update files

binary_classify.py

@@ -22,8 +22,8 @@
 from types import SimpleNamespace
 from utils.kinetics_utils import *
 
-sys.path.append('/local/vondrick/dave/fails')
-sys.path.append('/local/vondrick/dave/fails/cnns')
+sys.path.append("PATH/TO/fails")
+sys.path.append("PATH/TO/cnns")
 
 
 def make_uint8(im):
@@ -180,7 +180,7 @@ def load_checkpoint(checkpoint, filename='model_best.pth'):
     parser.add_argument('-j', '--workers', default=8, type=int, metavar='N',
                         help='number of data loading workers')
     parser.add_argument('-c', '--checkpoint',
-                        default='/proj/vondrick/dave/checkpoints/sliding_weaksup/checkpoint_bincls_newborders', type=str,
+                        default="PATH/TO/checkpoint_bincls_newborders", type=str,
                         metavar='PATH',
                         help='path to save checkpoint')
     parser.add_argument('--resume_training', action='store_true')
@@ -214,16 +214,16 @@ def load_checkpoint(checkpoint, filename='model_best.pth'):
     parser.add_argument('--clips_per_video', type=int, default=5)
     # parser.add_argument('--in_vids', type=int, default=4)
     # parser.add_argument('--in_step', type=int, default=1)
-    parser.add_argument('--save_path', default='/proj/vondrick/dave/results/sliding_weaksup/test_results_bincls_newborders')
+    parser.add_argument('--save_path', default="PATH/TO/test_results_bincls_newborders")
     parser.add_argument(
-        '--fails_path', default='/local/vondrick/datasets/fails/scenes')
+        '--fails_path', default="PATH/TO/scenes")
     parser.add_argument(
-        '--kinetics_path', default='/local/vondrick/datasets/Kinetics-600/data')
+        '--kinetics_path', default="PATH/TO/data")
     parser.add_argument('--border_path', default="PATH/TO/borders.json")
-    parser.add_argument('--pretrain_path', default='/local/vondrick/dave/fails/checkpoint_kinetics/model_best.pt.tar')
+    parser.add_argument('--pretrain_path', default="PATH/TO/model_best.pt.tar")
 
-    parser.add_argument('--dataset_path', default='/proj/vondrick/dave/datasets')
-    # '--data_root', default='/proj/vondrick/datasets/fails/scenes_flow')
+    parser.add_argument('--dataset_path', default="PATH/TO/datasets")
+    # '--data_root', default="PATH/TO/scenes_flow")
     parser.add_argument('--sample_size', type=int, default=112)
     parser.add_argument('--sample_duration', type=int, default=16)
     parser.add_argument('--n_kinetics_classes', type=int, default=600)

dataloader.py

@@ -98,8 +98,8 @@ def __init__(self, fails_path, kinetics_path, frames_per_clip, step_between_clip
             assert fails_path is None or fails_video_list is None
             video_list = fails_video_list or glob(os.path.join(fails_path, '**', '*.mp4'), recursive=True)
             if not fails_only:
-                kinetics_cls = torch.load('/local/vondrick/dave/fails/kinetics_classes.pt')
-                kinetics_dist = torch.load('/local/vondrick/dave/slidingwindow/fails_kinetics_features/dist.pt')
+                kinetics_cls = torch.load("PATH/TO/kinetics_classes.pt")
+                kinetics_dist = torch.load("PATH/TO/dist.pt")
                 s = len(video_list)
                 for i, n in kinetics_dist.items():
                     n *= s
@@ -213,14 +213,10 @@ def __init__(self, fails_path, kinetics_path, frames_per_clip, step_between_clip
             self.video_clips.cumulative_sizes = clip_lengths.cumsum(0).tolist()
             if kwargs['local_rank'] <= 0:
                 print(f'removed videos from {fns_removed} out of {len(self.video_clips.video_paths)} files')
-        # if not fails_path.startswith('/local/vondrick/datasets/fails/scenes'):
+        # if not fails_path.startswith("PATH/TO/scenes"):
         for i, p in enumerate(self.video_clips.video_paths):
-            if '/local/vondrick' in p:
-                self.video_clips.video_paths[i] = p.replace('/local/vondrick/datasets/fails/scenes',
-                                                            os.path.dirname(fails_path))
-            elif '/local3/vondrick3' in p:
-                self.video_clips.video_paths[i] = p.replace('/local3/vondrick3/datasets/fails/scenes',
-                                                            os.path.dirname(fails_path))
+            self.video_clips.video_paths[i] = p.replace("PATH/TO/scenes",
+                                                        os.path.dirname(fails_path))
         self.debug_dataset = debug_dataset
         if debug_dataset:
             # self.video_clips = self.video_clips.subset([0])

eval_results.py

@@ -368,9 +368,9 @@ def percentile(l, p):
         data = json.load(f)
 
     # fns = glob(
-    #     '/local/vondrick/dave/slidingwindow/fails_kinetics_features/fails_kinetics_features_*.json')
+    #     '"PATH/TO/fails_kinetics_features_*.json')"
     #
-    # kin_cls = torch.load('/local/vondrick/dave/fails/kinetics_classes.pt')
+    # kin_cls = torch.load("PATH/TO/kinetics_classes.pt")
     #
     # kindata = {}
     # for k, v in kinetics.process().items():

fails_classify.py

@@ -12,8 +12,8 @@
 import torch.distributed as distrib
 import torch.nn as nn
 import torch.nn.functional as F
-from captum.attr import LayerAttribution
-from captum.attr._core.grad_cam import LayerGradCam
+# from captum.attr import LayerAttribution
+# from captum.attr._core.grad_cam import LayerGradCam
 from moviepy.video.io.ImageSequenceClip import ImageSequenceClip
 from torchvision.models.video import r3d_18
 from torchvision.utils import save_image, make_grid
@@ -28,8 +28,8 @@
 # from utils.kinetics_utils import *
 
 
-# sys.path.append('/local/vondrick/dave/fails')
-# sys.path.append('/local/vondrick/dave/fails/cnns')
+# sys.path.append("PATH/TO/fails")
+# sys.path.append("PATH/TO/cnns")
 
 
 def make_uint8(im):
@@ -489,17 +489,17 @@ def show(img):
     # parser.add_argument('--in_step', type=int, default=1)
     parser.add_argument('--save_path')
     parser.add_argument(
-        '--fails_path', default='/local/vondrick/datasets/fails/scenes')
+        '--fails_path', default="PATH/TO/scenes")
     parser.add_argument(
-        '--fails_flow_path', default='/local/vondrick/datasets/fails/scenes_flow_small')
+        '--fails_flow_path', default="PATH/TO/scenes_flow_small")
     parser.add_argument(
-        '--kinetics_path', default='/local/vondrick/datasets/Kinetics-600/data')
+        '--kinetics_path', default="PATH/TO/data")
     parser.add_argument('--border_path', default="PATH/TO/borders.json")
-    parser.add_argument('--pretrain_path', default='/local/vondrick/dave/fails/checkpoint_kinetics/model_best.pt.tar')
+    parser.add_argument('--pretrain_path', default="PATH/TO/model_best.pt.tar")
 
-    parser.add_argument('--dataset_path', default='/proj/vondrick/dave/datasets')
+    parser.add_argument('--dataset_path', default="PATH/TO/datasets")
     parser.add_argument('--remove_fns')
-    # '--data_root', default='/proj/vondrick/datasets/fails/scenes_flow')
+    # '--data_root', default="PATH/TO/scenes_flow")
     parser.add_argument('--sample_size', type=int, default=112)
     parser.add_argument('--sample_duration', type=int, default=16)
     parser.add_argument('--n_kinetics_classes', type=int, default=600)

plotting.py

@@ -8,7 +8,7 @@
 import plotly.graph_objects as go
 import torch
 
-plotly.io.orca.config.executable = '/proj/vondrick/shared/envs/pytorch12/bin/orca'
+plotly.io.orca.config.executable = "PATH/TO/orca"
 
 '''
 Notes: 73% of videos pass the filter
@@ -41,7 +41,7 @@ def __exit__(self, type, value, traceback):
 
 
 class FigureBuilder:
-    def __init__(self, anns, confusion=None, filetype='pdf', basepath='/proj/vondrick/dave/www/figures/oops/tmp',
+    def __init__(self, anns, confusion=None, filetype='pdf', basepath="PATH/TO/tmp",
                  ok_names=None):
         self.anns = anns
         self.filetype = filetype
@@ -160,7 +160,7 @@ def gen_fig(self, name):
                 }
             }
         elif name == 'vid_len':
-            vid_basepath = '/proj/vondrick/datasets/fails/scenes/'
+            vid_basepath = "PATH/TO/"
             with open(os.path.join(vid_basepath, 'validcliplens.json'), 'r') as fff:
                 validcliplens = json.load(fff)
 

utils/compute_kinetics_features.py

@@ -16,8 +16,8 @@
 from spatial_transforms import Normalize, Compose, ToTensor, Scale, RandomScaleCrop
 from torchvision.datasets.folder import default_loader
 import json
-sys.path.append('/local/vondrick/dave/fails')
-sys.path.append('/local/vondrick/dave/fails/cnns')
+sys.path.append("PATH/TO/fails")
+sys.path.append("PATH/TO/cnns")
 
 
 def get_mean(norm_value=255, dataset='activitynet'):
@@ -141,7 +141,7 @@ def trim_borders(img, fn):
         torch.distributed.init_process_group(
             backend='nccl', init_method='env://')
 
-    basepath = '/local/vondrick/datasets/fails/scenes_small'
+    basepath = "PATH/TO/scenes_small"
     with open("PATH/TO/borders.json") as f:
         border_file = json.load(f)
     img_xform = Compose([RandomScaleCrop((1,), center=True), Resize((args.sample_size, args.sample_size)), ToTensor(
@@ -184,7 +184,7 @@ def loaderfn(fn): return (default_loader(fn), fn)
         model = torch.nn.DataParallel(model)
 
     checkpoint = torch.load(
-        '/local/vondrick/dave/fails/checkpoint_kinetics/model_best.pt.tar', map_location=device)
+        "PATH/TO/model_best.pt.tar", map_location=device)
     model.load_state_dict(checkpoint['state_dict'])
 
     results = []

utils/compute_places_features.py

@@ -89,7 +89,7 @@ def trim_borders(img, fn):
         torch.distributed.init_process_group(
             backend='nccl', init_method='env://')
 
-    basepath = '/local/vondrick/datasets/fails/scenes_small'
+    basepath = "PATH/TO/scenes_small"
     with open("PATH/TO/borders.json") as f:
         border_file = json.load(f)
     # img_xform = Compose([RandomScaleCrop((1,), center=True), Resize((args.sample_size, args.sample_size)), ToTensor(
@@ -167,7 +167,7 @@ def loaderfn(fn): return (default_loader(fn), fn)
         model = torch.nn.DataParallel(model)
 
     # checkpoint = torch.load(
-    #     '/local/vondrick/dave/fails/checkpoint_kinetics/model_best.pt.tar', map_location=device)
+    #     "PATH/TO/model_best.pt.tar", map_location=device)
     # model.load_state_dict(checkpoint['state_dict'])
 
     results = []

utils/crop_videos.py

@@ -10,9 +10,9 @@
 with open("PATH/TO/borders.json") as f:
     fails_borders = json.load(f)
 
-path = '/local3/vondrick3/datasets/fails/scenes'
+path = 'PATH/TO/scenes'
 
-newpath = '/proj/vondrick/datasets/fails/scene_clips_split_cropped'
+newpath = "PATH/TO/scene_clips_split_cropped"
 
 vids = glob(os.path.join(path, '*', '*', '*.mp4'))
 

utils/get_pose.py

@@ -13,8 +13,8 @@
 def job(fn, gpuid):
     env = os.environ.copy()
     env['CUDA_VISIBLE_DEVICES'] = str(gpuid)
-    result = subprocess.run(['python', '/local/vondrick/tools/alphapose/video_demo.py', '--video', fn, '--outdir', 
-        '/local/vondrick/datasets/fails/scenes_pose', '--save_video', '--sp', '--nThreads', str(1)], env=env, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+    result = subprocess.run(['python', "PATH/TO/video_demo.py', '--video', fn, '--outdir",
+        "PATH/TO/scenes_pose', '--save_video', '--sp', '--nThreads", str(1)], env=env, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
     return result
 
 def worker(inq, outq, lock, wid, ngpus):
@@ -26,7 +26,7 @@ def worker(inq, outq, lock, wid, ngpus):
 
 
 if __name__ == "__main__":
-    fns = glob('/local/vondrick/datasets/fails/scenes/*.mp4')
+    fns = glob("PATH/TO/*.mp4")
     inq = Queue()
     outq = Queue()
     lock = Lock()

utils/imgs_to_flow.py

@@ -12,7 +12,7 @@
 def job(path, gpuid):
     env = os.environ.copy()
     env['CUDA_VISIBLE_DEVICES'] = str(gpuid)
-    result = subprocess.run(['python', '/local/vondrick/shared/flownet2-pytorch/main.py', '--inference', '--model', 'FlowNet2', '--save_flow', '--inference_dataset', 'ImagesFromFolder', '--inference_dataset_root', path, '--resume', '/local/vondrick/shared/flownet2-pytorch/checkpoint/FlowNet2_checkpoint.pth.tar', '--save', path, '--name', 'flow'], env=env, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+    result = subprocess.run(['python', "PATH/TO/FlowNet2_checkpoint.pth.tar', '--save', path, '--name', 'flow"], env=env, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
     return result
 
 def worker(inq, outq, lock, wid, ngpus):
@@ -24,7 +24,7 @@ def worker(inq, outq, lock, wid, ngpus):
 
 
 if __name__ == "__main__":
-    fns = sorted(glob('/proj/vondrick/datasets/fails/scenes/*/*/*'))
+    fns = sorted(glob("PATH/TO/*"))
     inq = Queue()
     outq = Queue()
     lock = Lock()

utils/kinetics_utils.py

@@ -4,8 +4,8 @@
 import collections
 import sys
 if True:    
-    sys.path.append('/local/vondrick/dave/fails')
-    sys.path.append('/local/vondrick/dave/fails/cnns')
+    sys.path.append("PATH/TO/fails")
+    sys.path.append("PATH/TO/cnns")
 # from cnns import model as model3d
 from types import SimpleNamespace
 import cv2

utils/parse_kinetics_features.py

@@ -8,7 +8,7 @@
 
 def process():
     fns = glob(
-        '/local/vondrick/dave/slidingwindow/fails_kinetics_features/fails_kinetics_preds_*.json')
+        "PATH/TO/fails_kinetics_preds_*.json")
 
     data = []
     for fn in fns:

utils/progress/.gitignore

@@ -0,0 +1,4 @@
+*.pyc
+*.egg-info
+build/
+dist/

utils/vids_to_imgs.py

@@ -28,7 +28,7 @@ def worker(inq, outq, lock):
     outq = Queue()
     lock = Lock()
     nproc = 40
-    #basepath = "/local/vondrick/datasets/fails/scenes"
+    #basepath = "PATH/TO/scenes"
     basepath = "YOUR PATH HERE"
     outdir = "YOUR PATH HERE"
     data=glob(os.path.join(basepath, '**/*.mp4'), recursive=True)