| from PIL import Image | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| pred = "model/inference/monotemporalL2/export/epoch_1/test/img-0_pred.npy" | |
| target = "model/inference/monotemporalL2/export/epoch_1/test/img-0_target.npy" | |
| var = "model/inference/monotemporalL2/export/epoch_1/test/img-0_var.npy" | |
| pred = np.load(pred) | |
| target = np.load(target) | |
| var = np.load(var) | |
| print(pred.shape) # (13, 256, 256) | |
| print(target.shape) # (13, 256, 256) | |
| print(var.shape) # (0, 256, 256) | |
| print(pred.dtype) # float32 | |
| print(target.dtype) # float32 | |
| print(var.dtype) # float32 | |
| print(pred.min(), pred.max()) # 0.0 1.0 | |
| print(target.min(), target.max()) # 0.0 1.0 | |
| # print(var.min(), var.max()) # nan nan | |
| rgb = [3, 2, 1] | |
| fig, ax = plt.subplots() | |
| # get discrete colormap | |
| cmap = plt.get_cmap("gray", 13) | |
| ax.matshow(np.transpose(pred, (1, 2, 0)), cmap=cmap, vmin=0, vmax=1) | |
| ax.axis("off") | |
| fig.tight_layout() | |
| plt.savefig("pred.png") |