Initial codebase (#1)

* Add project code

* Logger improvements

* Improvements to web demo code

* added create_wlasl_landmarks_dataset.py and xtract_mediapipe_landmarks.py

* Fix rotation augmentation

* fixed error in docstring, and removed unnecessary replace -1 -> 0

* Readme updates

* Share base notebooks

* Add notebooks and unify for different datasets

* requirements update

* fixes

* Make evaluate more deterministic

* Allow training with clearml

* refactor preprocessing and apply linter

* Minor fixes

* Minor notebook tweaks

* Readme updates

* Fix PR comments

* Remove unneeded code

* Add banner to Readme

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Co-authored-by: Gabriel Lema <gabriel.lema@xmartlabs.com>

training/train_utils.py

@@ -0,0 +1,71 @@
+import os
+import random
+import numpy as np
+import pandas as pd
+import plotly.express as px
+import torch
+
+from models import embeddings_scatter_plot, embeddings_scatter_plot_splits
+
+
+def train_setup(seed, experiment_name):
+    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
+    g = torch.Generator()
+    g.manual_seed(seed)
+    return g
+
+
+def create_embedding_scatter_plots(tracker, model, train_loader, val_loader, device, id_to_label, epoch, model_name):
+    tsne_results, labels = embeddings_scatter_plot(model, train_loader, device, id_to_label, perplexity=40, n_iter=1000)
+
+    df = pd.DataFrame({'x': tsne_results[:, 0],
+                       'y': tsne_results[:, 1],
+                       'label': labels})
+    fig = px.scatter(df, y="y", x="x", color="label")
+
+    tracker.log_chart(
+        title="Training Scatter Plot with Best Model: " + model_name,
+        series="Scatter Plot",
+        iteration=epoch,
+        figure=fig
+    )
+
+    tsne_results, labels = embeddings_scatter_plot(model, val_loader, device, id_to_label, perplexity=40, n_iter=1000)
+
+    df = pd.DataFrame({'x': tsne_results[:, 0],
+                       'y': tsne_results[:, 1],
+                       'label': labels})
+    fig = px.scatter(df, y="y", x="x", color="label")
+
+    tracker.log_chart(
+        title="Validation Scatter Plot with Best Model: " + model_name,
+        series="Scatter Plot",
+        iteration=epoch,
+        figure=fig,
+    )
+
+    dataloaders = {'train': train_loader,
+                   'val': val_loader}
+    splits = list(dataloaders.keys())
+    tsne_results_splits, labels_splits = embeddings_scatter_plot_splits(model, dataloaders,
+                                                                        device, id_to_label, perplexity=40, n_iter=1000)
+    tsne_results = np.vstack([tsne_results_splits[split] for split in splits])
+    labels = np.concatenate([labels_splits[split] for split in splits])
+    split = np.concatenate([[split]*len(labels_splits[split]) for split in splits])
+    df = pd.DataFrame({'x': tsne_results[:, 0],
+                       'y': tsne_results[:, 1],
+                       'label': labels,
+                       'split': split})
+    fig = px.scatter(df, y="y", x="x", color="label", symbol='split')
+    tracker.log_chart(
+        title="Scatter Plot of train and val with Best Model: " + model_name,
+        series="Scatter Plot",
+        iteration=epoch,
+        figure=fig,
+    )