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PolarPointBEV/augment.py

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+#######################################################################################################
+# This file is borrowed from COiLTRAiNE https://github.com/felipecode/coiltraine by Felipe Codevilla  #
+# COiLTRAiNE itself is under MIT License                                                              #
+#######################################################################################################
+
+
+import imgaug as ia
+from imgaug import augmenters as iaa
+
+
+def hard(image_iteration):
+
+    iteration = image_iteration/32
+    frequency_factor = min(0.05 + float(iteration)/200000.0, 1.0)
+    color_factor = min(float(iteration)/1000000.0, 1.0)
+    dropout_factor = 0.198667 + (0.03856658 - 0.198667) / (1 + (iteration / 196416.6) ** 1.863486)
+
+    blur_factor = min(0.5 + (0.5*iteration/100000.0), 1.0)
+
+    add_factor = 10 + 10*iteration/100000.0
+
+    multiply_factor_pos = 1 + (2.5*iteration/200000.0)
+    multiply_factor_neg = 1 - (0.91 * iteration / 500000.0)
+
+    contrast_factor_pos = 1 + (0.5*iteration/500000.0)
+    contrast_factor_neg = 1 - (0.5 * iteration / 500000.0)
+
+    augmenter = iaa.Sequential([
+
+        iaa.Sometimes(frequency_factor, iaa.GaussianBlur((0, blur_factor))),
+        # blur images with a sigma between 0 and 1.5
+        iaa.Sometimes(frequency_factor, iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, dropout_factor),
+                                                                  per_channel=color_factor)),
+        # add gaussian noise to images
+        iaa.Sometimes(frequency_factor, iaa.CoarseDropout((0.0, dropout_factor), size_percent=(
+            0.08, 0.2), per_channel=color_factor)),
+        # randomly remove up to X% of the pixels
+        iaa.Sometimes(frequency_factor, iaa.Dropout((0.0, dropout_factor), per_channel=color_factor)),
+        # randomly remove up to X% of the pixels
+        iaa.Sometimes(frequency_factor,
+                      iaa.Add((-add_factor, add_factor), per_channel=color_factor)),
+        # change brightness of images (by -X to Y of original value)
+        iaa.Sometimes(frequency_factor,
+                      iaa.Multiply((multiply_factor_neg, multiply_factor_pos), per_channel=color_factor)),
+        # change brightness of images (X-Y% of original value)
+        # iaa.Sometimes(frequency_factor, iaa.ContrastNormalization((contrast_factor_neg, contrast_factor_pos),
+        #                                                                per_channel=color_factor)),
+        iaa.Sometimes(frequency_factor, iaa.contrast.LinearContrast((contrast_factor_neg, contrast_factor_pos),
+                                                                    per_channel=color_factor)),
+        # improve or worsen the contrast
+        iaa.Sometimes(frequency_factor, iaa.Grayscale((0.0, 1))),  # put grayscale
+
+    ],
+        random_order=True  # do all of the above in random order
+    )
+
+    return augmenter
+
+
+def hard_1(image_iteration):
+
+    iteration = image_iteration/32
+    frequency_factor = min(0.05 + float(iteration)/200000.0, 1.0)
+    color_factor = min(float(iteration)/1000000.0, 1.0)
+    dropout_factor = 0.198667 + (0.03856658 - 0.198667) / (1 + (iteration / 196416.6) ** 1.863486)
+
+    blur_factor = min(0.5 + (0.5*iteration/100000.0), 1.0)
+
+    add_factor = 10 + 10*iteration/100000.0
+
+    multiply_factor_pos = 1 + (2.5*iteration/200000.0)
+    multiply_factor_neg = 1 - (0.91 * iteration / 500000.0)
+
+    contrast_factor_pos = 1 + (0.5*iteration/500000.0)
+    contrast_factor_neg = 1 - (0.5 * iteration / 500000.0)
+
+    augmenter = iaa.Sequential([
+
+        iaa.Sometimes(frequency_factor, iaa.GaussianBlur((0, blur_factor))),
+        # blur images with a sigma between 0 and 1.5
+        iaa.Sometimes(frequency_factor, iaa.AdditiveGaussianNoise(loc=0, scale=(0.0, dropout_factor),
+                                                                  per_channel=color_factor)),
+        # add gaussian noise to images
+        iaa.Sometimes(frequency_factor, iaa.CoarseDropout((0.0, dropout_factor), size_percent=(
+            0.02, 0.2), per_channel=color_factor)),
+        # randomly remove up to X% of the pixels
+        iaa.Sometimes(frequency_factor, iaa.Dropout((0.0, dropout_factor), per_channel=color_factor)),
+        # randomly remove up to X% of the pixels
+        iaa.Sometimes(frequency_factor,
+                      iaa.Add((-add_factor, add_factor), per_channel=color_factor)),
+        # change brightness of images (by -X to Y of original value)
+        iaa.Sometimes(frequency_factor,
+                      iaa.Multiply((multiply_factor_neg, multiply_factor_pos), per_channel=color_factor)),
+        # change brightness of images (X-Y% of original value)
+        # iaa.Sometimes(frequency_factor, iaa.ContrastNormalization((contrast_factor_neg, contrast_factor_pos),
+        #                                                                per_channel=color_factor)),
+        iaa.Sometimes(frequency_factor, iaa.contrast.LinearContrast((contrast_factor_neg, contrast_factor_pos),
+                                                                    per_channel=color_factor)),
+        # improve or worsen the contrast
+        iaa.Sometimes(frequency_factor, iaa.Grayscale((0.0, 1))),  # put grayscale
+
+    ],
+        random_order=True  # do all of the above in random order
+    )
+
+    return augmenter

PolarPointBEV/config.py

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+import os
+
+class GlobalConfig:
+	""" base architecture configurations """
+	# Data
+	seq_len = 1 # input timesteps
+	pred_len = 4 # future waypoints predicted
+
+	# data root for pretrain
+	# root_dir_all = "tcp_carla_data"
+	# data root for training
+	root_dir_all = "/workspace/dataset/carla_data/"
+
+	train_towns = ['train_data']
+	val_towns = ['val_data']
+	train_data, val_data = [], []
+	for town in train_towns:		
+		train_data.append(os.path.join(root_dir_all, town))
+		# train_data.append(os.path.join(root_dir_all, town+'_addition'))
+	for town in val_towns:
+		val_data.append(os.path.join(root_dir_all, town))
+
+	ignore_sides = True # don't consider side cameras
+	ignore_rear = True # don't consider rear cameras
+
+	input_resolution = 256
+
+	scale = 1 # image pre-processing
+	crop = 256 # image pre-processing
+
+	lr = 1e-4 # learning rate
+
+	# Controller
+	turn_KP = 0.75
+	turn_KI = 0.75
+	turn_KD = 0.3
+	turn_n = 40 # buffer size
+
+	speed_KP = 5.0
+	speed_KI = 0.5
+	speed_KD = 1.0
+	speed_n = 40 # buffer size
+
+	max_throttle = 0.75 # upper limit on throttle signal value in dataset
+	brake_speed = 0.4 # desired speed below which brake is triggered
+	brake_ratio = 1.1 # ratio of speed to desired speed at which brake is triggered
+	clip_delta = 0.25 # maximum change in speed input to logitudinal controller
+
+
+	aim_dist = 4.0 # distance to search around for aim point
+	angle_thresh = 0.3 # outlier control detection angle
+	dist_thresh = 10 # target point y-distance for outlier filtering
+
+
+	speed_weight = 0.05
+	value_weight = 0.001
+	features_weight = 0.05
+	bev_weight = 1.0
+	graph_weight = 1.0
+
+	rl_ckpt = "/workspace/published_project/polar_BEV/roach/log/ckpt_11833344.pth"
+
+	img_aug = True
+
+
+	def __init__(self, **kwargs):
+		for k,v in kwargs.items():
+			setattr(self, k, v)