Update README.md

VainF · web-flow · commit 2a31ea2f3dcc · 2024-11-17T17:33:28.000+08:00
diff --git a/README.md b/README.md
@@ -192,7 +192,7 @@ pruner = tp.pruner.MetaPruner( # We can always choose MetaPruner if sparse train
     pruning_ratio=0.5, # remove 50% channels, ResNet18 = {64, 128, 256, 512} => ResNet18_Half = {32, 64, 128, 256}
     # pruning_ratio_dict = {model.conv1: 0.2, model.layer2: 0.8}, # customized pruning ratios for layers or blocks
     ignored_layers=ignored_layers,
-    round_to=8, # It's recommended to round dim/channels to 4x or 8x for acceleration. Please see: https://docs.nvidia.com/deeplearning/performance/dl-performance-convolutional/index.html
+    round_to=8, # It's recommended to round dims/channels to 4x or 8x for acceleration. Please see: https://docs.nvidia.com/deeplearning/performance/dl-performance-convolutional/index.html
 )
 
 # 3. Prune & finetune the model

Original file line number	Diff line number	Diff line change
`@@ -192,7 +192,7 @@ pruner = tp.pruner.MetaPruner( # We can always choose MetaPruner if sparse train`
`192`	`192`	`pruning_ratio=0.5, # remove 50% channels, ResNet18 = {64, 128, 256, 512} => ResNet18_Half = {32, 64, 128, 256}`
`193`	`193`	`# pruning_ratio_dict = {model.conv1: 0.2, model.layer2: 0.8}, # customized pruning ratios for layers or blocks`
`194`	`194`	`ignored_layers=ignored_layers,`
`195`		`- round_to=8, # It's recommended to round dim/channels to 4x or 8x for acceleration. Please see: https://docs.nvidia.com/deeplearning/performance/dl-performance-convolutional/index.html`
	`195`	`+ round_to=8, # It's recommended to round dims/channels to 4x or 8x for acceleration. Please see: https://docs.nvidia.com/deeplearning/performance/dl-performance-convolutional/index.html`
`196`	`196`	`)`
`197`	`197`
`198`	`198`	`# 3. Prune & finetune the model`