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Fixed issues

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Jordon Brooks 2023-07-26 00:12:23 +01:00
parent bfa8caa032
commit 0c2d4a5ce7
1 changed files with 55 additions and 20 deletions
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75
train_model.py
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@ -55,6 +55,7 @@ def load_video_from_list(list_path):
video_details['preset_speed'] = PRESET_SPEED video_details['preset_speed'] = PRESET_SPEED
train_frames, w, h = load_frames_from_video(os.path.join("test_data/", VIDEO_FILE), NUM_FRAMES * TRAIN_SAMPLES) train_frames, w, h = load_frames_from_video(os.path.join("test_data/", VIDEO_FILE), NUM_FRAMES * TRAIN_SAMPLES)
all_frames.extend(train_frames) all_frames.extend(train_frames)
all_details.append({ all_details.append({
"frames": train_frames, "frames": train_frames,
@ -69,7 +70,7 @@ def load_video_from_list(list_path):
def generate_frame_sequences(frames): def generate_frame_sequences(frames):
sequences = [] sequences = []
labels = [] labels = []
for i in range(len(frames) - NUM_FRAMES + 2): for i in range(len(frames) - NUM_FRAMES + 1):
sequence = frames[i:i+NUM_FRAMES-1] sequence = frames[i:i+NUM_FRAMES-1]
sequences.append(sequence) sequences.append(sequence)
labels.append(sequence[-1]) labels.append(sequence[-1])
@ -87,39 +88,73 @@ def main():
model = VideoCompressionModel(NUM_CHANNELS, NUM_FRAMES) model = VideoCompressionModel(NUM_CHANNELS, NUM_FRAMES)
model.compile(loss='mean_squared_error', optimizer='adam') model.compile(loss='mean_squared_error', optimizer='adam')
early_stop = EarlyStopping(monitor='val_loss', patience=3, verbose=1, restore_best_weights=True) early_stop = EarlyStopping(monitor='val_loss', patience=3, verbose=1, restore_best_weights=True)
# Load and concatenate all sequences and labels
all_train_sequences = []
all_val_sequences = []
all_train_labels = []
all_val_labels = []
all_crf_train = []
all_crf_val = []
all_preset_speed_train = []
all_preset_speed_val = []
for video_details_train, video_details_val in zip(all_video_details_train, all_video_details_val): for video_details_train, video_details_val in zip(all_video_details_train, all_video_details_val):
train_frames = video_details_train["frames"] train_frames = video_details_train["frames"]
val_frames = video_details_val["frames"] val_frames = video_details_val["frames"]
train_differences = frame_difference(preprocess(train_frames)) train_differences = frame_difference(preprocess(train_frames))
val_differences = frame_difference(preprocess(val_frames)) val_differences = frame_difference(preprocess(val_frames))
#print(len(train_differences), train_differences[0].shape)
train_sequences, train_labels = generate_frame_sequences(train_differences) train_sequences, train_labels = generate_frame_sequences(train_differences)
val_sequences, val_labels = generate_frame_sequences(val_differences) val_sequences, val_labels = generate_frame_sequences(val_differences)
num_sequences_train = len(train_sequences) crf_array_train = np.full((len(train_sequences), 1), video_details_train['crf'])
num_sequences_val = len(val_sequences) crf_array_val = np.full((len(val_sequences), 1), video_details_val['crf'])
crf_array_train = np.full((num_sequences_train, 1), video_details_train['crf']) preset_speed_array_train = np.full((len(train_sequences), 1), video_details_train['preset_speed'])
crf_array_val = np.full((num_sequences_val, 1), video_details_val['crf']) preset_speed_array_val = np.full((len(val_sequences), 1), video_details_val['preset_speed'])
preset_speed_array_train = np.full((num_sequences_train, 1), video_details_train['preset_speed'])
preset_speed_array_val = np.full((num_sequences_val, 1), video_details_val['preset_speed'])
print(len(train_sequences)) all_train_sequences.extend(train_sequences)
print(len(val_sequences)) all_val_sequences.extend(val_sequences)
all_train_labels.extend(train_labels)
all_val_labels.extend(val_labels)
all_crf_train.extend(crf_array_train)
all_crf_val.extend(crf_array_val)
all_preset_speed_train.extend(preset_speed_array_train)
all_preset_speed_val.extend(preset_speed_array_val)
print("\nTraining the model for video:", video_details_train["video_file"]) # Convert lists to numpy arrays
model.fit( all_train_sequences = np.array(all_train_sequences)
{"frames": train_sequences, "crf": crf_array_train, "preset_speed": preset_speed_array_train}, all_val_sequences = np.array(all_val_sequences)
train_labels, all_train_labels = np.array(all_train_labels)
batch_size=BATCH_SIZE, all_val_labels = np.array(all_val_labels)
epochs=EPOCHS, all_crf_train = np.array(all_crf_train)
validation_data=({"frames": val_sequences, "crf": crf_array_val, "preset_speed": preset_speed_array_val}, val_labels), all_crf_val = np.array(all_crf_val)
callbacks=[early_stop] all_preset_speed_train = np.array(all_preset_speed_train)
) all_preset_speed_val = np.array(all_preset_speed_val)
print("\nTraining completed for video:", video_details_train["video_file"])
# Shuffle the training data
indices_train = np.arange(all_train_sequences.shape[0])
np.random.shuffle(indices_train)
all_train_sequences = all_train_sequences[indices_train]
all_train_labels = all_train_labels[indices_train]
all_crf_train = all_crf_train[indices_train]
all_preset_speed_train = all_preset_speed_train[indices_train]
print("\nTraining the model on mixed sequences...")
model.fit(
{"frames": all_train_sequences, "crf": all_crf_train, "preset_speed": all_preset_speed_train},
all_train_labels,
batch_size=BATCH_SIZE,
epochs=EPOCHS,
validation_data=({"frames": all_val_sequences, "crf": all_crf_val, "preset_speed": all_preset_speed_val}, all_val_labels),
callbacks=[early_stop]
)
print("\nTraining completed!")
save_model(model, 'model_differencing.keras') save_model(model, 'model_differencing.keras')