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https://github.com/open-mmlab/mmselfsup.git
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304e81650a
* [Feature]: Add shape bias vis * [Fix]: Fix lint * [Feature]: Add shape bias metrics * [Fix]: Fix lint * [Fix]: Delete redundant code * [Feature]: Add shape bias doc * [Fix]: Fix lint * [Feature]: add UT * [Fix]: Fix lint * [Fix]: Fix typo * [Fix]: Fix typo * [Fix]: Fix args param style * [Feature]: Download pic automatically
265 lines
8.5 KiB
Python
265 lines
8.5 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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# Modified from https://github.com/bethgelab/model-vs-human
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import argparse
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import os
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import os.path as osp
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import matplotlib as mpl
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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from utils import FormatStrFormatter, ShapeBias
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# global default boundary settings for thin gray transparent
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# boundaries to avoid not being able to see the difference
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# between two partially overlapping datapoints of the same color:
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PLOTTING_EDGE_COLOR = (0.3, 0.3, 0.3, 0.3)
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PLOTTING_EDGE_WIDTH = 0.02
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ICONS_DIR = osp.join(
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osp.dirname(__file__), '..', '..', 'resources', 'shape_bias_icons')
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parser = argparse.ArgumentParser()
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parser.add_argument('--csv-dir', type=str, help='directory of csv files')
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parser.add_argument(
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'--result-dir', type=str, help='directory to save plotting results')
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parser.add_argument('--model-names', nargs='+', default=[], help='model name')
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parser.add_argument(
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'--colors',
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nargs='+',
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type=float,
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default=[],
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help= # noqa
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'the colors for the plots of each model, and they should be in the same order as model_names' # noqa: E501
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)
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parser.add_argument(
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'--markers',
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nargs='+',
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type=str,
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default=[],
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help= # noqa
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'the markers for the plots of each model, and they should be in the same order as model_names' # noqa: E501
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)
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parser.add_argument(
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'--plotting-names',
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nargs='+',
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default=[],
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help= # noqa
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'the plotting names for the plots of each model, and they should be in the same order as model_names' # noqa: E501
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)
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humans = [
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'subject-01', 'subject-02', 'subject-03', 'subject-04', 'subject-05',
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'subject-06', 'subject-07', 'subject-08', 'subject-09', 'subject-10'
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]
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def read_csvs(csv_dir: str) -> pd.DataFrame:
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"""Reads all csv files in a directory and returns a single dataframe.
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Args:
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csv_dir (str): directory of csv files.
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Returns:
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pd.DataFrame: dataframe containing all csv files
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"""
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df = pd.DataFrame()
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for csv in os.listdir(csv_dir):
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if csv.endswith('.csv'):
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cur_df = pd.read_csv(osp.join(csv_dir, csv))
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cur_df.columns = [c.lower() for c in cur_df.columns]
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df = df.append(cur_df)
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df.condition = df.condition.astype(str)
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return df
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def plot_shape_bias_matrixplot(args, analysis=ShapeBias()) -> None:
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"""Plots a matrixplot of shape bias.
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Args:
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args (argparse.Namespace): arguments.
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analysis (ShapeBias): shape bias analysis. Defaults to ShapeBias().
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"""
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mpl.rcParams['font.family'] = ['serif']
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mpl.rcParams['font.serif'] = ['Times New Roman']
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plt.figure(figsize=(9, 7))
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df = read_csvs(args.csv_dir)
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fontsize = 15
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ticklength = 10
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markersize = 250
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label_size = 20
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classes = df['category'].unique()
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num_classes = len(classes)
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# plot setup
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fig = plt.figure(1, figsize=(12, 12), dpi=300.)
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ax = plt.gca()
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ax.set_xlim([0, 1])
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ax.set_ylim([-.5, num_classes - 0.5])
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# secondary reversed x axis
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ax_top = ax.secondary_xaxis(
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'top', functions=(lambda x: 1 - x, lambda x: 1 - x))
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# labels, ticks
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plt.tick_params(
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axis='y', which='both', left=False, right=False, labelleft=False)
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ax.set_ylabel('Shape categories', labelpad=60, fontsize=label_size)
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ax.set_xlabel(
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"Fraction of 'texture' decisions", fontsize=label_size, labelpad=25)
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ax_top.set_xlabel(
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"Fraction of 'shape' decisions", fontsize=label_size, labelpad=25)
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ax.xaxis.set_major_formatter(FormatStrFormatter('%g'))
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ax_top.xaxis.set_major_formatter(FormatStrFormatter('%g'))
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ax.get_xaxis().set_ticks(np.arange(0, 1.1, 0.1))
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ax_top.set_ticks(np.arange(0, 1.1, 0.1))
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ax.tick_params(
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axis='both', which='major', labelsize=fontsize, length=ticklength)
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ax_top.tick_params(
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axis='both', which='major', labelsize=fontsize, length=ticklength)
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# arrows on x axes
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plt.arrow(
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x=0,
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y=-1.75,
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dx=1,
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dy=0,
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fc='black',
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head_width=0.4,
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head_length=0.03,
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clip_on=False,
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length_includes_head=True,
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overhang=0.5)
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plt.arrow(
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x=1,
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y=num_classes + 0.75,
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dx=-1,
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dy=0,
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fc='black',
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head_width=0.4,
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head_length=0.03,
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clip_on=False,
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length_includes_head=True,
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overhang=0.5)
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# icons besides y axis
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# determine order of icons
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df_selection = df.loc[(df['subj'].isin(humans))]
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class_avgs = []
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for cl in classes:
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df_class_selection = df_selection.query("category == '{}'".format(cl))
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class_avgs.append(1 - analysis.analysis(
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df=df_class_selection)['shape-bias'])
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sorted_indices = np.argsort(class_avgs)
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classes = classes[sorted_indices]
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# icon placement is calculated in axis coordinates
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WIDTH = 1 / num_classes
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# placement left of yaxis (-WIDTH) plus some spacing (-.25*WIDTH)
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XPOS = -1.25 * WIDTH
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YPOS = -0.5
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HEIGHT = 1
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MARGINX = 1 / 10 * WIDTH # vertical whitespace between icons
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MARGINY = 1 / 10 * HEIGHT # horizontal whitespace between icons
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left = XPOS + MARGINX
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right = XPOS + WIDTH - MARGINX
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for i in range(num_classes):
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bottom = i + MARGINY + YPOS
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top = (i + 1) - MARGINY + YPOS
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iconpath = osp.join(ICONS_DIR, '{}.png'.format(classes[i]))
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plt.imshow(
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plt.imread(iconpath),
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extent=[left, right, bottom, top],
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aspect='auto',
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clip_on=False)
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# plot horizontal intersection lines
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for i in range(num_classes - 1):
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plt.plot([0, 1], [i + .5, i + .5],
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c='gray',
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linestyle='dotted',
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alpha=0.4)
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# plot average shapebias + scatter points
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for i in range(len(args.model_names)):
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df_selection = df.loc[(df['subj'].isin(args.model_names[i]))]
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result_df = analysis.analysis(df=df_selection)
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avg = 1 - result_df['shape-bias']
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ax.plot([avg, avg], [-1, num_classes], color=args.colors[i])
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class_avgs = []
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for cl in classes:
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df_class_selection = df_selection.query(
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"category == '{}'".format(cl))
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class_avgs.append(1 - analysis.analysis(
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df=df_class_selection)['shape-bias'])
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ax.scatter(
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class_avgs,
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classes,
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color=args.colors[i],
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marker=args.markers[i],
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label=args.plotting_names[i],
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s=markersize,
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clip_on=False,
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edgecolors=PLOTTING_EDGE_COLOR,
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linewidths=PLOTTING_EDGE_WIDTH,
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zorder=3)
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plt.legend(frameon=True, labelspacing=1, loc=9)
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figure_path = osp.join(args.result_dir,
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'cue-conflict_shape-bias_matrixplot.pdf')
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fig.savefig(figure_path, bbox_inches='tight')
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plt.close()
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if __name__ == '__main__':
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icon_names = [
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'airplane.png', 'response_icons_vertical_reverse.png', 'bottle.png',
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'car.png', 'oven.png', 'elephant.png', 'dog.png', 'boat.png',
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'clock.png', 'chair.png', 'keyboard.png', 'bird.png', 'bicycle.png',
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'response_icons_horizontal.png', 'cat.png', 'bear.png', 'colorbar.pdf',
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'knife.png', 'response_icons_vertical.png', 'truck.png'
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]
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root_url = 'https://github.com/bethgelab/model-vs-human/raw/master/assets/icons' # noqa: E501
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os.makedirs(ICONS_DIR, exist_ok=True)
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for icon_name in icon_names:
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url = osp.join(root_url, icon_name)
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os.system('wget -O {} {}'.format(osp.join(ICONS_DIR, icon_name), url))
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args = parser.parse_args()
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assert len(args.model_names) * 3 == len(args.colors), 'Number of colors \
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must be 3 times the number of models. Every three colors are the RGB \
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values for one model.'
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# preprocess colors
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args.colors = [c / 255. for c in args.colors]
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colors = []
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for i in range(len(args.model_names)):
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colors.append(args.colors[3 * i:3 * i + 3])
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args.colors = colors
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args.colors.append([165 / 255., 30 / 255., 55 / 255.]) # human color
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# if plotting names are not specified, use model names
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if len(args.plotting_names) == 0:
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args.plotting_names = args.model_names
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# preprocess markers
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args.markers.append('D') # human marker
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# preprocess model names
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args.model_names = [[m] for m in args.model_names]
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args.model_names.append(humans)
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# preprocess plotting names
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args.plotting_names.append('Humans')
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plot_shape_bias_matrixplot(args)
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os.system('rm -rf {}'.format(ICONS_DIR))
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