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app.py

@@ -11,7 +11,10 @@ import plotly.express as px
 from fastcore.all import *
 import streamlit as st
 
-# %% 03a_image_archs.ipynb 13
+# %% 03a_image_archs.ipynb 12
+st.set_page_config(page_title="Which Image Model is best?",layout="wide")
+
+# %% 03a_image_archs.ipynb 14
 def get_results(result_option = 'original'):
   suffix = "" if result_option == 'original' else "-real"
   url_results = f"https://github.com/huggingface/pytorch-image-models/raw/main/results/results-imagenet{suffix}.csv"
@@ -20,7 +23,7 @@ def get_results(result_option = 'original'):
   df_results['model'] = df_results['model'].str.split('.').str[0]
   return df_results
 
-# %% 03a_image_archs.ipynb 15
+# %% 03a_image_archs.ipynb 16
 def get_integrated_data(activity_option, result_option):
   df_results = get_results(result_option)
   url_benchmark = f"https://github.com/huggingface/pytorch-image-models/raw/main/results/benchmark-{activity_option}-amp-nhwc-pt112-cu113-rtx3090.csv"
@@ -32,7 +35,7 @@ def get_integrated_data(activity_option, result_option):
   df_integrated.loc[df_integrated.model.str.contains('resnet.*d'), 'family'] = df_integrated.loc[df_integrated.model.str.contains('resnet.*d'), 'family'] + "d"
   return df_integrated[~df_integrated.model.str.endswith('gn')] # Group norm models. Why Jeremy eliminated them from analysis?
 
-# %% 03a_image_archs.ipynb 17
+# %% 03a_image_archs.ipynb 18
 @st.cache_data
 def get_filtered_data(df_integrated, subs, is_fullmatch=False, drop_tf=True):
   if drop_tf: df_integrated = df_integrated[~df_integrated.is_tensorflow_model]
@@ -40,7 +43,7 @@ def get_filtered_data(df_integrated, subs, is_fullmatch=False, drop_tf=True):
   elif is_fullmatch: return df_integrated[df_integrated.family.str.fullmatch(subs)]
   else: return df_integrated[df_integrated.model.str.contains(subs)]
 
-# %% 03a_image_archs.ipynb 18
+# %% 03a_image_archs.ipynb 19
 def get_data(col_option, activity_option, result_option, subs, is_fullmatch=False, drop_tf=True):
   col = "_".join([activity_option, col_option])
   df_integrated = get_integrated_data(activity_option, result_option)
@@ -48,7 +51,7 @@ def get_data(col_option, activity_option, result_option, subs, is_fullmatch=Fals
   df_integrated['secs'] =1./df_integrated[col]
   return df_integrated
 
-# %% 03a_image_archs.ipynb 19
+# %% 03a_image_archs.ipynb 20
 def plot_selection(df, title, col_option, activity_option, w=1000, h=800):
   size_col = "_".join([activity_option, col_option])
   return px.scatter(df, width=w, height=h, size=df[size_col]**2,trendline="ols", trendline_options={'log_x':True},
@@ -57,7 +60,7 @@ def plot_selection(df, title, col_option, activity_option, w=1000, h=800):
                     hover_data=[size_col])
 
 
-# %% 03a_image_archs.ipynb 20
+# %% 03a_image_archs.ipynb 21
 result_options = ['original', 'real'] #result = 'real'
 activity_options = ['train', 'infer']
 col_options = ['samples_per_sec', 'step_time', 'batch_size', 'img_size', 'gmacs', 'macts']
@@ -74,8 +77,7 @@ title_dict = dict(zip(activity_options, ['Training', "Inference"]))
 df = get_data(col_option, activity_option, result_option, subs, is_fullmatch=is_fullmatch, drop_tf=drop_tf)
 fig = plot_selection(df, title_dict[activity_option], size_col_option, activity_option)
 
-# %% 03a_image_archs.ipynb 24
-st.set_page_config(page_title="Which Image Model is best?",layout="wide")
+# %% 03a_image_archs.ipynb 25
 st.title("Which Image Model is best?")
 col1, col2 = st.columns([1,3])
 with col1: