def vlad_experiment(X,
list_of_K,
list_of_PC,
label_map,
frm_map,
figure_name,
list_of_layers=constants.alex_net_layers):
assert len(list_of_K) != 0
assert len(list_of_PC) != 0
for K in list_of_K:
for PC in list_of_PC:
for layer in list_of_layers:
print("[VLAD] K = %3d PC = %3d layer = %s " % (K, PC, layer))
X_layer = X[layer]
X_vlad = encoding.encode_VLAD(X_layer, K, PC)
X_vlad_pca = pca(X_vlad)
X_vlad_tsne = tsne(X_vlad)
name = figure_name + '_VLAD_' + 'K' + str(K) + '_PC' + str(
PC) + '_' + layer
plot_annotated_embedding(X_vlad_pca,
label_map,
frm_map,
name + '_pca',
title="PCA - " + name)
plot_annotated_embedding(X_vlad_tsne,
label_map,
frm_map,
name + '_tsne',
title="t-SNE - " + name)
def process_batch(self, PATH_TO_DATA, annotations, list_of_layers, sampling_rate, batch_size, LCD):
i = 0
label_map = {}
frm_map = {}
X = {}
map_index_data = pickle.load(open(annotations, "rb"))
for index in map_index_data:
segments = map_index_data[index]
# print "Processing images for label " + str(index)
for seg in segments:
# print str(seg)
frm_num = seg[0]
b = 1 #Running count of num frames in batch
batch_data = {}
while frm_num <= (seg[1] + batch_size):
# Initialize Batch
if b == 1:
label_map[i] = index
frm_map[i] = frm_num
# Process frames and build up features in batches
im = caffe.io.load_image(utils.get_full_image_path(PATH_TO_DATA, frm_num))
self.net.blobs['data'].data[...] = self.transformer.preprocess('data', im)
out = self.net.forward()
for layer in list_of_layers:
if LCD:
if layer == 'input':
print "ERROR: Cannot do LCD on input layer"
sys.exit()
data = self.net.blobs[layer].data[0]
data = lcd.LCD(data)
utils.dict_insert(layer, data, batch_data, axis = 0)
else:
if layer == 'input':
data = cv2.imread(full_image_path)
else:
data = self.net.blobs[layer].data[0]
data = utils.flatten(data)
utils.dict_insert(layer, data, batch_data, axis = 1)
if b == batch_size:
print("Batch %3d" % i)
b = 0
i += 1
# Concatenate with main data dictionary
for layer in list_of_layers:
data = encoding.encode_VLAD(batch_data[layer] , 5)
utils.dict_insert(layer, data, X)
batch_data = {}
b += 1
frm_num += sampling_rate
return X, label_map, frm_map
def vlad_experiment(X, list_of_K, list_of_PC, label_map, frm_map, figure_name, list_of_layers = constants.alex_net_layers):
assert len(list_of_K) != 0
assert len(list_of_PC) != 0
for K in list_of_K:
for PC in list_of_PC:
for layer in list_of_layers:
print("[VLAD] K = %3d PC = %3d layer = %s " % (K, PC, layer))
X_layer = X[layer]
X_vlad = encoding.encode_VLAD(X_layer, K, PC)
X_vlad_pca = pca(X_vlad)
X_vlad_tsne = tsne(X_vlad)
name = figure_name + '_VLAD_' + 'K' + str(K) + '_PC'+ str(PC) + '_' + layer
plot_annotated_embedding(X_vlad_pca, label_map, frm_map, name + '_pca', title = "PCA - " + name)
plot_annotated_embedding(X_vlad_tsne, label_map, frm_map, name + '_tsne', title = "t-SNE - " + name)
def featurize_LCD_VLAD(list_of_demonstrations,
kinematics,
layer,
net_name,
folder,
dimensions,
batch_size,
fname,
config=[True, True, True]):
M = dimensions[0]
a = dimensions[1]
print "Featurizing LCD + VLAD: ", layer, net_name, folder, M, a, batch_size
BATCH_SIZE = batch_size
if constants.SIMULATION:
BATCH_SIZE = 5
data_X_PCA = {}
data_X_CCA = {}
data_X_GRP = {}
size_sampled_matrices = [
utils.sample_matrix(kinematics[demo],
sampling_rate=BATCH_SIZE).shape[0]
for demo in list_of_demonstrations
]
PC = min(100, min(size_sampled_matrices))
print "PC: ", PC
for demonstration in list_of_demonstrations:
print demonstration
W = kinematics[demonstration]
Z = load_cnn_features(demonstration, layer, folder, net_name)
W_new = utils.sample_matrix(W, sampling_rate=BATCH_SIZE)
Z_batch = None
W_batch = None
j = 1
Z_new = None
IPython.embed()
PATH_TO_ANNOTATION = constants.PATH_TO_DATA + constants.ANNOTATIONS_FOLDER + demonstration + "_" + str(
constants.CAMERA) + ".p"
start, end = utils.get_start_end_annotations(PATH_TO_ANNOTATION)
Z = []
IPython.embed()
for i in range(Z):
vector_W = W[i]
W_batch = utils.safe_concatenate(W_batch, vector_W)
vector_Z = Z[i]
vector_Z = vector_Z.reshape(M, a, a)
vector_Z = lcd.LCD(vector_Z)
Z_batch = utils.safe_concatenate(Z_batch, vector_Z)
if (j == BATCH_SIZE):
print "NEW BATCH", str(i)
Z_batch_VLAD = encoding.encode_VLAD(Z_batch)
Z_new = utils.safe_concatenate(Z_new, Z_batch_VLAD)
# Re-initialize batch variables
j = 0
Z_batch = None
W_batch = None
j += 1
# tail case
if Z_batch is not None:
print "TAIL CASE"
print "NEW BATCH", str(i)
Z_batch_VLAD = encoding.encode_VLAD(Z_batch)
Z_new = utils.safe_concatenate(Z_new, Z_batch_VLAD)
if config[0]:
Z_new_pca = utils.pca_incremental(Z_new, PC=PC)
print Z_new_pca.shape
assert W_new.shape[0] == Z_new_pca.shape[0]
X_PCA = np.concatenate((W_new, Z_new_pca), axis=1)
data_X_PCA[demonstration] = X_PCA
if config[1]:
Z_new_cca = utils.cca(W_new, Z_new)
print Z_new_cca.shape
assert W_new.shape[0] == Z_new_cca.shape[0]
X_CCA = np.concatenate((W_new, Z_new_cca), axis=1)
data_X_CCA[demonstration] = X_CCA
if config[2]:
Z_new_grp = utils.grp(Z_new)
print Z_new_grp.shape
assert W_new.shape[0] == Z_new_grp.shape[0]
X_GRP = np.concatenate((W_new, Z_new_grp), axis=1)
data_X_GRP[demonstration] = X_GRP
if config[0]:
pickle.dump(data_X_PCA,
open(PATH_TO_FEATURES + fname + "_PCA" + ".p", "wb"))
if config[1]:
pickle.dump(data_X_CCA,
open(PATH_TO_FEATURES + fname + "_CCA" + ".p", "wb"))
if config[2]:
pickle.dump(data_X_GRP,
open(PATH_TO_FEATURES + fname + "_GRP" + ".p", "wb"))
def featurize_LCD_VLAD(list_of_demonstrations, kinematics, layer, net_name, folder, dimensions, batch_size, fname, config = [True, True, True]):
M = dimensions[0]
a = dimensions[1]
print "Featurizing LCD + VLAD: ", layer, net_name, folder, M, a, batch_size
BATCH_SIZE = batch_size
if constants.SIMULATION:
BATCH_SIZE = 5
data_X_PCA = {}
data_X_CCA = {}
data_X_GRP = {}
size_sampled_matrices = [utils.sample_matrix(kinematics[demo], sampling_rate = BATCH_SIZE).shape[0] for demo in list_of_demonstrations]
PC = min(100, min(size_sampled_matrices))
print "PC: ", PC
for demonstration in list_of_demonstrations:
print demonstration
W = kinematics[demonstration]
Z = load_cnn_features(demonstration, layer, folder, net_name)
W_new = utils.sample_matrix(W, sampling_rate = BATCH_SIZE)
Z_batch = None
W_batch = None
j = 1
Z_new = None
IPython.embed()
PATH_TO_ANNOTATION = constants.PATH_TO_DATA + constants.ANNOTATIONS_FOLDER + demonstration + "_" + str(constants.CAMERA) + ".p"
start, end = utils.get_start_end_annotations(PATH_TO_ANNOTATION)
Z = []
IPython.embed()
for i in range(Z):
vector_W = W[i]
W_batch = utils.safe_concatenate(W_batch, vector_W)
vector_Z = Z[i]
vector_Z = vector_Z.reshape(M, a, a)
vector_Z = lcd.LCD(vector_Z)
Z_batch = utils.safe_concatenate(Z_batch, vector_Z)
if (j == BATCH_SIZE):
print "NEW BATCH", str(i)
Z_batch_VLAD = encoding.encode_VLAD(Z_batch)
Z_new = utils.safe_concatenate(Z_new, Z_batch_VLAD)
# Re-initialize batch variables
j = 0
Z_batch = None
W_batch = None
j += 1
# tail case
if Z_batch is not None:
print "TAIL CASE"
print "NEW BATCH", str(i)
Z_batch_VLAD = encoding.encode_VLAD(Z_batch)
Z_new = utils.safe_concatenate(Z_new, Z_batch_VLAD)
if config[0]:
Z_new_pca = utils.pca_incremental(Z_new, PC = PC)
print Z_new_pca.shape
assert W_new.shape[0] == Z_new_pca.shape[0]
X_PCA = np.concatenate((W_new, Z_new_pca), axis = 1)
data_X_PCA[demonstration] = X_PCA
if config[1]:
Z_new_cca = utils.cca(W_new, Z_new)
print Z_new_cca.shape
assert W_new.shape[0] == Z_new_cca.shape[0]
X_CCA = np.concatenate((W_new, Z_new_cca), axis = 1)
data_X_CCA[demonstration] = X_CCA
if config[2]:
Z_new_grp = utils.grp(Z_new)
print Z_new_grp.shape
assert W_new.shape[0] == Z_new_grp.shape[0]
X_GRP = np.concatenate((W_new, Z_new_grp), axis = 1)
data_X_GRP[demonstration] = X_GRP
if config[0]:
pickle.dump(data_X_PCA, open(PATH_TO_FEATURES + fname + "_PCA" + ".p", "wb"))
if config[1]:
pickle.dump(data_X_CCA, open(PATH_TO_FEATURES + fname + "_CCA" + ".p", "wb"))
if config[2]:
pickle.dump(data_X_GRP, open(PATH_TO_FEATURES + fname + "_GRP" + ".p", "wb"))
