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evaluate.py
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evaluate.py
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import pickle
import numpy as np
import pprint
from urbanlanegraph_evaluator.evaluator import GraphEvaluator
from urbanlanegraph_evaluator.utils import adjust_node_positions
from aggregation.utils import visualize_graph, laplacian_smoothing, filter_graph
import matplotlib.pyplot as plt
from glob import glob
from PIL import Image
import os
city_names = [
"austin",
"detroit",
"miami",
"paloalto",
"pittsburgh",
"washington"
]
def evaluate_successor_lgp(graphs_gt, graphs_pred, split):
'''Evaluate the successor graph prediction task.'''
metric_names = ["TOPO Precision",
"TOPO Recall",
"GEO Precision",
"GEO Recall",
"APLS",
"SDA20",
"SDA50",
"Graph IoU"
]
metrics_all = {}
for city in city_names:
metrics_all[city] = {}
metrics_all[city][split] = {}
for sample_id in graphs_gt[city][split]:
metrics_all[city][split][sample_id] = {}
# print("Successor-LGP evaluating sample", sample_id)
if not sample_id in graphs_pred[city][split]:
print("No prediction for sample", sample_id)
metrics_sample = {metric_name: 0.0 for metric_name in metric_names}
else:
evaluator = GraphEvaluator()
metrics = evaluator.evaluate_graph(graphs_gt[city][split][sample_id],
graphs_pred[city][split][sample_id],
area_size=[256, 256])
metrics_sample = {
"TOPO Precision": metrics['topo_precision'],
"TOPO Recall": metrics['topo_recall'],
"GEO Precision": metrics['topo_precision'],
"GEO Recall": metrics['geo_recall'],
"APLS": metrics['apls'],
"SDA20": metrics['sda@20'],
"SDA50": metrics['sda@50'],
"Graph IoU": metrics['iou'],
}
metrics_all[city][split][sample_id].update(metrics_sample)
# Now we average over the samples
for city in city_names:
metrics_all[city][split]["avg"] = {}
for metric_name in metric_names:
metrics_all[city][split]["avg"][metric_name] = np.nanmean(
[metrics_all[city][split][sample_id][metric_name] for sample_id in graphs_gt[city][split]])
# also get the average over all cities
metrics_all[split] = {}
metrics_all[split]["avg"] = {}
for metric_name in metric_names:
metrics_all[split]["avg"][metric_name] = np.nanmean(
[metrics_all[city][split]["avg"][metric_name] for city in city_names])
return metrics_all
def evaluate_full_lgp(graphs_gt, graphs_pred, split):
metric_names = ["TOPO Precision",
"TOPO Recall",
"GEO Precision",
"GEO Recall",
"APLS",
"Graph IoU"
]
metrics_all = {}
metrics_all[split] = {}
for city in city_names:
metrics_all[split][city] = {}
for sample_id in graphs_gt[city][split]:
metrics_all[split][city][sample_id] = {}
print("Full-LGP evaluating sample", sample_id)
if graphs_pred[city][split][sample_id] is None:
print(" No prediction for sample", sample_id)
metrics_sample = {metric_name: 0.0 for metric_name in metric_names}
else:
graph_pred = graphs_pred[city][split][sample_id]
graph_gt = graphs_gt[city][split][sample_id]
# adjust node positions
x_offset = float(sample_id.split("_")[2])
y_offset = float(sample_id.split("_")[3])
graph_pred = adjust_node_positions(graph_pred, x_offset, y_offset)
graph_gt = adjust_node_positions(graph_gt, x_offset, y_offset)
evaluator = GraphEvaluator()
metrics = evaluator.evaluate_graph(graph_gt,
graph_pred,
area_size=[5000, 5000])
metrics_sample = {
"TOPO Precision": metrics['topo_precision'],
"TOPO Recall": metrics['topo_recall'],
"GEO Precision": metrics['topo_precision'],
"GEO Recall": metrics['geo_recall'],
"APLS": metrics['apls'],
"Graph IoU": metrics['iou'],
}
metrics_all[split][city][sample_id].update(metrics_sample)
# Now we average over the samples
for city in city_names:
metrics_all[split][city]["avg"] = {}
for metric_name in metric_names:
metrics_all[split][city]["avg"][metric_name] = np.nanmean(
[metrics_all[split][city][sample_id][metric_name] for sample_id in graphs_gt[city][split]])
# also get the average over all cities
metrics_all[split]["avg"] = {}
for metric_name in metric_names:
metrics_all[split]["avg"][metric_name] = np.nanmean([metrics_all[split][city]["avg"][metric_name] for city in city_names])
return metrics_all
def evaluate_planning(graphs_gt, graphs_pred, split):
metric_names = ["MMD", "MED", "SR"]
metrics_all = {}
metrics_all[split] = {}
for city in city_names:
metrics_all[split][city] = {}
for sample_id in graphs_gt[city][split]:
metrics_all[split][city][sample_id] = {}
print("Planning evaluating sample", sample_id)
if graphs_pred[city][split][sample_id] is None:
print(" No prediction for sample", sample_id)
metrics_sample = {metric_name: 0.0 for metric_name in metric_names}
else:
graph_gt = graphs_gt[city][split][sample_id]
graph_pred = graphs_pred[city][split][sample_id]
# adjust node positions
x_offset = float(sample_id.split("_")[2])
y_offset = float(sample_id.split("_")[3])
graph_pred = adjust_node_positions(graph_pred, x_offset, y_offset)
graph_gt = adjust_node_positions(graph_gt, x_offset, y_offset)
evaluator = GraphEvaluator()
paths_gt, paths_pred = evaluator.generate_paths(graph_gt, graph_pred, num_planning_paths=100)
metrics = evaluator.evaluate_paths(graph_gt, graph_pred, paths_gt, paths_pred)
metrics_sample = {
"MMD": metrics['mmd'],
"MED": metrics['med'],
"SR": metrics['sr'],
}
metrics_all[split][city][sample_id].update(metrics_sample)
# Now we average over the samples
for city in city_names:
metrics_all[split][city]["avg"] = {}
for metric_name in metric_names:
metrics_all[split][city]["avg"][metric_name] = np.nanmean(
[metrics_all[split][city][sample_id][metric_name] for sample_id in graphs_gt[city][split]])
# also get the average over all cities
metrics_all[split]["avg"] = {}
for metric_name in metric_names:
metrics_all[split]["avg"][metric_name] = np.nanmean(
[metrics_all[split][city]["avg"][metric_name] for city in city_names])
return metrics_all
def evaluate(annotation_file, user_submission_file, phase_codename, split, **kwargs):
with open(annotation_file, 'rb') as f:
graphs_gt = pickle.load(f)
with open(user_submission_file, 'rb') as f:
graphs_pred = pickle.load(f)
output = {}
if phase_codename == "phase_successor_lgp":
print("%%%%%%%%%%%%%%%%%%%%%\n%%%%%%\tEvaluating for Phase: phase_successor_lgp\n%%%%%%%%%%%%%%%%%%%%%")
out_dict = evaluate_successor_lgp(graphs_gt, graphs_pred, split)
# this goes to the leaderboard (average of all cities
metrics_successor = out_dict[split]["avg"]
output["result"] = [{"{}_split_succ".format(split): metrics_successor}]
# To display the results in the result file (all cities)
output["submission_result"] = out_dict
elif phase_codename == "phase_full_lgp":
print("%%%%%%%%%%%%%%%%%%%%%\n%%%%%%\tEvaluating for Phase: phase_full_lgp\n%%%%%%%%%%%%%%%%%%%%%")
out_dict = evaluate_full_lgp(graphs_gt, graphs_pred, split)
# the average over all cities for the eval split is this dict entry:
metrics_full = out_dict[split]["avg"]
output["result"] = [{"{}_split_full".format(split): metrics_full}]
# To display the results in the result file
output["submission_result"] = output["result"][0]
elif phase_codename == "phase_planning":
print("%%%%%%%%%%%%%%%%%%%%%\n%%%%%%\tEvaluating for Phase: phase_planning\n%%%%%%%%%%%%%%%%%%%%%")
out_dict = evaluate_planning(graphs_gt, graphs_pred, split)
# the average over all cities for the eval split is this dict entry:
metrics_planning = out_dict[split]["avg"]
output["result"] = [{"{}_split_planning".format(split): metrics_planning}]
# To display the results in the result file
output["submission_result"] = output["result"][0]
else:
raise ValueError("Unknown phase codename: {}".format(phase_codename))
return output
def evaluate_single_full_lgp(graph_gt, graph_pred):
evaluator = GraphEvaluator()
metrics = evaluator.evaluate_graph(graph_gt,
graph_pred,
area_size=[5000, 5000])
metrics_sample = {
"TOPO Precision": metrics['topo_precision'],
"TOPO Recall": metrics['topo_recall'],
"GEO Precision": metrics['topo_precision'],
"GEO Recall": metrics['geo_recall'],
"APLS": metrics['apls'],
"Graph IoU": metrics['iou'],
}
return metrics_sample
if __name__ == "__main__":
tile_ids = glob("/data/lanegraph/urbanlanegraph-dataset-dev/*/tiles/eval/*.png")
tile_ids = [os.path.basename(t).split(".")[0] for t in tile_ids]
for tile_id in tile_ids:
graph_gt = glob('/data/lanegraph/urbanlanegraph-dataset-dev/*/tiles/*/{}.gpickle'.format(tile_id))[0]
graph_pred = '/home/zuern/Desktop/autograph/G_agg/{}/G_agg_naive_all.pickle'.format(tile_id)
aerial_image = glob('/data/lanegraph/urbanlanegraph-dataset-dev/*/tiles/*/{}.png'.format(tile_id))[0]
aerial_image = Image.open(aerial_image)
aerial_image = np.array(aerial_image)
with open(graph_gt, 'rb') as f:
graph_gt = pickle.load(f)
with open(graph_pred, 'rb') as f:
graph_pred = pickle.load(f)
# adjust node positions
x_offset = float(tile_id.split("_")[2])
y_offset = float(tile_id.split("_")[3])
graph_gt = adjust_node_positions(graph_gt, x_offset, y_offset)
graph_pred = filter_graph(target=graph_gt, source=graph_pred, threshold=50)
# metrics_dict = evaluate_single_full_lgp(graph_gt, graph_pred)
# print(metrics_dict)
# fig, ax = plt.subplots(1, 3, figsize=(20, 10), sharex=True, sharey=True, dpi=300)
# ax[0].set_aspect('equal')
# ax[1].set_aspect('equal')
# ax[2].set_aspect('equal')
# visualize_graph(graph_gt, ax[0])
# visualize_graph(graph_pred, ax[1])
# visualize_graph(laplacian_smoothing(graph_pred, gamma=0.2), ax[2])
# ax[0].set_title("Ground Truth")
# ax[1].set_title("Naive")
# ax[2].set_title("Smoothed")
fig, ax = plt.subplots(dpi=600)
ax.imshow(aerial_image)
visualize_graph(laplacian_smoothing(graph_pred, gamma=0.2), ax)
plt.savefig("/home/zuern/Desktop/autograph/keep-viz/{}_pred_smoothed.svg".format(tile_id))
plt.savefig("/home/zuern/Desktop/autograph/keep-viz/{}_pred_smoothed.png".format(tile_id))
exit()
# Evaluate the submission for each task
# Task: Successor LGP, Eval Split
# results_dict = evaluate(annotation_file="annotations_successor_lgp_eval.pickle",
# user_submission_file="succ_lgp_eval_autograph.pickle",
# phase_codename="phase_successor_lgp")
# # Task: Full LGP, Eval Split
results_dict = evaluate(annotation_file="annotations_full_lgp_eval.pickle",
user_submission_file="/home/zuern/Desktop/autograph/tmp/G_agg/0011_G_agg_cvpr.pickle",
phase_codename="phase_full_lgp",
split="eval")
#
# # Task: Planning, Eval Split
# results_dict = evaluate(annotation_file="annotations_full_lgp_eval.pickle",
# user_submission_file="annotations_full_lgp_eval.pickle",
# phase_codename="phase_planning")