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traces.py
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traces.py
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import matplotlib.pyplot as plt
import os
import numpy as np
import time
from itertools import chain
class Trace(object):
def __init__(self, model, n, sample_fn, batches_per_epoch=None, name='traces', batch_thinout=1,
epoch_thinout=1, columns=2):
self.model = model
self.n = n
self.traces = {}
self.sample_fn = sample_fn
self.name = name
self.batch_thinout = batch_thinout
self.epoch_thinout = epoch_thinout
self.columns = columns
self.batches_per_epoch = batches_per_epoch
self.metrics_names = set()
self.weight_names = [name for name, _ in chain(self.model.named_parameters(), self.model.named_buffers())]
super(Trace, self).__init__()
def on_epoch_begin(self, epoch, logs=None):
self.epoch = epoch
def on_epoch_end(self, epoch, logs=None):
self.batches_per_epoch = self.batches_per_epoch or self.last_batch
self.add_metrics_to_traces(self.batches_per_epoch, logs)
if epoch % self.epoch_thinout == 0:
self.plot_all()
def plot_all(self, use_MPLD3=False):
for plot_fn, descr in [(self.plot_all_items, 'weights'),
(self.plot_all_metrics, 'metrics'),
(self.plot_all_samples, 'samples')
]:
try:
fig = plot_fn(self.batches_per_epoch)
filename = self.name + '/' + descr + ('.html' if use_MPLD3 else '.png')
if not os.path.exists(os.path.dirname(filename)):
try:
os.makedirs(os.path.dirname(filename))
except OSError as exc: # Guard against race condition
if exc.errno != errno.EEXIST:
raise
if use_MPLD3:
with open(filename, 'w') as file:
mpld3.save_html(fig, file)
else:
plt.savefig(filename)
plt.close(fig)
except TypeError: # in particular catch missing sample_fn
pass
def on_batch_end(self, batch, logs=None):
self.last_batch = batch
def some_weights(w, n):
sz = w.size
if sz > n:
return w.flatten()[np.round(np.linspace(0, sz - 1, n)).astype(np.int)]
else:
return w.flatten()
if batch % self.batch_thinout == 0:
for name, w in chain(self.model.named_parameters(), self.model.named_buffers()):
# w = w.detach().cpu().numpy()
w = w.data.cpu().numpy()
# w = np.random.randn(100,200)
v = some_weights(w, self.n)
item = self.traces.setdefault(name, {'values': [], 'batch': [], 'epoch': []})
item['values'].append(v)
item['batch'].append(batch)
item['epoch'].append(self.epoch)
self.add_metrics_to_traces(batch, logs)
def add_metrics_to_traces(self, batch, logs):
logs = logs or {}
for name, v in logs.items():
item = self.traces.setdefault(name, {'values': [], 'batch': [], 'epoch': []})
item['values'].append(v)
item['batch'].append(batch)
item['epoch'].append(self.epoch)
self.metrics_names = self.metrics_names.union(
{k.replace('val_', '') for k, v in logs.items() if k.startswith('val_')})
self.metrics_names = self.metrics_names.union(
{k for k, v in logs.items() if not k.startswith('val_') and k is not 'size' and k is not 'batch'})
def plot_item(self, ax, item, batches_per_epoch):
x = np.array(item['batch'], dtype='double') / batches_per_epoch + np.array(item['epoch'])
y = np.stack(item['values'])
ylim = np.percentile(y, [2.5, 97.5])
ylim = [ylim[0] - 0.1 / 0.95 * (ylim[1] - ylim[0]), ylim[1] + 0.1 / 0.95 * (ylim[1] - ylim[0])]
if ax.lines:
ylim_ = ax.get_ylim()
ylim = [min(ylim[0], ylim_[0]), max(ylim[1], ylim_[1])]
if x.shape[0] < 1000:
_ = ax.plot(x, y, '-')
ax.set_ylim(*ylim)
else:
i = np.round(np.linspace(0, x.shape[0] - 1, 1000)).astype(np.int32)
_ = ax.plot(x[i], y[i], '-')
ax.set_ylim(*ylim)
def plot_all_items(self, batches_per_epoch):
M = self.columns
N = len(self.weight_names) // M + 1
fig = plt.figure(figsize=(6 * M, 4 * N))
fig.clf()
fig.subplots(nrows=N, ncols=M)
# for i,(name,item) in enumerate(sorted(self.traces.iteritems())):
for i, name in enumerate(sorted(self.weight_names)):
try:
item = self.traces[name]
ax = plt.subplot(N, M, i + 1)
self.plot_item(ax, item, batches_per_epoch)
plt.title(name)
except:
pass
ax = plt.axes([0, 0.9, 1.0, 0.1], frameon=False)
# ax.axis('off')
ax.grid(False)
# ax.set_axis_off()
# ax.set_frame_on(False)
ax.set_xticks([])
ax.set_yticks([])
ax.text(0.5, 0.5, self.name + ' \nweight traces \n' + time.strftime("%c"), ha='center', fontsize=24)
return fig
def plot_all_metrics(self, batches_per_epoch):
M = 2
N = (len(self.metrics_names) + 1) // M
fig = plt.figure(figsize=(6 * M, 4 * N))
fig.clf()
fig.subplots(nrows=N, ncols=M)
# for i,(name,item) in enumerate(sorted(self.traces.iteritems())):
for i, name in enumerate(sorted(self.metrics_names)):
for name_ in [name, 'val_' + name]:
try:
item = self.traces[name_]
ax = plt.subplot(N, M, i + 1)
self.plot_item(ax, item, batches_per_epoch)
ax.grid(True)
# ax.set_xscale('log')
except:
pass
plt.title(name)
ax = plt.axes([0, 0.9, 1.0, 0.1], frameon=False)
# ax.axis('off')
ax.grid(False)
ax.set_xscale('log')
# ax.set_axis_off()
# ax.set_frame_on(False)
ax.set_xticks([])
ax.set_yticks([])
ax.text(0.5, 0.5, self.name + ' \nmetrics \n' + time.strftime("%c"), ha='center', fontsize=24)
return fig
def plot_all_samples(self, batches_per_epoch):
fig = self.sample_fn()
ax = plt.axes([0, 0.9, 1.0, 0.1], frameon=False)
# ax.axis('off')
ax.grid(False)
# ax.set_axis_off()
# ax.set_frame_on(False)
ax.set_xticks([])
ax.set_yticks([])
ax.text(0.5, 0.5, self.name + ' \nsamples \n' + time.strftime("%c"), ha='center', fontsize=24)
return fig