def fit(self, data_dir_path, model_dir_path, vgg16_include_top=True, data_set_name='UCF-101', test_size=0.2, random_state=42,from_picture=False):
self.vgg16_include_top = vgg16_include_top
self.from_picture = from_picture
config_file_path = self.get_config_file_path(model_dir_path, vgg16_include_top)
weight_file_path = self.get_weight_file_path(model_dir_path, vgg16_include_top)
architecture_file_path = self.get_architecture_file_path(model_dir_path, vgg16_include_top)
vgg16_model = VGG16(include_top=self.vgg16_include_top, weights='imagenet')
### Ucommnet if you want to have intermidiate layers ###
#self.vgg16_model.summary()
#block5_conv3 = vgg16_model.get_layer("block3_conv3").output
#f0 = block5_conv3
#vgg16_model = Model(inputs=vgg16_model.input, outputs=f0)
vgg16_model.compile(optimizer=SGD(), loss='categorical_crossentropy', metrics=['accuracy'])
self.vgg16_model = vgg16_model
feature_dir_name = data_set_name + '-VGG16-Features'
if not vgg16_include_top:
feature_dir_name = data_set_name + '-VGG16-HiDimFeatures'
print('Read Features from :', feature_dir_name)
max_frames = 0
self.labels = dict()
x_samples, y_samples = scan_and_extract_vgg16_features(data_dir_path,
output_dir_path=feature_dir_name,
model=self.vgg16_model,
data_set_name=data_set_name,
is_picture=self.from_picture)
self.num_input_tokens = x_samples[0].shape[1]
frames_list = []
for x in x_samples:
frames = x.shape[0]
frames_list.append(frames)
max_frames = max(frames, max_frames)
self.expected_frames = int(np.mean(frames_list))
print('max frames: ', max_frames)
print('expected frames: ', self.expected_frames)
for i in range(len(x_samples)):
x = x_samples[i]
frames = x.shape[0]
print(x.shape)
if frames > self.expected_frames:
x = x[0:self.expected_frames, :]
x_samples[i] = x
elif frames < self.expected_frames:
temp = np.zeros(shape=(self.expected_frames, x.shape[1]))
temp[0:frames, :] = x
x_samples[i] = temp
for y in y_samples:
if y not in self.labels:
self.labels[y] = len(self.labels)
print(self.labels)
for i in range(len(y_samples)):
y_samples[i] = self.labels[y_samples[i]]
self.nb_classes = len(self.labels)
y_samples = np_utils.to_categorical(y_samples, self.nb_classes)
config = dict()
config['labels'] = self.labels
config['nb_classes'] = self.nb_classes
config['num_input_tokens'] = self.num_input_tokens
config['expected_frames'] = self.expected_frames
config['vgg16_include_top'] = self.vgg16_include_top
self.config = config
np.save(config_file_path, config)
model = self.create_model()
open(architecture_file_path, 'w').write(model.to_json())
Xtrain, Xtest, Ytrain, Ytest = train_test_split(x_samples, y_samples, test_size=test_size,
random_state=random_state)
print(len(x_samples))
print(len(Xtrain))
print(len(Xtest))
train_gen = generate_batch(Xtrain, Ytrain)
test_gen = generate_batch(Xtest, Ytest)
train_num_batches = len(Xtrain) // BATCH_SIZE
test_num_batches = len(Xtest) // BATCH_SIZE
checkpoint = ModelCheckpoint(filepath=weight_file_path, save_best_only=True)
history = model.fit_generator(generator=train_gen, steps_per_epoch=train_num_batches,
epochs=NUM_EPOCHS,
verbose=1, validation_data=test_gen, validation_steps=test_num_batches,
callbacks=[checkpoint])
print(weight_file_path)
model.save_weights(weight_file_path)
return history
def fit(self,
data_dir_path,
model_dir_path,
vgg16_include_top=True,
data_set_name='UCF-101',
test_size=0.3,
random_state=42):
self.vgg16_include_top = vgg16_include_top
config_file_path = self.get_config_file_path(model_dir_path,
vgg16_include_top)
weight_file_path = self.get_weight_file_path(model_dir_path,
vgg16_include_top)
architecture_file_path = self.get_architecture_file_path(
model_dir_path, vgg16_include_top)
self.vgg16_model = VGG16(include_top=self.vgg16_include_top,
weights='imagenet')
self.vgg16_model.compile(optimizer=SGD(),
loss='categorical_crossentropy',
metrics=['accuracy'])
feature_dir_name = data_set_name + '-VGG16-Features'
if not vgg16_include_top:
feature_dir_name = data_set_name + '-VGG16-HiDimFeatures'
max_frames = 0
self.labels = dict()
x_samples, y_samples = scan_and_extract_vgg16_features(
data_dir_path,
output_dir_path=feature_dir_name,
model=self.vgg16_model,
data_set_name=data_set_name)
print(x_samples[0])
self.num_input_tokens = x_samples[0].shape[1]
frames_list = []
for x in x_samples:
print(x)
frames = x.shape[0]
frames_list.append(frames)
max_frames = max(frames, max_frames)
self.expected_frames = int(np.mean(frames_list))
print('max frames: ', max_frames)
print('expected frames: ', self.expected_frames)
for i in range(len(x_samples)):
x = x_samples[i]
frames = x.shape[0]
if frames > self.expected_frames:
x = x[0:self.expected_frames, :]
x_samples[i] = x
elif frames < self.expected_frames:
print('shape: ', x.shape)
print('before error expected_frames = ', self.expected_frames,
'x.shape[1] =', x.shape[1])
temp = np.zeros(shape=(self.expected_frames, x.shape[1]))
temp[0:frames, :] = x
x_samples[i] = temp
for y in y_samples:
if y not in self.labels:
self.labels[y] = len(self.labels)
print(self.labels)
for i in range(len(y_samples)):
y_samples[i] = self.labels[y_samples[i]]
self.nb_classes = len(self.labels)
y_samples = np_utils.to_categorical(y_samples, self.nb_classes)
config = dict()
config['labels'] = self.labels
config['nb_classes'] = self.nb_classes
config['num_input_tokens'] = self.num_input_tokens
config['expected_frames'] = self.expected_frames
config['vgg16_include_top'] = self.vgg16_include_top
self.config = config
np.save(config_file_path, config)
model = self.create_model()
open(architecture_file_path, 'w').write(model.to_json())
Xtrain, Xtest, Ytrain, Ytest = train_test_split(
x_samples,
y_samples,
test_size=test_size,
random_state=random_state)
train_gen = generate_batch(Xtrain, Ytrain)
test_gen = generate_batch(Xtest, Ytest)
print('Xtrain', len(Xtrain), 'Xtest', len(Xtest))
train_num_batches = len(Xtrain) // BATCH_SIZE
test_num_batches = len(Xtest) // BATCH_SIZE
print(test_num_batches, len(Xtest))
checkpoint = ModelCheckpoint(filepath=weight_file_path,
save_best_only=True)
history = model.fit_generator(generator=train_gen,
steps_per_epoch=train_num_batches,
epochs=NUM_EPOCHS,
verbose=1,
validation_data=test_gen,
validation_steps=test_num_batches,
callbacks=[checkpoint])
model.save_weights(weight_file_path)
return history
def fit(self,
data_dir_path,
model_dir_path,
vgg16_include_top=True,
data_set_name='UCF-101',
test_size=0.2,
random_state=42):
self.vgg16_include_top = vgg16_include_top
config_file_path = self.get_config_file_path(model_dir_path,
vgg16_include_top)
weight_file_path = self.get_weight_file_path(model_dir_path,
vgg16_include_top)
architecture_file_path = self.get_architecture_file_path(
model_dir_path, vgg16_include_top)
vgg16_model = VGG16(include_top=self.vgg16_include_top,
weights='imagenet')
vgg16_model.compile(optimizer=SGD(),
loss='categorical_crossentropy',
metrics=['accuracy'])
self.vgg16_model = vgg16_model
feature_dir_name = data_set_name + '-VGG16-Features'
if not vgg16_include_top:
feature_dir_name = data_set_name + '-VGG16-HiDimFeatures'
max_frames = 0
self.labels = dict()
x_samples, y_samples = scan_and_extract_vgg16_features(
data_dir_path,
output_dir_path=feature_dir_name,
model=self.vgg16_model,
data_set_name=data_set_name)
self.num_input_tokens = x_samples[0].shape[1]
print("num_input_tokens= :", self.num_input_tokens) # = 7x7x512
frames_list = []
for x in x_samples:
frames = x.shape[0]
#print("frames in x: = " , frames)
frames_list.append(frames)
#print("frames_list: = " , frames_list)
max_frames = max(frames, max_frames)
#print("max_frames: = " , max_frames)
self.expected_frames = int(np.mean(frames_list))
#print("expected_frames: = " , self.expected_frames)
print("frames_list: = ", frames_list)
print('max frames: ', max_frames)
print('expected frames: ', self.expected_frames)
print("len(x_samples): = ", len(x_samples))
for i in range(len(x_samples)):
x = x_samples[i]
frames = x.shape[0]
#print("x.shape",x.shape) #(frames,7x7x512)
if frames > self.expected_frames:
x = x[0:self.expected_frames, :]
#print("x.shape if frame > expected frame ",x.shape)
x_samples[i] = x
elif frames < self.expected_frames:
temp = np.zeros(shape=(self.expected_frames, x.shape[1]))
temp[0:frames, :] = x
x_samples[i] = temp
#print("x.shape if frame < expected frame (temp) ",temp.shape)
print("WERAWERAWER")
for y in y_samples:
print("for y in y_samples: passed ")
if y not in self.labels:
self.labels[y] = len(self.labels)
print("self.labels:", self.labels)
for i in range(len(y_samples)):
y_samples[i] = self.labels[y_samples[i]]
self.nb_classes = len(self.labels)
y_samples = np_utils.to_categorical(y_samples, self.nb_classes)
config = dict()
config['labels'] = self.labels
config['nb_classes'] = self.nb_classes
config['num_input_tokens'] = self.num_input_tokens
config['expected_frames'] = self.expected_frames
config['vgg16_include_top'] = self.vgg16_include_top
self.config = config
np.save(config_file_path, config)
print("PASS NP SAVE in RCNN")
model = self.create_model()
open(architecture_file_path, 'w').write(model.to_json())
print("PASS model = self.create_model() in RCNN")
Xtrain, Xtest, Ytrain, Ytest = train_test_split(
x_samples,
y_samples,
test_size=test_size,
random_state=random_state)
print("Xtrain", np.shape(Xtrain))
print("Xtest", np.shape(Xtest))
print("Ytrain", np.shape(Ytrain))
print("Ytest", np.shape(Ytest))
train_gen = generate_batch(Xtrain, Ytrain)
test_gen = generate_batch(Xtest, Ytest)
train_num_batches = len(Xtrain) // BATCH_SIZE
test_num_batches = len(Xtest) // BATCH_SIZE
checkpoint = ModelCheckpoint(filepath=weight_file_path,
save_best_only=True)
history = model.fit_generator(generator=train_gen,
steps_per_epoch=train_num_batches,
epochs=NUM_EPOCHS,
verbose=VERBOSE,
validation_data=test_gen,
validation_steps=test_num_batches,
callbacks=[checkpoint])
model.save_weights(weight_file_path)
return history
