81bbf66aab
* 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 --------- Co-authored-by: Gabriel Lema <gabriel.lema@xmartlabs.com>
72 lines
2.6 KiB
Python
72 lines
2.6 KiB
Python
import os
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import random
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import numpy as np
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import pandas as pd
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import plotly.express as px
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import torch
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from models import embeddings_scatter_plot, embeddings_scatter_plot_splits
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def train_setup(seed, experiment_name):
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random.seed(seed)
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np.random.seed(seed)
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os.environ["PYTHONHASHSEED"] = str(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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torch.backends.cudnn.deterministic = True
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g = torch.Generator()
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g.manual_seed(seed)
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return g
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def create_embedding_scatter_plots(tracker, model, train_loader, val_loader, device, id_to_label, epoch, model_name):
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tsne_results, labels = embeddings_scatter_plot(model, train_loader, device, id_to_label, perplexity=40, n_iter=1000)
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df = pd.DataFrame({'x': tsne_results[:, 0],
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'y': tsne_results[:, 1],
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'label': labels})
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fig = px.scatter(df, y="y", x="x", color="label")
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tracker.log_chart(
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title="Training Scatter Plot with Best Model: " + model_name,
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series="Scatter Plot",
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iteration=epoch,
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figure=fig
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)
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tsne_results, labels = embeddings_scatter_plot(model, val_loader, device, id_to_label, perplexity=40, n_iter=1000)
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df = pd.DataFrame({'x': tsne_results[:, 0],
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'y': tsne_results[:, 1],
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'label': labels})
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fig = px.scatter(df, y="y", x="x", color="label")
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tracker.log_chart(
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title="Validation Scatter Plot with Best Model: " + model_name,
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series="Scatter Plot",
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iteration=epoch,
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figure=fig,
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)
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dataloaders = {'train': train_loader,
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'val': val_loader}
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splits = list(dataloaders.keys())
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tsne_results_splits, labels_splits = embeddings_scatter_plot_splits(model, dataloaders,
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device, id_to_label, perplexity=40, n_iter=1000)
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tsne_results = np.vstack([tsne_results_splits[split] for split in splits])
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labels = np.concatenate([labels_splits[split] for split in splits])
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split = np.concatenate([[split]*len(labels_splits[split]) for split in splits])
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df = pd.DataFrame({'x': tsne_results[:, 0],
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'y': tsne_results[:, 1],
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'label': labels,
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'split': split})
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fig = px.scatter(df, y="y", x="x", color="label", symbol='split')
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tracker.log_chart(
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title="Scatter Plot of train and val with Best Model: " + model_name,
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series="Scatter Plot",
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iteration=epoch,
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figure=fig,
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)
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