def get_activations(layer_num, base_model, mode='summation', folder='ILSVRC2012_img_val'):
"""
Returns a matrix s.t. activations[i][j] is the summed/maxed activation of image[i] at filter[j] for a given layer
This matrix of activations is saved as a .csv file and will later be used by 'find_strongest_images'
:param layer_num: Specifies layer, whose filters' activations are returned
:param base_model: Pass alexnet_model
:param mode: either 'summation' or 'maximum'
:param folder: Specify folder that contains validation images
"""
assert mode in ('summation', 'maximum'), "Mode has to be either 'summation' or 'maximum'"
print('Working on layer {}\n'.format(layer_num))
filters = AlexNet.channels[layer_num]
activation_matrix_filename = 'Data/Strongest_Activation_Layer{}.csv'.format(layer_num)
# Init array to save activations, Filter and image numbers start with 1!!
activations = np.full((AlexNet.val_set_size + 1, filters + 1), fill_value=0.0, dtype=np.float32)
# Create Model up to layer_num
model = AlexNet(layer_num, base_model)
# For timing
timesteps = [time.time()]
save_interval = 10000
time_interval = 1000
for img_id in range(1, AlexNet.val_set_size + 1):
# Convert image_id to file location
id_str = str(img_id)
while len(id_str) < 5:
id_str = '0' + id_str
img_name = folder + '/ILSVRC2012_val_000' + id_str + '.JPEG'
# Get activations for shortened model
activation_img = model.predict(img_name)
# Make sure that dimensions 2 and 3 are spacial (Image is square)
assert activation_img.shape[2] == activation_img.shape[3], "Index ordering incorrect"
if mode == 'summing':
# Sum over spacial dimension to get activation for given filter and given image
assert activation_img.shape[2] == activation_img.shape[3], "Index ordering incorrect"
activation_img = activation_img.sum(3)
activation_img = activation_img.sum(2)
if mode == 'maximum':
# Find maximum activation for each filter for a given image
activation_img = np.nanmax(activation_img, axis=3)
activation_img = np.nanmax(activation_img, axis=2)
# Remove batch size dimension
assert activation_img.shape[0] == 1
activation_img = activation_img.sum(0)
# Make activations 1-based indexing
activation_img = np.insert(activation_img, 0, 0.0)
# activation_image is now a vector of length equal to number of filters (plus one for one-based indexing)
# each entry corresponds to the maximum/summed activation of each filter for a given image form validation set
# Copy activation results for image to matrix
activations[img_id] = activation_img[:]
# Print progress
if img_id % time_interval == 0:
timesteps.append(time.time())
last_interval = timesteps[-1] - timesteps[-2]
total_execution = timesteps[-1] - timesteps[0]
expected_remaining_time = total_execution / img_id * (AlexNet.val_set_size - img_id)
print("Current image ID: {}".format(img_id))
print("The last 100 images took {0:.2f} s.".format(100 * last_interval / time_interval))
print("The average time per 100 images so far is {0:.2f} s".format(100 * total_execution / img_id))
print("Total execution time so far: {0:.2f} min".format(total_execution / 60))
print("The extrapolated remaining time is {0:.2f} min".format(expected_remaining_time / 60))
print('\n')
# Update output file
if img_id % save_interval == 0:
print("Results for first {} images saved\n".format(img_id))
try:
os.remove(activation_matrix_filename)
except OSError:
pass
np.savetxt(activation_matrix_filename, activations, delimiter=',')
np.savetxt(activation_matrix_filename, activations, delimiter=',')
def get_activations(layer_num, base_model, mode='summation',
folder='/home/z840/ALISURE/Data/VOC2012/ILSVRC2012_img_val'):
"""
返回矩阵 activations[i][j] 是 summed/maxed 过得 image[i] at filter[j]
矩阵保存到 .csv 文件, 之后'find_strongest_images'会使用
:param layer_num: Specifies layer, whose filters' activations are returned
:param base_model: Pass alexnet_model
:param mode: either 'summation' or 'maximum'
:param folder: Specify folder that contains validation images
"""
assert mode in ('summation', 'maximum'), "Mode has to be either 'summation' or 'maximum'"
print('Working on layer {}\n'.format(layer_num))
filters = AlexNet.channels[layer_num]
activation_matrix_filename = 'data/Strongest_Activation_Layer{}.csv'.format(layer_num)
# 初始化激活矩阵, Filter and image numbers start with 1!!
activations = np.full((AlexNet.val_set_size + 1, filters + 1), fill_value=0.0, dtype=np.float32)
# Create Model up to layer_num
model = AlexNet(layer_num, base_model)
# For timing
timesteps = [time.time()]
save_interval = 10000
time_interval = 1000
for img_id in range(1, AlexNet.val_set_size + 1):
# Convert image_id to file location
id_str = str(img_id)
while len(id_str) < 5:
id_str = '0' + id_str
img_name = folder + '/ILSVRC2012_val_000' + id_str + '.JPEG'
# 获得对应层的特征图
activation_img = model.predict(img_name)
# Make sure that dimensions 2 and 3 are spacial (Image is square)
assert activation_img.shape[2] == activation_img.shape[3], "Index ordering incorrect"
# 降维
if mode == 'summation':
assert activation_img.shape[2] == activation_img.shape[3], "Index ordering incorrect"
activation_img = activation_img.sum(3)
activation_img = activation_img.sum(2)
if mode == 'maximum':
activation_img = np.nanmax(activation_img, axis=3)
activation_img = np.nanmax(activation_img, axis=2)
# Remove batch size dimension
assert activation_img.shape[0] == 1
activation_img = activation_img.sum(0)
# 使得激活图从1开始
activation_img = np.insert(activation_img, 0, 0.0)
# activation_image现在存储filter（下标从1开始）
# 每个filter通过maximum/summed 处理
# 拷贝激活值到图片
activations[img_id] = activation_img[:]
# Print progress
if img_id % time_interval == 0:
timesteps.append(time.time())
last_interval = timesteps[-1] - timesteps[-2]
total_execution = timesteps[-1] - timesteps[0]
expected_remaining_time = total_execution / img_id * (AlexNet.val_set_size - img_id)
print("Current image ID: {}".format(img_id))
print("The last 100 images took {0:.2f} s.".format(100 * last_interval / time_interval))
print("The average time per 100 images so far is {0:.2f} s".format(100 * total_execution / img_id))
print("Total execution time so far: {0:.2f} min".format(total_execution / 60))
print("The extrapolated remaining time is {0:.2f} min".format(expected_remaining_time / 60))
print('\n')
# save
if img_id % save_interval == 0:
print("Results for first {} images saved\n".format(img_id))
try:
os.remove(activation_matrix_filename)
except OSError:
pass
np.savetxt(activation_matrix_filename, activations, delimiter=',')
np.savetxt(activation_matrix_filename, activations, delimiter=',')
steps=test_num//Batch_Size)
print("Loss = " + str(evals[0]))
print("Test Accuracy = " + str(evals[1]))
# Predict the classifcation given the implicit steps=7301 for selecting the specific image number.
predict_datagen = ImageDataGenerator(rescale=1.0/255)
predict_generator = predict_datagen.flow_from_directory(predict_dir,
target_size=(image_width,image_height),
batch_size=Batch_Size,
class_mode='categorical')
predict_num = predict_generator.samples
# Make the prediction for any one of the predicted images
predictions = model.predict(predict_generator,
verbose=1,
batch_size=Batch_Size,
steps=predict_num//Batch_Size)
# Plot the discriptive diagram
imshow(predict_generator[5800][0][0])
plt.imsave("predicted1.png", predict_generator[5800][0][0])
predictions[5800]
print(predictions[5800])
