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fast tensor mult #2

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fast tensor mult #2

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16 changes: 13 additions & 3 deletions ResNet_CAM.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,8 @@
from keras.preprocessing import image
from keras.models import Model
import sys
import tensorflow as tf
import keras.backend as K

def pretrained_path_to_tensor(img_path):
# loads RGB image as PIL.Image.Image type
Expand Down Expand Up @@ -36,11 +38,19 @@ def ResNet_CAM(img_path, model, all_amp_layer_weights):
# get model's prediction (number between 0 and 999, inclusive)
pred = np.argmax(pred_vec)
# bilinear upsampling to resize each filtered image to size of original image
mat_for_mult = scipy.ndimage.zoom(last_conv_output, (32, 32, 1), order=1) # dim: 224 x 224 x 2048
mat_for_mult = tf.image.resize_images(last_conv_output, [224, 224]) # dim: 224 x 224 x 2048
# get AMP layer weights
amp_layer_weights = all_amp_layer_weights[:, pred] # dim: (2048,)
# get class activation map for object class that is predicted to be in the image
final_output = np.dot(mat_for_mult.reshape((224*224, 2048)), amp_layer_weights).reshape(224,224) # dim: 224 x 224
# convert weights to tensorflow tensor
amp_layer_weights=tf.convert_to_tensor(amp_layer_weights)
# reshape mat_for_mult to 224x224, 2048
mat_for_mult=tf.reshape(mat_for_mult,(224*224, 2048))
# get class activation output tensor for object class that is predicted to be in the image
final_output = tf.tensordot(mat_for_mult, amp_layer_weights,axes=1)
# reshape output
final_output=tf.reshape(final_output,(224,224)) # dim: 224 x 224
# convert output tensor to numpy array
final_output=final_output.eval(session=K.get_session())
# return class activation map
return final_output, pred

Expand Down