Upload app.py

app.py

@@ -1,5 +1,7 @@
 import os
 import random
+import time
+import datetime
 import cv2
 import numpy as np
 import torch
@@ -17,6 +19,7 @@ import utils.misc
 import utils.saveload
 from nets.blocks import InputPadder
 from nets.net34 import Net
+from tensorboardX import SummaryWriter
 import imageio
 from demo_dense_visualize import Tracker
 import spaces
@@ -47,25 +50,11 @@ seed_everything(42)
 torch.set_grad_enabled(False)
 
 # -------------------- Model Loading -------------------- #
-# Adjust these paths as needed.
-init_dir = '648Ai4i4i3n4s_1e-5m_c5c_stage3_from_kub_ns_wa_kk_lsh_dyk_46470'
-ckpt_dir = 'checkpoints'
-load_dir = os.path.join(ckpt_dir, init_dir)
-
-# Create the model and load weights.
+url = "https://huggingface.co/aharley/alltracker/resolve/main/alltracker.pth"
+state_dict = torch.hub.load_state_dict_from_url(url, map_location='cpu')
 model = Net(16)
 count_parameters(model)
-_ = utils.saveload.load(
-    None,
-    load_dir,
-    model,
-    optimizer=None,
-    scheduler=None,
-    ignore_load=None,
-    strict=True,
-    verbose=False,
-    weights_only=False,
-)
+model.load_state_dict(state_dict, strict=True)
 model.cuda()
 for n, p in model.named_parameters():
     p.requires_grad = False
@@ -260,7 +249,7 @@ if __name__ == '__main__':
         
         with gr.Row():
             with gr.Column():
-                video_input = gr.Video(label="Upload Video", value="data/172620-847860540_small.mp4")
+                video_input = gr.Video(label="Upload Video", value="data/244754_medium.mp4")
                 extract_btn = gr.Button("Extract First Frame")
                 # Add sliders for resolution and sliding window length.
                 resolution_slider = gr.Slider(minimum=512, maximum=1024, step=256, value=1024, label="Target Resolution")