Delete training

training/pipelines/app.py

@@ -1,67 +0,0 @@
-import gradio as gr
-import argparse
-import os
-import re
-import time
-
-import torch
-import pandas as pd
-
-import os, sys
-root_folder = os.path.abspath(
-    os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-)
-sys.path.append(root_folder)
-from kernel_utils import VideoReader, FaceExtractor, confident_strategy, predict_on_video_set
-from training.zoo.classifiers import DeepFakeClassifier
-
-    
-    
-def predict(video_index):
-    video_index = int(video_index)
-    
-    frames_per_video = 32
-    video_reader = VideoReader()
-    video_read_fn = lambda x: video_reader.read_frames(x, num_frames=frames_per_video)
-    face_extractor = FaceExtractor(video_read_fn)
-    input_size = 380
-    strategy = confident_strategy
-    
-    test_videos = sorted([x for x in os.listdir(args.test_dir) if x[-4:] == ".mp4"])[video_index]
-    print(f"Predicting {video_index} videos")
-    predictions = predict_on_video_set(face_extractor=face_extractor, input_size=input_size, models=models,
-                                       strategy=strategy, frames_per_video=frames_per_video, videos=test_videos,
-                                       num_workers=6, test_dir=args.test_dir)
-    return predictions
-
-def get_args_models():
-    parser = argparse.ArgumentParser("Predict test videos")
-    arg = parser.add_argument
-    arg('--weights-dir', type=str, default="weights", help="path to directory with checkpoints")
-    arg('--models', type=str, default='classifier_DeepFakeClassifier_tf_efficientnet_b7_ns_1_best_dice', help="checkpoint files")  # nargs='+',
-    arg('--test-dir', type=str, default='test_dataset', help="path to directory with videos")
-    arg('--output', type=str, required=False, help="path to output csv", default="submission.csv")
-    args = parser.parse_args()
-
-    models = []
-    # model_paths = [os.path.join(args.weights_dir, model) for model in args.models]
-    model_paths = [os.path.join(args.weights_dir, args.models)]
-    for path in model_paths:
-        model = DeepFakeClassifier(encoder="tf_efficientnet_b7_ns").to("cuda")
-        print("loading state dict {}".format(path))
-        checkpoint = torch.load(path, map_location="cpu")
-        state_dict = checkpoint.get("state_dict", checkpoint)
-        model.load_state_dict({re.sub("^module.", "", k): v for k, v in state_dict.items()}, strict=True)
-        model.eval()
-        del checkpoint
-        models.append(model.half())
-    return args, models
-
-if __name__ == '__main__':
-    global models, args
-    stime = time.time()
-    print("Elapsed:", time.time() - stime)
-    args, models = get_args_models()
-    demo = gr.Interface(fn=predict, inputs="text", outputs="text")
-    demo.launch()  
-

training/pipelines/train_classifier_gradio.py

@@ -1,67 +0,0 @@
-import gradio as gr
-import argparse
-import os
-import re
-import time
-
-import torch
-import pandas as pd
-
-import os, sys
-root_folder = os.path.abspath(
-    os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
-)
-sys.path.append(root_folder)
-from kernel_utils import VideoReader, FaceExtractor, confident_strategy, predict_on_video_set
-from training.zoo.classifiers import DeepFakeClassifier
-
-    
-    
-def predict(video_index):
-    video_index = int(video_index)
-    
-    frames_per_video = 32
-    video_reader = VideoReader()
-    video_read_fn = lambda x: video_reader.read_frames(x, num_frames=frames_per_video)
-    face_extractor = FaceExtractor(video_read_fn)
-    input_size = 380
-    strategy = confident_strategy
-    
-    test_videos = sorted([x for x in os.listdir(args.test_dir) if x[-4:] == ".mp4"])[video_index]
-    print(f"Predicting {video_index} videos")
-    predictions = predict_on_video_set(face_extractor=face_extractor, input_size=input_size, models=models,
-                                       strategy=strategy, frames_per_video=frames_per_video, videos=test_videos,
-                                       num_workers=6, test_dir=args.test_dir)
-    return predictions
-
-def get_args_models():
-    parser = argparse.ArgumentParser("Predict test videos")
-    arg = parser.add_argument
-    arg('--weights-dir', type=str, default="weights", help="path to directory with checkpoints")
-    arg('--models', type=str, default='classifier_DeepFakeClassifier_tf_efficientnet_b7_ns_1_best_dice', help="checkpoint files")  # nargs='+',
-    arg('--test-dir', type=str, default='test_dataset', help="path to directory with videos")
-    arg('--output', type=str, required=False, help="path to output csv", default="submission.csv")
-    args = parser.parse_args()
-
-    models = []
-    # model_paths = [os.path.join(args.weights_dir, model) for model in args.models]
-    model_paths = [os.path.join(args.weights_dir, args.models)]
-    for path in model_paths:
-        model = DeepFakeClassifier(encoder="tf_efficientnet_b7_ns").to("cuda")
-        print("loading state dict {}".format(path))
-        checkpoint = torch.load(path, map_location="cpu")
-        state_dict = checkpoint.get("state_dict", checkpoint)
-        model.load_state_dict({re.sub("^module.", "", k): v for k, v in state_dict.items()}, strict=True)
-        model.eval()
-        del checkpoint
-        models.append(model.half())
-    return args, models
-
-if __name__ == '__main__':
-    global models, args
-    stime = time.time()
-    print("Elapsed:", time.time() - stime)
-    args, models = get_args_models()
-    demo = gr.Interface(fn=predict, inputs="text", outputs="text")
-    demo.launch()  
-