def plot_final_scores(): ''' Plot the scores ''' font = {'size': 12} mpl.rc('font', **font) fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(7, 4)) # create figure & 1 axis outfiles = [ RESULT_DIR + 'seq2seq_teacher_imagenet_%s_iter_5000.json', RESULT_DIR + 'seq2seq_sample_imagenet_%s_iter_20000.json', RESULT_DIR + '%s_stop_agent.json', RESULT_DIR + '%s_random_agent.json' ] for split in ['val_seen']: ev = Evaluation([split]) for i, outfile in enumerate(outfiles): score_summary, scores = ev.score(outfile % split) if i == 0: method = 'Teacher-forcing' ax.hist(scores['nav_errors'], bins=range(0, 30, 3), label=method, normed=True, histtype='step', linewidth=2.5, color='C1') elif i == 1: method = 'Student-forcing' ax.hist(scores['nav_errors'], bins=range(0, 30, 3), label=method, alpha=0.7, normed=True, color='C0') elif i == 2: method = 'Start locations' ax.hist(scores['nav_errors'], bins=range(0, 30, 3), label=method, normed=True, histtype='step', linewidth=2.5, color='C3') elif i == 3: method = 'Random agent' ax.hist(scores['nav_errors'], bins=range(0, 30, 3), label=method, normed=True, histtype='step', linewidth=2.5, color='C2') ax.set_title('Val Seen Navigation Error') ax.set_xlabel('Error (m)') ax.set_ylabel('Frequency') ax.set_ylim([0, 0.14]) ax.set_xlim([0, 30]) plt.axvline(x=3, color='black', linestyle='--') legend = ax.legend(loc='upper right') plt.tight_layout() plt.savefig('%s/val_seen_error.png' % (PLOT_DIR)) plt.close(fig)
def plot_final_scores(): ''' Plot the scores ''' font = { 'size' : 12 } mpl.rc('font', **font) fig, ax = plt.subplots( nrows=1, ncols=1, figsize=(7,4) ) # create figure & 1 axis outfiles = [ RESULT_DIR + 'seq2seq_sample_imagenet_%s_iter_20000.json', RESULT_DIR + 'seq2seq_teacher_imagenet_%s_iter_5000.json', RESULT_DIR + '%s_stop_agent.json', RESULT_DIR + '%s_random_agent.json' ] for split in ['val_seen']: ev = Evaluation([split]) for i,outfile in enumerate(outfiles): score_summary,scores = ev.score(outfile % split) if i == 1: method = 'Teacher-forcing' ax.hist(scores['nav_errors'], bins=range(0,30,3), label=method, normed=True, histtype = 'step', linewidth=2.5, color='C1') elif i == 0: method = 'Student-forcing' ax.hist(scores['nav_errors'], bins=range(0,30,3), label=method, alpha=0.7, normed=True, color='C0') elif i == 2: method = 'Start locations' ax.hist(scores['nav_errors'], bins=range(0,30,3), label=method, normed=True, histtype = 'step', linewidth=2.5, color='C3') elif i == 3: method = 'Random agent' ax.hist(scores['nav_errors'], bins=range(0,30,3), label=method, normed=True, histtype = 'step', linewidth=2.5, color='C2') ax.set_title('Val Seen Navigation Error') ax.set_xlabel('Error (m)') ax.set_ylabel('Frequency') ax.set_ylim([0,0.14]) ax.set_xlim([0,30]) plt.axvline(x=3, color='black', linestyle='--') legend = ax.legend(loc='upper right') plt.tight_layout() plt.savefig('%s/val_seen_error.png' % (PLOT_DIR)) plt.close(fig)
def plot_final_scores(): """ Plot the scores """ font = {"size": 12} mpl.rc("font", **font) fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(7, 4)) # create figure & 1 axis outfiles = [ RESULT_DIR + "seq2seq_sample_imagenet_%s_iter_20000.json", RESULT_DIR + "seq2seq_teacher_imagenet_%s_iter_5000.json", RESULT_DIR + "%s_stop_agent.json", RESULT_DIR + "%s_random_agent.json", ] for split in ["val_seen"]: ev = Evaluation([split]) for i, outfile in enumerate(outfiles): score_summary, scores = ev.score(outfile % split) if i == 1: method = "Teacher-forcing" ax.hist( scores["nav_errors"], bins=list(range(0, 30, 3)), label=method, normed=True, histtype="step", linewidth=2.5, color="C1", ) elif i == 0: method = "Student-forcing" ax.hist( scores["nav_errors"], bins=list(range(0, 30, 3)), label=method, alpha=0.7, normed=True, color="C0", ) elif i == 2: method = "Start locations" ax.hist( scores["nav_errors"], bins=list(range(0, 30, 3)), label=method, normed=True, histtype="step", linewidth=2.5, color="C3", ) elif i == 3: method = "Random agent" ax.hist( scores["nav_errors"], bins=list(range(0, 30, 3)), label=method, normed=True, histtype="step", linewidth=2.5, color="C2", ) ax.set_title("Val Seen Navigation Error") ax.set_xlabel("Error (m)") ax.set_ylabel("Frequency") ax.set_ylim([0, 0.14]) ax.set_xlim([0, 30]) plt.axvline(x=3, color="black", linestyle="--") legend = ax.legend(loc="upper right") plt.tight_layout() plt.savefig("%s/val_seen_error.png" % (PLOT_DIR)) plt.close(fig)