def trainingoverview(dataset, dists, filts, filtnames, pdf=None,
other_dists=None, parts_to_show=None, exp_id='default'):
# Initialize blank page
fig = plt.figure(figsize=(8.5,11), dpi=100, facecolor='w')
# Default parts to show performance results
if parts_to_show is None:
parts_to_show = []
if dataset == 'flic': parts_to_show += [('Face',['leye','reye','nose'])]
else: parts_to_show += [('Head',['head','neck']),
('Ank',['lank','rank']),
('Knee',['lkne','rkne'])]
parts_to_show += [('Sho',['lsho','rsho']),
('Elb',['lelb','relb']),
('Wri',['lwri','rwri']),
('Hip',['lhip','rhip'])]
part_labels = [parts_to_show[i][0] for i in xrange(len(parts_to_show))]
# Load training logs
log_dir = ref.posedir + '/exp/' + dataset + '/' + exp_id
train_log = np.loadtxt(log_dir + '/train.log', skiprows=1)
test_log = np.loadtxt(log_dir + '/test.log', skiprows=1)
# Plot loss
ax = fig.add_subplot(4,1,2)
plottraintest(ax, train_log, test_log)
# Setup table to hold performance numbers
ax_table = fig.add_subplot(4,1,1)
cols = ['Label'] + part_labels
rows = filtnames
if other_dists is not None:
rows += other_dists.keys()
setuptable(ax_table, len(rows)+1, len(cols), row_labels=rows, col_labels=cols)
ax_table.set_title('%s - Experiment: %s' % (dataset.upper(), exp_id), y=1.05)
# Generate PDJ charts for each part
num_cols = 4
for i,pts in enumerate(parts_to_show):
ax = fig.add_subplot(4,num_cols,i+2*num_cols+1)
setuppdjplot(ax, i, len(parts_to_show), num_cols)
for j,filt in enumerate(filts):
d, t = eval.pdjdata(dataset, dists, filt=filt, partnames=pts[1])
# Plot PDJ curve
ax.plot(t,d,label=filtnames[j])
# Display performance number in table
tabletext(ax_table, '%04.1f'%(d[-1]*100), j+1, i+1)
if other_dists is not None:
for j,k in enumerate(other_dists):
d, t = eval.pdjdata(dataset, other_dists[k][0], filt=other_dists[k][1], partnames=pts[1])
# Plot PDJ curve
ax.plot(t,d,label=k)
# Display performance number in table
tabletext(ax_table, '%04.1f'%(d[-1]*100), j+1+len(filts), i+1)
ax.set_title(pts[0], fontsize=10)
ax.set_ylim([0,1])
if i == len(parts_to_show) - 1:
ax_labels = filtnames
if other_dists is not None:
ax_labels += other_dists.keys()
ax.legend(loc='upper left', bbox_to_anchor=(1.1, 1), fontsize=11)
if pdf: pdf.savefig()
else: plt.show()
def trainingoverview(dataset, dists, filts, filtnames, pdf=None,
other_dists=None, parts_to_show=None, exp_id='default'):
# Initialize blank page
fig = plt.figure(figsize=(8.5,11), dpi=100, facecolor='w')
# Default parts to show performance results
if parts_to_show is None:
parts_to_show = []
if dataset == 'flic': parts_to_show += [('Face',['leye','reye','nose'])]
else: parts_to_show += [('Head',['head','neck']),
('Ank',['lank','rank']),
('Knee',['lkne','rkne'])]
parts_to_show += [('Sho',['lsho','rsho']),
('Elb',['lelb','relb']),
('Wri',['lwri','rwri']),
('Hip',['lhip','rhip'])]
part_labels = [parts_to_show[i][0] for i in xrange(len(parts_to_show))]
# Load training logs
log_dir = ref.posedir + '/exp/' + dataset + '/' + exp_id
train_log = np.loadtxt(log_dir + '/train.log', skiprows=1)
test_log = np.loadtxt(log_dir + '/test.log', skiprows=1)
# Plot loss
ax = fig.add_subplot(4,1,2)
plottraintest(ax, train_log, test_log)
# Setup table to hold performance numbers
ax_table = fig.add_subplot(4,1,1)
cols = ['Label'] + part_labels
rows = filtnames
if other_dists is not None:
rows += other_dists.keys()
setuptable(ax_table, len(rows)+1, len(cols), row_labels=rows, col_labels=cols)
ax_table.set_title('%s - Experiment: %s' % (dataset.upper(), exp_id), y=1.05)
# Generate PDJ charts for each part
num_cols = 4
for i,pts in enumerate(parts_to_show):
ax = fig.add_subplot(4,num_cols,i+2*num_cols+1)
setuppdjplot(ax, i, len(parts_to_show), num_cols)
for j,filt in enumerate(filts):
d, t = eval.pdjdata(dataset, dists, filt=filt, partnames=pts[1])
# Plot PDJ curve
ax.plot(t,d,label=filtnames[j])
# Display performance number in table
tabletext(ax_table, '%04.1f'%(d[-1]*100), j+1, i+1)
if other_dists is not None:
for j,k in enumerate(other_dists):
d, t = eval.pdjdata(dataset, other_dists[k][0], filt=other_dists[k][1], partnames=pts[1])
# Plot PDJ curve
ax.plot(t,d,label=k)
# Display performance number in table
tabletext(ax_table, '%04.1f'%(d[-1]*100), j+1+len(filts), i+1)
ax.set_title(pts[0], fontsize=10)
ax.set_ylim([0,1])
if i == len(parts_to_show) - 1:
ax_labels = filtnames
if other_dists is not None:
ax_labels += other_dists.keys()
ax.legend(loc='upper left', bbox_to_anchor=(1.1, 1), fontsize=11)
if pdf: pdf.savefig()
else: plt.show()
def filtercomparison(dataset, dists, filts, filtnames, pdf=None, title='Performance comparison', other_dists=None, parts_to_show=None):
# Initialize blank page
# plt.clf()
fig = plt.figure(figsize=(8.5,11), dpi=100, facecolor='w')
if parts_to_show is None:
part_labels = ref.pair_names[dataset]
parts_to_show = ref.part_pairs[dataset]
else:
part_labels = [parts_to_show[i][0] for i in xrange(len(parts_to_show))]
# Configuration of ticks in plots
major_ticks_y = np.arange(0,1.01,.2)
minor_ticks_y = np.arange(0,1.01,.1)
#-------------------------------------------------------------------
# Table with performance numbers
#-------------------------------------------------------------------
ax_table = fig.add_subplot(5,1,1)
cols = ['', '#', 'Full'] + part_labels
rows = ['Label'] + filtnames
num_samples = [len(dists) if filt is None else filt.sum() for filt in filts]
num_samples = [''] + num_samples
if other_dists is not None:
rows += other_dists.keys()
num_samples += [len(dists) if other_dists[k][1] is None else other_dists[k][1].sum()
for k in other_dists.keys()]
# Initial table set up
for r in xrange(len(rows)):
# Filter labels
ax_table.text(.1,len(rows)-.75-r,rows[r],fontsize=10,horizontalalignment='left')
# Number of samples available from each filter
ax_table.text(2.5,len(rows)-.75-r,num_samples[r],fontsize=10,horizontalalignment='center')
if r < len(rows) - 1:
thk = 1
else:
thk = 2
plt.plot([0,len(cols)+1],[r,r],color='k',linewidth=thk)
for c in xrange(1, len(cols) + 1):
ax_table.text(c+.5,len(rows)-.75,cols[c-1],fontsize=10,horizontalalignment='center')
if c > 2:
thk = 1
else:
thk = 2
if c < len(cols):
plt.plot([c+1, c+1],[0,len(rows)],color='k',linewidth=thk)
# Performance numbers get filled in as we create the PDJ charts
ax_table.set_xlim(0,len(cols)+1)
ax_table.get_xaxis().set_visible(False)
ax_table.get_yaxis().set_visible(False)
ax_table.set_title(title, y=1.05)
#-------------------------------------------------------------------
# Overall performance chart
#-------------------------------------------------------------------
ax = fig.add_subplot(5,3,4)
ax.set_yticks(major_ticks_y)
ax.set_yticks(minor_ticks_y, minor=True)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(8)
tick.label.set_visible(False)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(8)
ax.grid()
ax.grid(which='minor', alpha=0.5)
for i,filt in enumerate(filts):
d, t = eval.pdjdata(dataset, dists, filt=filt)
# Plot PDJ curve
ax.plot(t,d)
# Display performance number in table
ax_table.text(3.5, len(rows)-i-1.75, '%04.1f' % (d[-1]*100),
fontsize=10, horizontalalignment='center')
if other_dists is not None:
for i,k in enumerate(other_dists):
d, t = eval.pdjdata(dataset, other_dists[k][0], filt=other_dists[k][1])
# Plot PDJ curve
ax.plot(t,d)
# Display performance number in table
ax_table.text(3.5, len(rows)-i-len(filts)-1.75, '%04.1f' % (d[-1]*100),
fontsize=10, horizontalalignment='center')
ax.set_ylim([0,1])
# box = ax.get_position()
# ax.set_position([box.x0, box.y0, box.width * 0.9, box.height])
ax_labels = filtnames
if other_dists is not None:
ax_labels += other_dists.keys()
ax.legend(loc='center left', bbox_to_anchor=(1.1, 0.5), labels=ax_labels, fontsize=11)
ax.set_title('Overall', fontsize=10)
#-------------------------------------------------------------------
# Separate charts for each part
#-------------------------------------------------------------------
num_cols = 3
for i,pts in enumerate(parts_to_show):
ax = fig.add_subplot(5,num_cols,i+7)
setuppdjplot(ax, i, len(parts_to_show), num_cols)
for j,filt in enumerate(filts):
d, t = eval.pdjdata(dataset, dists, filt=filt, partnames=pts)
# Plot PDJ curve
ax.plot(t,d)
# Display performance number in table
ax_table.text(i+4.5, len(rows)-j-1.75, '%04.1f' % (d[-1]*100), fontsize=10, horizontalalignment='center')
if other_dists is not None:
for j,k in enumerate(other_dists):
d, t = eval.pdjdata(dataset, other_dists[k][0], filt=other_dists[k][1], partnames=pts)
# Plot PDJ curve
ax.plot(t,d)
# Display performance number in table
ax_table.text(i+4.5, len(rows)-j-1.75-len(filts), '%04.1f' % (d[-1]*100),
fontsize=10, horizontalalignment='center')
ax.set_title(part_labels[i], fontsize=10)
ax.set_ylim([0,1])
if pdf:
pdf.savefig()
else:
plt.show()
def filtercomparison(dataset, dists, filts, filtnames, pdf=None, title='Performance comparison', other_dists=None, parts_to_show=None):
# Initialize blank page
# plt.clf()
fig = plt.figure(figsize=(8.5,11), dpi=100, facecolor='w')
if parts_to_show is None:
part_labels = ref.pair_names[dataset]
parts_to_show = ref.part_pairs[dataset]
else:
part_labels = [parts_to_show[i][0] for i in xrange(len(parts_to_show))]
# Configuration of ticks in plots
major_ticks_y = np.arange(0,1.01,.2)
minor_ticks_y = np.arange(0,1.01,.1)
#-------------------------------------------------------------------
# Table with performance numbers
#-------------------------------------------------------------------
ax_table = fig.add_subplot(5,1,1)
cols = ['', '#', 'Full'] + part_labels
rows = ['Label'] + filtnames
num_samples = [len(dists) if filt is None else filt.sum() for filt in filts]
num_samples = [''] + num_samples
if other_dists is not None:
rows += other_dists.keys()
num_samples += [len(dists) if other_dists[k][1] is None else other_dists[k][1].sum()
for k in other_dists.keys()]
# Initial table set up
for r in xrange(len(rows)):
# Filter labels
ax_table.text(.1,len(rows)-.75-r,rows[r],fontsize=10,horizontalalignment='left')
# Number of samples available from each filter
ax_table.text(2.5,len(rows)-.75-r,num_samples[r],fontsize=10,horizontalalignment='center')
if r < len(rows) - 1:
thk = 1
else:
thk = 2
plt.plot([0,len(cols)+1],[r,r],color='k',linewidth=thk)
for c in xrange(1, len(cols) + 1):
ax_table.text(c+.5,len(rows)-.75,cols[c-1],fontsize=10,horizontalalignment='center')
if c > 2:
thk = 1
else:
thk = 2
if c < len(cols):
plt.plot([c+1, c+1],[0,len(rows)],color='k',linewidth=thk)
# Performance numbers get filled in as we create the PDJ charts
ax_table.set_xlim(0,len(cols)+1)
ax_table.get_xaxis().set_visible(False)
ax_table.get_yaxis().set_visible(False)
ax_table.set_title(title, y=1.05)
#-------------------------------------------------------------------
# Overall performance chart
#-------------------------------------------------------------------
ax = fig.add_subplot(5,3,4)
ax.set_yticks(major_ticks_y)
ax.set_yticks(minor_ticks_y, minor=True)
for tick in ax.xaxis.get_major_ticks():
tick.label.set_fontsize(8)
tick.label.set_visible(False)
for tick in ax.yaxis.get_major_ticks():
tick.label.set_fontsize(8)
ax.grid()
ax.grid(which='minor', alpha=0.5)
for i,filt in enumerate(filts):
d, t = eval.pdjdata(dataset, dists, filt=filt)
# Plot PDJ curve
ax.plot(t,d)
# Display performance number in table
ax_table.text(3.5, len(rows)-i-1.75, '%04.1f' % (d[-1]*100),
fontsize=10, horizontalalignment='center')
if other_dists is not None:
for i,k in enumerate(other_dists):
d, t = eval.pdjdata(dataset, other_dists[k][0], filt=other_dists[k][1])
# Plot PDJ curve
ax.plot(t,d)
# Display performance number in table
ax_table.text(3.5, len(rows)-i-len(filts)-1.75, '%04.1f' % (d[-1]*100),
fontsize=10, horizontalalignment='center')
ax.set_ylim([0,1])
# box = ax.get_position()
# ax.set_position([box.x0, box.y0, box.width * 0.9, box.height])
ax_labels = filtnames
if other_dists is not None:
ax_labels += other_dists.keys()
ax.legend(loc='center left', bbox_to_anchor=(1.1, 0.5), labels=ax_labels, fontsize=11)
ax.set_title('Overall', fontsize=10)
#-------------------------------------------------------------------
# Separate charts for each part
#-------------------------------------------------------------------
num_cols = 3
for i,pts in enumerate(parts_to_show):
ax = fig.add_subplot(5,num_cols,i+7)
setuppdjplot(ax, i, len(parts_to_show), num_cols)
for j,filt in enumerate(filts):
d, t = eval.pdjdata(dataset, dists, filt=filt, partnames=pts)
# Plot PDJ curve
ax.plot(t,d)
# Display performance number in table
ax_table.text(i+4.5, len(rows)-j-1.75, '%04.1f' % (d[-1]*100), fontsize=10, horizontalalignment='center')
if other_dists is not None:
for j,k in enumerate(other_dists):
d, t = eval.pdjdata(dataset, other_dists[k][0], filt=other_dists[k][1], partnames=pts)
# Plot PDJ curve
ax.plot(t,d)
# Display performance number in table
ax_table.text(i+4.5, len(rows)-j-1.75-len(filts), '%04.1f' % (d[-1]*100),
fontsize=10, horizontalalignment='center')
ax.set_title(part_labels[i], fontsize=10)
ax.set_ylim([0,1])
if pdf:
pdf.savefig()
else:
plt.show()
