UnboundLocalError Traceback (most recent call last) in ----> 1 fe.display_from_map(layer_no=2, cell_no=4)

~/SageMaker/speedlimit/mapextrackt/MapExtrackt/extractor.py in display_from_map(self, layer_no, cell_no, out_type, colourize, outsize, border, picture_in_picture, write_text) 194 if self.write_text.lower() != "none": 195 subtext = "" --> 196 img = self._write_text(img, layer_no, cell_no) 197 198 if self.out_type.lower() == "pil":

~/SageMaker/speedlimit/mapextrackt/MapExtrackt/extractor.py in _write_text(self, img, layer_no, cell_no, flip) 242 if flip: 243 text, subtext = subtext, text --> 244 img = draw_text(img, text, subtext) 245 246 return img

~/SageMaker/speedlimit/mapextrackt/MapExtrackt/functions.py in draw_text(img, text, subtext) 194 195 --> 196 font = ImageFont.truetype(font_loc, size) 197 smaller_font = ImageFont.truetype(font_loc, smaller_size) 198

UnboundLocalError: local variable 'font_loc' referenced before assignment

EDIT: SORRY MISSED VERSIONS REQUIREMENTS, WILL FIX THEM AND SEE IF ITS CORRECTED I'm using resnet18 with last layer softmax classifier and getting this error. By the way thank you for your work 👍

`from MapExtrackt import FeatureExtractor

fe = FeatureExtractor(model_conv)

fe.set_image('im2.jpg')

fe.display_from_map(layer_no=1) #Tried several just in case: Layers -> 0-72`

ERROR:

`---------------------------------------------------------------------------
error                                     Traceback (most recent call last)
 in 
      5 fe.set_image('im2.jpg')
      6 
----> 7 fe.display_from_map(layer_no=1)

C:\ProgramData\Anaconda3\envs\vc\lib\site-packages\MapExtrackt\extractor.py in display_from_map(self, layer_no, cell_no, out_type, colourize, outsize, border, picture_in_picture, write_text)
    179 
    180         # get image map
--> 181         img = self.__return_feature_map(layer_no, single=cell_no)
    182 
    183         # return type

C:\ProgramData\Anaconda3\envs\vc\lib\site-packages\MapExtrackt\extractor.py in __return_feature_map(self, layer_no, single)
    726                 # if colourize
    727                 if self.colourize > -1:
--> 728                     img = colourize_image(img, self.colourize)
    729 
    730                 # if border pad

C:\ProgramData\Anaconda3\envs\vc\lib\site-packages\MapExtrackt\functions.py in colourize_image(img, colour_type)
    168         return base
    169     else:
--> 170         return cv2.applyColorMap(img, colour_type, None)
    171 
    172 

error: OpenCV(3.4.2) C:\Miniconda3\conda-bld\opencv-suite_1534379934306\work\modules\imgproc\src\colormap.cpp:558: error: (-5:Bad argument) Unknown colormap id; use one of COLORMAP_* in function 'cv::applyColorMap'`

Feature request: Possibility to visualize the input of a specific layer. It is useful if you want to visualize the activations (weights after the activation function, e.g., Relu) because the inputs to the next layer after the Relu are effectively the activation.

Thanks for the great library.

When setting the image, one options is to normalize the layer for viewing (parameter normalize_layer), if normalize_layer=False all features (independently of the others in the same layer) are normalized using min-max feature scaling. When normalize_layer=True, one would expect the layer to be normalized across all features using the same strategy, namely min-max feature scaling. However, the denominator doesn't contain the difference max-min but only max. Is this intentioned? If yes, why?