Moved all argparse configs to .yaml files

All configs are parsed from the .yaml file. If necessary, any parameter can be written to the terminal as before and these parameters will overwrite the .yaml file. All args are also stored in args_text variable as string

configs/inference.yaml

@@ -0,0 +1,16 @@
+data: 'dataset/test'          # path to dataset
+output_dir: 'output/'         # path to output files
+model: 'tf_efficientnet_b5'   # model architecture (default: dpn92)
+workers: 4                    # number of McD loading workers (default: 2
+batch_size: 256               # mini-batch size (default: 256)
+img_size: 224                 # Input image dimension
+mean: null                    # Override mean pixel value of dataset
+std: null                     # Override std deviation of of dataset
+interpolation: ''             # Image resize interpolation type (overrides model)
+num_classes: 1000             # Number classes in dataset
+log_freq: 10                  # batch logging frequency (default: 10)
+checkpoint: ''                # path to latest checkpoint (default: none)
+pretrained: True              # use pre-trained model
+num_gpu: 1                    # Number of GPUS to use
+no_test_pool: False           # disable test time pool
+topk: 5                       # Top-k to output to CSV

configs/train.yaml

@@ -0,0 +1,102 @@
+# Dataset / Model parameters
+data: 'dataset/splitted'  # path to dataset
+model: 'tf_efficientnet_b0'      # Name of model to train (default: "countception")
+pretrained: True          # Start with pretrained version of specified network (if avail)
+initial_checkpoint: ''    # Initialize model from this checkpoint (default: none)
+resume: ''                # Resume full model and optimizer state from checkpoint (default: none)
+no_resume_opt: False      # prevent resume of optimizer state when resuming model
+num_classes: 2            # number of label classes (default: 1000)
+gp: null                  # Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None.
+img_size: 224             # Image patch size (default: None => model default)
+crop_pct: null            # Input image center crop percent (for validation only)
+mean: null                # Override mean pixel value of dataset
+std: null                 # Override std deviation of of dataset
+interpolation: ''         # Image resize interpolation type (overrides model)
+batch_size: 16            # input batch size for training (default: 32)
+validation_batch_size_multiplier: 1 # ratio of validation batch size to training batch size (default: 1)
+
+# Optimizer parameters
+opt: 'Adam'               # Optimizer (default: "sgd"
+opt_eps: null             # Optimizer Epsilon (default: None, use opt default)
+opt_betas: null           # Optimizer Betas (default: None, use opt default)
+momentum: 0.9             # Optimizer momentum (default: 0.9)
+weight_decay: 0.0         # weight decay (default: 0.0001)
+clip_grad: null           # Clip gradient norm (default: None, no clipping)
+
+# Learning rate schedule parameters
+sched: 'plateau'          # LR scheduler (default: "step")
+lr: 0.0001                # learning rate (default: 0.01)
+lr_noise: null            # learning rate noise on/off epoch percentages
+lr_noise_pct: 0.67        # learning rate noise limit percent (default: 0.67)
+lr_noise_std: 1.0         # learning rate noise std-dev (default: 1.0)
+lr_cycle_mul: 1.0         # learning rate cycle len multiplier (default: 1.0)
+lr_cycle_limit: 1         # learning rate cycle limit
+warmup_lr: 0.0001         # warmup learning rate (default: 0.0001)
+min_lr: 0.00001           # lower lr bound for cyclic schedulers that hit 0 (1e-5)
+epochs: 30                # number of epochs to train (default: 2)
+start_epoch: null         # manual epoch number (useful on restarts)
+decay_epochs: 5           # epoch interval to decay LR
+warmup_epochs: 10         # epochs to warmup LR, if scheduler supports
+cooldown_epochs: 0        # epochs to cooldown LR at min_lr, after cyclic schedule ends
+patience_epochs: 5        # patience epochs for Plateau LR scheduler (default: 10)
+decay_rate: 0.1           # LR decay rate (default: 0.1)
+
+# Augmentation & regularization parameters
+no_aug: False            # Disable all training augmentation, override other train aug args
+scale: [1, 1]            # Random resize scale (default: 0.08 1.0)
+ratio: [0.8, 1.2]        # Random resize aspect ratio (default: 0.75 1.33)
+hflip: 0.5               # Horizontal flip training aug probability
+vflip: 0.0               # Vertical flip training aug probability
+color_jitter: 0.1        # Color jitter factor (default: 0.4)
+aa: null                 # Use AutoAugment policy. "v0" or "original". (default: None)
+aug_splits: 0            # Number of augmentation splits (default: 0, valid: 0 or >=2)
+jsd: False               # Enable Jensen-Shannon Divergence + CE loss. Use with `--aug-splits`.
+reprob: 0.0              # Random erase prob (default: 0.)
+remode: 'const'          # Random erase mode (default: "const")
+recount: 1               # Random erase count (default: 1)
+resplit: False           # Do not random erase first (clean) augmentation split
+mixup: 0.0               # mixup alpha, mixup enabled if > 0. (default: 0.)
+cutmix: 0.0              # cutmix alpha, cutmix enabled if > 0. (default: 0.
+cutmix_minmax:           # cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)
+mixup_prob: 1.0          # Probability of performing mixup or cutmix when either/both is enabled
+mixup_switch_prob: 0.5   # Probability of switching to cutmix when both mixup and cutmix enabled
+mixup_mode: 'batch'      # How to apply mixup/cutmix params. Per "batch", "pair", or "elem"
+mixup_off_epoch: 0       # Turn off mixup after this epoch, disabled if 0 (default: 0)
+smoothing: 0.0           # Label smoothing (default: 0.1)
+train_interpolation: 'random' # Training interpolation (random, bilinear, bicubic default: "random"
+drop: 0.0                # Dropout rate (default: 0.)
+drop_connect: null       # Drop connect rate, DEPRECATED, use drop-path (default: None)
+drop_path: null          # Drop path rate (default: None)
+drop_block: null         # Drop block rate (default: None)
+
+# Batch norm parameters (only works with gen_efficientnet based models currently)
+bn_tf: bull              # Use Tensorflow BatchNorm defaults for models that support it (default: False)
+bn_momentum: null        # BatchNorm momentum override (if not None)
+bn_eps: null             # BatchNorm epsilon override (if not None)
+sync_bn: False           # Enable NVIDIA Apex or Torch synchronized BatchNorm.
+dist_bn: ''              # Distribute BatchNorm stats between nodes after each epoch ("broadcast", "reduce", or "")
+split_bn: False          # Enable separate BN layers per augmentation split.
+
+# Model Exponential Moving Average
+model_ema: False         # Enable tracking moving average of model weights
+model_ema_force_cpu: False # Force ema to be tracked on CPU, rank=0 node only. Disables EMA validation.
+model_ema_decay: 0.9998  # decay factor for model weights moving average (default: 0.9998)
+
+# Misc
+seed: 42                 # random seed (default: 42)
+log_interval: 50         # how many batches to wait before logging training status
+recovery_interval: 0     # how many batches to wait before writing recovery checkpoint
+workers: 1               # how many training processes to use (default: 1)
+num_gpu: 1               # Number of GPUS to use
+save_images: False       # save images of input bathes every log interval for debugging
+amp: False               # use NVIDIA Apex AMP or Native AMP for mixed precision training
+apex_amp: False          # Use NVIDIA Apex AMP mixed precision
+native_amp: False        # Use Native Torch AMP mixed precision
+channels_last: False     # Use channels_last memory layout
+pin_mem: False           # Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.
+no_prefetcher: False     # disable fast prefetcher
+output: ''               # path to output folder (default: none, current dir)
+eval_metric: 'top1'      # Best metric (default: "top1"
+tta: 0                   # Test/inference time augmentation (oversampling) factor. 0=None (default: 0)
+local_rank: 0            #
+use_multi_epochs_loader: False # use the multi-epochs-loader to save time at the beginning of every epoch

configs/validate.yaml

@@ -0,0 +1,34 @@
+data: 'dataset/splitted/val'       # path to dataset
+model: 'tf_efficientnet_b0'      # Name of model to train (default: "countception"
+# path to latest checkpoint (default: none)
+checkpoint: 'output/train/tf_efficientnet_b0-224/model_best.pth.tar'
+
+workers: 4                # number of McD loading workers (default: 2)
+batch_size: 16            # mini-batch size (default: 256)
+img_size: 224             # Input image dimension, uses model default if empty
+crop_pct: null            # Input image center crop pct
+mean: null                # Override mean pixel value of dataset
+std: null                 # Override std deviation of of dataset
+interpolation: ''         # Image resize interpolation type (overrides model)
+num_classes: 2            # Number classes in dataset
+class_map: ''             # path to class to idx mapping file (default: "")
+gp: null                  # Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None.
+log_freq: 10              # batch logging frequency (default: 10)
+pretrained: False         # use pre-trained model
+num_gpu: 1                # Number of GPUS to use
+no_test_pool: False       # disable test time pool
+no_prefetcher: False      # disable fast prefetcher
+pin_mem: False            # Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU
+channels_last: False      # Use channels_last memory layout
+amp: False                # Use AMP mixed precision. Defaults to Apex, fallback to native Torch AMP.
+apex_amp: False           # Use NVIDIA Apex AMP mixed precision
+native_amp: False         # Use Native Torch AMP mixed precision
+tf_preprocessing: False   # Use Tensorflow preprocessing pipeline (require CPU TF installed
+use_ema: False            # use ema version of weights if present
+torchscript: False        # convert model torchscript for inference
+lagacy_jit: False         # use legacy jit mode for pytorch 1.5/1.5.1/1.6 to get back fusion performance
+results_file: ''          # Output csv file for validation results (summary)
+real_labels: ''           # Real labels JSON file for imagenet evaluation
+valid_labels: ''          # Valid label indices txt file for validation of partial label space
+
+

inference.py

@@ -7,8 +7,11 @@ Hacked together by / Copyright 2020 Ross Wightman (https://github.com/rwightman)
 """
 import os
 import time
-import argparse
 import logging
+import yaml
+from fire import Fire
+from addict import Dict
+
 import numpy as np
 import torch
 
@@ -20,44 +23,42 @@ torch.backends.cudnn.benchmark = True
 _logger = logging.getLogger('inference')
 
 
-parser = argparse.ArgumentParser(description='PyTorch ImageNet Inference')
+def _update_config(config, params):
-parser.add_argument('data', metavar='DIR',
+    for k, v in params.items():
-                    help='path to dataset')
+        *path, key = k.split(".")
-parser.add_argument('--output_dir', metavar='DIR', default='./',
+        config.update({k: v})
-                    help='path to output files')
+        print(f"Overwriting {k} = {v} (was {config.get(key)})")
-parser.add_argument('--model', '-m', metavar='MODEL', default='dpn92',
+    return config
-                    help='model architecture (default: dpn92)')
+
-parser.add_argument('-j', '--workers', default=2, type=int, metavar='N',
+
-                    help='number of data loading workers (default: 2)')
+def _fit(config_path, **kwargs):
-parser.add_argument('-b', '--batch-size', default=256, type=int,
+    with open(config_path) as stream:
-                    metavar='N', help='mini-batch size (default: 256)')
+        base_config = yaml.safe_load(stream)
-parser.add_argument('--img-size', default=None, type=int,
+
-                    metavar='N', help='Input image dimension')
+    if "config" in kwargs.keys():
-parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
+        cfg_path = kwargs["config"]
-                    help='Override mean pixel value of dataset')
+        with open(cfg_path) as cfg:
-parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
+            cfg_yaml = yaml.load(cfg, Loader=yaml.FullLoader)
-                    help='Override std deviation of of dataset')
+
-parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
+        merged_cfg = _update_config(base_config, cfg_yaml)
-                    help='Image resize interpolation type (overrides model)')
+    else:
-parser.add_argument('--num-classes', type=int, default=1000,
+        merged_cfg = base_config
-                    help='Number classes in dataset')
+
-parser.add_argument('--log-freq', default=10, type=int,
+    update_cfg = _update_config(merged_cfg, kwargs)
-                    metavar='N', help='batch logging frequency (default: 10)')
+    return update_cfg
-parser.add_argument('--checkpoint', default='', type=str, metavar='PATH',
+
-                    help='path to latest checkpoint (default: none)')
+
-parser.add_argument('--pretrained', dest='pretrained', action='store_true',
+def _parse_args(config_path):
-                    help='use pre-trained model')
+    args = Dict(Fire(_fit(config_path)))
-parser.add_argument('--num-gpu', type=int, default=1,
+
-                    help='Number of GPUS to use')
+    # Cache the args as a text string to save them in the output dir later
-parser.add_argument('--no-test-pool', dest='no_test_pool', action='store_true',
+    args_text = yaml.safe_dump(args.__dict__, default_flow_style=False)
-                    help='disable test time pool')
+    return args, args_text
-parser.add_argument('--topk', default=5, type=int,
-                    metavar='N', help='Top-k to output to CSV')
 
 
 def main():
     setup_default_logging()
-    args = parser.parse_args()
+    args, args_text = _parse_args('configs/inference.yaml')
     # might as well try to do something useful...
     args.pretrained = args.pretrained or not args.checkpoint
 

requirements.txt

@@ -1,3 +1,5 @@
 torch>=1.4.0
 torchvision>=0.5.0
 pyyaml
+fire
+addict

train.py

@@ -14,7 +14,6 @@ NVIDIA CUDA specific speedups adopted from NVIDIA Apex examples
 
 Hacked together by / Copyright 2020 Ross Wightman (https://github.com/rwightman)
 """
-import argparse
 import time
 import yaml
 import os
@@ -22,6 +21,8 @@ import logging
 from collections import OrderedDict
 from contextlib import suppress
 from datetime import datetime
+from fire import Fire
+from addict import Dict
 
 import torch
 import torch.nn as nn
@@ -54,220 +55,34 @@ except AttributeError:
 torch.backends.cudnn.benchmark = True
 _logger = logging.getLogger('train')
 
-# The first arg parser parses out only the --config argument, this argument is used to
+
-# load a yaml file containing key-values that override the defaults for the main parser below
+def _update_config(config, params):
-config_parser = parser = argparse.ArgumentParser(description='Training Config', add_help=False)
+    for k, v in params.items():
-parser.add_argument('-c', '--config', default='', type=str, metavar='FILE',
+        *path, key = k.split(".")
-                    help='YAML config file specifying default arguments')
+        config.update({k: v})
-
+        print(f"Overwriting {k} = {v} (was {config.get(key)})")
-
+    return config
-parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
+
-
+
-# Dataset / Model parameters
+def _fit(config_path, **kwargs):
-parser.add_argument('data', metavar='DIR',
+    with open(config_path) as stream:
-                    help='path to dataset')
+        base_config = yaml.safe_load(stream)
-parser.add_argument('--model', default='resnet101', type=str, metavar='MODEL',
+
-                    help='Name of model to train (default: "countception"')
+    if "config" in kwargs.keys():
-parser.add_argument('--pretrained', action='store_true', default=False,
+        cfg_path = kwargs["config"]
-                    help='Start with pretrained version of specified network (if avail)')
+        with open(cfg_path) as cfg:
-parser.add_argument('--initial-checkpoint', default='', type=str, metavar='PATH',
+            cfg_yaml = yaml.load(cfg, Loader=yaml.FullLoader)
-                    help='Initialize model from this checkpoint (default: none)')
+
-parser.add_argument('--resume', default='', type=str, metavar='PATH',
+        merged_cfg = _update_config(base_config, cfg_yaml)
-                    help='Resume full model and optimizer state from checkpoint (default: none)')
+    else:
-parser.add_argument('--no-resume-opt', action='store_true', default=False,
+        merged_cfg = base_config
-                    help='prevent resume of optimizer state when resuming model')
+
-parser.add_argument('--num-classes', type=int, default=1000, metavar='N',
+    update_cfg = _update_config(merged_cfg, kwargs)
-                    help='number of label classes (default: 1000)')
+    return update_cfg
-parser.add_argument('--gp', default=None, type=str, metavar='POOL',
+
-                    help='Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None.')
+
-parser.add_argument('--img-size', type=int, default=None, metavar='N',
+def _parse_args(config_path):
-                    help='Image patch size (default: None => model default)')
+    args = Dict(Fire(_fit(config_path)))
-parser.add_argument('--crop-pct', default=None, type=float,
-                    metavar='N', help='Input image center crop percent (for validation only)')
-parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
-                    help='Override mean pixel value of dataset')
-parser.add_argument('--std', type=float, nargs='+', default=None, metavar='STD',
-                    help='Override std deviation of of dataset')
-parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
-                    help='Image resize interpolation type (overrides model)')
-parser.add_argument('-b', '--batch-size', type=int, default=32, metavar='N',
-                    help='input batch size for training (default: 32)')
-parser.add_argument('-vb', '--validation-batch-size-multiplier', type=int, default=1, metavar='N',
-                    help='ratio of validation batch size to training batch size (default: 1)')
-
-# Optimizer parameters
-parser.add_argument('--opt', default='sgd', type=str, metavar='OPTIMIZER',
-                    help='Optimizer (default: "sgd"')
-parser.add_argument('--opt-eps', default=None, type=float, metavar='EPSILON',
-                    help='Optimizer Epsilon (default: None, use opt default)')
-parser.add_argument('--opt-betas', default=None, type=float, nargs='+', metavar='BETA',
-                    help='Optimizer Betas (default: None, use opt default)')
-parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
-                    help='Optimizer momentum (default: 0.9)')
-parser.add_argument('--weight-decay', type=float, default=0.0001,
-                    help='weight decay (default: 0.0001)')
-parser.add_argument('--clip-grad', type=float, default=None, metavar='NORM',
-                    help='Clip gradient norm (default: None, no clipping)')
-
-
-
-# Learning rate schedule parameters
-parser.add_argument('--sched', default='step', type=str, metavar='SCHEDULER',
-                    help='LR scheduler (default: "step"')
-parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
-                    help='learning rate (default: 0.01)')
-parser.add_argument('--lr-noise', type=float, nargs='+', default=None, metavar='pct, pct',
-                    help='learning rate noise on/off epoch percentages')
-parser.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT',
-                    help='learning rate noise limit percent (default: 0.67)')
-parser.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV',
-                    help='learning rate noise std-dev (default: 1.0)')
-parser.add_argument('--lr-cycle-mul', type=float, default=1.0, metavar='MULT',
-                    help='learning rate cycle len multiplier (default: 1.0)')
-parser.add_argument('--lr-cycle-limit', type=int, default=1, metavar='N',
-                    help='learning rate cycle limit')
-parser.add_argument('--warmup-lr', type=float, default=0.0001, metavar='LR',
-                    help='warmup learning rate (default: 0.0001)')
-parser.add_argument('--min-lr', type=float, default=1e-5, metavar='LR',
-                    help='lower lr bound for cyclic schedulers that hit 0 (1e-5)')
-parser.add_argument('--epochs', type=int, default=200, metavar='N',
-                    help='number of epochs to train (default: 2)')
-parser.add_argument('--start-epoch', default=None, type=int, metavar='N',
-                    help='manual epoch number (useful on restarts)')
-parser.add_argument('--decay-epochs', type=float, default=30, metavar='N',
-                    help='epoch interval to decay LR')
-parser.add_argument('--warmup-epochs', type=int, default=3, metavar='N',
-                    help='epochs to warmup LR, if scheduler supports')
-parser.add_argument('--cooldown-epochs', type=int, default=10, metavar='N',
-                    help='epochs to cooldown LR at min_lr, after cyclic schedule ends')
-parser.add_argument('--patience-epochs', type=int, default=10, metavar='N',
-                    help='patience epochs for Plateau LR scheduler (default: 10')
-parser.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE',
-                    help='LR decay rate (default: 0.1)')
-
-# Augmentation & regularization parameters
-parser.add_argument('--no-aug', action='store_true', default=False,
-                    help='Disable all training augmentation, override other train aug args')
-parser.add_argument('--scale', type=float, nargs='+', default=[0.08, 1.0], metavar='PCT',
-                    help='Random resize scale (default: 0.08 1.0)')
-parser.add_argument('--ratio', type=float, nargs='+', default=[3./4., 4./3.], metavar='RATIO',
-                    help='Random resize aspect ratio (default: 0.75 1.33)')
-parser.add_argument('--hflip', type=float, default=0.5,
-                    help='Horizontal flip training aug probability')
-parser.add_argument('--vflip', type=float, default=0.,
-                    help='Vertical flip training aug probability')
-parser.add_argument('--color-jitter', type=float, default=0.4, metavar='PCT',
-                    help='Color jitter factor (default: 0.4)')
-parser.add_argument('--aa', type=str, default=None, metavar='NAME',
-                    help='Use AutoAugment policy. "v0" or "original". (default: None)'),
-parser.add_argument('--aug-splits', type=int, default=0,
-                    help='Number of augmentation splits (default: 0, valid: 0 or >=2)')
-parser.add_argument('--jsd', action='store_true', default=False,
-                    help='Enable Jensen-Shannon Divergence + CE loss. Use with `--aug-splits`.')
-parser.add_argument('--reprob', type=float, default=0., metavar='PCT',
-                    help='Random erase prob (default: 0.)')
-parser.add_argument('--remode', type=str, default='const',
-                    help='Random erase mode (default: "const")')
-parser.add_argument('--recount', type=int, default=1,
-                    help='Random erase count (default: 1)')
-parser.add_argument('--resplit', action='store_true', default=False,
-                    help='Do not random erase first (clean) augmentation split')
-parser.add_argument('--mixup', type=float, default=0.0,
-                    help='mixup alpha, mixup enabled if > 0. (default: 0.)')
-parser.add_argument('--cutmix', type=float, default=0.0,
-                    help='cutmix alpha, cutmix enabled if > 0. (default: 0.)')
-parser.add_argument('--cutmix-minmax', type=float, nargs='+', default=None,
-                    help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)')
-parser.add_argument('--mixup-prob', type=float, default=1.0,
-                    help='Probability of performing mixup or cutmix when either/both is enabled')
-parser.add_argument('--mixup-switch-prob', type=float, default=0.5,
-                    help='Probability of switching to cutmix when both mixup and cutmix enabled')
-parser.add_argument('--mixup-mode', type=str, default='batch',
-                    help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"')
-parser.add_argument('--mixup-off-epoch', default=0, type=int, metavar='N',
-                    help='Turn off mixup after this epoch, disabled if 0 (default: 0)')
-parser.add_argument('--smoothing', type=float, default=0.1,
-                    help='Label smoothing (default: 0.1)')
-parser.add_argument('--train-interpolation', type=str, default='random',
-                    help='Training interpolation (random, bilinear, bicubic default: "random")')
-parser.add_argument('--drop', type=float, default=0.0, metavar='PCT',
-                    help='Dropout rate (default: 0.)')
-parser.add_argument('--drop-connect', type=float, default=None, metavar='PCT',
-                    help='Drop connect rate, DEPRECATED, use drop-path (default: None)')
-parser.add_argument('--drop-path', type=float, default=None, metavar='PCT',
-                    help='Drop path rate (default: None)')
-parser.add_argument('--drop-block', type=float, default=None, metavar='PCT',
-                    help='Drop block rate (default: None)')
-
-# Batch norm parameters (only works with gen_efficientnet based models currently)
-parser.add_argument('--bn-tf', action='store_true', default=False,
-                    help='Use Tensorflow BatchNorm defaults for models that support it (default: False)')
-parser.add_argument('--bn-momentum', type=float, default=None,
-                    help='BatchNorm momentum override (if not None)')
-parser.add_argument('--bn-eps', type=float, default=None,
-                    help='BatchNorm epsilon override (if not None)')
-parser.add_argument('--sync-bn', action='store_true',
-                    help='Enable NVIDIA Apex or Torch synchronized BatchNorm.')
-parser.add_argument('--dist-bn', type=str, default='',
-                    help='Distribute BatchNorm stats between nodes after each epoch ("broadcast", "reduce", or "")')
-parser.add_argument('--split-bn', action='store_true',
-                    help='Enable separate BN layers per augmentation split.')
-
-# Model Exponential Moving Average
-parser.add_argument('--model-ema', action='store_true', default=False,
-                    help='Enable tracking moving average of model weights')
-parser.add_argument('--model-ema-force-cpu', action='store_true', default=False,
-                    help='Force ema to be tracked on CPU, rank=0 node only. Disables EMA validation.')
-parser.add_argument('--model-ema-decay', type=float, default=0.9998,
-                    help='decay factor for model weights moving average (default: 0.9998)')
-
-# Misc
-parser.add_argument('--seed', type=int, default=42, metavar='S',
-                    help='random seed (default: 42)')
-parser.add_argument('--log-interval', type=int, default=50, metavar='N',
-                    help='how many batches to wait before logging training status')
-parser.add_argument('--recovery-interval', type=int, default=0, metavar='N',
-                    help='how many batches to wait before writing recovery checkpoint')
-parser.add_argument('-j', '--workers', type=int, default=4, metavar='N',
-                    help='how many training processes to use (default: 1)')
-parser.add_argument('--num-gpu', type=int, default=1,
-                    help='Number of GPUS to use')
-parser.add_argument('--save-images', action='store_true', default=False,
-                    help='save images of input bathes every log interval for debugging')
-parser.add_argument('--amp', action='store_true', default=False,
-                    help='use NVIDIA Apex AMP or Native AMP for mixed precision training')
-parser.add_argument('--apex-amp', action='store_true', default=False,
-                    help='Use NVIDIA Apex AMP mixed precision')
-parser.add_argument('--native-amp', action='store_true', default=False,
-                    help='Use Native Torch AMP mixed precision')
-parser.add_argument('--channels-last', action='store_true', default=False,
-                    help='Use channels_last memory layout')
-parser.add_argument('--pin-mem', action='store_true', default=False,
-                    help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
-parser.add_argument('--no-prefetcher', action='store_true', default=False,
-                    help='disable fast prefetcher')
-parser.add_argument('--output', default='', type=str, metavar='PATH',
-                    help='path to output folder (default: none, current dir)')
-parser.add_argument('--eval-metric', default='top1', type=str, metavar='EVAL_METRIC',
-                    help='Best metric (default: "top1"')
-parser.add_argument('--tta', type=int, default=0, metavar='N',
-                    help='Test/inference time augmentation (oversampling) factor. 0=None (default: 0)')
-parser.add_argument("--local_rank", default=0, type=int)
-parser.add_argument('--use-multi-epochs-loader', action='store_true', default=False,
-                    help='use the multi-epochs-loader to save time at the beginning of every epoch')
-
-
-def _parse_args():
-    # Do we have a config file to parse?
-    args_config, remaining = config_parser.parse_known_args()
-    if args_config.config:
-        with open(args_config.config, 'r') as f:
-            cfg = yaml.safe_load(f)
-            parser.set_defaults(**cfg)
-
-    # The main arg parser parses the rest of the args, the usual
-    # defaults will have been overridden if config file specified.
-    args = parser.parse_args(remaining)
 
     # Cache the args as a text string to save them in the output dir later
     args_text = yaml.safe_dump(args.__dict__, default_flow_style=False)
@@ -276,7 +91,7 @@ def _parse_args():
 
 def main():
     setup_default_logging()
-    args, args_text = _parse_args()
+    args, args_text = _parse_args('configs/train.yaml')
 
     args.prefetcher = not args.no_prefetcher
     args.distributed = False

validate.py

@@ -7,17 +7,19 @@ canonical PyTorch, standard Python style, and good performance. Repurpose as you
 
 Hacked together by Ross Wightman (https://github.com/rwightman)
 """
-import argparse
 import os
 import csv
 import glob
 import time
+import yaml
 import logging
 import torch
 import torch.nn as nn
 import torch.nn.parallel
 from collections import OrderedDict
 from contextlib import suppress
+from fire import Fire
+from addict import Dict
 
 from timm.models import create_model, apply_test_time_pool, load_checkpoint, is_model, list_models
 from timm.data import Dataset, DatasetTar, create_loader, resolve_data_config, RealLabelsImagenet
@@ -41,67 +43,37 @@ torch.backends.cudnn.benchmark = True
 _logger = logging.getLogger('validate')
 
 
-parser = argparse.ArgumentParser(description='PyTorch ImageNet Validation')
+def _update_config(config, params):
-parser.add_argument('data', metavar='DIR',
+    for k, v in params.items():
-                    help='path to dataset')
+        *path, key = k.split(".")
-parser.add_argument('--model', '-m', metavar='MODEL', default='dpn92',
+        config.update({k: v})
-                    help='model architecture (default: dpn92)')
+        print(f"Overwriting {k} = {v} (was {config.get(key)})")
-parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
+    return config
-                    help='number of data loading workers (default: 2)')
+
-parser.add_argument('-b', '--batch-size', default=256, type=int,
+
-                    metavar='N', help='mini-batch size (default: 256)')
+def _fit(config_path, **kwargs):
-parser.add_argument('--img-size', default=None, type=int,
+    with open(config_path) as stream:
-                    metavar='N', help='Input image dimension, uses model default if empty')
+        base_config = yaml.safe_load(stream)
-parser.add_argument('--crop-pct', default=None, type=float,
+
-                    metavar='N', help='Input image center crop pct')
+    if "config" in kwargs.keys():
-parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
+        cfg_path = kwargs["config"]
-                    help='Override mean pixel value of dataset')
+        with open(cfg_path) as cfg:
-parser.add_argument('--std', type=float,  nargs='+', default=None, metavar='STD',
+            cfg_yaml = yaml.load(cfg, Loader=yaml.FullLoader)
-                    help='Override std deviation of of dataset')
+
-parser.add_argument('--interpolation', default='', type=str, metavar='NAME',
+        merged_cfg = _update_config(base_config, cfg_yaml)
-                    help='Image resize interpolation type (overrides model)')
+    else:
-parser.add_argument('--num-classes', type=int, default=1000,
+        merged_cfg = base_config
-                    help='Number classes in dataset')
+
-parser.add_argument('--class-map', default='', type=str, metavar='FILENAME',
+    update_cfg = _update_config(merged_cfg, kwargs)
-                    help='path to class to idx mapping file (default: "")')
+    return update_cfg
-parser.add_argument('--gp', default=None, type=str, metavar='POOL',
+
-                    help='Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None.')
+
-parser.add_argument('--log-freq', default=10, type=int,
+def _parse_args(config_path):
-                    metavar='N', help='batch logging frequency (default: 10)')
+    args = Dict(Fire(_fit(config_path)))
-parser.add_argument('--checkpoint', default='', type=str, metavar='PATH',
+
-                    help='path to latest checkpoint (default: none)')
+    # Cache the args as a text string to save them in the output dir later
-parser.add_argument('--pretrained', dest='pretrained', action='store_true',
+    args_text = yaml.safe_dump(args.__dict__, default_flow_style=False)
-                    help='use pre-trained model')
+    return args, args_text
-parser.add_argument('--num-gpu', type=int, default=1,
-                    help='Number of GPUS to use')
-parser.add_argument('--no-test-pool', dest='no_test_pool', action='store_true',
-                    help='disable test time pool')
-parser.add_argument('--no-prefetcher', action='store_true', default=False,
-                    help='disable fast prefetcher')
-parser.add_argument('--pin-mem', action='store_true', default=False,
-                    help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.')
-parser.add_argument('--channels-last', action='store_true', default=False,
-                    help='Use channels_last memory layout')
-parser.add_argument('--amp', action='store_true', default=False,
-                    help='Use AMP mixed precision. Defaults to Apex, fallback to native Torch AMP.')
-parser.add_argument('--apex-amp', action='store_true', default=False,
-                    help='Use NVIDIA Apex AMP mixed precision')
-parser.add_argument('--native-amp', action='store_true', default=False,
-                    help='Use Native Torch AMP mixed precision')
-parser.add_argument('--tf-preprocessing', action='store_true', default=False,
-                    help='Use Tensorflow preprocessing pipeline (require CPU TF installed')
-parser.add_argument('--use-ema', dest='use_ema', action='store_true',
-                    help='use ema version of weights if present')
-parser.add_argument('--torchscript', dest='torchscript', action='store_true',
-                    help='convert model torchscript for inference')
-parser.add_argument('--legacy-jit', dest='legacy_jit', action='store_true',
-                    help='use legacy jit mode for pytorch 1.5/1.5.1/1.6 to get back fusion performance')
-parser.add_argument('--results-file', default='', type=str, metavar='FILENAME',
-                    help='Output csv file for validation results (summary)')
-parser.add_argument('--real-labels', default='', type=str, metavar='FILENAME',
-                    help='Real labels JSON file for imagenet evaluation')
-parser.add_argument('--valid-labels', default='', type=str, metavar='FILENAME',
-                    help='Valid label indices txt file for validation of partial label space')
 
 
 def validate(args):
@@ -254,14 +226,15 @@ def validate(args):
         interpolation=data_config['interpolation'])
 
     _logger.info(' * Acc@1 {:.3f} ({:.3f}) Acc@5 {:.3f} ({:.3f})'.format(
-       results['top1'], results['top1_err'], results['top5'], results['top5_err']))
+        results['top1'], results['top1_err'], results['top5'], results['top5_err']))
 
     return results
 
 
 def main():
     setup_default_logging()
-    args = parser.parse_args()
+    args, args_text = _parse_args('configs/validate.yaml')
+
     model_cfgs = []
     model_names = []
     if os.path.isdir(args.checkpoint):