adding basic x3d inference script

inference.py

@@ -0,0 +1,101 @@
+import os
+import torch
+import json
+import urllib.request
+from pytorchvideo.data.encoded_video import EncodedVideo
+
+# === Config ===
+model_name = "x3d_m"   # options: x3d_xs, x3d_s, x3d_m, x3d_l
+device = torch.device("cpu")
+
+# === Load Model ===
+model = torch.hub.load(
+    "facebookresearch/pytorchvideo",
+    model_name,
+    pretrained=True
+).to(device).eval()
+
+# === Load Kinetics-400 Labels ===
+label_map_path = "kinetics_classnames.json"
+if not os.path.exists(label_map_path):
+    urllib.request.urlretrieve(
+        "https://dl.fbaipublicfiles.com/pyslowfast/dataset/class_names/kinetics_classnames.json",
+        label_map_path
+    )
+with open(label_map_path, "r") as f:
+    raw = json.load(f)
+# raw may map label->id or be a list; invert mapping if necessary
+if isinstance(raw, dict):
+    # raw: {label: id}
+    kinetics_labels = {v: k for k, v in raw.items()}
+elif isinstance(raw, list):
+    # raw: [label0, label1, ...]
+    kinetics_labels = {i: raw[i] for i in range(len(raw))}
+else:
+    raise ValueError("Unexpected label map format")
+
+# === Preprocessing ===
+def preprocess(frames, num_samples: int = 16, size: int = 224) -> torch.Tensor:
+    """
+    frames: Tensor [C, T, H, W]
+    returns: Tensor [1, C, T_sampled, size, size]
+    """
+    # Permute to [T, C, H, W]
+    C, T, H, W = frames.shape
+    vid = frames.permute(1, 0, 2, 3).float() / 255.0
+
+    # Log shapes
+    print(f"[DEBUG] Raw frames shape: {frames.shape}")
+
+    # Uniform temporal subsampling
+    idx = torch.linspace(0, T - 1, num_samples).long()
+    clip = vid[idx]  # [num_samples, C, H, W]
+
+    # Center crop spatially
+    top = max((H - size) // 2, 0)
+    left = max((W - size) // 2, 0)
+    clip = clip[:, :, top:top+size, left:left+size]
+
+    # Log clipped shape
+    print(f"[DEBUG] After crop & sample clip shape: {clip.shape}")
+
+    # Permute to [C, T, H, W] and add batch => [1, C, T, H, W]
+    clip = clip.permute(1, 0, 2, 3).unsqueeze(0)
+
+    # Normalize channels
+    mean = torch.tensor([0.45, 0.45, 0.45], device=device).view(1, 3, 1, 1, 1)
+    std = torch.tensor([0.225, 0.225, 0.225], device=device).view(1, 3, 1, 1, 1)
+    clip = (clip - mean) / std
+
+    print(f"[DEBUG] Final input shape: {clip.shape}")
+    return clip
+
+# === Prediction ===
+def predict_clip(path: str):
+    print(f"[INFO] Processing clip: {path}")
+    video = EncodedVideo.from_path(path)
+    frames = video.get_clip(0, 2.0)["video"]  # [C, T, H, W]
+
+    inp = preprocess(frames).to(device)
+
+    with torch.no_grad():
+        logits = model(inp)
+        probs = torch.softmax(logits, dim=1)[0]
+
+    top5 = torch.topk(probs, k=5)
+    results = []
+    for idx_tensor, score in zip(top5.indices, top5.values):
+        idx = idx_tensor.item()
+        label = kinetics_labels.get(idx, f"Class_{idx}")
+        results.append((label, float(score)))
+    return results
+
+# === CLI ===
+if __name__ == "__main__":
+    import sys
+    clip_path = sys.argv[1] if len(sys.argv) > 1 else "segments/segment_000.mp4"
+    preds = predict_clip(clip_path)
+    print("\nTop-5 labels:")
+    for label, score in preds:
+        print(f"{label:>30s} : {score:.3f}")
+

kinetics_classnames.json

@@ -0,0 +1 @@
+{"\"sharpening knives\"": 290, "\"eating ice cream\"": 115, "\"cutting nails\"": 81, "\"changing wheel\"": 53, "\"bench pressing\"": 19, "deadlifting": 88, "\"eating carrots\"": 111, "marching": 192, "\"throwing discus\"": 358, "\"playing flute\"": 231, "\"cooking on campfire\"": 72, "\"breading or breadcrumbing\"": 33, "\"playing badminton\"": 218, "\"ripping paper\"": 276, "\"playing saxophone\"": 244, "\"milking cow\"": 197, "\"juggling balls\"": 169, "\"flying kite\"": 130, "capoeira": 43, "\"making jewelry\"": 187, "drinking": 100, "\"playing cymbals\"": 228, "\"cleaning gutters\"": 61, "\"hurling (sport)\"": 161, "\"playing organ\"": 239, "\"tossing coin\"": 361, "wrestling": 395, "\"driving car\"": 103, "headbutting": 150, "\"gymnastics tumbling\"": 147, "\"making bed\"": 186, "abseiling": 0, "\"holding snake\"": 155, "\"rock climbing\"": 278, "\"cooking egg\"": 71, "\"long jump\"": 182, "\"bee keeping\"": 17, "\"trimming or shaving beard\"": 365, "\"cleaning shoes\"": 63, "\"dancing gangnam style\"": 86, "\"catching or throwing softball\"": 50, "\"ice skating\"": 164, "jogging": 168, "\"eating spaghetti\"": 116, "bobsledding": 28, "\"assembling computer\"": 8, "\"playing cricket\"": 227, "\"playing monopoly\"": 238, "\"golf putting\"": 143, "\"making pizza\"": 188, "\"javelin throw\"": 166, "\"peeling potatoes\"": 211, "clapping": 57, "\"brushing hair\"": 36, "\"flipping pancake\"": 129, "\"drinking beer\"": 101, "\"dribbling basketball\"": 99, "\"playing bagpipes\"": 219, "somersaulting": 325, "\"canoeing or kayaking\"": 42, "\"riding unicycle\"": 275, "texting": 355, "\"tasting beer\"": 352, "\"hockey stop\"": 154, "\"playing clarinet\"": 225, "\"waxing legs\"": 389, "\"curling hair\"": 80, "\"running on treadmill\"": 281, "\"tai chi\"": 346, "\"driving tractor\"": 104, "\"shaving legs\"": 293, "\"sharpening pencil\"": 291, "\"making sushi\"": 190, "\"spray painting\"": 327, "situp": 305, "\"playing kickball\"": 237, "\"sticking tongue out\"": 331, "headbanging": 149, "\"folding napkins\"": 132, "\"playing piano\"": 241, "skydiving": 312, "\"dancing charleston\"": 85, "\"ice fishing\"": 163, "tickling": 359, "bandaging": 13, "\"high jump\"": 151, "\"making a sandwich\"": 185, "\"riding mountain bike\"": 271, "\"cutting pineapple\"": 82, "\"feeding goats\"": 125, "\"dancing macarena\"": 87, "\"playing basketball\"": 220, "krumping": 179, "\"high kick\"": 152, "\"balloon blowing\"": 12, "\"playing accordion\"": 217, "\"playing chess\"": 224, "\"hula hooping\"": 159, "\"pushing wheelchair\"": 263, "\"riding camel\"": 268, "\"blowing out candles\"": 27, "\"extinguishing fire\"": 121, "\"using computer\"": 373, "\"jumpstyle dancing\"": 173, "yawning": 397, "writing": 396, "\"jumping into pool\"": 172, "\"doing laundry\"": 96, "\"egg hunting\"": 118, "\"sanding floor\"": 284, "\"moving furniture\"": 200, "\"exercising arm\"": 119, "\"sword fighting\"": 345, "\"sign language interpreting\"": 303, "\"counting money\"": 74, "bartending": 15, "\"cleaning windows\"": 65, "\"blasting sand\"": 23, "\"petting cat\"": 213, "sniffing": 320, "bowling": 31, "\"playing poker\"": 242, "\"taking a shower\"": 347, "\"washing hands\"": 382, "\"water sliding\"": 384, "\"presenting weather forecast\"": 254, "tobogganing": 360, "celebrating": 51, "\"getting a haircut\"": 138, "snorkeling": 321, "\"weaving basket\"": 390, "\"playing squash or racquetball\"": 245, "parasailing": 206, "\"news anchoring\"": 202, "\"belly dancing\"": 18, "windsurfing": 393, "\"braiding hair\"": 32, "\"crossing river\"": 78, "\"laying bricks\"": 181, "\"roller skating\"": 280, "hopscotch": 156, "\"playing trumpet\"": 248, "\"dying hair\"": 108, "\"trimming trees\"": 366, "\"pumping fist\"": 256, "\"playing keyboard\"": 236, "snowboarding": 322, "\"garbage collecting\"": 136, "\"playing controller\"": 226, "dodgeball": 94, "\"recording music\"": 266, "\"country line dancing\"": 75, "\"dancing ballet\"": 84, "gargling": 137, "ironing": 165, "\"push up\"": 260, "\"frying vegetables\"": 135, "\"ski jumping\"": 307, "\"mowing lawn\"": 201, "\"getting a tattoo\"": 139, "\"rock scissors paper\"": 279, "cheerleading": 55, "\"using remote controller (not gaming)\"": 374, "\"shaking head\"": 289, "sailing": 282, "\"training dog\"": 363, "hurdling": 160, "\"fixing hair\"": 128, "\"climbing ladder\"": 67, "\"filling eyebrows\"": 126, "\"springboard diving\"": 329, "\"eating watermelon\"": 117, "\"drumming fingers\"": 106, "\"waxing back\"": 386, "\"playing didgeridoo\"": 229, "\"swimming backstroke\"": 339, "\"biking through snow\"": 22, "\"washing feet\"": 380, "\"mopping floor\"": 198, "\"throwing ball\"": 357, "\"eating doughnuts\"": 113, "\"drinking shots\"": 102, "\"tying bow tie\"": 368, "dining": 91, "\"surfing water\"": 337, "\"sweeping floor\"": 338, "\"grooming dog\"": 145, "\"catching fish\"": 47, "\"pumping gas\"": 257, "\"riding or walking with horse\"": 273, "\"massaging person's head\"": 196, "archery": 5, "\"ice climbing\"": 162, "\"playing recorder\"": 243, "\"decorating the christmas tree\"": 89, "\"peeling apples\"": 210, "snowmobiling": 324, "\"playing ukulele\"": 249, "\"eating burger\"": 109, "\"building cabinet\"": 38, "\"stomping grapes\"": 332, "\"drop kicking\"": 105, "\"passing American football (not in game)\"": 209, "applauding": 3, "hugging": 158, "\"eating hotdog\"": 114, "\"pole vault\"": 253, "\"reading newspaper\"": 265, "\"snatch weight lifting\"": 318, "zumba": 399, "\"playing ice hockey\"": 235, "breakdancing": 34, "\"feeding fish\"": 124, "\"shredding paper\"": 300, "\"catching or throwing frisbee\"": 49, "\"exercising with an exercise ball\"": 120, "\"pushing cart\"": 262, "\"swimming butterfly stroke\"": 341, "\"riding scooter\"": 274, "spraying": 328, "\"folding paper\"": 133, "\"golf driving\"": 142, "\"robot dancing\"": 277, "\"bending back\"": 20, "testifying": 354, "\"waxing chest\"": 387, "\"carving pumpkin\"": 46, "\"hitting baseball\"": 153, "\"riding elephant\"": 269, "\"brushing teeth\"": 37, "\"pull ups\"": 255, "\"riding a bike\"": 267, "skateboarding": 306, "\"cleaning pool\"": 62, "\"playing paintball\"": 240, "\"massaging back\"": 193, "\"shoveling snow\"": 299, "\"surfing crowd\"": 336, "unboxing": 371, "faceplanting": 122, "trapezing": 364, "\"swinging legs\"": 343, "hoverboarding": 157, "\"playing violin\"": 250, "\"wrapping present\"": 394, "\"blowing nose\"": 26, "\"kicking field goal\"": 174, "\"picking fruit\"": 214, "\"swinging on something\"": 344, "\"giving or receiving award\"": 140, "\"planting trees\"": 215, "\"water skiing\"": 383, "\"washing dishes\"": 379, "\"punching bag\"": 258, "\"massaging legs\"": 195, "\"throwing axe\"": 356, "\"salsa dancing\"": 283, "bookbinding": 29, "\"tying tie\"": 370, "\"skiing crosscountry\"": 309, "\"shining shoes\"": 295, "\"making snowman\"": 189, "\"front raises\"": 134, "\"doing nails\"": 97, "\"massaging feet\"": 194, "\"playing drums\"": 230, "smoking": 316, "\"punching person (boxing)\"": 259, "cartwheeling": 45, "\"passing American football (in game)\"": 208, "\"shaking hands\"": 288, "plastering": 216, "\"watering plants\"": 385, "kissing": 176, "slapping": 314, "\"playing harmonica\"": 233, "welding": 391, "\"smoking hookah\"": 317, "\"scrambling eggs\"": 285, "\"cooking chicken\"": 70, "\"pushing car\"": 261, "\"opening bottle\"": 203, "\"cooking sausages\"": 73, "\"catching or throwing baseball\"": 48, "\"swimming breast stroke\"": 340, "digging": 90, "\"playing xylophone\"": 252, "\"doing aerobics\"": 95, "\"playing trombone\"": 247, "knitting": 178, "\"waiting in line\"": 377, "\"tossing salad\"": 362, "squat": 330, "vault": 376, "\"using segway\"": 375, "\"crawling baby\"": 77, "\"reading book\"": 264, "motorcycling": 199, "barbequing": 14, "\"cleaning floor\"": 60, "\"playing cello\"": 223, "drawing": 98, "auctioning": 9, "\"carrying baby\"": 44, "\"diving cliff\"": 93, "busking": 41, "\"cutting watermelon\"": 83, "\"scuba diving\"": 286, "\"riding mechanical bull\"": 270, "\"making tea\"": 191, "\"playing tennis\"": 246, "crying": 79, "\"dunking basketball\"": 107, "\"cracking neck\"": 76, "\"arranging flowers\"": 7, "\"building shed\"": 39, "\"golf chipping\"": 141, "\"tasting food\"": 353, "\"shaving head\"": 292, "\"answering questions\"": 2, "\"climbing tree\"": 68, "\"skipping rope\"": 311, "kitesurfing": 177, "\"juggling fire\"": 170, "laughing": 180, "paragliding": 205, "\"contact juggling\"": 69, "slacklining": 313, "\"arm wrestling\"": 6, "\"making a cake\"": 184, "\"finger snapping\"": 127, "\"grooming horse\"": 146, "\"opening present\"": 204, "\"tapping pen\"": 351, "singing": 304, "\"shot put\"": 298, "\"cleaning toilet\"": 64, "\"spinning poi\"": 326, "\"setting table\"": 287, "\"tying knot (not on a tie)\"": 369, "\"blowing glass\"": 24, "\"eating chips\"": 112, "\"tap dancing\"": 349, "\"climbing a rope\"": 66, "\"brush painting\"": 35, "\"chopping wood\"": 56, "\"stretching leg\"": 334, "\"petting animal (not cat)\"": 212, "\"baking cookies\"": 11, "\"stretching arm\"": 333, "beatboxing": 16, "jetskiing": 167, "\"bending metal\"": 21, "sneezing": 319, "\"folding clothes\"": 131, "\"sled dog racing\"": 315, "\"tapping guitar\"": 350, "\"bouncing on trampoline\"": 30, "\"waxing eyebrows\"": 388, "\"air drumming\"": 1, "\"kicking soccer ball\"": 175, "\"washing hair\"": 381, "\"riding mule\"": 272, "\"blowing leaves\"": 25, "\"strumming guitar\"": 335, "\"playing cards\"": 222, "snowkiting": 323, "\"playing bass guitar\"": 221, "\"applying cream\"": 4, "\"shooting basketball\"": 296, "\"walking the dog\"": 378, "\"triple jump\"": 367, "\"shearing sheep\"": 294, "\"clay pottery making\"": 58, "\"bungee jumping\"": 40, "\"unloading truck\"": 372, "\"shuffling cards\"": 301, "\"shooting goal (soccer)\"": 297, "\"tango dancing\"": 348, "\"side kick\"": 302, "\"grinding meat\"": 144, "yoga": 398, "\"hammer throw\"": 148, "\"changing oil\"": 52, "\"checking tires\"": 54, "parkour": 207, "\"eating cake\"": 110, "\"skiing slalom\"": 310, "\"juggling soccer ball\"": 171, "whistling": 392, "\"feeding birds\"": 123, "\"playing volleyball\"": 251, "\"swing dancing\"": 342, "\"skiing (not slalom or crosscountry)\"": 308, "lunge": 183, "\"disc golfing\"": 92, "\"clean and jerk\"": 59, "\"playing guitar\"": 232, "\"baby waking up\"": 10, "\"playing harp\"": 234}

requirements.txt

@@ -0,0 +1,6 @@
+torch
+torchvision
+pytorchvideo
+transformers
+huggingface_hub
+ffmpeg-python

results.json

@@ -0,0 +1,288 @@
+{
+  "segment_001.mov": [
+    [
+      "bobsledding",
+      0.0029806457459926605
+    ],
+    [
+      "archery",
+      0.0028982453513890505
+    ],
+    [
+      "skateboarding",
+      0.002631985815241933
+    ],
+    [
+      "barbequing",
+      0.002604125998914242
+    ],
+    [
+      "\"changing wheel\"",
+      0.0026030109729617834
+    ]
+  ],
+  "segment_002.mov": [
+    [
+      "archery",
+      0.003014166606590152
+    ],
+    [
+      "\"golf driving\"",
+      0.002740557538345456
+    ],
+    [
+      "\"mowing lawn\"",
+      0.002675712341442704
+    ],
+    [
+      "\"golf putting\"",
+      0.0026040568482130766
+    ],
+    [
+      "bobsledding",
+      0.0025902960915118456
+    ]
+  ],
+  "segment_003.mov": [
+    [
+      "\"changing wheel\"",
+      0.003073550760746002
+    ],
+    [
+      "barbequing",
+      0.002854740945622325
+    ],
+    [
+      "\"mowing lawn\"",
+      0.0026648251805454493
+    ],
+    [
+      "\"changing oil\"",
+      0.0025920344050973654
+    ],
+    [
+      "archery",
+      0.0025756466202437878
+    ]
+  ],
+  "segment_004.mov": [
+    [
+      "\"tossing coin\"",
+      0.004114203620702028
+    ],
+    [
+      "\"side kick\"",
+      0.002894123550504446
+    ],
+    [
+      "\"rock scissors paper\"",
+      0.002691109199076891
+    ],
+    [
+      "\"punching person (boxing)\"",
+      0.0026034375187009573
+    ],
+    [
+      "\"shaking hands\"",
+      0.0025962668005377054
+    ]
+  ],
+  "segment_005.mov": [
+    [
+      "\"tossing coin\"",
+      0.003616387490183115
+    ],
+    [
+      "\"passing American football (not in game)\"",
+      0.0027686816174536943
+    ],
+    [
+      "\"passing American football (in game)\"",
+      0.002704756101593375
+    ],
+    [
+      "\"kicking field goal\"",
+      0.002686359453946352
+    ],
+    [
+      "\"side kick\"",
+      0.0026586230378597975
+    ]
+  ],
+  "segment_006.mov": [
+    [
+      "\"tossing coin\"",
+      0.0035721457097679377
+    ],
+    [
+      "\"passing American football (in game)\"",
+      0.0033913173247128725
+    ],
+    [
+      "\"side kick\"",
+      0.002801080234348774
+    ],
+    [
+      "\"passing American football (not in game)\"",
+      0.0025844001211225986
+    ],
+    [
+      "\"juggling soccer ball\"",
+      0.0025617412757128477
+    ]
+  ],
+  "segment_007.mov": [
+    [
+      "beatboxing",
+      0.0027317821513861418
+    ],
+    [
+      "\"pumping fist\"",
+      0.002660650759935379
+    ],
+    [
+      "\"arm wrestling\"",
+      0.002654405776411295
+    ],
+    [
+      "bobsledding",
+      0.002652029972523451
+    ],
+    [
+      "archery",
+      0.002651914255693555
+    ]
+  ],
+  "segment_008.mov": [
+    [
+      "crying",
+      0.0034351919312030077
+    ],
+    [
+      "beatboxing",
+      0.0026230965740978718
+    ],
+    [
+      "\"pumping fist\"",
+      0.0026218448765575886
+    ],
+    [
+      "\"fixing hair\"",
+      0.0026146825402975082
+    ],
+    [
+      "\"getting a haircut\"",
+      0.0026112159248441458
+    ]
+  ],
+  "segment_009.mov": [
+    [
+      "\"passing American football (in game)\"",
+      0.004364537075161934
+    ],
+    [
+      "\"side kick\"",
+      0.003033441724255681
+    ],
+    [
+      "\"passing American football (not in game)\"",
+      0.002836051397025585
+    ],
+    [
+      "\"kicking field goal\"",
+      0.0027268915437161922
+    ],
+    [
+      "\"high kick\"",
+      0.0025182710960507393
+    ]
+  ],
+  "segment_010.mov": [
+    [
+      "\"passing American football (in game)\"",
+      0.005220369435846806
+    ],
+    [
+      "\"kicking field goal\"",
+      0.0030636927112936974
+    ],
+    [
+      "\"passing American football (not in game)\"",
+      0.0026047772262245417
+    ],
+    [
+      "\"side kick\"",
+      0.002511046826839447
+    ],
+    [
+      "\"high kick\"",
+      0.0024939693976193666
+    ]
+  ],
+  "segment_011.mov": [
+    [
+      "\"passing American football (in game)\"",
+      0.00662989029660821
+    ],
+    [
+      "\"passing American football (not in game)\"",
+      0.002533461432904005
+    ],
+    [
+      "\"side kick\"",
+      0.0024943421594798565
+    ],
+    [
+      "\"kicking field goal\"",
+      0.0024910743813961744
+    ],
+    [
+      "\"high kick\"",
+      0.002490129554644227
+    ]
+  ],
+  "segment_012.mov": [
+    [
+      "\"passing American football (in game)\"",
+      0.006737703923135996
+    ],
+    [
+      "\"passing American football (not in game)\"",
+      0.002498970367014408
+    ],
+    [
+      "\"kicking field goal\"",
+      0.0024903472512960434
+    ],
+    [
+      "\"side kick\"",
+      0.0024894215166568756
+    ],
+    [
+      "celebrating",
+      0.002489363308995962
+    ]
+  ],
+  "segment_013.mov": [
+    [
+      "\"passing American football (in game)\"",
+      0.006262147333472967
+    ],
+    [
+      "\"kicking field goal\"",
+      0.0025951964780688286
+    ],
+    [
+      "\"tossing coin\"",
+      0.0025495628360658884
+    ],
+    [
+      "\"passing American football (not in game)\"",
+      0.002519148401916027
+    ],
+    [
+      "\"shaking hands\"",
+      0.002490811515599489
+    ]
+  ]
+}

run_all_clips.py

@@ -0,0 +1,56 @@
+#!/usr/bin/env python3
+"""
+Loop over all .mov/.mp4 files in the `data/` directory, call `predict_clip` from inference.py on each,
+print the results, and append each clip's scores to a JSON file as they're generated.
+"""
+import os
+import glob
+import json
+from inference import predict_clip
+
+
+def main(input_dir: str = "data", output_file: str = "results.json"):
+    # find video files
+    patterns = ["*.mov", "*.mp4"]
+    clips = []
+    for pat in patterns:
+        clips.extend(glob.glob(os.path.join(input_dir, pat)))
+    clips = sorted(clips)
+    if not clips:
+        print(f"No clips found in '{input_dir}'.")
+        return
+
+    results = {}
+    # Ensure output file exists with empty JSON if first write fails
+    try:
+        with open(output_file, 'w') as f:
+            json.dump(results, f, indent=2)
+    except Exception as e:
+        print(f"[WARN] Could not initialize {output_file}: {e}")
+
+    for clip in clips:
+        clip_name = os.path.basename(clip)
+        print(f"\n=== Scoring {clip_name} ===")
+        scores = predict_clip(clip)
+        # Record even if empty (error cases will yield empty list)
+        results[clip_name] = scores
+        # Print to console
+        if scores:
+            for label, score in scores:
+                print(f"{label:>30s} : {score:.3f}")
+        else:
+            print(f"[INFO] No scores for {clip_name} (skipped or error)")
+
+        # Write incremental results after each clip
+        try:
+            with open(output_file, 'w') as f:
+                json.dump(results, f, indent=2)
+        except Exception as e:
+            print(f"[ERROR] Failed to write {output_file}: {e}")
+
+    print(f"\nFinished scoring. Results saved to '{output_file}'")
+
+
+if __name__ == "__main__":
+    main()
+