Add checkpoint averaging script. Add headers, shebangs, exec perms to all scripts

avg_checkpoint.py

@@ -0,0 +1,113 @@
+#!/usr/bin/env python
+""" Checkpoint Averaging Script
+
+This script averages all model weights for checkpoints in specified path that match
+the specified filter wildcard. All checkpoints must be from the exact same model.
+
+For any hope of decent results, the checkpoints should be from the same or child
+(via resumes) training session. This can be viewed as similar to maintaining running
+EMA (exponential moving average) of the model weights or performing SWA (stochastic
+weight averaging), but post-training.
+
+Hacked together by Ross Wightman (https://github.com/rwightman)
+"""
+import torch
+import argparse
+import os
+import glob
+import hashlib
+from timm.models.helpers import load_state_dict
+
+parser = argparse.ArgumentParser(description='PyTorch Checkpoint Averager')
+parser.add_argument('--input', default='', type=str, metavar='PATH',
+                    help='path to base input folder containing checkpoints')
+parser.add_argument('--filter', default='*.pth.tar', type=str, metavar='WILDCARD',
+                    help='checkpoint filter (path wildcard)')
+parser.add_argument('--output', default='./averaged.pth', type=str, metavar='PATH',
+                    help='output filename')
+parser.add_argument('--no-use-ema', dest='no_use_ema', action='store_true',
+                    help='Force not using ema version of weights (if present)')
+parser.add_argument('--no-sort', dest='no_sort', action='store_true',
+                    help='Do not sort and select by checkpoint metric, also makes "n" argument irrelevant')
+parser.add_argument('-n', type=int, default=10, metavar='N',
+                    help='Number of checkpoints to average')
+
+
+def checkpoint_metric(checkpoint_path):
+    if not checkpoint_path or not os.path.isfile(checkpoint_path):
+        return {}
+    print("=> Extracting metric from checkpoint '{}'".format(checkpoint_path))
+    checkpoint = torch.load(checkpoint_path, map_location='cpu')
+    metric = None
+    if 'metric' in checkpoint:
+        metric = checkpoint['metric']
+    return metric
+
+
+def main():
+    args = parser.parse_args()
+    # by default use the EMA weights (if present)
+    args.use_ema = not args.no_use_ema
+    # by default sort by checkpoint metric (if present) and avg top n checkpoints
+    args.sort = not args.no_sort
+
+    if os.path.exists(args.output):
+        print("Error: Output filename ({}) already exists.".format(args.output))
+        exit(1)
+
+    pattern = args.input
+    if not args.input.endswith(os.path.sep) and not args.filter.startswith(os.path.sep):
+        pattern += os.path.sep
+    pattern += args.filter
+    checkpoints = glob.glob(pattern, recursive=True)
+
+    if args.sort:
+        checkpoint_metrics = []
+        for c in checkpoints:
+            metric = checkpoint_metric(c)
+            if metric is not None:
+                checkpoint_metrics.append((metric, c))
+        checkpoint_metrics = list(sorted(checkpoint_metrics))
+        checkpoint_metrics = checkpoint_metrics[-args.n:]
+        print("Selected checkpoints:")
+        [print(m, c) for m, c in checkpoint_metrics]
+        avg_checkpoints = [c for m, c in checkpoint_metrics]
+    else:
+        avg_checkpoints = checkpoints
+        print("Selected checkpoints:")
+        [print(c) for c in checkpoints]
+
+    avg_state_dict = {}
+    avg_counts = {}
+    for c in avg_checkpoints:
+        new_state_dict = load_state_dict(c, args.use_ema)
+        if not new_state_dict:
+            print("Error: Checkpoint ({}) doesn't exist".format(args.checkpoint))
+            continue
+
+        for k, v in new_state_dict.items():
+            if k not in avg_state_dict:
+                avg_state_dict[k] = v.clone().to(dtype=torch.float64)
+                avg_counts[k] = 1
+            else:
+                avg_state_dict[k] += v.to(dtype=torch.float64)
+                avg_counts[k] += 1
+
+    for k, v in avg_state_dict.items():
+        v.div_(avg_counts[k])
+
+    # float32 overflow seems unlikely based on weights seen to date, but who knows
+    float32_info = torch.finfo(torch.float32)
+    final_state_dict = {}
+    for k, v in avg_state_dict.items():
+        v = v.clamp(float32_info.min, float32_info.max)
+        final_state_dict[k] = v.to(dtype=torch.float32)
+
+    torch.save(final_state_dict, args.output)
+    with open(args.output, 'rb') as f:
+        sha_hash = hashlib.sha256(f.read()).hexdigest()
+    print("=> Saved state_dict to '{}, SHA256: {}'".format(args.output, sha_hash))
+
+
+if __name__ == '__main__':
+    main()

clean_checkpoint.py

@@ -1,3 +1,12 @@
+#!/usr/bin/env python
+""" Checkpoint Cleaning Script
+
+Takes training checkpoints with GPU tensors, optimizer state, extra dict keys, etc.
+and outputs a CPU  tensor checkpoint with only the `state_dict` along with SHA256
+calculation for model zoo compatibility.
+
+Hacked together by Ross Wightman (https://github.com/rwightman)
+"""
 import torch
 import argparse
 import os
@@ -5,7 +14,7 @@ import hashlib
 import shutil
 from collections import OrderedDict
 
-parser = argparse.ArgumentParser(description='PyTorch ImageNet Validation')
+parser = argparse.ArgumentParser(description='PyTorch Checkpoint Cleaner')
 parser.add_argument('--checkpoint', default='', type=str, metavar='PATH',
                     help='path to latest checkpoint (default: none)')
 parser.add_argument('--output', default='', type=str, metavar='PATH',

inference.py

@@ -1,10 +1,10 @@
-"""Sample PyTorch Inference script
+#!/usr/bin/env python
+"""PyTorch Inference Script
+
+An example inference script that outputs top-k class ids for images in a folder into a csv.
+
+Hacked together by Ross Wightman (https://github.com/rwightman)
 """
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
 import os
 import time
 import argparse

timm/models/helpers.py

@@ -5,12 +5,11 @@ import logging
 from collections import OrderedDict
 
 
-def load_checkpoint(model, checkpoint_path, use_ema=False):
+def load_state_dict(checkpoint_path, use_ema=False):
     if checkpoint_path and os.path.isfile(checkpoint_path):
         checkpoint = torch.load(checkpoint_path, map_location='cpu')
-        state_dict_key = ''
-        if isinstance(checkpoint, dict):
         state_dict_key = 'state_dict'
+        if isinstance(checkpoint, dict):
             if use_ema and 'state_dict_ema' in checkpoint:
                 state_dict_key = 'state_dict_ema'
         if state_dict_key and state_dict_key in checkpoint:
@@ -19,15 +18,21 @@ def load_checkpoint(model, checkpoint_path, use_ema=False):
                 # strip `module.` prefix
                 name = k[7:] if k.startswith('module') else k
                 new_state_dict[name] = v
-            model.load_state_dict(new_state_dict)
+            state_dict = new_state_dict
         else:
-            model.load_state_dict(checkpoint)
+            state_dict = checkpoint
-        logging.info("Loaded {} from checkpoint '{}'".format(state_dict_key or 'weights', checkpoint_path))
+        logging.info("Loaded {} from checkpoint '{}'".format(state_dict_key, checkpoint_path))
+        return state_dict
     else:
         logging.error("No checkpoint found at '{}'".format(checkpoint_path))
         raise FileNotFoundError()
 
 
+def load_checkpoint(model, checkpoint_path, use_ema=False):
+    state_dict = load_state_dict(checkpoint_path, use_ema)
+    model.load_state_dict(state_dict)
+
+
 def resume_checkpoint(model, checkpoint_path):
     other_state = {}
     resume_epoch = None

train.py

@@ -1,4 +1,19 @@
+#!/usr/bin/env python
+""" ImageNet Training Script
 
+This is intended to be a lean and easily modifiable ImageNet training script that reproduces ImageNet
+training results with some of the latest networks and training techniques. It favours canonical PyTorch
+and standard Python style over trying to be able to 'do it all.' That said, it offers quite a few speed
+and training result improvements over the usual PyTorch example scripts. Repurpose as you see fit.
+
+This script was started from an early version of the PyTorch ImageNet example
+(https://github.com/pytorch/examples/tree/master/imagenet)
+
+NVIDIA CUDA specific speedups adopted from NVIDIA Apex examples
+(https://github.com/NVIDIA/apex/tree/master/examples/imagenet)
+
+Hacked together by Ross Wightman (https://github.com/rwightman)
+"""
 import argparse
 import time
 import logging
@@ -35,7 +50,7 @@ parser.add_argument('-c', '--config', default='', type=str, metavar='FILE',
                     help='YAML config file specifying default arguments')
 
 
-parser = argparse.ArgumentParser(description='Training')
+parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
 # Dataset / Model parameters
 parser.add_argument('data', metavar='DIR',
                     help='path to dataset')

validate.py

@@ -1,7 +1,12 @@
-from __future__ import absolute_import
+#!/usr/bin/env python
-from __future__ import division
+""" ImageNet Validation Script
-from __future__ import print_function
 
+This is intended to be a lean and easily modifiable ImageNet validation script for evaluating pretrained
+models or training checkpoints against ImageNet or similarly organized image datasets. It prioritizes
+canonical PyTorch, standard Python style, and good performance. Repurpose as you see fit.
+
+Hacked together by Ross Wightman (https://github.com/rwightman)
+"""
 import argparse
 import os
 import csv
@@ -182,6 +187,7 @@ def main():
         # validate all checkpoints in a path with same model
         checkpoints = glob.glob(args.checkpoint + '/*.pth.tar')
         checkpoints += glob.glob(args.checkpoint + '/*.pth')
+        model_names = list_models(args.model)
         model_cfgs = [(args.model, c) for c in sorted(checkpoints, key=natural_key)]
     else:
         if args.model == 'all':
@@ -195,7 +201,7 @@ def main():
             model_cfgs = [(n, '') for n in model_names]
 
     if len(model_cfgs):
-        print('Running bulk validation on these pretrained models:', ', '.join(model_names))
+        logging.info('Running bulk validation on these pretrained models: {}'.format(', '.join(model_names)))
         header_written = False
         with open('./results-all.csv', mode='w') as cf:
             for m, c in model_cfgs: