Add MobileNets version.
@@ -241,7 +241,7 @@ def get_raw_data(i):
data.append(datum)
index = [
- 'platform', 'library', 'model', 'multiplier', 'resolution', 'batch_size', 'convolution_method', 'data_layout', 'repetition_id'
+ 'platform', 'library', 'model', 'version', 'multiplier', 'resolution', 'batch_size', 'convolution_method', 'data_layout', 'repetition_id'
]
# Construct a DataFrame.
@@ -266,8 +266,8 @@ def get_raw_data(i):
df = df_performance
accuracy_top1_list, accuracy_top5_list = [], []
for index, row in df.iterrows():
- (platform, lib, model, multiplier, resolution, batch_size, convolution_method, data_layout, repetition_id) = index
- row = df_accuracy.loc[(platform, lib, model, multiplier, resolution, batch_size, convolution_method, data_layout)]
+ (platform, library, model, version, multiplier, resolution, batch_size, convolution_method, data_layout, repetition_id) = index
+ row = df_accuracy.loc[(platform, library, model, version, multiplier, resolution, batch_size, convolution_method, data_layout)]
accuracy_top1_list.append(row['accuracy_top1'][0])
accuracy_top5_list.append(row['accuracy_top5'][0])
df = df.assign(accuracy_top1=accuracy_top1_list)
@@ -281,15 +281,12 @@ def get_raw_data(i):
time_avg_min_ms, time_avg_max_ms, time_avg_mean_ms = [], [], []
time_min_min_ms, time_min_max_ms = [], []
for index, row in df.iterrows():
- (platform, lib, model, multiplier, resolution, batch_size, convolution_method, data_layout, repetition_id) = index
+ (platform, library, model, version, multiplier, resolution, batch_size, convolution_method, data_layout, repetition_id) = index
# Handle abnormal situation when no corresponding performance data is available.
try:
- row = df_performance.loc[(platform, lib, model, multiplier, resolution, batch_size, convolution_method, data_layout)]
+ row = df_performance.loc[(platform, library, model, version, multiplier, resolution, batch_size, convolution_method, data_layout)]
except:
ck.out('[Warning] Found no performance data corresponding to accuracy data with index: "%s". Plotting at zero time...' % str(index))
- for index_p, row_p in df_performance.iterrows():
- ck.out(str(index_p))
- break
row = None
if row is not None:
