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* add plot * add assets/plot/model_complexity_cmp_bsrn.png * update readme * update readme * update readme * update readme
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| Original file line number | Diff line number | Diff line change |
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| import re | ||
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| def read_data_from_tensorboard(log_path, tag): | ||
| """Get raw data (steps and values) from tensorboard events. | ||
| Args: | ||
| log_path (str): Path to the tensorboard log. | ||
| tag (str): tag to be read. | ||
| """ | ||
| from tensorboard.backend.event_processing.event_accumulator import EventAccumulator | ||
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| # tensorboard event | ||
| event_acc = EventAccumulator(log_path) | ||
| event_acc.Reload() | ||
| scalar_list = event_acc.Tags()['scalars'] | ||
| print('tag list: ', scalar_list) | ||
| steps = [int(s.step) for s in event_acc.Scalars(tag)] | ||
| values = [s.value for s in event_acc.Scalars(tag)] | ||
| return steps, values | ||
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| def read_data_from_txt_2v(path, pattern, step_one=False): | ||
| """Read data from txt with 2 returned values (usually [step, value]). | ||
| Args: | ||
| path (str): path to the txt file. | ||
| pattern (str): re (regular expression) pattern. | ||
| step_one (bool): add 1 to steps. Default: False. | ||
| """ | ||
| with open(path) as f: | ||
| lines = f.readlines() | ||
| lines = [line.strip() for line in lines] | ||
| steps = [] | ||
| values = [] | ||
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| pattern = re.compile(pattern) | ||
| for line in lines: | ||
| match = pattern.match(line) | ||
| if match: | ||
| steps.append(int(match.group(1))) | ||
| values.append(float(match.group(2))) | ||
| if step_one: | ||
| steps = [v + 1 for v in steps] | ||
| return steps, values | ||
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| def read_data_from_txt_1v(path, pattern): | ||
| """Read data from txt with 1 returned values. | ||
| Args: | ||
| path (str): path to the txt file. | ||
| pattern (str): re (regular expression) pattern. | ||
| """ | ||
| with open(path) as f: | ||
| lines = f.readlines() | ||
| lines = [line.strip() for line in lines] | ||
| data = [] | ||
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| pattern = re.compile(pattern) | ||
| for line in lines: | ||
| match = pattern.match(line) | ||
| if match: | ||
| data.append(float(match.group(1))) | ||
| return data | ||
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| def smooth_data(values, smooth_weight): | ||
| """ Smooth data using 1st-order IIR low-pass filter (what tensorflow does). | ||
| Ref: https://github.com/tensorflow/tensorboard/blob/f801ebf1f9fbfe2baee1ddd65714d0bccc640fb1/\ | ||
| tensorboard/plugins/scalar/vz_line_chart/vz-line-chart.ts#L704 | ||
| Args: | ||
| values (list): A list of values to be smoothed. | ||
| smooth_weight (float): Smooth weight. | ||
| """ | ||
| values_sm = [] | ||
| last_sm_value = values[0] | ||
| for value in values: | ||
| value_sm = last_sm_value * smooth_weight + (1 - smooth_weight) * value | ||
| values_sm.append(value_sm) | ||
| last_sm_value = value_sm | ||
| return values_sm |
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| # Plot Figures | ||
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| We provide source codes for some representative figures. | ||
| You can easily modify those figures to fit your needs. | ||
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| The commonly used functions are defined in [basicsr/utils/plot_util.py](https://github.com/XPixelGroup/BasicSR/blob/plot/basicsr/utils/plot_util.py), such as `read_data_from_tensorboard`, `smooth_data`, `read_data_from_txt_2v`, *etc*. | ||
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| - [model_complexity_cmp_bsrn.py](model_complexity_cmp_bsrn.py) by Haoming Cai. [[Paper](https://openaccess.thecvf.com/content/CVPR2022W/NTIRE/papers/Li_Blueprint_Separable_Residual_Network_for_Efficient_Image_Super-Resolution_CVPRW_2022_paper.pdf)] | ||
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| <p align="center"> | ||
| <img src="../../assets/plot/model_complexity_cmp_bsrn.png" height=250> | ||
| </p> |
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| def main(): | ||
| import matplotlib.pyplot as plt | ||
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| fig, ax = plt.subplots(figsize=(15, 10)) | ||
| radius = 9.5 | ||
| notation_size = 27 | ||
| '''0 - 10''' | ||
| # BSRN-S, FSRCNN | ||
| x = [156, 13] | ||
| y = [32.16, 30.71] | ||
| area = (30) * radius**2 | ||
| ax.scatter(x, y, s=area, alpha=0.8, marker='.', c='#4D96FF', edgecolors='white', linewidths=2.0) | ||
| plt.annotate('FSRCNN', (13 + 10, 30.71 + 0.1), fontsize=notation_size) | ||
| plt.annotate('BSRN-S(Ours)', (156 - 70, 32.16 + 0.15), fontsize=notation_size) | ||
| '''10 - 25''' | ||
| # BSRN, RFDN | ||
| x = [357, 550] | ||
| y = [32.30, 32.24] | ||
| area = (75) * radius**2 | ||
| ax.scatter(x, y, s=area, alpha=1.0, marker='.', c='#FFD93D', edgecolors='white', linewidths=2.0) | ||
| plt.annotate('BSRN(Ours)', (357 - 70, 32.35 + 0.10), fontsize=notation_size) | ||
| plt.annotate('RFDN', (550 - 70, 32.24 + 0.15), fontsize=notation_size) | ||
| '''25 - 50''' | ||
| # IDN, IMDN, PAN | ||
| x = [553, 715, 272] | ||
| y = [31.82, 32.21, 32.13] | ||
| area = (140) * radius**2 | ||
| ax.scatter(x, y, s=area, alpha=0.6, marker='.', c='#95CD41', edgecolors='white', linewidths=2.0) | ||
| plt.annotate('IDN', (553 - 60, 31.82 + 0.15), fontsize=notation_size) | ||
| plt.annotate('IMDN', (715 + 10, 32.21 + 0.15), fontsize=notation_size) | ||
| plt.annotate('PAN', (272 - 70, 32.13 - 0.25), fontsize=notation_size) | ||
| '''50 - 100''' | ||
| # SRCNN, CARN, LAPAR-A | ||
| x = [57, 1592, 659] | ||
| y = [30.48, 32.13, 32.15] | ||
| area = 175 * radius**2 | ||
| ax.scatter(x, y, s=area, alpha=0.8, marker='.', c='#EAE7C6', edgecolors='white', linewidths=2.0) | ||
| plt.annotate('SRCNN', (57 + 30, 30.48 + 0.1), fontsize=notation_size) | ||
| plt.annotate('LAPAR-A', (659 - 75, 32.15 + 0.20), fontsize=notation_size) | ||
| '''1M+''' | ||
| # LapSRCN, VDSR, DRRN, MemNet | ||
| x = [502, 666, 298, 678] | ||
| y = [31.54, 31.35, 31.68, 31.74] | ||
| area = (250) * radius**2 | ||
| ax.scatter(x, y, s=area, alpha=0.3, marker='.', c='#264653', edgecolors='white', linewidths=2.0) | ||
| plt.annotate('LapSRCN', (502 - 90, 31.54 - 0.35), fontsize=notation_size) | ||
| plt.annotate('VDSR', (666 - 70, 31.35 - 0.35), fontsize=notation_size) | ||
| plt.annotate('DRRN', (298 - 65, 31.68 - 0.35), fontsize=notation_size) | ||
| plt.annotate('MemNet', (678 + 15, 31.74 + 0.18), fontsize=notation_size) | ||
| '''Ours marker''' | ||
| x = [156] | ||
| y = [32.16] | ||
| ax.scatter(x, y, alpha=1.0, marker='*', c='r', s=300) | ||
| x = [357] | ||
| y = [32.30] | ||
| ax.scatter(x, y, alpha=1.0, marker='*', c='r', s=700) | ||
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| plt.xlim(0, 800) | ||
| plt.ylim(29.75, 32.75) | ||
| plt.xlabel('Parameters (K)', fontsize=35) | ||
| plt.ylabel('PSNR (dB)', fontsize=35) | ||
| plt.title('PSNR vs. Parameters vs. Multi-Adds', fontsize=35) | ||
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| h = [ | ||
| plt.plot([], [], color=c, marker='.', ms=i, alpha=a, ls='')[0] for i, c, a in zip( | ||
| [40, 60, 80, 95, 110], ['#4D96FF', '#FFD93D', '#95CD41', '#EAE7C6', '#264653'], [0.8, 1.0, 0.6, 0.8, 0.3]) | ||
| ] | ||
| ax.legend( | ||
| labelspacing=0.1, | ||
| handles=h, | ||
| handletextpad=1.0, | ||
| markerscale=1.0, | ||
| fontsize=17, | ||
| title='Multi-Adds', | ||
| title_fontsize=25, | ||
| labels=['<10k', '10k-25k', '25k-50k', '50k-100k', '1M+'], | ||
| scatteryoffsets=[0.0], | ||
| loc='lower right', | ||
| ncol=5, | ||
| shadow=True, | ||
| handleheight=6) | ||
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| for size in ax.get_xticklabels(): # Set fontsize for x-axis | ||
| size.set_fontsize('30') | ||
| for size in ax.get_yticklabels(): # Set fontsize for y-axis | ||
| size.set_fontsize('30') | ||
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| ax.grid(b=True, linestyle='-.', linewidth=0.5) | ||
| plt.show() | ||
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| fig.savefig('model_complexity_cmp_bsrn.png') | ||
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| if __name__ == '__main__': | ||
| main() |