refine mobilenetv2 (#153)

* refine retinanet

* update case readme

* upadte case readme for bs=512

* remove 1x4 config

---------

Co-authored-by: zhouyu <[email protected]>

training/benchmarks/mobilenetv2/pytorch/config/_base.py

@@ -1,80 +1,61 @@
-from typing import ClassVar
-#from train.event.base import BaseTrainingEventInterface
+# DO NOT MODIFY THESE REQUIRED PARAMETERS
 
-# case info
-# chip vendor: nvidia, kunlunxin,  iluvatar, cambricon etc. key vendor is required.
+# Required parameters
 vendor: str = None
-# model name
-name: str = "MobileNetV2"
+data_dir: str = None
+name: str = "mobilenetv2"
+cudnn_benchmark: bool = False
+cudnn_deterministic: bool = True
+
+# Optional parameters
 
-do_train = True
-fp16 = True
 # =========================================================
 # data
 # =========================================================
-data_dir: str = None
 train_data: str = "train"
 eval_data: str = "val"
-output_dir: str = ""
-init_checkpoint: str = ""
 
 # =========================================================
-# train && evaluate
+# loss scale
 # =========================================================
-train_batch_size: int = 8
-eval_batch_size: int = 8
-dist_backend: str = 'nccl'
-
 lr: float = 0.045
-lr_step_size: int = 1
-lr_gamma: float = 0.98
-
 weight_decay: float = 0.00004
-gradient_accumulation_steps: int = 1
 momentum: float = 0.9
+lr_steps: list = 1
+lr_gamma: float = 0.98
 
-max_steps: int = 5005 * 300  # 300 epoch
-seed: int = 41
-# torch.backends.cudnn.benchmark
-cudnn_benchmark: bool = False
-# torch.backends.cudnn.deterministic
-cudnn_deterministic: bool = True
-
-# Stop training after reaching this accuracy
-target_acc1: float = 70.634
+# =========================================================
+# train && evaluate
+# =========================================================
+train_batch_size: int = 8
+eval_batch_size: int = 8
 
-# Sample to begin performing eval.
-eval_iter_start_samples: int = 100
+# https://github.com/pytorch/vision/blob/main/torchvision/models/mobilenetv2.py#L193
+target_acc1: float = 68.6
+# https://github.com/pytorch/vision/tree/main/references/classification
+max_epoch: int = 300
 
-# If set to -1, disable eval, else evaluate every eval_iter_samples during training
-eval_interval_samples: int = 5005 * 256 * 1  # 1 epoch
 
-# Total number of training samples to run.
-max_samples_termination: float = 5005 * 256 * 300  # 300 epoch
+do_train = True
+fp16 = False
+amp: bool = False
+distributed: bool = True
 
-# number workers for dataloader
+# =========================================================
+# utils
+# =========================================================
+seed: int = 41
+dist_backend: str = 'nccl'
 num_workers: int = 16
+device: str = None
 
-# local_rank for distributed training on gpus
-local_rank: int = 0
-# Whether to read local rank from ENVVAR
+# =========================================================
+# for driver
+# =========================================================
+local_rank: int = -1
 use_env: bool = True
-
-# Number of epochs to plan seeds for. Same set across all workers.
-num_epochs_to_generate_seeds_for: int = 2
-
-# frequency of logging loss. If not positive, no logging is provided for training loss
-log_freq: int = 10
-
-# Whether to resume training from checkpoint.
-# If set, precedes init_checkpoint/init_tf_checkpoint
-resume_from_checkpoint: bool = False
-
-# A object to provide some core components in training
-#training_event: ClassVar[BaseTrainingEventInterface] = None
-
-#training_event_instance: BaseTrainingEventInterface = None
-
-# device
-device: str = None
+log_freq: int = 100
+print_freq: int = 100
 n_device: int = 1
+sync_bn: bool = False
+gradient_accumulation_steps: int = 1

training/benchmarks/mobilenetv2/pytorch/config/mutable_params.py

@@ -1,9 +1,6 @@
 mutable_params = [
-    'train_data', 'eval_data', 'init_checkpoint', 'train_batch_size',
-    'eval_batch_size', 'dist_backend', 'lr', 'weight_decay',
-    'gradient_accumulation_steps', 'max_samples_termination', "vendor",
-    'cudnn_benchmark',
-    'cudnn_deterministic'
+    'vendor', 'data_dir', 'train_data', 'eval_data', 'lr', 'weight_decay',
+    'momentum', 'lr_steps', 'lr_gamma', 'train_batch_size', 'eval_batch_size',
+    'do_train', 'fp16', 'distributed', 'dist_backend', 'num_workers', 'device',
+    'cudnn_benchmark', 'cudnn_deterministic'
 ]
-
-mutable_params += ["local_rank", "do_train", "data_dir", "log_freq"]

training/benchmarks/mobilenetv2/pytorch/dataloaders/__init__.py

@@ -1,2 +0,0 @@
-from .dataloader import (build_train_dataset, build_eval_dataset,
-                         build_train_dataloader, build_eval_dataloader)

training/benchmarks/mobilenetv2/pytorch/dataloaders/dataloader.py

@@ -1,99 +1,89 @@
-# coding=utf-8
-
-import os
-import sys
-import random
-import numpy as np
-import torch
-from torch.utils.data import Dataset
-from torchvision import datasets, models, transforms
-import torch.distributed as dist
-from torch.utils.data.dataloader import default_collate
-
-CURR_PATH = os.path.abspath(os.path.dirname(__file__))
-sys.path.append(os.path.abspath(os.path.join(CURR_PATH, "../../../")))
-from driver import dist_pytorch
-
-
-def build_train_dataset(args):
-    traindir = os.path.join(args.data_dir, args.train_data)
-    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
-                                     std=[0.229, 0.224, 0.225])
-
-    train_dataset = datasets.ImageFolder(
-        traindir,
-        transforms.Compose([
-            transforms.RandomResizedCrop(224),
-            transforms.RandomHorizontalFlip(),
-            transforms.ToTensor(),
-            normalize,
-        ]))
-    return train_dataset
-
-
-def build_eval_dataset(args):
-    valdir = os.path.join(args.data_dir, args.eval_data)
-    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
-                                     std=[0.229, 0.224, 0.225])
-
-    val_dataset = datasets.ImageFolder(
-        valdir,
-        transforms.Compose([
-            transforms.Resize(256),
-            transforms.CenterCrop(224),
-            transforms.ToTensor(),
-            normalize,
-        ]))
-
-    return val_dataset
-
-
-def build_train_dataloader(train_dataset, args):
-    """Traing dataloaders."""
-    dist_pytorch.main_proc_print('building train dataloaders ...')
-
-    if torch.distributed.is_available() and torch.distributed.is_initialized():
-        train_sampler = torch.utils.data.distributed.DistributedSampler(
-            train_dataset)
-        dist_pytorch.main_proc_print(
-            f"use sampler: DistributedSampler, num_replicas:{args.n_device}")
-    else:
-        train_sampler = None
-
-    train_dataloader = torch.utils.data.DataLoader(
-        train_dataset,
-        batch_size=args.train_batch_size,
-        shuffle=(train_sampler is None),
-        num_workers=args.num_workers,
-        pin_memory=True,
-        sampler=train_sampler)
-
-    dist_pytorch.main_proc_print(
-        f'train samples:{len(train_dataset)}, batch size:{args.train_batch_size}'
-    )
-    return train_dataloader
-
-
-def build_eval_dataloader(eval_dataset, args):
-    """Traing and validation dataloaders."""
-    dist_pytorch.main_proc_print('building eval dataloaders ...')
-
-    if torch.distributed.is_available() and torch.distributed.is_initialized():
-        val_sampler = torch.utils.data.distributed.DistributedSampler(
-            eval_dataset, shuffle=False, drop_last=True)
-        dist_pytorch.main_proc_print(
-            f"use sampler: DistributedSampler, num_replicas:{args.n_device}")
-    else:
-        val_sampler = None
-
-    eval_dataloader = torch.utils.data.DataLoader(
-        eval_dataset,
-        batch_size=args.eval_batch_size,
-        shuffle=False,
-        num_workers=args.num_workers,
-        pin_memory=True,
-        sampler=val_sampler)
-
-    dist_pytorch.main_proc_print(
-        f'eval samples:{len(eval_dataset)}, batch size:{args.eval_batch_size}')
-    return eval_dataloader
+import os
+import torch
+import torch.utils.data
+import torchvision
+import torchvision.transforms as t
+
+
+class ToFloat16(object):
+
+    def __call__(self, tensor):
+        return tensor.to(dtype=torch.float16)
+
+
+def build_train_dataset(config):
+    normalize = t.Normalize(mean=[0.485, 0.456, 0.406],
+                            std=[0.229, 0.224, 0.225])
+    traindir = os.path.join(config.data_dir, config.train_data)
+    if config.fp16:
+        dataset = torchvision.datasets.ImageFolder(
+            traindir,
+            t.Compose([
+                t.RandomResizedCrop(224),
+                t.RandomHorizontalFlip(),
+                t.ToTensor(),
+                ToFloat16(), normalize
+            ]))
+    else:
+        dataset = torchvision.datasets.ImageFolder(
+            traindir,
+            t.Compose([
+                t.RandomResizedCrop(224),
+                t.RandomHorizontalFlip(),
+                t.ToTensor(), normalize
+            ]))
+    return dataset
+
+
+def build_eval_dataset(config):
+    normalize = t.Normalize(mean=[0.485, 0.456, 0.406],
+                            std=[0.229, 0.224, 0.225])
+    evaldir = os.path.join(config.data_dir, config.eval_data)
+    if config.fp16:
+        dataset = torchvision.datasets.ImageFolder(
+            evaldir,
+            t.Compose([
+                t.Resize(256),
+                t.CenterCrop(224),
+                t.ToTensor(),
+                ToFloat16(), normalize
+            ]))
+    else:
+        dataset = torchvision.datasets.ImageFolder(
+            evaldir,
+            t.Compose(
+                [t.Resize(256),
+                 t.CenterCrop(224),
+                 t.ToTensor(), normalize]))
+    return dataset
+
+
+def build_train_dataloader(dataset, config):
+    if config.distributed:
+        train_sampler = torch.utils.data.distributed.DistributedSampler(
+            dataset)
+    else:
+        train_sampler = torch.utils.data.RandomSampler(dataset)
+
+    data_loader = torch.utils.data.DataLoader(
+        dataset,
+        batch_size=config.train_batch_size,
+        sampler=train_sampler,
+        num_workers=config.num_workers,
+        pin_memory=True)
+    return data_loader
+
+
+def build_eval_dataloader(dataset, config):
+    if config.distributed:
+        test_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
+    else:
+        test_sampler = torch.utils.data.SequentialSampler(dataset)
+
+    data_loader = torch.utils.data.DataLoader(
+        dataset,
+        batch_size=config.eval_batch_size,
+        sampler=test_sampler,
+        num_workers=config.num_workers,
+        pin_memory=True)
+    return data_loader

training/benchmarks/mobilenetv2/pytorch/model/__init__.py

@@ -1,7 +1,5 @@
-import torch
-import torchvision
+from torchvision.models import mobilenet_v2
 
 
-def create_model(config):
-    model = torchvision.models.mobilenet_v2()
-    return model
+def create_model():
+    return mobilenet_v2()

training/benchmarks/mobilenetv2/pytorch/optimizers/__init__.py

@@ -0,0 +1,10 @@
+from torch.optim import SGD
+
+
+def create_optimizer(model, args):
+    params = [p for p in model.parameters() if p.requires_grad]
+    opt = SGD(params,
+              lr=args.lr,
+              momentum=args.momentum,
+              weight_decay=args.weight_decay)
+    return opt