ptrendx commented on a change in pull request #17265: Add bfloat16
floating-point format support based on AMP
URL: https://github.com/apache/incubator-mxnet/pull/17265#discussion_r365371650
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File path: example/quantization/imagenet_inference.py
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@@ -99,7 +100,34 @@ def score(sym, arg_params, aux_params, data, devs,
label_name, max_num_examples,
logger.info(m.get())
-def benchmark_score(symbol_file, ctx, batch_size, num_batches,
data_layer_type, logger=None):
+def low_precison_convert(model_name, low_precision, sym, arg_params,
aux_params, excluded_sym_names=[]):
+ if low_precision == 'bfloat16':
+ if model_name.find('imagenet1k-resnet-152') != -1:
+ excluded_sym_names += ['conv0']
+ elif model_name.find('imagenet1k-inception-bn') != -1:
+ excluded_sym_names += ['conv_1']
+ elif model_name.find('resnet') != -1 and model_name.find('v1') != -1:
+ excluded_sym_names += ['resnetv10_conv0_fwd']
+ elif model_name.find('resnet') != -1 and model_name.find('v2') != -1:
+ excluded_sym_names += ['resnetv20_conv0_fwd']
+ elif model_name.find('vgg') != -1:
+ excluded_sym_names += ['vgg0_conv0_fwd']
+ elif model_name.find('squeezenet1') != -1:
+ excluded_sym_names += ['squeezenet0_conv0_fwd']
+ elif model_name.find('mobilenet') != -1 and model_name.find('v2') ==
-1:
+ excluded_sym_names += ['mobilenet0_conv0_fwd']
+ elif model_name.find('mobilenet') != -1 and model_name.find('v2') !=
-1:
+ excluded_sym_names += ['mobilenetv20_conv0_fwd']
+ elif model_name.find('inceptionv3') != -1:
+ excluded_sym_names += ['inception30_conv0_fwd']
Review comment:
Why? Is there an accuracy issue without those exclusions?
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