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train_spv.py
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train_spv.py
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#!/usr/bin/env python2.7
# Li Ding
# Mar. 2018
# Modified from https://github.com/colincsl/TemporalConvolutionalNetworks/blob/master/code/TCN_main.py
from __future__ import division
import os
import time
import numpy as np
from scipy import io as sio
from keras.utils import np_utils
from utils import utils, metrics, tf_models, weak_model
# ---------- Directories & User inputs --------------
# Location of data/features folder
data_dir = './data' # make sure there are ./data/breakfast_data/s1/... and ./data/segmentation_coarse/... placed
# Parameters
n_nodes = [48, 64, 96]
nb_epoch = 100
conv_len = 25
splits = ['s1', 's2', 's3', 's4']
sample_rate = 15
model_types = ['ED-TCN', 'TC-FPN']
save_predictions = True
batch_size = 8
# ------------------------------------------------------------------
# Evaluate using different filter lengths
if 1:
# Load all the data, takes up to 3 min
breakfast_data = utils.breakfast_dataset(data_dir)
print
asct = time.asctime()
for model_type in model_types:
# Initialize dataset loader & metrics
trial_metrics_tr = metrics.ComputeMetrics(overlap=.1, bg_class=0)
trial_metrics_te = metrics.ComputeMetrics(overlap=.1, bg_class=0)
# Load data for each split
print 'Model type:', model_type
for split in splits:
print 'Start:', split
split_start = time.time()
asct = time.asctime()
# Load data for each split
X_train, y_train = breakfast_data.get_split(split, "train")
X_test, y_test = breakfast_data.get_split(split, "test")
X_train = [i[::sample_rate] for i in X_train]
y_train = [i[::sample_rate] for i in y_train]
X_test = [i[::sample_rate] for i in X_test]
y_test = [i[::sample_rate] for i in y_test]
n_classes = 48
trial_metrics_tr.set_classes(n_classes)
trial_metrics_te.set_classes(n_classes)
train_lengths = [x.shape[0] for x in X_train]
test_lengths = [x.shape[0] for x in X_test]
n_train = len(X_train)
n_test = len(X_test)
n_feat = 64
print "# Feat:", n_feat
# ------------------ Models ----------------------------
# Go from y_t = {1...C} to one-hot vector (e.g. y_t = [0, 0, 1, 0])
Y_train = [np_utils.to_categorical(y, n_classes) for y in y_train]
Y_test = [np_utils.to_categorical(y, n_classes) for y in y_test]
# In order process batches simultaneously all data needs to be of the same length
# So make all same length and mask out the ends of each.
n_layers = len(n_nodes)
max_len = max(max(train_lengths), max(test_lengths))
max_len = int(np.ceil(np.float(max_len) / (2 ** n_layers))) * 2 ** n_layers
print "Max length:", max_len
X_train_m, Y_train_, M_train = utils.mask_data(X_train, Y_train, max_len, mask_value=-1)
X_test_m, Y_test_, M_test = utils.mask_data(X_test, Y_test, max_len, mask_value=-1)
M_train_temp = M_train[:, :, 0]
param_str = None
model = None
if model_type == 'ED-TCN':
model, param_str = tf_models.ED_TCN(n_nodes, conv_len, n_classes, n_feat, return_param_str=True,
max_len=max_len)
elif model_type == 'TC-FPN':
model, param_str = weak_model.TCFPN(n_nodes, conv_len, n_classes, n_feat, return_param_str=True,
in_len=max_len)
elif model_type == 'GRU':
model, param_str = weak_model.GRU64(n_nodes, conv_len, n_classes, n_feat, return_param_str=True,
in_len=max_len)
print param_str
model.fit(X_train_m, Y_train_, nb_epoch=nb_epoch, batch_size=batch_size,
verbose=1, sample_weight=M_train_temp)
AP_train = model.predict(X_train_m, verbose=0)
AP_test = model.predict(X_test_m, verbose=0)
AP_train = utils.unmask(AP_train, M_train)
AP_test = utils.unmask(AP_test, M_test)
P_train = [p.argmax(1) for p in AP_train]
P_test = [p.argmax(1) for p in AP_test]
# --------- Metrics ----------
print
print 'training'
trial_metrics_tr.add_predictions(split, P_train, y_train)
trial_metrics_tr.print_trials()
print
print 'testing'
trial_metrics_te.add_predictions(split, P_test, y_test)
trial_metrics_te.print_trials()
print
# ----- Save prediction -----
if save_predictions:
dir_out = "prediction/{}/{}/{}".format('supv', asct, param_str)
# Make sure folder exists
if not os.path.isdir(dir_out):
os.makedirs(dir_out)
out = {"P": P_test, "Y": y_test, "S": AP_test}
sio.savemat(dir_out + "/{}.mat".format(split), out)
split_end = time.time()
print 'Time elapsed:', time.strftime("%H:%M:%S", time.gmtime(split_end - split_start))
print
print
print 'training'
trial_metrics_tr.print_scores()
print
print 'testing'
trial_metrics_te.print_scores()
print
print "Done!"