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| import matplotlib | |
| matplotlib.use("Agg") | |
| import matplotlib.pylab as plt | |
| import numpy as np | |
| def save_figure_to_numpy(fig): | |
| # save it to a numpy array. | |
| data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='') | |
| data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) | |
| return data | |
| def plot_alignment_to_numpy(alignment, info=None): | |
| fig, ax = plt.subplots(figsize=(6, 4)) | |
| im = ax.imshow(alignment, aspect='auto', origin='lower', | |
| interpolation='none') | |
| fig.colorbar(im, ax=ax) | |
| xlabel = 'Decoder timestep' | |
| if info is not None: | |
| xlabel += '\n\n' + info | |
| plt.xlabel(xlabel) | |
| plt.ylabel('Encoder timestep') | |
| plt.tight_layout() | |
| fig.canvas.draw() | |
| data = save_figure_to_numpy(fig) | |
| plt.close() | |
| data = data.transpose(2, 0, 1) | |
| return data | |
| def plot_gst_scores_to_numpy(gst_scores, info=None): | |
| fig, ax = plt.subplots(figsize=(6, 4)) | |
| im = ax.imshow(gst_scores, aspect='auto', origin='lower', | |
| interpolation='none') | |
| fig.colorbar(im, ax=ax) | |
| xlabel = 'Validation samples' | |
| if info is not None: | |
| xlabel += '\n\n' + info | |
| plt.xlabel(xlabel) | |
| plt.ylabel('Style Tokens') | |
| plt.tight_layout() | |
| fig.canvas.draw() | |
| data = save_figure_to_numpy(fig) | |
| plt.close() | |
| data = data.transpose(2, 0, 1) | |
| return data | |
| def plot_spectrogram_to_numpy(spectrogram): | |
| fig, ax = plt.subplots(figsize=(12, 3)) | |
| im = ax.imshow(spectrogram, aspect="auto", origin="lower", | |
| interpolation='none') | |
| plt.colorbar(im, ax=ax) | |
| plt.xlabel("Frames") | |
| plt.ylabel("Channels") | |
| plt.tight_layout() | |
| fig.canvas.draw() | |
| data = save_figure_to_numpy(fig) | |
| plt.close() | |
| data = data.transpose(2, 0, 1) | |
| return data | |
| def plot_gate_outputs_to_numpy(gate_targets, gate_outputs): | |
| fig, ax = plt.subplots(figsize=(12, 3)) | |
| ax.scatter(range(len(gate_targets)), gate_targets, alpha=0.5, | |
| color='green', marker='+', s=1, label='target') | |
| ax.scatter(range(len(gate_outputs)), gate_outputs, alpha=0.5, | |
| color='red', marker='.', s=1, label='predicted') | |
| plt.xlabel("Frames (Green target, Red predicted)") | |
| plt.ylabel("Gate State") | |
| plt.tight_layout() | |
| fig.canvas.draw() | |
| data = save_figure_to_numpy(fig) | |
| plt.close() | |
| data = data.transpose(2, 0, 1) | |
| return data | |