File size: 7,333 Bytes
eec42bd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 |
import argparse
import math
import os
os.sys.path += ['expman']
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
from matplotlib.image import imread
from matplotlib.backends.backend_pdf import PdfPages
import numpy as np
import pandas as pd
import seaborn as sns
from glob import glob
import expman
def ee(args):
sns.set_theme(context='notebook', style='whitegrid')
exps = expman.gather(args.run).filter(args.filter)
mask_metrics = exps.collect('test_pred/mask_metrics.csv').groupby('exp_id')[['dice', 'iou']].max()
flops_nparams = exps.collect('flops_nparams.csv')
data = pd.merge(mask_metrics, flops_nparams, on='exp_id')
data['dice'] *= 100
named_data = data.rename({
'nparams': '# Params',
'dice': 'mean Dice Coeff. (%)',
'conv_type': '$t$ (Conv. Type)',
'grow_factor': r'$\gamma$',
'num_filters': '$k$ (# Filters)',
'flops': 'FLOPs',
'num_stages': '$s$ (# Stages)',
}, axis=1).replace({
'bn-conv': 'conv-bn',
'sep-bn-conv': 'sep-conv-bn'
})
g = sns.relplot(data=named_data,
x='FLOPs', y='mean Dice Coeff. (%)',
hue='$t$ (Conv. Type)',
hue_order=['conv', 'conv-bn', 'sep-conv', 'sep-conv-bn'],
col='$s$ (# Stages)', style='$k$ (# Filters)', markers=True, markersize=9,
kind='line', dashes=True, facet_kws=dict(despine=False, legend_out=False), legend=True,
height=3.8, aspect=1.3, markeredgecolor='white')
b_formatter = ticker.FuncFormatter(lambda x, pos: '{:.2f}'.format(x / 10 ** 9) + 'B')
h, l = g.axes.flatten()[0].get_legend_handles_labels()
for hi in h:
hi.set_markeredgecolor('white')
g.axes.flatten()[0].legend_.remove()
g.fig.legend(h, l, ncol=2, bbox_to_anchor=(0.53 ,0.53),
fancybox=False, columnspacing=0, framealpha=1, handlelength=1.2)
for ax in g.axes.flatten():
ax.yaxis.set_minor_locator(ticker.AutoMinorLocator())
ax.set_ylim(bottom=40, top=90)
ax.set_xscale('symlog')
ax.set_xlim(left=0.04 * 10 ** 9, right=2 * 10 ** 9)
ax.xaxis.set_minor_locator(ticker.SymmetricalLogLocator(base=10, linthresh=2, subs=[1.5, 2,3,4,5,6,8]))
ax.xaxis.set_minor_formatter(b_formatter)
ax.grid(which='minor', linestyle='--', color='#eeeeee')
ax.xaxis.set_major_formatter(b_formatter)
ax.tick_params(axis="x", which="both", rotation=90)
plt.savefig(args.output, bbox_inches='tight')
def bd(args):
exps = expman.gather(args.run).filter(args.filter)
blink_metrics = exps.collect('test_pred/all_blink_roc_metrics.csv')
blink_metrics = blink_metrics.iloc[3::4].rename({'0': 'auc'}, axis=1)
aucs = blink_metrics.auc.values
print(f'{aucs.mean()} +- {aucs.std()}')
def dice_fps(args):
exps = expman.gather(args.run).filter(args.filter)
mask_metrics = exps.collect('test_pred/mask_metrics.csv')
mask_metrics = mask_metrics.groupby('exp_name').dice.max()
time_metrics = exps.collect('timings.csv')
time_metrics = time_metrics.rename({'Unnamed: 0': 'metrics', '0':'value'}, axis=1)
time_metrics = time_metrics.pivot_table(index='exp_name', columns='metrics', values='value')
flops_nparams = exps.collect('flops_nparams.csv')
flops_nparams = flops_nparams.set_index('exp_name')[['flops','nparams']]
table = pd.concat((time_metrics, mask_metrics, flops_nparams), axis=1)[['dice', 'fps', 'throughput', 'flops', 'nparams']]
table['dice'] = table.dice.map('{:.1%}'.format)
table['fps'] = table.fps.map('{:.1f}'.format)
table['throughput'] = (table.throughput*1000).map('{:.1f}ms'.format)
table['flops'] = (table.flops / 10**9).map('{:.1f}G'.format)
table['nparams'] = (table.nparams / 10**6).map('{:.2f}M'.format)
print(table)
def metrics(args):
exps = expman.gather(args.run).filter(args.filter)
mask_metrics = exps.collect('test_pred/mask_metrics.csv')
sns.lineplot(data=mask_metrics, x='thr', y='dice', hue='conv_type', size='grow_factor', style='num_filters')
plt.savefig(args.output)
def log(args):
exps = expman.gather(args.run).filter(args.filter)
with PdfPages(args.output) as pdf:
for exp_name, exp in sorted(exps.items()):
print(exp_name)
log = pd.read_csv(exp.path_to('log.csv'), index_col='epoch')
train_cols = [c for c in log.columns if 'val' not in c]
val_cols = [c for c in log.columns if 'val' in c]
test_images = glob(os.path.join(exp.path_to('test_pred'), '*_samples.png'))
fig = plt.figure(figsize=(14, 10))
fig_shape = (2, 2) if test_images else (2, 1)
ax1 = plt.subplot2grid(fig_shape, (0, 0))
ax2 = plt.subplot2grid(fig_shape, (1, 0))
log[train_cols].plot(ax=ax1)
log[val_cols].plot(ax=ax2)
ax1.legend(loc='center right', bbox_to_anchor=(-0.05, 0.5))
ax2.legend(loc='center right', bbox_to_anchor=(-0.05, 0.5))
ax2.set_ylim((0, 1))
if test_images:
test_images = sorted(test_images)
test_images = list(map(imread, test_images))
max_w = max(i.shape[1] for i in test_images)
pads = [((0,0), (0, max_w - i.shape[1]), (0, 0)) for i in test_images]
test_images = np.concatenate([np.pad(i, pad) for i, pad in zip(test_images, pads)], axis=0)
ax3 = plt.subplot2grid(fig_shape, (0, 1), rowspan=2)
ax3.imshow(test_images)
ax3.set_axis_off()
log_plot_file = exp.path_to('log_plot.pdf')
plt.suptitle(exp_name)
plt.savefig(log_plot_file, bbox_inches='tight')
pdf.savefig(fig, bbox_inches='tight')
plt.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Show stuff')
parser.add_argument('-f', '--filter', default={}, type=expman.exp_filter)
subparsers = parser.add_subparsers()
parser_log = subparsers.add_parser('log')
parser_log.add_argument('run', default='runs/')
parser_log.add_argument('-o', '--output', default='log_summary.pdf')
parser_log.set_defaults(func=log)
parser_metrics = subparsers.add_parser('metrics')
parser_metrics.add_argument('run', default='runs/')
parser_metrics.add_argument('-o', '--output', default='mask_metrics_summary.pdf')
parser_metrics.set_defaults(func=metrics)
parser_ee = subparsers.add_parser('ee')
parser_ee.add_argument('run', default='runs/')
parser_ee.add_argument('-o', '--output', default='ee_summary.pdf')
parser_ee.set_defaults(func=ee)
parser_bd = subparsers.add_parser('bd')
parser_bd.add_argument('run', default='runs/')
parser_bd.set_defaults(func=bd)
parser_dice_fps = subparsers.add_parser('dice-fps')
parser_dice_fps.add_argument('run', default='runs/')
parser_dice_fps.set_defaults(func=dice_fps)
args = parser.parse_args()
args.func(args)
|