def visualize_activations(self, stacked_shreds):
# TODO: hard-coded left and right indexes. change if you like. don't want to spend time.
if self._comparator.t > 2:
left, right = self._comparator.t + 1, self._comparator.t + 2
else:
left, right = 0, 1
tensor = ComparatorCNN._prepare_left_right_check(
stacked_shreds[0][left], stacked_shreds[0][right])
root = os.path.join(os.path.dirname(__file__), 'class_activation_maps')
os.makedirs('class_activation_maps', exist_ok=True)
for i, layer in enumerate(self._comparator._model.layers[1:]):
try:
layer_name = layer.name
if 'conv' not in layer_name:
continue
cam = visualize_cam(self._comparator._model, i, None, tensor)
plt.imshow(stacked_shreds[0][left], cmap='gray')
plt.imshow(cam,
cmap='hot',
interpolation='nearest',
alpha=0.15)
plt.title('#{}:{}'.format(i, layer_name))
current_milli_time = lambda: int(round(time.time() * 1000))
plt.savefig(
os.path.join(
root, '{}_{}_{}.png'.format(i, layer_name,
current_milli_time())))
plt.clf()
print(cam)
except:
print("Exception!")
def main():
if 'large' in sys.argv:
number_of_samples = sys.maxsize
else:
number_of_samples = 20
ts = list()
if '2' in sys.argv:
ts.append(2)
if '4' in sys.argv:
ts.append(4)
if '5' in sys.argv:
ts = [
5,
]
if 0 == len(ts):
ts = (2, 4, 5)
image_types = list()
if 'image' in sys.argv:
image_types.append(ImageType.IMAGES)
if 'document' in sys.argv:
image_types.append(ImageType.DOCUMENTS)
if 0 == len(image_types):
image_types = ImageType
np.random.seed(42)
for t in ts:
for image_type in image_types:
print('t={}. image type is {}'.format(t, image_type.value))
if image_type == ImageType.IMAGES:
get_images = DataProvider().get_fish_images
else:
get_images = DataProvider().get_docs_images
images = get_images(num_samples=number_of_samples)
images_train, images_validation = train_test_split(images,
random_state=42)
clf = ComparatorCNN(t,
IMAGE_TYPE_TO_T_TO_COMPARATOR_CNN_WEIGHT_FILE_ID_AND_FILE_PATH[image_type][t].width,
IMAGE_TYPE_TO_T_TO_COMPARATOR_CNN_WEIGHT_FILE_ID_AND_FILE_PATH[image_type][t].height,
image_type) \
.load_weights(IMAGE_TYPE_TO_T_TO_COMPARATOR_CNN_WEIGHT_FILE_ID_AND_FILE_PATH[image_type][t].model_path)
cam = ComparatorActivationMap(clf)
for image in images_train:
cam.visualize_activations(
shred_and_resize_to([image], t, (clf.width, clf.height)))
def load_models():
global image_type_to_t_to_comparator
image_type_to_t_to_comparator = {
image_type: {
t: ComparatorCNN(
t,
IMAGE_TYPE_TO_T_TO_COMPARATOR_CNN_WEIGHT_FILE_ID_AND_FILE_PATH[
image_type][t].width,
IMAGE_TYPE_TO_T_TO_COMPARATOR_CNN_WEIGHT_FILE_ID_AND_FILE_PATH[
image_type][t].height, image_type,
IMAGE_TYPE_TO_MEAN[image_type], IMAGE_TYPE_TO_STD[image_type]).
load_weights(
IMAGE_TYPE_TO_T_TO_COMPARATOR_CNN_WEIGHT_FILE_ID_AND_FILE_PATH[
image_type][t].model_path)
for t in TS
}
for image_type in ImageType
}
def debug():
names_train = ['n01440764_11593.JPEG', 'n01440764_11602.JPEG', 'n01440764_4562.JPEG', 'n01440764_5148.JPEG',
'n01440764_11897.JPEG', 'n01440764_29057.JPEG', 'n01440764_22135.JPEG', 'n01440764_8003.JPEG',
'n01440764_3566.JPEG', 'n01440764_44.JPEG', 'n01440764_10910.JPEG', 'n01440764_10382.JPEG',
'n01440764_6508.JPEG', 'n01440764_10290.JPEG', 'n01440764_910.JPEG']
images_train, names_train = DataProvider.read_images('../images', names_train)
indices = [4, 5, 13]
images_validation = [images_train[index] for index in indices]
index = 4
t = 4
permutation = [2, 8, 4, 12, 0, 10, 6, 5, 7, 3, 13, 15, 11, 9, 1, 14]
width = 224
height = 224
image_type = ImageType.IMAGES
cmp = ComparatorCNN(t, width, height, image_type) \
._fit_standardisation(images_train) \
.load_weights(IMAGE_TYPE_TO_T_TO_COMPARATOR_CNN_WEIGHT_FILE_ID_AND_FILE_PATH[image_type][t].model_path)
slv = SolverGreedy({t: cmp})
score = slv.evaluate_image_for_permutation(images_validation[0], permutation, sample_index=index)
print('done with ', score)
def main():
if 'debug' in sys.argv:
print('Debug')
number_of_samples = 20
epochs = 1
else:
print('Release')
number_of_samples = sys.maxsize
epochs = 1
ts = list()
if '2' in sys.argv:
ts.append(2)
if '4' in sys.argv:
ts.append(4)
if '5' in sys.argv:
ts = [
5,
]
if 0 == len(ts):
ts = (2, 4, 5)
image_types = list()
if 'image' in sys.argv:
image_types.append(ImageType.IMAGES)
if 'document' in sys.argv:
image_types.append(ImageType.DOCUMENTS)
if 0 == len(image_types):
image_types = ImageType
np.random.seed(42)
for image_type in image_types:
print(image_type.value)
if image_type == ImageType.IMAGES:
get_images = DataProvider().get_fish_images
mean = 100.52933494138787
std = 65.69793156777682
else:
get_images = DataProvider().get_docs_images
mean = 241.46115784237548
std = 49.512839464023564
images, names = get_images(num_samples=number_of_samples,
return_names=True)
images_train, images_validation, names_train, names_validation = train_test_split(
images, names, random_state=42)
t_to_comparator = {
t: ComparatorCNN(
t,
IMAGE_TYPE_TO_T_TO_COMPARATOR_CNN_WEIGHT_FILE_ID_AND_FILE_PATH[
image_type][t].width,
IMAGE_TYPE_TO_T_TO_COMPARATOR_CNN_WEIGHT_FILE_ID_AND_FILE_PATH[
image_type][t].height,
image_type,
mean=mean,
std=std).
load_weights(
IMAGE_TYPE_TO_T_TO_COMPARATOR_CNN_WEIGHT_FILE_ID_AND_FILE_PATH[
image_type][t].model_path)
for t in ts
}
clf = SolverLP(t_to_comparator, image_type=image_type)
print('Train: ', names_train)
accuracy = clf.evaluate(images_train, epochs=epochs, ts=ts)
print('Train 0-1 accuracy on {}: {}'.format(image_type.value,
accuracy))
print('Validation: ', names_validation)
accuracy = clf.evaluate(images_validation, epochs=epochs, ts=ts)
print('Validation 0-1 accuracy on {}: {}'.format(
image_type.value, accuracy))
from solvers.solver_greedy import SolverGreedy
from utils.shredder import Shredder
from utils.image_type import ImageType
from models.fish_or_doc_classifier import FishOrDocClassifier
from models.comparator_cnn import ComparatorCNN
fish_or_doc_classifier = FishOrDocClassifier(
weights_file=IMAGE_OR_DOCUMENT_WEIGHT_FILE_ID_AND_FILE_PATH.model_path)
image_type_to_solver_with_comparator = {
image_type: SolverGreedy(
{
t: ComparatorCNN(
t,
IMAGE_TYPE_TO_T_TO_COMPARATOR_CNN_WEIGHT_FILE_ID_AND_FILE_PATH[
image_type][t].width,
IMAGE_TYPE_TO_T_TO_COMPARATOR_CNN_WEIGHT_FILE_ID_AND_FILE_PATH[
image_type][t].height, image_type,
IMAGE_TYPE_TO_MEAN[image_type], IMAGE_TYPE_TO_STD[image_type]).
load_weights(
IMAGE_TYPE_TO_T_TO_COMPARATOR_CNN_WEIGHT_FILE_ID_AND_FILE_PATH[
image_type][t].model_path)
for t in TS
},
image_type=image_type)
for image_type in ImageType
}
def predict(images):
reconstructed_image = Shredder.reconstruct(images)
print('It is ', end='')
def main():
if 'debug' in sys.argv:
print('Debug')
number_of_samples = 20
epochs = 1
else:
print('Release')
number_of_samples = sys.maxsize
epochs = 1
ts = list()
if '2' in sys.argv:
ts.append(2)
if '4' in sys.argv:
ts.append(4)
if '5' in sys.argv:
ts = [5, ]
if 0 == len(ts):
ts = (2, 4, 5)
image_types = list()
if 'image' in sys.argv:
image_types.append(ImageType.IMAGES)
if 'document' in sys.argv:
image_types.append(ImageType.DOCUMENTS)
if 'version' in sys.argv:
version = int(sys.argv[sys.argv.index('version') + 1])
else:
version = 2
if 0 == version:
iterate_on_bottom_values = (False, )
iterate_on_right_values = (False, )
column_then_row_values = (False, )
iterate_first_shred = False
try_to_improve_with_row_permutation = False
width = 224
height = 224
elif 1 == version:
iterate_on_bottom_values = (False, )
iterate_on_right_values = (False, )
column_then_row_values = (False, )
iterate_first_shred = True
try_to_improve_with_row_permutation = False
width = 224
height = 224
elif 2 == version:
iterate_on_bottom_values = (False, True)
iterate_on_right_values = (False, True)
column_then_row_values = (False, True)
iterate_first_shred = True
try_to_improve_with_row_permutation = False
width = 224
height = 224
else: # if 3 <= version
iterate_on_bottom_values = (False, True)
iterate_on_right_values = (False, True)
column_then_row_values = (False, True)
iterate_first_shred = True
try_to_improve_with_row_permutation = True
width = 2200 // 5
height = 2200 // 5
if 0 == len(image_types):
image_types = ImageType
np.random.seed(42)
for image_type in image_types:
print(image_type.value)
if image_type == ImageType.IMAGES:
get_images = DataProvider().get_fish_images
mean = 100.52933494138787
std = 65.69793156777682
else:
get_images = DataProvider().get_docs_images
mean = 241.46115784237548
std = 49.512839464023564
images, names = get_images(num_samples=number_of_samples, return_names=True)
images_train, images_validation, names_train, names_validation = train_test_split(images, names,
random_state=42)
t_to_comparator = {
t: ComparatorCNN(t, width, height, image_type, mean=mean, std=std)
.load_weights()
for t in ts
}
clf = SolverGreedy(t_to_comparator,
image_type=image_type,
iterate_on_bottom_values=iterate_on_bottom_values,
iterate_on_right_values=iterate_on_right_values,
column_then_row_values=column_then_row_values,
iterate_first_shred=iterate_first_shred,
try_to_improve_with_row_permutation=try_to_improve_with_row_permutation)
print('Train: ', names_train)
accuracy = clf.evaluate(images_train, epochs=epochs, ts=ts)
print('Train 0-1 accuracy on {}: {}'.format(image_type.value, accuracy))
print('Validation: ', names_validation)
accuracy = clf.evaluate(images_validation, epochs=epochs, ts=ts)
print('Validation 0-1 accuracy on {}: {}'.format(image_type.value, accuracy))
def main():
if 'debug' in sys.argv:
print('Debug')
number_of_samples = 20
epochs = 1
else:
print('Release')
number_of_samples = sys.maxsize
epochs = 5
ts = list()
if '2' in sys.argv:
ts.append(2)
if '4' in sys.argv:
ts.append(4)
if '5' in sys.argv:
ts = [
5,
]
if 0 == len(ts):
ts = (2, 4, 5)
image_types = list()
if 'image' in sys.argv:
image_types.append(ImageType.IMAGES)
if 'document' in sys.argv:
image_types.append(ImageType.DOCUMENTS)
if 0 == len(image_types):
image_types = ImageType
np.random.seed(42)
for image_type in image_types:
print(image_type.value)
if image_type == ImageType.IMAGES:
get_images = DataProvider().get_fish_images
mean = IMAGE_TYPE_TO_MEAN[image_type]
std = IMAGE_TYPE_TO_STD[image_type]
else:
get_images = DataProvider().get_docs_images
mean = IMAGE_TYPE_TO_MEAN[image_type]
std = IMAGE_TYPE_TO_STD[image_type]
images, names = get_images(num_samples=number_of_samples,
return_names=True)
images_train, images_validation, names_train, names_validation = train_test_split(
images, names, random_state=42)
t_to_comparator = {
t: ComparatorCNN(
t,
IMAGE_TYPE_TO_T_TO_COMPARATOR_CNN_WEIGHT_FILE_ID_AND_FILE_PATH[
image_type][t].width,
IMAGE_TYPE_TO_T_TO_COMPARATOR_CNN_WEIGHT_FILE_ID_AND_FILE_PATH[
image_type][t].height,
image_type,
mean=mean,
std=std).
load_weights(
IMAGE_TYPE_TO_T_TO_COMPARATOR_CNN_WEIGHT_FILE_ID_AND_FILE_PATH[
image_type][t].model_path)
for t in ts
}
t_to_backup_solver = {
t: SolverGreedy(t_to_comparator, image_type=image_type)
for t in ts
}
clf = SolverPairwiseMerge(t_to_comparator,
t_to_backup_solver=t_to_backup_solver,
image_type=image_type)
print('Train: ', names_train)
accuracy = clf.evaluate(images_train, epochs=epochs, ts=ts)
print('Train 0-1 accuracy on {}: {}'.format(image_type.value,
accuracy))
print('Validation: ', names_validation)
accuracy = clf.evaluate(images_validation, epochs=epochs, ts=ts)
print('Validation 0-1 accuracy on {}: {}'.format(
image_type.value, accuracy))