https://github.com/shyhuai/B-Caffe
Tip revision: 1e8a70d84cbe9524a249fa907c634a0ca89c57fb authored by shyhuai on 21 January 2019, 03:28:40 UTC
merge benchmark scripts from dlbench
merge benchmark scripts from dlbench
Tip revision: 1e8a70d
common_plot.py
import re
import os
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
import scipy.spatial as spatial
def get_test_accuracy(log, top_k):
iteration = re.findall(r'Iteration (\d*), Testing net \(#0\)', log)
accuracy = re.findall(r'Test net output #\d: accuracy/top-{top_k} = (\d*.\d*)'.format(top_k=top_k), log)
if len(accuracy)==0:
accuracy = re.findall(r'Test net output #\d: top-{top_k} = (\d*.\d*)'.format(top_k=top_k), log)
if len(accuracy)==0:
accuracy = re.findall(r'Test net output #\d: loss/top-{top_k} = (\d*.\d*)'.format(top_k=top_k), log)
if len(accuracy)==0:
accuracy = re.findall(r'Test net output #\d: accuracy/top{top_k} = (\d*.\d*)'.format(top_k=top_k), log)
if len(accuracy)==0:
accuracy = re.findall(r'Test net output #\d: accuracy = (\d*.\d*)', log)
iteration = [int(i) for i in iteration]
accuracy = [float(i) for i in accuracy]
return iteration, accuracy
def get_test_loss(log):
iteration = re.findall(r'Iteration (\d*), Testing net ', log)
loss = re.findall(r'Test net output #\d: loss = (\d*.\d*)', log)
if len(loss)==0:
loss = re.findall(r'Test net output #\d: loss/loss = (\d*.\d*)', log)
iteration = [int(i) for i in iteration]
loss = [float(i) for i in loss]
return iteration, loss
def get_train_loss(log):
iteration = re.findall(r'Iteration (\d*), lr = ', log)
loss = re.findall(r'Train net output #\d: loss = (\d*.\d*)', log)
iteration = [int(i) for i in iteration]
loss = [float(i) for i in loss]
return iteration, loss
def get_epochs(log):
gpus = re.findall(r' GPU (\d*):', log)
num_gpus = len(gpus)
#print num_gpus
max_iter = re.findall(r'max_iter: (\d*)', log)
iter_size = re.findall(r'iter_size: (\d*)', log)
batch_size = re.findall(r'batch_size: (\d*)',log)
max_iter = int(max_iter[0])
if len(iter_size) >0:
iter_size=int(iter_size[0])
else:
iter_size=1
batch_size = int(batch_size[0])
# print max_iter, iter_size, batch_size
num_epochs = int(round( (max_iter * iter_size * batch_size*num_gpus) / 1281167. +0.5))
return max_iter, num_epochs
def get_net_name(log):
return re.findall(r"Solving (.*)\n", log)[0]
def parse_files(files, top_k=1, separate=False):
data = {}
for file in files:
with open(file, 'r') as fp:
log = fp.read()
net_name = os.path.basename(file) if separate else get_net_name(log)
if net_name not in data.keys():
data[net_name] = {}
data[net_name]["accuracy"] = {}
data[net_name]["accuracy"]["accuracy"] = []
data[net_name]["accuracy"]["iteration"] = []
data[net_name]["loss"] = {}
data[net_name]["loss"]["loss"] = []
data[net_name]["loss"]["iteration"] = []
data[net_name]["train_loss"] = {}
data[net_name]["train_loss"]["loss"] = []
data[net_name]["train_loss"]["iteration"] = []
max_iter, epochs = get_epochs(log)
#print epochs
scale = float(epochs) / max_iter
iteration, accuracy = get_test_accuracy(log, top_k)
iteration = [k*scale for k in iteration]
data[net_name]["accuracy"]["iteration"].extend(iteration)
data[net_name]["accuracy"]["accuracy"].extend(accuracy)
iteration, loss = get_test_loss(log)
iteration = [k*scale for k in iteration]
data[net_name]["loss"]["iteration"].extend(iteration)
data[net_name]["loss"]["loss"].extend(loss)
iteration, loss = get_train_loss(log)
iteration = [k*scale for k in iteration]
data[net_name]["train_loss"]["iteration"].extend(iteration)
data[net_name]["train_loss"]["loss"].extend(loss)
return data
def fmt(x, y):
return 'x: {x:0.2f}\ny: {y:0.2f}'.format(x=x, y=y)
class FollowDotCursor(object):
"""Display the x,y location of the nearest data point.
http://stackoverflow.com/a/4674445/190597 (Joe Kington)
http://stackoverflow.com/a/20637433/190597 (unutbu)
"""
def __init__(self, ax, x, y, formatter=fmt, offsets=(-20, 20)):
try:
x = np.asarray(x, dtype='float')
except (TypeError, ValueError):
x = np.asarray(mdates.date2num(x), dtype='float')
y = np.asarray(y, dtype='float')
mask = ~(np.isnan(x) | np.isnan(y))
x = x[mask]
y = y[mask]
self._points = np.column_stack((x, y))
self.offsets = offsets
y = y[np.abs(y - y.mean()) <= 3 * y.std()]
self.scale = x.ptp()
self.scale = y.ptp() / self.scale if self.scale else 1
self.tree = spatial.cKDTree(self.scaled(self._points))
self.formatter = formatter
self.ax = ax
self.fig = ax.figure
self.ax.xaxis.set_label_position('top')
self.dot = ax.scatter(
[x.min()], [y.min()], s=130, color='green', alpha=0.7)
self.annotation = self.setup_annotation()
plt.connect('motion_notify_event', self)
def scaled(self, points):
points = np.asarray(points)
return points * (self.scale, 1)
def __call__(self, event):
ax = self.ax
# event.inaxes is always the current axis. If you use twinx, ax could be
# a different axis.
if event.inaxes == ax:
x, y = event.xdata, event.ydata
elif event.inaxes is None:
return
else:
inv = ax.transData.inverted()
x, y = inv.transform([(event.x, event.y)]).ravel()
annotation = self.annotation
x, y = self.snap(x, y)
annotation.xy = x, y
annotation.set_text(self.formatter(x, y))
self.dot.set_offsets((x, y))
event.canvas.draw()
def setup_annotation(self):
"""Draw and hide the annotation box."""
annotation = self.ax.annotate(
'', xy=(0, 0), ha = 'right',
xytext = self.offsets, textcoords = 'offset points', va = 'bottom',
bbox = dict(
boxstyle='round,pad=0.5', fc='yellow', alpha=0.75),
arrowprops = dict(
arrowstyle='->', connectionstyle='arc3,rad=0'))
return annotation
def snap(self, x, y):
"""Return the value in self.tree closest to x, y."""
dist, idx = self.tree.query(self.scaled((x, y)), k=1, p=1)
try:
return self._points[idx]
except IndexError:
# IndexError: index out of bounds
return self._points[0]
def plot_accuracy(top_k, data, value_at_hover=False):
nets = data.keys()
colors = iter(cm.rainbow(np.linspace(0, 1, len(nets))))
fig = plt.figure()
ax = fig.add_subplot(111)
for net in nets:
iteration = data[net]["accuracy"]["iteration"]
accuracy = data[net]["accuracy"]["accuracy"]
iteration, accuracy = (np.array(t) for t in zip(*sorted(zip(iteration, accuracy))))
ax.plot(iteration, accuracy*100, color=next(colors), linestyle='-')
if value_at_hover:
cursor = FollowDotCursor(ax, iteration, accuracy*100)
plt.legend(nets, loc='lower right')
plt.title("Top {}".format(top_k))
plt.xlabel("Epochs")
plt.ylabel("Accuracy [%]")
plt.ylim(0,100)
plt.grid()
return plt
def plot_loss(data, value_at_hover=False):
nets = data.keys()
colors = iter(cm.rainbow(np.linspace(0, 1, len(nets))))
fig = plt.figure()
ax = fig.add_subplot(111)
for net in nets:
iteration = data[net]["loss"]["iteration"]
loss = data[net]["loss"]["loss"]
iteration, loss = (list(t) for t in zip(*sorted(zip(iteration, loss))))
ax.scatter(iteration, loss, color=next(colors))
if value_at_hover:
cursor = FollowDotCursor(ax, iteration, loss)
plt.legend(nets, loc='upper right')
plt.title("Log Loss")
plt.xlabel("Iteration")
plt.ylabel("Log Loss")
plt.xlim(0)
plt.grid()
return plt
def plot_train_loss(data, value_at_hover=False):
nets = data.keys()
colors = iter(cm.rainbow(np.linspace(0, 1, len(nets))))
fig = plt.figure()
ax = fig.add_subplot(111)
for net in nets:
iteration = data[net]["train_loss"]["iteration"]
loss = data[net]["train_loss"]["loss"]
iteration, loss = (list(t) for t in zip(*sorted(zip(iteration, loss))))
ax.scatter(iteration, loss, color=next(colors))
if value_at_hover:
cursor = FollowDotCursor(ax, iteration, loss)
plt.legend(nets, loc='upper right')
plt.title("Log Loss")
plt.xlabel("Iteration")
plt.ylabel("Log Loss")
plt.xlim(0)
plt.grid()
return plt