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pylint refactor (1)

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lorenzo 2019-05-21 18:34:57 +02:00
parent 02ac6626d6
commit eeea8945fb
4 changed files with 39 additions and 75 deletions
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1
src/main.py
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@ -58,6 +58,7 @@ def cli():
# 2) Monoloco argument # 2) Monoloco argument
predict_parser.add_argument('--model', help='path of MonoLoco model to load', predict_parser.add_argument('--model', help='path of MonoLoco model to load',
default="data/models/best_model__seed_2_.pickle") default="data/models/best_model__seed_2_.pickle")
predict_parser.add_argument('--hidden_size', type=int, help='Number of hidden units in the model', default=256)
predict_parser.add_argument('--path_gt', help='path of json file with gt 3d localization', predict_parser.add_argument('--path_gt', help='path of json file with gt 3d localization',
default='data/arrays/names-kitti-190513-1754.json') default='data/arrays/names-kitti-190513-1754.json')
predict_parser.add_argument('--transform', help='transformation for the pose', default='None') predict_parser.add_argument('--transform', help='transformation for the pose', default='None')

5
src/predict/predict_2d_3d.py
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@ -4,7 +4,6 @@ import json
import os import os
import numpy as np import numpy as np
import torch
from openpifpaf.network import nets from openpifpaf.network import nets
from openpifpaf import decoder, show from openpifpaf import decoder, show
@ -47,7 +46,7 @@ class ImageList(torch.utils.data.Dataset):
return len(self.image_paths) return len(self.image_paths)
def elaborate_cli(args): def factory_from_args(args):
# Merge the model_pifpaf argument # Merge the model_pifpaf argument
if not args.checkpoint: if not args.checkpoint:
@ -77,7 +76,7 @@ def elaborate_cli(args):
def predict(args): def predict(args):
elaborate_cli(args) factory_from_args(args)
# load model # load model
model, _ = nets.factory_from_args(args) model, _ = nets.factory_from_args(args)

99
src/predict/predict_monoloco.py
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@ -3,36 +3,33 @@
From a json file output images and json annotations From a json file output images and json annotations
""" """
import torch
import sys import sys
import numpy as np
from collections import defaultdict from collections import defaultdict
import os import os
import json import json
import logging import logging
import time import time
from models.architectures import LinearModel from models.architectures import LinearModel
from utils.camera import preprocess_single, get_keypoints, get_depth
from utils.misc import epistemic_variance, laplace_sampling, get_idx_max
from visuals.printer import Printer
from utils.normalize import unnormalize_bi
from utils.kitti import get_simplified_calibration, get_calibration
from utils.pifpaf import get_input_data
import numpy as np
import torch
class PredictMonoLoco: class PredictMonoLoco:
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
output_size = 2
input_size = 17 * 2
def __init__(self, boxes, keypoints, image_path, output_path, args): def __init__(self, boxes, keypoints, image_path, output_path, args):
logging.basicConfig(level=logging.INFO)
self.logger = logging.getLogger(__name__)
basename, _ = os.path.splitext(os.path.basename(image_path))
# Check for ground-truth file
try:
with open(args.path_gt, 'r') as f:
self.dic_names = json.load(f)
except FileNotFoundError:
self.dic_names = None
print('-' * 120)
print("Monoloco: ground truth file not found")
print('-' * 120)
self.boxes = boxes self.boxes = boxes
self.keypoints = keypoints self.keypoints = keypoints
self.image_path = image_path self.image_path = image_path
@ -43,7 +40,6 @@ class PredictMonoLoco:
self.z_max = args.z_max self.z_max = args.z_max
self.output_types = args.output_types self.output_types = args.output_types
self.show = args.show self.show = args.show
output_size = 2
self.n_samples = 100 self.n_samples = 100
self.n_dropout = args.n_dropout self.n_dropout = args.n_dropout
if self.n_dropout > 0: if self.n_dropout > 0:
@ -52,43 +48,23 @@ class PredictMonoLoco:
self.epistemic = False self.epistemic = False
self.iou_min = 0.25 self.iou_min = 0.25
# load the model # load the model parameters
input_size = 17 * 2 self.model = LinearModel(input_size=self.input_size, output_size=self.output_size, linear_size=args.hidden_size)
# self.model = TriLinear(input_size=input_size, output_size=output_size, p_dropout=dropout)
self.model = LinearModel(input_size=input_size, output_size=output_size)
self.model.load_state_dict(torch.load(args.model, map_location=lambda storage, loc: storage)) self.model.load_state_dict(torch.load(args.model, map_location=lambda storage, loc: storage))
self.model.eval() # Default is train self.model.eval() # Default is train
self.model.to(self.device) self.model.to(self.device)
# Import # Check for ground-truth file
from utils.camera import preprocess_single, get_keypoints, get_depth try:
self.preprocess_single = preprocess_single with open(args.path_gt, 'r') as f:
self.get_keypoints = get_keypoints self.dic_names = json.load(f)
self.get_depth = get_depth except FileNotFoundError:
self.dic_names = None
from utils.misc import epistemic_variance, laplace_sampling, get_idx_max print('-' * 120 + "\nMonoloco: ground truth file not found\n" + '-' * 120)
self.epistemic_variance = epistemic_variance
self.laplace_sampling = laplace_sampling
self.get_idx_max = get_idx_max
from visuals.printer import Printer
self.Printer = Printer
from utils.normalize import unnormalize_bi
self.unnormalize_bi = unnormalize_bi
from utils.kitti import get_simplified_calibration, get_calibration
self.get_simplified_calibration = get_simplified_calibration
self.get_calibration = get_calibration
from utils.pifpaf import get_input_data
self.get_input_data = get_input_data
def run(self): def run(self):
cnt = 0
# Extract calibration matrix if ground-truth file is present or use a default one # Extract calibration matrix if ground-truth file is present or use a default one
cnt = 0
name = os.path.basename(self.image_path) name = os.path.basename(self.image_path)
if self.dic_names: if self.dic_names:
kk = self.dic_names[name]['K'] kk = self.dic_names[name]['K']
@ -97,7 +73,7 @@ class PredictMonoLoco:
kk = [[718.3351, 0., 600.3891], [0., 718.3351, 181.5122], [0., 0., 1.]] kk = [[718.3351, 0., 600.3891], [0., 718.3351, 181.5122], [0., 0., 1.]]
(inputs_norm, xy_kps), (uv_kps, uv_boxes, uv_centers, uv_shoulders) = \ (inputs_norm, xy_kps), (uv_kps, uv_boxes, uv_centers, uv_shoulders) = \
self.get_input_data(self.boxes, self.keypoints, kk, left_to_right=True) get_input_data(self.boxes, self.keypoints, kk, left_to_right=True)
# Conversion into torch tensor # Conversion into torch tensor
if len(inputs_norm) > 0: if len(inputs_norm) > 0:
@ -113,8 +89,8 @@ class PredictMonoLoco:
if self.n_dropout > 0: if self.n_dropout > 0:
for ii in range(self.n_dropout): for ii in range(self.n_dropout):
outputs = self.model(inputs) outputs = self.model(inputs)
outputs = self.unnormalize_bi(outputs) outputs = unnormalize_bi(outputs)
samples = self.laplace_sampling(outputs, self.n_samples) samples = laplace_sampling(outputs, self.n_samples)
total_outputs = torch.cat((total_outputs, samples), 0) total_outputs = torch.cat((total_outputs, samples), 0)
varss = total_outputs.std(0) varss = total_outputs.std(0)
else: else:
@ -124,7 +100,7 @@ class PredictMonoLoco:
start_single = time.time() start_single = time.time()
self.model.dropout.training = False self.model.dropout.training = False
outputs = self.model(inputs) outputs = self.model(inputs)
outputs = self.unnormalize_bi(outputs) outputs = unnormalize_bi(outputs)
end = time.time() end = time.time()
print("Total Forward pass time = {:.2f} ms".format((end-start) * 1000)) print("Total Forward pass time = {:.2f} ms".format((end-start) * 1000))
print("Single pass time = {:.2f} ms".format((end - start_single) * 1000)) print("Single pass time = {:.2f} ms".format((end - start_single) * 1000))
@ -141,13 +117,11 @@ class PredictMonoLoco:
# Find the corresponding ground truth if available # Find the corresponding ground truth if available
if self.dic_names: if self.dic_names:
idx_max, iou_max = get_idx_max(box, boxes_gt)
idx_max, iou_max = self.get_idx_max(box, boxes_gt)
if iou_max > self.iou_min: if iou_max > self.iou_min:
dd_real = dds_gt[idx_max] dd_real = dds_gt[idx_max]
boxes_gt.pop(idx_max) boxes_gt.pop(idx_max)
dds_gt.pop(idx_max) dds_gt.pop(idx_max)
# In case of no matching # In case of no matching
else: else:
dd_real = 0 dd_real = 0
@ -156,9 +130,8 @@ class PredictMonoLoco:
dd_real = dd_pred dd_real = dd_pred
uv_center = uv_centers[idx] uv_center = uv_centers[idx]
xyz_real = self.get_depth(uv_center, kk, dd_real) xyz_real = get_depth(uv_center, kk, dd_real)
xyz_pred = self.get_depth(uv_center, kk, dd_pred) xyz_pred = get_depth(uv_center, kk, dd_pred)
dic_out['boxes'].append(box) dic_out['boxes'].append(box)
dic_out['dds_real'].append(dd_real) dic_out['dds_real'].append(dd_real)
dic_out['dds_pred'].append(dd_pred) dic_out['dds_pred'].append(dd_pred)
@ -172,14 +145,12 @@ class PredictMonoLoco:
dic_out['uv_shoulders'].append(uv_shoulders[idx]) dic_out['uv_shoulders'].append(uv_shoulders[idx])
if any((xx in self.output_types for xx in ['front', 'bird', 'combined'])): if any((xx in self.output_types for xx in ['front', 'bird', 'combined'])):
printer = Printer(self.image_path, self.output_path, dic_out, kk,
printer = self.Printer(self.image_path, self.output_path, dic_out, kk, y_scale=self.y_scale, output_types=self.output_types,
y_scale=self.y_scale, output_types=self.output_types, show=self.show, z_max=self.z_max, epistemic=self.epistemic)
show=self.show, z_max=self.z_max, epistemic=self.epistemic)
printer.print() printer.print()
if 'json' in self.output_types: if 'json' in self.output_types:
with open(os.path.join(self.output_path + '.monoloco.json'), 'w') as ff: with open(os.path.join(self.output_path + '.monoloco.json'), 'w') as ff:
json.dump(dic_out, ff) json.dump(dic_out, ff)

7
src/utils/camera.py
View File

@ -90,12 +90,6 @@ def preprocess_single(kps, kk):
kps_orig.append(float(kp_orig[0])) kps_orig.append(float(kp_orig[0]))
kps_orig.append(float(kp_orig[1])) kps_orig.append(float(kp_orig[1]))
# Append the y of the ground foot to the keypoints
# kp_gr = np.array([0, vv_gr, 1])
# xy1_gr = pixel_to_camera(kp_gr, kk, 1)
# kps_0c.append(float(xy1_gr[1]))
return kps_0c, kps_orig return kps_0c, kps_orig
@ -198,5 +192,4 @@ def get_depth(uv_center, kk, dd):
zz = dd / math.sqrt(1 + xyz_norm[0] ** 2 + xyz_norm[1] ** 2) zz = dd / math.sqrt(1 + xyz_norm[0] ** 2 + xyz_norm[1] ** 2)
xyz = pixel_to_camera(uv_center_np, kk, zz).tolist() xyz = pixel_to_camera(uv_center_np, kk, zz).tolist()
return xyz return xyz