diff --git a/.gitignore b/.gitignore index f417c17..abb3de6 100644 --- a/.gitignore +++ b/.gitignore @@ -155,3 +155,4 @@ out-img/ converted_models/ *.pth *.onnx +.devcontainer diff --git a/README2.md b/README2.md new file mode 100644 index 0000000..8d6614d --- /dev/null +++ b/README2.md @@ -0,0 +1,20 @@ +# Spoter Embeddings + +## Creating dataset +First, make a folder where all you're videos are located. When this is done, all keypoints can be extracted from the videos using the following command. This will extract the keypoints and store them in \. +``` +python3 preprocessing.py extract --videos-folder --output-landmark +``` + +When this is done, the dataset can be created using the following command: +``` +python3 preprocessing.py create --landmarks-dataset --videos-folder --dataset-folder (--create-new-split --test-size ) +``` +The above command generates a train (and val) csv file which includes all the extracted keypoints. These can then be used to train or generates embeddings. + +## Creating Embeddings +The embeddings can be created using the following command: +``` +python3 export_embeddings.py --checkpoint --dataset --output +``` +The command above generates the embeddings for a given dataset and saves them as a csv file. \ No newline at end of file diff --git a/checkpoints/checkpoint_embed_3006.pth b/checkpoints/checkpoint_embed_3006.pth new file mode 100644 index 0000000..7123eca Binary files /dev/null and b/checkpoints/checkpoint_embed_3006.pth differ diff --git a/checkpoints/checkpoint_embed_3835.pth b/checkpoints/checkpoint_embed_3835.pth new file mode 100644 index 0000000..d3195e2 Binary files /dev/null and b/checkpoints/checkpoint_embed_3835.pth differ diff --git a/conversion_requirements.txt b/conversion_requirements.txt index 4d37abe..7526746 100644 --- a/conversion_requirements.txt +++ b/conversion_requirements.txt @@ -17,5 +17,4 @@ requests==2.28.1 onnx==1.12.0 onnx-tf==1.10.0 onnxruntime==1.12.1 -tensorflow -tensorflow-probability +coremltools==6.3.0 \ No newline at end of file diff --git a/embeddings/basic-signs/embeddings-online-dict.csv b/embeddings/basic-signs/embeddings-online-dict.csv new file mode 100644 index 0000000..2b86d35 --- /dev/null +++ b/embeddings/basic-signs/embeddings-online-dict.csv @@ -0,0 +1,91 @@ +embeddings,label_name,labels,embeddings2 +"[[ 0.01295076 -0.1074132 0.5111911 0.08452003 -0.7638924 0.6458221 + -1.3892679 -1.1791427 -0.30736607 -0.41543546 -0.6358013 0.31411174 + 0.878936 1.7265923 -0.09298562 0.12005516 0.26995468 -1.5934305 + -0.12619524 0.9111336 0.91827893 0.5948979 0.90334046 -0.84089845 + -0.29575598 -0.3024254 -0.03074584 1.4402957 -0.22309914 0.54750854 + -0.68767273 -0.0665718 ]]",BEDANKEN,0,"[0.012950755655765533, -0.10741320252418518, 0.5111911296844482, 0.08452003449201584, -0.763892412185669, 0.6458221077919006, -1.389267921447754, -1.179142713546753, -0.3073660731315613, -0.41543546319007874, 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-1.048095464706421, -0.04290715977549553, -0.3627583384513855]" +"[[ 1.0733138 -0.66348886 0.36737278 0.01765811 0.5730918 1.7617474 + -0.45580968 -1.0973133 0.4730748 -0.4665998 0.48594972 0.548745 + -0.91712314 0.4846773 0.3774526 -0.5996147 0.28750947 -1.0166394 + -0.6239797 1.7935088 -0.03666669 -0.51941574 2.045076 -0.9045104 + -0.6312211 0.9698636 1.0522215 -0.14772844 0.7406032 0.2901447 + -0.48710442 -0.4619298 ]]",TOT-ZIENS,14,"[1.07331383228302, -0.6634888648986816, 0.3673727810382843, 0.017658105120062828, 0.5730918049812317, 1.7617473602294922, -0.45580968260765076, -1.0973132848739624, 0.4730747938156128, -0.46659979224205017, 0.48594972491264343, 0.5487449765205383, -0.9171231389045715, 0.4846773147583008, 0.3774526119232178, -0.599614679813385, 0.2875094711780548, -1.0166393518447876, -0.6239796876907349, 1.793508768081665, -0.036666687577962875, -0.5194157361984253, 2.0450758934020996, -0.9045103788375854, -0.6312211155891418, 0.9698635935783386, 1.0522215366363525, -0.14772844314575195, 0.7406032085418701, 0.2901447117328644, -0.4871044158935547, -0.4619297981262207]" diff --git a/export_embeddings.py b/export_embeddings.py new file mode 100644 index 0000000..c305308 --- /dev/null +++ b/export_embeddings.py @@ -0,0 +1,97 @@ +import multiprocessing +import os +import torch +import argparse +from datasets.dataset_loader import LocalDatasetLoader +from datasets.embedding_dataset import SLREmbeddingDataset +from torch.utils.data import DataLoader +from datasets import SLREmbeddingDataset, collate_fn_padd +from models.spoter_embedding_model import SPOTER_EMBEDDINGS +import numpy as np +import random +import pandas as pd + +seed = 43 +random.seed(seed) +np.random.seed(seed) +os.environ["PYTHONHASHSEED"] = str(seed) +torch.manual_seed(seed) +torch.cuda.manual_seed(seed) +torch.cuda.manual_seed_all(seed) +torch.backends.cudnn.deterministic = True +torch.use_deterministic_algorithms(True) +generator = torch.Generator() +generator.manual_seed(seed) + +def seed_worker(worker_id): + worker_seed = torch.initial_seed() % 2**32 + np.random.seed(worker_seed) + random.seed(worker_seed) + +generator = torch.Generator() +generator.manual_seed(seed) +import os +os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" + +def parse_args(): + parser = argparse.ArgumentParser(description='Export embeddings') + parser.add_argument('--checkpoint', type=str, default=None, help='Path to checkpoint') + parser.add_argument('--output', type=str, default=None, help='Path to output') + parser.add_argument('--dataset', type=str, default=None, help='Path to data') + parser.add_argument('--format', type=str, default='csv', help='Format of the output file (csv, json)') + args = parser.parse_args() + return args + +args = parse_args() + +device = torch.device("cpu") +if torch.cuda.is_available(): + device = torch.device("cuda") + +# load the model +checkpoint = torch.load(args.checkpoint, map_location=device) + +model = SPOTER_EMBEDDINGS( + features=checkpoint["config_args"].vector_length, + hidden_dim=checkpoint["config_args"].hidden_dim, + norm_emb=checkpoint["config_args"].normalize_embeddings, +).to(device) + +model.load_state_dict(checkpoint["state_dict"]) + +dataset_loader = LocalDatasetLoader() +dataset = SLREmbeddingDataset(args.dataset, triplet=False, augmentations=False) + +data_loader = DataLoader( + dataset, + batch_size=1, + shuffle=False, + collate_fn=collate_fn_padd, + pin_memory=torch.cuda.is_available(), + #num_workers=0, # Uncomment this line (and comment out next line) if you want to disable multithreading + num_workers=multiprocessing.cpu_count(), + worker_init_fn=seed_worker, + generator=generator, + ) + +embeddings = [] +k = 0 +with torch.no_grad(): + for i, (inputs, labels, masks) in enumerate(data_loader): + k += 1 + inputs = inputs.to(device) + masks = masks.to(device) + outputs = model(inputs, masks) + + for n in range(outputs.shape[0]): + embeddings.append(outputs[n].cpu().numpy()) + +df = pd.read_csv(args.dataset) +df["embeddings"] = embeddings +df = df[['embeddings', 'label_name', 'labels']] +df['embeddings'] = df['embeddings'].apply(lambda x: x.tolist()[0]) + +if args.format == 'json': + df.to_json(args.output, orient='records') +elif args.format == 'csv': + df.to_csv(args.output, index=False) \ No newline at end of file diff --git a/export_model.py b/export_model.py new file mode 100644 index 0000000..51163f2 --- /dev/null +++ b/export_model.py @@ -0,0 +1,67 @@ +# to run this script, you need torch 1.13.1 and torchvision 0.14.1 + +import numpy as np +import onnx +import torch +import torchvision +import os + +from models.spoter_embedding_model import SPOTER_EMBEDDINGS + +# set parameters of the model +model_name = 'fingerspelling_embedding_model' + +# load PyTorch model from .pth file + +device = torch.device("cpu") +# if torch.cuda.is_available(): +# device = torch.device("cuda") + +CHECKPOINT_PATH = "checkpoints/fingerspelling_checkpoint.pth" +checkpoint = torch.load(CHECKPOINT_PATH, map_location=device) + +model = SPOTER_EMBEDDINGS( + features=checkpoint["config_args"].vector_length, + hidden_dim=checkpoint["config_args"].hidden_dim, + norm_emb=checkpoint["config_args"].normalize_embeddings, +).to(device) +model.load_state_dict(checkpoint["state_dict"]) +# set model to evaluation mode +model.eval() + +dummy_input = torch.randn(1, 10, 54, 2) + +# check if models folder exists +if not os.path.exists('out-models'): + os.makedirs('out-models') + +for model_export in ["onnx", "coreml"]: + if model_export == "coreml": + # set device for dummy input + dummy_input = dummy_input.to(device) + traced_model = torch.jit.trace(model, dummy_input) + + out = traced_model(dummy_input) + import coremltools as ct + + # Convert to Core ML + coreml_model = ct.convert( + traced_model, + inputs=[ct.TensorType(name="input", shape=dummy_input.shape)], + ) + + # Save Core ML model + coreml_model.save("out-models/" + model_name + ".mlmodel") + else: + # set device for dummy input + dummy_input = dummy_input.to(device) + + torch.onnx.export(model, # model being run + dummy_input, # model input (or a tuple for multiple inputs) + 'out-models/' + model_name + '.onnx', # where to save the model (can be a file or file-like object) + export_params=True, # store the trained parameter weights inside the model file + opset_version=9, # the ONNX version to export the model to + do_constant_folding=True, # whether to execute constant folding for optimization + input_names = ['X'], # the model's input names + output_names = ['Y'] # the model's output names + ) \ No newline at end of file diff --git a/hyperparam_opt.py b/hyperparam_opt.py new file mode 100644 index 0000000..a7caabe --- /dev/null +++ b/hyperparam_opt.py @@ -0,0 +1,73 @@ +from clearml.automation import UniformParameterRange, UniformIntegerParameterRange, DiscreteParameterRange +from clearml.automation import HyperParameterOptimizer +from clearml.automation.optuna import OptimizerOptuna +from optuna.pruners import HyperbandPruner, MedianPruner +from clearml import Task + +task = Task.init( + project_name='SpoterEmbedding', + task_name='Automatic Hyper-Parameter Optimization', + task_type=Task.TaskTypes.optimizer, + reuse_last_task_id=False +) + + +optimizer = HyperParameterOptimizer( + # specifying the task to be optimized, task must be in system already so it can be cloned + base_task_id="4504e0b3ec6745249d3d4c94d3d40652", + # setting the hyperparameters to optimize + hyper_parameters=[ + # epochs: + DiscreteParameterRange('Args/epochs', [200]), + # learning rate + UniformParameterRange('Args/lr', 0.000001, 0.01), + # optimizer + DiscreteParameterRange('Args/optimizer', ['ADAM', 'SGD']), + # vector length + UniformIntegerParameterRange('Args/vector_length', 10, 100), + ], + # setting the objective metric we want to maximize/minimize + objective_metric_title='train_loss', + objective_metric_series='loss', + objective_metric_sign='min', + + # setting optimizer + optimizer_class=OptimizerOptuna, + + # configuring optimization parameters + execution_queue='default', + optimization_time_limit=360, + compute_time_limit=480, + total_max_jobs=20, + min_iteration_per_job=0, + max_iteration_per_job=150000, + pool_period_min=0.1, + + save_top_k_tasks_only=3, + optuna_pruner=MedianPruner(), + + ) + +def job_complete_callback( + job_id, # type: str + objective_value, # type: float + objective_iteration, # type: int + job_parameters, # type: dict + top_performance_job_id # type: str +): + print('Job completed!', job_id, objective_value, objective_iteration, job_parameters) + if job_id == top_performance_job_id: + print('WOOT WOOT we broke the record! Objective reached {}'.format(objective_value)) + +task.execute_remotely(queue_name='hypertuning', exit_process=True) + +optimizer.set_report_period(0.3) + +optimizer.start(job_complete_callback=job_complete_callback) + +optimizer.wait() + +top_exp = optimizer.get_top_experiments(top_k=3) +print([t.id for t in top_exp]) + +optimizer.stop() \ No newline at end of file diff --git a/models/utils.py b/models/utils.py index 498affa..899f421 100644 --- a/models/utils.py +++ b/models/utils.py @@ -88,9 +88,10 @@ def train_epoch_embedding_online(model, epoch_iters, train_loader, val_loader, c if enable_batch_sorting: if labels_size < train_loader.batch_size: trim_count = labels_size % mini_batch - inputs = inputs[:-trim_count] - labels = labels[:-trim_count] - masks = masks[:-trim_count] + if trim_count > 0: + inputs = inputs[:-trim_count] + labels = labels[:-trim_count] + masks = masks[:-trim_count] embeddings = None with torch.no_grad(): for j in range(batch_loop_count): diff --git a/normalization/blazepose_mapping.py b/normalization/blazepose_mapping.py index 666aba7..ad24b55 100644 --- a/normalization/blazepose_mapping.py +++ b/normalization/blazepose_mapping.py @@ -61,20 +61,25 @@ def map_blazepose_keypoint(column): return f"{mapped}_{hand}{suffix}" -def map_blazepose_df(df): +def map_blazepose_df(df, rename=True): + to_drop = [] + if rename: + renamings = {} + for column in df.columns: + mapped_column = map_blazepose_keypoint(column) + if mapped_column: + renamings[column] = mapped_column + else: + to_drop.append(column) + df = df.rename(columns=renamings) + for index, row in df.iterrows(): + sequence_size = len(row["leftEar_Y"]) lsx = row["leftShoulder_X"] rsx = row["rightShoulder_X"] lsy = row["leftShoulder_Y"] rsy = row["rightShoulder_Y"] - # convert all to list - lsx = lsx[1:-1].split(",") - rsx = rsx[1:-1].split(",") - lsy = lsy[1:-1].split(",") - rsy = rsy[1:-1].split(",") - sequence_size = len(lsx) - neck_x = [] neck_y = [] # Treat each element of the sequence (analyzed frame) individually @@ -84,4 +89,5 @@ def map_blazepose_df(df): df.loc[index, "neck_X"] = str(neck_x) df.loc[index, "neck_Y"] = str(neck_y) - return df + df.drop(columns=to_drop, inplace=True) + return df \ No newline at end of file diff --git a/normalization/main.py b/normalization/main.py index 9f4231f..2ed4891 100644 --- a/normalization/main.py +++ b/normalization/main.py @@ -5,30 +5,30 @@ import pandas as pd from normalization.hand_normalization import normalize_hands_full from normalization.body_normalization import normalize_body_full -DATASET_PATH = './data/wlasl' +DATASET_PATH = './data/processed' # Load the dataset -df = pd.read_csv(os.path.join(DATASET_PATH, "WLASL100_train.csv"), encoding="utf-8") +df = pd.read_csv(os.path.join(DATASET_PATH, "spoter_train.csv"), encoding="utf-8") print(df.head()) print(df.columns) # Retrieve metadata -video_size_heights = df["video_height"].to_list() -video_size_widths = df["video_width"].to_list() +# video_size_heights = df["video_height"].to_list() +# video_size_widths = df["video_width"].to_list() # Delete redundant (non-related) properties -del df["video_height"] -del df["video_width"] +# del df["video_height"] +# del df["video_width"] # Temporarily remove other relevant metadata labels = df["labels"].to_list() -video_fps = df["fps"].to_list() +signs = df["sign"].to_list() + del df["labels"] -del df["fps"] -del df["split"] -del df["video_id"] -del df["label_name"] -del df["length"] +del df["sign"] +del df["path"] +del df["participant_id"] +del df["sequence_id"] # Convert the strings into lists @@ -41,7 +41,7 @@ for column in df.columns: # Perform the normalizations df = normalize_hands_full(df) -df, invalid_row_indexes = normalize_body_full(df) +# df, invalid_row_indexes = normalize_body_full(df) # Clear lists of items from deleted rows # labels = [t for i, t in enumerate(labels) if i not in invalid_row_indexes] @@ -49,6 +49,6 @@ df, invalid_row_indexes = normalize_body_full(df) # Return the metadata back to the dataset df["labels"] = labels -df["fps"] = video_fps +df["sign"] = signs -df.to_csv(os.path.join(DATASET_PATH, "wlasl_train_norm.csv"), encoding="utf-8", index=False) +df.to_csv(os.path.join(DATASET_PATH, "spoter_train_norm.csv"), encoding="utf-8", index=False) diff --git a/notebooks/embeddings_evaluation.ipynb b/notebooks/embeddings_evaluation.ipynb index f04a1c0..d012c83 100644 --- a/notebooks/embeddings_evaluation.ipynb +++ b/notebooks/embeddings_evaluation.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 5, "id": "c20f7fd5", "metadata": {}, "outputs": [], @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "id": "ada032d0", "metadata": {}, "outputs": [], @@ -22,13 +22,12 @@ "import os\n", "import os.path as op\n", "import pandas as pd\n", - "import json\n", - "import base64" + "import json" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "id": "05682e73", "metadata": {}, "outputs": [], @@ -38,7 +37,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 8, "id": "fede7684", "metadata": {}, "outputs": [], @@ -48,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 9, "id": "ce531994", "metadata": {}, "outputs": [], @@ -64,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 10, "id": "f4a2d672", "metadata": {}, "outputs": [], @@ -87,17 +86,17 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 11, "id": "1d9db764", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -119,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "id": "71224139", "metadata": {}, "outputs": [], @@ -155,7 +154,7 @@ "# checkpoint = torch.load(model.get_weights())\n", "\n", "## Set your path to checkoint here\n", - "CHECKPOINT_PATH = \"../out-checkpoints/augment_rotate_75_x8/checkpoint_embed_6.pth\"\n", + "CHECKPOINT_PATH = \"../out-checkpoints/augment_rotate_75_x8/checkpoint_embed_1105.pth\"\n", "checkpoint = torch.load(CHECKPOINT_PATH, map_location=device)\n", "\n", "model = SPOTER_EMBEDDINGS(\n", @@ -169,27 +168,28 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 24, "id": "ba6b58f0", "metadata": {}, "outputs": [], "source": [ - "SL_DATASET = 'wlasl' # or 'lsa'\n", - "if SL_DATASET == 'wlasl':\n", + "SL_DATASET = 'basic-signs' # or 'wlasl'\n", + "\n", + "if SL_DATASET == 'fingerspelling':\n", + " dataset_name = \"fingerspelling\"\n", + " split_dataset_path = \"fingerspelling_{}.csv\"\n", + "elif SL_DATASET == 'wlasl':\n", " dataset_name = \"wlasl\"\n", - " num_classes = 100\n", - " split_dataset_path = \"WLASL100_train.csv\"\n", - "else:\n", - " dataset_name = \"lsa64_mapped_mediapipe_only_landmarks_25fps\"\n", - " num_classes = 64\n", - " split_dataset_path = \"LSA64_{}.csv\"\n", - " \n", + " split_dataset_path = \"WLASL100_{}.csv\"\n", + "elif SL_DATASET == 'basic-signs':\n", + " dataset_name = \"basic-signs\"\n", + " split_dataset_path = \"basic-signs_{}.csv\"\n", " " ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 25, "id": "5643a72c", "metadata": {}, "outputs": [], @@ -228,7 +228,7 @@ "outputs": [], "source": [ "dataloaders = {}\n", - "splits = ['train', 'val']\n", + "splits = ['train', 'val']\n", "dfs = {}\n", "for split in splits:\n", " split_set_path = op.join(dataset_folder, split_dataset_path.format(split))\n", @@ -269,6 +269,8 @@ " for i, (inputs, labels, masks) in enumerate(dataloader):\n", " k += 1\n", " inputs = inputs.to(device)\n", + " \n", + "\n", " masks = masks.to(device)\n", " outputs = model(inputs, masks)\n", " for n in range(outputs.shape[0]):\n", @@ -285,7 +287,7 @@ { "data": { "text/plain": [ - "(810, 810)" + "(164, 164)" ] }, "execution_count": 19, @@ -299,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "id": "ab83c6e2", "metadata": { "lines_to_next_cell": 2 @@ -311,6 +313,70 @@ " df['embeddings'] = embeddings_split[split]" ] }, + { + "cell_type": "code", + "execution_count": 26, + "id": "0b9fb9c2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 [1.7327625, -3.015248, -1.4775522, -0.7505071,...\n", + "1 [2.0936582, -0.596195, -0.7918601, -0.15896143...\n", + "2 [-1.4007742, -0.9608915, 1.3294879, -0.5185398...\n", + "3 [1.3280737, -3.299126, -1.0110444, -1.2528414,...\n", + "4 [-0.071124956, -0.79259753, 0.7182858, 0.38130...\n", + " ... \n", + "159 [-1.5968355, 1.9617733, 0.28859574, 1.256657, ...\n", + "160 [0.44801116, -1.8377966, 1.1004394, -1.195648,...\n", + "161 [2.0584257, 1.6986116, 0.5129896, 0.27279535, ...\n", + "162 [1.6695516, -2.967027, -1.5715427, -0.77170163...\n", + "163 [1.4977738, -2.6278958, -1.6123883, -0.8420623...\n", + "Name: embeddings, Length: 164, dtype: object\n", + "0 TOT-ZIENS\n", + "1 GOED\n", + "2 GOEDENACHT\n", + "3 NEE\n", + "4 SLECHT\n", + " ... \n", + "159 SORRY\n", + "160 GOEDEMORGEN\n", + "161 LINKS\n", + "162 TOT-ZIENS\n", + "163 GOED\n", + "Name: label_name, Length: 164, dtype: object\n", + "0 0\n", + "1 1\n", + "2 2\n", + "3 3\n", + "4 4\n", + " ..\n", + "159 7\n", + "160 5\n", + "161 13\n", + "162 0\n", + "163 1\n", + "Name: labels, Length: 164, dtype: int64\n" + ] + } + ], + "source": [ + "print(dfs['train'][\"embeddings\"])\n", + "print(dfs['train'][\"label_name\"])\n", + "print(dfs['train'][\"labels\"])\n", + "\n", + "# only keep these columns\n", + "dfs['train'] = dfs['train'][['embeddings', 'label_name', 'labels']]\n", + "\n", + "# convert embeddings to string\n", + "dfs['train']['embeddings2'] = dfs['train']['embeddings'].apply(lambda x: x.tolist())\n", + "\n", + "# save the dfs['train']\n", + "dfs['train'].to_csv(f'../data/{dataset_name}/embeddings.csv', index=False)" + ] + }, { "cell_type": "markdown", "id": "2951638d", @@ -322,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "id": "7399b8ae", "metadata": {}, "outputs": [ @@ -331,16 +397,16 @@ "output_type": "stream", "text": [ "Using centroids only\n", - "Top-1 accuracy: 5.19 %\n", - "Top-5 embeddings class match: 17.65 % (Picks any class in the 5 closest embeddings)\n", + "Top-1 accuracy: 80.00 %\n", + "Top-5 embeddings class match: 93.33 % (Picks any class in the 5 closest embeddings)\n", "\n", "################################\n", "\n", "Using all embeddings\n", - "Top-1 accuracy: 5.31 %\n", - "5-nn accuracy: 5.56 % (Picks the class that appears most often in the 5 closest embeddings)\n", - "Top-5 embeddings class match: 15.43 % (Picks any class in the 5 closest embeddings)\n", - "Top-5 unique class match: 15.56 % (Picks the 5 closest distinct classes)\n", + "Top-1 accuracy: 80.00 %\n", + "5-nn accuracy: 80.00 % (Picks the class that appears most often in the 5 closest embeddings)\n", + "Top-5 embeddings class match: 86.67 % (Picks any class in the 5 closest embeddings)\n", + "Top-5 unique class match: 93.33 % (Picks the 5 closest distinct classes)\n", "\n", "################################\n", "\n" @@ -375,13 +441,13 @@ " sorted_labels = labels[argsort]\n", " if sorted_labels[0] == true_label:\n", " top1 += 1\n", - " if use_centroids:\n", - " good_samples.append(df_val.loc[i, 'video_id'])\n", - " else:\n", - " good_samples.append((df_val.loc[i, 'video_id'],\n", - " df_train.loc[argsort[0], 'video_id'],\n", - " i,\n", - " argsort[0]))\n", + " # if use_centroids:\n", + " # good_samples.append(df_val.loc[i, 'video_id'])\n", + " # else:\n", + " # good_samples.append((df_val.loc[i, 'video_id'],\n", + " # df_train.loc[argsort[0], 'video_id'],\n", + " # i,\n", + " # argsort[0]))\n", "\n", "\n", " if true_label == Counter(sorted_labels[:5]).most_common()[0][0]:\n", diff --git a/notebooks/visualize_embeddings.ipynb b/notebooks/visualize_embeddings.ipynb index 4268c5a..e32d95e 100644 --- a/notebooks/visualize_embeddings.ipynb +++ b/notebooks/visualize_embeddings.ipynb @@ -91,7 +91,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 7, @@ -129,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 18, "id": "ead15a36", "metadata": {}, "outputs": [ @@ -139,7 +139,7 @@ "" ] }, - "execution_count": 9, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -151,7 +151,7 @@ "# checkpoint = torch.load(model.get_weights())\n", "\n", "\n", - "CHECKPOINT_PATH = \"../out-checkpoints/augment_rotate_75_x8/checkpoint_embed_18.pth\"\n", + "CHECKPOINT_PATH = \"../checkpoints/checkpoint_embed_411.pth\"\n", "checkpoint = torch.load(CHECKPOINT_PATH, map_location=device)\n", "\n", "\n", @@ -166,26 +166,28 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "id": "20f8036d", "metadata": {}, "outputs": [], "source": [ - "SL_DATASET = 'wlasl' # or 'lsa'\n", - "if SL_DATASET == 'wlasl':\n", - " dataset_name = \"processed\"\n", - " num_classes = 15\n", - " split_dataset_path = \"spoter_test.csv\"\n", - "else:\n", - " dataset_name = \"lsa64_mapped_mediapipe_only_landmarks_25fps\"\n", - " num_classes = 64\n", - " split_dataset_path = \"LSA64_{}.csv\"\n", + "SL_DATASET = 'fingerspelling' # or 'wlasl'\n", + "\n", + "if SL_DATASET == 'fingerspelling':\n", + " dataset_name = \"fingerspelling\"\n", + " split_dataset_path = \"{}.csv\"\n", + "elif SL_DATASET == 'wlasl':\n", + " dataset_name = \"wlasl\"\n", + " split_dataset_path = \"WLASL100_{}.csv\"\n", + "elif SL_DATASET == 'basic-signs':\n", + " dataset_name = \"basic-signs\"\n", + " split_dataset_path = \"{}.csv\"\n", " " ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "id": "758716b6", "metadata": {}, "outputs": [], @@ -205,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "id": "f1527959", "metadata": {}, "outputs": [ @@ -255,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 22, "id": "3c3af5bf", "metadata": { "lines_to_next_cell": 0 @@ -264,10 +266,10 @@ { "data": { "text/plain": [ - "1220" + "743" ] }, - "execution_count": 24, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -286,13 +288,13 @@ " # df['video_fn'] = df['video_id'].apply(lambda video_id: os.path.join(BASE_DATA_FOLDER, f'lsa/videos/{video_id}.mp4'))\n", " dfs[split] = df\n", "\n", - "df = pd.concat([dfs['train'].sample(20), dfs['val']]).reset_index(drop=True)\n", + "df = pd.concat([dfs['train'], dfs['val']]).reset_index(drop=True)\n", "len(df)" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 23, "id": "dccbe1b9", "metadata": {}, "outputs": [], @@ -315,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "id": "904298f0", "metadata": {}, "outputs": [], @@ -329,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 25, "id": "42832f7c", "metadata": { "scrolled": false @@ -340,7 +342,7 @@ "text/html": [ "
\n", " \n", - " Loading BokehJS ...\n", + " Loading BokehJS ...\n", "
\n" ] }, @@ -349,7 +351,7 @@ }, { "data": { - "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n // Clean up Bokeh references\n if (id != null && id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim();\n if (id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"1144\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; 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i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"1144\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));", + "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n // Clean up Bokeh references\n if (id != null && id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim();\n if (id in Bokeh.index) {\n Bokeh.index[id].model.document.clear();\n delete Bokeh.index[id];\n }\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; 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\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"1119\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"1119\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));", "application/vnd.bokehjs_load.v0+json": "" }, "metadata": {}, @@ -373,7 +375,12 @@ " \n", " \n", "\"\"\"\n", - "cmap = LinearColorMapper(palette=\"Turbo256\", low=0, high=len(set_labels))\n", + "\n", + "# get labels\n", + "labels = df['label_name'].values\n", + "# get unique labels\n", + "unique_labels = np.unique(labels)\n", + "cmap = LinearColorMapper(palette=\"Turbo256\", low=0, high=len(unique_labels))\n", "\n", "output_notebook()\n", "# or \n", @@ -387,32 +394,33 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 26, "id": "ead4daf7", "metadata": { "scrolled": false }, "outputs": [ { - "ename": "KeyError", - "evalue": "'label_name'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/indexes/base.py:3802\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3801\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 3802\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_engine\u001b[39m.\u001b[39;49mget_loc(casted_key)\n\u001b[1;32m 3803\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n", - "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/_libs/index.pyx:138\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/_libs/index.pyx:165\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5745\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:5753\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mKeyError\u001b[0m: 'label_name'", - "\nThe above exception was the direct cause of the following exception:\n", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[29], line 5\u001b[0m\n\u001b[1;32m 1\u001b[0m column_data \u001b[39m=\u001b[39m \u001b[39mdict\u001b[39m(\n\u001b[1;32m 2\u001b[0m x\u001b[39m=\u001b[39mdf[\u001b[39m'\u001b[39m\u001b[39mtsne_x\u001b[39m\u001b[39m'\u001b[39m],\n\u001b[1;32m 3\u001b[0m y\u001b[39m=\u001b[39mdf[\u001b[39m'\u001b[39m\u001b[39mtsne_y\u001b[39m\u001b[39m'\u001b[39m],\n\u001b[1;32m 4\u001b[0m label\u001b[39m=\u001b[39mdf[\u001b[39m'\u001b[39m\u001b[39msign\u001b[39m\u001b[39m'\u001b[39m],\n\u001b[0;32m----> 5\u001b[0m label_desc\u001b[39m=\u001b[39mdf[\u001b[39m'\u001b[39;49m\u001b[39mlabel_name\u001b[39;49m\u001b[39m'\u001b[39;49m],\n\u001b[1;32m 6\u001b[0m split\u001b[39m=\u001b[39mdf[\u001b[39m'\u001b[39m\u001b[39msplit\u001b[39m\u001b[39m'\u001b[39m],\n\u001b[1;32m 7\u001b[0m video_id\u001b[39m=\u001b[39mdf[\u001b[39m'\u001b[39m\u001b[39mvideo_id\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[1;32m 8\u001b[0m )\n\u001b[1;32m 10\u001b[0m \u001b[39mif\u001b[39;00m use_img_div:\n\u001b[1;32m 11\u001b[0m emb_videos \u001b[39m=\u001b[39m load_videos(df[\u001b[39m'\u001b[39m\u001b[39mvideo_fn\u001b[39m\u001b[39m'\u001b[39m])\n", - "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:3807\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3805\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcolumns\u001b[39m.\u001b[39mnlevels \u001b[39m>\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m 3806\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 3807\u001b[0m indexer \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcolumns\u001b[39m.\u001b[39;49mget_loc(key)\n\u001b[1;32m 3808\u001b[0m \u001b[39mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m 3809\u001b[0m indexer \u001b[39m=\u001b[39m [indexer]\n", - "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/pandas/core/indexes/base.py:3804\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m 3802\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_engine\u001b[39m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m 3803\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyError\u001b[39;00m \u001b[39mas\u001b[39;00m err:\n\u001b[0;32m-> 3804\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mKeyError\u001b[39;00m(key) \u001b[39mfrom\u001b[39;00m \u001b[39merr\u001b[39;00m\n\u001b[1;32m 3805\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m 3806\u001b[0m \u001b[39m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3807\u001b[0m \u001b[39m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3808\u001b[0m \u001b[39m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3809\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_indexing_error(key)\n", - "\u001b[0;31mKeyError\u001b[0m: 'label_name'" - ] + "data": { + "text/html": [ + "\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": "(function(root) {\n function embed_document(root) {\n const docs_json = {\"27aecfdc-14bf-4fd5-928c-7f15b2ea73c6\":{\"defs\":[],\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1131\"}],\"center\":[{\"id\":\"1134\"},{\"id\":\"1138\"},{\"id\":\"1172\"}],\"height\":800,\"left\":[{\"id\":\"1135\"}],\"renderers\":[{\"id\":\"1160\"}],\"title\":{\"id\":\"1121\"},\"toolbar\":{\"id\":\"1147\"},\"width\":1000,\"x_range\":{\"id\":\"1123\"},\"x_scale\":{\"id\":\"1127\"},\"y_range\":{\"id\":\"1125\"},\"y_scale\":{\"id\":\"1129\"}},\"id\":\"1120\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{},\"id\":\"1164\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.1},\"fill_color\":{\"field\":\"labels\",\"transform\":{\"id\":\"1118\"}},\"hatch_alpha\":{\"value\":0.1},\"hatch_color\":{\"field\":\"labels\",\"transform\":{\"id\":\"1118\"}},\"line_alpha\":{\"value\":0.1},\"line_color\":{\"field\":\"labels\",\"transform\":{\"id\":\"1118\"}},\"size\":{\"value\":10},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1158\",\"type\":\"Scatter\"},{\"attributes\":{},\"id\":\"1136\",\"type\":\"BasicTicker\"},{\"attributes\":{\"coordinates\":null,\"formatter\":{\"id\":\"1167\"},\"group\":null,\"major_label_policy\":{\"id\":\"1168\"},\"ticker\":{\"id\":\"1132\"}},\"id\":\"1131\",\"type\":\"LinearAxis\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"field\":\"labels\",\"transform\":{\"id\":\"1118\"}},\"hatch_alpha\":{\"value\":0.5},\"hatch_color\":{\"field\":\"labels\",\"transform\":{\"id\":\"1118\"}},\"line_alpha\":{\"value\":0.5},\"line_color\":{\"field\":\"labels\",\"transform\":{\"id\":\"1118\"}},\"size\":{\"value\":10},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1157\",\"type\":\"Scatter\"},{\"attributes\":{\"source\":{\"id\":\"1155\"}},\"id\":\"1161\",\"type\":\"CDSView\"},{\"attributes\":{\"coordinates\":null,\"data_source\":{\"id\":\"1155\"},\"glyph\":{\"id\":\"1157\"},\"group\":null,\"hover_glyph\":null,\"muted_glyph\":{\"id\":\"1159\"},\"nonselection_glyph\":{\"id\":\"1158\"},\"view\":{\"id\":\"1161\"}},\"id\":\"1160\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"coordinates\":null,\"group\":null,\"items\":[{\"id\":\"1173\"}]},\"id\":\"1172\",\"type\":\"Legend\"},{\"attributes\":{},\"id\":\"1132\",\"type\":\"BasicTicker\"},{\"attributes\":{},\"id\":\"1167\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"bottom_units\":\"screen\",\"coordinates\":null,\"fill_alpha\":0.5,\"fill_color\":\"lightgrey\",\"group\":null,\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":1.0,\"line_color\":\"black\",\"line_dash\":[4,4],\"line_width\":2,\"right_units\":\"screen\",\"syncable\":false,\"top_units\":\"screen\"},\"id\":\"1145\",\"type\":\"BoxAnnotation\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.2},\"fill_color\":{\"field\":\"labels\",\"transform\":{\"id\":\"1118\"}},\"hatch_alpha\":{\"value\":0.2},\"hatch_color\":{\"field\":\"labels\",\"transform\":{\"id\":\"1118\"}},\"line_alpha\":{\"value\":0.2},\"line_color\":{\"field\":\"labels\",\"transform\":{\"id\":\"1118\"}},\"size\":{\"value\":10},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1159\",\"type\":\"Scatter\"},{\"attributes\":{\"label\":{\"field\":\"label_desc\"},\"renderers\":[{\"id\":\"1160\"}]},\"id\":\"1173\",\"type\":\"LegendItem\"},{\"attributes\":{},\"id\":\"1170\",\"type\":\"Selection\"},{\"attributes\":{},\"id\":\"1125\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1127\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1129\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"1169\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"1144\",\"type\":\"HelpTool\"},{\"attributes\":{\"axis\":{\"id\":\"1135\"},\"coordinates\":null,\"dimension\":1,\"group\":null,\"ticker\":null},\"id\":\"1138\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1168\",\"type\":\"AllLabels\"},{\"attributes\":{},\"id\":\"1165\",\"type\":\"AllLabels\"},{\"attributes\":{\"axis\":{\"id\":\"1131\"},\"coordinates\":null,\"group\":null,\"ticker\":null},\"id\":\"1134\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1139\",\"type\":\"PanTool\"},{\"attributes\":{},\"id\":\"1123\",\"type\":\"DataRange1d\"},{\"attributes\":{\"coordinates\":null,\"group\":null,\"text\":\"Check label by hovering mouse over the dots\"},\"id\":\"1121\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1140\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"tools\":[{\"id\":\"1139\"},{\"id\":\"1140\"},{\"id\":\"1141\"},{\"id\":\"1142\"},{\"id\":\"1143\"},{\"id\":\"1144\"},{\"id\":\"1146\"}]},\"id\":\"1147\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"1142\",\"type\":\"SaveTool\"},{\"attributes\":{\"callback\":null,\"tooltips\":\"\\n
\\n \\n
\\n @label_desc - @split\\n [#@video_id]\\n
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Application\",\"version\":\"2.4.3\"}};\n const render_items = [{\"docid\":\"27aecfdc-14bf-4fd5-928c-7f15b2ea73c6\",\"root_ids\":[\"1120\"],\"roots\":{\"1120\":\"8aa5a5a3-57de-4695-bcff-8e64eb93f200\"}}];\n root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n }\n if (root.Bokeh !== undefined) {\n embed_document(root);\n } else {\n let attempts = 0;\n const timer = setInterval(function(root) {\n if (root.Bokeh !== undefined) {\n clearInterval(timer);\n embed_document(root);\n } else {\n attempts++;\n if (attempts > 100) {\n clearInterval(timer);\n console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n }\n }\n }, 10, root)\n }\n})(window);", + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "1120" + } + }, + "output_type": "display_data" } ], "source": [ @@ -420,38 +428,36 @@ " x=df['tsne_x'],\n", " y=df['tsne_y'],\n", " label=df['labels'],\n", - " label_desc=df['sign'],\n", + " label_desc=df['label_name'],\n", " split=df['split'],\n", - " video_id=df['video_id']\n", + " # video_id=df['video_id']\n", ")\n", "\n", + "# get unique labels\n", + "set_labels = list(set(column_data['label']))\n", + "\n", + "# map labels to 0 to num_classes\n", + "label_to_id = {label: i for i, label in enumerate(set_labels)}\n", + "column_data['labels'] = [label_to_id[label] for label in column_data['label']]\n", + "\n", + "\n", "if use_img_div:\n", " emb_videos = load_videos(df['video_fn'])\n", " column_data[\"videos\"] = emb_videos\n", "source = ColumnDataSource(data=column_data)\n", "\n", - "p.scatter('x', 'y',\n", - " size=10,\n", - " source=source,\n", - " fill_color={\"field\": 'label', \"transform\": cmap},\n", - " line_color={\"field\": 'label', \"transform\": cmap}, \n", - " #legend_label={\"field\": 'split', \"transform\": lambda x: df['split']},\n", - "# marker={\"field\": 'split'}\n", - " )\n", + "# scatter plot with for each label another color\n", + "p.scatter(x='x',\n", + " y='y',\n", + " source=source,\n", + " color={'field': 'labels', 'transform': cmap},\n", + " legend_field='label_desc',\n", + " size=10,\n", + " alpha=0.5)\n", "\n", "show(p)" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "1d761766", - "metadata": {}, - "outputs": [], - "source": [ - "df" - ] - }, { "cell_type": "code", "execution_count": null, diff --git a/preprocessing.py b/preprocessing.py index 34ac917..ac4b7f5 100644 --- a/preprocessing.py +++ b/preprocessing.py @@ -1,5 +1,5 @@ from argparse import ArgumentParser -from preprocessing.create_wlasl_landmarks_dataset import parse_create_args, create +from preprocessing.create_fingerspelling_dataset import parse_create_args, create from preprocessing.extract_mediapipe_landmarks import parse_extract_args, extract diff --git a/preprocessing/create_fingerspelling_dataset.py b/preprocessing/create_fingerspelling_dataset.py new file mode 100644 index 0000000..c3793cb --- /dev/null +++ b/preprocessing/create_fingerspelling_dataset.py @@ -0,0 +1,174 @@ +import os +import os.path as op +import json +import shutil + +import cv2 +import mediapipe as mp +import numpy as np +import pandas as pd +from utils import get_logger +from tqdm.auto import tqdm +from sklearn.model_selection import train_test_split +from normalization.blazepose_mapping import map_blazepose_df + +BASE_DATA_FOLDER = 'data/' + +mp_drawing = mp.solutions.drawing_utils +mp_drawing_styles = mp.solutions.drawing_styles +mp_hands = mp.solutions.hands +mp_holistic = mp.solutions.holistic +pose_landmarks = mp_holistic.PoseLandmark +hand_landmarks = mp_holistic.HandLandmark + + +def get_landmarks_names(): + ''' + Returns landmark names for mediapipe holistic model + ''' + pose_lmks = ','.join([f'{lmk.name.lower()}_x,{lmk.name.lower()}_y' for lmk in pose_landmarks]) + left_hand_lmks = ','.join([f'left_hand_{lmk.name.lower()}_x,left_hand_{lmk.name.lower()}_y' + for lmk in hand_landmarks]) + right_hand_lmks = ','.join([f'right_hand_{lmk.name.lower()}_x,right_hand_{lmk.name.lower()}_y' + for lmk in hand_landmarks]) + lmks_names = f'{pose_lmks},{left_hand_lmks},{right_hand_lmks}' + return lmks_names + + +def convert_to_str(arr, precision=6): + if isinstance(arr, np.ndarray): + values = [] + for val in arr: + if val == 0: + values.append('0') + else: + values.append(f'{val:.{precision}f}') + return f"[{','.join(values)}]" + else: + return str(arr) + + +def parse_create_args(parser): + parser.add_argument('--landmarks-dataset', '-lmks', required=True, + help='Path to folder with landmarks npy files. \ + You need to run `extract_mediapipe_landmarks.py` script first') + parser.add_argument('--dataset-folder', '-df', default='data/wlasl', + help='Path to folder where original `WLASL_v0.3.json` and `id_to_label.json` are stored. \ + Note that final CSV files will be saved in this folder too.') + parser.add_argument('--videos-folder', '-videos', default=None, + help='Path to folder with videos. If None, then no information of videos (fps, length, \ + width and height) will be stored in final csv file') + parser.add_argument('--num-classes', '-nc', default=100, type=int, help='Number of classes to use in WLASL dataset') + parser.add_argument('--create-new-split', action='store_true') + parser.add_argument('--test-size', '-ts', default=0.25, type=float, + help='Test split percentage size. Only required if --create-new-split is set') + + +# python3 preprocessing.py --landmarks-dataset=data/landmarks -videos data/wlasl/videos +def create(args): + logger = get_logger(__name__) + + landmarks_dataset = args.landmarks_dataset + videos_folder = args.videos_folder + dataset_folder = args.dataset_folder + num_classes = args.num_classes + test_size = args.test_size + + os.makedirs(dataset_folder, exist_ok=True) + + # shutil.copy(os.path.join(BASE_DATA_FOLDER, 'wlasl/id_to_label.json'), dataset_folder) + # shutil.copy(os.path.join(BASE_DATA_FOLDER, 'wlasl/WLASL_v0.3.json'), dataset_folder) + + # get files in landmarks_dataset folder + landmarks_files = os.listdir(landmarks_dataset) + + video_data = [] + for i, file in enumerate(tqdm(landmarks_files)): + + # split by ! + label = file.split('!')[0] + subset = file.split('!')[1].split('.')[0] + + # remove npy and set mp4 + video_id = file.replace('.npy', "") + + + video_dict = {'video_id': video_id, + 'label_name': label, + 'split': subset} + + if videos_folder is not None: + cap = cv2.VideoCapture(op.join(videos_folder, f'{video_id}.mp4')) + if not cap.isOpened(): + logger.warning(f'Video {video_id}.mp4 not found') + continue + width = cap.get(cv2.CAP_PROP_FRAME_WIDTH) + height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT) + fps = cap.get(cv2.CAP_PROP_FPS) + length = cap.get(cv2.CAP_PROP_FRAME_COUNT) / float(cap.get(cv2.CAP_PROP_FPS)) + video_info = {'video_width': width, + 'video_height': height, + 'fps': fps, + 'length': length} + video_dict.update(video_info) + video_data.append(video_dict) + + df_video = pd.DataFrame(video_data) + video_ids = df_video['video_id'].unique() + lmks_data = [] + lmks_names = get_landmarks_names().split(',') + + # get labels from df_video + labels = df_video['label_name'].unique() + # map labels to ids + label_to_id = {label: i for i, label in enumerate(labels)} + + # add label_id column to df_video + df_video['labels'] = df_video['label_name'].map(label_to_id) + + # export to json file as id to label + id_to_label = {i: label for label, i in label_to_id.items()} + with open(op.join(dataset_folder, 'id_to_label.json'), 'w') as f: + json.dump(id_to_label, f, indent=4) + + for video_id in video_ids: + lmk_fn = op.join(landmarks_dataset, f'{video_id}.npy') + if not op.exists(lmk_fn): + logger.warning(f'{lmk_fn} file not found. Skipping') + continue + lmk = np.load(lmk_fn).T + lmks_dict = {'video_id': video_id} + for lmk_, name in zip(lmk, lmks_names): + lmks_dict[name] = lmk_ + lmks_data.append(lmks_dict) + + df_lmks = pd.DataFrame(lmks_data) + df = pd.merge(df_video, df_lmks) + aux_columns = ['split', 'video_id', 'labels', 'label_name'] + if videos_folder is not None: + aux_columns += ['video_width', 'video_height', 'fps', 'length'] + df_aux = df[aux_columns] + df = map_blazepose_df(df) + df = pd.concat([df, df_aux], axis=1) + if args.create_new_split: + df_train, df_test = train_test_split(df, test_size=test_size, stratify=df['labels'], random_state=42) + + + print(f'Num classes: {num_classes}') + print(df_train['labels'].value_counts()) + print(df_test['labels'].value_counts()) + assert set(df_train['labels'].unique()) == set(df_test['labels'].unique( + )), 'The labels for train and test dataframe are different. We recommend to download the dataset again, or to use \ + the --create-new-split flag' + for split, df_split in zip(['train', 'val'], + [df_train, df_test]): + fn_out = op.join(dataset_folder, f'{split}.csv') + (df_split.reset_index(drop=True) + .applymap(convert_to_str) + .to_csv(fn_out, index=False)) + + else: + fn_out = op.join(dataset_folder, 'train.csv') + (df.reset_index(drop=True) + .applymap(convert_to_str) + .to_csv(fn_out, index=False)) \ No newline at end of file diff --git a/preprocessing/create_google_asl_landmarks_dataset.py b/preprocessing/create_google_asl_landmarks_dataset.py index 4a4beda..c128356 100644 --- a/preprocessing/create_google_asl_landmarks_dataset.py +++ b/preprocessing/create_google_asl_landmarks_dataset.py @@ -4,6 +4,8 @@ import pandas as pd from tqdm.auto import tqdm import json +from normalization.blazepose_mapping import map_blazepose_df + def create(train_landmark_files, train_csv, dataset_folder, test_size): os.makedirs(dataset_folder, exist_ok=True) @@ -17,15 +19,15 @@ def create(train_landmark_files, train_csv, dataset_folder, test_size): mapping = { 'pose_0': 'nose', 'pose_1': 'leftEye', - 'pose_2': 'rightEye', - 'pose_3': 'leftEar', - 'pose_4': 'rightEar', - 'pose_5': 'leftShoulder', - 'pose_6': 'rightShoulder', - 'pose_7': 'leftElbow', - 'pose_8': 'rightElbow', - 'pose_9': 'leftWrist', - 'pose_10': 'rightWrist', + 'pose_4': 'rightEye', + 'pose_7': 'leftEar', + 'pose_8': 'rightEar', + 'pose_11': 'leftShoulder', + 'pose_12': 'rightShoulder', + 'pose_13': 'leftElbow', + 'pose_14': 'rightElbow', + 'pose_15': 'leftWrist', + 'pose_16': 'rightWrist', 'left_hand_0': 'wrist_left', 'left_hand_1': 'thumbCMC_left', @@ -77,7 +79,7 @@ def create(train_landmark_files, train_csv, dataset_folder, test_size): columns.append(f'{v}_X') columns.append(f'{v}_Y') - for _, row in tqdm(train_df.head(6000).iterrows(), total=6000): + for _, row in tqdm(train_df.head(10000).iterrows(), total=10000): path, participant_id, sequence_id, sign = row['path'], row['participant_id'], row['sequence_id'], row['sign'] parquet_file = os.path.join(train_landmark_files, str(participant_id), f"{sequence_id}.parquet") @@ -136,6 +138,7 @@ def create(train_landmark_files, train_csv, dataset_folder, test_size): video_data.append(new_landmark_data) video_data = pd.concat(video_data, axis=0, ignore_index=True) + video_data = map_blazepose_df(video_data, rename=False) video_data.to_csv(os.path.join(dataset_folder, 'spoter.csv'), index=False) train_landmark_files = 'data/train_landmark_files' diff --git a/preprocessing/create_wlasl_landmarks_dataset.py b/preprocessing/create_wlasl_landmarks_dataset.py index c1e4d5c..53d5cf4 100644 --- a/preprocessing/create_wlasl_landmarks_dataset.py +++ b/preprocessing/create_wlasl_landmarks_dataset.py @@ -110,6 +110,7 @@ def create(args): 'length': length} video_dict.update(video_info) video_data.append(video_dict) + df_video = pd.DataFrame(video_data) video_ids = df_video['video_id'].unique() lmks_data = [] @@ -126,9 +127,7 @@ def create(args): lmks_data.append(lmks_dict) df_lmks = pd.DataFrame(lmks_data) - print(df_lmks) df = pd.merge(df_video, df_lmks) - print(df) aux_columns = ['split', 'video_id', 'labels', 'label_name'] if videos_folder is not None: aux_columns += ['video_width', 'video_height', 'fps', 'length'] diff --git a/preprocessing/extract_mediapipe_landmarks.py b/preprocessing/extract_mediapipe_landmarks.py index 6d63076..f02421f 100644 --- a/preprocessing/extract_mediapipe_landmarks.py +++ b/preprocessing/extract_mediapipe_landmarks.py @@ -35,7 +35,11 @@ class LandmarksResults: ): self.results = results self.num_landmarks_pose = num_landmarks_pose - self.num_landmarks_hand = num_landmarks_hand + self.num_landmarks_hand = num_landmarks_hand + + @property + def empty(self): + return self.results.pose_landmarks is None or (self.results.left_hand_landmarks is None and self.results.right_hand_landmarks is None) @property def pose_landmarks(self): @@ -67,6 +71,10 @@ def get_landmarks(image_orig, holistic, debug=False): # Convert the BGR image to RGB before processing. image = cv2.cvtColor(image_orig, cv2.COLOR_BGR2RGB) results = LandmarksResults(holistic.process(image)) + + if results.empty: + return None + if debug: lmks_pose = [] for lmk in results.pose_landmarks: @@ -94,6 +102,7 @@ def get_landmarks(image_orig, holistic, debug=False): len(lmks_right_hand) == 2 * LEN_LANDMARKS_HAND ), f"{len(lmks_right_hand)} != {2 * LEN_LANDMARKS_HAND}" landmarks = [] + for lmk in chain( results.pose_landmarks, results.left_hand_landmarks, @@ -128,10 +137,21 @@ def extract(args): videos_folder = args.videos_folder os.makedirs(landmarks_output, exist_ok=True) for fn_video in tqdm(sorted(glob.glob(op.join(videos_folder, "*mp4")))): + + # check if landmarks already exist + if op.exists(op.join(landmarks_output, op.basename(fn_video).split(".")[0] + ".npy")): + continue + cap = cv2.VideoCapture(fn_video) ret, image_orig = cap.read() height, width = image_orig.shape[:2] landmarks_video = [] + + # make sure fps is 20 by determining the number of frames to be skipped + frame_rate = int(cap.get(cv2.CAP_PROP_FPS)) + frame_skip = (frame_rate // 10) - 1 + + with tqdm(total=int(cap.get(cv2.CAP_PROP_FRAME_COUNT))) as pbar: with mp_holistic.Holistic( static_image_mode=False, @@ -145,7 +165,11 @@ def extract(args): print(e) landmarks = get_landmarks(image_orig, holistic, debug=True) ret, image_orig = cap.read() - landmarks_video.append(landmarks) + for _ in range(frame_skip): + ret, image_orig = cap.read() + pbar.update(1) + if landmarks: + landmarks_video.append(landmarks) pbar.update(1) landmarks_video = np.vstack(landmarks_video) np.save( diff --git a/preprocessing/split_dataset.py b/preprocessing/split_dataset.py index 17e1f55..24e3133 100644 --- a/preprocessing/split_dataset.py +++ b/preprocessing/split_dataset.py @@ -8,7 +8,6 @@ dataset = "data/processed/spoter.csv" # read the dataset df = pd.read_csv(dataset) -df = map_blazepose_df(df) with open("data/sign_to_prediction_index_map.json", "r") as f: sign_to_prediction_index_max = json.load(f) @@ -17,6 +16,9 @@ with open("data/sign_to_prediction_index_map.json", "r") as f: # filter df to make sure each sign has at least 4 samples df = df[df["sign"].map(df["sign"].value_counts()) > 4] +# print number of unique signs +print("Number of unique signs: ", len(df["sign"].unique())) + # use the path column to split the dataset paths = df["path"].unique() diff --git a/requirements.txt b/requirements.txt index 2308b4c..4ec4834 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,6 @@ pandas bokeh==2.4.3 boto3>=1.9 -clearml==1.6.4 ipywidgets==8.0.4 matplotlib==3.5.3 mediapipe==0.8.11 @@ -9,6 +8,9 @@ notebook==6.5.2 opencv-python==4.6.0.66 plotly==5.11.0 scikit-learn==1.0.2 -torch -torchvision +clearml==1.10.3 +torch==2.0.0 +torchvision==0.15.1 tqdm==4.54.1 +optuna==3.1.1 +onnx==1.14.0 \ No newline at end of file diff --git a/train.py b/train.py index 76fabfe..7c29d1e 100644 --- a/train.py +++ b/train.py @@ -15,7 +15,7 @@ from torchvision import transforms from torch.utils.data import DataLoader from pathlib import Path import copy - +import numpy as np from datasets import CzechSLRDataset, SLREmbeddingDataset, collate_fn_triplet_padd, collate_fn_padd from models import SPOTER, SPOTER_EMBEDDINGS, train_epoch, evaluate, train_epoch_embedding, \ train_epoch_embedding_online, evaluate_embedding @@ -32,7 +32,7 @@ except ImportError: pass -PROJECT_NAME = "spoter" +PROJECT_NAME = "SpoterEmbedding" CLEARML = "clearml" @@ -75,12 +75,25 @@ def train(args, tracker: Tracker): # Construct the model if not args.classification_model: - slrt_model = SPOTER_EMBEDDINGS( - features=args.vector_length, - hidden_dim=args.hidden_dim, - norm_emb=args.normalize_embeddings, - dropout=args.dropout - ) + # if finetune, load the weights from the classification model + if args.finetune: + checkpoint = torch.load(args.checkpoint_path, map_location=device) + + slrt_model = SPOTER_EMBEDDINGS( + features=checkpoint["config_args"].vector_length, + hidden_dim=checkpoint["config_args"].hidden_dim, + norm_emb=checkpoint["config_args"].normalize_embeddings, + dropout=checkpoint["config_args"].dropout, + ) + else: + slrt_model = SPOTER_EMBEDDINGS( + features=args.vector_length, + hidden_dim=args.hidden_dim, + norm_emb=args.normalize_embeddings, + dropout=args.dropout, + ) + + model_type = 'embed' if args.hard_triplet_mining == "None": cel_criterion = nn.TripletMarginLoss(margin=args.triplet_loss_margin, p=2) @@ -233,6 +246,9 @@ def train(args, tracker: Tracker): val_accs.append(val_acc) tracker.log_scalar_metric("acc", "val", epoch, val_acc) + create_embedding_scatter_plots(tracker, slrt_model, train_loader, val_loader, device, id_to_label, epoch, + top_model_name) + logger.info(f"Epoch time: {datetime.now() - start_time}") logger.info("[" + str(epoch) + "] TRAIN loss: " + str(train_loss) + " acc: " + str(train_accs[-1])) logger.info("[" + str(epoch) + "] VALIDATION acc: " + str(val_accs[-1])) diff --git a/train.sh b/train.sh index fe68689..9b97974 100755 --- a/train.sh +++ b/train.sh @@ -1,22 +1,24 @@ #!/bin/sh -python -m train \ +python3 -m train \ --save_checkpoints_every 10 \ - --experiment_name "augment_rotate_75_x8" \ - --epochs 300 \ + --experiment_name "Finetune Fingerspelling Signs" \ + --epochs 1000 \ --optimizer "ADAM" \ - --lr 0.001 \ - --batch_size 16 \ - --dataset_name "processed" \ - --training_set_path "spoter_train.csv" \ - --validation_set_path "spoter_test.csv" \ + --lr 0.00001 \ + --batch_size 8 \ + --dataset_name "FingerSpelling" \ + --training_set_path "train.csv" \ + --validation_set_path "val.csv" \ --vector_length 32 \ --epoch_iters -1 \ --scheduler_factor 0 \ --hard_triplet_mining "in_batch" \ --filter_easy_triplets \ - --triplet_loss_margin 1 \ + --start_mining_hard 50 \ + --triplet_loss_margin 4 \ --dropout 0.2 \ - --augmentations_prob=0.75 \ - --hard_mining_scheduler_triplets_threshold=0 \ - --normalize_embeddings \ - --num_classes 100 \ \ No newline at end of file + --tracker=clearml \ + --dataset_loader=clearml \ + --dataset_project="SpoterEmbedding" \ + --finetune \ + --checkpoint_path "checkpoints/checkpoint_embed_3835.pth" \ No newline at end of file diff --git a/training/train_arguments.py b/training/train_arguments.py index 5980aff..1a56874 100644 --- a/training/train_arguments.py +++ b/training/train_arguments.py @@ -81,4 +81,7 @@ def get_default_args(): help="Enables batching grouping scheduler if > 0. Defines threshold for when to decay the \ distance threshold of the batch sorter") + parser.add_argument("--finetune", action='store_true', default=False, help="Fintune the model") + parser.add_argument("--checkpoint_path", type=str, default="") + return parser diff --git a/visualize_data.ipynb b/visualize_data.ipynb new file mode 100644 index 0000000..2146c06 --- /dev/null +++ b/visualize_data.ipynb @@ -0,0 +1,1636 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from augmentations.augment import __preprocess_row_sign" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 path\n", + "1 participant_id\n", + "2 sequence_id\n", + "3 sign\n", + "4 labels\n", + "5 nose_X\n", + "6 nose_Y\n", + "7 leftEye_X\n", + "8 leftEye_Y\n", + "9 rightEye_X\n", + "10 rightEye_Y\n", + "11 leftEar_X\n", + "12 leftEar_Y\n", + "13 rightEar_X\n", + "14 rightEar_Y\n", + "15 leftShoulder_X\n", + "16 leftShoulder_Y\n", + "17 rightShoulder_X\n", + "18 rightShoulder_Y\n", + "19 leftElbow_X\n", + "20 leftElbow_Y\n", + "21 rightElbow_X\n", + "22 rightElbow_Y\n", + "23 leftWrist_X\n", + "24 leftWrist_Y\n", + "25 rightWrist_X\n", + "26 rightWrist_Y\n", + "27 wrist_left_X\n", + "28 wrist_left_Y\n", + "29 thumbCMC_left_X\n", + "30 thumbCMC_left_Y\n", + "31 thumbMP_left_X\n", + "32 thumbMP_left_Y\n", + "33 thumbIP_left_X\n", + "34 thumbIP_left_Y\n", + "35 thumbTip_left_X\n", + "36 thumbTip_left_Y\n", + "37 indexMCP_left_X\n", + "38 indexMCP_left_Y\n", + "39 indexPIP_left_X\n", + "40 indexPIP_left_Y\n", + "41 indexDIP_left_X\n", + "42 indexDIP_left_Y\n", + "43 indexTip_left_X\n", + "44 indexTip_left_Y\n", + "45 middleMCP_left_X\n", + "46 middleMCP_left_Y\n", + "47 middlePIP_left_X\n", + "48 middlePIP_left_Y\n", + "49 middleDIP_left_X\n", + "50 middleDIP_left_Y\n", + "51 middleTip_left_X\n", + "52 middleTip_left_Y\n", + "53 ringMCP_left_X\n", + "54 ringMCP_left_Y\n", + "55 ringPIP_left_X\n", + "56 ringPIP_left_Y\n", + "57 ringDIP_left_X\n", + "58 ringDIP_left_Y\n", + "59 ringTip_left_X\n", + "60 ringTip_left_Y\n", + "61 littleMCP_left_X\n", + "62 littleMCP_left_Y\n", + "63 littlePIP_left_X\n", + "64 littlePIP_left_Y\n", + "65 littleDIP_left_X\n", + "66 littleDIP_left_Y\n", + "67 littleTip_left_X\n", + "68 littleTip_left_Y\n", + "69 wrist_right_X\n", + "70 wrist_right_Y\n", + "71 thumbCMC_right_X\n", + "72 thumbCMC_right_Y\n", + "73 thumbMP_right_X\n", + "74 thumbMP_right_Y\n", + "75 thumbIP_right_X\n", + "76 thumbIP_right_Y\n", + "77 thumbTip_right_X\n", + "78 thumbTip_right_Y\n", + "79 indexMCP_right_X\n", + "80 indexMCP_right_Y\n", + "81 indexPIP_right_X\n", + "82 indexPIP_right_Y\n", + "83 indexDIP_right_X\n", + "84 indexDIP_right_Y\n", + "85 indexTip_right_X\n", + "86 indexTip_right_Y\n", + "87 middleMCP_right_X\n", + "88 middleMCP_right_Y\n", + "89 middlePIP_right_X\n", + "90 middlePIP_right_Y\n", + "91 middleDIP_right_X\n", + "92 middleDIP_right_Y\n", + "93 middleTip_right_X\n", + "94 middleTip_right_Y\n", + "95 ringMCP_right_X\n", + "96 ringMCP_right_Y\n", + "97 ringPIP_right_X\n", + "98 ringPIP_right_Y\n", + "99 ringDIP_right_X\n", + "100 ringDIP_right_Y\n", + "101 ringTip_right_X\n", + "102 ringTip_right_Y\n", + "103 littleMCP_right_X\n", + "104 littleMCP_right_Y\n", + "105 littlePIP_right_X\n", + "106 littlePIP_right_Y\n", + "107 littleDIP_right_X\n", + "108 littleDIP_right_Y\n", + "109 littleTip_right_X\n", + "110 littleTip_right_Y\n", + "111 neck_X\n", + "112 neck_Y\n" + ] + } + ], + "source": [ + "df = pd.read_csv('data/processed/spoter_train.csv')\n", + "for i, e in enumerate(df.columns):\n", + " print(i, e)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "# dataset = 'data/processed/spoter_train.csv'\n", + "\n", + "def plot(dataset):\n", + " df = pd.read_csv(dataset)\n", + " # get columns 0 - 26\n", + " # df = df.iloc[:, 5:27]\n", + "\n", + "\n", + " # df = df.iloc[:, 27:69]\n", + " # df = df.iloc[:, 69:]\n", + "\n", + " if 'wlasl' in dataset:\n", + " df = df.iloc[:, :108]\n", + "\n", + " else:\n", + " df = df.iloc[:, 5:]\n", + "\n", + "\n", + " # get first row\n", + " row = df.iloc[20]\n", + "\n", + " # use matplotlib to create a scatter plot (the columns are X, Y, X, Y, X, Y, X, Y)\n", + " import matplotlib.pyplot as plt\n", + " import ast\n", + "\n", + " def __process_row(coords):\n", + " coords = coords.tolist()\n", + "\n", + " new_coords = [] # string to list\n", + "\n", + " for x in coords:\n", + " new_coords.append(ast.literal_eval(x))\n", + "\n", + " coords = [x[0] for x in new_coords]\n", + "\n", + " return [float(x) for x in coords]\n", + "\n", + "\n", + " # extract X and Y coordinates from the row\n", + " x_coords = row.iloc[::2] # columns 0, 2, 4, 6, ...\n", + " y_coords = row.iloc[1::2] # columns 1, 3, 5, 7, ...\n", + "\n", + " x_coords = __process_row(x_coords)\n", + " y_coords = __process_row(y_coords)\n", + "\n", + " # create a scatter plot\n", + " plt.scatter(x_coords, y_coords)\n", + "\n", + "\n", + " plt.xlim(0, 1)\n", + " plt.ylim(0, 1)\n", + " \n", + " # add axis labels and a title\n", + " plt.xlabel('X')\n", + " plt.ylabel('Y')\n", + " plt.title('Scatter plot of X and Y coordinates')\n", + "\n", + " # show the plot\n", + " plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot('data/wlasl/WLASL100_train.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# read parquet file at data/train_landmark_files/2044/635217.parquet\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n", + "df = pd.read_parquet('data/train_landmark_files/37779/254554888.parquet')\n", + "\n", + "# print all unique frame numbers\n", + "print(df['frame'].unique())\n", + "\n", + "# filter where type is pose\n", + "df = df[df['frame'] == 25]\n", + "\n", + "print(df.head())\n", + "\n", + "mapping = {\n", + " 'pose_0': 'nose',\n", + " 'pose_1': 'leftEye',\n", + " 'pose_4': 'rightEye',\n", + " 'pose_7': 'leftEar',\n", + " 'pose_8': 'rightEar',\n", + " 'pose_11': 'leftShoulder',\n", + " 'pose_12': 'rightShoulder',\n", + " 'pose_13': 'leftElbow',\n", + " 'pose_14': 'rightElbow',\n", + " 'pose_15': 'leftWrist',\n", + " 'pose_16': 'rightWrist',\n", + "\n", + " 'left_hand_0': 'wrist_left',\n", + " 'left_hand_1': 'thumbCMC_left',\n", + " 'left_hand_2': 'thumbMP_left',\n", + " 'left_hand_3': 'thumbIP_left',\n", + " 'left_hand_4': 'thumbTip_left',\n", + " 'left_hand_5': 'indexMCP_left',\n", + " 'left_hand_6': 'indexPIP_left',\n", + " 'left_hand_7': 'indexDIP_left',\n", + " 'left_hand_8': 'indexTip_left',\n", + " 'left_hand_9': 'middleMCP_left',\n", + " 'left_hand_10': 'middlePIP_left',\n", + " 'left_hand_11': 'middleDIP_left',\n", + " 'left_hand_12': 'middleTip_left',\n", + " 'left_hand_13': 'ringMCP_left',\n", + " 'left_hand_14': 'ringPIP_left',\n", + " 'left_hand_15': 'ringDIP_left',\n", + " 'left_hand_16': 'ringTip_left',\n", + " 'left_hand_17': 'littleMCP_left',\n", + " 'left_hand_18': 'littlePIP_left',\n", + " 'left_hand_19': 'littleDIP_left',\n", + " 'left_hand_20': 'littleTip_left',\n", + "\n", + " 'right_hand_0': 'wrist_right',\n", + " 'right_hand_1': 'thumbCMC_right',\n", + " 'right_hand_2': 'thumbMP_right',\n", + " 'right_hand_3': 'thumbIP_right',\n", + " 'right_hand_4': 'thumbTip_right',\n", + " 'right_hand_5': 'indexMCP_right',\n", + " 'right_hand_6': 'indexPIP_right',\n", + " 'right_hand_7': 'indexDIP_right',\n", + " 'right_hand_8': 'indexTip_right',\n", + " 'right_hand_9': 'middleMCP_right',\n", + " 'right_hand_10': 'middlePIP_right',\n", + " 'right_hand_11': 'middleDIP_right',\n", + " 'right_hand_12': 'middleTip_right',\n", + " 'right_hand_13': 'ringMCP_right',\n", + " 'right_hand_14': 'ringPIP_right',\n", + " 'right_hand_15': 'ringDIP_right',\n", + " 'right_hand_16': 'ringTip_right',\n", + " 'right_hand_17': 'littleMCP_right',\n", + " 'right_hand_18': 'littlePIP_right',\n", + " 'right_hand_19': 'littleDIP_right',\n", + " 'right_hand_20': 'littleTip_right',\n", + " }\n", + "\n", + "# scatter plot and when hovering over the point, show the frame number\n", + "import plotly.express as px\n", + "\n", + "# combine type and landmark index\n", + "df['landmark_id'] = df['type'] + '_' + df['landmark_index'].astype(str)\n", + "# only keep rows where landmark_id is in the mapping\n", + "df = df[df['landmark_id'].isin(mapping.keys())]\n", + "df['name'] = df['landmark_id'].apply(lambda x: mapping[x])\n", + "\n", + "# flip vertically\n", + "df['y'] = 1 - df['y']\n", + "\n", + "# keep aspect ratio\n", + "\n", + "# scatter with px and set color of points based on the type\n", + "fig = px.scatter(df, x='x', y='y', color='type', hover_data=['name'])\n", + "\n", + "fig.update_xaxes(range=[0, 1], constrain='domain')\n", + "fig.update_yaxes(scaleanchor='x', scaleratio=1, range=[0, 1])\n", + "\n", + "# show the plot\n", + "fig.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# from training dataset\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/webcam.py b/webcam.py index d381c71..f832fc5 100644 --- a/webcam.py +++ b/webcam.py @@ -238,6 +238,7 @@ def distance_matrix(keypoints, embeddings, p=2, threshold=1000000): f"{kk}-dimensional vectors") if m*n*k <= threshold: + print("Using minkowski_distance") return minkowski_distance(x[:,np.newaxis,:],y[np.newaxis,:,:],p) else: result = np.empty((m,n),dtype=float) # FIXME: figure out the best dtype