From 421621d9297c5e0e08ea055df4ad9fbc5b0445e0 Mon Sep 17 00:00:00 2001
From: gent <jw02425@surrey.ac.uk>
Date: Sat, 27 Jan 2024 18:09:43 +0000
Subject: [PATCH] stronger regularization for progressive training
---
vitookit/datasets/ffcv_transform.py | 12 +++++++-----
vitookit/evaluation/eval_cls_ffcv.py | 6 ++++++
2 files changed, 13 insertions(+), 5 deletions(-)
diff --git a/vitookit/datasets/ffcv_transform.py b/vitookit/datasets/ffcv_transform.py
index 9541909..1ad463d 100644
--- a/vitookit/datasets/ffcv_transform.py
+++ b/vitookit/datasets/ffcv_transform.py
@@ -38,7 +38,7 @@ IMAGENET_STD = np.array([0.229, 0.224, 0.225]) * 255
@gin.configurable
class DynamicResolution:
def __init__(self, start_ramp=gin.REQUIRED, end_ramp=gin.REQUIRED,
- res=(224,224,0),lower_scale=(0.2,0.2,0), upper_scale=(1,1,0)):
+ res=(224,224,0),lower_scale=(0.08,0.08,0), upper_scale=(1,1,0)):
self.start_ramp = start_ramp
self.end_ramp = end_ramp
self.resolution = res
@@ -61,7 +61,7 @@ class DynamicResolution:
return rv
interp = np.interp([epoch], [self.start_ramp, self.end_ramp], [lv, rv])
- num_steps = int(np.round((interp[0]-lv) / step))
+ num_steps = int(((interp[0]-lv) / step))
return num_steps*step + lv
def get_lower_scale(self, epoch):
@@ -80,7 +80,7 @@ class DynamicResolution:
interp = np.interp([epoch], [self.start_ramp, self.end_ramp], [lv, rv])
- num_steps = int(np.round((interp[0]-lv) / step))
+ num_steps = int(((interp[0]-lv) / step))
return num_steps*step + lv
def get_upper_scale(self, epoch):
@@ -98,14 +98,16 @@ class DynamicResolution:
return rv
interp = np.interp([epoch], [self.start_ramp, self.end_ramp], [lv, rv])
- num_steps = int(np.round((interp[0]-lv) / step))
+ num_steps = int(((interp[0]-lv) / step))
return num_steps*step + lv
+
def __call__(self, loader, epoch,is_ffcv=False):
img_size = self.get_resolution(epoch)
lower_scale = self.get_lower_scale(epoch)
upper_scale = self.get_upper_scale(epoch)
print(f"resolution = {img_size}, scale = ({lower_scale:.2f},{upper_scale:.2f}) ")
+
if is_ffcv:
pipeline=loader.pipeline_specs['image']
if pipeline.decoder.output_size[0] != img_size:
@@ -581,7 +583,7 @@ def ThreeAugmentPipeline(img_size=224,scale=(0.08,1), color_jitter=None,downscal
[ RandomResizedCropRGBImageDecoder((img_size, img_size), scale=scale,),
RandomHorizontalFlip(),]+
# second_tfl
- ( [RandomColorJitter(jitter_prob=1, brightness=color_jitter, contrast=color_jitter, saturation=color_jitter, hue=0, seed=None)] if color_jitter else []) +
+ ( [RandomColorJitter(jitter_prob=0.8, brightness=color_jitter, contrast=color_jitter, saturation=color_jitter, hue=0, seed=None)] if color_jitter else []) +
# final_tfl
[
NormalizeImage(IMAGENET_MEAN, IMAGENET_STD, np.float32),
diff --git a/vitookit/evaluation/eval_cls_ffcv.py b/vitookit/evaluation/eval_cls_ffcv.py
index ad8d388..3f7de32 100644
--- a/vitookit/evaluation/eval_cls_ffcv.py
+++ b/vitookit/evaluation/eval_cls_ffcv.py
@@ -346,6 +346,12 @@ def main(args):
for epoch in range(args.start_epoch, args.epochs):
if dres:
+ if epoch == dres.end_ramp:
+ ## enhance augmentation, see efficientnetv2
+
+ data_loader_train = Loader(args.train_path, pipelines=ThreeAugmentPipeline(color_jitter=0.4),
+ batch_size=args.batch_size, num_workers=args.num_workers,
+ order=order, distributed=args.distributed,seed=args.seed)
dres(data_loader_train,epoch,True)
train_stats = train_one_epoch(
model, criterion, data_loader_train,
--
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