Create Experiment_Hollywood2.py

Experiment_Hollywood2.py

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+__author__ = "Yinchong Yang"
+__copyright__ = "Siemens AG, 2017"
+__licencse__ = "MIT"
+__version__ = "0.1"
+
+"""
+MIT License
+Copyright (c) 2017 Siemens AG
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+"""
+
+import os
+import numpy as np
+import pickle
+import datetime
+
+from keras.layers import Input, SimpleRNN, LSTM, GRU, Dense, Dropout, Masking, BatchNormalization
+from keras.models import Model
+from keras.optimizers import *
+from keras.regularizers import l2
+from keras.layers.wrappers import TimeDistributed
+from keras.preprocessing.sequence import pad_sequences
+from keras.utils.np_utils import to_categorical
+
+from sklearn.metrics import average_precision_score, roc_auc_score, accuracy_score
+
+# Custom Functions -----------------------------------------------------------------------------------------------------
+from TTRNN import TT_GRU, TT_LSTM
+
+
+def load_data(inds, mode='train'):
+    N = len(inds)
+    X = np.zeros((N, GLOBAL_MAX_LEN, 234*100*3), dtype='int8')
+    Y = np.zeros((N, 12), dtype='int8')
+
+    for i in range(N):
+        print i
+        if mode=='train':
+            read_in = open(data_path + 'actioncliptrain/' + tr_sample_filenames[inds[i]])
+        elif mode == 'test':
+            read_in = open(data_path + 'actioncliptest/' + te_sample_filenames[inds[i]])
+        this_clip = pickle.load(read_in)[0]
+        read_in.close()
+        # flatten the dimensions 1, 2 and 3
+        this_clip = this_clip.reshape(this_clip.shape[0], -1) # of shape (nb_frames, 240*320*3)
+        this_clip = (this_clip - 128).astype('int8')   # this_clip.mean()
+        X[i] = pad_sequences([this_clip], maxlen=GLOBAL_MAX_LEN, truncating='post', dtype='int8')[0]
+        if mode == 'train':
+            Y[i] = tr_labels[inds[i]]
+        elif mode == 'test':
+            Y[i] = te_labels[inds[i]]
+    return [X, Y]
+
+
+# Load the data --------------------------------------------------------------------------------------------------------
+np.random.seed(11111986)
+
+# Settings:
+model_type = 1
+use_TT = 0
+
+
+# Had to remove due to anonymity
+data_path = ''
+write_out_path = ''
+
+GLOBAL_MAX_LEN = 1496
+
+classes = ['AnswerPhone', 'DriveCar', 'Eat', 'FightPerson', 'GetOutCar', 'HandShake',
+           'HugPerson', 'Kiss', 'Run', 'SitDown', 'SitUp', 'StandUp']
+
+
+# filter out labels that are not included because their samples are longer than 50 frames:
+tr_sample_filenames = os.listdir(data_path + 'actioncliptrain/')
+tr_sample_filenames.sort()
+
+tr_sample_ids = np.array( [x[-10:-5] for x in tr_sample_filenames] ).astype('int16')
+
+tr_label_filename = data_path + 'labels_train.txt'
+tr_labels = np.loadtxt(tr_label_filename, dtype='int16')
+tr_label_ids = tr_labels[:, 0]
+tr_labels = tr_labels[:, 1::]
+
+
+# filter out labels that are not included because their samples are longer than 50 frames:
+te_sample_filenames = os.listdir(data_path + 'actioncliptest/')
+te_sample_filenames.sort()
+
+te_sample_ids = np.array( [x[-10:-5] for x in te_sample_filenames] ).astype('int16')
+
+te_label_filename = data_path + 'labels_test.txt'
+te_labels = np.loadtxt(te_label_filename, dtype='int16')
+te_label_ids = te_labels[:, 0]
+te_labels = te_labels[:, 1::]
+
+n_tr = tr_labels.shape[0]
+n_te = te_labels.shape[0]
+
+
+# X_train, Y_train = load_data(np.arange(0, 128), mode='train')  # small set
+X_train, Y_train = load_data(np.arange(0, n_tr), mode='train')
+# X_test, Y_test = load_data(np.arange(0, 128), mode='test')  # small set
+X_test, Y_test = load_data(np.arange(0, n_te), mode='test')
+
+
+# Define the model -----------------------------------------------------------------------------------------------------
+alpha = 1e-2
+
+tt_input_shape = [10, 18, 13, 30]
+tt_output_shape = [4, 4, 4, 4]
+tt_ranks = [1, 4, 4, 4, 1]
+
+dropoutRate = .25
+
+input = Input(shape=(GLOBAL_MAX_LEN, 234*100*3))
+if model_type == 0:
+    if use_TT ==0:
+        rnn_layer = GRU(np.prod(tt_output_shape),
+                        return_sequences=False,
+                        dropout=0.25, recurrent_dropout=0.25, activation='tanh')
+    else:
+        rnn_layer = TT_GRU(tt_input_shape=tt_input_shape, tt_output_shape=tt_output_shape,
+                           tt_ranks=tt_ranks,
+                           return_sequences=False,
+                           dropout=0.25, recurrent_dropout=0.25, activation='tanh')
+else:
+    if use_TT ==0:
+        rnn_layer = LSTM(np.prod(tt_output_shape),
+                         return_sequences=False,
+                         dropout=0.25, recurrent_dropout=0.25, activation='tanh')
+    else:
+        rnn_layer = TT_LSTM(tt_input_shape=tt_input_shape, tt_output_shape=tt_output_shape,
+                            tt_ranks=tt_ranks,
+                            return_sequences=False,
+                            dropout=0.25, recurrent_dropout=0.25, activation='tanh')
+h = rnn_layer(input)
+output = Dense(units=12, activation='sigmoid', kernel_regularizer=l2(alpha))(h)
+model = Model(input, output)
+model.compile(optimizer=Adam(1e-3), loss='binary_crossentropy')
+
+
+# Start training -------------------------------------------------------------------------------------------------------
+for l in range(501):
+    print 'iter ' + str(l)
+    model.fit(X_train, Y_train, nb_epoch=1, batch_size=32, verbose=1, validation_split=.15)
+
+    if l % 10 == 0:
+        Y_hat = model.predict(X_train)
+        Y_pred = model.predict(X_test)
+        train_res = average_precision_score(Y_train, Y_hat)
+        test_res = average_precision_score(Y_test, Y_pred)
+
+        print 'Training: '
+        print train_res
+        print 'Test: '
+        print test_res