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utils.py
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utils.py
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from collections import OrderedDict
def get_kernel_size(feature_map_number, layer_str):
"""return kernel size that corresponds to feature_map_number
Args:
feature_map_number: int
layer_str: layer in InceptionV1 [str]
Returns:
kernel_size_str: str with kernel size description
"""
if "_pre_relu" in layer_str:
layer_str = layer_str.replace("_pre_relu", "")
if (
feature_map_number
> idx_of_kernel_sizes_in_each_layer_dict[layer_str + "_" + kernel_size_list[-1]]
):
kernel_size_str = -1
print(
"The value of feature_map_number is too high! It is "
+ str(feature_map_number)
+ ". However, there are only "
+ str(
idx_of_kernel_sizes_in_each_layer_dict[
layer_str + "_" + kernel_size_list[-1]
]
)
+ " channels in layer "
+ layer_str
)
for kernel_size_i in reversed(kernel_size_list):
if (
feature_map_number
< idx_of_kernel_sizes_in_each_layer_dict[layer_str + "_" + kernel_size_i]
):
kernel_size_str = kernel_size_i
return kernel_size_str
def get_channel_number(feature_map_number, kernel_size_str, layer_str):
"""Return channel number that is specific to a kernel size.
Args:
feature_map_number: number of feature map, where the range goes over all kernel sizes jointly [int]
layer_str: layer in InceptionV1 [str]
kernel_size_str: str with kernel size description
Returns:
channel_number_str: number of channel, where the range is specific to a kernel size [str]
"""
if kernel_size_str == "1x1":
channel_number_str = feature_map_number
else:
n_channels_to_subtract = idx_of_kernel_sizes_in_each_layer_dict[
layer_str
+ "_"
+ kernel_size_list[kernel_size_list.index(kernel_size_str) - 1]
]
channel_number_str = feature_map_number - n_channels_to_subtract
return str(channel_number_str)
layer_names_experiment_i = [
# 'conv2d0',
# 'conv2d1',
# 'conv2d2',
"mixed3a",
"mixed3b",
"mixed4a",
"mixed4b",
"mixed4c",
"mixed4d",
"mixed4e",
"mixed5a",
"mixed5b",
]
# skip the non-inception-layers & skip every second layer in experiment II
layer_names_experiment_ii = [
# 'conv2d0',
# 'conv2d1',
# 'conv2d2',
"mixed3a",
# "mixed3b",
"mixed4a",
# "mixed4b",
"mixed4c",
# "mixed4d",
"mixed4e",
# "mixed5a",
"mixed5b",
]
kernel_size_list = ["1x1", "3x3", "5x5", "pool"]
# This is according to the InceptionV1 implementation in lucid 0.3.8.
# There is a mistake in channel mixed4a...
idx_of_kernel_sizes_in_each_layer_dict = OrderedDict()
idx_of_kernel_sizes_in_each_layer_dict["mixed3a_1x1"] = 64
idx_of_kernel_sizes_in_each_layer_dict["mixed3a_3x3"] = 192
idx_of_kernel_sizes_in_each_layer_dict["mixed3a_5x5"] = 224
idx_of_kernel_sizes_in_each_layer_dict["mixed3a_pool"] = 256
idx_of_kernel_sizes_in_each_layer_dict["mixed3b_1x1"] = 128
idx_of_kernel_sizes_in_each_layer_dict["mixed3b_3x3"] = 320
idx_of_kernel_sizes_in_each_layer_dict["mixed3b_5x5"] = 416
idx_of_kernel_sizes_in_each_layer_dict["mixed3b_pool"] = 480
idx_of_kernel_sizes_in_each_layer_dict["mixed4a_1x1"] = 192
idx_of_kernel_sizes_in_each_layer_dict["mixed4a_3x3"] = 396
idx_of_kernel_sizes_in_each_layer_dict["mixed4a_5x5"] = 444
idx_of_kernel_sizes_in_each_layer_dict[
"mixed4a_pool"
] = 508 # this one should be 512 according to Szegedy et al. 2014
idx_of_kernel_sizes_in_each_layer_dict["mixed4b_1x1"] = 160
idx_of_kernel_sizes_in_each_layer_dict["mixed4b_3x3"] = 384
idx_of_kernel_sizes_in_each_layer_dict["mixed4b_5x5"] = 448
idx_of_kernel_sizes_in_each_layer_dict["mixed4b_pool"] = 512
idx_of_kernel_sizes_in_each_layer_dict["mixed4c_1x1"] = 128
idx_of_kernel_sizes_in_each_layer_dict["mixed4c_3x3"] = 384
idx_of_kernel_sizes_in_each_layer_dict["mixed4c_5x5"] = 448
idx_of_kernel_sizes_in_each_layer_dict["mixed4c_pool"] = 512
idx_of_kernel_sizes_in_each_layer_dict["mixed4d_1x1"] = 112
idx_of_kernel_sizes_in_each_layer_dict["mixed4d_3x3"] = 400
idx_of_kernel_sizes_in_each_layer_dict["mixed4d_5x5"] = 464
idx_of_kernel_sizes_in_each_layer_dict["mixed4d_pool"] = 528
idx_of_kernel_sizes_in_each_layer_dict["mixed4e_1x1"] = 256
idx_of_kernel_sizes_in_each_layer_dict["mixed4e_3x3"] = 576
idx_of_kernel_sizes_in_each_layer_dict["mixed4e_5x5"] = 704
idx_of_kernel_sizes_in_each_layer_dict["mixed4e_pool"] = 832
idx_of_kernel_sizes_in_each_layer_dict["mixed5a_1x1"] = 256
idx_of_kernel_sizes_in_each_layer_dict["mixed5a_3x3"] = 576
idx_of_kernel_sizes_in_each_layer_dict["mixed5a_5x5"] = 704
idx_of_kernel_sizes_in_each_layer_dict["mixed5a_pool"] = 832
idx_of_kernel_sizes_in_each_layer_dict["mixed5b_1x1"] = 384
idx_of_kernel_sizes_in_each_layer_dict["mixed5b_3x3"] = 768
idx_of_kernel_sizes_in_each_layer_dict["mixed5b_5x5"] = 896
idx_of_kernel_sizes_in_each_layer_dict["mixed5b_pool"] = 1024
# These are the feature_map_numbers of feature_maps that Olah et al. considered
# intuitive in their appendix and the ones that we considered most interpretable.
feature_maps_judged_most_interpretable_warm_up = OrderedDict()
feature_maps_judged_most_interpretable_warm_up["mixed3a"] = [43]
feature_maps_judged_most_interpretable_warm_up["mixed4b"] = [504]
feature_maps_judged_most_interpretable_warm_up["mixed5b"] = [17]
feature_maps_judged_most_interpretable_sampled_trials = OrderedDict()
feature_maps_judged_most_interpretable_sampled_trials["mixed3a"] = [230]
feature_maps_judged_most_interpretable_sampled_trials["mixed3b"] = [462]
feature_maps_judged_most_interpretable_sampled_trials["mixed4a"] = [501]
feature_maps_judged_most_interpretable_sampled_trials["mixed4b"] = [465]
feature_maps_judged_most_interpretable_sampled_trials["mixed4c"] = [449]
feature_maps_judged_most_interpretable_sampled_trials["mixed4d"] = [516]
feature_maps_judged_most_interpretable_sampled_trials["mixed4e"] = [809]
feature_maps_judged_most_interpretable_sampled_trials["mixed5a"] = [720]
feature_maps_judged_most_interpretable_sampled_trials["mixed5b"] = [946]