#!/usr/local/lib/python3.5/dist-packages
import sys
import json
sys.path.append('/home/sexy/source/deep-visual-odometry/python/snippets/')
sys.path.append('/home/sexy/source/deep-visual-odometry/models/')
import formatData
from alexNet_3 import run_model, create_model
netNum = '3'
Xtr, Ytr, Xte, Yte = formatData.knownEnv(formatData.load_data(),
formatData.load_poses(),
training_ratio=(5 / 10.0))
#run = 0
#score, history = run_model(create_model(), Xtr, Ytr, Xte, Yte, "/home/sexy/source/deep-visual-odometry/models/alexNet_"+netNum+"/train_"+str(run)+".h5")
#with open("/home/sexy/source/deep-visual-odometry/models/alexNet_"+netNum+"/history_"+str(run)+".json", 'w') as f:
# json.dump(score, f, indent=4)
# json.dump(history, f, indent=4)
run = 5
score, history = run_model(
create_model(), Xtr, Ytr, Xte, Yte,
"/home/sexy/source/deep-visual-odometry/models/alexNet_" + netNum +
"/train_" + str(run) + ".h5")
with open(
"/home/sexy/source/deep-visual-odometry/models/alexNet_" + netNum +
"/history_" + str(run) + ".json", 'w') as f:
json.dump(score, f, indent=4)
json.dump(history, f, indent=4)
Xtr, Ytr, Xte, Yte = formatData.knownEnv(formatData.load_data(),
formatData.load_poses(),
def plot_model(netNum='3', which_model='gen_train_', tr_ratio=(10 / 10.0)):
model_dir = '/home/sexy/source/deep-visual-odometry/models/'
model = load_model(model_dir + "global_models/" + which_model + netNum +
".h5")
image_data = formatData.load_data()
pose_data = formatData.load_poses()
Xtr, Ytr, Xte, Yte = formatData.knownEnv(image_data,
pose_data,
training_ratio=tr_ratio)
# split training and tests into each sequence
Xtr_list = []
Ytr_list = []
Xte_list = []
Yte_list = []
for i in range(11):
_Xtr, _Ytr, _Xte, _Yte = formatData.knownEnv(image_data,
pose_data,
sequences=[i],
training_ratio=tr_ratio)
Xtr_list.append(_Xtr)
Ytr_list.append(_Ytr)
Xte_list.append(_Xte)
Yte_list.append(_Yte)
print("Xtr_list[" + str(i) + "] = " + str(Xtr_list[i].shape))
print("Ytr_list[" + str(i) + "] = " + str(Ytr_list[i].shape))
print("Xte_list[" + str(i) + "] = " + str(Xte_list[i].shape))
print("Yte_list[" + str(i) + "] = " + str(Yte_list[i].shape))
print("Xtr = " + str(Xtr.shape))
print("Ytr = " + str(Ytr.shape))
print("Xte = " + str(Xte.shape))
print("Yte = " + str(Yte.shape))
predicted_outputs = [[], [], []]
plot_data = [[], [], []]
# run through the model for each sequence
for i in range(11):
# create a temp array with all of training and test of sequence i
dim1 = Xtr_list[i][:, 0, 0, 0].shape[0] + Xte_list[i][:, 0, 0,
0].shape[0]
temp_s = np.zeros((dim1, 128, 128, 6))
temp_s[:Xtr_list[i][:, 0, 0, 0].shape[0]] = Xtr_list[i]
temp_s[Xtr_list[i][:, 0, 0, 0].shape[0]:] = Xte_list[i]
predicted_outputs[0].append(model.predict_proba(temp_s, batch_size=4))
predicted_outputs[1].append(
model.predict_proba(Xtr_list[i], batch_size=4))
predicted_outputs[2].append(
model.predict_proba(Xte_list[i], batch_size=4))
# Sets up the data for the ploting of X vs Z
for i in range(11):
temp_Ys = np.concatenate((Ytr_list[i], Yte_list[i]), axis=0)
print(temp_Ys.shape)
plot_data[0].append(np.zeros((temp_Ys.shape[0], 4)))
plot_data[1].append(np.zeros((Ytr_list[i].shape[0], 4)))
plot_data[2].append(np.zeros((Yte_list[i].shape[0], 4)))
for j in range(temp_Ys.shape[0]):
plot_data[0][i][j, 0] = np.sum(temp_Ys[:j, 0])
plot_data[0][i][j, 1] = np.sum(temp_Ys[:j, 2])
plot_data[0][i][j, 2] = np.sum(predicted_outputs[0][i][:j, 0])
plot_data[0][i][j, 3] = np.sum(predicted_outputs[0][i][:j, 2])
for j in range(Ytr_list[i].shape[0]):
plot_data[1][i][j, 0] = np.sum(Ytr_list[i][:j, 0])
plot_data[1][i][j, 1] = np.sum(Ytr_list[i][:j, 2])
plot_data[1][i][j, 2] = np.sum(predicted_outputs[1][i][:j, 0])
plot_data[1][i][j, 3] = np.sum(predicted_outputs[1][i][:j, 2])
for j in range(Yte_list[i].shape[0]):
plot_data[2][i][j, 0] = np.sum(Yte_list[i][:j, 0])
plot_data[2][i][j, 1] = np.sum(Yte_list[i][:j, 2])
plot_data[2][i][j, 2] = np.sum(predicted_outputs[2][i][:j, 0])
plot_data[2][i][j, 3] = np.sum(predicted_outputs[2][i][:j, 2])
# plots the result
plt.figure(1, figsize=(16, 12))
for i in range(11):
plt.subplot(3, 4, i + 1)
plt.plot(plot_data[0][i][:, 0], plot_data[0][i][:, 1],
plot_data[0][i][:, 2], plot_data[0][i][:, 3])
plt.figure(2, figsize=(16, 12))
for i in range(11):
plt.subplot(3, 4, i + 1)
plt.plot(plot_data[1][i][:, 0], plot_data[1][i][:, 1],
plot_data[1][i][:, 2], plot_data[1][i][:, 3])
plt.figure(3, figsize=(16, 12))
for i in range(11):
plt.subplot(3, 4, i + 1)
plt.plot(plot_data[2][i][:, 0], plot_data[2][i][:, 1],
plot_data[2][i][:, 2], plot_data[2][i][:, 3])
#plt.show()
# save models evaluation foward pass
with open(model_dir + "alexNet_" + netNum + "/eval_S.json", 'w') as f:
temp_plot = []
for i in range(11):
temp_plot.append(plot_data[0][i].tolist())
json.dump(temp_plot, f, indent=4)
with open(model_dir + "alexNet_" + netNum + "/eval_Tr.json", 'w') as f:
temp_plot = []
for i in range(11):
temp_plot.append(plot_data[1][i].tolist())
json.dump(temp_plot, f, indent=4)
with open(model_dir + "alexNet_" + netNum + "/eval_Te.json", 'w') as f:
temp_plot = []
for i in range(11):
temp_plot.append(plot_data[2][i].tolist())
json.dump(temp_plot, f, indent=4)
#!/usr/local/lib/python3.5/dist-packages
import sys
import json
repo_dir = "/home/sexy/source/deep-visual-odometry/"
sys.path.append(repo_dir + 'python/snippets/')
sys.path.append(repo_dir + 'models/')
import formatData
from alexNet_5 import run_model, create_model
netNum = '5'
path = repo_dir + "models/alexNet_" + netNum + "/"
score = [[] for i in range(10)]
history = [{} for i in range(10)]
run = 1
model = create_model()
for i in range(10):
Xtr, Ytr, Xte, Yte = formatData.knownEnv(formatData.load_data(),
formatData.load_poses(),
sequences=[i],
training_ratio=(8 / 10.0))
score[i], temp_hist[i] = run_model(model, Xtr, Ytr, Xte, Yte)
model.save("train_" + str(run) + ".h5")
with open(path + "history_" + str(run) + ".json", 'w') as f:
json.dump(score, f, indent=4)
json.dump(history, f, indent=4)