pylint refactor (1)
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lorenzo
parent
02ac6626d6
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eeea8945fb
@ -58,6 +58,7 @@ def cli():
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# 2) Monoloco argument
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predict_parser.add_argument('--model', help='path of MonoLoco model to load',
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default="data/models/best_model__seed_2_.pickle")
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predict_parser.add_argument('--hidden_size', type=int, help='Number of hidden units in the model', default=256)
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predict_parser.add_argument('--path_gt', help='path of json file with gt 3d localization',
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default='data/arrays/names-kitti-190513-1754.json')
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predict_parser.add_argument('--transform', help='transformation for the pose', default='None')
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@ -4,7 +4,6 @@ import json
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import os
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import numpy as np
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import torch
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from openpifpaf.network import nets
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from openpifpaf import decoder, show
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@ -47,7 +46,7 @@ class ImageList(torch.utils.data.Dataset):
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return len(self.image_paths)
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def elaborate_cli(args):
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def factory_from_args(args):
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# Merge the model_pifpaf argument
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if not args.checkpoint:
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@ -77,7 +76,7 @@ def elaborate_cli(args):
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def predict(args):
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elaborate_cli(args)
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factory_from_args(args)
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# load model
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model, _ = nets.factory_from_args(args)
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@ -3,36 +3,33 @@
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From a json file output images and json annotations
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"""
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import torch
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import sys
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import numpy as np
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from collections import defaultdict
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import os
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import json
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import logging
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import time
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from models.architectures import LinearModel
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from utils.camera import preprocess_single, get_keypoints, get_depth
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from utils.misc import epistemic_variance, laplace_sampling, get_idx_max
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from visuals.printer import Printer
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from utils.normalize import unnormalize_bi
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from utils.kitti import get_simplified_calibration, get_calibration
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from utils.pifpaf import get_input_data
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import numpy as np
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import torch
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class PredictMonoLoco:
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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output_size = 2
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input_size = 17 * 2
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def __init__(self, boxes, keypoints, image_path, output_path, args):
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logging.basicConfig(level=logging.INFO)
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self.logger = logging.getLogger(__name__)
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basename, _ = os.path.splitext(os.path.basename(image_path))
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# Check for ground-truth file
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try:
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with open(args.path_gt, 'r') as f:
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self.dic_names = json.load(f)
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except FileNotFoundError:
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self.dic_names = None
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print('-' * 120)
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print("Monoloco: ground truth file not found")
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print('-' * 120)
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self.boxes = boxes
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self.keypoints = keypoints
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self.image_path = image_path
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@ -43,7 +40,6 @@ class PredictMonoLoco:
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self.z_max = args.z_max
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self.output_types = args.output_types
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self.show = args.show
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output_size = 2
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self.n_samples = 100
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self.n_dropout = args.n_dropout
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if self.n_dropout > 0:
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@ -52,43 +48,23 @@ class PredictMonoLoco:
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self.epistemic = False
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self.iou_min = 0.25
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# load the model
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input_size = 17 * 2
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# self.model = TriLinear(input_size=input_size, output_size=output_size, p_dropout=dropout)
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self.model = LinearModel(input_size=input_size, output_size=output_size)
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# load the model parameters
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self.model = LinearModel(input_size=self.input_size, output_size=self.output_size, linear_size=args.hidden_size)
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self.model.load_state_dict(torch.load(args.model, map_location=lambda storage, loc: storage))
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self.model.eval() # Default is train
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self.model.to(self.device)
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# Import
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from utils.camera import preprocess_single, get_keypoints, get_depth
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self.preprocess_single = preprocess_single
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self.get_keypoints = get_keypoints
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self.get_depth = get_depth
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from utils.misc import epistemic_variance, laplace_sampling, get_idx_max
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self.epistemic_variance = epistemic_variance
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self.laplace_sampling = laplace_sampling
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self.get_idx_max = get_idx_max
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from visuals.printer import Printer
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self.Printer = Printer
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from utils.normalize import unnormalize_bi
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self.unnormalize_bi = unnormalize_bi
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from utils.kitti import get_simplified_calibration, get_calibration
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self.get_simplified_calibration = get_simplified_calibration
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self.get_calibration = get_calibration
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from utils.pifpaf import get_input_data
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self.get_input_data = get_input_data
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# Check for ground-truth file
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try:
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with open(args.path_gt, 'r') as f:
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self.dic_names = json.load(f)
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except FileNotFoundError:
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self.dic_names = None
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print('-' * 120 + "\nMonoloco: ground truth file not found\n" + '-' * 120)
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def run(self):
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cnt = 0
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# Extract calibration matrix if ground-truth file is present or use a default one
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cnt = 0
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name = os.path.basename(self.image_path)
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if self.dic_names:
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kk = self.dic_names[name]['K']
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@ -97,7 +73,7 @@ class PredictMonoLoco:
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kk = [[718.3351, 0., 600.3891], [0., 718.3351, 181.5122], [0., 0., 1.]]
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(inputs_norm, xy_kps), (uv_kps, uv_boxes, uv_centers, uv_shoulders) = \
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self.get_input_data(self.boxes, self.keypoints, kk, left_to_right=True)
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get_input_data(self.boxes, self.keypoints, kk, left_to_right=True)
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# Conversion into torch tensor
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if len(inputs_norm) > 0:
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@ -113,8 +89,8 @@ class PredictMonoLoco:
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if self.n_dropout > 0:
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for ii in range(self.n_dropout):
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outputs = self.model(inputs)
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outputs = self.unnormalize_bi(outputs)
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samples = self.laplace_sampling(outputs, self.n_samples)
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outputs = unnormalize_bi(outputs)
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samples = laplace_sampling(outputs, self.n_samples)
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total_outputs = torch.cat((total_outputs, samples), 0)
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varss = total_outputs.std(0)
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else:
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@ -124,7 +100,7 @@ class PredictMonoLoco:
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start_single = time.time()
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self.model.dropout.training = False
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outputs = self.model(inputs)
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outputs = self.unnormalize_bi(outputs)
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outputs = unnormalize_bi(outputs)
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end = time.time()
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print("Total Forward pass time = {:.2f} ms".format((end-start) * 1000))
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print("Single pass time = {:.2f} ms".format((end - start_single) * 1000))
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@ -141,13 +117,11 @@ class PredictMonoLoco:
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# Find the corresponding ground truth if available
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if self.dic_names:
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idx_max, iou_max = self.get_idx_max(box, boxes_gt)
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idx_max, iou_max = get_idx_max(box, boxes_gt)
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if iou_max > self.iou_min:
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dd_real = dds_gt[idx_max]
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boxes_gt.pop(idx_max)
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dds_gt.pop(idx_max)
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# In case of no matching
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else:
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dd_real = 0
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@ -156,9 +130,8 @@ class PredictMonoLoco:
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dd_real = dd_pred
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uv_center = uv_centers[idx]
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xyz_real = self.get_depth(uv_center, kk, dd_real)
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xyz_pred = self.get_depth(uv_center, kk, dd_pred)
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xyz_real = get_depth(uv_center, kk, dd_real)
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xyz_pred = get_depth(uv_center, kk, dd_pred)
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dic_out['boxes'].append(box)
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dic_out['dds_real'].append(dd_real)
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dic_out['dds_pred'].append(dd_pred)
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@ -172,14 +145,12 @@ class PredictMonoLoco:
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dic_out['uv_shoulders'].append(uv_shoulders[idx])
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if any((xx in self.output_types for xx in ['front', 'bird', 'combined'])):
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printer = self.Printer(self.image_path, self.output_path, dic_out, kk,
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y_scale=self.y_scale, output_types=self.output_types,
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show=self.show, z_max=self.z_max, epistemic=self.epistemic)
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printer = Printer(self.image_path, self.output_path, dic_out, kk,
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y_scale=self.y_scale, output_types=self.output_types,
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show=self.show, z_max=self.z_max, epistemic=self.epistemic)
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printer.print()
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if 'json' in self.output_types:
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with open(os.path.join(self.output_path + '.monoloco.json'), 'w') as ff:
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json.dump(dic_out, ff)
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@ -90,12 +90,6 @@ def preprocess_single(kps, kk):
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kps_orig.append(float(kp_orig[0]))
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kps_orig.append(float(kp_orig[1]))
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# Append the y of the ground foot to the keypoints
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# kp_gr = np.array([0, vv_gr, 1])
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# xy1_gr = pixel_to_camera(kp_gr, kk, 1)
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# kps_0c.append(float(xy1_gr[1]))
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return kps_0c, kps_orig
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@ -198,5 +192,4 @@ def get_depth(uv_center, kk, dd):
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zz = dd / math.sqrt(1 + xyz_norm[0] ** 2 + xyz_norm[1] ** 2)
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xyz = pixel_to_camera(uv_center_np, kk, zz).tolist()
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return xyz
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return xyz
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