Cleanup tranforms, add custom schedulers, tweak senet34 model

dataset.py

@@ -9,6 +9,7 @@ import re
 import torch
 from PIL import Image
 
+
 IMG_EXTENSIONS = ['.png', '.jpg', '.jpeg']
 
 
@@ -53,7 +54,7 @@ class Dataset(data.Dataset):
     def __init__(
             self,
             root,
-            transform=None):
+            transform):
 
         imgs, _, _ = find_images_and_targets(root)
         if len(imgs) == 0:
@@ -66,7 +67,6 @@ class Dataset(data.Dataset):
     def __getitem__(self, index):
         path, target = self.imgs[index]
         img = Image.open(path).convert('RGB')
-        if self.transform is not None:
         img = self.transform(img)
         if target is None:
             target = torch.zeros(1).long()
@@ -75,9 +75,6 @@ class Dataset(data.Dataset):
     def __len__(self):
         return len(self.imgs)
 
-    def set_transform(self, transform):
-        self.transform = transform
-
     def filenames(self, indices=[], basename=False):
         if indices:
             if basename:

models/__init__.py

@@ -1 +1,2 @@
-from .model_factory import create_model, get_transforms_eval, get_transforms_train
+from .model_factory import create_model
+from .transforms import transforms_imagenet_eval, transforms_imagenet_train

models/model_factory.py

@@ -129,66 +129,3 @@ def load_checkpoint(model, checkpoint_path):
     else:
         print("Error: No checkpoint found at %s." % checkpoint_path)
 
-
-class LeNormalize(object):
-    """Normalize to -1..1 in Google Inception style
-    """
-    def __call__(self, tensor):
-        for t in tensor:
-            t.sub_(0.5).mul_(2.0)
-        return tensor
-
-
-DEFAULT_CROP_PCT = 0.875
-
-
-def get_transforms_train(model_name, img_size=224):
-    if 'dpn' in model_name:
-        normalize = transforms.Normalize(
-            mean=[124 / 255, 117 / 255, 104 / 255],
-            std=[1 / (.0167 * 255)] * 3)
-    elif 'inception' in model_name:
-        normalize = LeNormalize()
-    else:
-        normalize = transforms.Normalize(
-            mean=[0.485, 0.456, 0.406],
-            std=[0.229, 0.224, 0.225])
-
-    return transforms.Compose([
-        transforms.RandomResizedCrop(img_size, scale=(0.3, 1.0)),
-        transforms.RandomHorizontalFlip(),
-        transforms.ColorJitter(0.3, 0.3, 0.3),
-        transforms.ToTensor(),
-        normalize])
-
-
-def get_transforms_eval(model_name, img_size=224, crop_pct=None):
-    crop_pct = crop_pct or DEFAULT_CROP_PCT
-    if 'dpn' in model_name:
-        if crop_pct is None:
-            # Use default 87.5% crop for model's native img_size
-            # but use 100% crop for larger than native as it
-            # improves test time results across all models.
-            if img_size == 224:
-                scale_size = int(math.floor(img_size / DEFAULT_CROP_PCT))
-            else:
-                scale_size = img_size
-        else:
-            scale_size = int(math.floor(img_size / crop_pct))
-        normalize = transforms.Normalize(
-            mean=[124 / 255, 117 / 255, 104 / 255],
-            std=[1 / (.0167 * 255)] * 3)
-    elif 'inception' in model_name:
-        scale_size = int(math.floor(img_size / crop_pct))
-        normalize = LeNormalize()
-    else:
-        scale_size = int(math.floor(img_size / crop_pct))
-        normalize = transforms.Normalize(
-            mean=[0.485, 0.456, 0.406],
-            std=[0.229, 0.224, 0.225])
-
-    return transforms.Compose([
-        transforms.Resize(scale_size, Image.BICUBIC),
-        transforms.CenterCrop(img_size),
-        transforms.ToTensor(),
-        normalize])

models/senet.py

@@ -441,7 +441,7 @@ def senet154(num_classes=1000, pretrained='imagenet'):
 
 
 def se_resnet18(num_classes=1000, pretrained='imagenet'):
-    model = SENet(SEResNetBottleneck, [2, 2, 2, 2], groups=1, reduction=16,
+    model = SENet(SEResNetBlock, [2, 2, 2, 2], groups=1, reduction=16,
                   dropout_p=None, inplanes=64, input_3x3=False,
                   downsample_kernel_size=1, downsample_padding=0,
                   num_classes=num_classes)

models/transforms.py

@@ -0,0 +1,73 @@
+import torch
+from torchvision import transforms
+from PIL import Image
+import math
+
+
+DEFAULT_CROP_PCT = 0.875
+
+IMAGENET_DPN_MEAN = [124 / 255, 117 / 255, 104 / 255]
+IMAGENET_DPN_STD = [1 / (.0167 * 255)] * 3
+IMAGENET_DEFAULT_MEAN = [0.485, 0.456, 0.406]
+IMAGENET_DEFAULT_STD = [0.229, 0.224, 0.225]
+
+
+class LeNormalize(object):
+    """Normalize to -1..1 in Google Inception style
+    """
+    def __call__(self, tensor):
+        for t in tensor:
+            t.sub_(0.5).mul_(2.0)
+        return tensor
+
+
+def transforms_imagenet_train(model_name, img_size=224, scale=(0.08, 1.0), color_jitter=(0.3, 0.3, 0.3)):
+    if 'dpn' in model_name:
+        normalize = transforms.Normalize(
+            mean=IMAGENET_DPN_MEAN,
+            std=IMAGENET_DPN_STD)
+    elif 'inception' in model_name:
+        normalize = LeNormalize()
+    else:
+        normalize = transforms.Normalize(
+            mean=IMAGENET_DEFAULT_MEAN,
+            std=IMAGENET_DEFAULT_STD)
+
+    return transforms.Compose([
+        transforms.RandomResizedCrop(img_size, scale=scale),
+        transforms.RandomHorizontalFlip(),
+        transforms.ColorJitter(*color_jitter),
+        transforms.ToTensor(),
+        normalize])
+
+
+def transforms_imagenet_eval(model_name, img_size=224, crop_pct=None):
+    crop_pct = crop_pct or DEFAULT_CROP_PCT
+    if 'dpn' in model_name:
+        if crop_pct is None:
+            # Use default 87.5% crop for model's native img_size
+            # but use 100% crop for larger than native as it
+            # improves test time results across all models.
+            if img_size == 224:
+                scale_size = int(math.floor(img_size / DEFAULT_CROP_PCT))
+            else:
+                scale_size = img_size
+        else:
+            scale_size = int(math.floor(img_size / crop_pct))
+        normalize = transforms.Normalize(
+            mean=IMAGENET_DPN_MEAN,
+            std=IMAGENET_DPN_STD)
+    elif 'inception' in model_name:
+        scale_size = int(math.floor(img_size / crop_pct))
+        normalize = LeNormalize()
+    else:
+        scale_size = int(math.floor(img_size / crop_pct))
+        normalize = transforms.Normalize(
+            mean=IMAGENET_DEFAULT_MEAN,
+            std=IMAGENET_DEFAULT_STD)
+
+    return transforms.Compose([
+        transforms.Resize(scale_size, Image.BICUBIC),
+        transforms.CenterCrop(img_size),
+        transforms.ToTensor(),
+        normalize])

scheduler/__init__.py

@@ -0,0 +1,3 @@
+from .cosine_lr import CosineLRScheduler
+from .plateau_lr import PlateauLRScheduler
+from .step_lr import StepLRScheduler

scheduler/cosine_lr.py

@@ -0,0 +1,72 @@
+import logging
+import math
+import numpy as np
+import torch
+
+from .scheduler import Scheduler
+
+
+logger = logging.getLogger(__name__)
+
+
+class CosineLRScheduler(Scheduler):
+    """
+    Cosine annealing with restarts.
+    This is described in the paper https://arxiv.org/abs/1608.03983.
+    """
+
+    def __init__(self,
+                 optimizer: torch.optim.Optimizer,
+                 t_initial: int,
+                 t_mul: float = 1.,
+                 lr_min: float = 0.,
+                 decay_rate: float = 1.,
+                 warmup_updates=0,
+                 warmup_lr_init=0,
+                 initialize=True) -> None:
+        super().__init__(optimizer, param_group_field="lr", initialize=initialize)
+
+        assert t_initial > 0
+        assert lr_min >= 0
+        if t_initial == 1 and t_mul == 1 and decay_rate == 1:
+            logger.warning("Cosine annealing scheduler will have no effect on the learning "
+                           "rate since t_initial = t_mul = eta_mul = 1.")
+        self.t_initial = t_initial
+        self.t_mul = t_mul
+        self.lr_min = lr_min
+        self.decay_rate = decay_rate
+        self.warmup_updates = warmup_updates
+        self.warmup_lr_init = warmup_lr_init
+        if self.warmup_updates:
+            self.warmup_steps = [(v - warmup_lr_init) / self.warmup_updates for v in self.base_values]
+        else:
+            self.warmup_steps = [1 for _ in self.base_values]
+        if self.warmup_lr_init:
+            super().update_groups(self.warmup_lr_init)
+
+    def get_epoch_values(self, epoch: int):
+        # this scheduler doesn't update on epoch
+        return None
+
+    def get_update_values(self, num_updates: int):
+        if num_updates < self.warmup_updates:
+            lrs = [self.warmup_lr_init + num_updates * s for s in self.warmup_steps]
+        else:
+            curr_updates = num_updates - self.warmup_updates
+            if self.t_mul != 1:
+                i = math.floor(math.log(1 - curr_updates / self.t_initial * (1 - self.t_mul), self.t_mul))
+                t_i = self.t_mul ** i * self.t_initial
+                t_curr = curr_updates - (1 - self.t_mul ** i) / (1 - self.t_mul) * self.t_initial
+            else:
+                i = curr_updates // self.t_initial
+                t_i = self.t_initial
+                t_curr = curr_updates - (self.t_initial * i)
+
+            gamma = self.decay_rate ** i
+            lr_min = self.lr_min * gamma
+            lr_max_values = [v * gamma for v in self.base_values]
+
+            lrs = [
+                lr_min + 0.5 * (lr_max - lr_min) * (1 + math.cos(math.pi * t_curr / t_i)) for lr_max in lr_max_values
+            ]
+        return lrs

scheduler/plateau_lr.py

@@ -0,0 +1,68 @@
+import torch
+
+from .scheduler import Scheduler
+
+
+class PlateauLRScheduler(Scheduler):
+    """Decay the LR by a factor every time the validation loss plateaus."""
+
+    def __init__(self,
+                 optimizer,
+                 factor=0.1,
+                 patience=10,
+                 verbose=False,
+                 threshold=1e-4,
+                 cooldown_epochs=0,
+                 warmup_updates=0,
+                 warmup_lr_init=0,
+                 lr_min=0,
+                 ):
+        super().__init__(optimizer, 'lr', initialize=False)
+
+        self.lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
+            self.optimizer.optimizer,
+            patience=patience,
+            factor=factor,
+            verbose=verbose,
+            threshold=threshold,
+            cooldown=cooldown_epochs,
+            min_lr=lr_min
+        )
+
+        self.warmup_updates = warmup_updates
+        self.warmup_lr_init = warmup_lr_init
+
+        if self.warmup_updates:
+            self.warmup_active = warmup_updates > 0  # this state updates with num_updates
+            self.warmup_steps = [(v - warmup_lr_init) / self.warmup_updates for v in self.base_values]
+            super().update_groups(self.warmup_lr_init)
+        else:
+            self.warmup_steps = [1 for _ in self.base_values]
+
+    def state_dict(self):
+        return {
+            'best': self.lr_scheduler.best,
+            'last_epoch': self.lr_scheduler.last_epoch,
+        }
+
+    def load_state_dict(self, state_dict):
+        self.lr_scheduler.best = state_dict['best']
+        if 'last_epoch' in state_dict:
+            self.lr_scheduler.last_epoch = state_dict['last_epoch']
+
+    # override the base class step fn completely
+    def step(self, epoch, val_loss=None):
+        """Update the learning rate at the end of the given epoch."""
+        if val_loss is not None and not self.warmup_active:
+            self.lr_scheduler.step(val_loss, epoch)
+        else:
+            self.lr_scheduler.last_epoch = epoch
+
+    def get_update_values(self, num_updates: int):
+        if num_updates < self.warmup_updates:
+            lrs = [self.warmup_lr_init + num_updates * s for s in self.warmup_steps]
+        else:
+            self.warmup_active = False  # warmup cancelled by first update past warmup_update count
+            lrs = None  # no change on update after warmup stage
+        return lrs
+

scheduler/scheduler.py

@@ -0,0 +1,73 @@
+from typing import Dict, Any
+
+import torch
+
+
+class Scheduler:
+    """ Parameter Scheduler Base Class
+    A scheduler base class that can be used to schedule any optimizer parameter groups.
+
+    Unlike the builtin PyTorch schedulers, this is intended to be consistently called
+    * At the END of each epoch, before incrementing the epoch count, to calculate next epoch's value
+    * At the END of each optimizer update, after incrementing the update count, to calculate next update's value
+
+    The schedulers built on this should try to remain as stateless as possible (for simplicity).
+
+    This family of schedulers is attempting to avoid the confusion of the meaning of 'last_epoch'
+    and -1 values for special behaviour. All epoch and update counts must be tracked in the training
+    code and explicitly passed in to the schedulers on the corresponding step or step_update call.
+
+    Based on ideas from:
+     * https://github.com/pytorch/fairseq/tree/master/fairseq/optim/lr_scheduler
+     * https://github.com/allenai/allennlp/tree/master/allennlp/training/learning_rate_schedulers
+    """
+
+    def __init__(self,
+                 optimizer: torch.optim.Optimizer,
+                 param_group_field: str,
+                 initialize: bool = True) -> None:
+        self.optimizer = optimizer
+        self.param_group_field = param_group_field
+        self._initial_param_group_field = f"initial_{param_group_field}"
+        if initialize:
+            for i, group in enumerate(self.optimizer.param_groups):
+                if param_group_field not in group:
+                    raise KeyError(f"{param_group_field} missing from param_groups[{i}]")
+                group.setdefault(self._initial_param_group_field, group[param_group_field])
+        else:
+            for i, group in enumerate(self.optimizer.param_groups):
+                if self._initial_param_group_field not in group:
+                    raise KeyError(f"{self._initial_param_group_field} missing from param_groups[{i}]")
+        self.base_values = [group[self._initial_param_group_field] for group in self.optimizer.param_groups]
+        self.metric = None  # any point to having this for all?
+        self.update_groups(self.base_values)
+
+    def state_dict(self) -> Dict[str, Any]:
+        return {key: value for key, value in self.__dict__.items() if key != 'optimizer'}
+
+    def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
+        self.__dict__.update(state_dict)
+
+    def get_epoch_values(self, epoch: int):
+        return None
+
+    def get_update_values(self, num_updates: int):
+        return None
+
+    def step(self, epoch: int, metric: float = None) -> None:
+        self.metric = metric
+        values = self.get_epoch_values(epoch)
+        if values is not None:
+            self.update_groups(values)
+
+    def step_update(self, num_updates: int, metric: float = None):
+        self.metric = metric
+        values = self.get_update_values(num_updates)
+        if values is not None:
+            self.update_groups(values)
+
+    def update_groups(self, values):
+        if not isinstance(values, (list, tuple)):
+            values = [values] * len(self.optimizer.param_groups)
+        for param_group, value in zip(self.optimizer.param_groups, values):
+            param_group[self.param_group_field] = value

scheduler/step_lr.py

@@ -0,0 +1,48 @@
+import math
+import torch
+
+from .scheduler import Scheduler
+
+
+class StepLRScheduler(Scheduler):
+    """
+    """
+
+    def __init__(self,
+                 optimizer: torch.optim.Optimizer,
+                 decay_epochs: int,
+                 decay_rate: float = 1.,
+                 warmup_updates=0,
+                 warmup_lr_init=0,
+                 initialize=True) -> None:
+        super().__init__(optimizer, param_group_field="lr", initialize=initialize)
+
+        self.decay_epochs = decay_epochs
+        self.decay_rate = decay_rate
+        self.warmup_updates = warmup_updates
+        self.warmup_lr_init = warmup_lr_init
+
+        if self.warmup_updates:
+            self.warmup_active = warmup_updates > 0  # this state updates with num_updates
+            self.warmup_steps = [(v - warmup_lr_init) / self.warmup_updates for v in self.base_values]
+            super().update_groups(self.warmup_lr_init)
+        else:
+            self.warmup_steps = [1 for _ in self.base_values]
+
+    def get_epoch_values(self, epoch: int):
+        if not self.warmup_active:
+            lrs = [v * (self.decay_rate ** ((epoch + 1) // self.decay_epochs))
+                   for v in self.base_values]
+        else:
+            lrs = None  # no epoch updates while warming up
+        return lrs
+
+    def get_update_values(self, num_updates: int):
+        if num_updates < self.warmup_updates:
+            lrs = [self.warmup_lr_init + num_updates * s for s in self.warmup_steps]
+        else:
+            self.warmup_active = False  # warmup cancelled by first update past warmup_update count
+            lrs = None  # no change on update afte warmup stage
+        return lrs
+
+

train.py

@@ -6,9 +6,10 @@ from collections import OrderedDict
 from datetime import datetime
 
 from dataset import Dataset
-from models import model_factory, get_transforms_eval, get_transforms_train
+from models import model_factory, transforms_imagenet_eval, transforms_imagenet_train
 from utils import *
 from optim import nadam
+import scheduler
 
 import torch
 import torch.nn
@@ -48,6 +49,8 @@ parser.add_argument('--decay-epochs', type=int, default=30, metavar='N',
                     help='epoch interval to decay LR')
 parser.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE',
                     help='LR decay rate (default: 0.1)')
+parser.add_argument('--sched', default='step', type=str, metavar='SCHEDULER',
+                    help='LR scheduler (default: "step"')
 parser.add_argument('--drop', type=float, default=0.0, metavar='DROP',
                     help='Dropout rate (default: 0.1)')
 parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
@@ -93,22 +96,9 @@ def main():
     num_epochs = args.epochs
     torch.manual_seed(args.seed)
 
-    model = model_factory.create_model(
-        args.model,
-        pretrained=args.pretrained,
-        num_classes=1000,
-        drop_rate=args.drop,
-        global_pool=args.gp,
-        checkpoint_path=args.initial_checkpoint)
-
-    if args.initial_batch_size:
-        batch_size = adjust_batch_size(
-            epoch=0, initial_bs=args.initial_batch_size, target_bs=args.batch_size)
-        print('Setting batch-size to %d' % batch_size)
-
     dataset_train = Dataset(
         os.path.join(args.data, 'train'),
-        transform=get_transforms_train(args.model))
+        transform=transforms_imagenet_train(args.model))
 
     loader_train = data.DataLoader(
         dataset_train,
@@ -119,7 +109,7 @@ def main():
 
     dataset_eval = Dataset(
         os.path.join(args.data, 'validation'),
-        transform=get_transforms_eval(args.model))
+        transform=transforms_imagenet_eval(args.model))
 
     loader_eval = data.DataLoader(
         dataset_eval,
@@ -128,38 +118,17 @@ def main():
         num_workers=args.workers
     )
 
-    train_loss_fn = validate_loss_fn = torch.nn.CrossEntropyLoss()
-    train_loss_fn = train_loss_fn.cuda()
-    validate_loss_fn = validate_loss_fn.cuda()
-
-    if args.opt.lower() == 'sgd':
-        optimizer = optim.SGD(
-            model.parameters(), lr=args.lr,
-            momentum=args.momentum, weight_decay=args.weight_decay, nesterov=True)
-    elif args.opt.lower() == 'adam':
-        optimizer = optim.Adam(
-            model.parameters(), lr=args.lr, weight_decay=args.weight_decay, eps=args.opt_eps)
-    elif args.opt.lower() == 'nadam':
-        optimizer = nadam.Nadam(
-            model.parameters(), lr=args.lr, weight_decay=args.weight_decay, eps=args.opt_eps)
-    elif args.opt.lower() == 'adadelta':
-        optimizer = optim.Adadelta(
-            model.parameters(), lr=args.lr, weight_decay=args.weight_decay, eps=args.opt_eps)
-    elif args.opt.lower() == 'rmsprop':
-        optimizer = optim.RMSprop(
-            model.parameters(), lr=args.lr, alpha=0.9, eps=args.opt_eps,
-            momentum=args.momentum, weight_decay=args.weight_decay)
-    else:
-        assert False and "Invalid optimizer"
-        exit(1)
-
-    if not args.decay_epochs:
-        lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=8)
-    else:
-        lr_scheduler = None
+    model = model_factory.create_model(
+        args.model,
+        pretrained=args.pretrained,
+        num_classes=1000,
+        drop_rate=args.drop,
+        global_pool=args.gp,
+        checkpoint_path=args.initial_checkpoint)
 
     # optionally resume from a checkpoint
     start_epoch = 0 if args.start_epoch is None else args.start_epoch
+    optimizer_state = None
     if args.resume:
         if os.path.isfile(args.resume):
             print("=> loading checkpoint '{}'".format(args.resume))
@@ -174,7 +143,7 @@ def main():
                     new_state_dict[name] = v
                 model.load_state_dict(new_state_dict)
                 if 'optimizer' in checkpoint:
-                    optimizer.load_state_dict(checkpoint['optimizer'])
+                    optimizer_state = checkpoint['optimizer']
                 print("=> loaded checkpoint '{}' (epoch {})".format(args.resume, checkpoint['epoch']))
                 start_epoch = checkpoint['epoch'] if args.start_epoch is None else args.start_epoch
             else:
@@ -183,55 +152,73 @@ def main():
             print("=> no checkpoint found at '{}'".format(args.resume))
             return False
 
-    saver = CheckpointSaver(checkpoint_dir=output_dir)
-
     if args.num_gpu > 1:
         model = torch.nn.DataParallel(model, device_ids=list(range(args.num_gpu))).cuda()
     else:
         model.cuda()
 
+    train_loss_fn = validate_loss_fn = torch.nn.CrossEntropyLoss()
+    train_loss_fn = train_loss_fn.cuda()
+    validate_loss_fn = validate_loss_fn.cuda()
+
+    if args.opt.lower() == 'sgd':
+        optimizer = optim.SGD(
+            model.parameters(), lr=args.lr,
+            momentum=args.momentum, weight_decay=args.weight_decay, nesterov=True)
+    elif args.opt.lower() == 'adam':
+        optimizer = optim.Adam(
+            model.parameters(), lr=args.lr, weight_decay=args.weight_decay, eps=args.opt_eps)
+    elif args.opt.lower() == 'nadam':
+        optimizer = nadam.Nadam(
+            model.parameters(), lr=args.lr, weight_decay=args.weight_decay, eps=args.opt_eps)
+    elif args.opt.lower() == 'adadelta':
+        optimizer = optim.Adadelta(
+            model.parameters(), lr=args.lr, weight_decay=args.weight_decay, eps=args.opt_eps)
+    elif args.opt.lower() == 'rmsprop':
+        optimizer = optim.RMSprop(
+            model.parameters(), lr=args.lr, alpha=0.9, eps=args.opt_eps,
+            momentum=args.momentum, weight_decay=args.weight_decay)
+    else:
+        assert False and "Invalid optimizer"
+        exit(1)
+
+    if optimizer_state is not None:
+        optimizer.load_state_dict(optimizer_state)
+
+    if args.sched == 'cosine':
+        lr_scheduler = scheduler.CosineLRScheduler(
+            optimizer,
+            t_initial=13 * len(loader_train),
+            t_mul=2.0,
+            lr_min=0,
+            decay_rate=0.5,
+            warmup_lr_init=1e-4,
+            warmup_updates=len(loader_train)
+        )
+    else:
+        lr_scheduler = scheduler.StepLRScheduler(
+            optimizer,
+            decay_epochs=args.decay_epochs,
+            decay_rate=args.decay_rate,
+        )
+
+    saver = CheckpointSaver(checkpoint_dir=output_dir)
     best_loss = None
     try:
         for epoch in range(start_epoch, num_epochs):
-            if args.decay_epochs:
-                adjust_learning_rate(
-                    optimizer, epoch, initial_lr=args.lr,
-                    decay_rate=args.decay_rate, decay_epochs=args.decay_epochs)
-
-            if args.initial_batch_size:
-                next_batch_size = adjust_batch_size(
-                    epoch, initial_bs=args.initial_batch_size, target_bs=args.batch_size)
-                if next_batch_size > batch_size:
-                    print("Changing batch size from %d to %d" % (batch_size, next_batch_size))
-                    batch_size = next_batch_size
-                    loader_train = data.DataLoader(
-                        dataset_train,
-                        batch_size=batch_size,
-                        pin_memory=True,
-                        shuffle=True,
-                        # sampler=sampler,
-                        num_workers=args.workers)
 
             train_metrics = train_epoch(
                 epoch, model, loader_train, optimizer, train_loss_fn, args,
-                saver=saver, output_dir=output_dir)
+                lr_scheduler=lr_scheduler, saver=saver, output_dir=output_dir)
 
-            step = epoch * len(loader_train)
             eval_metrics = validate(
-                step, model, loader_eval, validate_loss_fn, args,
-                output_dir=output_dir)
+                model, loader_eval, validate_loss_fn, args)
 
             if lr_scheduler is not None:
-                lr_scheduler.step(eval_metrics['eval_loss'])
+                lr_scheduler.step(epoch, eval_metrics['eval_loss'])
 
-            rowd = OrderedDict(epoch=epoch)
-            rowd.update(train_metrics)
-            rowd.update(eval_metrics)
-            with open(os.path.join(output_dir, 'summary.csv'), mode='a') as cf:
-                dw = csv.DictWriter(cf, fieldnames=rowd.keys())
-                if best_loss is None:  # first iteration (epoch == 1 can't be used)
-                    dw.writeheader()
-                dw.writerow(rowd)
+            update_summary(
+                epoch, train_metrics, eval_metrics, output_dir, write_header=best_loss is None)
 
             # save proper checkpoint with eval metric
             best_loss = saver.save_checkpoint({
@@ -252,9 +239,8 @@ def main():
 
 def train_epoch(
         epoch, model, loader, optimizer, loss_fn, args,
-        saver=None, output_dir=''):
+        lr_scheduler=None, saver=None, output_dir=''):
 
-    epoch_step = (epoch - 1) * len(loader)
     batch_time_m = AverageMeter()
     data_time_m = AverageMeter()
     losses_m = AverageMeter()
@@ -263,9 +249,9 @@ def train_epoch(
 
     end = time.time()
     last_idx = len(loader) - 1
+    num_updates = epoch * len(loader)
     for batch_idx, (input, target) in enumerate(loader):
         last_batch = batch_idx == last_idx
-        step = epoch_step + batch_idx
         data_time_m.update(time.time() - end)
 
         input = input.cuda()
@@ -283,20 +269,27 @@ def train_epoch(
         loss.backward()
         optimizer.step()
 
+        num_updates += 1
+
         batch_time_m.update(time.time() - end)
         if last_batch or batch_idx % args.log_interval == 0:
+            lrl = [param_group['lr'] for param_group in optimizer.param_groups]
+            lr = sum(lrl) / len(lrl)
+
             print('Train: {} [{}/{} ({:.0f}%)]  '
                   'Loss: {loss.val:.6f} ({loss.avg:.4f})  '
                   'Time: {batch_time.val:.3f}s, {rate:.3f}/s  '
                   '({batch_time.avg:.3f}s, {rate_avg:.3f}/s)  '
+                  'LR: {lr:.4f}  '
                   'Data: {data_time.val:.3f} ({data_time.avg:.3f})'.format(
                 epoch,
-                batch_idx * len(input), len(loader.sampler),
+                batch_idx, len(loader),
                 100. * batch_idx / last_idx,
                 loss=losses_m,
                 batch_time=batch_time_m,
                 rate=input.size(0) / batch_time_m.val,
                 rate_avg=input.size(0) / batch_time_m.avg,
+                lr=lr,
                 data_time=data_time_m))
 
             if args.save_images:
@@ -319,12 +312,15 @@ def train_epoch(
                 epoch=save_epoch,
                 batch_idx=batch_idx)
 
+        if lr_scheduler is not None:
+            lr_scheduler.step_update(num_updates=num_updates, metric=losses_m.avg)
+
         end = time.time()
 
     return OrderedDict([('train_loss', losses_m.avg)])
 
 
-def validate(step, model, loader, loss_fn, args, output_dir=''):
+def validate(model, loader, loss_fn, args):
     batch_time_m = AverageMeter()
     losses_m = AverageMeter()
     prec1_m = AverageMeter()
@@ -345,7 +341,6 @@ def validate(step, model, loader, loss_fn, args, output_dir=''):
                 target = target.cuda()
 
             output = model(input)
-
             if isinstance(output, (tuple, list)):
                 output = output[0]
 
@@ -381,17 +376,15 @@ def validate(step, model, loader, loss_fn, args, output_dir=''):
     return metrics
 
 
-def adjust_learning_rate(optimizer, epoch, initial_lr, decay_rate=0.1, decay_epochs=30):
-    """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
-    lr = initial_lr * (decay_rate ** (epoch // decay_epochs))
-    print('Setting LR to', lr)
-    for param_group in optimizer.param_groups:
-        param_group['lr'] = lr
-
-
-def adjust_batch_size(epoch, initial_bs, target_bs, decay_epochs=1):
-    batch_size = min(target_bs, initial_bs * (2 ** (epoch // decay_epochs)))
-    return batch_size
+def update_summary(epoch, train_metrics, eval_metrics, output_dir, write_header=False):
+    rowd = OrderedDict(epoch=epoch)
+    rowd.update(train_metrics)
+    rowd.update(eval_metrics)
+    with open(os.path.join(output_dir, 'summary.csv'), mode='a') as cf:
+        dw = csv.DictWriter(cf, fieldnames=rowd.keys())
+        if write_header:  # first iteration (epoch == 1 can't be used)
+            dw.writeheader()
+        dw.writerow(rowd)
 
 
 if __name__ == '__main__':