Complete classifier app with activation map plotting.

app.py

@@ -1,44 +0,0 @@
-# AUTOGENERATED! DO NOT EDIT! File to edit: four-way-classifier.ipynb.
-
-# %% auto 0
-__all__ = ['learn', 'categories', 'description', 'image', 'label', 'examples', 'interf', 'classify_image']
-
-# %% four-way-classifier.ipynb 1
-from fastai.vision.all import *
-import gradio as gr
-import PIL
-import glob
-
-# %% four-way-classifier.ipynb 3
-learn = load_learner( 'simple-image-classifier.pkl' )
-
-# %% four-way-classifier.ipynb 5
-categories = [ 'bird', 'forest', 'otter', 'snake' ]
-
-def classify_image( img ):
-    pred, idx, probs = learn.predict( img )
-    # print( 'This is a %s'%pred )
-    return dict( zip( categories, map( float, probs ) ) )
-
-# %% four-way-classifier.ipynb 7
-description='''
-A simple 4-way classifier that categorizes images as 'snake', 'bird', 'otter' or 'forest'. 
-Refined from an initial ResNet18 model downloaded from HuggingFace.
-
-**DISCLAIMER**: the images here are merely for demonstration purposes. I don't own any of them 
-and I'm not making money from them.  
-'''
-image = gr.components.Image()
-label = gr.components.Label()
-examples = glob.glob( './*.jpg' )
-
-interf = gr.Interface( 
-    title='Simple 4-way image classifier', 
-    description=description, 
-    fn=classify_image, 
-    inputs=image, 
-    outputs=label, 
-    examples=examples, 
-    allow_flagging='manual' 
-)
-interf.launch( inline=True )

simple_image_classifier/.ipynb_checkpoints/app-checkpoint.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: ../gradio_test.ipynb.
+
+# %% auto 0
+__all__ = ['iface', 'greet']
+
+# %% ../gradio_test.ipynb 1
+import gradio as gr 
+
+# %% ../gradio_test.ipynb 3
+def greet( name ): 
+    return 'Hello %s!!'%name
+    
+iface = gr.Interface( fn=greet, inputs='text', outputs='text' ) 
+iface.launch( inline=True )
+

simple_image_classifier/app.py

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+# AUTOGENERATED! DO NOT EDIT! File to edit: ../four-way-classifier.ipynb.
+
+# %% auto 0
+__all__ = ['learn', 'categories', 'model', 'description', 'image', 'label', 'examples', 'interf', 'get_best_layout',
+           'create_plots', 'classify_image']
+
+# %% ../four-way-classifier.ipynb 1
+from fastai.vision.all import *
+import gradio as gr
+import PIL
+import glob
+
+import primefac
+
+from torchviz import make_dot
+from pytorch_grad_cam.utils.image import preprocess_image
+
+# %% ../four-way-classifier.ipynb 2
+warnings.filterwarnings( 'ignore' )
+warnings.simplefilter( 'ignore' )
+
+# plt.rcParams[ 'figure.figsize' ] = [ 15, 15 ]
+plt.rcParams[ 'figure.autolayout' ] = True
+
+# %% ../four-way-classifier.ipynb 3
+def get_best_layout( num_imgs, img_size ):
+    prime_factors = list( primefac.primefac( num_imgs ) )
+    aspect_ratios = []
+    for n in range( len( prime_factors[1:-1] ) ):
+        n1 = np.prod( prime_factors[:n] )
+        n2 = np.prod( prime_factors[n:] )
+        assert n1 * n2 == num_imgs
+        aspect_ratios.append( [ n1, n2, np.abs( img_size[0]/img_size[1] - ( n1/n2 ) ) ] )
+    # print( aspect_ratios )
+    n1_final, n2_final = tuple( aspect_ratios[ np.argmin( [ elem[2] for elem in aspect_ratios ] ) ] )[:2]
+    return n1_final, n2_final
+
+def create_plots( output_tensor, title, fontsize=24 ):
+    temp = output_tensor.detach().numpy()
+    mn, mx = temp.min(), temp.max()
+    img_list = [ output_tensor[0][n].detach().numpy() for n in range( output_tensor.shape[1] ) ]
+    fig = plt.figure()
+    M, N = get_best_layout( len( img_list ), img_list[0].shape )
+    ax = fig.subplots(  M, N, gridspec_kw={ 'wspace':0.01, 'hspace':0.01 } )
+    for N0, img in enumerate( img_list ):
+        m = N0//N
+        n = N0%N
+        im = ax[m,n].imshow( img, cmap='gray' )
+        ax[m,n].axis( 'off' )
+        im.set_clim( [ mn, mx ] )
+
+    plt.suptitle( title, fontsize=fontsize )
+    # plt.subplots_adjust( hspace=0.01, wspace=0.01 )
+    plt.tight_layout()
+    img_buf = io.BytesIO()
+    plt.savefig(img_buf, format='png')
+    im = Image.open(img_buf)
+    return im
+
+def classify_image( img ):
+    pred, idx, probs = learn.predict( img )
+    # print( 'This is a %s'%pred )
+    model = learn.model.eval()
+    rgb_img = np.float32( np.array( img ) ) / 255
+    input_tensor = preprocess_image(
+        rgb_img,
+        mean=[0.485, 0.456, 0.406],
+        std=[0.229, 0.224, 0.225]
+    )
+    im1 = create_plots( model[0][:3]( input_tensor ), title='Inferred features at model[0][:3]' )
+    im2 = create_plots( model[0][:5]( input_tensor ), title='Inferred features at model[0][:5]' )
+    im3 = create_plots( model[0][:7]( input_tensor ), title='Inferred features at model[0][:7]' )
+    
+    return ( dict( zip( categories, map( float, probs ) ) ), im1, im2, im3 )
+
+# %% ../four-way-classifier.ipynb 4
+learn = load_learner( 'simple-image-classifier.pkl' )
+categories = [ 'bird', 'forest', 'otter', 'snake' ]
+model = learn.model.eval()
+
+# %% ../four-way-classifier.ipynb 5
+description='''
+A simple 4-way classifier that categorizes images as 'snake', 'bird', 'otter' or 'forest'. 
+Refined from an initial ResNet18 model downloaded from HuggingFace.
+
+**DISCLAIMER**: the images here are merely for demonstration purposes. I don't own any of them 
+and I'm not making money from them.  
+'''
+
+# with gr.Blocks() as layout: 
+image = gr.components.Image()
+label = [ gr.components.Label(), gr.components.Image(), gr.components.Image(), gr.components.Image() ]
+examples = glob.glob( './*.jpg' )
+
+# with gr.Row():
+interf = gr.Interface( 
+    title='Simple 4-way image classifier', 
+    description=description, 
+    fn=classify_image, 
+    inputs=image, 
+    outputs=label, 
+    examples=examples, 
+    allow_flagging='manual' 
+)
+interf.launch( inline=True )