import os
import sys
from copy import deepcopy
from enum import Enum
from typing import Union, Tuple, Mapping
import numpy as np
import pkg_resources
import torch
import torchvision.transforms as transforms
from deprecated import deprecated
from torch import nn
from torch.cuda.amp import GradScaler, autocast
from torchmetrics import MetricCollection
from tqdm import tqdm
from piptools.scripts.sync import _get_installed_distributions
from super_gradients.common.decorators.factory_decorator import resolve_param
from super_gradients.common.environment import env_helpers
from super_gradients.common.abstractions.abstract_logger import get_logger
from super_gradients.common.factories.datasets_factory import DatasetsFactory
from super_gradients.common.factories.list_factory import ListFactory
from super_gradients.common.factories.losses_factory import LossesFactory
from super_gradients.common.factories.metrics_factory import MetricsFactory
from super_gradients.common.sg_loggers import SG_LOGGERS
from super_gradients.common.sg_loggers.abstract_sg_logger import AbstractSGLogger
from super_gradients.common.sg_loggers.base_sg_logger import BaseSGLogger
from super_gradients.training import ARCHITECTURES, utils as core_utils
from super_gradients.training.utils import sg_model_utils
from super_gradients.training import metrics
from super_gradients.training.exceptions.sg_model_exceptions import UnsupportedOptimizerFormat
from super_gradients.training.datasets import DatasetInterface
from super_gradients.training.losses import LOSSES
from super_gradients.training.metrics.metric_utils import get_metrics_titles, get_metrics_results_tuple, \
get_logging_values, \
get_metrics_dict, get_train_loop_description_dict
from super_gradients.training.models import SgModule
from super_gradients.training.params import TrainingParams
from super_gradients.training.utils.detection_utils import DetectionPostPredictionCallback
from super_gradients.training.utils.distributed_training_utils import MultiGPUModeAutocastWrapper, \
reduce_results_tuple_for_ddp, compute_precise_bn_stats
from super_gradients.training.utils.ema import ModelEMA
from super_gradients.training.utils.optimizer_utils import build_optimizer
from super_gradients.training.utils.weight_averaging_utils import ModelWeightAveraging
from super_gradients.training.metrics import Accuracy, Top5
from super_gradients.training.utils import random_seed
from super_gradients.training.utils.checkpoint_utils import get_ckpt_local_path, read_ckpt_state_dict, \
load_checkpoint_to_model, load_pretrained_weights
from super_gradients.training.datasets.datasets_utils import DatasetStatisticsTensorboardLogger
from super_gradients.training.utils.callbacks import CallbackHandler, Phase, LR_SCHEDULERS_CLS_DICT, PhaseContext, \
MetricsUpdateCallback, LR_WARMUP_CLS_DICT
from super_gradients.common.environment import environment_config
from super_gradients.training.pretrained_models import PRETRAINED_NUM_CLASSES
logger = get_logger(__name__)
[docs]class StrictLoad(Enum):
"""
Wrapper for adding more functionality to torch's strict_load parameter in load_state_dict().
Attributes:
OFF - Native torch "strict_load = off" behaviour. See nn.Module.load_state_dict() documentation for more details.
ON - Native torch "strict_load = on" behaviour. See nn.Module.load_state_dict() documentation for more details.
NO_KEY_MATCHING - Allows the usage of SuperGradient's adapt_checkpoint function, which loads a checkpoint by matching each
layer's shapes (and bypasses the strict matching of the names of each layer (ie: disregards the state_dict key matching)).
"""
OFF = False
ON = True
NO_KEY_MATCHING = 'no_key_matching'
[docs]class MultiGPUMode(str, Enum):
"""
MultiGPUMode
Attributes:
OFF - Single GPU Mode / CPU Mode
DATA_PARALLEL - Multiple GPUs, Synchronous
DISTRIBUTED_DATA_PARALLEL - Multiple GPUs, Asynchronous
"""
OFF = 'Off'
DATA_PARALLEL = 'DP'
DISTRIBUTED_DATA_PARALLEL = 'DDP'
AUTO = "AUTO"
[docs]class EvaluationType(str, Enum):
"""
EvaluationType
Passed to SgModel.evaluate(..), and controls which phase callbacks should be triggered (if at all).
Attributes:
TEST
VALIDATION
"""
TEST = 'TEST'
VALIDATION = 'VALIDATION'
[docs]class SgModel:
"""
SuperGradient Model - Base Class for Sg Models
Methods
-------
train(max_epochs : int, initial_epoch : int, save_model : bool)
the main function used for the training, h.p. updating, logging etc.
predict(idx : int)
returns the predictions and label of the current inputs
test(epoch : int, idx : int, save : bool):
returns the test loss, accuracy and runtime
"""
def __init__(self, experiment_name: str, device: str = None, multi_gpu: Union[MultiGPUMode, str] = MultiGPUMode.AUTO,
model_checkpoints_location: str = 'local',
overwrite_local_checkpoint: bool = True, ckpt_name: str = 'ckpt_latest.pth',
post_prediction_callback: DetectionPostPredictionCallback = None, ckpt_root_dir=None):
"""
:param experiment_name: Used for logging and loading purposes
:param device: If equal to 'cpu' runs on the CPU otherwise on GPU
:param multi_gpu: If True, runs on all available devices
:param model_checkpoints_location: If set to 's3' saves the Checkpoints in AWS S3
otherwise saves the Checkpoints Locally
:param overwrite_local_checkpoint: If set to False keeps the current local checkpoint when importing
checkpoint from cloud service, otherwise overwrites the local checkpoints file
:param ckpt_name: The Checkpoint to Load
:ckpt_root_dir: Local root directory path where all experiment logging directories will
reside. When none is give, it is assumed that
pkg_resources.resource_filename('checkpoints', "") exists and will be used.
"""
# SET THE EMPTY PROPERTIES
self.net, self.architecture, self.arch_params, self.dataset_interface = None, None, None, None
self.architecture_cls, self.device, self.multi_gpu = None, None, None
self.dataset_params, self.train_loader, self.valid_loader, self.test_loader, self.classes = None, None, None, None, None
self.ema = None
self.ema_model = None
self.sg_logger = None
self.update_param_groups = None
self.post_prediction_callback = None
self.criterion = None
self.training_params = None
self.scaler = None
self.phase_callbacks = None
# SET THE DEFAULT PROPERTIES
self.half_precision = False
self.load_checkpoint = False
self.load_backbone = False
self.load_weights_only = False
self.ddp_silent_mode = False
self.source_ckpt_folder_name = None
self.model_weight_averaging = None
self.average_model_checkpoint_filename = 'average_model.pth'
self.start_epoch = 0
self.best_metric = np.inf
self.external_checkpoint_path = None
# DETERMINE THE LOCATION OF THE LOSS AND ACCURACY IN THE RESULTS TUPLE OUTPUTED BY THE TEST
self.loss_idx_in_results_tuple, self.acc_idx_in_results_tuple = None, None
# METRICS
self.loss_logging_items_names = None
self.train_metrics = None
self.valid_metrics = None
self.greater_metric_to_watch_is_better = None
# SETTING THE PROPERTIES FROM THE CONSTRUCTOR
self.experiment_name = experiment_name
self.ckpt_name = ckpt_name
self.overwrite_local_checkpoint = overwrite_local_checkpoint
self.model_checkpoints_location = model_checkpoints_location
# CREATING THE LOGGING DIR BASED ON THE INPUT PARAMS TO PREVENT OVERWRITE OF LOCAL VERSION
if ckpt_root_dir:
self.checkpoints_dir_path = os.path.join(ckpt_root_dir, self.experiment_name)
elif pkg_resources.resource_exists("checkpoints", ""):
self.checkpoints_dir_path = pkg_resources.resource_filename('checkpoints', self.experiment_name)
else:
raise ValueError("Illegal checkpoints directory: pass ckpt_root_dir that exists, or add 'checkpoints' to"
"resources.")
# INITIALIZE THE DEVICE FOR THE MODEL
self._initialize_device(requested_device=device, requested_multi_gpu=multi_gpu)
self.post_prediction_callback = post_prediction_callback
# SET THE DEFAULTS
# TODO: SET DEFAULT TRAINING PARAMS FOR EACH TASK
default_results_titles = ['Train Loss', 'Train Acc', 'Train Top5', 'Valid Loss', 'Valid Acc', 'Valid Top5']
self.results_titles = default_results_titles
self.loss_idx_in_results_tuple, self.acc_idx_in_results_tuple = 0, 1
default_train_metrics, default_valid_metrics = MetricCollection([Accuracy(), Top5()]), MetricCollection(
[Accuracy(), Top5()])
default_loss_logging_items_names = ["Loss"]
self.train_metrics, self.valid_metrics = default_train_metrics, default_valid_metrics
self.loss_logging_items_names = default_loss_logging_items_names
[docs] @resolve_param('dataset_interface', DatasetsFactory())
def connect_dataset_interface(self, dataset_interface: DatasetInterface, data_loader_num_workers: int = 8):
"""
:param dataset_interface: DatasetInterface object
:param data_loader_num_workers: The number of threads to initialize the Data Loaders with
The dataset to be connected
"""
self.dataset_interface = dataset_interface
self.train_loader, self.valid_loader, self.test_loader, self.classes = \
self.dataset_interface.get_data_loaders(batch_size_factor=self.num_devices,
num_workers=data_loader_num_workers,
distributed_sampler=self.multi_gpu == MultiGPUMode.DISTRIBUTED_DATA_PARALLEL)
self.dataset_params = self.dataset_interface.get_dataset_params()
# FIXME - we need to resolve flake8's 'function is too complex' for this function
[docs] def build_model(self, # noqa: C901 - too complex
architecture: Union[str, nn.Module],
arch_params={},
load_checkpoint: bool = False,
strict_load: StrictLoad = StrictLoad.ON,
source_ckpt_folder_name: str = None,
load_weights_only: bool = False,
load_backbone: bool = False,
external_checkpoint_path: str = None,
load_ema_as_net: bool = False):
"""
:param architecture: Defines the network's architecture from models/ALL_ARCHITECTURES
:param arch_params: Architecture H.P. e.g.: block, num_blocks, num_classes, etc.
:param load_checkpoint: Load a pre-trained checkpoint
:param strict_load: See StrictLoad class documentation for details.
:param source_ckpt_folder_name: folder name to load the checkpoint from (self.experiment_name if none is given)
:param load_weights_only: loads only the weight from the checkpoint and zeroize the training params
:param load_backbone: loads the provided checkpoint to self.net.backbone instead of self.net
:param external_checkpoint_path: The path to the external checkpoint to be loaded. Can be absolute or relative
(ie: path/to/checkpoint.pth). If provided, will automatically attempt to
load the checkpoint even if the load_checkpoint flag is not provided.
"""
if 'num_classes' not in arch_params.keys():
if self.dataset_interface is None:
raise Exception('Error', 'Number of classes not defined in arch params and dataset is not defined')
else:
arch_params['num_classes'] = len(self.classes)
self.arch_params = core_utils.HpmStruct(**arch_params)
pretrained_weights = core_utils.get_param(self.arch_params, 'pretrained_weights', default_val=None)
if pretrained_weights is not None:
num_classes_new_head = self.arch_params.num_classes
self.arch_params.num_classes = PRETRAINED_NUM_CLASSES[pretrained_weights]
# OVERRIDE THE INPUT PARAMS WITH THE arch_params VALUES
load_weights_only = core_utils.get_param(self.arch_params, 'load_weights_only', default_val=load_weights_only)
self.source_ckpt_folder_name = core_utils.get_param(self.arch_params, 'source_ckpt_folder_name',
default_val=source_ckpt_folder_name)
strict_load = core_utils.get_param(self.arch_params, 'strict_load', default_val=strict_load)
self.arch_params.sync_bn = core_utils.get_param(self.arch_params, 'sync_bn', default_val=False)
self.load_checkpoint = load_checkpoint or core_utils.get_param(self.arch_params, 'load_checkpoint',
default_val=False)
self.load_backbone = core_utils.get_param(self.arch_params, 'load_backbone', default_val=load_backbone)
self.external_checkpoint_path = core_utils.get_param(self.arch_params, 'external_checkpoint_path',
default_val=external_checkpoint_path)
if isinstance(architecture, str):
self.architecture_cls = ARCHITECTURES[architecture]
self.net = self.architecture_cls(arch_params=self.arch_params)
elif isinstance(architecture, SgModule.__class__):
self.net = architecture(self.arch_params)
else:
self.net = architecture
# SAVE THE ARCHITECTURE FOR NEURAL ARCHITECTURE SEARCH
if hasattr(self.net, 'structure'):
self.architecture = self.net.structure
self.net.to(self.device)
# FOR MULTI-GPU TRAINING (not distributed)
if self.multi_gpu == MultiGPUMode.DATA_PARALLEL:
self.net = torch.nn.DataParallel(self.net, device_ids=self.device_ids)
elif self.multi_gpu == MultiGPUMode.DISTRIBUTED_DATA_PARALLEL:
if self.arch_params.sync_bn:
if not self.ddp_silent_mode:
logger.info('DDP - Using Sync Batch Norm... Training time will be affected accordingly')
self.net = torch.nn.SyncBatchNorm.convert_sync_batchnorm(self.net).to(self.device)
local_rank = int(self.device.split(':')[1])
self.net = torch.nn.parallel.DistributedDataParallel(self.net,
device_ids=[local_rank],
output_device=local_rank,
find_unused_parameters=True)
else:
self.net = core_utils.WrappedModel(self.net)
# SET THE FLAG FOR DIFFERENT PARAMETER GROUP OPTIMIZER UPDATE
self.update_param_groups = hasattr(self.net.module, 'update_param_groups')
# LOAD AN EXISTING CHECKPOINT IF INDICATED
self.checkpoint = {}
if self.load_checkpoint or self.external_checkpoint_path:
self.load_weights_only = load_weights_only
self._load_checkpoint_to_model(strict=strict_load, load_backbone=self.load_backbone,
source_ckpt_folder_name=self.source_ckpt_folder_name,
load_ema_as_net=load_ema_as_net)
if pretrained_weights:
load_pretrained_weights(self.net, architecture, pretrained_weights)
if num_classes_new_head != self.arch_params.num_classes:
self.net.module.replace_head(new_num_classes=num_classes_new_head)
self.arch_params.num_classes = num_classes_new_head
self.net.to(self.device)
def _train_epoch(self, epoch: int, silent_mode: bool = False) -> tuple:
"""
train_epoch - A single epoch training procedure
:param optimizer: The optimizer for the network
:param epoch: The current epoch
:param silent_mode: No verbosity
"""
# SET THE MODEL IN training STATE
self.net.train()
# THE DISABLE FLAG CONTROLS WHETHER THE PROGRESS BAR IS SILENT OR PRINTS THE LOGS
progress_bar_train_loader = tqdm(self.train_loader, bar_format="{l_bar}{bar:10}{r_bar}", dynamic_ncols=True,
disable=silent_mode)
progress_bar_train_loader.set_description(f"Train epoch {epoch}")
# RESET/INIT THE METRIC LOGGERS
self.train_metrics.reset()
self.train_metrics.to(self.device)
loss_avg_meter = core_utils.utils.AverageMeter()
context = PhaseContext(epoch=epoch,
optimizer=self.optimizer,
metrics_compute_fn=self.train_metrics,
loss_avg_meter=loss_avg_meter,
criterion=self.criterion,
device=self.device,
lr_warmup_epochs=self.training_params.lr_warmup_epochs,
sg_logger=self.sg_logger)
for batch_idx, batch_items in enumerate(progress_bar_train_loader):
batch_items = core_utils.tensor_container_to_device(batch_items, self.device, non_blocking=True)
inputs, targets, additional_batch_items = sg_model_utils.unpack_batch_items(batch_items)
# AUTOCAST IS ENABLED ONLY IF self.training_params.mixed_precision - IF enabled=False AUTOCAST HAS NO EFFECT
with autocast(enabled=self.training_params.mixed_precision):
# FORWARD PASS TO GET NETWORK'S PREDICTIONS
outputs = self.net(inputs)
# COMPUTE THE LOSS FOR BACK PROP + EXTRA METRICS COMPUTED DURING THE LOSS FORWARD PASS
loss, loss_log_items = self._get_losses(outputs, targets)
context.update_context(batch_idx=batch_idx,
inputs=inputs,
preds=outputs,
target=targets,
loss_log_items=loss_log_items,
**additional_batch_items)
self.phase_callback_handler(Phase.TRAIN_BATCH_END, context)
# LOG LR THAT WILL BE USED IN CURRENT EPOCH AND AFTER FIRST WARMUP/LR_SCHEDULER UPDATE BEFORE WEIGHT UPDATE
if not self.ddp_silent_mode and batch_idx == 0:
self._write_lrs(epoch)
self.backward_step(loss, epoch, batch_idx, context)
# COMPUTE THE RUNNING USER METRICS AND LOSS RUNNING ITEMS. RESULT TUPLE IS THEIR CONCATENATION.
logging_values = loss_avg_meter.average + get_metrics_results_tuple(self.train_metrics)
gpu_memory_utilization = torch.cuda.memory_cached() / 1E9 if torch.cuda.is_available() else 0
# RENDER METRICS PROGRESS
pbar_message_dict = get_train_loop_description_dict(logging_values,
self.train_metrics,
self.loss_logging_items_names,
gpu_mem=gpu_memory_utilization)
progress_bar_train_loader.set_postfix(**pbar_message_dict)
if not self.ddp_silent_mode:
self.sg_logger.upload()
return logging_values
def _get_losses(self, outputs: torch.Tensor, targets: torch.Tensor) -> Tuple[torch.Tensor, tuple]:
# GET THE OUTPUT OF THE LOSS FUNCTION
loss = self.criterion(outputs, targets)
if isinstance(loss, tuple):
loss, loss_logging_items = loss
# IF ITS NOT A TUPLE THE LOGGING ITEMS CONTAIN ONLY THE LOSS FOR BACKPROP (USER DEFINED LOSS RETURNS SCALAR)
else:
loss_logging_items = loss.unsqueeze(0).detach()
if len(loss_logging_items) != len(self.loss_logging_items_names):
raise ValueError("Loss output length must match loss_logging_items_names. Got " + str(
len(loss_logging_items)) + ', and ' + str(len(self.loss_logging_items_names)))
# RETURN AND THE LOSS LOGGING ITEMS COMPUTED DURING LOSS FORWARD PASS
return loss, loss_logging_items
[docs] def backward_step(self, loss: torch.Tensor, epoch: int, batch_idx: int, context: PhaseContext):
"""
Run backprop on the loss and perform a step
:param loss: The value computed by the loss function
:param optimizer: An object that can perform a gradient step and zeroize model gradient
:param epoch: number of epoch the training is on
:param batch_idx: number of iteration inside the current epoch
:param context: current phase context
:return:
"""
# SCALER IS ENABLED ONLY IF self.training_params.mixed_precision=True
self.scaler.scale(loss).backward()
# ACCUMULATE GRADIENT FOR X BATCHES BEFORE OPTIMIZING
integrated_batches_num = batch_idx + len(self.train_loader) * epoch + 1
if integrated_batches_num % self.batch_accumulate == 0:
# SCALER IS ENABLED ONLY IF self.training_params.mixed_precision=True
self.scaler.step(self.optimizer)
self.scaler.update()
self.optimizer.zero_grad()
if self.ema:
self.ema_model.update(self.net, integrated_batches_num / (len(self.train_loader) * self.max_epochs))
# RUN PHASE CALLBACKS
self.phase_callback_handler(Phase.TRAIN_BATCH_STEP, context)
[docs] def save_checkpoint(self, optimizer=None, epoch: int = None, validation_results_tuple: tuple = None, context: PhaseContext = None):
"""
Save the current state dict as latest (always), best (if metric was improved), epoch# (if determined in training
params)
"""
# WHEN THE validation_results_tuple IS NONE WE SIMPLY SAVE THE state_dict AS LATEST AND Return
if validation_results_tuple is None:
self.sg_logger.add_checkpoint(tag='ckpt_latest_weights_only.pth', state_dict={'net': self.net.state_dict()},
global_step=epoch)
return
# COMPUTE THE CURRENT metric
# IF idx IS A LIST - SUM ALL THE VALUES STORED IN THE LIST'S INDICES
metric = validation_results_tuple[self.metric_idx_in_results_tuple] if isinstance(
self.metric_idx_in_results_tuple, int) else \
sum([validation_results_tuple[idx] for idx in self.metric_idx_in_results_tuple])
# BUILD THE state_dict
state = {'net': self.net.state_dict(), 'acc': metric, 'epoch': epoch}
if optimizer is not None:
state['optimizer_state_dict'] = optimizer.state_dict()
if self.scaler is not None:
state['scaler_state_dict'] = self.scaler.state_dict()
if self.ema:
state['ema_net'] = self.ema_model.ema.state_dict()
# SAVES CURRENT MODEL AS ckpt_latest
self.sg_logger.add_checkpoint(tag='ckpt_latest.pth', state_dict=state, global_step=epoch)
# SAVE MODEL AT SPECIFIC EPOCHS DETERMINED BY save_ckpt_epoch_list
if epoch in self.training_params.save_ckpt_epoch_list:
self.sg_logger.add_checkpoint(tag=f'ckpt_epoch_{epoch}.pth', state_dict=state, global_step=epoch)
# OVERRIDE THE BEST CHECKPOINT AND best_metric IF metric GOT BETTER THAN THE PREVIOUS BEST
if (metric > self.best_metric and self.greater_metric_to_watch_is_better) or (
metric < self.best_metric and not self.greater_metric_to_watch_is_better):
# STORE THE CURRENT metric AS BEST
self.best_metric = metric
self.sg_logger.add_checkpoint(tag='ckpt_best.pth', state_dict=state, global_step=epoch)
# RUN PHASE CALLBACKS
self.phase_callback_handler(Phase.VALIDATION_END_BEST_EPOCH, context)
if isinstance(metric, torch.Tensor):
metric = metric.item()
logger.info("Best checkpoint overriden: validation " + self.metric_to_watch + ": " + str(metric))
if self.training_params.average_best_models:
net_for_averaging = self.ema_model.ema if self.ema else self.net
averaged_model_sd = self.model_weight_averaging.get_average_model(net_for_averaging,
validation_results_tuple=validation_results_tuple)
self.sg_logger.add_checkpoint(tag=self.average_model_checkpoint_filename,
state_dict={'net': averaged_model_sd}, global_step=epoch)
# FIXME - we need to resolve flake8's 'function is too complex' for this function
[docs] def train(self, training_params: dict = dict()): # noqa: C901
"""
train - Trains the Model
IMPORTANT NOTE: Additional batch parameters can be added as a third item (optional) if a tuple is returned by
the data loaders, as dictionary. The phase context will hold the additional items, under an attribute with
the same name as the key in this dictionary. Then such items can be accessed through phase callbacks.
:param training_params:
- `max_epochs` : int
Number of epochs to run training.
- `lr_updates` : list(int)
List of fixed epoch numbers to perform learning rate updates when `lr_mode='step'`.
- `lr_decay_factor` : float
Decay factor to apply to the learning rate at each update when `lr_mode='step'`.
- `lr_mode` : str
Learning rate scheduling policy, one of ['step','poly','cosine','function']. 'step' refers to
constant updates at epoch numbers passed through `lr_updates`. 'cosine' refers to Cosine Anealing
policy as mentioned in https://arxiv.org/abs/1608.03983. 'poly' refers to polynomial decrease i.e
in each epoch iteration `self.lr = self.initial_lr * pow((1.0 - (current_iter / max_iter)),
0.9)` 'function' refers to user defined learning rate scheduling function, that is passed through
`lr_schedule_function`.
- `lr_schedule_function` : Union[callable,None]
Learning rate scheduling function to be used when `lr_mode` is 'function'.
- `lr_warmup_epochs` : int (default=0)
Number of epochs for learning rate warm up - see https://arxiv.org/pdf/1706.02677.pdf (Section 2.2).
- `cosine_final_lr_ratio` : float (default=0.01)
Final learning rate ratio (only relevant when `lr_mode`='cosine'). The cosine starts from initial_lr and reaches
initial_lr * cosine_final_lr_ratio in last epoch
- `inital_lr` : float
Initial learning rate.
- `loss` : Union[nn.module, str]
Loss function for training.
One of SuperGradient's built in options:
"cross_entropy": LabelSmoothingCrossEntropyLoss,
"mse": MSELoss,
"r_squared_loss": RSquaredLoss,
"detection_loss": YoLoV3DetectionLoss,
"shelfnet_ohem_loss": ShelfNetOHEMLoss,
"shelfnet_se_loss": ShelfNetSemanticEncodingLoss,
"yolo_v5_loss": YoLoV5DetectionLoss,
"ssd_loss": SSDLoss,
or user defined nn.module loss function.
IMPORTANT: forward(...) should return a (loss, loss_items) tuple where loss is the tensor used
for backprop (i.e what your original loss function returns), and loss_items should be a tensor of
shape (n_items), of values computed during the forward pass which we desire to log over the
entire epoch. For example- the loss itself should always be logged. Another example is a scenario
where the computed loss is the sum of a few components we would like to log- these entries in
loss_items).
When training, set the loss_logging_items_names parameter in train_params to be a list of
strings, of length n_items who's ith element is the name of the ith entry in loss_items. Then
each item will be logged, rendered on tensorboard and "watched" (i.e saving model checkpoints
according to it).
Since running logs will save the loss_items in some internal state, it is recommended that
loss_items are detached from their computational graph for memory efficiency.
- `optimizer` : Union[str, torch.optim.Optimizer]
Optimization algorithm. One of ['Adam','SGD','RMSProp'] corresponding to the torch.optim
optimzers implementations, or any object that implements torch.optim.Optimizer.
- `criterion_params` : dict
Loss function parameters.
- `optimizer_params` : dict
When `optimizer` is one of ['Adam','SGD','RMSProp'], it will be initialized with optimizer_params.
(see https://pytorch.org/docs/stable/optim.html for the full list of
parameters for each optimizer).
- `train_metrics_list` : list(torchmetrics.Metric)
Metrics to log during training. For more information on torchmetrics see
https://torchmetrics.rtfd.io/en/latest/.
- `valid_metrics_list` : list(torchmetrics.Metric)
Metrics to log during validation/testing. For more information on torchmetrics see
https://torchmetrics.rtfd.io/en/latest/.
- `loss_logging_items_names` : list(str)
The list of names/titles for the outputs returned from the loss functions forward pass (reminder-
the loss function should return the tuple (loss, loss_items)). These names will be used for
logging their values.
- `metric_to_watch` : str (default="Accuracy")
will be the metric which the model checkpoint will be saved according to, and can be set to any
of the following:
a metric name (str) of one of the metric objects from the valid_metrics_list
a "metric_name" if some metric in valid_metrics_list has an attribute component_names which
is a list referring to the names of each entry in the output metric (torch tensor of size n)
one of "loss_logging_items_names" i.e which will correspond to an item returned during the
loss function's forward pass.
At the end of each epoch, if a new best metric_to_watch value is achieved, the models checkpoint
is saved in YOUR_PYTHON_PATH/checkpoints/ckpt_best.pth
- `greater_metric_to_watch_is_better` : bool
When choosing a model's checkpoint to be saved, the best achieved model is the one that maximizes the
metric_to_watch when this parameter is set to True, and a one that minimizes it otherwise.
- `ema` : bool (default=False)
Whether to use Model Exponential Moving Average (see
https://github.com/rwightman/pytorch-image-models ema implementation)
- `batch_accumulate` : int (default=1)
Number of batches to accumulate before every backward pass.
- `ema_params` : dict
Parameters for the ema model.
- `zero_weight_decay_on_bias_and_bn` : bool (default=False)
Whether to apply weight decay on batch normalization parameters or not (ignored when the passed
optimizer has already been initialized).
- `load_opt_params` : bool (default=True)
Whether to load the optimizers parameters as well when loading a model's checkpoint.
- `run_validation_freq` : int (default=1)
The frequency in which validation is performed during training (i.e the validation is ran every
`run_validation_freq` epochs.
- `save_model` : bool (default=True)
Whether to save the model checkpoints.
- `silent_mode` : bool
Silents the print outs.
- `mixed_precision` : bool
Whether to use mixed precision or not.
- `save_ckpt_epoch_list` : list(int) (default=[])
List of fixed epoch indices the user wishes to save checkpoints in.
- `average_best_models` : bool (default=False)
If set, a snapshot dictionary file and the average model will be saved / updated at every epoch
and evaluated only when training is completed. The snapshot file will only be deleted upon
completing the training. The snapshot dict will be managed on cpu.
- `precise_bn` : bool (default=False)
Whether to use precise_bn calculation during the training.
- `precise_bn_batch_size` : int (default=None)
The effective batch size we want to calculate the batchnorm on. For example, if we are training a model
on 8 gpus, with a batch of 128 on each gpu, a good rule of thumb would be to give it 8192
(ie: effective_batch_size * num_gpus = batch_per_gpu * num_gpus * num_gpus).
If precise_bn_batch_size is not provided in the training_params, the latter heuristic will be taken.
- `seed` : int (default=42)
Random seed to be set for torch, numpy, and random. When using DDP each process will have it's seed
set to seed + rank.
- `log_installed_packages` : bool (default=False)
When set, the list of all installed packages (and their versions) will be written to the tensorboard
and logfile (useful when trying to reproduce results).
- `dataset_statistics` : bool (default=False)
Enable a statistic analysis of the dataset. If set to True the dataset will be analyzed and a report
will be added to the tensorboard along with some sample images from the dataset. Currently only
detection datasets are supported for analysis.
- `save_full_train_log` : bool (default=False)
When set, a full log (of all super_gradients modules, including uncaught exceptions from any other
module) of the training will be saved in the checkpoint directory under full_train_log.log
- `sg_logger` : Union[AbstractSGLogger, str] (defauls=base_sg_logger)
Define the SGLogger object for this training process. The SGLogger handles all disk writes, logs, TensorBoard, remote logging
and remote storage. By overriding the default base_sg_logger, you can change the storage location, support external monitoring and logging
or support remote storage.
- `sg_logger_params` : dict
SGLogger parameters
:return:
"""
global logger
if self.net is None:
raise Exception('Model', 'No model found')
if self.dataset_interface is None:
raise Exception('Data', 'No dataset found')
self.training_params = TrainingParams()
self.training_params.override(**training_params)
# SET RANDOM SEED
random_seed(is_ddp=self.multi_gpu == MultiGPUMode.DISTRIBUTED_DATA_PARALLEL,
device=self.device, seed=self.training_params.seed)
silent_mode = self.training_params.silent_mode or self.ddp_silent_mode
# METRICS
self._set_train_metrics(train_metrics_list=self.training_params.train_metrics_list)
self._set_valid_metrics(valid_metrics_list=self.training_params.valid_metrics_list)
self.loss_logging_items_names = self.training_params.loss_logging_items_names
self.results_titles = ["Train_" + t for t in
self.loss_logging_items_names + get_metrics_titles(self.train_metrics)] + \
["Valid_" + t for t in
self.loss_logging_items_names + get_metrics_titles(self.valid_metrics)]
# Store the metric to follow (loss\accuracy) and initialize as the worst value
self.metric_to_watch = self.training_params.metric_to_watch
self.greater_metric_to_watch_is_better = self.training_params.greater_metric_to_watch_is_better
self.metric_idx_in_results_tuple = (self.loss_logging_items_names + get_metrics_titles(self.valid_metrics)).index(self.metric_to_watch)
# Allowing loading instantiated loss or string
if isinstance(self.training_params.loss, str):
criterion_cls = LOSSES[self.training_params.loss]
self.criterion = criterion_cls(**self.training_params.criterion_params)
elif isinstance(self.training_params.loss, Mapping):
self.criterion = LossesFactory().get(self.training_params.loss)
elif isinstance(self.training_params.loss, nn.Module):
self.criterion = self.training_params.loss
self.criterion.to(self.device)
self.max_epochs = self.training_params.max_epochs
self.ema = self.training_params.ema
self.precise_bn = self.training_params.precise_bn
self.precise_bn_batch_size = self.training_params.precise_bn_batch_size
self.batch_accumulate = self.training_params.batch_accumulate
num_batches = len(self.train_loader)
if self.ema:
ema_params = self.training_params.ema_params
logger.info(f'Using EMA with params {ema_params}')
self.ema_model = ModelEMA(self.net, **ema_params)
self.ema_model.updates = self.start_epoch * num_batches // self.batch_accumulate
if self.load_checkpoint:
if 'ema_net' in self.checkpoint.keys():
self.ema_model.ema.load_state_dict(self.checkpoint['ema_net'])
else:
self.ema = False
logger.warning(
"[Warning] Checkpoint does not include EMA weights, continuing training without EMA.")
self.run_validation_freq = self.training_params.run_validation_freq
validation_results_tuple = (0, 0)
inf_time = 0
timer = core_utils.Timer(self.device)
# IF THE LR MODE IS NOT DEFAULT TAKE IT FROM THE TRAINING PARAMS
self.lr_mode = self.training_params.lr_mode
load_opt_params = self.training_params.load_opt_params
self.phase_callbacks = self.training_params.phase_callbacks
if self.lr_mode is not None:
sg_lr_callback_cls = LR_SCHEDULERS_CLS_DICT[self.lr_mode]
self.phase_callbacks.append(sg_lr_callback_cls(train_loader_len=len(self.train_loader),
net=self.net,
training_params=self.training_params,
update_param_groups=self.update_param_groups,
**self.training_params.to_dict()))
if self.training_params.lr_warmup_epochs > 0:
warmup_callback_cls = LR_WARMUP_CLS_DICT[self.training_params.warmup_mode]
self.phase_callbacks.append(warmup_callback_cls(train_loader_len=len(self.train_loader),
net=self.net,
training_params=self.training_params,
update_param_groups=self.update_param_groups,
**self.training_params.to_dict()))
self.phase_callbacks.append(MetricsUpdateCallback(Phase.TRAIN_BATCH_END))
self.phase_callbacks.append(MetricsUpdateCallback(Phase.VALIDATION_BATCH_END))
self.phase_callback_handler = CallbackHandler(callbacks=self.phase_callbacks)
if not self.ddp_silent_mode:
self._initialize_sg_logger_objects()
if self.training_params.dataset_statistics:
dataset_statistics_logger = DatasetStatisticsTensorboardLogger(self.sg_logger)
dataset_statistics_logger.analyze(self.train_loader, dataset_params=self.dataset_params,
title="Train-set", anchors=self.net.module.arch_params.anchors)
dataset_statistics_logger.analyze(self.valid_loader, dataset_params=self.dataset_params,
title="val-set")
# AVERAGE BEST 10 MODELS PARAMS
if self.training_params.average_best_models:
self.model_weight_averaging = ModelWeightAveraging(self.checkpoints_dir_path,
greater_is_better=self.greater_metric_to_watch_is_better,
source_ckpt_folder_name=self.source_ckpt_folder_name,
metric_to_watch=self.metric_to_watch,
metric_idx=self.metric_idx_in_results_tuple,
load_checkpoint=self.load_checkpoint,
model_checkpoints_location=self.model_checkpoints_location)
if self.training_params.save_full_train_log and not self.ddp_silent_mode:
logger = get_logger(__name__,
training_log_path=self.sg_logger.log_file_path.replace('.txt', 'full_train_log.log'))
sg_model_utils.log_uncaught_exceptions(logger)
if not self.load_checkpoint or self.load_weights_only:
# WHEN STARTING TRAINING FROM SCRATCH, DO NOT LOAD OPTIMIZER PARAMS (EVEN IF LOADING BACKBONE)
self.start_epoch = 0
self.best_metric = -1 * np.inf if self.greater_metric_to_watch_is_better else np.inf
load_opt_params = False
if isinstance(self.training_params.optimizer, str):
self.optimizer = build_optimizer(net=self.net, lr=self.training_params.initial_lr,
training_params=self.training_params)
elif isinstance(self.training_params.optimizer, torch.optim.Optimizer):
self.optimizer = self.training_params.optimizer
else:
raise UnsupportedOptimizerFormat()
if self.load_checkpoint and load_opt_params:
self.optimizer.load_state_dict(self.checkpoint['optimizer_state_dict'])
self._initialize_mixed_precision(self.training_params.mixed_precision)
context = PhaseContext(optimizer=self.optimizer, net=self.net, experiment_name=self.experiment_name,
ckpt_dir=self.checkpoints_dir_path,
lr_warmup_epochs=self.training_params.lr_warmup_epochs, sg_logger=self.sg_logger)
self.phase_callback_handler(Phase.PRE_TRAINING, context)
try:
# HEADERS OF THE TRAINING PROGRESS
if not silent_mode:
logger.info(
f'Started training for {self.max_epochs - self.start_epoch} epochs ({self.start_epoch}/'f'{self.max_epochs - 1})\n')
for epoch in range(self.start_epoch, self.max_epochs):
if context.stop_training:
logger.info("Request to stop training has been received, stopping training")
break
# Phase.TRAIN_EPOCH_START
# RUN PHASE CALLBACKS
context.update_context(epoch=epoch)
self.phase_callback_handler(Phase.TRAIN_EPOCH_START, context)
# IN DDP- SET_EPOCH WILL CAUSE EVERY PROCESS TO BE EXPOSED TO THE ENTIRE DATASET BY SHUFFLING WITH A
# DIFFERENT SEED EACH EPOCH START
if self.multi_gpu == MultiGPUMode.DISTRIBUTED_DATA_PARALLEL:
self.train_loader.sampler.set_epoch(epoch)
train_metrics_tuple = self._train_epoch(epoch=epoch, silent_mode=silent_mode)
# Phase.TRAIN_EPOCH_END
# RUN PHASE CALLBACKS
train_metrics_dict = get_metrics_dict(train_metrics_tuple, self.train_metrics,
self.loss_logging_items_names)
context.update_context(metrics_dict=train_metrics_dict)
self.phase_callback_handler(Phase.TRAIN_EPOCH_END, context)
# CALCULATE PRECISE BATCHNORM STATS
if self.precise_bn:
compute_precise_bn_stats(model=self.net, loader=self.train_loader,
precise_bn_batch_size=self.precise_bn_batch_size,
num_gpus=self.num_devices)
if self.ema:
compute_precise_bn_stats(model=self.ema_model.ema, loader=self.train_loader,
precise_bn_batch_size=self.precise_bn_batch_size,
num_gpus=self.num_devices)
# model switch - we replace self.net.module with the ema model for the testing and saving part
# and then switch it back before the next training epoch
if self.ema:
self.ema_model.update_attr(self.net)
keep_model = self.net
self.net = self.ema_model.ema
# RUN TEST ON VALIDATION SET EVERY self.run_validation_freq EPOCHS
if (epoch + 1) % self.run_validation_freq == 0:
timer.start()
validation_results_tuple = self._validate_epoch(epoch=epoch, silent_mode=silent_mode)
inf_time = timer.stop()
# Phase.VALIDATION_EPOCH_END
# RUN PHASE CALLBACKS
valid_metrics_dict = get_metrics_dict(validation_results_tuple, self.valid_metrics,
self.loss_logging_items_names)
context.update_context(metrics_dict=valid_metrics_dict)
self.phase_callback_handler(Phase.VALIDATION_EPOCH_END, context)
if self.ema:
self.net = keep_model
if not self.ddp_silent_mode:
# SAVING AND LOGGING OCCURS ONLY IN THE MAIN PROCESS (IN CASES THERE ARE SEVERAL PROCESSES - DDP)
self._write_to_disk_operations(train_metrics_tuple, validation_results_tuple, inf_time, epoch, context)
# Evaluating the average model and removing snapshot averaging file if training is completed
if self.training_params.average_best_models:
self._validate_final_average_model(cleanup_snapshots_pkl_file=True)
except KeyboardInterrupt:
logger.info(
'\n[MODEL TRAINING EXECUTION HAS BEEN INTERRUPTED]... Please wait until SOFT-TERMINATION process '
'finishes and saves all of the Model Checkpoints and log files before terminating...')
logger.info('For HARD Termination - Stop the process again')
finally:
if self.multi_gpu == MultiGPUMode.DISTRIBUTED_DATA_PARALLEL:
# CLEAN UP THE MULTI-GPU PROCESS GROUP WHEN DONE
if torch.distributed.is_initialized():
torch.distributed.destroy_process_group()
# PHASE.TRAIN_END
self.phase_callback_handler(Phase.POST_TRAINING, context)
if not self.ddp_silent_mode:
if self.model_checkpoints_location != 'local':
logger.info('[CLEANUP] - Saving Checkpoint files')
self.sg_logger.upload()
self.sg_logger.close()
@resolve_param('train_metrics_list', ListFactory(MetricsFactory()))
def _set_train_metrics(self, train_metrics_list):
self.train_metrics = MetricCollection(train_metrics_list)
@resolve_param('valid_metrics_list', ListFactory(MetricsFactory()))
def _set_valid_metrics(self, valid_metrics_list):
self.valid_metrics = MetricCollection(valid_metrics_list)
def _initialize_mixed_precision(self, mixed_precision_enabled: bool):
# SCALER IS ALWAYS INITIALIZED BUT IS DISABLED IF MIXED PRECISION WAS NOT SET
self.scaler = GradScaler(enabled=mixed_precision_enabled)
if mixed_precision_enabled:
assert self.device.startswith('cuda'), "mixed precision is not available for CPU"
if self.multi_gpu == MultiGPUMode.DATA_PARALLEL:
# IN DATAPARALLEL MODE WE NEED TO WRAP THE FORWARD FUNCTION OF OUR MODEL SO IT WILL RUN WITH AUTOCAST.
# BUT SINCE THE MODULE IS CLONED TO THE DEVICES ON EACH FORWARD CALL OF A DATAPARALLEL MODEL,
# WE HAVE TO REGISTER THE WRAPPER BEFORE EVERY FORWARD CALL
def hook(module, _):
module.forward = MultiGPUModeAutocastWrapper(module.forward)
self.net.module.register_forward_pre_hook(hook=hook)
if self.load_checkpoint:
scaler_state_dict = core_utils.get_param(self.checkpoint, 'scaler_state_dict')
if scaler_state_dict is None:
logger.warning(
'Mixed Precision - scaler state_dict not found in loaded model. This may case issues '
'with loss scaling')
else:
self.scaler.load_state_dict(scaler_state_dict)
def _validate_final_average_model(self, cleanup_snapshots_pkl_file=False):
"""
Testing the averaged model by loading the last saved average checkpoint and running test.
Will be loaded to each of DDP processes
:param cleanup_pkl_file: a flag for deleting the 10 best snapshots dictionary
"""
logger.info('RUNNING ADDITIONAL TEST ON THE AVERAGED MODEL...')
keep_state_dict = deepcopy(self.net.state_dict())
# SETTING STATE DICT TO THE AVERAGE MODEL FOR EVALUATION
average_model_ckpt_path = os.path.join(self.checkpoints_dir_path, self.average_model_checkpoint_filename)
average_model_sd = read_ckpt_state_dict(average_model_ckpt_path)['net']
self.net.load_state_dict(average_model_sd)
# testing the averaged model and save instead of best model if needed
averaged_model_results_tuple = self._validate_epoch(epoch=self.max_epochs)
# Reverting the current model
self.net.load_state_dict(keep_state_dict)
if not self.ddp_silent_mode:
# Adding values to sg_logger
# looping over last titles which corresponds to validation (and average model) metrics.
all_titles = self.results_titles[-1 * len(averaged_model_results_tuple):]
result_dict = {all_titles[i]: averaged_model_results_tuple[i] for i in
range(len(averaged_model_results_tuple))}
self.sg_logger.add_scalars(tag_scalar_dict=result_dict, global_step=self.max_epochs)
average_model_tb_titles = ['Averaged Model ' + x for x in
self.results_titles[-1 * len(averaged_model_results_tuple):]]
write_struct = ''
for ind, title in enumerate(average_model_tb_titles):
write_struct += '%s: %.3f \n ' % (title, averaged_model_results_tuple[ind])
self.sg_logger.add_scalar(title, averaged_model_results_tuple[ind], global_step=self.max_epochs)
self.sg_logger.add_text("Averaged_Model_Performance", write_struct, self.max_epochs)
if cleanup_snapshots_pkl_file:
self.model_weight_averaging.cleanup()
# FIXME - we need to resolve flake8's 'function is too complex' for this function
[docs] @deprecated(version='0.1', reason="directly predict using the nn_module") # noqa: C901
def predict(self, inputs, targets=None, half=False, normalize=False, verbose=False,
move_outputs_to_cpu=True):
"""
A fast predictor for a batch of inputs
:param inputs: torch.tensor or numpy.array
a batch of inputs
:param targets: torch.tensor()
corresponding labels - if non are given - accuracy will not be computed
:param verbose: bool
print the results to screen
:param normalize: bool
If true, normalizes the tensor according to the dataloader's normalization values
:param half:
Performs half precision evaluation
:param move_outputs_to_cpu:
Moves the results from the GPU to the CPU
:return: outputs, acc, net_time, gross_time
networks predictions, accuracy calculation, forward pass net time, function gross time
"""
transform_list = []
# Create a 'to_tensor' transformation and a place holder of input_t
if type(inputs) == torch.Tensor:
inputs_t = torch.zeros_like(inputs)
else:
transform_list.append(transforms.ToTensor())
inputs_t = torch.zeros(size=(inputs.shape[0], inputs.shape[3], inputs.shape[1], inputs.shape[2]))
# Create a normalization transformation
if normalize:
try:
mean, std = self.dataset_interface.lib_dataset_params['mean'], self.dataset_interface.lib_dataset_params['std']
except AttributeError:
raise AttributeError('In \'predict()\', Normalization is set to True while the dataset has no default '
'mean & std => deactivate normalization or inject it to the datasets library.')
transform_list.append(transforms.Normalize(mean, std))
# Compose all transformations into one
transformation = transforms.Compose(transform_list)
# Transform the input
for idx in range(len(inputs_t)):
inputs_t[idx] = transformation(inputs[idx])
# Timer instances
gross_timer = core_utils.Timer('cpu')
gross_timer.start()
net_timer = core_utils.Timer(self.device)
# Set network in eval mode
self.net.eval()
# Half is not supported on CPU
if self.device != 'cuda' and half:
half = False
logger.warning('NOTICE: half is set to True but is not supported on CPU ==> using full precision')
# Apply half precision to network and input
if half:
self.net.half()
inputs_t = inputs_t.half()
with torch.no_grad():
# Move input to compute device
inputs_t = inputs_t.to(self.device)
# Forward pass (timed...)
net_timer.start()
outputs = self.net(inputs_t)
net_time = net_timer.stop()
if move_outputs_to_cpu:
outputs = outputs.cpu()
gross_time = gross_timer.stop()
# Convert targets to tensor
targets = torch.tensor(targets) if (type(targets) != torch.Tensor and targets is not None) else targets
# Compute accuracy
acc = metrics.accuracy(outputs.float(), targets.cpu())[0] if targets is not None else None
acc_str = '%.2f' % acc if targets is not None else 'N/A'
if verbose:
logger.info('%s\nPredicted %d examples: \n\t%.2f ms (gross) --> %.2f ms (net)\n\tWith accuracy %s\n%s' %
('-' * 50, inputs_t.shape[0], gross_time, net_time, acc_str, '-' * 50))
# Undo the half precision
if half and not self.half_precision:
self.net = self.net.float()
return outputs, acc, net_time, gross_time
[docs] def compute_model_runtime(self, input_dims: tuple = None,
batch_sizes: Union[tuple, list, int] = (1, 8, 16, 32, 64),
verbose: bool = True):
"""
Compute the "atomic" inference time and throughput.
Atomic refers to calculating the forward pass independently, discarding effects such as data augmentation,
data upload to device, multi-gpu distribution etc.
:param input_dims: tuple
shape of a basic input to the network (without the first index) e.g. (3, 224, 224)
if None uses an input from the test loader
:param batch_sizes: int or list
Batch sizes for latency calculation
:param verbose: bool
Prints results to screen
:return: log: dict
Latency and throughput for each tested batch size
"""
assert input_dims or self.test_loader is not None, 'Must get \'input_dims\' or connect a dataset interface'
assert self.multi_gpu not in (MultiGPUMode.DATA_PARALLEL, MultiGPUMode.DISTRIBUTED_DATA_PARALLEL), \
'The model is on multiple GPUs, move it to a single GPU is order to compute runtime'
# TRANSFER THE MODEL TO EVALUATION MODE BUT REMEMBER THE MODE TO RETURN TO
was_in_training_mode = True if self.net.training else False
self.net.eval()
# INITIALIZE LOGS AND PRINTS
timer = core_utils.Timer(self.device)
logs = {}
log_print = f"{'-' * 35}\n" \
f"Batch Time per Batch Throughput\n" \
f"size (ms) (im/s)\n" \
f"{'-' * 35}\n"
# GET THE INPUT SHAPE FROM THE DATA LOADER IF NOT PROVIDED EXPLICITLY
input_dims = input_dims or next(iter(self.test_loader))[0].shape[1:]
# DEFINE NUMBER ACCORDING TO DEVICE
repetitions = 200 if self.device == 'cuda' else 20
# CREATE A LIST OF BATCH SIZES
batch_sizes = [batch_sizes] if type(batch_sizes) == int else batch_sizes
for batch_size in sorted(batch_sizes):
try:
# CREATE A RANDOM TENSOR AS INPUT
dummy_batch = torch.randn((batch_size, *input_dims), device=self.device)
# WARM UP
for _ in range(10):
_ = self.net(dummy_batch)
# RUN & TIME
accumulated_time = 0
with torch.no_grad():
for _ in range(repetitions):
timer.start()
_ = self.net(dummy_batch)
accumulated_time += timer.stop()
# PERFORMANCE CALCULATION
time_per_batch = accumulated_time / repetitions
throughput = batch_size * 1000 / time_per_batch
logs[batch_size] = {'time_per_batch': time_per_batch, 'throughput': throughput}
log_print += f"{batch_size:4.0f} {time_per_batch:12.1f} {throughput:12.0f}\n"
except RuntimeError as e:
# ONLY FOR THE CASE OF CUDA OUT OF MEMORY WE CATCH THE EXCEPTION AND CONTINUE THE FUNCTION
if 'CUDA out of memory' in str(e):
log_print += f"{batch_size:4d}\t{'CUDA out of memory':13s}\n"
else:
raise
# PRINT RESULTS
if verbose:
logger.info(log_print)
# RETURN THE MODEL TO THE PREVIOUS MODE
self.net.train(was_in_training_mode)
return logs
[docs] def get_arch_params(self):
return self.arch_params.to_dict()
[docs] def get_structure(self):
return self.net.module.structure
[docs] def get_architecture(self):
return self.architecture
[docs] def set_experiment_name(self, experiment_name):
self.experiment_name = experiment_name
[docs] def re_build_model(self, arch_params={}):
"""
arch_params : dict
Architecture H.P. e.g.: block, num_blocks, num_classes, etc.
:return:
"""
if 'num_classes' not in arch_params.keys():
if self.dataset_interface is None:
raise Exception('Error', 'Number of classes not defined in arch params and dataset is not defined')
else:
arch_params['num_classes'] = len(self.classes)
self.arch_params = core_utils.HpmStruct(**arch_params)
self.classes = self.arch_params.num_classes
self.net = self.architecture_cls(arch_params=self.arch_params)
# save the architecture for neural architecture search
if hasattr(self.net, 'structure'):
self.architecture = self.net.structure
self.net.to(self.device)
if self.multi_gpu == MultiGPUMode.DISTRIBUTED_DATA_PARALLEL:
logger.warning("Warning: distributed training is not supported in re_build_model()")
self.net = torch.nn.DataParallel(self.net,
device_ids=self.device_ids) if self.multi_gpu else core_utils.WrappedModel(
self.net)
[docs] def update_architecture(self, structure):
'''
architecture : str
Defines the network's architecture according to the options in models/all_architectures
load_checkpoint : bool
Loads a checkpoint according to experiment_name
arch_params : dict
Architecture H.P. e.g.: block, num_blocks, num_classes, etc.
:return:
'''
if hasattr(self.net.module, 'update_structure'):
self.net.module.update_structure(structure)
self.net.to(self.device)
else:
raise Exception("architecture is not valid for NAS")
[docs] def get_module(self):
return self.net
[docs] def set_module(self, module):
self.net = module
def _initialize_device(self, requested_device: str, requested_multi_gpu: Union[MultiGPUMode, str]):
"""
_initialize_device - Initializes the device for the model - Default is CUDA
:param requested_device: Device to initialize ('cuda' / 'cpu')
:param requested_multi_gpu: Get Multiple GPU
"""
if isinstance(requested_multi_gpu, str):
requested_multi_gpu = MultiGPUMode(requested_multi_gpu)
# SELECT CUDA DEVICE
if requested_device == 'cuda':
if torch.cuda.is_available():
self.device = 'cuda' # TODO - we may want to set the device number as well i.e. 'cuda:1'
else:
raise RuntimeError('CUDA DEVICE NOT FOUND... EXITING')
# SELECT CPU DEVICE
elif requested_device == 'cpu':
self.device = 'cpu'
self.multi_gpu = False
else:
# SELECT CUDA DEVICE BY DEFAULT IF AVAILABLE
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
# DEFUALT IS SET TO 1 - IT IS CHANGED IF MULTI-GPU IS USED
self.num_devices = 1
# IN CASE OF MULTIPLE GPUS UPDATE THE LEARNING AND DATA PARAMETERS
# FIXME - CREATE A DISCUSSION ON THESE PARAMETERS - WE MIGHT WANT TO CHANGE THE WAY WE USE THE LR AND
if requested_multi_gpu != MultiGPUMode.OFF:
if 'cuda' in self.device:
# COLLECT THE AVAILABLE GPU AND COUNT THE AVAILABLE GPUS AMOUNT
self.device_ids = list(range(torch.cuda.device_count()))
self.num_devices = len(self.device_ids)
if self.num_devices == 1:
self.multi_gpu = MultiGPUMode.OFF
if requested_multi_gpu != MultiGPUMode.AUTO:
# if AUTO mode was set - do not log a warning
logger.warning('\n[WARNING] - Tried running on multiple GPU but only a single GPU is available\n')
else:
if requested_multi_gpu == MultiGPUMode.AUTO:
if env_helpers.is_distributed():
requested_multi_gpu = MultiGPUMode.DISTRIBUTED_DATA_PARALLEL
else:
requested_multi_gpu = MultiGPUMode.DATA_PARALLEL
self.multi_gpu = requested_multi_gpu
if self.multi_gpu == MultiGPUMode.DISTRIBUTED_DATA_PARALLEL:
self._initialize_ddp()
else:
# MULTIPLE GPUS CAN BE ACTIVE ONLY IF A GPU IS AVAILABLE
self.multi_gpu = MultiGPUMode.OFF
logger.warning('\n[WARNING] - Tried running on multiple GPU but none are available => running on CPU\n')
def _initialize_ddp(self):
"""
Initializes Distributed Data Parallel
Usage:
python -m torch.distributed.launch --nproc_per_node=n YOUR_TRAINING_SCRIPT.py
where n is the number of GPUs required, e.g., n=8
Important note: (1) in distributed training it is customary to specify learning rates and batch sizes per GPU.
Whatever learning rate and schedule you specify will be applied to the each GPU individually.
Since gradients are passed and summed (reduced) from all to all GPUs, the effective batch size is the
batch you specify times the number of GPUs. In the literature there are several "best practices" to set
learning rates and schedules for large batch sizes.
"""
logger.info("Distributed training starting...")
local_rank = environment_config.DDP_LOCAL_RANK
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend='nccl', init_method='env://')
if local_rank > 0:
f = open(os.devnull, 'w')
sys.stdout = f # silent all printing for non master process
torch.cuda.set_device(local_rank)
self.device = 'cuda:%d' % local_rank
# MAKE ALL HIGHER-RANK GPUS SILENT (DISTRIBUTED MODE)
self.ddp_silent_mode = local_rank > 0
if torch.distributed.get_rank() == 0:
logger.info(f"Training in distributed mode... with {str(torch.distributed.get_world_size())} GPUs")
def _switch_device(self, new_device):
self.device = new_device
self.net.to(self.device)
# FIXME - we need to resolve flake8's 'function is too complex' for this function
def _load_checkpoint_to_model(self, strict: StrictLoad, load_backbone: bool, source_ckpt_folder_name: str,
load_ema_as_net: bool): # noqa: C901 - too complex
"""
Copies the source checkpoint to a local folder and loads the checkpoint's data to the model
:param strict: See StrictLoad class documentation for details.
:param load_backbone: loads the provided checkpoint to self.net.backbone instead of self.net
:param source_ckpt_folder_name: The folder where the checkpoint is saved. By default uses the self.experiment_name
NOTE: 'acc', 'epoch', 'optimizer_state_dict' and the logs are NOT loaded if self.zeroize_prev_train_params is True
"""
# GET LOCAL PATH TO THE CHECKPOINT FILE FIRST
ckpt_local_path = get_ckpt_local_path(source_ckpt_folder_name=source_ckpt_folder_name,
experiment_name=self.experiment_name,
ckpt_name=self.ckpt_name,
model_checkpoints_location=self.model_checkpoints_location,
external_checkpoint_path=self.external_checkpoint_path,
overwrite_local_checkpoint=self.overwrite_local_checkpoint,
load_weights_only=self.load_weights_only)
# LOAD CHECKPOINT TO MODEL
self.checkpoint = load_checkpoint_to_model(ckpt_local_path=ckpt_local_path,
load_backbone=load_backbone,
net=self.net,
strict=strict.value if isinstance(strict, StrictLoad) else strict,
load_weights_only=self.load_weights_only,
load_ema_as_net=load_ema_as_net)
if 'ema_net' in self.checkpoint.keys():
logger.warning("[WARNING] Main network has been loaded from checkpoint but EMA network exists as well. It "
" will only be loaded during validation when training with ema=True. ")
# UPDATE TRAINING PARAMS IF THEY EXIST & WE ARE NOT LOADING AN EXTERNAL MODEL's WEIGHTS
self.best_metric = self.checkpoint['acc'] if 'acc' in self.checkpoint.keys() else -1
self.start_epoch = self.checkpoint['epoch'] if 'epoch' in self.checkpoint.keys() else 0
def _prep_for_test(self, test_loader: torch.utils.data.DataLoader = None, loss=None, post_prediction_callback=None,
test_metrics_list=None,
loss_logging_items_names=None, test_phase_callbacks=None):
"""Run commands that are common to all SgModels"""
# SET THE MODEL IN evaluation STATE
self.net.eval()
# IF SPECIFIED IN THE FUNCTION CALL - OVERRIDE THE self ARGUMENTS
self.test_loader = test_loader or self.test_loader
self.criterion = loss or self.criterion
self.post_prediction_callback = post_prediction_callback or self.post_prediction_callback
self.loss_logging_items_names = loss_logging_items_names or self.loss_logging_items_names
self.phase_callbacks = test_phase_callbacks or self.phase_callbacks
if self.phase_callbacks is None:
self.phase_callbacks = []
if test_metrics_list:
self.test_metrics = MetricCollection(test_metrics_list)
self.phase_callbacks.append(MetricsUpdateCallback(Phase.TEST_BATCH_END))
self.phase_callback_handler = CallbackHandler(self.phase_callbacks)
# WHEN TESTING WITHOUT A LOSS FUNCTION- CREATE EPOCH HEADERS FOR PRINTS
if self.criterion is None:
self.loss_logging_items_names = []
if self.test_metrics is None:
raise ValueError("Metrics are required to perform test. Pass them through test_metrics_list arg when "
"calling test or through training_params when calling train(...)")
if self.test_loader is None:
raise ValueError("Test dataloader is required to perform test. Make sure to either pass it through "
"test_loader arg or calling connect_dataset_interface upon a DatasetInterface instance "
"with a non empty testset attribute.")
# RESET METRIC RUNNERS
self.test_metrics.reset()
self.test_metrics.to(self.device)
def _initialize_sg_logger_objects(self):
"""Initialize object that collect, write to disk, monitor and store remotely all training outputs"""
sg_logger = core_utils.get_param(self.training_params, 'sg_logger')
# OVERRIDE SOME PARAMETERS TO MAKE SURE THEY MATCH THE TRAINING PARAMETERS
general_sg_logger_params = {'experiment_name': self.experiment_name,
'storage_location': self.model_checkpoints_location,
'resumed': self.load_checkpoint,
'training_params': self.training_params,
'checkpoints_dir_path': self.checkpoints_dir_path}
if sg_logger is None:
raise RuntimeError('sg_logger must be defined in training params (see default_training_params)')
if isinstance(sg_logger, AbstractSGLogger):
self.sg_logger = sg_logger
elif isinstance(sg_logger, str):
sg_logger_params = core_utils.get_param(self.training_params, 'sg_logger_params', {})
if issubclass(SG_LOGGERS[sg_logger], BaseSGLogger):
sg_logger_params = {**sg_logger_params, **general_sg_logger_params}
if sg_logger not in SG_LOGGERS:
raise RuntimeError('sg_logger not defined in SG_LOGGERS')
self.sg_logger = SG_LOGGERS[sg_logger](**sg_logger_params)
else:
raise RuntimeError('sg_logger can be either an sg_logger name (str) or a subcalss of AbstractSGLogger')
if not isinstance(self.sg_logger, BaseSGLogger):
logger.warning("WARNING! Using a user-defined sg_logger: files will not be automatically written to disk!\n"
"Please make sure the provided sg_logger writes to disk or compose your sg_logger to BaseSGLogger")
# IN CASE SG_LOGGER UPDATED THE DIR PATH
self.checkpoints_dir_path = self.sg_logger.local_dir()
additional_log_items = {'initial_LR': self.training_params.initial_lr,
'num_devices': self.num_devices,
'multi_gpu': str(self.multi_gpu),
'device_type': torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'cpu'}
# ADD INSTALLED PACKAGE LIST + THEIR VERSIONS
if self.training_params.log_installed_packages:
pkg_list = list(map(lambda pkg: str(pkg), _get_installed_distributions()))
additional_log_items['installed_packages'] = pkg_list
self.sg_logger.add_config("hyper_params", {"arch_params": self.arch_params.__dict__,
"training_hyperparams": self.training_params.__dict__,
"dataset_params": self.dataset_params.__dict__,
"additional_log_items": additional_log_items})
self.sg_logger.flush()
def _write_to_disk_operations(self, train_metrics: tuple, validation_results: tuple, inf_time: float, epoch: int, context: PhaseContext):
"""Run the various logging operations, e.g.: log file, Tensorboard, save checkpoint etc."""
# STORE VALUES IN A TENSORBOARD FILE
train_results = list(train_metrics) + list(validation_results) + [inf_time]
all_titles = self.results_titles + ['Inference Time']
result_dict = {all_titles[i]: train_results[i] for i in range(len(train_results))}
self.sg_logger.add_scalars(tag_scalar_dict=result_dict, global_step=epoch)
# SAVE THE CHECKPOINT
if self.training_params.save_model:
self.save_checkpoint(self.optimizer, epoch + 1, validation_results, context)
def _write_lrs(self, epoch):
lrs = [self.optimizer.param_groups[i]['lr'] for i in range(len(self.optimizer.param_groups))]
lr_titles = ['LR/Param_group_' + str(i) for i in range(len(self.optimizer.param_groups))] if len(
self.optimizer.param_groups) > 1 else ['LR']
lr_dict = {lr_titles[i]: lrs[i] for i in range(len(lrs))}
self.sg_logger.add_scalars(tag_scalar_dict=lr_dict, global_step=epoch)
[docs] def test(self, # noqa: C901
test_loader: torch.utils.data.DataLoader = None,
loss: torch.nn.modules.loss._Loss = None,
silent_mode: bool = False,
test_metrics_list=None,
loss_logging_items_names=None, metrics_progress_verbose=False, test_phase_callbacks=None,
use_ema_net=True) -> tuple:
"""
Evaluates the model on given dataloader and metrics.
:param test_loader: dataloader to perform test on.
:param test_metrics_list: (list(torchmetrics.Metric)) metrics list for evaluation.
:param silent_mode: (bool) controls verbosity
:param metrics_progress_verbose: (bool) controls the verbosity of metrics progress (default=False). Slows down the program.
:param use_ema_net (bool) whether to perform test on self.ema_model.ema (when self.ema_model.ema exists,
otherwise self.net will be tested) (default=True)
:return: results tuple (tuple) containing the loss items and metric values.
All of the above args will override SgModel's corresponding attribute when not equal to None. Then evaluation
is ran on self.test_loader with self.test_metrics.
"""
# IN CASE TRAINING WAS PERFROMED BEFORE TEST- MAKE SURE TO TEST THE EMA MODEL (UNLESS SPECIFIED OTHERWISE BY
# use_ema_net)
if use_ema_net and self.ema_model is not None:
keep_model = self.net
self.net = self.ema_model.ema
self._prep_for_test(test_loader=test_loader,
loss=loss,
test_metrics_list=test_metrics_list,
loss_logging_items_names=loss_logging_items_names,
test_phase_callbacks=test_phase_callbacks,
)
test_results = self.evaluate(data_loader=self.test_loader,
metrics=self.test_metrics,
evaluation_type=EvaluationType.TEST,
silent_mode=silent_mode,
metrics_progress_verbose=metrics_progress_verbose)
# SWITCH BACK BETWEEN NETS SO AN ADDITIONAL TRAINING CAN BE DONE AFTER TEST
if use_ema_net and self.ema_model is not None:
self.net = keep_model
return test_results
def _validate_epoch(self, epoch: int, silent_mode: bool = False) -> tuple:
"""
Runs evaluation on self.valid_loader, with self.valid_metrics.
:param epoch: (int) epoch idx
:param silent_mode: (bool) controls verbosity
:return: results tuple (tuple) containing the loss items and metric values.
"""
self.net.eval()
self.valid_metrics.reset()
self.valid_metrics.to(self.device)
return self.evaluate(data_loader=self.valid_loader, metrics=self.valid_metrics,
evaluation_type=EvaluationType.VALIDATION, epoch=epoch, silent_mode=silent_mode)
[docs] def evaluate(self, data_loader: torch.utils.data.DataLoader, metrics: MetricCollection,
evaluation_type: EvaluationType, epoch: int = None, silent_mode: bool = False,
metrics_progress_verbose: bool = False):
"""
Evaluates the model on given dataloader and metrics.
:param data_loader: dataloader to perform evaluataion on
:param metrics: (MetricCollection) metrics for evaluation
:param evaluation_type: (EvaluationType) controls which phase callbacks will be used (for example, on batch end,
when evaluation_type=EvaluationType.VALIDATION the Phase.VALIDATION_BATCH_END callbacks will be triggered)
:param epoch: (int) epoch idx
:param silent_mode: (bool) controls verbosity
:param metrics_progress_verbose: (bool) controls the verbosity of metrics progress (default=False).
Slows down the program significantly.
:return: results tuple (tuple) containing the loss items and metric values.
"""
# THE DISABLE FLAG CONTROLS WHETHER THE PROGRESS BAR IS SILENT OR PRINTS THE LOGS
progress_bar_data_loader = tqdm(data_loader, bar_format="{l_bar}{bar:10}{r_bar}", dynamic_ncols=True,
disable=silent_mode)
loss_avg_meter = core_utils.utils.AverageMeter()
logging_values = None
loss_tuple = None
lr_warmup_epochs = self.training_params.lr_warmup_epochs if self.training_params else None
context = PhaseContext(epoch=epoch,
metrics_compute_fn=metrics,
loss_avg_meter=loss_avg_meter,
criterion=self.criterion,
device=self.device,
lr_warmup_epochs=lr_warmup_epochs,
sg_logger=self.sg_logger)
if not silent_mode:
# PRINT TITLES
pbar_start_msg = f"Validation epoch {epoch}" if evaluation_type == EvaluationType.VALIDATION else "Test"
progress_bar_data_loader.set_description(pbar_start_msg)
with torch.no_grad():
for batch_idx, batch_items in enumerate(progress_bar_data_loader):
batch_items = core_utils.tensor_container_to_device(batch_items, self.device, non_blocking=True)
inputs, targets, additional_batch_items = sg_model_utils.unpack_batch_items(batch_items)
output = self.net(inputs)
if self.criterion is not None:
# STORE THE loss_items ONLY, THE 1ST RETURNED VALUE IS THE loss FOR BACKPROP DURING TRAINING
loss_tuple = self._get_losses(output, targets)[1].cpu()
context.update_context(batch_idx=batch_idx,
inputs=inputs,
preds=output,
target=targets,
loss_log_items=loss_tuple,
**additional_batch_items)
# TRIGGER PHASE CALLBACKS CORRESPONDING TO THE EVALUATION TYPE
if evaluation_type == EvaluationType.VALIDATION:
self.phase_callback_handler(Phase.VALIDATION_BATCH_END, context)
else:
self.phase_callback_handler(Phase.TEST_BATCH_END, context)
# COMPUTE METRICS IF PROGRESS VERBOSITY IS SET
if metrics_progress_verbose and not silent_mode:
# COMPUTE THE RUNNING USER METRICS AND LOSS RUNNING ITEMS. RESULT TUPLE IS THEIR CONCATENATION.
logging_values = get_logging_values(loss_avg_meter, metrics, self.criterion)
pbar_message_dict = get_train_loop_description_dict(logging_values,
metrics,
self.loss_logging_items_names)
progress_bar_data_loader.set_postfix(**pbar_message_dict)
# NEED TO COMPUTE METRICS FOR THE FIRST TIME IF PROGRESS VERBOSITY IS NOT SET
if not metrics_progress_verbose:
# COMPUTE THE RUNNING USER METRICS AND LOSS RUNNING ITEMS. RESULT TUPLE IS THEIR CONCATENATION.
logging_values = get_logging_values(loss_avg_meter, metrics, self.criterion)
pbar_message_dict = get_train_loop_description_dict(logging_values,
metrics,
self.loss_logging_items_names)
progress_bar_data_loader.set_postfix(**pbar_message_dict)
# TODO: SUPPORT PRINTING AP PER CLASS- SINCE THE METRICS ARE NOT HARD CODED ANYMORE (as done in
# calc_batch_prediction_accuracy_per_class in metric_utils.py), THIS IS ONLY RELEVANT WHEN CHOOSING
# DETECTIONMETRICS, WHICH ALREADY RETURN THE METRICS VALUEST HEMSELVES AND NOT THE ITEMS REQUIRED FOR SUCH
# COMPUTATION. ALSO REMOVE THE BELOW LINES BY IMPLEMENTING CRITERION AS A TORCHMETRIC.
if self.multi_gpu == MultiGPUMode.DISTRIBUTED_DATA_PARALLEL:
logging_values = reduce_results_tuple_for_ddp(logging_values, next(self.net.parameters()).device)
return logging_values