Caffe—根据log日志绘制loss曲线和accuracy

#!/usr/bin/env python import inspect import os import random import sys import matplotlib.cm as cmx import matplotlib.colors as colors import matplotlib.pyplot as plt import matplotlib.legend as lgd import matplotlib.markers as mks def get_log_parsing_script(): dirname = os.path.dirname(os.path.abspath(inspect.getfile( inspect.currentframe()))) return dirname + '/parse_log.sh' def get_log_file_suffix(): return '.log' def get_chart_type_description_separator(): return ' vs. ' def is_x_axis_field(field): x_axis_fields = ['Iters', 'Seconds'] return field in x_axis_fields def create_field_index(): train_key = 'Train' test_key = 'Test' field_index = {train_key:{'Iters':0, 'Seconds':1, train_key + ' loss':2, train_key + ' learning rate':3}, test_key:{'Iters':0, 'Seconds':1, test_key + ' accuracy':2, test_key + ' loss':3}} fields = set() for data_file_type in field_index.keys(): fields = fields.union(set(field_index[data_file_type].keys())) fields = list(fields) fields.sort() return field_index, fields def get_supported_chart_types(): field_index, fields = create_field_index() num_fields = len(fields) supported_chart_types = [] for i in xrange(num_fields): if not is_x_axis_field(fields[i]): for j in xrange(num_fields): if i != j and is_x_axis_field(fields[j]): supported_chart_types.append('%s%s%s' % ( fields[i], get_chart_type_description_separator(), fields[j])) return supported_chart_types def get_chart_type_description(chart_type): supported_chart_types = get_supported_chart_types() chart_type_description = supported_chart_types[chart_type] return chart_type_description def get_data_file_type(chart_type): description = get_chart_type_description(chart_type) data_file_type = description.split()[0] return data_file_type def get_data_file(chart_type, path_to_log): return (os.path.basename(path_to_log) + '.' + get_data_file_type(chart_type).lower()) def get_field_descriptions(chart_type): description = get_chart_type_description(chart_type).split( get_chart_type_description_separator()) y_axis_field = description[0] x_axis_field = description[1] return x_axis_field, y_axis_field def get_field_indices(x_axis_field, y_axis_field): data_file_type = get_data_file_type(chart_type) fields = create_field_index()[0][data_file_type] return fields[x_axis_field], fields[y_axis_field] def load_data(data_file, field_idx0, field_idx1): data = [[], []] with open(data_file, 'r') as f: for line in f: line = line.strip() if line[0] != '#': line=','.join(filter(lambda x: x, line.split(' '))) print line fields = line.split(',') print fields data[0].append(float(fields[field_idx0].strip())) data[1].append(float(fields[field_idx1].strip())) return data def random_marker(): markers = mks.MarkerStyle.markers num = len(markers.values()) idx = random.randint(0, num - 1) return markers.values()[idx] def get_data_label(path_to_log): label = path_to_log[path_to_log.rfind('/')+1 : path_to_log.rfind( get_log_file_suffix())] return label def get_legend_loc(chart_type): x_axis, y_axis = get_field_descriptions(chart_type) loc = 'lower right' if y_axis.find('accuracy') != -1: pass if y_axis.find('loss') != -1 or y_axis.find('learning rate') != -1: loc = 'upper right' return loc def plot_chart(chart_type, path_to_png, path_to_log_list): for path_to_log in path_to_log_list: os.system('%s %s' % (get_log_parsing_script(), path_to_log)) data_file = get_data_file(chart_type, path_to_log) x_axis_field, y_axis_field = get_field_descriptions(chart_type) x, y = get_field_indices(x_axis_field, y_axis_field) data = load_data(data_file, x, y) ## TODO: more systematic color cycle for lines color = [random.random(), random.random(), random.random()] label = get_data_label(path_to_log) linewidth = 0.75 ## If there too many datapoints, do not use marker. ## use_marker = False use_marker = True if not use_marker: plt.plot(data[0], data[1], label = label, color = color, linewidth = linewidth) else: ok = False ## Some markers throw ValueError: Unrecognized marker style while not ok: try: marker = random_marker() plt.plot(data[0], data[1], label = label, color = color, marker = marker, linewidth = linewidth) ok = True except: pass legend_loc = get_legend_loc(chart_type) plt.legend(loc = legend_loc, ncol = 1) # ajust ncol to fit the space plt.title(get_chart_type_description(chart_type)) plt.xlabel(x_axis_field) plt.ylabel(y_axis_field) plt.savefig(path_to_png) plt.show() def print_help(): print """This script mainly serves as the basis of your customizations. Customization is a must. You can copy, paste, edit them in whatever way you want. Be warned that the fields in the training log may change in the future. You had better check the data files and change the mapping from field name to field index in create_field_index before designing your own plots. Usage: ./plot_training_log.py chart_type[0-%s] /where/to/save.png /path/to/first.log ... Notes: 1. Supporting multiple logs. 2. Log file name must end with the lower-cased "%s". Supported chart types:""" % (len(get_supported_chart_types()) - 1, get_log_file_suffix()) supported_chart_types = get_supported_chart_types() num = len(supported_chart_types) for i in xrange(num): print ' %d: %s' % (i, supported_chart_types[i]) sys.exit() def is_valid_chart_type(chart_type): return chart_type >= 0 and chart_type < len(get_supported_chart_types()) if __name__ == '__main__': if len(sys.argv) < 4: print_help() else: chart_type = int(sys.argv[1]) if not is_valid_chart_type(chart_type): print '%s is not a valid chart type.' % chart_type print_help() path_to_png = sys.argv[2] if not path_to_png.endswith('.png'): print 'Path must ends with png' % path_to_png sys.exit() path_to_logs = sys.argv[3:] for path_to_log in path_to_logs: if not os.path.exists(path_to_log): print 'Path does not exist: %s' % path_to_log sys.exit() if not path_to_log.endswith(get_log_file_suffix()): print 'Log file must end in %s.' % get_log_file_suffix() print_help() ## plot_chart accpets multiple path_to_logs plot_chart(chart_type, path_to_png, path_to_logs)