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| import numpy as np | |
| from sklearn.svm import SVC | |
| from sklearn.model_selection import train_test_split | |
| import torch | |
| import PIL | |
| def get_separation_space(type_bin, annotations, df): | |
| abstracts = np.array([float(ann) for ann in df[type_bin]]) | |
| abstract_idxs = list(np.argsort(abstracts))[:200] | |
| repr_idxs = list(np.argsort(abstracts))[-200:] | |
| X = np.array([annotations['z_vectors'][i] for i in abstract_idxs+repr_idxs]) | |
| X = X.reshape((400, 512)) | |
| y = np.array([1]*200 + [0]*200) | |
| x_train, x_val, y_train, y_val = train_test_split(X, y, test_size=0.2) | |
| svc = SVC(gamma='auto', kernel='linear') | |
| svc.fit(x_train, y_train) | |
| print(svc.score(x_val, y_val)) | |
| imp_features = (np.abs(svc.coef_) > 0.1).sum() | |
| return svc.coef_, imp_features | |
| def regenerate_images(model, z, decision_boundary, min_epsilon=-3, max_epsilon=3, count=5): | |
| device = torch.device('cpu') | |
| G = model.to(device) # type: ignore | |
| # Labels. | |
| label = torch.zeros([1, G.c_dim], device=device) | |
| z = torch.from_numpy(z.copy()).to(device) | |
| decision_boundary = torch.from_numpy(decision_boundary.copy()).to(device) | |
| lambdas = np.linspace(min_epsilon, max_epsilon, count) | |
| images = [] | |
| # Generate images. | |
| for _, lambda_ in enumerate(lambdas): | |
| z_0 = z + lambda_ * decision_boundary | |
| # Construct an inverse rotation/translation matrix and pass to the generator. The | |
| # generator expects this matrix as an inverse to avoid potentially failing numerical | |
| # operations in the network. | |
| #if hasattr(G.synthesis, 'input'): | |
| #m = make_transform(translate, rotate) | |
| #m = np.linalg.inv(m) | |
| #G.synthesis.input.transform.copy_(torch.from_numpy(m)) | |
| img = G(z_0, label, truncation_psi=0.7, noise_mode='random') | |
| img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) | |
| images.append(PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB')) | |
| return images, lambdas | |
| def generate_original_image(z, model): | |
| device = torch.device('cpu') | |
| G = model.to(device) # type: ignore | |
| # Labels. | |
| label = torch.zeros([1, G.c_dim], device=device) | |
| z = torch.from_numpy(z.copy()).to(device) | |
| img = G(z, label, truncation_psi=0.7, noise_mode='random') | |
| img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) | |
| return PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB') |