_n_params(model, return_str=True): |
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tot = 0 |
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for p in model.parameters(): |
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w = 1 |
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for x in p.shape: |
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w *= x |
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tot += w |
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if return_str: |
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if tot >= 1e6: |
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return '{:.1f}M'.format(tot / 1e6) |
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else: |
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return '{:.1f}K'.format(tot / 1e3) |
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else: |
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return tot |
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def setup_seed(seed): |
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torch.manual_seed(seed) |
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np.random.seed(seed) |
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random.seed(seed) |
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def remove_files_if_exist(file_paths): |
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for fp in file_paths: |
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if os.path.isfile(fp): |
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os.remove(fp) |
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def save_json(data, filename, save_pretty=False, sort_keys=False): |
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with open(filename, "w") as f: |
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if save_pretty: |
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f.write(json.dumps(data, indent=4, sort_keys=sort_keys)) |
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else: |
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json.dump(data, f) |
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def load_json(filename): |
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with open(filename, "r") as f: |
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return json.load(f) |
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def flat_list_of_lists(l): |
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"""flatten a list of lists [[1,2], [3,4]] to [1,2,3,4]""" |
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return [item for sublist in l for item in sublist] |
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def find_files_by_suffix_recursively(root: str, suffix: Union[str, List[str]]): |
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""" |
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Args: |
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root: path to the directory to start search files |
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suffix: any str as suffix, or can match multiple such strings |
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when input is List[str]. |
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Example 1, e.g., suffix: `.jpg` or [`.jpg`, `.png`] |
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Example 2, e.g., use a `*` in the `suffix`: `START*.jpg.`. |
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""" |
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if isinstance(suffix, str): |
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suffix = [suffix, ] |
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filepaths = flat_list_of_lists( |
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[list(Path(root).rglob(f"*{e}")) for e in suffix]) |
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return filepaths |
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def match_key_and_shape(state_dict1, state_dict2): |
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keys1 = set(state_dict1.keys()) |
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keys2 = set(state_dict2.keys()) |
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print(f"keys1 - keys2: {keys1 - keys2}") |
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print(f"keys2 - keys1: {keys2 - keys1}") |
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mismatch = 0 |
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for k in list(keys1): |
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if state_dict1[k].shape != state_dict2[k].shape: |
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print( |
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f"k={k}, state_dict1[k].shape={state_dict1[k].shape}, state_dict2[k].shape={state_dict2[k].shape}") |
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mismatch += 1 |
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print(f"mismatch {mismatch}") |
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def merge_dicts(list_dicts): |
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merged_dict = list_dicts[0].copy() |
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for i in range(1, len(list_dicts)): |
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merged_dict.update(list_dicts[i]) |
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return merged_dict |
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