speech_recognition/FunASR
1
0
Fork 0
mirror of https://github.com/modelscope/FunASR synced 2025-09-15 14:48:36 +08:00
Code Issues Actions Packages Projects Releases Wiki Activity

Funasr1.0 (#1261)

Browse Source 
* funasr1.0 funetine

* funasr1.0 pbar

* update with main (#1260)

* Update websocket_protocol_zh.md

* update

---------

Co-authored-by: Yabin Li <wucong.lyb@alibaba-inc.com>
Co-authored-by: shixian.shi <shixian.shi@alibaba-inc.com>

---------

Co-authored-by: Yabin Li <wucong.lyb@alibaba-inc.com>
Co-authored-by: shixian.shi <shixian.shi@alibaba-inc.com>
This commit is contained in:
zhifu gao 2024-01-17 18:28:28 +08:00 committed by GitHub
parent b1857837dd
commit 9a9c3b75b5
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
10 changed files with 298 additions and 147 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
examples/industrial_data_pretraining/paraformer/finetune.sh
View File

@ -9,9 +9,11 @@
python funasr/bin/train.py \
+model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" \
+model_revision="v2.0.2" \
+train_data_set_list="/Users/zhifu/funasr_github/test_local/aishell2_dev_ios/asr_task_debug_len.jsonl" \
+train_data_set_list="/Users/zhifu/funasr_github/test_local/aishell2_dev_ios/asr_task_debug_len_10.jsonl" \
+valid_data_set_list="/Users/zhifu/funasr_github/test_local/aishell2_dev_ios/asr_task_debug_len_10.jsonl" \
++dataset_conf.batch_size=2 \
++dataset_conf.batch_type="example" \
++train_conf.max_epoch=2 \
+output_dir="outputs/debug/ckpt/funasr2/exp2" \
+device="cpu" \
+debug="true"

2
examples/industrial_data_pretraining/seaco_paraformer/demo.py
View File

@ -15,6 +15,6 @@ model = AutoModel(model="damo/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-co
spk_model_revision="v2.0.2",
)
res = model.generate(input=f"{model.model_path}/example/asr_example.wav",
res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
hotword='达摩院 魔搭')
print(res)

34
funasr/auto/auto_model.py
View File

@ -221,7 +221,8 @@ class AutoModel:
speed_stats = {}
asr_result_list = []
num_samples = len(data_list)
pbar = tqdm(colour="blue", total=num_samples+1, dynamic_ncols=True)
disable_pbar = kwargs.get("disable_pbar", False)
pbar = tqdm(colour="blue", total=num_samples+1, dynamic_ncols=True) if not disable_pbar else None
time_speech_total = 0.0
time_escape_total = 0.0
for beg_idx in range(0, num_samples, batch_size):
@ -239,8 +240,7 @@ class AutoModel:
time2 = time.perf_counter()
asr_result_list.extend(results)
pbar.update(1)
# batch_data_time = time_per_frame_s * data_batch_i["speech_lengths"].sum().item()
batch_data_time = meta_data.get("batch_data_time", -1)
time_escape = time2 - time1
@ -252,12 +252,15 @@ class AutoModel:
description = (
f"{speed_stats}, "
)
pbar.set_description(description)
if pbar:
pbar.update(1)
pbar.set_description(description)
time_speech_total += batch_data_time
time_escape_total += time_escape
pbar.update(1)
pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}")
if pbar:
pbar.update(1)
pbar.set_description(f"rtf_avg: {time_escape_total/time_speech_total:0.3f}")
torch.cuda.empty_cache()
return asr_result_list
@ -309,8 +312,11 @@ class AutoModel:
time_speech_total_per_sample = speech_lengths/16000
time_speech_total_all_samples += time_speech_total_per_sample
pbar_sample = tqdm(colour="blue", total=n + 1, dynamic_ncols=True)
all_segments = []
for j, _ in enumerate(range(0, n)):
pbar_sample.update(1)
batch_size_ms_cum += (sorted_data[j][0][1] - sorted_data[j][0][0])
if j < n - 1 and (
batch_size_ms_cum + sorted_data[j + 1][0][1] - sorted_data[j + 1][0][0]) < batch_size and (
@ -319,13 +325,14 @@ class AutoModel:
batch_size_ms_cum = 0
end_idx = j + 1
speech_j, speech_lengths_j = slice_padding_audio_samples(speech, speech_lengths, sorted_data[beg_idx:end_idx])
results = self.inference(speech_j, input_len=None, model=model, kwargs=kwargs, **cfg)
results = self.inference(speech_j, input_len=None, model=model, kwargs=kwargs, disable_pbar=True, **cfg)
if self.spk_model is not None:
# compose vad segments: [[start_time_sec, end_time_sec, speech], [...]]
for _b in range(len(speech_j)):
vad_segments = [[sorted_data[beg_idx:end_idx][_b][0][0]/1000.0, \
sorted_data[beg_idx:end_idx][_b][0][1]/1000.0, \
vad_segments = [[sorted_data[beg_idx:end_idx][_b][0][0]/1000.0,
sorted_data[beg_idx:end_idx][_b][0][1]/1000.0,
speech_j[_b]]]
segments = sv_chunk(vad_segments)
all_segments.extend(segments)
@ -338,12 +345,13 @@ class AutoModel:
results_sorted.extend(results)
pbar_total.update(1)
end_asr_total = time.time()
time_escape_total_per_sample = end_asr_total - beg_asr_total
pbar_total.set_description(f"rtf_avg_per_sample: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
pbar_sample.set_description(f"rtf_avg_per_sample: {time_escape_total_per_sample / time_speech_total_per_sample:0.3f}, "
f"time_speech_total_per_sample: {time_speech_total_per_sample: 0.3f}, "
f"time_escape_total_per_sample: {time_escape_total_per_sample:0.3f}")
restored_data = [0] * n
for j in range(n):

17
funasr/bin/train.py
View File

@ -141,30 +141,37 @@ def main(**kwargs):
scheduler_class = scheduler_classes.get(scheduler)
scheduler = scheduler_class(optim, **kwargs.get("scheduler_conf"))
# import pdb;
# pdb.set_trace()
# dataset
dataset_class = tables.dataset_classes.get(kwargs.get("dataset", "AudioDataset"))
dataset_tr = dataset_class(kwargs.get("train_data_set_list"), frontend=frontend, tokenizer=tokenizer, **kwargs.get("dataset_conf"))
dataset_val = dataset_class(kwargs.get("valid_data_set_list"), frontend=frontend, tokenizer=tokenizer,
**kwargs.get("dataset_conf"))
# dataloader
batch_sampler = kwargs["dataset_conf"].get("batch_sampler", "DynamicBatchLocalShuffleSampler")
batch_sampler_class = tables.batch_sampler_classes.get(batch_sampler)
batch_sampler_val = None
if batch_sampler is not None:
batch_sampler_class = tables.batch_sampler_classes.get(batch_sampler)
batch_sampler = batch_sampler_class(dataset_tr, **kwargs.get("dataset_conf"))
batch_sampler_val = batch_sampler_class(dataset_tr, is_training=False, **kwargs.get("dataset_conf"))
dataloader_tr = torch.utils.data.DataLoader(dataset_tr,
collate_fn=dataset_tr.collator,
batch_sampler=batch_sampler,
num_workers=kwargs.get("dataset_conf").get("num_workers", 4),
pin_memory=True)
dataloader_val = torch.utils.data.DataLoader(dataset_val,
collate_fn=dataset_val.collator,
batch_sampler=batch_sampler_val,
num_workers=kwargs.get("dataset_conf").get("num_workers", 4),
pin_memory=True)
trainer = Trainer(
model=model,
optim=optim,
scheduler=scheduler,
dataloader_train=dataloader_tr,
dataloader_val=None,
dataloader_val=dataloader_val,
local_rank=local_rank,
use_ddp=use_ddp,
use_fsdp=use_fsdp,

6
funasr/datasets/audio_datasets/index_ds.py
View File

@ -54,7 +54,11 @@ class IndexDSJsonl(torch.utils.data.Dataset):
return len(self.contents)
def __getitem__(self, index):
return self.contents[index]
try:
data = self.contents[index]
except:
print(index)
return data
def get_source_len(self, data_dict):
return data_dict["source_len"]

3
funasr/datasets/audio_datasets/samplers.py
View File

@ -13,6 +13,7 @@ class BatchSampler(torch.utils.data.BatchSampler):
buffer_size: int = 30,
drop_last: bool = False,
shuffle: bool = True,
is_training: bool = True,
**kwargs):
self.drop_last = drop_last
@ -24,7 +25,7 @@ class BatchSampler(torch.utils.data.BatchSampler):
self.buffer_size = buffer_size
self.max_token_length = kwargs.get("max_token_length", 5000)
self.shuffle_idx = np.arange(self.total_samples)
self.shuffle = shuffle
self.shuffle = shuffle and is_training
def __len__(self):
return self.total_samples

1
funasr/models/paraformer/model.py
View File

@ -164,6 +164,7 @@ class Paraformer(torch.nn.Module):
self.use_1st_decoder_loss = use_1st_decoder_loss
self.length_normalized_loss = length_normalized_loss
self.beam_search = None
self.error_calculator = None
def forward(
self,

1
funasr/models/paraformer/template.yaml
View File

@ -95,6 +95,7 @@ train_conf:
- acc
- max
keep_nbest_models: 10
avg_nbest_model: 5
log_interval: 50
optim: adam

268
funasr/train_utils/average_nbest_models.py
View File

@ -9,117 +9,173 @@ from io import BytesIO
import torch
from typing import Collection
import os
import torch
import re
from collections import OrderedDict
from functools import cmp_to_key
from funasr.train.reporter import Reporter
# @torch.no_grad()
# def average_nbest_models(
# output_dir: Path,
# best_model_criterion: Sequence[Sequence[str]],
# nbest: Union[Collection[int], int],
# suffix: Optional[str] = None,
# oss_bucket=None,
# pai_output_dir=None,
# ) -> None:
# """Generate averaged model from n-best models
#
# Args:
# output_dir: The directory contains the model file for each epoch
# reporter: Reporter instance
# best_model_criterion: Give criterions to decide the best model.
# e.g. [("valid", "loss", "min"), ("train", "acc", "max")]
# nbest: Number of best model files to be averaged
# suffix: A suffix added to the averaged model file name
# """
# if isinstance(nbest, int):
# nbests = [nbest]
# else:
# nbests = list(nbest)
# if len(nbests) == 0:
# warnings.warn("At least 1 nbest values are required")
# nbests = [1]
# if suffix is not None:
# suffix = suffix + "."
# else:
# suffix = ""
#
# # 1. Get nbests: List[Tuple[str, str, List[Tuple[epoch, value]]]]
# nbest_epochs = [
# (ph, k, reporter.sort_epochs_and_values(ph, k, m)[: max(nbests)])
# for ph, k, m in best_model_criterion
# if reporter.has(ph, k)
# ]
#
# _loaded = {}
# for ph, cr, epoch_and_values in nbest_epochs:
# _nbests = [i for i in nbests if i <= len(epoch_and_values)]
# if len(_nbests) == 0:
# _nbests = [1]
#
# for n in _nbests:
# if n == 0:
# continue
# elif n == 1:
# # The averaged model is same as the best model
# e, _ = epoch_and_values[0]
# op = output_dir / f"{e}epoch.pb"
# sym_op = output_dir / f"{ph}.{cr}.ave_1best.{suffix}pb"
# if sym_op.is_symlink() or sym_op.exists():
# sym_op.unlink()
# sym_op.symlink_to(op.name)
# else:
# op = output_dir / f"{ph}.{cr}.ave_{n}best.{suffix}pb"
# logging.info(
# f"Averaging {n}best models: " f'criterion="{ph}.{cr}": {op}'
# )
#
# avg = None
# # 2.a. Averaging model
# for e, _ in epoch_and_values[:n]:
# if e not in _loaded:
# if oss_bucket is None:
# _loaded[e] = torch.load(
# output_dir / f"{e}epoch.pb",
# map_location="cpu",
# )
# else:
# buffer = BytesIO(
# oss_bucket.get_object(os.path.join(pai_output_dir, f"{e}epoch.pb")).read())
# _loaded[e] = torch.load(buffer)
# states = _loaded[e]
#
# if avg is None:
# avg = states
# else:
# # Accumulated
# for k in avg:
# avg[k] = avg[k] + states[k]
# for k in avg:
# if str(avg[k].dtype).startswith("torch.int"):
# # For int type, not averaged, but only accumulated.
# # e.g. BatchNorm.num_batches_tracked
# # (If there are any cases that requires averaging
# # or the other reducing method, e.g. max/min, for integer type,
# # please report.)
# pass
# else:
# avg[k] = avg[k] / n
#
# # 2.b. Save the ave model and create a symlink
# if oss_bucket is None:
# torch.save(avg, op)
# else:
# buffer = BytesIO()
# torch.save(avg, buffer)
# oss_bucket.put_object(os.path.join(pai_output_dir, f"{ph}.{cr}.ave_{n}best.{suffix}pb"),
# buffer.getvalue())
#
# # 3. *.*.ave.pb is a symlink to the max ave model
# if oss_bucket is None:
# op = output_dir / f"{ph}.{cr}.ave_{max(_nbests)}best.{suffix}pb"
# sym_op = output_dir / f"{ph}.{cr}.ave.{suffix}pb"
# if sym_op.is_symlink() or sym_op.exists():
# sym_op.unlink()
# sym_op.symlink_to(op.name)
def _get_checkpoint_paths(output_dir: str, last_n: int=5):
"""
Get the paths of the last 'last_n' checkpoints by parsing filenames
in the output directory.
"""
# List all files in the output directory
files = os.listdir(output_dir)
# Filter out checkpoint files and extract epoch numbers
checkpoint_files = [f for f in files if f.startswith("model.pt.e")]
# Sort files by epoch number in descending order
checkpoint_files.sort(key=lambda x: int(re.search(r'(\d+)', x).group()), reverse=True)
# Get the last 'last_n' checkpoint paths
checkpoint_paths = [os.path.join(output_dir, f) for f in checkpoint_files[:last_n]]
return checkpoint_paths
@torch.no_grad()
def average_nbest_models(
output_dir: Path,
reporter: Reporter,
best_model_criterion: Sequence[Sequence[str]],
nbest: Union[Collection[int], int],
suffix: Optional[str] = None,
oss_bucket=None,
pai_output_dir=None,
) -> None:
"""Generate averaged model from n-best models
Args:
output_dir: The directory contains the model file for each epoch
reporter: Reporter instance
best_model_criterion: Give criterions to decide the best model.
e.g. [("valid", "loss", "min"), ("train", "acc", "max")]
nbest: Number of best model files to be averaged
suffix: A suffix added to the averaged model file name
def average_checkpoints(output_dir: str, last_n: int=5):
"""
if isinstance(nbest, int):
nbests = [nbest]
else:
nbests = list(nbest)
if len(nbests) == 0:
warnings.warn("At least 1 nbest values are required")
nbests = [1]
if suffix is not None:
suffix = suffix + "."
else:
suffix = ""
Average the last 'last_n' checkpoints' model state_dicts.
If a tensor is of type torch.int, perform sum instead of average.
"""
checkpoint_paths = _get_checkpoint_paths(output_dir, last_n)
state_dicts = []
# 1. Get nbests: List[Tuple[str, str, List[Tuple[epoch, value]]]]
nbest_epochs = [
(ph, k, reporter.sort_epochs_and_values(ph, k, m)[: max(nbests)])
for ph, k, m in best_model_criterion
if reporter.has(ph, k)
]
# Load state_dicts from checkpoints
for path in checkpoint_paths:
if os.path.isfile(path):
state_dicts.append(torch.load(path, map_location='cpu')['state_dict'])
else:
print(f"Checkpoint file {path} not found.")
continue
_loaded = {}
for ph, cr, epoch_and_values in nbest_epochs:
_nbests = [i for i in nbests if i <= len(epoch_and_values)]
if len(_nbests) == 0:
_nbests = [1]
# Check if we have any state_dicts to average
if not state_dicts:
raise RuntimeError("No checkpoints found for averaging.")
for n in _nbests:
if n == 0:
continue
elif n == 1:
# The averaged model is same as the best model
e, _ = epoch_and_values[0]
op = output_dir / f"{e}epoch.pb"
sym_op = output_dir / f"{ph}.{cr}.ave_1best.{suffix}pb"
if sym_op.is_symlink() or sym_op.exists():
sym_op.unlink()
sym_op.symlink_to(op.name)
else:
op = output_dir / f"{ph}.{cr}.ave_{n}best.{suffix}pb"
logging.info(
f"Averaging {n}best models: " f'criterion="{ph}.{cr}": {op}'
)
avg = None
# 2.a. Averaging model
for e, _ in epoch_and_values[:n]:
if e not in _loaded:
if oss_bucket is None:
_loaded[e] = torch.load(
output_dir / f"{e}epoch.pb",
map_location="cpu",
)
else:
buffer = BytesIO(
oss_bucket.get_object(os.path.join(pai_output_dir, f"{e}epoch.pb")).read())
_loaded[e] = torch.load(buffer)
states = _loaded[e]
if avg is None:
avg = states
else:
# Accumulated
for k in avg:
avg[k] = avg[k] + states[k]
for k in avg:
if str(avg[k].dtype).startswith("torch.int"):
# For int type, not averaged, but only accumulated.
# e.g. BatchNorm.num_batches_tracked
# (If there are any cases that requires averaging
# or the other reducing method, e.g. max/min, for integer type,
# please report.)
pass
else:
avg[k] = avg[k] / n
# 2.b. Save the ave model and create a symlink
if oss_bucket is None:
torch.save(avg, op)
else:
buffer = BytesIO()
torch.save(avg, buffer)
oss_bucket.put_object(os.path.join(pai_output_dir, f"{ph}.{cr}.ave_{n}best.{suffix}pb"),
buffer.getvalue())
# 3. *.*.ave.pb is a symlink to the max ave model
if oss_bucket is None:
op = output_dir / f"{ph}.{cr}.ave_{max(_nbests)}best.{suffix}pb"
sym_op = output_dir / f"{ph}.{cr}.ave.{suffix}pb"
if sym_op.is_symlink() or sym_op.exists():
sym_op.unlink()
sym_op.symlink_to(op.name)
# Average or sum weights
avg_state_dict = OrderedDict()
for key in state_dicts[0].keys():
tensors = [state_dict[key].cpu() for state_dict in state_dicts]
# Check the type of the tensor
if str(tensors[0].dtype).startswith("torch.int"):
# Perform sum for integer tensors
summed_tensor = sum(tensors)
avg_state_dict[key] = summed_tensor
else:
# Perform average for other types of tensors
stacked_tensors = torch.stack(tensors)
avg_state_dict[key] = torch.mean(stacked_tensors, dim=0)
torch.save({'state_dict': avg_state_dict}, os.path.join(output_dir, f"model.pt.avg{last_n}"))
return avg_state_dict

109
funasr/train_utils/trainer.py
View File

@ -7,10 +7,11 @@ import torch.distributed as dist
from contextlib import nullcontext
# from torch.utils.tensorboard import SummaryWriter
from tensorboardX import SummaryWriter
from pathlib import Path
from funasr.train_utils.device_funcs import to_device
from funasr.train_utils.recursive_op import recursive_average
from funasr.train_utils.average_nbest_models import average_checkpoints
class Trainer:
"""
@ -66,10 +67,9 @@ class Trainer:
self.use_ddp = use_ddp
self.use_fsdp = use_fsdp
self.device = next(model.parameters()).device
self.avg_nbest_model = kwargs.get("avg_nbest_model", 5)
self.kwargs = kwargs
if self.resume:
self._resume_checkpoint(self.resume)
try:
rank = dist.get_rank()
@ -102,9 +102,17 @@ class Trainer:
}
# Create output directory if it does not exist
os.makedirs(self.output_dir, exist_ok=True)
filename = os.path.join(self.output_dir, f'model.e{epoch}.pb')
filename = os.path.join(self.output_dir, f'model.pt.ep{epoch}')
torch.save(state, filename)
print(f'Checkpoint saved to {filename}')
latest = Path(os.path.join(self.output_dir, f'model.pt'))
try:
latest.unlink()
except:
pass
latest.symlink_to(filename)
def _resume_checkpoint(self, resume_path):
"""
@ -114,29 +122,50 @@ class Trainer:
Args:
resume_path (str): The file path to the checkpoint to resume from.
"""
if os.path.isfile(resume_path):
checkpoint = torch.load(resume_path)
ckpt = os.path.join(resume_path, "model.pt")
if os.path.isfile(ckpt):
checkpoint = torch.load(ckpt)
self.start_epoch = checkpoint['epoch'] + 1
self.model.load_state_dict(checkpoint['state_dict'])
self.optim.load_state_dict(checkpoint['optimizer'])
self.scheduler.load_state_dict(checkpoint['scheduler'])
print(f"Checkpoint loaded successfully from '{resume_path}' at (epoch {checkpoint['epoch']})")
print(f"Checkpoint loaded successfully from '{ckpt}'")
else:
print(f"No checkpoint found at '{resume_path}', starting from scratch")
print(f"No checkpoint found at '{ckpt}', starting from scratch")
if self.use_ddp or self.use_fsdp:
dist.barrier()
def run(self):
"""
Starts the training process, iterating over epochs, training the model,
and saving checkpoints at the end of each epoch.
"""
if self.resume:
self._resume_checkpoint(self.output_dir)
for epoch in range(self.start_epoch, self.max_epoch + 1):
self._train_epoch(epoch)
# self._validate_epoch(epoch)
self._validate_epoch(epoch)
if self.rank == 0:
self._save_checkpoint(epoch)
self.scheduler.step()
if self.use_ddp or self.use_fsdp:
dist.barrier()
self.scheduler.step()
if self.rank == 0:
average_checkpoints(self.output_dir, self.avg_nbest_model)
if self.use_ddp or self.use_fsdp:
dist.barrier()
self.writer.close()
def _train_epoch(self, epoch):
"""
@ -157,8 +186,7 @@ class Trainer:
for batch_idx, batch in enumerate(self.dataloader_train):
time1 = time.perf_counter()
speed_stats["data_load"] = f"{time1-time5:0.3f}"
# import pdb;
# pdb.set_trace()
batch = to_device(batch, self.device)
my_context = self.model.no_sync if batch_idx % accum_grad != 0 else nullcontext
@ -211,13 +239,12 @@ class Trainer:
speed_stats["optim_time"] = f"{time5 - time4:0.3f}"
speed_stats["total_time"] = total_time
# import pdb;
# pdb.set_trace()
pbar.update(1)
if self.local_rank == 0:
description = (
f"Epoch: {epoch + 1}/{self.max_epoch}, "
f"Epoch: {epoch}/{self.max_epoch}, "
f"step {batch_idx}/{len(self.dataloader_train)}, "
f"{speed_stats}, "
f"(loss: {loss.detach().cpu().item():.3f}), "
@ -248,6 +275,50 @@ class Trainer:
"""
self.model.eval()
with torch.no_grad():
for data, target in self.dataloader_val:
# Implement the model validation steps here
pass
pbar = tqdm(colour="red", desc=f"Training Epoch: {epoch + 1}", total=len(self.dataloader_val),
dynamic_ncols=True)
speed_stats = {}
time5 = time.perf_counter()
for batch_idx, batch in enumerate(self.dataloader_val):
time1 = time.perf_counter()
speed_stats["data_load"] = f"{time1 - time5:0.3f}"
batch = to_device(batch, self.device)
time2 = time.perf_counter()
retval = self.model(**batch)
time3 = time.perf_counter()
speed_stats["forward_time"] = f"{time3 - time2:0.3f}"
loss, stats, weight = retval
stats = {k: v for k, v in stats.items() if v is not None}
if self.use_ddp or self.use_fsdp:
# Apply weighted averaging for loss and stats
loss = (loss * weight.type(loss.dtype)).sum()
# if distributed, this method can also apply all_reduce()
stats, weight = recursive_average(stats, weight, distributed=True)
# Now weight is summation over all workers
loss /= weight
# Multiply world_size because DistributedDataParallel
# automatically normalizes the gradient by world_size.
loss *= self.world_size
# Scale the loss since we're not updating for every mini-batch
loss = loss
time4 = time.perf_counter()
pbar.update(1)
if self.local_rank == 0:
description = (
f"validation: \nEpoch: {epoch}/{self.max_epoch}, "
f"step {batch_idx}/{len(self.dataloader_train)}, "
f"{speed_stats}, "
f"(loss: {loss.detach().cpu().item():.3f}), "
f"{[(k, round(v.cpu().item(), 3)) for k, v in stats.items()]}"
)
pbar.set_description(description)
if self.writer:
self.writer.add_scalar('Loss/val', loss.item(),
epoch*len(self.dataloader_train) + batch_idx)
for key, var in stats.items():
self.writer.add_scalar(f'{key}/val', var.item(),
epoch * len(self.dataloader_train) + batch_idx)
for key, var in speed_stats.items():
self.writer.add_scalar(f'{key}/val', eval(var),
epoch * len(self.dataloader_train) + batch_idx)