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2 changes: 1 addition & 1 deletion exec.py
Original file line number Diff line number Diff line change
Expand Up @@ -100,7 +100,7 @@ def train(logger):
model_selector.run_model_selection(net, optimizer, monitor_metrics, epoch)

# update monitoring and prediction plots
TrainingPlot.update_and_save(monitor_metrics, epoch)
TrainingPlot.update_and_save(monitor_metrics, starting_epoch, epoch)
epoch_time = time.time() - start_time
logger.info('trained epoch {}: took {} sec. ({} train / {} val)'.format(
epoch, epoch_time, train_time, epoch_time-train_time))
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8 changes: 4 additions & 4 deletions plotting.py
Original file line number Diff line number Diff line change
Expand Up @@ -180,17 +180,17 @@ def __init__(self, cf):
self.figure_list[0].ax1.set_ylim(0, 1.5)
self.color_palette = ['b', 'c', 'r', 'purple', 'm', 'y', 'k', 'tab:gray']

def update_and_save(self, metrics, epoch):
def update_and_save(self, metrics, starting_epoch, epoch):

for figure_ix in range(len(self.figure_list)):
fig = self.figure_list[figure_ix]
detection_monitoring_plot(fig.ax1, metrics, self.exp_name, self.color_palette, epoch, figure_ix,
detection_monitoring_plot(fig.ax1, metrics, starting_epoch, self.exp_name, self.color_palette, epoch, figure_ix,
self.separate_values_dict,
self.do_validation)
fig.savefig(self.file_name + '_{}'.format(figure_ix))


def detection_monitoring_plot(ax1, metrics, exp_name, color_palette, epoch, figure_ix, separate_values_dict, do_validation):
def detection_monitoring_plot(ax1, metrics, starting_epoch, exp_name, color_palette, epoch, figure_ix, separate_values_dict, do_validation):

monitor_values_keys = metrics['train']['monitor_values'][1][0].keys()
separate_values = [v for fig_ix in separate_values_dict.values() for v in fig_ix]
Expand All @@ -217,7 +217,7 @@ def detection_monitoring_plot(ax1, metrics, exp_name, color_palette, epoch, figu
if do_validation:
ax1.plot(x, y_val, label='val_{}'.format(pk), linestyle='-', color=color_palette[kix])

if epoch == 1:
if epoch == starting_epoch:
box = ax1.get_position()
ax1.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax1.legend(loc='center left', bbox_to_anchor=(1, 0.5))
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