diff --git a/Config.py b/Config.py index c8043fb..462e34a 100644 --- a/Config.py +++ b/Config.py @@ -1,17 +1,18 @@ import json -from Parameters import ARGS +from Parameters import parse class Config(dict): - def __init__(self, init_dict=None, ARGS=ARGS, config_file_name=None): + def __init__(self, init_dict=None, parser=parse, config_file_name=None): """ Takes in command-line arguments as well as the configuration file and parses them. Parameters ----------------- :param init_dict: : a dictionary of configuration settings to use - :param ARGS: : the command-line arguments to take, structured like a dictionary :param config_file_name: : relative filepath to config file """ + + ARGS = parse() if init_dict: self.load_config(init_dict) config_file_name = './configs/' + (config_file_name or (ARGS and ARGS.config) or '') @@ -49,5 +50,3 @@ def load_config(self, kv, ARGS=None): self[key] = Config(init_dict=kv[key], ARGS=ARGS, config_file_name=None) else: self[key] = kv[key] - -config = Config() diff --git a/Parameters.py b/Parameters.py index 44daea5..c24892b 100644 --- a/Parameters.py +++ b/Parameters.py @@ -2,66 +2,69 @@ import argparse # default python library for command line argument parsing import os -parser = argparse.ArgumentParser( # pylint: disable=invalid-name - description='Train DNNs on model car data.', - formatter_class=argparse.ArgumentDefaultsHelpFormatter) +def parse(): + parser = argparse.ArgumentParser( # pylint: disable=invalid-name + description='Train DNNs on model car data.', + formatter_class=argparse.ArgumentDefaultsHelpFormatter) -parser.add_argument('--gpu', default=-1, type=int, help='Cuda GPU ID') -parser.add_argument('--epoch', default=0, type=int, help='Cuda GPU ID') -parser.add_argument('--batch-size', default=100, type=int) -parser.add_argument('--display', dest='display', action='store_true') -parser.add_argument('--no-display', dest='display', action='store_false') -parser.set_defaults(display=True) + parser.add_argument('--gpu', default=-1, type=int, help='Cuda GPU ID') + parser.add_argument('--epoch', default=0, type=int, help='Cuda GPU ID') + parser.add_argument('--batch-size', default=100, type=int) + parser.add_argument('--display', dest='display', action='store_true') + parser.add_argument('--no-display', dest='display', action='store_false') + parser.set_defaults(display=True) -parser.add_argument('--verbose', default=True, type=bool, help='Debugging mode') -parser.add_argument('--aruco', default=True, type=bool, help='Use Aruco data') -parser.add_argument('--data-path', default='/hostroot/home/dataset/' + - 'bair_car_data', type=str) -parser.add_argument('--resume-path', default=None, type=str, help='Path to' + - ' resume file containing network state dictionary') -parser.add_argument('--bkup', default=None, type=str, help='Path to' + - ' resume file containing network state dictionary') -parser.add_argument('--save-path', default='save', type=str, help='Path to' + - ' folder to save net state dictionaries.') + parser.add_argument('--verbose', default=True, type=bool, help='Debugging mode') + parser.add_argument('--aruco', default=True, type=bool, help='Use Aruco data') + parser.add_argument('--data-path', default='/hostroot/home/dataset/' + + 'bair_car_data', type=str) + parser.add_argument('--resume-path', default=None, type=str, help='Path to' + + ' resume file containing network state dictionary') + parser.add_argument('--bkup', default=None, type=str, help='Path to' + + ' resume file containing network state dictionary') + parser.add_argument('--save-path', default='save', type=str, help='Path to' + + ' folder to save net state dictionaries.') # nargs='+' allows for multiple arguments and stores arguments in a list -parser.add_argument( - '--ignore', - default=( - 'reject_run', - 'left', - 'out1_in2', - 'play', - 'Smyth', - 'racing'), - type=str, - nargs='+', - help='Skips these labels in data.') + parser.add_argument( + '--ignore', + default=( + 'reject_run', + 'left', + 'out1_in2', + 'play', + 'Smyth', + 'racing'), + type=str, + nargs='+', + help='Skips these labels in data.') -parser.add_argument('--require-one', default=(), type=str, nargs='+', - help='Skips data without these labels in data.') -parser.add_argument('--use-states', default=(1, 3, 5, 6, 7), type=str, - nargs='+', help='Skips data outside of these states.') + parser.add_argument('--require-one', default=(), type=str, nargs='+', + help='Skips data without these labels in data.') + parser.add_argument('--use-states', default=(1, 3, 5, 6, 7), type=str, + nargs='+', help='Skips data outside of these states.') -parser.add_argument('--nframes', default=8, type=int, - help='# timesteps of camera input') -parser.add_argument('--nsteps', default=10, type=int, - help='# of steps of time to predict in the future') -parser.add_argument('--stride', default=10, type=int, - help="number of timesteps between network predictions") -parser.add_argument('--log-interval', default=1, type=int, - help="Number of batches in between logging") + parser.add_argument('--nframes', default=8, type=int, + help='# timesteps of camera input') + parser.add_argument('--nsteps', default=10, type=int, + help='# of steps of time to predict in the future') + parser.add_argument('--stride', default=10, type=int, + help="number of timesteps between network predictions") + parser.add_argument('--log-interval', default=1, type=int, + help="Number of batches in between logging") -parser.add_argument('--print-moments', default=1000, type=int, - help='# of moments between printing stats') -parser.add_argument('--save-moments', default=400000, type=int, - help='# of moments between printing stats') + parser.add_argument('--print-moments', default=1000, type=int, + help='# of moments between printing stats') + parser.add_argument('--save-moments', default=400000, type=int, + help='# of moments between printing stats') -parser.add_argument('--config', default='default.json', type=str, help='Path of config file to use in ' + - 'experiment from ./configs') + parser.add_argument('--config', default='default.json', type=str, help='Path of config file to use in ' + + 'experiment from ./configs') -ARGS = parser.parse_args() + ARGS = parser.parse_args() # Check for $DISPLAY being blank -if 'DISPLAY' not in os.environ: - ARGS.display = False + if 'DISPLAY' not in os.environ: + ARGS.display = False + + return ARGS diff --git a/Train.py b/Train.py index 7b57c97..9127558 100644 --- a/Train.py +++ b/Train.py @@ -6,7 +6,8 @@ import os import importlib -from Config import config +from Config import Config + from Dataset import Dataset import Utils @@ -14,11 +15,9 @@ from torch.autograd import Variable import torch.nn.utils as nnutils import torch -Net = importlib.import_module(config['model']['py_path']).Net def iterate(net, loss_func, optimizer=None, input=None, truth=None, mask=None, train=True): - """ - Encapsulates a training or validation iteration. + """Encapsulates a training or validation iteration. :param net: : network to train :param optimizer: : optimizer to use @@ -52,6 +51,8 @@ def iterate(net, loss_func, optimizer=None, input=None, truth=None, mask=None, t return loss.cpu().data[0] def main(): + """Main training loop""" + # Configure logging logging.basicConfig(filename=config['logging']['path'], level=logging.DEBUG) logging.debug(config) @@ -169,4 +170,6 @@ def main(): sys.exit(1) if __name__ == '__main__': + config = Config() + Net = importlib.import_module(config['model']['py_path']).Net main() diff --git a/Utils.py b/Utils.py index 8fed947..18d9e21 100644 --- a/Utils.py +++ b/Utils.py @@ -2,7 +2,6 @@ import os import operator import time -from Parameters import ARGS import matplotlib.pyplot as plt import numpy as np import torch diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..009455b --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = sphinx-build +SPHINXPROJ = BDDModelCarTraining +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: 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When it is not found, a full rebuild will be done. +config: e75c1114160e13dcb17c687f7c4ed2cf +tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/_build/html/.nojekyll b/docs/_build/html/.nojekyll new file mode 100644 index 0000000..e69de29 diff --git a/docs/_build/html/_modules/index.html b/docs/_build/html/_modules/index.html new file mode 100644 index 0000000..4ed7b61 --- /dev/null +++ b/docs/_build/html/_modules/index.html @@ -0,0 +1,244 @@ + + + + + + + + + + + Overview: module code — BDD Model Car Training documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + +
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+ + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
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  • Overview: module code
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All modules for which code is available

+ + +
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+ +
+

+ © Copyright 2017, Sauhaarda Chowdhuri. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
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+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/training/ArucoDataset.html b/docs/_build/html/_modules/training/ArucoDataset.html new file mode 100644 index 0000000..517a096 --- /dev/null +++ b/docs/_build/html/_modules/training/ArucoDataset.html @@ -0,0 +1,418 @@ + + + + + + + + + + + training.ArucoDataset — BDD Model Car Training documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +

Source code for training.ArucoDataset

+import numpy as np
+import time
+import h5py
+import torch
+import torch.utils.data as data
+import sys
+from random import shuffle
+import os
+import matplotlib.pyplot as plt
+
+
+
[docs]class ArucoDataset(data.Dataset):
+    def __init__(self, data_folder_dir, require_one, ignore_list, stride=10, max_len=-1):
+        self.max_len = max_len
+        self.runs = os.walk(os.path.join(data_folder_dir, 'processed_h5py')).next()[1]
+        shuffle(self.runs)  # shuffle each epoch to allow shuffle False
+        self.run_files = []
+
+        # Initialize List of Files
+        self.invisible = []
+        self.visible = []
+        self.total_length = 0 
+        self.full_length = 0 
+
+        run_num = 0
+        for run in self.runs:
+            run_num += 1
+            segs_in_run = os.walk(os.path.join(data_folder_dir, 'processed_h5py', run)).next()[1]
+            shuffle(segs_in_run)  # shuffle on each epoch to allow shuffle False
+
+            run_labels = h5py.File(
+                os.path.join(data_folder_dir,
+                            'processed_h5py',
+                             run,
+                             'run_labels.h5py'),
+                'r')
+
+            # Ignore invalid runs
+            ignored = False
+            for ignore in ignore_list:
+                if ignore in run_labels and run_labels[ignore][0]:
+                    ignored = True
+                    break
+            if ignored:
+                continue
+
+            ignored = len(require_one) > 0 
+            for require in require_one:
+                if require in run_labels and run_labels[require][0]:
+                    ignored = False
+                    break
+            if ignored:
+                continue
+
+            print 'Loading Run {}/{}'.format(run_num, len(self.runs))
+            for seg in segs_in_run:
+                images = h5py.File(
+                    os.path.join(
+                        data_folder_dir,
+                        'processed_h5py',
+                        run,
+                        seg,
+                        'images.h5py'),
+                    'r')
+
+                metadata = h5py.File(
+                    os.path.join(data_folder_dir,
+                        'processed_h5py',
+                         run,
+                         seg,
+                         'metadata.h5py'),
+                    'r')
+
+
+                length = len(images['left'])
+                self.run_files.append({'images': images, 'metadata': metadata, 'run_labels' : run_labels})
+
+                self.visible.append(self.total_length)  # visible indicies
+
+                # invisible is not actually used at all, but is extremely useful
+                # for debugging indexing problems and gives very little slowdown
+                self.invisible.append(self.full_length + 7) # actual indicies mapped
+
+                self.total_length += 4 * (length - 7)
+                self.full_length += length
+
+        # Create row gradient
+        self.row_gradient = torch.FloatTensor(94, 168)
+        for row in range(94):
+            self.row_gradient[row, :] = row / 93.
+
+        # Create col gradient
+        self.col_gradient = torch.FloatTensor(94, 168)
+        for col in range(168):
+            self.col_gradient[:, col] = col / 167.
+
+        self.stride = stride
+        self.aruco_idx_to_key = ['cwdirect', 'ccwdirect', 'cwfollow', 'ccwfollow']
+
+    def __getitem__(self, index):
+        run_idx, t = self.create_map(index)
+        camera_t = t // 4
+        aruco_idx = t % 4
+        aruco_key = self.aruco_idx_to_key[aruco_idx]
+
+        list_camera_input = []
+
+        list_camera_input.append(
+            torch.from_numpy(
+                self.run_files[
+                    run_idx]['images']['left'][camera_t - 7]))
+
+        for delta_time in range(6, -1, -1):
+            list_camera_input.append(
+                torch.from_numpy(
+                    self.run_files[
+                        run_idx]['images']['left'][camera_t - delta_time,:,:,1:2]))
+
+        list_camera_input.append(
+            torch.from_numpy(
+                self.run_files[
+                    run_idx]['images']['right'][camera_t - 1,:,:,1:2]))
+
+        list_camera_input.append(
+            torch.from_numpy(
+                self.run_files[
+                    run_idx]['images']['right'][camera_t,:,:,1:2]))
+
+        camera_data = torch.cat(list_camera_input, 2)
+        camera_data = camera_data.float() / 255. - 0.5
+        camera_data = torch.transpose(camera_data, 0, 2)
+        camera_data = torch.transpose(camera_data, 1, 2)
+
+        final_camera_data = torch.FloatTensor(14, 94, 168)
+        final_camera_data[0:12, :, :] = camera_data
+        final_camera_data[12, :, :] = self.row_gradient
+        final_camera_data[13, :, :] = self.col_gradient
+
+        # Get behavioral mode
+        metadata_raw = self.run_files[run_idx]['run_labels']
+        metadata = torch.FloatTensor(20, 11, 20)
+        metadata[:] = 0.
+
+        if aruco_idx < 2: # Direct
+            metadata[2, :, :] = 1.
+        else:  # Follow
+            metadata[1, :, :] = 1.
+        if aruco_idx % 2 == 0:  # Clockwise
+            metadata[5, :, :] = 1.
+        else:  # Counterclockwise
+            metadata[6, :, :] = 1.
+
+        # Get Ground Truth
+        steer = []
+        motor = []
+
+        steer.append(float(self.run_files[run_idx]['metadata'][aruco_key][0]))
+        for i in range(0, self.stride * 9, self.stride):
+            steer.append(0.)
+
+        motor.append(float(self.run_files[run_idx]['metadata']['motor'][0]))
+        for i in range(0, self.stride * 29, self.stride):
+            motor.append(0.)
+
+        final_ground_truth = torch.FloatTensor(steer + motor) / 99.
+
+        mask = torch.FloatTensor([1, 0, 0, 0, 0, 0, 0, 0, 0, 0,  # ONLY VALIDATE ON ONE STEERING AND MOTOR
+                                  1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                                  0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+                                  0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
+
+        return final_camera_data, metadata, final_ground_truth, mask
+
+    def __len__(self):
+        if self.max_len == -1:
+            return self.total_length
+        return min(self.total_length, self.max_len)
+
+
[docs]    def create_map(self, global_index):
+        for idx, length in enumerate(self.visible[::-1]):
+            if global_index >= length:
+                return len(self.visible) - idx - 1, global_index - length + 7

+
+if __name__ == '__main__':
+    train_dataset = Dataset('/hostroot/data/dataset/bair_car_data_new_28April2017', [], [])
+    train_data_loader = torch.utils.data.DataLoader(train_dataset,
+                                                    batch_size=500,
+                                                    shuffle=False, pin_memory=False)
+    start = time.time()
+    for cam, meta, truth, mask in train_data_loader:
+        cur = time.time()
+        print(500./(cur - start))
+        start = cur
+        pass
+
+ +
+
+ +
+
+
+ + +
+ +
+

+ © Copyright 2017, Sauhaarda Chowdhuri. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/training/Config.html b/docs/_build/html/_modules/training/Config.html new file mode 100644 index 0000000..31d2eb3 --- /dev/null +++ b/docs/_build/html/_modules/training/Config.html @@ -0,0 +1,276 @@ + + + + + + + + + + + training.Config — BDD Model Car Training documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + +
+
+ + + + + + + + + + + + + + + + +
+ + + + +
+
+
+
+ +

Source code for training.Config

+import json
+from Parameters import parse
+
+
[docs]class Config(dict):
+    def __init__(self, init_dict=None, parser=parse, config_file_name=None):
+        """
+        Takes in command-line arguments as well as the configuration file and parses them.
+
+        Parameters
+        -----------------
+        :param init_dict: <dict>: a dictionary of configuration settings to use
+        :param config_file_name: <str>: relative filepath to config file
+        """
+
+        ARGS = parse()
+        if init_dict:
+            self.load_config(init_dict)
+        config_file_name = './configs/' + (config_file_name or (ARGS and ARGS.config) or '')
+        config_file_name = config_file_name.replace('configs/configs', 'configs')
+        if ARGS:
+            ARGS.config = ''
+        if config_file_name != './configs/':
+            self.load_helper(config_file_name, ARGS)
+
+
[docs]    def load_helper(self, path=None, ARGS=None):
+        if path is None:
+            base_config = json.load(open('./configs/default.json', 'r'))
+            self.load_config(base_config)
+        else:
+            config_dict = json.load(open(path, 'r'))
+            if config_dict['parent_config']:
+                self.load_helper(('./configs/' + config_dict['parent_config'])
+                                 .replace('./configs/configs/', './configs/'))
+            self.load_config(config_dict, ARGS)

+
+
[docs]    def load_config(self, kv, ARGS=None):
+        """
+        Loads in a configuration from a dictionary
+
+        :param kv: <key-val>: a key-val data structure
+        :return: None
+        """
+        for key in kv:
+            if key == 'gpu' and ARGS and ARGS.gpu != -1:
+                self['gpu'] = ARGS
+            if type(kv[key]) is dict:
+                if key in self:
+                    self[key].load_config(kv[key])
+                else:
+                    self[key] = Config(init_dict=kv[key], ARGS=ARGS, config_file_name=None)
+            else:
+             self[key] = kv[key]

+
+ +
+
+ +
+
+
+ + +
+ +
+

+ © Copyright 2017, Sauhaarda Chowdhuri. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/training/Dataset.html b/docs/_build/html/_modules/training/Dataset.html new file mode 100644 index 0000000..0f5cd2b --- /dev/null +++ b/docs/_build/html/_modules/training/Dataset.html @@ -0,0 +1,457 @@ + + + + + + + + + + + training.Dataset — BDD Model Car Training documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + +
+
+ + + + + + + + + + + + + + + + +
+ + + + +
+
+
+
+ +

Source code for training.Dataset

+import numpy as np
+import time
+import h5py
+import torch
+import torch.utils.data as data
+import sys
+from random import shuffle
+import os
+import matplotlib.pyplot as plt
+from random import shuffle
+import random
+
+
+
+
[docs]class Dataset(data.Dataset):
+
+    def __init__(self, data_folder_dir, require_one=[], ignore_list=[], stride=10, max_len=-1,
+                 train_ratio=0.9, seed=None, nframes=2, nsteps=10, separate_frames=False,
+                 metadata_shape=[], p_exclude_run=0.):
+        self.max_len = max_len
+        self.runs = os.walk(os.path.join(data_folder_dir, 'processed_h5py'), followlinks=True).next()[1]
+        self.run_files = []
+
+        # Initialize List of Files
+        self.invisible = []
+        self.visible = []
+        self.total_length = 0 
+        self.full_length = 0
+
+        self.train_part = None
+        self.val_part = None
+
+        self.separate_frames = separate_frames
+
+        self.train_ratio = train_ratio
+
+        self.nframes = nframes
+        self.nsteps = nsteps
+
+        self.metadata_shape = metadata_shape
+
+        random.seed(seed * 2)
+
+        for run in self.runs:
+            segs_in_run = os.walk(os.path.join(data_folder_dir, 'processed_h5py', run), followlinks=True).next()[1]
+
+            run_labels = None
+            try:
+                run_labels = h5py.File(
+                    os.path.join(data_folder_dir, 'processed_h5py', run, 'run_labels.h5py'),
+                    'r')
+            except Exception:
+                continue
+
+            if random.random() < p_exclude_run:
+                continue
+
+            # Ignore invalid runs
+            ignored = False
+            for ignore in ignore_list:
+                if ignore in run_labels and run_labels[ignore][0]:
+                    ignored = True
+                    break
+            if ignored:
+                continue
+
+            ignored = len(require_one) > 0 
+            for require in require_one:
+                if require in run_labels and run_labels[require][0]:
+                    ignored = False
+                    break
+            if ignored:
+                continue
+
+            print 'Loading Run ' + run
+            for seg in segs_in_run:
+                images = h5py.File(
+                    os.path.join(
+                        data_folder_dir,
+                        'processed_h5py',
+                        run,
+                        seg,
+                        'images.h5py'),
+                    'r')
+
+                metadata = h5py.File(
+                    os.path.join(data_folder_dir,
+                        'processed_h5py',
+                         run,
+                         seg,
+                         'metadata.h5py'),
+                    'r')
+
+                length = len(images['left'])
+
+                self.run_files.append({'images': images, 'metadata': metadata, 'run_labels' : run_labels})
+                self.visible.append(self.total_length)  # visible indicies
+
+                # invisible is not actually used at all, but is extremely useful
+                # for debugging indexing problems and gives very little slowdown
+                self.invisible.append(self.full_length + 7) # actual indicies mapped
+
+                self.total_length += (length - (self.nsteps * stride - 1) - 7)
+                self.full_length += length
+
+        # Create row gradient
+        self.row_gradient = torch.FloatTensor(94, 168)
+        for row in range(94):
+            self.row_gradient[row, :] = row / 93.
+
+        # Create col gradient
+        self.col_gradient = torch.FloatTensor(94, 168)
+        for col in range(168):
+            self.col_gradient[:, col] = col / 167.
+
+        self.stride = stride
+
+        self.seed = seed or self.total_length
+        self.subsampled_train_part = None
+
+    def __getitem__(self, index):
+        run_idx, t = self.create_map(index)
+
+        data_file = self.run_files[run_idx]['images']
+        metadata_file = self.run_files[run_idx]['metadata']
+
+        list_camera_input = []
+
+        for t in range(self.nframes):
+            for camera in ('left', 'right'):
+                list_camera_input.append(torch.from_numpy(data_file[camera][t]))
+                camera_data = torch.cat(list_camera_input, 2)
+                camera_data = camera_data.cuda().float() / 255. - 0.5
+                camera_data = torch.transpose(camera_data, 0, 2)
+                camera_data = torch.transpose(camera_data, 1, 2)
+
+        # Get behavioral mode
+        metadata_raw = self.run_files[run_idx]['run_labels']
+
+        metadata = torch.FloatTensor(self.nframes, 64, 23, 41)
+        metadata = torch.FloatTensor(*self.metadata_shape)
+
+        metadata[:] = 0.
+        for label_idx, cur_label in enumerate(['racing', 'follow', 'direct', 'play', 'furtive', 'clockwise', 'counterclockwise']):
+            if self.separate_frames:
+                metadata[:, label_idx, :, :] = int(cur_label in metadata_raw and metadata_raw[cur_label][0])
+            else:
+                metadata[label_idx, :, :] = int(cur_label in metadata_raw and metadata_raw[cur_label][0])
+
+        # Get Ground Truth
+        steer = []
+        motor = []
+
+        for i in range(0, self.stride * self.nsteps, self.stride):
+            steer.append(float(self.run_files[run_idx]['metadata']['steer'][t + i]))
+        for i in range(0, self.stride * self.nsteps, self.stride):
+            motor.append(float(self.run_files[run_idx]['metadata']['motor'][t + i]))
+        for i in range(0, self.stride * self.nsteps * 2, self.stride):
+            motor.append(0.)
+
+        final_ground_truth = torch.FloatTensor(steer + motor) / 99.
+
+        mask = torch.FloatTensor([1] * (2 * self.nsteps) + # use all data
+                                [0] * (2 * self.nsteps)) # no mask
+
+        return camera_data, metadata, final_ground_truth, mask
+
+    def __len__(self):
+        if self.max_len == -1:
+            return self.total_length
+        return min(self.total_length, self.max_len)
+
+
[docs]    def train_len(self):
+        return len(self.train_part)

+
+
[docs]    def val_len(self):
+        return len(self.val_part)

+
+
[docs]    def get_train_partition(self):
+        if self.train_part:
+            return self.train_part
+        else:
+            self.train_part = set()
+            self.val_part = set()
+            random.seed(self.seed)
+            for i in range(len(self)):
+                if random.random() < self.train_ratio:
+                    self.train_part.add(i)
+                else:
+                    self.val_part.add(i)
+            return self.train_part

+
+
[docs]    def get_val_partition(self):
+        if self.val_part:
+            return self.val_part
+        else:
+            self.get_train_partition()
+            return self.val_part

+
+
[docs]    def get_train_loader(self, p_subsample=None, seed=None, *args, **kwargs):
+        random.seed(seed)
+        remove_train, train_part = set(), set(self.train_part or self.get_train_partition())
+        for i in train_part:
+            if random.random() > p_subsample:
+                remove_train.add(i)
+        for i in remove_train:
+            train_part.remove(i)
+
+        self.subsampled_train_part = train_part
+
+        kwargs['sampler'] = torch.utils.data.sampler.SubsetRandomSampler(list(train_part))
+        return torch.utils.data.DataLoader(self, *args, **kwargs)

+
+
[docs]    def get_val_loader(self, *args, **kwargs):
+        kwargs['sampler'] = torch.utils.data.sampler.SubsetRandomSampler(list(self.get_val_partition()))
+        return torch.utils.data.DataLoader(self, *args, **kwargs)

+
+
[docs]    def create_map(self, global_index):
+        for idx, length in enumerate(self.visible[::-1]):
+            if global_index >= length:
+                return len(self.visible) - idx - 1, global_index - length + 7

+
+if __name__ == '__main__':
+    train_dataset = Dataset('/hostroot/data/dataset/bair_car_data_Main_Dataset', ['furtive'], [])
+    train_data_loader = torch.utils.data.DataLoader(train_dataset,
+                                                    batch_size=500,
+                                                    shuffle=False, pin_memory=False)
+    start = time.time()
+    for cam, meta, truth, mask in train_data_loader:
+        cur = time.time()
+        print(500./(cur - start))
+        start = cur
+        pass
+
+ +
+
+ +
+
+
+ + +
+ +
+

+ © Copyright 2017, Sauhaarda Chowdhuri. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/training/Parameters.html b/docs/_build/html/_modules/training/Parameters.html new file mode 100644 index 0000000..afacef2 --- /dev/null +++ b/docs/_build/html/_modules/training/Parameters.html @@ -0,0 +1,294 @@ + + + + + + + + + + + training.Parameters — BDD Model Car Training documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + +
+
+ + + + + + + + + + + + + + + + +
+ + + + +
+
+
+
+ +

Source code for training.Parameters

+"""Command line arguments parser configuration."""
+import argparse  # default python library for command line argument parsing
+import os
+
+
[docs]def parse():
+    parser = argparse.ArgumentParser(  # pylint: disable=invalid-name
+        description='Train DNNs on model car data.',
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+
+    parser.add_argument('--gpu', default=-1, type=int, help='Cuda GPU ID')
+    parser.add_argument('--epoch', default=0, type=int, help='Cuda GPU ID')
+    parser.add_argument('--batch-size', default=100, type=int)
+    parser.add_argument('--display', dest='display', action='store_true')
+    parser.add_argument('--no-display', dest='display', action='store_false')
+    parser.set_defaults(display=True)
+
+    parser.add_argument('--verbose', default=True, type=bool, help='Debugging mode')
+    parser.add_argument('--aruco', default=True, type=bool, help='Use Aruco data')
+    parser.add_argument('--data-path', default='/hostroot/home/dataset/' +
+                        'bair_car_data', type=str)
+    parser.add_argument('--resume-path', default=None, type=str, help='Path to' +
+                        ' resume file containing network state dictionary')
+    parser.add_argument('--bkup', default=None, type=str, help='Path to' +
+                        ' resume file containing network state dictionary')
+    parser.add_argument('--save-path', default='save', type=str, help='Path to' +
+                        ' folder to save net state dictionaries.')
+
+# nargs='+' allows for multiple arguments and stores arguments in a list
+    parser.add_argument(
+        '--ignore',
+        default=(
+            'reject_run',
+            'left',
+            'out1_in2',
+            'play',
+            'Smyth',
+            'racing'),
+        type=str,
+        nargs='+',
+        help='Skips these labels in data.')
+
+    parser.add_argument('--require-one', default=(), type=str, nargs='+',
+                        help='Skips data without these labels in data.')
+    parser.add_argument('--use-states', default=(1, 3, 5, 6, 7), type=str,
+                        nargs='+', help='Skips data outside of these states.')
+
+    parser.add_argument('--nframes', default=8, type=int,
+                        help='# timesteps of camera input')
+    parser.add_argument('--nsteps', default=10, type=int,
+                        help='# of steps of time to predict in the future')
+    parser.add_argument('--stride', default=10, type=int,
+                        help="number of timesteps between network predictions")
+    parser.add_argument('--log-interval', default=1, type=int,
+                        help="Number of batches in between logging")
+
+    parser.add_argument('--print-moments', default=1000, type=int,
+                        help='# of moments between printing stats')
+    parser.add_argument('--save-moments', default=400000, type=int,
+                        help='# of moments between printing stats')
+
+    parser.add_argument('--config', default='default.json', type=str, help='Path of config file to use in ' +
+                                                                           'experiment from ./configs')
+
+    ARGS = parser.parse_args()
+
+# Check for $DISPLAY being blank
+    if 'DISPLAY' not in os.environ:
+        ARGS.display = False
+
+    return ARGS

+
+ +
+
+ +
+
+
+ + +
+ +
+

+ © Copyright 2017, Sauhaarda Chowdhuri. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/training/Train.html b/docs/_build/html/_modules/training/Train.html new file mode 100644 index 0000000..3db2488 --- /dev/null +++ b/docs/_build/html/_modules/training/Train.html @@ -0,0 +1,399 @@ + + + + + + + + + + + training.Train — BDD Model Car Training documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + +
+
+ + + + + + + + + + + + + + + + +
+ + + + +
+
+
+
+ +

Source code for training.Train

+"""Training and validation code for bddmodelcar."""
+import sys
+import traceback
+import logging
+import time
+import os
+import importlib
+
+from Config import Config
+
+from Dataset import Dataset
+
+import Utils
+
+from torch.autograd import Variable
+import torch.nn.utils as nnutils
+import torch
+
+
[docs]def iterate(net, loss_func, optimizer=None, input=None, truth=None, mask=None, train=True):
+    """Encapsulates a training or validation iteration.
+
+    :param net: <nn.Module>: network to train
+    :param optimizer: <torch.optim>: optimizer to use
+    :param input: <tuple>: tuple of np.array or tensors to pass into net. Should contain data for this iteration
+    :param truth: <np.array | tensor>: tuple of np.array to pass into optimizer. Should contain data for this iteration
+    :param mask: <np.array | tensor>: mask to ignore unnecessary outputs.
+    :return: loss
+    """
+
+    if train:
+        net.train()
+        optimizer.zero_grad()
+    else:
+        net.eval()
+
+    # Transform inputs into Variables for pytorch and run forward prop
+    input = tuple([Variable(tensor) for tensor in input])
+    outputs = net(*input).cuda() * Variable(mask)
+    loss = loss_func(outputs, Variable(truth))
+
+    if not train:
+        return loss.cpu().data[0]
+
+    # Run backprop, gradient clipping
+    loss.backward()
+    nnutils.clip_grad_norm(net.parameters(), 1.0)
+
+    # Apply backprop gradients
+    optimizer.step()
+
+    return loss.cpu().data[0]

+
+
[docs]def main():
+    """Main training loop"""
+
+    # Configure logging
+    logging.basicConfig(filename=config['logging']['path'], level=logging.DEBUG)
+    logging.debug(config)
+
+    # Set Up PyTorch Environment
+    # torch.set_default_tensor_type('torch.FloatTensor')
+    torch.cuda.set_device(config['hardware']['gpu'])
+    torch.cuda.device(config['hardware']['gpu'])
+
+    # Define basic training and network parameters
+    net, loss_func = Net(n_steps=config['model']['future_frames'],
+                        n_frames=config['model']['past_frames']).cuda(), \
+                    torch.nn.MSELoss().cuda()
+
+    # Iterate over all epochs
+    for epoch in range(config['training']['start_epoch'], config['training']['num_epochs']):
+        try:
+            torch.cuda.set_device(config['hardware']['gpu'])
+            torch.cuda.device(config['hardware']['gpu'])
+            if not epoch == 0:
+                print("Resuming")
+                save_data = torch.load(os.path.join(config['model']['save_path'], config['model']['name'] + "epoch%02d.weights" % (epoch - 1,)))
+                net.load_state_dict(save_data)
+
+            net.cuda()
+
+            optimizer = torch.optim.Adam(net.parameters())
+
+            logging.debug('Starting training epoch #{}'.format(epoch))
+
+            train_dataset = Dataset(config['training']['dataset']['path'],
+                                    require_one=config['dataset']['include_labels'],
+                                    ignore_list=config['dataset']['ignore_labels'],
+                                    stride=config['model']['frame_stride'],
+                                    seed=config['training']['rand_seed'],
+                                    nframes=config['model']['past_frames'],
+                                    nsteps=config['model']['future_frames'],
+                                    train_ratio=config['training']['dataset']['train_ratio'],
+                                    separate_frames=config['model']['separate_frames'],
+                                    metadata_shape=config['model']['metadata_shape'],
+                                    p_exclude_run=config['training']['p_exclude_run'])
+
+            train_data_loader = train_dataset.get_train_loader(batch_size=config['training']['dataset']['batch_size'],
+                                                               shuffle=config['training']['dataset']['shuffle'],
+                                                               p_subsample=config['training']['dataset']['p_subsample'],
+                                                               seed=(epoch+config['training']['rand_seed']),
+                                                               pin_memory=False)
+
+            train_loss = Utils.LossLog()
+            start = time.time()
+
+            for batch_idx, (camera, meta, truth, mask) in enumerate(train_data_loader):
+                # Cuda everything
+                camera, meta, truth, mask = camera.cuda(), meta.cuda(), truth.cuda(), mask.cuda()
+                truth = truth * mask
+
+                loss = iterate(net, loss_func=loss_func, optimizer=optimizer,
+                               input=(camera, meta), truth=truth, mask=mask)
+
+                # Logging Loss
+                train_loss.add(loss)
+
+                print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                epoch, batch_idx * len(camera), len(train_data_loader.dataset.subsampled_train_part),
+                100. * batch_idx / len(train_data_loader), loss))
+
+                cur = time.time()
+                print('{} Hz'.format(float(len(camera))/(cur - start)))
+                start = cur
+
+
+            Utils.csvwrite(config['logging']['training_loss'], [train_loss.average()])
+
+            logging.debug('Finished training epoch #{}'.format(epoch))
+            logging.debug('Starting validation epoch #{}'.format(epoch))
+
+            val_dataset = Dataset(config['validation']['dataset']['path'],
+                                    require_one=config['dataset']['include_labels'],
+                                    ignore_list=config['dataset']['ignore_labels'],
+                                    stride=config['model']['frame_stride'],
+                                    seed=config['validation']['rand_seed'],
+                                    nframes=config['model']['past_frames'],
+                                    train_ratio=config['validation']['dataset']['train_ratio'],
+                                    nsteps=config['model']['future_frames'],
+                                    separate_frames=config['model']['separate_frames'],
+                                    metadata_shape=config['model']['metadata_shape'])
+
+            val_data_loader = val_dataset.get_val_loader(batch_size=config['validation']['dataset']['batch_size'],
+                                                               shuffle=config['validation']['dataset']['shuffle'],
+                                                               pin_memory=False)
+            val_loss = Utils.LossLog()
+
+            net.eval()
+
+            for batch_idx, (camera, meta, truth, mask) in enumerate(val_data_loader):
+                # Cuda everything
+                camera, meta, truth, mask = camera.cuda(), meta.cuda(), truth.cuda(), mask.cuda()
+                truth = truth * mask
+
+                loss = iterate(net, loss_func=loss_func, truth=truth, input=(camera, meta), mask=mask, train=False)
+
+                # Logging Loss
+                val_loss.add(loss)
+
+                print('Val Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'
+                      .format(epoch, batch_idx * len(camera), len(val_data_loader.dataset.val_part),
+                              100. * batch_idx / len(val_data_loader), loss))
+
+            Utils.csvwrite(config['logging']['validation_loss'], [val_loss.average()])
+            logging.debug('Finished validation epoch #{}'.format(epoch))
+            Utils.save_net(config['model']['save_path'], config['model']['name'] + "epoch%02d" % (epoch,), net)
+
+        except Exception:
+            logging.error(traceback.format_exc())  # Log exception
+            sys.exit(1)

+
+if __name__ == '__main__':
+    config = Config()
+    Net = importlib.import_module(config['model']['py_path']).Net
+    main()
+
+ +
+
+ +
+
+
+ + +
+ +
+

+ © Copyright 2017, Sauhaarda Chowdhuri. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/training/Utils.html b/docs/_build/html/_modules/training/Utils.html new file mode 100644 index 0000000..3cea3de --- /dev/null +++ b/docs/_build/html/_modules/training/Utils.html @@ -0,0 +1,302 @@ + + + + + + + + + + + training.Utils — BDD Model Car Training documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + +
+
+ + + + + + + + + + + + + + + + +
+ + + + +
+
+
+
+ +

Source code for training.Utils

+"""Utility classes for training."""
+import os
+import operator
+import time
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+
+
+
[docs]class MomentCounter:
+    """Notify after N Data Moments Passed"""
+
+    def __init__(self, n):
+        self.start = 0
+        self.n = n
+
+
[docs]    def step(self, data_index):
+        if data_index.ctr - self.start >= self.n:
+            self.start = data_index.ctr
+            return True
+        return False

+
+
+
[docs]def csvwrite(filename, objs):
+    with open(filename, 'a') as csvfile:
+        csvfile.write(",".join([str(x) for x in objs]) +'\n')

+
+
+
[docs]class LossLog:
+    """Keep Track of Loss, can be used within epoch or for per epoch."""
+
+    def __init__(self):
+        self.ctr = 0
+        self.total_loss = 0
+
+
[docs]    def add(self, loss):
+        self.total_loss += loss
+        self.ctr += 1

+
+
[docs]    def average(self):
+        return self.total_loss / (self.ctr * 1.)

+
+
[docs]def save_net(save_path, save_name, net):
+    torch.save(
+        net.state_dict(),
+        os.path.join(
+            save_path + save_name +
+            '.weights'))
+
+    # Next, save for inference (creates ['net'] and moves net to GPU #0)
+    weights = {'net': net.state_dict().copy()}
+    for key in weights['net']:
+        weights['net'][key] = weights['net'][key].cuda(device=0)
+    torch.save(weights,
+               os.path.join(save_path + save_name + '.infer'))

+
+
+
[docs]def display_sort_data_moment_loss(data_moment_loss_record, data):
+    sorted_data_moment_loss_record = sorted(data_moment_loss_record.items(),
+                                            key=operator.itemgetter(1))
+    low_loss_range = range(20)
+    high_loss_range = range(-1, -20, -1)
+
+    for i in low_loss_range + high_loss_range:
+        l = sorted_data_moment_loss_record[i]
+        run_code, seg_num, offset = sorted_data_moment_loss_record[i][0][0]
+        t = sorted_data_moment_loss_record[i][0][1]
+        o = sorted_data_moment_loss_record[i][0][2]
+
+        sorted_data = data.get_data(run_code, seg_num, offset)
+        plt.figure(22)
+        plt.clf()
+        plt.ylim(0, 1)
+        plt.plot(t, 'r.')
+        plt.plot(o, 'g.')
+        plt.plot([0, 20], [0.5, 0.5], 'k')
+        mi(sorted_data['right'][0, :, :], 23, img_title=d2s(l[1]))
+        plt.pause(1)

+
+ +
+
+ +
+
+
+ + +
+ +
+

+ © Copyright 2017, Sauhaarda Chowdhuri. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/training/nets/SqueezeNet.html b/docs/_build/html/_modules/training/nets/SqueezeNet.html new file mode 100644 index 0000000..db2a842 --- /dev/null +++ b/docs/_build/html/_modules/training/nets/SqueezeNet.html @@ -0,0 +1,319 @@ + + + + + + + + + + + training.nets.SqueezeNet — BDD Model Car Training documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + +
+
+ + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • training.nets.SqueezeNet
    • + + +
  • + + + +
    • + +
+ + +
+
+
+
+ +

Source code for training.nets.SqueezeNet

+"""SqueezeNet 1.1 modified for regression."""
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+from torch.autograd import Variable
+import logging
+logging.basicConfig(filename='training.log', level=logging.DEBUG)
+
+
+
[docs]class Fire(nn.Module):
+
+    def __init__(self, inplanes, squeeze_planes,
+                 expand1x1_planes, expand3x3_planes):
+        super(Fire, self).__init__()
+        self.inplanes = inplanes
+        self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
+        self.squeeze_activation = nn.ReLU(inplace=True)
+        self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes,
+                                   kernel_size=1)
+        self.expand1x1_activation = nn.ReLU(inplace=True)
+        self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes,
+                                   kernel_size=3, padding=1)
+        self.expand3x3_activation = nn.ReLU(inplace=True)
+
+
[docs]    def forward(self, x):
+        x = self.squeeze_activation(self.squeeze(x))
+        return torch.cat([
+            self.expand1x1_activation(self.expand1x1(x)),
+            self.expand3x3_activation(self.expand3x3(x))
+        ], 1)

+
+
+
[docs]class SqueezeNet(nn.Module):
+
+    def __init__(self, n_steps=10, n_frames=2):
+        super(SqueezeNet, self).__init__()
+
+        self.n_steps = n_steps
+        self.n_frames = n_frames
+        self.pre_metadata_features = nn.Sequential(
+            nn.Conv2d(3 * 2 * self.n_frames, 64, kernel_size=3, stride=2),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(64, 16, 64, 64)
+        )
+        self.post_metadata_features = nn.Sequential(
+            Fire(256, 16, 64, 64),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(128, 32, 128, 128),
+            Fire(256, 32, 128, 128),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(256, 48, 192, 192),
+            Fire(384, 48, 192, 192),
+            Fire(384, 64, 256, 256),
+            Fire(512, 64, 256, 256),
+        )
+        final_conv = nn.Conv2d(512, self.n_steps * 4, kernel_size=1)
+        self.final_output = nn.Sequential(
+            nn.Dropout(p=0.5),
+            final_conv,
+            # nn.ReLU(inplace=True),
+            nn.AvgPool2d(kernel_size=5, stride=6)
+        )
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                if m is final_conv:
+                    init.normal(m.weight.data, mean=0.0, std=0.01)
+                else:
+                    init.kaiming_uniform(m.weight.data)
+                if m.bias is not None:
+                    m.bias.data.zero_()
+
+
[docs]    def forward(self, x, metadata):
+        x = self.pre_metadata_features(x)
+        x = torch.cat((x, metadata), 1)
+        x = self.post_metadata_features(x)
+        x = self.final_output(x)
+        x = x.view(x.size(0), -1)
+        return x

+
+
[docs]    def num_params(self):
+        return sum([reduce(lambda x, y: x * y, [dim for dim in p.size()], 1) for p in self.parameters()])

+
+
[docs]def unit_test():
+    test_net = SqueezeNet(20, 6)
+    a = test_net(Variable(torch.randn(5, 36, 94, 168)),
+                 Variable(torch.randn(5, 128, 23, 41)))
+    logging.debug('Net Test Output = {}'.format(a))
+    logging.debug('Network was Unit Tested')
+    print(test_net.num_params())

+
+unit_test()
+
+Net = SqueezeNet
+
+ +
+
+ +
+
+
+ + +
+ +
+

+ © Copyright 2017, Sauhaarda Chowdhuri. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/training/nets/SqueezeNetAruco1.html b/docs/_build/html/_modules/training/nets/SqueezeNetAruco1.html new file mode 100644 index 0000000..119c4d8 --- /dev/null +++ b/docs/_build/html/_modules/training/nets/SqueezeNetAruco1.html @@ -0,0 +1,318 @@ + + + + + + + + + + + training.nets.SqueezeNetAruco1 — BDD Model Car Training documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + +
+
+ + + + + + + + + + + + + + + + +
+ +
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    • + +
  • training.nets.SqueezeNetAruco1
    • + + +
  • + + + +
    • + +
+ + +
+
+
+
+ +

Source code for training.nets.SqueezeNetAruco1

+"""SqueezeNet 1.1 modified for Aruco markers."""
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+from torch.autograd import Variable
+
+
+
[docs]class Fire(nn.Module):
+
+    def __init__(self, inplanes, squeeze_planes,
+                 expand1x1_planes, expand3x3_planes):
+        super(Fire, self).__init__()
+        self.inplanes = inplanes
+        self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
+        self.squeeze_activation = nn.ReLU(inplace=True)
+        self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes,
+                                   kernel_size=1)
+        self.expand1x1_activation = nn.ReLU(inplace=True)
+        self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes,
+                                   kernel_size=3, padding=1)
+        self.expand3x3_activation = nn.ReLU(inplace=True)
+
+
[docs]    def forward(self, x):
+        x = self.squeeze_activation(self.squeeze(x))
+        return torch.cat([
+            self.expand1x1_activation(self.expand1x1(x)),
+            self.expand3x3_activation(self.expand3x3(x))
+        ], 1)

+
+
+
[docs]class SqueezeNet(nn.Module):
+
+    def __init__(self):
+        super(SqueezeNet, self).__init__()
+
+        self.lr = 0.01
+        self.momentum = 0.01
+        self.N_FRAMES = 2
+        self.N_STEPS = 10
+        self.pre_metadata_features = nn.Sequential(
+            nn.Conv2d(12, 64, kernel_size=3, stride=2),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(64, 16, 64, 64),
+        )
+        self.post_metadata_features = nn.Sequential(
+            Fire(256, 16, 64, 64),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(128, 32, 128, 128),
+            Fire(256, 32, 128, 128),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(256, 48, 192, 192),
+            Fire(384, 48, 192, 192),
+            Fire(384, 64, 256, 256),
+            Fire(512, 64, 256, 256),
+        )
+        final_conv = nn.Conv2d(512, 66, kernel_size=1)
+        self.final_output_Aruco = nn.Sequential(
+            nn.Dropout(p=0.5),
+            final_conv,
+            # nn.ReLU(inplace=True),
+            nn.AvgPool2d(kernel_size=5, stride=6)
+        )
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                if m is final_conv:
+                    init.normal(m.weight.data, mean=0.0, std=0.01)
+                else:
+                    init.kaiming_uniform(m.weight.data)
+                if m.bias is not None:
+                    m.bias.data.zero_()
+
+
[docs]    def forward(self, x, metadata):
+        x = self.pre_metadata_features(x)
+        x = torch.cat((x, metadata), 1)
+        x = self.post_metadata_features(x)
+        x = self.final_output_Aruco(x)
+        x = x.view(x.size(0), -1)
+        return x

+
+
+
[docs]def unit_test():
+    test_net = SqueezeNet()
+    a = test_net(
+        Variable(
+            torch.randn(
+                5, 12, 94, 168)), Variable(
+            torch.randn(
+                5, 128, 23, 41)))
+    print(a)

+
+
+unit_test()
+
+ +
+
+ +
+
+
+ + +
+ +
+

+ © Copyright 2017, Sauhaarda Chowdhuri. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/training/nets/Z2ColorBatchNorm.html b/docs/_build/html/_modules/training/nets/Z2ColorBatchNorm.html new file mode 100644 index 0000000..5d4b5bd --- /dev/null +++ b/docs/_build/html/_modules/training/nets/Z2ColorBatchNorm.html @@ -0,0 +1,304 @@ + + + + + + + + + + + training.nets.Z2ColorBatchNorm — BDD Model Car Training documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + +
+
+ + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • training.nets.Z2ColorBatchNorm
    • + + +
  • + + + +
    • + +
+ + +
+
+
+
+ +

Source code for training.nets.Z2ColorBatchNorm

+"""z2_color implementation with batch normalization."""
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+
+
+
[docs]class Z2ColorBatchNorm(nn.Module):
+    def __init__(self):
+        super(Z2ColorBatchNorm, self).__init__()
+
+        self.lr = 0.1
+        self.momentum = 0.1
+        self.N_FRAMES = 2
+        self.N_STEPS = 10
+
+        self.conv1 = nn.Conv2d(
+            in_channels=12,
+            out_channels=96,
+            kernel_size=11,
+            stride=3,
+            groups=1)
+        self.conv1_pool = nn.MaxPool2d(kernel_size=3, stride=2)
+        self.conv1_pool_norm = nn.BatchNorm2d(96)
+
+        self.conv2 = nn.Conv2d(
+            in_channels=102,
+            out_channels=256,
+            kernel_size=3,
+            stride=2,
+            groups=2)
+        self.conv2_pool = nn.MaxPool2d(kernel_size=3, stride=2)
+        self.conv2_pool_norm = nn.BatchNorm2d(256)
+        self.ip1 = nn.Linear(in_features=2560, out_features=512)
+        self.ip1_norm = nn.BatchNorm1d(512)
+        self.ip2 = nn.Linear(in_features=512, out_features=20)
+
+        # Initialize weights
+        nn.init.normal(self.conv1.weight, std=0.00001)
+        nn.init.normal(self.conv2.weight, std=0.1)
+
+        nn.init.xavier_normal(self.ip1.weight)
+        nn.init.xavier_normal(self.ip2.weight)
+
+
[docs]    def forward(self, x, metadata):
+        # conv1
+        x = self.conv1(x)
+        x = F.relu(x)
+        x = self.conv1_pool(x)
+        x = self.conv1_pool_norm(x)
+
+        # metadata_concat
+        x = torch.cat((metadata, x), 1)
+
+        # conv2
+        x = self.conv2_pool_norm(self.conv2_pool(F.relu(self.conv2(x))))
+
+        x = x.view(-1, 2560)
+
+        # ip1
+        x = self.ip1_norm(F.relu(self.ip1(x)))
+
+        # ip2
+        x = self.ip2(x)
+
+        return x

+
+
+
[docs]def unit_test():
+    test_net = Z2ColorBatchNorm()
+    a = test_net(
+        Variable(
+            torch.randn(
+                5, 12, 94, 168)), Variable(
+            torch.randn(
+                5, 6, 13, 26)))
+    print(a)

+
+
+unit_test()
+
+ +
+
+ +
+
+
+ + +
+ +
+

+ © Copyright 2017, Sauhaarda Chowdhuri. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/training/nets/eric/nonsqueeze/Feedforward.html b/docs/_build/html/_modules/training/nets/eric/nonsqueeze/Feedforward.html new file mode 100644 index 0000000..70b3e18 --- /dev/null +++ b/docs/_build/html/_modules/training/nets/eric/nonsqueeze/Feedforward.html @@ -0,0 +1,294 @@ + + + + + + + + + + + training.nets.eric.nonsqueeze.Feedforward — BDD Model Car Training documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + +
+
+ + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • training.nets.eric.nonsqueeze.Feedforward
    • + + +
  • + + + +
    • + +
+ + +
+
+
+
+ +

Source code for training.nets.eric.nonsqueeze.Feedforward

+"""SqueezeNet 1.1 modified for regression."""
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+from torch.autograd import Variable
+import logging
+logging.basicConfig(filename='training.log', level=logging.DEBUG)
+
+
[docs]class Feedforward(nn.Module):
+
+    def __init__(self, n_steps=10, n_frames=2):
+        super(Feedforward, self).__init__()
+
+        self.n_steps = n_steps
+        self.n_frames = n_frames
+        self.pre_metadata_features = nn.Sequential(
+            nn.Conv2d(3 * 2 * n_frames, 8, kernel_size=3, stride=2),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            nn.Conv2d(8, 8, kernel_size=3, padding=1)
+        )
+        self.post_metadata_features = nn.Sequential(
+            nn.Conv2d(16, 12, kernel_size=3, padding=1),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            nn.Conv2d(12, 12, kernel_size=3, padding=1),
+            nn.Conv2d(12, 16, kernel_size=3, padding=1),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            nn.Conv2d(16, 16, kernel_size=3, padding=1),
+            nn.Conv2d(16, 24, kernel_size=3, padding=1),
+            nn.Conv2d(24, 24, kernel_size=3, padding=1)
+        )
+        final_conv = nn.Conv2d(24, self.n_steps * 2, kernel_size=1)
+        self.final_output = nn.Sequential(
+            nn.Dropout(p=0.5),
+            final_conv,
+            # nn.ReLU(inplace=True),
+            nn.AvgPool2d(kernel_size=5, stride=5)
+        )
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                if m is final_conv:
+                    init.normal(m.weight.data, mean=0.0, std=0.01)
+                else:
+                    init.kaiming_uniform(m.weight.data)
+                if m.bias is not None:
+                    m.bias.data.zero_()
+
+
[docs]    def forward(self, x, metadata):
+        x = self.pre_metadata_features(x)
+        x = torch.cat((x, metadata), 1)
+        x = self.post_metadata_features(x)
+        x = self.final_output(x)
+        x = x.view(x.size(0), -1)
+        return x

+
+
[docs]    def num_params(self):
+        return sum([reduce(lambda x, y: x * y, [dim for dim in p.size()], 1) for p in self.parameters()])

+
+
[docs]def unit_test():
+    test_net = Feedforward(20, 6)
+    a = test_net(Variable(torch.randn(5, 36, 94, 168)),
+                 Variable(torch.randn(5, 8, 23, 41)))
+    logging.debug('Net Test Output = {}'.format(a))
+    logging.debug('Network was Unit Tested')
+    print(test_net.num_params())

+
+unit_test()
+
+Net = Feedforward
+
+ +
+
+ +
+
+
+ + +
+ +
+

+ © Copyright 2017, Sauhaarda Chowdhuri. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/training/nets/eric/nonsqueeze/Nvidia.html b/docs/_build/html/_modules/training/nets/eric/nonsqueeze/Nvidia.html new file mode 100644 index 0000000..8a4d0a0 --- /dev/null +++ b/docs/_build/html/_modules/training/nets/eric/nonsqueeze/Nvidia.html @@ -0,0 +1,273 @@ + + + + + + + + + + + training.nets.eric.nonsqueeze.Nvidia — BDD Model Car Training documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + +
+
+ + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • training.nets.eric.nonsqueeze.Nvidia
    • + + +
  • + + + +
    • + +
+ + +
+
+
+
+ +

Source code for training.nets.eric.nonsqueeze.Nvidia

+"""SqueezeNet 1.1 modified for regression."""
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+from torch.autograd import Variable
+import logging
+logging.basicConfig(filename='training.log', level=logging.DEBUG)
+
+
[docs]class Nvidia(nn.Module):
+
+    def __init__(self, n_steps=10, n_frames=2):
+        super(Nvidia, self).__init__()
+
+        self.n_steps = n_steps
+        self.n_frames = n_frames
+        self.conv_nets = nn.Sequential(
+            nn.Conv2d(3 * 2 * self.n_frames, 24, kernel_size=5, stride=2),
+            nn.Conv2d(24, 36, kernel_size=5, stride=2),
+            nn.Conv2d(36, 48, kernel_size=5, stride=2),
+            nn.Conv2d(48, 64, kernel_size=3, stride=2),
+            nn.Conv2d(64, 64, kernel_size=3, stride=1)
+        )
+        self.fcl = nn.Sequential(
+            nn.Linear(768, 100),
+            nn.Linear(100, 4 * self.n_steps)
+        )
+
+
+
[docs]    def forward(self, x, metadata):
+        x = self.conv_nets(x)
+        x = x.view(x.size(0), -1)
+        x = self.fcl(x)
+        x = x.view(x.size(0), -1, 4)
+        return x

+
+
[docs]    def num_params(self):
+        return sum([reduce(lambda x, y: x * y, [dim for dim in p.size()], 1) for p in self.parameters()])

+
+
[docs]def unit_test():
+    test_net = Nvidia(20, 6)
+    a = test_net(Variable(torch.randn(5, 36, 94, 168)),
+                 Variable(torch.randn(5, 12, 23, 41)))
+    logging.debug('Net Test Output = {}'.format(a))
+    logging.debug('Network was Unit Tested')
+    print(test_net.num_params())

+
+unit_test()
+
+Net = Nvidia
+
+ +
+
+ +
+
+
+ + +
+ +
+

+ © Copyright 2017, Sauhaarda Chowdhuri. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/training/nets/eric/nonsqueeze/NvidiaMetadata.html b/docs/_build/html/_modules/training/nets/eric/nonsqueeze/NvidiaMetadata.html new file mode 100644 index 0000000..ffa0aa5 --- /dev/null +++ b/docs/_build/html/_modules/training/nets/eric/nonsqueeze/NvidiaMetadata.html @@ -0,0 +1,277 @@ + + + + + + + + + + + training.nets.eric.nonsqueeze.NvidiaMetadata — BDD Model Car Training documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + +
+
+ + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • training.nets.eric.nonsqueeze.NvidiaMetadata
    • + + +
  • + + + +
    • + +
+ + +
+
+
+
+ +

Source code for training.nets.eric.nonsqueeze.NvidiaMetadata

+"""SqueezeNet 1.1 modified for regression."""
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+from torch.autograd import Variable
+import logging
+logging.basicConfig(filename='training.log', level=logging.DEBUG)
+
+
[docs]class NvidiaMetadata(nn.Module):
+
+    def __init__(self, n_steps=10, n_frames=2):
+        super(NvidiaMetadata, self).__init__()
+
+        self.n_steps = n_steps
+        self.n_frames = n_frames
+        self.pre_metadata = nn.Sequential(
+            nn.Conv2d(3 * 2 * self.n_frames, 24, kernel_size=5, stride=2),
+            nn.Conv2d(24, 36, kernel_size=5, stride=2)
+        )
+        self.post_metadata = nn.Sequential(
+            nn.Conv2d(48, 48, kernel_size=5, stride=2),
+            nn.Conv2d(48, 64, kernel_size=3, stride=2),
+            nn.Conv2d(64, 64, kernel_size=3, stride=1)
+        )
+        self.fcl = nn.Sequential(
+            nn.Linear(768, 100),
+            nn.Linear(100, 4 * self.n_steps)
+        )
+
+
+
[docs]    def forward(self, x, metadata):
+        x = self.pre_metadata(x)
+        x = torch.cat([x, metadata], 1)
+        x = self.post_metadata(x)
+        x = x.view(x.size(0), -1)
+        x = self.fcl(x)
+        x = x.view(x.size(0), -1, 4)
+        return x

+
+
[docs]    def num_params(self):
+        return sum([reduce(lambda x, y: x * y, [dim for dim in p.size()], 1) for p in self.parameters()])

+
+
[docs]def unit_test():
+    test_net = NvidiaMetadata(20, 6)
+    a = test_net(Variable(torch.randn(5, 36, 94, 168)),
+                 Variable(torch.randn(5, 12, 21, 39)))
+    logging.debug('Net Test Output = {}'.format(a))
+    logging.debug('Network was Unit Tested')
+    print(test_net.num_params())

+
+unit_test()
+
+Net = NvidiaMetadata
+
+ +
+
+ +
+
+
+ + +
+ +
+

+ © Copyright 2017, Sauhaarda Chowdhuri. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/training/nets/eric/nonsqueeze/TimeLSTM.html b/docs/_build/html/_modules/training/nets/eric/nonsqueeze/TimeLSTM.html new file mode 100644 index 0000000..aac5fa0 --- /dev/null +++ b/docs/_build/html/_modules/training/nets/eric/nonsqueeze/TimeLSTM.html @@ -0,0 +1,363 @@ + + + + + + + + + + + training.nets.eric.nonsqueeze.TimeLSTM — BDD Model Car Training documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + +
+
+ + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • training.nets.eric.nonsqueeze.TimeLSTM
    • + + +
  • + + + +
    • + +
+ + +
+
+
+
+ +

Source code for training.nets.eric.nonsqueeze.TimeLSTM

+"""SqueezeNet 1.1 modified for LSTM regression."""
+import logging
+
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+from torch.autograd import Variable
+
+logging.basicConfig(filename='training.log', level=logging.DEBUG)
+
+
+# from Parameters import ARGS
+
+
+
[docs]class Fire(nn.Module):  # pylint: disable=too-few-public-methods
+    """Implementation of Fire module"""
+
+    def __init__(self, inplanes, squeeze_planes,
+                 expand1x1_planes, expand3x3_planes):
+        """Sets up layers for Fire module"""
+        super(Fire, self).__init__()
+        self.inplanes = inplanes
+        self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
+        self.squeeze_activation = nn.ReLU(inplace=True)
+        self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes,
+                                   kernel_size=1)
+        self.expand1x1_activation = nn.ReLU(inplace=True)
+        self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes,
+                                   kernel_size=3, padding=1)
+        self.expand3x3_activation = nn.ReLU(inplace=True)
+
+
[docs]    def forward(self, input_data):
+        """Forward-propagates data through Fire module"""
+        output_data = self.squeeze_activation(self.squeeze(input_data))
+        return torch.cat([
+            self.expand1x1_activation(self.expand1x1(output_data)),
+            self.expand3x3_activation(self.expand3x3(output_data))
+        ], 1)

+
+
+
[docs]class SqueezeNetTimeLSTM(nn.Module):  # pylint: disable=too-few-public-methods
+    """SqueezeNet+LSTM for end to end autonomous driving"""
+
+    def __init__(self, n_frames=2, n_steps=10):
+        """Sets up layers"""
+        super(SqueezeNetTimeLSTM, self).__init__()
+
+        self.is_cuda = False
+
+        self.n_frames = n_frames
+        self.n_steps = n_steps
+        self.pre_metadata_features = nn.Sequential(
+            nn.Conv2d(3 * 2, 8, kernel_size=3, stride=2),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            nn.Conv2d(8, 8, kernel_size=3, padding=1)
+        )
+        self.post_metadata_features = nn.Sequential(
+            nn.Conv2d(16, 12, kernel_size=3, padding=1),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            nn.Conv2d(12, 12, kernel_size=3, padding=1),
+            nn.Conv2d(12, 16, kernel_size=3, padding=1),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            nn.Conv2d(16, 16, kernel_size=3, padding=1),
+            nn.Conv2d(16, 24, kernel_size=3, padding=1),
+            nn.Conv2d(24, 8, kernel_size=3, padding=1)
+        )
+        final_conv = nn.Conv2d(8, 2, kernel_size=1)
+        self.pre_lstm_output = nn.Sequential(
+            nn.Dropout(p=0.5),
+            final_conv,
+            nn.AvgPool2d(kernel_size=3, stride=2),
+        )
+        self.lstm_encoder = nn.ModuleList([
+            nn.LSTM(16, 32, 1, batch_first=True)
+        ])
+        self.lstm_decoder = nn.ModuleList([
+            nn.LSTM(1, 32, 1, batch_first=True),
+            nn.LSTM(32, 4, 1, batch_first=True)
+        ])
+
+        for mod in self.modules():
+            if isinstance(mod, nn.Conv2d):
+                if mod is final_conv:
+                    init.normal(mod.weight.data, mean=0.0, std=0.01)
+                else:
+                    init.kaiming_uniform(mod.weight.data)
+                if mod.bias is not None:
+                    mod.bias.data.zero_()
+
+
[docs]    def forward(self, camera_data, metadata):
+        """Forward-propagates data through SqueezeNetTimeLSTM"""
+        batch_size = camera_data.size(0)
+        nframes = camera_data.size(1) // 6
+        metadata = metadata.contiguous().view(-1, 8, 23, 41)
+        net_output = camera_data.contiguous().view(-1, 6, 94, 168)
+        net_output = self.pre_metadata_features(net_output)
+        net_output = torch.cat((net_output, metadata), 1)
+        net_output = self.post_metadata_features(net_output)
+        net_output = self.pre_lstm_output(net_output)
+        net_output = net_output.contiguous().view(batch_size, -1, 16)
+        for lstm in self.lstm_encoder:
+            net_output, last_hidden_cell = lstm(net_output)
+            last_hidden_cell = list(last_hidden_cell)
+        for lstm in self.lstm_decoder:
+            if last_hidden_cell:
+                # last_hidden_cell[0] = last_hidden_cell[0].contiguous().view(batch_size, -1, 256)
+                # last_hidden_cell[1] = last_hidden_cell[1].contiguous().view(batch_size, -1, 256)
+                net_output = lstm(self.get_decoder_seq(batch_size, self.n_steps), last_hidden_cell)[0]
+                last_hidden_cell = None
+            else:
+                net_output = lstm(net_output)[0]
+        net_output = net_output.contiguous().view(net_output.size(0), -1)
+        return net_output

+
+
[docs]    def get_decoder_seq(self, batch_size, timesteps):
+        decoder_input_seq = Variable(torch.zeros(batch_size, timesteps, 1))
+        return decoder_input_seq.cuda() if self.is_cuda else decoder_input_seq

+
+
[docs]    def cuda(self, device_id=None):
+        self.is_cuda = True
+        return super(SqueezeNetTimeLSTM, self).cuda(device_id)

+
+
[docs]    def num_params(self):
+        return sum([reduce(lambda x, y: x * y, [dim for dim in p.size()], 1) for p in self.parameters()])

+
+
[docs]def unit_test():
+    """Tests SqueezeNetTimeLSTM for size constitency"""
+    test_net = SqueezeNetTimeLSTM(6, 20)
+    test_net_output = test_net(
+        Variable(torch.randn(5, 36, 94, 168)),
+        Variable(torch.randn(5, 8, 6, 23, 41)))
+    logging.debug('Net Test Output = {}'.format(test_net_output))
+    logging.debug('Network was Unit Tested')
+    print(test_net.num_params())

+
+unit_test()
+
+Net = SqueezeNetTimeLSTM
+
+ +
+
+ +
+
+
+ + +
+ +
+

+ © Copyright 2017, Sauhaarda Chowdhuri. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/training/nets/eric/smallmodels/SqueezeNet.html b/docs/_build/html/_modules/training/nets/eric/smallmodels/SqueezeNet.html new file mode 100644 index 0000000..2d4c7f7 --- /dev/null +++ b/docs/_build/html/_modules/training/nets/eric/smallmodels/SqueezeNet.html @@ -0,0 +1,319 @@ + + + + + + + + + + + training.nets.eric.smallmodels.SqueezeNet — BDD Model Car Training documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + +
+
+ + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • training.nets.eric.smallmodels.SqueezeNet
    • + + +
  • + + + +
    • + +
+ + +
+
+
+
+ +

Source code for training.nets.eric.smallmodels.SqueezeNet

+"""SqueezeNet 1.1 modified for regression."""
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+from torch.autograd import Variable
+import logging
+logging.basicConfig(filename='training.log', level=logging.DEBUG)
+
+
+
[docs]class Fire(nn.Module):
+
+    def __init__(self, inplanes, squeeze_planes,
+                 expand1x1_planes, expand3x3_planes):
+        super(Fire, self).__init__()
+        self.inplanes = inplanes
+        self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
+        self.squeeze_activation = nn.ReLU(inplace=True)
+        self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes,
+                                   kernel_size=1)
+        self.expand1x1_activation = nn.ReLU(inplace=True)
+        self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes,
+                                   kernel_size=3, padding=1)
+        self.expand3x3_activation = nn.ReLU(inplace=True)
+
+
[docs]    def forward(self, x):
+        x = self.squeeze_activation(self.squeeze(x))
+        return torch.cat([
+            self.expand1x1_activation(self.expand1x1(x)),
+            self.expand3x3_activation(self.expand3x3(x))
+        ], 1)

+
+
+
[docs]class SqueezeNet(nn.Module):
+
+    def __init__(self, n_steps=10, n_frames=2):
+        super(SqueezeNet, self).__init__()
+
+        self.n_steps = n_steps
+        self.n_frames = n_frames
+        self.pre_metadata_features = nn.Sequential(
+            nn.Conv2d(3 * 2 * self.n_frames, 16, kernel_size=3, stride=2),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(16, 6, 12, 12)
+        )
+        self.post_metadata_features = nn.Sequential(
+            Fire(36, 8, 16, 16),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(32, 12, 24, 24),
+            Fire(48, 12, 24, 24),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(48, 16, 32, 32),
+            Fire(64, 16, 32, 32),
+            Fire(64, 24, 48, 48),
+            Fire(96, 24, 48, 48),
+        )
+        final_conv = nn.Conv2d(96, self.n_steps * 4, kernel_size=1)
+        self.final_output = nn.Sequential(
+            nn.Dropout(p=0.5),
+            final_conv,
+            # nn.ReLU(inplace=True),
+            nn.AvgPool2d(kernel_size=5, stride=6)
+        )
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                if m is final_conv:
+                    init.normal(m.weight.data, mean=0.0, std=0.01)
+                else:
+                    init.kaiming_uniform(m.weight.data)
+                if m.bias is not None:
+                    m.bias.data.zero_()
+
+
[docs]    def forward(self, x, metadata):
+        x = self.pre_metadata_features(x)
+        x = torch.cat((x, metadata), 1)
+        x = self.post_metadata_features(x)
+        x = self.final_output(x)
+        x = x.view(x.size(0), -1)
+        return x

+
+
[docs]    def num_params(self):
+        return sum([reduce(lambda x, y: x * y, [dim for dim in p.size()], 1) for p in self.parameters()])

+
+
[docs]def unit_test():
+    test_net = SqueezeNet(20, 6)
+    a = test_net(Variable(torch.randn(5, 36, 94, 168)),
+                 Variable(torch.randn(5, 12, 23, 41)))
+    logging.debug('Net Test Output = {}'.format(a))
+    logging.debug('Network was Unit Tested')
+    print(test_net.num_params())

+
+unit_test()
+
+Net = SqueezeNet
+
+ +
+
+ +
+
+
+ + +
+ +
+

+ © Copyright 2017, Sauhaarda Chowdhuri. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/training/nets/eric/smallmodels/SqueezeNetLSTM.html b/docs/_build/html/_modules/training/nets/eric/smallmodels/SqueezeNetLSTM.html new file mode 100644 index 0000000..262fc1b --- /dev/null +++ b/docs/_build/html/_modules/training/nets/eric/smallmodels/SqueezeNetLSTM.html @@ -0,0 +1,336 @@ + + + + + + + + + + + training.nets.eric.smallmodels.SqueezeNetLSTM — BDD Model Car Training documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + +
+
+ + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • training.nets.eric.smallmodels.SqueezeNetLSTM
    • + + +
  • + + + +
    • + +
+ + +
+
+
+
+ +

Source code for training.nets.eric.smallmodels.SqueezeNetLSTM

+"""SqueezeNet 1.1 modified for LSTM regression."""
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+from torch.autograd import Variable
+import logging
+logging.basicConfig(filename='training.log', level=logging.DEBUG)
+
+#from Parameters import ARGS
+
+
+
[docs]class Fire(nn.Module):  # pylint: disable=too-few-public-methods
+    """Implementation of Fire module"""
+
+    def __init__(self, inplanes, squeeze_planes,
+                 expand1x1_planes, expand3x3_planes):
+        """Sets up layers for Fire module"""
+        super(Fire, self).__init__()
+        self.inplanes = inplanes
+        self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
+        self.squeeze_activation = nn.ReLU(inplace=True)
+        self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes,
+                                   kernel_size=1)
+        self.expand1x1_activation = nn.ReLU(inplace=True)
+        self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes,
+                                   kernel_size=3, padding=1)
+        self.expand3x3_activation = nn.ReLU(inplace=True)
+
+
[docs]    def forward(self, input_data):
+        """Forward-propagates data through Fire module"""
+        output_data = self.squeeze_activation(self.squeeze(input_data))
+        return torch.cat([
+            self.expand1x1_activation(self.expand1x1(output_data)),
+            self.expand3x3_activation(self.expand3x3(output_data))
+        ], 1)

+
+
+
[docs]class SqueezeNetLSTM(nn.Module):  # pylint: disable=too-few-public-methods
+    """SqueezeNet+LSTM for end to end autonomous driving"""
+
+    def __init__(self, n_frames=2, n_steps=10):
+        """Sets up layers"""
+        super(SqueezeNetLSTM, self).__init__()
+
+        self.n_frames = n_frames
+        self.n_steps = n_steps
+        self.pre_metadata_features = nn.Sequential(
+            nn.Conv2d(3 * 2 * self.n_frames, 16, kernel_size=3, stride=2),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(16, 6, 12, 12)
+        )
+        self.post_metadata_features = nn.Sequential(
+            Fire(36, 8, 16, 16),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(32, 12, 24, 24),
+            Fire(48, 12, 24, 24),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(48, 16, 32, 32),
+            Fire(64, 16, 32, 32),
+            Fire(64, 24, 48, 48),
+            Fire(96, 24, 48, 48),
+        )
+        final_conv = nn.Conv2d(96, self.n_steps * 2, kernel_size=1)
+        self.pre_lstm_output = nn.Sequential(
+            nn.Dropout(p=0.5),
+            final_conv,
+            nn.AvgPool2d(kernel_size=3, stride=2),
+        )
+        self.lstms = nn.ModuleList([
+            nn.LSTM(16, 32, 2, batch_first=True),
+            nn.LSTM(32, 4, 1, batch_first=True)
+        ])
+
+        for mod in self.modules():
+            if isinstance(mod, nn.Conv2d):
+                if mod is final_conv:
+                    init.normal(mod.weight.data, mean=0.0, std=0.01)
+                else:
+                    init.kaiming_uniform(mod.weight.data)
+                if mod.bias is not None:
+                    mod.bias.data.zero_()
+
+
[docs]    def forward(self, camera_data, metadata):
+        """Forward-propagates data through SqueezeNetLSTM"""
+        net_output = self.pre_metadata_features(camera_data)
+        net_output = torch.cat((net_output, metadata), 1)
+        net_output = self.post_metadata_features(net_output)
+        net_output = self.pre_lstm_output(net_output)
+        net_output = net_output.view(net_output.size(0), self.n_steps, -1)
+        for lstm in self.lstms:
+            net_output = lstm(net_output)[0]
+        net_output = net_output.contiguous().view(net_output.size(0), -1)
+        return net_output

+
+
[docs]    def num_params(self):
+        return sum([reduce(lambda x, y: x * y, [dim for dim in p.size()], 1) for p in self.parameters()])

+
+
+
[docs]def unit_test():
+    """Tests SqueezeNetLSTM for size constitency"""
+    test_net = SqueezeNetLSTM()
+    test_net_output = test_net(
+        Variable(torch.randn(5, 12, 94, 168)),
+        Variable(torch.randn(5, 12, 23, 41)))
+    logging.debug('Net Test Output = {}'.format(test_net_output))
+    logging.debug('Network was Unit Tested')
+    print(test_net.num_params())

+
+unit_test()
+
+Net = SqueezeNetLSTM
+
+ +
+
+ +
+
+
+ + +
+ +
+

+ © Copyright 2017, Sauhaarda Chowdhuri. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/training/nets/eric/smallmodels/SqueezeNetSqueezeLSTM.html b/docs/_build/html/_modules/training/nets/eric/smallmodels/SqueezeNetSqueezeLSTM.html new file mode 100644 index 0000000..e73bacd --- /dev/null +++ b/docs/_build/html/_modules/training/nets/eric/smallmodels/SqueezeNetSqueezeLSTM.html @@ -0,0 +1,342 @@ + + + + + + + + + + + training.nets.eric.smallmodels.SqueezeNetSqueezeLSTM — BDD Model Car Training documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + +
+
+ + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • training.nets.eric.smallmodels.SqueezeNetSqueezeLSTM
    • + + +
  • + + + +
    • + +
+ + +
+
+
+
+ +

Source code for training.nets.eric.smallmodels.SqueezeNetSqueezeLSTM

+"""SqueezeNet 1.1 modified for LSTM regression."""
+import logging
+
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+from torch.autograd import Variable
+
+logging.basicConfig(filename='training.log', level=logging.DEBUG)
+
+
+# from Parameters import ARGS
+
+
+
[docs]class Fire(nn.Module):  # pylint: disable=too-few-public-methods
+    """Implementation of Fire module"""
+
+    def __init__(self, inplanes, squeeze_planes,
+                 expand1x1_planes, expand3x3_planes):
+        """Sets up layers for Fire module"""
+        super(Fire, self).__init__()
+        self.inplanes = inplanes
+        self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
+        self.squeeze_activation = nn.ReLU(inplace=True)
+        self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes,
+                                   kernel_size=1)
+        self.expand1x1_activation = nn.ReLU(inplace=True)
+        self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes,
+                                   kernel_size=3, padding=1)
+        self.expand3x3_activation = nn.ReLU(inplace=True)
+
+
[docs]    def forward(self, input_data):
+        """Forward-propagates data through Fire module"""
+        output_data = self.squeeze_activation(self.squeeze(input_data))
+        return torch.cat([
+            self.expand1x1_activation(self.expand1x1(output_data)),
+            self.expand3x3_activation(self.expand3x3(output_data))
+        ], 1)

+
+
+
[docs]class SqueezeNetSqueezeLSTM(nn.Module):  # pylint: disable=too-few-public-methods
+    """SqueezeNet+LSTM for end to end autonomous driving"""
+
+    def __init__(self, n_steps=10, n_frames=2):
+        """Sets up layers"""
+        super(SqueezeNetSqueezeLSTM, self).__init__()
+
+        self.n_frames = n_frames
+        self.n_steps = n_steps
+        self.pre_metadata_features = nn.Sequential(
+            nn.Conv2d(3 * 2 * self.n_frames, 16, kernel_size=3, stride=2),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(16, 6, 12, 12)
+        )
+        self.post_metadata_features = nn.Sequential(
+            Fire(36, 8, 16, 16),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(32, 12, 24, 24),
+            Fire(48, 12, 24, 24),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(48, 16, 32, 32),
+            Fire(64, 16, 32, 32),
+            Fire(64, 24, 48, 48),
+            Fire(96, 24, 48, 48),
+        )
+        final_conv = nn.Conv2d(96, self.n_steps * 2, kernel_size=1)
+        self.pre_lstm_output = nn.Sequential(
+            nn.Dropout(p=0.5),
+            final_conv,
+            nn.AvgPool2d(kernel_size=3, stride=2),
+        )
+        self.lstms = nn.ModuleList([
+            nn.LSTM(16, 32, 1, batch_first=True),
+            nn.LSTM(32, 8, 1, batch_first=True),
+            nn.LSTM(8, 16, 1, batch_first=True),
+            nn.LSTM(16, 4, 1, batch_first=True)
+        ])
+
+        for mod in self.modules():
+            if isinstance(mod, nn.Conv2d):
+                if mod is final_conv:
+                    init.normal(mod.weight.data, mean=0.0, std=0.01)
+                else:
+                    init.kaiming_uniform(mod.weight.data)
+                if mod.bias is not None:
+                    mod.bias.data.zero_()
+
+
[docs]    def forward(self, camera_data, metadata):
+        """Forward-propagates data through SqueezeNetSqueezeLSTM"""
+        net_output = self.pre_metadata_features(camera_data)
+        net_output = torch.cat((net_output, metadata), 1)
+        net_output = self.post_metadata_features(net_output)
+        net_output = self.pre_lstm_output(net_output)
+        net_output = net_output.view(net_output.size(0), self.n_steps, -1)
+        for lstm in self.lstms:
+            net_output = lstm(net_output)[0]
+        net_output = net_output.contiguous().view(net_output.size(0), -1)
+        return net_output

+
+
[docs]    def num_params(self):
+        return sum([reduce(lambda x, y: x * y, [dim for dim in p.size()], 1) for p in self.parameters()])

+
+
+
[docs]def unit_test():
+    """Tests SqueezeNetSqueezeLSTM for size constitency"""
+    test_net = SqueezeNetSqueezeLSTM(20, 6)
+    test_net_output = test_net(
+        Variable(torch.randn(5, 36, 94, 168)),
+        Variable(torch.randn(5, 12, 23, 41)))
+    logging.debug('Net Test Output = {}'.format(test_net_output))
+    logging.debug('Network was Unit Tested')
+    print(test_net.num_params())

+    # for param in test_net.parameters():
+
+
+unit_test()
+Net = SqueezeNetSqueezeLSTM
+
+ +
+
+ +
+
+
+ + +
+ +
+

+ © Copyright 2017, Sauhaarda Chowdhuri. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/training/nets/eric/smallmodels/SqueezeNetSqueezeTimeLSTM.html b/docs/_build/html/_modules/training/nets/eric/smallmodels/SqueezeNetSqueezeTimeLSTM.html new file mode 100644 index 0000000..097b18e --- /dev/null +++ b/docs/_build/html/_modules/training/nets/eric/smallmodels/SqueezeNetSqueezeTimeLSTM.html @@ -0,0 +1,366 @@ + + + + + + + + + + + training.nets.eric.smallmodels.SqueezeNetSqueezeTimeLSTM — BDD Model Car Training documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + +
+
+ + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • training.nets.eric.smallmodels.SqueezeNetSqueezeTimeLSTM
    • + + +
  • + + + +
    • + +
+ + +
+
+
+
+ +

Source code for training.nets.eric.smallmodels.SqueezeNetSqueezeTimeLSTM

+"""SqueezeNet 1.1 modified for LSTM regression."""
+import logging
+
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+from torch.autograd import Variable
+
+logging.basicConfig(filename='training.log', level=logging.DEBUG)
+
+
+# from Parameters import ARGS
+
+
+
[docs]class Fire(nn.Module):  # pylint: disable=too-few-public-methods
+    """Implementation of Fire module"""
+
+    def __init__(self, inplanes, squeeze_planes,
+                 expand1x1_planes, expand3x3_planes):
+        """Sets up layers for Fire module"""
+        super(Fire, self).__init__()
+        self.inplanes = inplanes
+        self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
+        self.squeeze_activation = nn.ReLU(inplace=True)
+        self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes,
+                                   kernel_size=1)
+        self.expand1x1_activation = nn.ReLU(inplace=True)
+        self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes,
+                                   kernel_size=3, padding=1)
+        self.expand3x3_activation = nn.ReLU(inplace=True)
+
+
[docs]    def forward(self, input_data):
+        """Forward-propagates data through Fire module"""
+        output_data = self.squeeze_activation(self.squeeze(input_data))
+        return torch.cat([
+            self.expand1x1_activation(self.expand1x1(output_data)),
+            self.expand3x3_activation(self.expand3x3(output_data))
+        ], 1)

+
+
+
[docs]class SqueezeNetTimeLSTM(nn.Module):  # pylint: disable=too-few-public-methods
+    """SqueezeNet+LSTM for end to end autonomous driving"""
+
+    def __init__(self, n_frames=2, n_steps=10):
+        """Sets up layers"""
+        super(SqueezeNetTimeLSTM, self).__init__()
+
+        self.is_cuda = False
+
+        self.n_frames = n_frames
+        self.n_steps = n_steps
+        self.pre_metadata_features = nn.Sequential(
+            nn.Conv2d(3 * 2, 16, kernel_size=3, stride=2),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(16, 6, 12, 12)
+        )
+        self.post_metadata_features = nn.Sequential(
+            Fire(36, 8, 16, 16),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(32, 12, 24, 24),
+            Fire(48, 12, 24, 24),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(48, 16, 32, 32),
+            Fire(64, 16, 32, 32),
+            Fire(64, 24, 48, 48),
+            Fire(96, 24, 48, 48),
+        )
+        final_conv = nn.Conv2d(96, 2, kernel_size=1)
+        self.pre_lstm_output = nn.Sequential(
+            nn.Dropout(p=0.5),
+            final_conv,
+            nn.AvgPool2d(kernel_size=3, stride=2),
+        )
+        self.lstm_encoder = nn.ModuleList([
+            nn.LSTM(16, 32, 1, batch_first=True)
+        ])
+        self.lstm_decoder = nn.ModuleList([
+            nn.LSTM(1, 32, 1, batch_first=True),
+            nn.LSTM(32, 8, 1, batch_first=True),
+            nn.LSTM(8, 16, 1, batch_first=True),
+            nn.LSTM(16, 4, 1, batch_first=True),
+        ])
+
+        for mod in self.modules():
+            if isinstance(mod, nn.Conv2d):
+                if mod is final_conv:
+                    init.normal(mod.weight.data, mean=0.0, std=0.01)
+                else:
+                    init.kaiming_uniform(mod.weight.data)
+                if mod.bias is not None:
+                    mod.bias.data.zero_()
+
+
[docs]    def forward(self, camera_data, metadata):
+        """Forward-propagates data through SqueezeNetTimeLSTM"""
+        batch_size = camera_data.size(0)
+        nframes = camera_data.size(1) // 6
+        metadata = metadata.contiguous().view(-1, 12, 23, 41)
+        net_output = camera_data.contiguous().view(-1, 6, 94, 168)
+        net_output = self.pre_metadata_features(net_output)
+        net_output = torch.cat((net_output, metadata), 1)
+        net_output = self.post_metadata_features(net_output)
+        net_output = self.pre_lstm_output(net_output)
+        net_output = net_output.contiguous().view(batch_size, -1, 16)
+        for lstm in self.lstm_encoder:
+            net_output, last_hidden_cell = lstm(net_output)
+            last_hidden_cell = list(last_hidden_cell)
+        for lstm in self.lstm_decoder:
+            if last_hidden_cell:
+                # last_hidden_cell[0] = last_hidden_cell[0].contiguous().view(batch_size, -1, 256)
+                # last_hidden_cell[1] = last_hidden_cell[1].contiguous().view(batch_size, -1, 256)
+                net_output = lstm(self.get_decoder_seq(batch_size, self.n_steps), last_hidden_cell)[0]
+                last_hidden_cell = None
+            else:
+                net_output = lstm(net_output)[0]
+        net_output = net_output.contiguous().view(net_output.size(0), -1)
+        return net_output

+
+
[docs]    def get_decoder_seq(self, batch_size, timesteps):
+        decoder_input_seq = Variable(torch.zeros(batch_size, timesteps, 1))
+        return decoder_input_seq.cuda() if self.is_cuda else decoder_input_seq

+
+
[docs]    def cuda(self, device_id=None):
+        self.is_cuda = True
+        return super(SqueezeNetTimeLSTM, self).cuda(device_id)

+
+
[docs]    def num_params(self):
+        return sum([reduce(lambda x, y: x * y, [dim for dim in p.size()], 1) for p in self.parameters()])

+
+
[docs]def unit_test():
+    """Tests SqueezeNetTimeLSTM for size constitency"""
+    test_net = SqueezeNetTimeLSTM(6, 20)
+    test_net_output = test_net(
+        Variable(torch.randn(5, 36, 94, 168)),
+        Variable(torch.randn(5, 12, 6, 23, 41)))
+    logging.debug('Net Test Output = {}'.format(test_net_output))
+    logging.debug('Network was Unit Tested')
+    print(test_net.num_params())

+
+unit_test()
+
+Net = SqueezeNetTimeLSTM
+
+ +
+
+ +
+
+
+ + +
+ +
+

+ © Copyright 2017, Sauhaarda Chowdhuri. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/training/nets/eric/smallmodels/SqueezeNetTimeLSTM.html b/docs/_build/html/_modules/training/nets/eric/smallmodels/SqueezeNetTimeLSTM.html new file mode 100644 index 0000000..5b72e7e --- /dev/null +++ b/docs/_build/html/_modules/training/nets/eric/smallmodels/SqueezeNetTimeLSTM.html @@ -0,0 +1,364 @@ + + + + + + + + + + + training.nets.eric.smallmodels.SqueezeNetTimeLSTM — BDD Model Car Training documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + +
+
+ + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • training.nets.eric.smallmodels.SqueezeNetTimeLSTM
    • + + +
  • + + + +
    • + +
+ + +
+
+
+
+ +

Source code for training.nets.eric.smallmodels.SqueezeNetTimeLSTM

+"""SqueezeNet 1.1 modified for LSTM regression."""
+import logging
+
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+from torch.autograd import Variable
+
+logging.basicConfig(filename='training.log', level=logging.DEBUG)
+
+
+# from Parameters import ARGS
+
+
+
[docs]class Fire(nn.Module):  # pylint: disable=too-few-public-methods
+    """Implementation of Fire module"""
+
+    def __init__(self, inplanes, squeeze_planes,
+                 expand1x1_planes, expand3x3_planes):
+        """Sets up layers for Fire module"""
+        super(Fire, self).__init__()
+        self.inplanes = inplanes
+        self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
+        self.squeeze_activation = nn.ReLU(inplace=True)
+        self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes,
+                                   kernel_size=1)
+        self.expand1x1_activation = nn.ReLU(inplace=True)
+        self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes,
+                                   kernel_size=3, padding=1)
+        self.expand3x3_activation = nn.ReLU(inplace=True)
+
+
[docs]    def forward(self, input_data):
+        """Forward-propagates data through Fire module"""
+        output_data = self.squeeze_activation(self.squeeze(input_data))
+        return torch.cat([
+            self.expand1x1_activation(self.expand1x1(output_data)),
+            self.expand3x3_activation(self.expand3x3(output_data))
+        ], 1)

+
+
+
[docs]class SqueezeNetTimeLSTM(nn.Module):  # pylint: disable=too-few-public-methods
+    """SqueezeNet+LSTM for end to end autonomous driving"""
+
+    def __init__(self, n_frames=2, n_steps=10):
+        """Sets up layers"""
+        super(SqueezeNetTimeLSTM, self).__init__()
+
+        self.is_cuda = False
+
+        self.n_frames = n_frames
+        self.n_steps = n_steps
+        self.pre_metadata_features = nn.Sequential(
+            nn.Conv2d(3 * 2, 16, kernel_size=3, stride=2),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(16, 6, 12, 12)
+        )
+        self.post_metadata_features = nn.Sequential(
+            Fire(36, 8, 16, 16),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(32, 12, 24, 24),
+            Fire(48, 12, 24, 24),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(48, 16, 32, 32),
+            Fire(64, 16, 32, 32),
+            Fire(64, 24, 48, 48),
+            Fire(96, 24, 48, 48),
+        )
+        final_conv = nn.Conv2d(96, 2, kernel_size=1)
+        self.pre_lstm_output = nn.Sequential(
+            nn.Dropout(p=0.5),
+            final_conv,
+            nn.AvgPool2d(kernel_size=3, stride=2),
+        )
+        self.lstm_encoder = nn.ModuleList([
+            nn.LSTM(16, 32, 1, batch_first=True)
+        ])
+        self.lstm_decoder = nn.ModuleList([
+            nn.LSTM(1, 32, 1, batch_first=True),
+            nn.LSTM(32, 4, 1, batch_first=True)
+        ])
+
+        for mod in self.modules():
+            if isinstance(mod, nn.Conv2d):
+                if mod is final_conv:
+                    init.normal(mod.weight.data, mean=0.0, std=0.01)
+                else:
+                    init.kaiming_uniform(mod.weight.data)
+                if mod.bias is not None:
+                    mod.bias.data.zero_()
+
+
[docs]    def forward(self, camera_data, metadata):
+        """Forward-propagates data through SqueezeNetTimeLSTM"""
+        batch_size = camera_data.size(0)
+        nframes = camera_data.size(1) // 6
+        metadata = metadata.contiguous().view(-1, 12, 23, 41)
+        net_output = camera_data.contiguous().view(-1, 6, 94, 168)
+        net_output = self.pre_metadata_features(net_output)
+        net_output = torch.cat((net_output, metadata), 1)
+        net_output = self.post_metadata_features(net_output)
+        net_output = self.pre_lstm_output(net_output)
+        net_output = net_output.contiguous().view(batch_size, -1, 16)
+        for lstm in self.lstm_encoder:
+            net_output, last_hidden_cell = lstm(net_output)
+            last_hidden_cell = list(last_hidden_cell)
+        for lstm in self.lstm_decoder:
+            if last_hidden_cell:
+                # last_hidden_cell[0] = last_hidden_cell[0].contiguous().view(batch_size, -1, 256)
+                # last_hidden_cell[1] = last_hidden_cell[1].contiguous().view(batch_size, -1, 256)
+                net_output = lstm(self.get_decoder_seq(batch_size, self.n_steps), last_hidden_cell)[0]
+                last_hidden_cell = None
+            else:
+                net_output = lstm(net_output)[0]
+        net_output = net_output.contiguous().view(net_output.size(0), -1)
+        return net_output

+
+
[docs]    def get_decoder_seq(self, batch_size, timesteps):
+        decoder_input_seq = Variable(torch.zeros(batch_size, timesteps, 1))
+        return decoder_input_seq.cuda() if self.is_cuda else decoder_input_seq

+
+
[docs]    def cuda(self, device_id=None):
+        self.is_cuda = True
+        return super(SqueezeNetTimeLSTM, self).cuda(device_id)

+
+
[docs]    def num_params(self):
+        return sum([reduce(lambda x, y: x * y, [dim for dim in p.size()], 1) for p in self.parameters()])

+
+
[docs]def unit_test():
+    """Tests SqueezeNetTimeLSTM for size constitency"""
+    test_net = SqueezeNetTimeLSTM(6, 20)
+    test_net_output = test_net(
+        Variable(torch.randn(5, 36, 94, 168)),
+        Variable(torch.randn(5, 12, 6, 23, 41)))
+    logging.debug('Net Test Output = {}'.format(test_net_output))
+    logging.debug('Network was Unit Tested')
+    print(test_net.num_params())

+
+unit_test()
+
+Net = SqueezeNetTimeLSTM
+
+ +
+
+ +
+
+
+ + +
+ +
+

+ © Copyright 2017, Sauhaarda Chowdhuri. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/training/nets/eric/tinymodels/SqueezeNet.html b/docs/_build/html/_modules/training/nets/eric/tinymodels/SqueezeNet.html new file mode 100644 index 0000000..3464b8b --- /dev/null +++ b/docs/_build/html/_modules/training/nets/eric/tinymodels/SqueezeNet.html @@ -0,0 +1,319 @@ + + + + + + + + + + + training.nets.eric.tinymodels.SqueezeNet — BDD Model Car Training documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + +
+
+ + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • training.nets.eric.tinymodels.SqueezeNet
    • + + +
  • + + + +
    • + +
+ + +
+
+
+
+ +

Source code for training.nets.eric.tinymodels.SqueezeNet

+"""SqueezeNet 1.1 modified for regression."""
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+from torch.autograd import Variable
+import logging
+logging.basicConfig(filename='training.log', level=logging.DEBUG)
+
+
+
[docs]class Fire(nn.Module):
+
+    def __init__(self, inplanes, squeeze_planes,
+                 expand1x1_planes, expand3x3_planes):
+        super(Fire, self).__init__()
+        self.inplanes = inplanes
+        self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
+        self.squeeze_activation = nn.ReLU(inplace=True)
+        self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes,
+                                   kernel_size=1)
+        self.expand1x1_activation = nn.ReLU(inplace=True)
+        self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes,
+                                   kernel_size=3, padding=1)
+        self.expand3x3_activation = nn.ReLU(inplace=True)
+
+
[docs]    def forward(self, x):
+        x = self.squeeze_activation(self.squeeze(x))
+        return torch.cat([
+            self.expand1x1_activation(self.expand1x1(x)),
+            self.expand3x3_activation(self.expand3x3(x))
+        ], 1)

+
+
+
[docs]class SqueezeNet(nn.Module):
+
+    def __init__(self, n_steps=10, n_frames=2):
+        super(SqueezeNet, self).__init__()
+
+        self.n_steps = n_steps
+        self.n_frames = n_frames
+        self.pre_metadata_features = nn.Sequential(
+            nn.Conv2d(3 * 2 * self.n_frames, 16, kernel_size=3, stride=2),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(16, 4, 8, 8)
+        )
+        self.post_metadata_features = nn.Sequential(
+            Fire(24, 6, 12, 12),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(24, 8, 16, 16),
+            Fire(32, 8, 16, 16),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(32, 12, 24, 24),
+            Fire(48, 12, 24, 24),
+            Fire(48, 16, 32, 32),
+            Fire(64, 16, 32, 32),
+        )
+        final_conv = nn.Conv2d(64, self.n_steps * 2, kernel_size=1)
+        self.final_output = nn.Sequential(
+            nn.Dropout(p=0.5),
+            final_conv,
+            # nn.ReLU(inplace=True),
+            nn.AvgPool2d(kernel_size=5, stride=5)
+        )
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                if m is final_conv:
+                    init.normal(m.weight.data, mean=0.0, std=0.01)
+                else:
+                    init.kaiming_uniform(m.weight.data)
+                if m.bias is not None:
+                    m.bias.data.zero_()
+
+
[docs]    def forward(self, x, metadata):
+        x = self.pre_metadata_features(x)
+        x = torch.cat((x, metadata), 1)
+        x = self.post_metadata_features(x)
+        x = self.final_output(x)
+        x = x.view(x.size(0), -1)
+        return x

+
+
[docs]    def num_params(self):
+        return sum([reduce(lambda x, y: x * y, [dim for dim in p.size()], 1) for p in self.parameters()])

+
+
[docs]def unit_test():
+    test_net = SqueezeNet(20, 6)
+    a = test_net(Variable(torch.randn(5, 36, 94, 168)),
+                 Variable(torch.randn(5, 8, 23, 41)))
+    logging.debug('Net Test Output = {}'.format(a))
+    logging.debug('Network was Unit Tested')
+    print(test_net.num_params())

+
+unit_test()
+
+Net = SqueezeNet
+
+ +
+
+ +
+
+
+ + +
+ +
+

+ © Copyright 2017, Sauhaarda Chowdhuri. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/training/nets/eric/tinymodels/SqueezeNetLSTM.html b/docs/_build/html/_modules/training/nets/eric/tinymodels/SqueezeNetLSTM.html new file mode 100644 index 0000000..83f2ff2 --- /dev/null +++ b/docs/_build/html/_modules/training/nets/eric/tinymodels/SqueezeNetLSTM.html @@ -0,0 +1,336 @@ + + + + + + + + + + + training.nets.eric.tinymodels.SqueezeNetLSTM — BDD Model Car Training documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + +
+
+ + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • training.nets.eric.tinymodels.SqueezeNetLSTM
    • + + +
  • + + + +
    • + +
+ + +
+
+
+
+ +

Source code for training.nets.eric.tinymodels.SqueezeNetLSTM

+"""SqueezeNet 1.1 modified for LSTM regression."""
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+from torch.autograd import Variable
+import logging
+logging.basicConfig(filename='training.log', level=logging.DEBUG)
+
+#from Parameters import ARGS
+
+
+
[docs]class Fire(nn.Module):  # pylint: disable=too-few-public-methods
+    """Implementation of Fire module"""
+
+    def __init__(self, inplanes, squeeze_planes,
+                 expand1x1_planes, expand3x3_planes):
+        """Sets up layers for Fire module"""
+        super(Fire, self).__init__()
+        self.inplanes = inplanes
+        self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
+        self.squeeze_activation = nn.ReLU(inplace=True)
+        self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes,
+                                   kernel_size=1)
+        self.expand1x1_activation = nn.ReLU(inplace=True)
+        self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes,
+                                   kernel_size=3, padding=1)
+        self.expand3x3_activation = nn.ReLU(inplace=True)
+
+
[docs]    def forward(self, input_data):
+        """Forward-propagates data through Fire module"""
+        output_data = self.squeeze_activation(self.squeeze(input_data))
+        return torch.cat([
+            self.expand1x1_activation(self.expand1x1(output_data)),
+            self.expand3x3_activation(self.expand3x3(output_data))
+        ], 1)

+
+
+
[docs]class SqueezeNetLSTM(nn.Module):  # pylint: disable=too-few-public-methods
+    """SqueezeNet+LSTM for end to end autonomous driving"""
+
+    def __init__(self, n_frames=2, n_steps=10):
+        """Sets up layers"""
+        super(SqueezeNetLSTM, self).__init__()
+
+        self.n_frames = n_frames
+        self.n_steps = n_steps
+        self.pre_metadata_features = nn.Sequential(
+            nn.Conv2d(3 * 2 * self.n_frames, 16, kernel_size=3, stride=2),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(16, 4, 8, 8)
+        )
+        self.post_metadata_features = nn.Sequential(
+            Fire(24, 6, 12, 12),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(24, 8, 16, 16),
+            Fire(32, 8, 16, 16),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(32, 12, 24, 24),
+            Fire(48, 12, 24, 24),
+            Fire(48, 16, 32, 32),
+            Fire(64, 16, 32, 32),
+        )
+        final_conv = nn.Conv2d(64, self.n_steps * 2, kernel_size=1)
+        self.pre_lstm_output = nn.Sequential(
+            nn.Dropout(p=0.5),
+            final_conv,
+            nn.AvgPool2d(kernel_size=3, stride=2),
+        )
+        self.lstms = nn.ModuleList([
+            nn.LSTM(16, 32, 1, batch_first=True),
+            nn.LSTM(32, 4, 1, batch_first=True)
+        ])
+
+        for mod in self.modules():
+            if isinstance(mod, nn.Conv2d):
+                if mod is final_conv:
+                    init.normal(mod.weight.data, mean=0.0, std=0.01)
+                else:
+                    init.kaiming_uniform(mod.weight.data)
+                if mod.bias is not None:
+                    mod.bias.data.zero_()
+
+
[docs]    def forward(self, camera_data, metadata):
+        """Forward-propagates data through SqueezeNetLSTM"""
+        net_output = self.pre_metadata_features(camera_data)
+        net_output = torch.cat((net_output, metadata), 1)
+        net_output = self.post_metadata_features(net_output)
+        net_output = self.pre_lstm_output(net_output)
+        net_output = net_output.view(net_output.size(0), self.n_steps, -1)
+        for lstm in self.lstms:
+            net_output = lstm(net_output)[0]
+        net_output = net_output.contiguous().view(net_output.size(0), -1)
+        return net_output

+
+
[docs]    def num_params(self):
+        return sum([reduce(lambda x, y: x * y, [dim for dim in p.size()], 1) for p in self.parameters()])

+
+
+
[docs]def unit_test():
+    """Tests SqueezeNetLSTM for size constitency"""
+    test_net = SqueezeNetLSTM()
+    test_net_output = test_net(
+        Variable(torch.randn(5, 12, 94, 168)),
+        Variable(torch.randn(5, 8, 23, 41)))
+    logging.debug('Net Test Output = {}'.format(test_net_output))
+    logging.debug('Network was Unit Tested')
+    print(test_net.num_params())

+
+unit_test()
+
+Net = SqueezeNetLSTM
+
+ +
+
+ +
+
+
+ + +
+ +
+

+ © Copyright 2017, Sauhaarda Chowdhuri. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/training/nets/eric/tinymodels/SqueezeNetSqueezeLSTM.html b/docs/_build/html/_modules/training/nets/eric/tinymodels/SqueezeNetSqueezeLSTM.html new file mode 100644 index 0000000..442bf09 --- /dev/null +++ b/docs/_build/html/_modules/training/nets/eric/tinymodels/SqueezeNetSqueezeLSTM.html @@ -0,0 +1,342 @@ + + + + + + + + + + + training.nets.eric.tinymodels.SqueezeNetSqueezeLSTM — BDD Model Car Training documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + +
+
+ + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • training.nets.eric.tinymodels.SqueezeNetSqueezeLSTM
    • + + +
  • + + + +
    • + +
+ + +
+
+
+
+ +

Source code for training.nets.eric.tinymodels.SqueezeNetSqueezeLSTM

+"""SqueezeNet 1.1 modified for LSTM regression."""
+import logging
+
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+from torch.autograd import Variable
+
+logging.basicConfig(filename='training.log', level=logging.DEBUG)
+
+
+# from Parameters import ARGS
+
+
+
[docs]class Fire(nn.Module):  # pylint: disable=too-few-public-methods
+    """Implementation of Fire module"""
+
+    def __init__(self, inplanes, squeeze_planes,
+                 expand1x1_planes, expand3x3_planes):
+        """Sets up layers for Fire module"""
+        super(Fire, self).__init__()
+        self.inplanes = inplanes
+        self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
+        self.squeeze_activation = nn.ReLU(inplace=True)
+        self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes,
+                                   kernel_size=1)
+        self.expand1x1_activation = nn.ReLU(inplace=True)
+        self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes,
+                                   kernel_size=3, padding=1)
+        self.expand3x3_activation = nn.ReLU(inplace=True)
+
+
[docs]    def forward(self, input_data):
+        """Forward-propagates data through Fire module"""
+        output_data = self.squeeze_activation(self.squeeze(input_data))
+        return torch.cat([
+            self.expand1x1_activation(self.expand1x1(output_data)),
+            self.expand3x3_activation(self.expand3x3(output_data))
+        ], 1)

+
+
+
[docs]class SqueezeNetSqueezeLSTM(nn.Module):  # pylint: disable=too-few-public-methods
+    """SqueezeNet+LSTM for end to end autonomous driving"""
+
+    def __init__(self, n_steps=10, n_frames=2):
+        """Sets up layers"""
+        super(SqueezeNetSqueezeLSTM, self).__init__()
+
+        self.n_frames = n_frames
+        self.n_steps = n_steps
+        self.pre_metadata_features = nn.Sequential(
+            nn.Conv2d(3 * 2 * self.n_frames, 16, kernel_size=3, stride=2),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(16, 4, 8, 8)
+        )
+        self.post_metadata_features = nn.Sequential(
+            Fire(24, 6, 12, 12),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(24, 8, 16, 16),
+            Fire(32, 8, 16, 16),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(32, 12, 24, 24),
+            Fire(48, 12, 24, 24),
+            Fire(48, 16, 32, 32),
+            Fire(64, 16, 32, 32),
+        )
+        final_conv = nn.Conv2d(64, self.n_steps * 2, kernel_size=1)
+        self.pre_lstm_output = nn.Sequential(
+            nn.Dropout(p=0.5),
+            final_conv,
+            nn.AvgPool2d(kernel_size=3, stride=2),
+        )
+        self.lstms = nn.ModuleList([
+            nn.LSTM(16, 32, 1, batch_first=True),
+            nn.LSTM(32, 8, 1, batch_first=True),
+            nn.LSTM(8, 16, 1, batch_first=True),
+            nn.LSTM(16, 4, 1, batch_first=True)
+        ])
+
+        for mod in self.modules():
+            if isinstance(mod, nn.Conv2d):
+                if mod is final_conv:
+                    init.normal(mod.weight.data, mean=0.0, std=0.01)
+                else:
+                    init.kaiming_uniform(mod.weight.data)
+                if mod.bias is not None:
+                    mod.bias.data.zero_()
+
+
[docs]    def forward(self, camera_data, metadata):
+        """Forward-propagates data through SqueezeNetSqueezeLSTM"""
+        net_output = self.pre_metadata_features(camera_data)
+        net_output = torch.cat((net_output, metadata), 1)
+        net_output = self.post_metadata_features(net_output)
+        net_output = self.pre_lstm_output(net_output)
+        net_output = net_output.view(net_output.size(0), self.n_steps, -1)
+        for lstm in self.lstms:
+            net_output = lstm(net_output)[0]
+        net_output = net_output.contiguous().view(net_output.size(0), -1)
+        return net_output

+
+
[docs]    def num_params(self):
+        return sum([reduce(lambda x, y: x * y, [dim for dim in p.size()], 1) for p in self.parameters()])

+
+
+
[docs]def unit_test():
+    """Tests SqueezeNetSqueezeLSTM for size constitency"""
+    test_net = SqueezeNetSqueezeLSTM(20, 6)
+    test_net_output = test_net(
+        Variable(torch.randn(5, 36, 94, 168)),
+        Variable(torch.randn(5, 8, 23, 41)))
+    logging.debug('Net Test Output = {}'.format(test_net_output))
+    logging.debug('Network was Unit Tested')
+    print(test_net.num_params())

+    # for param in test_net.parameters():
+
+
+unit_test()
+Net = SqueezeNetSqueezeLSTM
+
+ +
+
+ +
+
+
+ + +
+ +
+

+ © Copyright 2017, Sauhaarda Chowdhuri. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/training/nets/eric/tinymodels/SqueezeNetSqueezeTimeLSTM.html b/docs/_build/html/_modules/training/nets/eric/tinymodels/SqueezeNetSqueezeTimeLSTM.html new file mode 100644 index 0000000..666c77d --- /dev/null +++ b/docs/_build/html/_modules/training/nets/eric/tinymodels/SqueezeNetSqueezeTimeLSTM.html @@ -0,0 +1,366 @@ + + + + + + + + + + + training.nets.eric.tinymodels.SqueezeNetSqueezeTimeLSTM — BDD Model Car Training documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + +
+
+ + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
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    • + +
  • training.nets.eric.tinymodels.SqueezeNetSqueezeTimeLSTM
    • + + +
  • + + + +
    • + +
+ + +
+
+
+
+ +

Source code for training.nets.eric.tinymodels.SqueezeNetSqueezeTimeLSTM

+"""SqueezeNet 1.1 modified for LSTM regression."""
+import logging
+
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+from torch.autograd import Variable
+
+logging.basicConfig(filename='training.log', level=logging.DEBUG)
+
+
+# from Parameters import ARGS
+
+
+
[docs]class Fire(nn.Module):  # pylint: disable=too-few-public-methods
+    """Implementation of Fire module"""
+
+    def __init__(self, inplanes, squeeze_planes,
+                 expand1x1_planes, expand3x3_planes):
+        """Sets up layers for Fire module"""
+        super(Fire, self).__init__()
+        self.inplanes = inplanes
+        self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
+        self.squeeze_activation = nn.ReLU(inplace=True)
+        self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes,
+                                   kernel_size=1)
+        self.expand1x1_activation = nn.ReLU(inplace=True)
+        self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes,
+                                   kernel_size=3, padding=1)
+        self.expand3x3_activation = nn.ReLU(inplace=True)
+
+
[docs]    def forward(self, input_data):
+        """Forward-propagates data through Fire module"""
+        output_data = self.squeeze_activation(self.squeeze(input_data))
+        return torch.cat([
+            self.expand1x1_activation(self.expand1x1(output_data)),
+            self.expand3x3_activation(self.expand3x3(output_data))
+        ], 1)

+
+
+
[docs]class SqueezeNetTimeLSTM(nn.Module):  # pylint: disable=too-few-public-methods
+    """SqueezeNet+LSTM for end to end autonomous driving"""
+
+    def __init__(self, n_frames=2, n_steps=10):
+        """Sets up layers"""
+        super(SqueezeNetTimeLSTM, self).__init__()
+
+        self.is_cuda = False
+
+        self.n_frames = n_frames
+        self.n_steps = n_steps
+        self.pre_metadata_features = nn.Sequential(
+            nn.Conv2d(3 * 2, 16, kernel_size=3, stride=2),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(16, 4, 8, 8)
+        )
+        self.post_metadata_features = nn.Sequential(
+            Fire(24, 6, 12, 12),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(24, 8, 16, 16),
+            Fire(32, 8, 16, 16),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(32, 12, 24, 24),
+            Fire(48, 12, 24, 24),
+            Fire(48, 16, 32, 32),
+            Fire(64, 16, 32, 32),
+        )
+        final_conv = nn.Conv2d(64, 2, kernel_size=1)
+        self.pre_lstm_output = nn.Sequential(
+            nn.Dropout(p=0.5),
+            final_conv,
+            nn.AvgPool2d(kernel_size=3, stride=2),
+        )
+        self.lstm_encoder = nn.ModuleList([
+            nn.LSTM(16, 32, 1, batch_first=True)
+        ])
+        self.lstm_decoder = nn.ModuleList([
+            nn.LSTM(1, 32, 1, batch_first=True),
+            nn.LSTM(32, 8, 1, batch_first=True),
+            nn.LSTM(8, 16, 1, batch_first=True),
+            nn.LSTM(16, 4, 1, batch_first=True),
+        ])
+
+        for mod in self.modules():
+            if isinstance(mod, nn.Conv2d):
+                if mod is final_conv:
+                    init.normal(mod.weight.data, mean=0.0, std=0.01)
+                else:
+                    init.kaiming_uniform(mod.weight.data)
+                if mod.bias is not None:
+                    mod.bias.data.zero_()
+
+
[docs]    def forward(self, camera_data, metadata):
+        """Forward-propagates data through SqueezeNetTimeLSTM"""
+        batch_size = camera_data.size(0)
+        nframes = camera_data.size(1) // 6
+        metadata = metadata.contiguous().view(-1, 8, 23, 41)
+        net_output = camera_data.contiguous().view(-1, 6, 94, 168)
+        net_output = self.pre_metadata_features(net_output)
+        net_output = torch.cat((net_output, metadata), 1)
+        net_output = self.post_metadata_features(net_output)
+        net_output = self.pre_lstm_output(net_output)
+        net_output = net_output.contiguous().view(batch_size, -1, 16)
+        for lstm in self.lstm_encoder:
+            net_output, last_hidden_cell = lstm(net_output)
+            last_hidden_cell = list(last_hidden_cell)
+        for lstm in self.lstm_decoder:
+            if last_hidden_cell:
+                # last_hidden_cell[0] = last_hidden_cell[0].contiguous().view(batch_size, -1, 256)
+                # last_hidden_cell[1] = last_hidden_cell[1].contiguous().view(batch_size, -1, 256)
+                net_output = lstm(self.get_decoder_seq(batch_size, self.n_steps), last_hidden_cell)[0]
+                last_hidden_cell = None
+            else:
+                net_output = lstm(net_output)[0]
+        net_output = net_output.contiguous().view(net_output.size(0), -1)
+        return net_output

+
+
[docs]    def get_decoder_seq(self, batch_size, timesteps):
+        decoder_input_seq = Variable(torch.zeros(batch_size, timesteps, 1))
+        return decoder_input_seq.cuda() if self.is_cuda else decoder_input_seq

+
+
[docs]    def cuda(self, device_id=None):
+        self.is_cuda = True
+        return super(SqueezeNetTimeLSTM, self).cuda(device_id)

+
+
[docs]    def num_params(self):
+        return sum([reduce(lambda x, y: x * y, [dim for dim in p.size()], 1) for p in self.parameters()])

+
+
[docs]def unit_test():
+    """Tests SqueezeNetTimeLSTM for size constitency"""
+    test_net = SqueezeNetTimeLSTM(6, 20)
+    test_net_output = test_net(
+        Variable(torch.randn(5, 36, 94, 168)),
+        Variable(torch.randn(5, 8, 6, 23, 41)))
+    logging.debug('Net Test Output = {}'.format(test_net_output))
+    logging.debug('Network was Unit Tested')
+    print(test_net.num_params())

+
+unit_test()
+
+Net = SqueezeNetTimeLSTM
+
+ +
+
+ +
+
+
+ + +
+ +
+

+ © Copyright 2017, Sauhaarda Chowdhuri. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_modules/training/nets/eric/tinymodels/SqueezeNetTimeLSTM.html b/docs/_build/html/_modules/training/nets/eric/tinymodels/SqueezeNetTimeLSTM.html new file mode 100644 index 0000000..159db54 --- /dev/null +++ b/docs/_build/html/_modules/training/nets/eric/tinymodels/SqueezeNetTimeLSTM.html @@ -0,0 +1,363 @@ + + + + + + + + + + + training.nets.eric.tinymodels.SqueezeNetTimeLSTM — BDD Model Car Training documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + +
+ + + + + + +
+
+ + + + + + + + + + + + + + + + +
+ +
    + +
  • Docs »
    • + +
  • Module code »
    • + +
  • training.nets.eric.tinymodels.SqueezeNetTimeLSTM
    • + + +
  • + + + +
    • + +
+ + +
+
+
+
+ +

Source code for training.nets.eric.tinymodels.SqueezeNetTimeLSTM

+"""SqueezeNet 1.1 modified for LSTM regression."""
+import logging
+
+import torch
+import torch.nn as nn
+import torch.nn.init as init
+from torch.autograd import Variable
+
+logging.basicConfig(filename='training.log', level=logging.DEBUG)
+
+
+# from Parameters import ARGS
+
+
+
[docs]class Fire(nn.Module):  # pylint: disable=too-few-public-methods
+    """Implementation of Fire module"""
+
+    def __init__(self, inplanes, squeeze_planes,
+                 expand1x1_planes, expand3x3_planes):
+        """Sets up layers for Fire module"""
+        super(Fire, self).__init__()
+        self.inplanes = inplanes
+        self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1)
+        self.squeeze_activation = nn.ReLU(inplace=True)
+        self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes,
+                                   kernel_size=1)
+        self.expand1x1_activation = nn.ReLU(inplace=True)
+        self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes,
+                                   kernel_size=3, padding=1)
+        self.expand3x3_activation = nn.ReLU(inplace=True)
+
+
[docs]    def forward(self, input_data):
+        """Forward-propagates data through Fire module"""
+        output_data = self.squeeze_activation(self.squeeze(input_data))
+        return torch.cat([
+            self.expand1x1_activation(self.expand1x1(output_data)),
+            self.expand3x3_activation(self.expand3x3(output_data))
+        ], 1)

+
+
+
[docs]class SqueezeNetTimeLSTM(nn.Module):  # pylint: disable=too-few-public-methods
+    """SqueezeNet+LSTM for end to end autonomous driving"""
+
+    def __init__(self, n_frames=2, n_steps=10):
+        """Sets up layers"""
+        super(SqueezeNetTimeLSTM, self).__init__()
+
+        self.is_cuda = False
+
+        self.n_frames = n_frames
+        self.n_steps = n_steps
+        self.pre_metadata_features = nn.Sequential(
+            nn.Conv2d(3 * 2, 16, kernel_size=3, stride=2),
+            nn.ReLU(inplace=True),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(16, 4, 8, 8),
+        )
+        self.post_metadata_features = nn.Sequential(
+            Fire(24, 6, 12, 12),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(24, 8, 16, 16),
+            Fire(32, 8, 16, 16),
+            nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True),
+            Fire(32, 12, 24, 24),
+            Fire(48, 12, 24, 24),
+            Fire(48, 16, 32, 32),
+            Fire(64, 16, 32, 32),
+        )
+        final_conv = nn.Conv2d(64, 2, kernel_size=1)
+        self.pre_lstm_output = nn.Sequential(
+            nn.Dropout(p=0.5),
+            final_conv,
+            nn.AvgPool2d(kernel_size=3, stride=2),
+        )
+        self.lstm_encoder = nn.ModuleList([
+            nn.LSTM(16, 32, 1, batch_first=True)
+        ])
+        self.lstm_decoder = nn.ModuleList([
+            nn.LSTM(1, 32, 1, batch_first=True),
+            nn.LSTM(32, 4, 1, batch_first=True)
+        ])
+
+        for mod in self.modules():
+            if isinstance(mod, nn.Conv2d):
+                if mod is final_conv:
+                    init.normal(mod.weight.data, mean=0.0, std=0.01)
+                else:
+                    init.kaiming_uniform(mod.weight.data)
+                if mod.bias is not None:
+                    mod.bias.data.zero_()
+
+
[docs]    def forward(self, camera_data, metadata):
+        """Forward-propagates data through SqueezeNetTimeLSTM"""
+        batch_size = camera_data.size(0)
+        metadata = metadata.contiguous().view(-1, 8, 23, 41)
+        net_output = camera_data.contiguous().view(-1, 6, 94, 168)
+        net_output = self.pre_metadata_features(net_output)
+        net_output = torch.cat((net_output, metadata), 1)
+        net_output = self.post_metadata_features(net_output)
+        net_output = self.pre_lstm_output(net_output)
+        net_output = net_output.contiguous().view(batch_size, -1, 16)
+        for lstm in self.lstm_encoder:
+            net_output, last_hidden_cell = lstm(net_output)
+            last_hidden_cell = list(last_hidden_cell)
+        for lstm in self.lstm_decoder:
+            if last_hidden_cell:
+                # last_hidden_cell[0] = last_hidden_cell[0].contiguous().view(batch_size, -1, 256)
+                # last_hidden_cell[1] = last_hidden_cell[1].contiguous().view(batch_size, -1, 256)
+                net_output = lstm(self.get_decoder_seq(batch_size, self.n_steps), last_hidden_cell)[0]
+                last_hidden_cell = None
+            else:
+                net_output = lstm(net_output)[0]
+        net_output = net_output.contiguous().view(net_output.size(0), -1)
+        return net_output

+
+
[docs]    def get_decoder_seq(self, batch_size, timesteps):
+        decoder_input_seq = Variable(torch.zeros(batch_size, timesteps, 1))
+        return decoder_input_seq.cuda() if self.is_cuda else decoder_input_seq

+
+
[docs]    def cuda(self, device_id=None):
+        self.is_cuda = True
+        return super(SqueezeNetTimeLSTM, self).cuda(device_id)

+
+
[docs]    def num_params(self):
+        return sum([reduce(lambda x, y: x * y, [dim for dim in p.size()], 1) for p in self.parameters()])

+
+
[docs]def unit_test():
+    """Tests SqueezeNetTimeLSTM for size constitency"""
+    test_net = SqueezeNetTimeLSTM(6, 20)
+    test_net_output = test_net(
+        Variable(torch.randn(5, 36, 94, 168)),
+        Variable(torch.randn(5, 8, 6, 23, 41)))
+    logging.debug('Net Test Output = {}'.format(test_net_output))
+    logging.debug('Network was Unit Tested')
+    print(test_net.num_params())

+
+unit_test()
+
+Net = SqueezeNetTimeLSTM
+
+ +
+
+ +
+
+
+ + +
+ +
+

+ © Copyright 2017, Sauhaarda Chowdhuri. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
+ + + + + + + + + + + + + + + + + + + + + + + + + \ No newline at end of file diff --git a/docs/_build/html/_sources/index.rst.txt b/docs/_build/html/_sources/index.rst.txt new file mode 100644 index 0000000..95bbf30 --- /dev/null +++ b/docs/_build/html/_sources/index.rst.txt @@ -0,0 +1,24 @@ +.. BDD Model Car Training documentation master file, created by + sphinx-quickstart on Sat Dec 23 21:19:21 2017. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to BDD Model Car Training's documentation! +================================================== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + source/modules + wiki/computersetup + wiki/computermonitor + wiki/dataset + wiki/TX2 + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/_build/html/_sources/source/modules.rst.txt b/docs/_build/html/_sources/source/modules.rst.txt new file mode 100644 index 0000000..54b4dcc --- /dev/null +++ b/docs/_build/html/_sources/source/modules.rst.txt @@ -0,0 +1,7 @@ +training +======== + +.. toctree:: + :maxdepth: 4 + + training diff --git a/docs/_build/html/_sources/source/training.nets.eric.nonsqueeze.rst.txt b/docs/_build/html/_sources/source/training.nets.eric.nonsqueeze.rst.txt new file mode 100644 index 0000000..2b5cda4 --- /dev/null +++ b/docs/_build/html/_sources/source/training.nets.eric.nonsqueeze.rst.txt @@ -0,0 +1,46 @@ +training\.nets\.eric\.nonsqueeze package +======================================== + +Submodules +---------- + +training\.nets\.eric\.nonsqueeze\.Feedforward module +---------------------------------------------------- + +.. automodule:: training.nets.eric.nonsqueeze.Feedforward + :members: + :undoc-members: + :show-inheritance: + +training\.nets\.eric\.nonsqueeze\.Nvidia module +----------------------------------------------- + +.. automodule:: training.nets.eric.nonsqueeze.Nvidia + :members: + :undoc-members: + :show-inheritance: + +training\.nets\.eric\.nonsqueeze\.NvidiaMetadata module +------------------------------------------------------- + +.. automodule:: training.nets.eric.nonsqueeze.NvidiaMetadata + :members: + :undoc-members: + :show-inheritance: + +training\.nets\.eric\.nonsqueeze\.TimeLSTM module +------------------------------------------------- + +.. automodule:: training.nets.eric.nonsqueeze.TimeLSTM + :members: + :undoc-members: + :show-inheritance: + + +Module contents +--------------- + +.. automodule:: training.nets.eric.nonsqueeze + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_build/html/_sources/source/training.nets.eric.rst.txt b/docs/_build/html/_sources/source/training.nets.eric.rst.txt new file mode 100644 index 0000000..d4d4a53 --- /dev/null +++ b/docs/_build/html/_sources/source/training.nets.eric.rst.txt @@ -0,0 +1,19 @@ +training\.nets\.eric package +============================ + +Subpackages +----------- + +.. toctree:: + + training.nets.eric.nonsqueeze + training.nets.eric.smallmodels + training.nets.eric.tinymodels + +Module contents +--------------- + +.. automodule:: training.nets.eric + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_build/html/_sources/source/training.nets.eric.smallmodels.rst.txt b/docs/_build/html/_sources/source/training.nets.eric.smallmodels.rst.txt new file mode 100644 index 0000000..03e0791 --- /dev/null +++ b/docs/_build/html/_sources/source/training.nets.eric.smallmodels.rst.txt @@ -0,0 +1,54 @@ +training\.nets\.eric\.smallmodels package +========================================= + +Submodules +---------- + +training\.nets\.eric\.smallmodels\.SqueezeNet module +---------------------------------------------------- + +.. automodule:: training.nets.eric.smallmodels.SqueezeNet + :members: + :undoc-members: + :show-inheritance: + +training\.nets\.eric\.smallmodels\.SqueezeNetLSTM module +-------------------------------------------------------- + +.. automodule:: training.nets.eric.smallmodels.SqueezeNetLSTM + :members: + :undoc-members: + :show-inheritance: + +training\.nets\.eric\.smallmodels\.SqueezeNetSqueezeLSTM module +--------------------------------------------------------------- + +.. automodule:: training.nets.eric.smallmodels.SqueezeNetSqueezeLSTM + :members: + :undoc-members: + :show-inheritance: + +training\.nets\.eric\.smallmodels\.SqueezeNetSqueezeTimeLSTM module +------------------------------------------------------------------- + +.. automodule:: training.nets.eric.smallmodels.SqueezeNetSqueezeTimeLSTM + :members: + :undoc-members: + :show-inheritance: + +training\.nets\.eric\.smallmodels\.SqueezeNetTimeLSTM module +------------------------------------------------------------ + +.. automodule:: training.nets.eric.smallmodels.SqueezeNetTimeLSTM + :members: + :undoc-members: + :show-inheritance: + + +Module contents +--------------- + +.. automodule:: training.nets.eric.smallmodels + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_build/html/_sources/source/training.nets.eric.tinymodels.rst.txt b/docs/_build/html/_sources/source/training.nets.eric.tinymodels.rst.txt new file mode 100644 index 0000000..e30d713 --- /dev/null +++ b/docs/_build/html/_sources/source/training.nets.eric.tinymodels.rst.txt @@ -0,0 +1,54 @@ +training\.nets\.eric\.tinymodels package +======================================== + +Submodules +---------- + +training\.nets\.eric\.tinymodels\.SqueezeNet module +--------------------------------------------------- + +.. automodule:: training.nets.eric.tinymodels.SqueezeNet + :members: + :undoc-members: + :show-inheritance: + +training\.nets\.eric\.tinymodels\.SqueezeNetLSTM module +------------------------------------------------------- + +.. automodule:: training.nets.eric.tinymodels.SqueezeNetLSTM + :members: + :undoc-members: + :show-inheritance: + +training\.nets\.eric\.tinymodels\.SqueezeNetSqueezeLSTM module +-------------------------------------------------------------- + +.. automodule:: training.nets.eric.tinymodels.SqueezeNetSqueezeLSTM + :members: + :undoc-members: + :show-inheritance: + +training\.nets\.eric\.tinymodels\.SqueezeNetSqueezeTimeLSTM module +------------------------------------------------------------------ + +.. automodule:: training.nets.eric.tinymodels.SqueezeNetSqueezeTimeLSTM + :members: + :undoc-members: + :show-inheritance: + +training\.nets\.eric\.tinymodels\.SqueezeNetTimeLSTM module +----------------------------------------------------------- + +.. automodule:: training.nets.eric.tinymodels.SqueezeNetTimeLSTM + :members: + :undoc-members: + :show-inheritance: + + +Module contents +--------------- + +.. automodule:: training.nets.eric.tinymodels + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_build/html/_sources/source/training.nets.rst.txt b/docs/_build/html/_sources/source/training.nets.rst.txt new file mode 100644 index 0000000..c1d2438 --- /dev/null +++ b/docs/_build/html/_sources/source/training.nets.rst.txt @@ -0,0 +1,45 @@ +training\.nets package +====================== + +Subpackages +----------- + +.. toctree:: + + training.nets.eric + +Submodules +---------- + +training\.nets\.SqueezeNet module +--------------------------------- + +.. automodule:: training.nets.SqueezeNet + :members: + :undoc-members: + :show-inheritance: + +training\.nets\.SqueezeNetAruco1 module +--------------------------------------- + +.. automodule:: training.nets.SqueezeNetAruco1 + :members: + :undoc-members: + :show-inheritance: + +training\.nets\.Z2ColorBatchNorm module +--------------------------------------- + +.. automodule:: training.nets.Z2ColorBatchNorm + :members: + :undoc-members: + :show-inheritance: + + +Module contents +--------------- + +.. automodule:: training.nets + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_build/html/_sources/source/training.rst.txt b/docs/_build/html/_sources/source/training.rst.txt new file mode 100644 index 0000000..a81ab55 --- /dev/null +++ b/docs/_build/html/_sources/source/training.rst.txt @@ -0,0 +1,69 @@ +training package +================ + +Subpackages +----------- + +.. toctree:: + + training.nets + +Submodules +---------- + +training\.ArucoDataset module +----------------------------- + +.. automodule:: training.ArucoDataset + :members: + :undoc-members: + :show-inheritance: + +training\.Config module +----------------------- + +.. automodule:: training.Config + :members: + :undoc-members: + :show-inheritance: + +training\.Dataset module +------------------------ + +.. automodule:: training.Dataset + :members: + :undoc-members: + :show-inheritance: + +training\.Parameters module +--------------------------- + +.. automodule:: training.Parameters + :members: + :undoc-members: + :show-inheritance: + +training\.Train module +---------------------- + +.. automodule:: training.Train + :members: + :undoc-members: + :show-inheritance: + +training\.Utils module +---------------------- + +.. automodule:: training.Utils + :members: + :undoc-members: + :show-inheritance: + + +Module contents +--------------- + +.. automodule:: training + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/_build/html/_sources/wiki/TX2.md.txt b/docs/_build/html/_sources/wiki/TX2.md.txt new file mode 100644 index 0000000..f210fac --- /dev/null +++ b/docs/_build/html/_sources/wiki/TX2.md.txt @@ -0,0 +1,5 @@ +# TX2 +## TX2 Cloning +TX2 Cloning can be a complicated process, but it is extremely useful to setup multiple TX2s all running the same inference code in a short period of time. It is much faster than compiling PyTorch/Caffe and installing all necessary components. Here is a guide I wrote on how to clone the TX2 properly: + +[https://slack-files.com/T62KXFY93-F6DPGDJP3-f7185694c1](https://slack-files.com/T62KXFY93-F6DPGDJP3-f7185694c1) diff --git a/docs/_build/html/_sources/wiki/computermonitor.md.txt b/docs/_build/html/_sources/wiki/computermonitor.md.txt new file mode 100644 index 0000000..d10a83b --- /dev/null +++ b/docs/_build/html/_sources/wiki/computermonitor.md.txt @@ -0,0 +1,153 @@ +# Computer Monitoring + +## Run once per client machine + +### Install distributed shell +#### Linux +```bash +sudo apt-get --assume-yes install dsh +``` +#### Mac OSX +http://macappstore.org/dsh/ + + +### Setup Config Files +#### Now create a `~/.ssh/config` file and add all of the machines to it. Here is an example: +``` +Host bdd3.tspankaj.com + User sauhaarda + Port 1022 +Host bdd6.tspankaj.com + User sauhaarda + Port 1022 +Host bdd5.neuro.berkeley.edu + User sauhaarda + Port 1022 +Host bdd4.dyn.berkeley.edu + User sauhaarda + Port 1022 +``` + +#### Create a `~/.dsh/machines.list` file. In this file put in all of the host names from your ssh config. Here is an example: +``` +bdd3.tspankaj.com +bdd6.tspankaj.com +bdd4.dyn.berkeley.edu +bdd5.neuro.berkeley.edu +``` + +#### Finally add an alias to allow for even easier access for monitoring computers: +```bash +echo "alias monitorall='dsh -Mac -- remote-log'" >> ~/.bashrc + +## Finished! +``` +Now you'll be able to run `monitorall` anytime you want to see the status of each computer. The output will look something like this: +``` +bdd5.dyn.berkeley.edu: PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND +bdd5.dyn.berkeley.edu: 30193 root 20 0 30.638g 6.504g 528376 R 93.3 5.2 23:02.27 python +bdd5.dyn.berkeley.edu: Wed Aug 16 21:10:31 2017 +bdd5.dyn.berkeley.edu: +-----------------------------------------------------------------------------+ +bdd5.dyn.berkeley.edu: | NVIDIA-SMI 375.66 Driver Version: 375.66 | +bdd5.dyn.berkeley.edu: |-------------------------------+----------------------+----------------------+ +bdd5.dyn.berkeley.edu: | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | +bdd5.dyn.berkeley.edu: | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | +bdd5.dyn.berkeley.edu: |===============================+======================+======================| +bdd5.dyn.berkeley.edu: | 0 GeForce GTX 108... Off | 0000:4B:00.0 Off | N/A | +bdd5.dyn.berkeley.edu: | 28% 38C P8 16W / 250W | 2MiB / 11172MiB | 0% Default | +bdd5.dyn.berkeley.edu: +-------------------------------+----------------------+----------------------+ +bdd5.dyn.berkeley.edu: | 1 GeForce GTX 108... Off | 0000:4C:00.0 Off | N/A | +bdd5.dyn.berkeley.edu: | 55% 79C P2 81W / 250W | 1877MiB / 11170MiB | 22% Default | +bdd5.dyn.berkeley.edu: +-------------------------------+----------------------+----------------------+ +bdd5.dyn.berkeley.edu: +bdd5.dyn.berkeley.edu: +-----------------------------------------------------------------------------+ +bdd5.dyn.berkeley.edu: | Processes: GPU Memory | +bdd5.dyn.berkeley.edu: | GPU PID Type Process name Usage | +bdd5.dyn.berkeley.edu: |=============================================================================| +bdd5.dyn.berkeley.edu: | 1 30193 C python 1875MiB | +bdd5.dyn.berkeley.edu: +-----------------------------------------------------------------------------+ +bdd3.tspankaj.com: PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND +bdd3.tspankaj.com: 10854 bala 20 0 40.226g 0.012t 166016 R 86.7 9.9 449:50.57 python +bdd3.tspankaj.com: 10875 bala 20 0 41.614g 0.014t 165668 R 100.0 11.0 523:46.08 python +bdd3.tspankaj.com: 11149 bala 20 0 29.662g 6.465g 86512 R 80.0 5.1 499:47.42 python +bdd3.tspankaj.com: 11393 bala 20 0 30.072g 6.391g 86568 R 73.3 5.1 463:29.35 python +bdd3.tspankaj.com: 31902 bala 20 0 29.506g 6.038g 87740 R 86.7 4.8 171:49.60 python +bdd3.tspankaj.com: 10854 bala 20 0 40.226g 0.012t 166016 R 66.7 9.9 449:51.16 python +bdd3.tspankaj.com: 10875 bala 20 0 41.614g 0.014t 165668 R 81.2 11.0 523:46.79 python +bdd3.tspankaj.com: Wed Aug 16 21:10:32 2017 +bdd3.tspankaj.com: +-----------------------------------------------------------------------------+ +bdd3.tspankaj.com: | NVIDIA-SMI 375.66 Driver Version: 375.66 | +bdd3.tspankaj.com: |-------------------------------+----------------------+----------------------+ +bdd3.tspankaj.com: | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | +bdd3.tspankaj.com: | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | +bdd3.tspankaj.com: |===============================+======================+======================| +bdd3.tspankaj.com: | 0 TITAN X (Pascal) Off | 0000:4C:00.0 Off | N/A | +bdd3.tspankaj.com: | 41% 69C P2 184W / 250W | 6103MiB / 12189MiB | 74% Default | +bdd3.tspankaj.com: +-------------------------------+----------------------+----------------------+ +bdd3.tspankaj.com: | 1 TITAN X (Pascal) Off | 0000:4D:00.0 Off | N/A | +bdd3.tspankaj.com: | 51% 84C P2 104W / 250W | 3728MiB / 12186MiB | 17% Default | +bdd3.tspankaj.com: +-------------------------------+----------------------+----------------------+ +bdd3.tspankaj.com: +bdd3.tspankaj.com: +-----------------------------------------------------------------------------+ +bdd3.tspankaj.com: | Processes: GPU Memory | +bdd3.tspankaj.com: | GPU PID Type Process name Usage | +bdd3.tspankaj.com: |=============================================================================| +bdd3.tspankaj.com: | 0 10854 C python 315MiB | +bdd3.tspankaj.com: | 0 10875 C python 315MiB | +bdd3.tspankaj.com: | 0 11149 C python 1565MiB | +bdd3.tspankaj.com: | 0 11393 C python 2067MiB | +bdd3.tspankaj.com: | 0 31902 C python 1839MiB | +bdd3.tspankaj.com: | 1 10854 C python 1863MiB | +bdd3.tspankaj.com: | 1 10875 C python 1863MiB | +bdd3.tspankaj.com: +-----------------------------------------------------------------------------+ +bdd6.tspankaj.com: PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND +bdd6.tspankaj.com: 10323 tpankaj 20 0 45.916g 0.021t 89460 R 553.3 34.8 7462:51 python +bdd6.tspankaj.com: 10412 tpankaj 20 0 45.972g 0.022t 89444 R 546.7 36.0 7469:08 python +bdd6.tspankaj.com: 23431 tpankaj 20 0 33.389g 0.011t 88680 R 46.7 17.7 280:30.31 python +bdd6.tspankaj.com: 17937 root 20 0 218564 11612 10968 S 0.0 0.0 0:12.94 Xorg +bdd6.tspankaj.com: Wed Aug 16 21:10:32 2017 +bdd6.tspankaj.com: +-----------------------------------------------------------------------------+ +bdd6.tspankaj.com: | NVIDIA-SMI 375.66 Driver Version: 375.66 | +bdd6.tspankaj.com: |-------------------------------+----------------------+----------------------+ +bdd6.tspankaj.com: | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | +bdd6.tspankaj.com: | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | +bdd6.tspankaj.com: |===============================+======================+======================| +bdd6.tspankaj.com: | 0 GeForce GTX 108... Off | 0000:4B:00.0 Off | N/A | +bdd6.tspankaj.com: | 47% 67C P2 82W / 250W | 4034MiB / 11172MiB | 9% Default | +bdd6.tspankaj.com: +-------------------------------+----------------------+----------------------+ +bdd6.tspankaj.com: | 1 GeForce GTX 108... Off | 0000:4C:00.0 On | N/A | +bdd6.tspankaj.com: | 40% 58C P8 30W / 250W | 55MiB / 11169MiB | 0% Default | +bdd6.tspankaj.com: +-------------------------------+----------------------+----------------------+ +bdd6.tspankaj.com: +bdd6.tspankaj.com: +-----------------------------------------------------------------------------+ +bdd6.tspankaj.com: | Processes: GPU Memory | +bdd6.tspankaj.com: | GPU PID Type Process name Usage | +bdd6.tspankaj.com: |=============================================================================| +bdd6.tspankaj.com: | 0 10323 C python 1585MiB | +bdd6.tspankaj.com: | 0 10412 C python 1585MiB | +bdd6.tspankaj.com: | 0 23431 C python 861MiB | +bdd6.tspankaj.com: | 1 17937 G /usr/lib/xorg/Xorg 50MiB | +bdd6.tspankaj.com: +-----------------------------------------------------------------------------+ +bdd4.dyn.berkeley.edu: PID USER PR NI VIRT RES SHR S %CPU %MEM TIME+ COMMAND +bdd4.dyn.berkeley.edu: 1334 root 20 0 219436 51404 36972 S 0.0 0.0 0:01.68 Xorg +bdd4.dyn.berkeley.edu: Wed Aug 16 21:10:32 2017 +bdd4.dyn.berkeley.edu: +-----------------------------------------------------------------------------+ +bdd4.dyn.berkeley.edu: | NVIDIA-SMI 375.82 Driver Version: 375.82 | +bdd4.dyn.berkeley.edu: |-------------------------------+----------------------+----------------------+ +bdd4.dyn.berkeley.edu: | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | +bdd4.dyn.berkeley.edu: | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | +bdd4.dyn.berkeley.edu: |===============================+======================+======================| +bdd4.dyn.berkeley.edu: | 0 TITAN X (Pascal) Off | 0000:4B:00.0 Off | N/A | +bdd4.dyn.berkeley.edu: | 23% 29C P8 8W / 250W | 1MiB / 12189MiB | 0% Default | +bdd4.dyn.berkeley.edu: +-------------------------------+----------------------+----------------------+ +bdd4.dyn.berkeley.edu: | 1 TITAN X (Pascal) Off | 0000:4C:00.0 On | N/A | +bdd4.dyn.berkeley.edu: | 23% 33C P8 10W / 250W | 43MiB / 12186MiB | 0% Default | +bdd4.dyn.berkeley.edu: +-------------------------------+----------------------+----------------------+ +bdd4.dyn.berkeley.edu: +bdd4.dyn.berkeley.edu: +-----------------------------------------------------------------------------+ +bdd4.dyn.berkeley.edu: | Processes: GPU Memory | +bdd4.dyn.berkeley.edu: | GPU PID Type Process name Usage | +bdd4.dyn.berkeley.edu: |=============================================================================| +bdd4.dyn.berkeley.edu: | 1 1334 G /usr/lib/xorg/Xorg 40MiB | +bdd4.dyn.berkeley.edu: +-----------------------------------------------------------------------------+ +``` diff --git a/docs/_build/html/_sources/wiki/computersetup.md.txt b/docs/_build/html/_sources/wiki/computersetup.md.txt new file mode 100644 index 0000000..d602d30 --- /dev/null +++ b/docs/_build/html/_sources/wiki/computersetup.md.txt @@ -0,0 +1,101 @@ +# Computer Setup + +The purpose of this document is to establish a standard set of procedures for setting up the servers in the BDD Model Car project. + +**FOR ADMINS ONLY** + +## Network Setup (IF AND ONLY IF, NOT AT DATACENTER COLOCATION) + +#### Find the Mac Address and Register it with Berkeley + +``` +ifconfig -a +``` +To get the mac address (of the form XX:XX:XX:XX where XX are in hex). Then register this mac address at: + +``` +https://netreg.berkeley.edu/ +``` + +## Software and Drivers +#### Install CUDA 8.0 +``` +wget https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64-deb +sudo dpkg -i cuda-repo-ubuntu1604-8-0-local-ga2_8.0.61-1_amd64.deb +sudo apt-get update +sudo apt-get install cuda +``` +#### Install cuDNN 5.1 +- Download CUDNN from https://developer.nvidia.com/rdp/cudnn-download#a-collapseTwo (Make an Account) +``` +tar -xzvf cudnn-8.0-linux-x64-v5.1.tgz +sudo cp cuda/include/cudnn.h /usr/local/cuda/include +sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 +sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn* +``` +#### Assorted utilities: +- Install Cuda Profiling Tools + ``` + sudo apt-get install libcupti-dev + ``` +#### Install All Dependencies for Running Training Code +- Install docker from https://docs.docker.com/engine/installation/linux/docker-ce/ubuntu/#install-docker-ce (use the amd64 version) +- Install nvidia-enabled docker: + ``` + wget https://github.com/NVIDIA/nvidia-docker/releases/download/v1.0.1/nvidia-docker_1.0.1-1_amd64.deb + sudo dpkg -i nvidia-docker_1.0.1-1_amd64.deb + ``` +- Install Python Docker Environment for our Code: + ``` + sudo docker pull tpankaj/bdd-pytorch # will take awhile + ``` + +## Misc. Setup +#### Update Ubuntu +``` +sudo apt-get update # Fetches the list of available updates +sudo apt-get upgrade # Strictly upgrades the current packages +sudo apt-get dist-upgrade # Installs updates (new ones) +``` + +#### Setup SSH Server +- Install OpenSSH Server and Curl + ``` + sudo apt-get install openssh-server curl + ``` +- Get secure sshd_config from @sauhaardac 's GitHub Gists + ``` + wget https://gist.githubusercontent.com/sauhaardac/017f6b26b3826299109f8be42aa1602e/raw/7b895873720af196535a4f56720aefdd5b7f25e1/sshd_config + sudo rm /etc/ssh/sshd_config + sudo mv sshd_config /etc/ssh/sshd_config + ``` +- Start ssh server + ``` + sudo service ssh restart + ``` + +#### Establish IP Address for computer: +- Ask network adminstrator for static IP +- Set IP Address following [this guide](http://www.configserverfirewall.com/ubuntu-linux/ubuntu-set-static-ip-address/) + +#### Securing Computer from Attacks +- Install UFW + ``` + sudo apt-get install ufw + sudo ufw disable + sudo ufw default deny + sudo ufw allow 1022 # for ssh, open ports for other services as well + sudo ufw allow http + sudo ufw allow https + sudo ufw enable + ``` +- Set Strict Password Policy following [this guide](https://help.ubuntu.com/16.04/serverguide/user-management.html) + +#### Setup Public Key for Each User (IMPORTANT) +``` +git clone https://github.com/bddmodelcar/users_allowed_keys +cd users_allowed_keys +sudo chmod +x install_all_users.sh +sudo ./install_all_users.sh +``` +Some warning/error messages may pop up, it's usually just best to ignore them. diff --git a/docs/_build/html/_sources/wiki/dataset.md.txt b/docs/_build/html/_sources/wiki/dataset.md.txt new file mode 100644 index 0000000..12c7fe3 --- /dev/null +++ b/docs/_build/html/_sources/wiki/dataset.md.txt @@ -0,0 +1,5 @@ +# Dataset +## Sorting List of Data Moments +The list @karlzipser normally makes for the datamoments "...all_steer.pkl" are not sorted in any meaningful order. Here is a short program for loading one of these files sorting them temporally and saving them again: + +[https://slack-files.com/T62KXFY93-F6G3YB98T-ba427618f9](https://slack-files.com/T62KXFY93-F6G3YB98T-ba427618f9) diff --git a/docs/_build/html/_static/ajax-loader.gif b/docs/_build/html/_static/ajax-loader.gif new file mode 100644 index 0000000..61faf8c Binary files /dev/null and b/docs/_build/html/_static/ajax-loader.gif differ diff --git a/docs/_build/html/_static/basic.css b/docs/_build/html/_static/basic.css new file mode 100644 index 0000000..3c7223b --- /dev/null +++ b/docs/_build/html/_static/basic.css @@ -0,0 +1,643 @@ +/* + * basic.css + * ~~~~~~~~~ + * + * Sphinx stylesheet -- basic theme. + * + * :copyright: Copyright 2007-2017 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; +} + +/* -- relbar 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b/docs/_build/html/_static/doctools.js new file mode 100644 index 0000000..24992e6 --- /dev/null +++ b/docs/_build/html/_static/doctools.js @@ -0,0 +1,311 @@ +/* + * doctools.js + * ~~~~~~~~~~~ + * + * Sphinx JavaScript utilities for all documentation. + * + * :copyright: Copyright 2007-2017 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/** + * select a different prefix for underscore + */ +$u = _.noConflict(); + +/** + * make the code below compatible with browsers without + * an installed firebug like debugger +if (!window.console || !console.firebug) { + var names = ["log", "debug", "info", "warn", "error", "assert", "dir", + "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace", + "profile", "profileEnd"]; + window.console = {}; + for (var i = 0; i < names.length; ++i) + window.console[names[i]] = function() {}; +} + */ + +/** + * small helper function to urldecode strings + */ +jQuery.urldecode = function(x) { + return decodeURIComponent(x).replace(/\+/g, ' '); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. Multiple values per key are supported, + * it will always return arrays of strings for the value parts. + */ +jQuery.getQueryParameters = function(s) { + if (typeof s === 'undefined') + s = document.location.search; + var parts = s.substr(s.indexOf('?') + 1).split('&'); + var result = {}; + for (var i = 0; i < parts.length; i++) { + var tmp = parts[i].split('=', 2); + var key = jQuery.urldecode(tmp[0]); + var value = jQuery.urldecode(tmp[1]); + if (key in result) + result[key].push(value); + else + result[key] = [value]; + } + return result; +}; + +/** + * highlight a given string on a jquery object by wrapping it in + * span elements with the given class name. + */ +jQuery.fn.highlightText = function(text, className) { + function highlight(node, addItems) { + if (node.nodeType === 3) { + var val = node.nodeValue; + var pos = val.toLowerCase().indexOf(text); + if (pos >= 0 && !jQuery(node.parentNode).hasClass(className)) { + var span; + var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.className = className; + } + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + node.parentNode.insertBefore(span, node.parentNode.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling)); + node.nodeValue = val.substr(0, pos); + if (isInSVG) { + var bbox = span.getBBox(); + var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute('class', className); + var parentOfText = node.parentNode.parentNode; + addItems.push({ + "parent": node.parentNode, + "target": rect}); + } + } + } + else if (!jQuery(node).is("button, select, textarea")) { + jQuery.each(node.childNodes, function() { + highlight(this, addItems); + }); + } + } + var addItems = []; + var result = this.each(function() { + highlight(this, addItems); + }); + for (var i = 0; i < addItems.length; ++i) { + jQuery(addItems[i].parent).before(addItems[i].target); + } + return result; +}; + +/* + * backward compatibility for jQuery.browser + * This will be supported until firefox bug is fixed. + */ +if (!jQuery.browser) { + jQuery.uaMatch = function(ua) { + ua = ua.toLowerCase(); + + var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || + /(webkit)[ \/]([\w.]+)/.exec(ua) || + /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || + /(msie) ([\w.]+)/.exec(ua) || + ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || + []; + + return { + browser: match[ 1 ] || "", + version: match[ 2 ] || "0" + }; + }; + jQuery.browser = {}; + jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; +} + +/** + * Small JavaScript module for the documentation. + */ +var Documentation = { + + init : function() { + this.fixFirefoxAnchorBug(); + this.highlightSearchWords(); + this.initIndexTable(); + + }, + + /** + * i18n support + */ + TRANSLATIONS : {}, + PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, + LOCALE : 'unknown', + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext : function(string) { + var translated = Documentation.TRANSLATIONS[string]; + if (typeof translated === 'undefined') + return string; + return (typeof translated === 'string') ? translated : translated[0]; + }, + + ngettext : function(singular, plural, n) { + var translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated === 'undefined') + return (n == 1) ? singular : plural; + return translated[Documentation.PLURALEXPR(n)]; + }, + + addTranslations : function(catalog) { + for (var key in catalog.messages) + this.TRANSLATIONS[key] = catalog.messages[key]; + this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); + this.LOCALE = catalog.locale; + }, + + /** + * add context elements like header anchor links + */ + addContextElements : function() { + $('div[id] > :header:first').each(function() { + $('\u00B6'). + attr('href', '#' + this.id). + attr('title', _('Permalink to this headline')). + appendTo(this); + }); + $('dt[id]').each(function() { + $('\u00B6'). + attr('href', '#' + this.id). + attr('title', _('Permalink to this definition')). + appendTo(this); + }); + }, + + /** + * workaround a firefox stupidity + * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 + */ + fixFirefoxAnchorBug : function() { + if (document.location.hash) + window.setTimeout(function() { + document.location.href += ''; + }, 10); + }, + + /** + * highlight the search words provided in the url in the text + */ + highlightSearchWords : function() { + var params = $.getQueryParameters(); + var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; + if (terms.length) { + var body = $('div.body'); + if (!body.length) { + body = $('body'); + } + window.setTimeout(function() { + $.each(terms, function() { + body.highlightText(this.toLowerCase(), 'highlighted'); + }); + }, 10); + $('

' + _('Hide Search Matches') + '

') + .appendTo($('#searchbox')); + } + }, + + /** + * init the domain index toggle buttons + */ + initIndexTable : function() { + var togglers = $('img.toggler').click(function() { + var src = $(this).attr('src'); + var idnum = $(this).attr('id').substr(7); + $('tr.cg-' + idnum).toggle(); + if (src.substr(-9) === 'minus.png') + $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); + else + $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); + }).css('display', ''); + if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { + togglers.click(); + } + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords : function() { + $('#searchbox .highlight-link').fadeOut(300); + $('span.highlighted').removeClass('highlighted'); + }, + + /** + * make the url absolute + */ + makeURL : function(relativeURL) { + return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; + }, + + /** + * get the current relative url + */ + getCurrentURL : function() { + var path = document.location.pathname; + var parts = path.split(/\//); + $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { + if (this === '..') + parts.pop(); + }); + var url = parts.join('/'); + return path.substring(url.lastIndexOf('/') + 1, path.length - 1); + }, + + initOnKeyListeners: function() { + $(document).keyup(function(event) { + var activeElementType = document.activeElement.tagName; + // don't navigate when in search box or textarea + if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') { + switch (event.keyCode) { + case 37: // left + var prevHref = $('link[rel="prev"]').prop('href'); + if (prevHref) { + window.location.href = prevHref; + return false; + } + case 39: // right + var nextHref = $('link[rel="next"]').prop('href'); + if (nextHref) { + window.location.href = nextHref; + return false; + } + } + } + }); + } +}; + +// quick alias for translations +_ = Documentation.gettext; + +$(document).ready(function() { 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https://sizzlejs.com/ + * + * Copyright jQuery Foundation and other contributors + * Released under the MIT license + * https://jquery.org/license + * + * Date: 2016-07-07T21:44Z + */ +( function( global, factory ) { + + "use strict"; + + if ( typeof module === "object" && typeof module.exports === "object" ) { + + // For CommonJS and CommonJS-like environments where a proper `window` + // is present, execute the factory and get jQuery. + // For environments that do not have a `window` with a `document` + // (such as Node.js), expose a factory as module.exports. + // This accentuates the need for the creation of a real `window`. + // e.g. var jQuery = require("jquery")(window); + // See ticket #14549 for more info. + module.exports = global.document ? + factory( global, true ) : + function( w ) { + if ( !w.document ) { + throw new Error( "jQuery requires a window with a document" ); + } + return factory( w ); + }; + } else { + factory( global ); + } + +// Pass this if window is not defined yet +} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { + +// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 +// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode +// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common +// enough that all such attempts are guarded in a try block. +"use strict"; + +var arr = []; + +var document = window.document; + +var getProto = Object.getPrototypeOf; + +var slice = arr.slice; + +var concat = arr.concat; + +var push = arr.push; + +var indexOf = arr.indexOf; + +var class2type = {}; + +var toString = class2type.toString; + +var hasOwn = class2type.hasOwnProperty; + +var fnToString = hasOwn.toString; + +var ObjectFunctionString = fnToString.call( Object ); + +var support = {}; + + + + function DOMEval( code, doc ) { + doc = doc || document; + + var script = doc.createElement( "script" ); + + script.text = code; + doc.head.appendChild( script ).parentNode.removeChild( script ); + } +/* global Symbol */ +// Defining this global in .eslintrc would create a danger of using the global +// unguarded in another place, it seems safer to define global only for this module + + + +var + version = "3.1.0", + + // Define a local copy of jQuery + jQuery = function( selector, context ) { + + // The jQuery object is actually just the init constructor 'enhanced' + // Need init if jQuery is called (just allow error to be thrown if not included) + return new jQuery.fn.init( selector, context ); + }, + + // Support: Android <=4.0 only + // Make sure we trim BOM and NBSP + rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g, + + // Matches dashed string for camelizing + rmsPrefix = /^-ms-/, + rdashAlpha = /-([a-z])/g, + + // Used by jQuery.camelCase as callback to replace() + fcamelCase = function( all, letter ) { + return letter.toUpperCase(); + }; + +jQuery.fn = jQuery.prototype = { + + // The current version of jQuery being used + jquery: version, + + constructor: jQuery, + + // The default length of a jQuery object is 0 + length: 0, + + toArray: function() { + return slice.call( this ); + }, + + // Get the Nth element in the matched element set OR + // Get the whole matched element set as a clean array + get: function( num ) { + return num != null ? + + // Return just the one element from the set + ( num < 0 ? this[ num + this.length ] : this[ num ] ) : + + // Return all the elements in a clean array + slice.call( this ); + }, + + // Take an array of elements and push it onto the stack + // (returning the new matched element set) + pushStack: function( elems ) { + + // Build a new jQuery matched element set + var ret = jQuery.merge( this.constructor(), elems ); + + // Add the old object onto the stack (as a reference) + ret.prevObject = this; + + // Return the newly-formed element set + return ret; + }, + + // Execute a callback for every element in the matched set. + each: function( callback ) { + return jQuery.each( this, callback ); + }, + + map: function( callback ) { + return this.pushStack( jQuery.map( this, function( elem, i ) { + return callback.call( elem, i, elem ); + } ) ); + }, + + slice: function() { + return this.pushStack( slice.apply( this, arguments ) ); + }, + + first: function() { + return this.eq( 0 ); + }, + + last: function() { + return this.eq( -1 ); + }, + + eq: function( i ) { + var len = this.length, + j = +i + ( i < 0 ? len : 0 ); + return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); + }, + + end: function() { + return this.prevObject || this.constructor(); + }, + + // For internal use only. + // Behaves like an Array's method, not like a jQuery method. + push: push, + sort: arr.sort, + splice: arr.splice +}; + +jQuery.extend = jQuery.fn.extend = function() { + var options, name, src, copy, copyIsArray, clone, + target = arguments[ 0 ] || {}, + i = 1, + length = arguments.length, + deep = false; + + // Handle a deep copy situation + if ( typeof target === "boolean" ) { + deep = target; + + // Skip the boolean and the target + target = arguments[ i ] || {}; + i++; + } + + // Handle case when target is a string or something (possible in deep copy) + if ( typeof target !== "object" && !jQuery.isFunction( target ) ) { + target = {}; + } + + // Extend jQuery itself if only one argument is passed + if ( i === length ) { + target = this; + i--; + } + + for ( ; i < length; i++ ) { + + // Only deal with non-null/undefined values + if ( ( options = arguments[ i ] ) != null ) { + + // Extend the base object + for ( name in options ) { + src = target[ name ]; + copy = options[ name ]; + + // Prevent never-ending loop + if ( target === copy ) { + continue; + } + + // Recurse if we're merging plain objects or arrays + if ( deep && copy && ( jQuery.isPlainObject( copy ) || + ( copyIsArray = jQuery.isArray( copy ) ) ) ) { + + if ( copyIsArray ) { + copyIsArray = false; + clone = src && jQuery.isArray( src ) ? src : []; + + } else { + clone = src && jQuery.isPlainObject( src ) ? src : {}; + } + + // Never move original objects, clone them + target[ name ] = jQuery.extend( deep, clone, copy ); + + // Don't bring in undefined values + } else if ( copy !== undefined ) { + target[ name ] = copy; + } + } + } + } + + // Return the modified object + return target; +}; + +jQuery.extend( { + + // Unique for each copy of jQuery on the page + expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), + + // Assume jQuery is ready without the ready module + isReady: true, + + error: function( msg ) { + throw new Error( msg ); + }, + + noop: function() {}, + + isFunction: function( obj ) { + return jQuery.type( obj ) === "function"; + }, + + isArray: Array.isArray, + + isWindow: function( obj ) { + return obj != null && obj === obj.window; + }, + + isNumeric: function( obj ) { + + // As of jQuery 3.0, isNumeric is limited to + // strings and numbers (primitives or objects) + // that can be coerced to finite numbers (gh-2662) + var type = jQuery.type( obj ); + return ( type === "number" || type === "string" ) && + + // parseFloat NaNs numeric-cast false positives ("") + // ...but misinterprets leading-number strings, particularly hex literals ("0x...") + // subtraction forces infinities to NaN + !isNaN( obj - parseFloat( obj ) ); + }, + + isPlainObject: function( obj ) { + var proto, Ctor; + + // Detect obvious negatives + // Use toString instead of jQuery.type to catch host objects + if ( !obj || toString.call( obj ) !== "[object Object]" ) { + return false; + } + + proto = getProto( obj ); + + // Objects with no prototype (e.g., `Object.create( null )`) are plain + if ( !proto ) { + return true; + } + + // Objects with prototype are plain iff they were constructed by a global Object function + Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; + return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; + }, + + isEmptyObject: function( obj ) { + + /* eslint-disable no-unused-vars */ + // See https://github.com/eslint/eslint/issues/6125 + var name; + + for ( name in obj ) { + return false; + } + return true; + }, + + type: function( obj ) { + if ( obj == null ) { + return obj + ""; + } + + // Support: Android <=2.3 only (functionish RegExp) + return typeof obj === "object" || typeof obj === "function" ? + class2type[ toString.call( obj ) ] || "object" : + typeof obj; + }, + + // Evaluates a script in a global context + globalEval: function( code ) { + DOMEval( code ); + }, + + // Convert dashed to camelCase; used by the css and data modules + // Support: IE <=9 - 11, Edge 12 - 13 + // Microsoft forgot to hump their vendor prefix (#9572) + camelCase: function( string ) { + return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); + }, + + nodeName: function( elem, name ) { + return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); + }, + + each: function( obj, callback ) { + var length, i = 0; + + if ( isArrayLike( obj ) ) { + length = obj.length; + for ( ; i < length; i++ ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } else { + for ( i in obj ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } + + return obj; + }, + + // Support: Android <=4.0 only + trim: function( text ) { + return text == null ? + "" : + ( text + "" ).replace( rtrim, "" ); + }, + + // results is for internal usage only + makeArray: function( arr, results ) { + var ret = results || []; + + if ( arr != null ) { + if ( isArrayLike( Object( arr ) ) ) { + jQuery.merge( ret, + typeof arr === "string" ? + [ arr ] : arr + ); + } else { + push.call( ret, arr ); + } + } + + return ret; + }, + + inArray: function( elem, arr, i ) { + return arr == null ? -1 : indexOf.call( arr, elem, i ); + }, + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + merge: function( first, second ) { + var len = +second.length, + j = 0, + i = first.length; + + for ( ; j < len; j++ ) { + first[ i++ ] = second[ j ]; + } + + first.length = i; + + return first; + }, + + grep: function( elems, callback, invert ) { + var callbackInverse, + matches = [], + i = 0, + length = elems.length, + callbackExpect = !invert; + + // Go through the array, only saving the items + // that pass the validator function + for ( ; i < length; i++ ) { + callbackInverse = !callback( elems[ i ], i ); + if ( callbackInverse !== callbackExpect ) { + matches.push( elems[ i ] ); + } + } + + return matches; + }, + + // arg is for internal usage only + map: function( elems, callback, arg ) { + var length, value, + i = 0, + ret = []; + + // Go through the array, translating each of the items to their new values + if ( isArrayLike( elems ) ) { + length = elems.length; + for ( ; i < length; i++ ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + + // Go through every key on the object, + } else { + for ( i in elems ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + } + + // Flatten any nested arrays + return concat.apply( [], ret ); + }, + + // A global GUID counter for objects + guid: 1, + + // Bind a function to a context, optionally partially applying any + // arguments. + proxy: function( fn, context ) { + var tmp, args, proxy; + + if ( typeof context === "string" ) { + tmp = fn[ context ]; + context = fn; + fn = tmp; + } + + // Quick check to determine if target is callable, in the spec + // this throws a TypeError, but we will just return undefined. + if ( !jQuery.isFunction( fn ) ) { + return undefined; + } + + // Simulated bind + args = slice.call( arguments, 2 ); + proxy = function() { + return fn.apply( context || this, args.concat( slice.call( arguments ) ) ); + }; + + // Set the guid of unique handler to the same of original handler, so it can be removed + proxy.guid = fn.guid = fn.guid || jQuery.guid++; + + return proxy; + }, + + now: Date.now, + + // jQuery.support is not used in Core but other projects attach their + // properties to it so it needs to exist. + support: support +} ); + +if ( typeof Symbol === "function" ) { + jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; +} + +// Populate the class2type map +jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), +function( i, name ) { + class2type[ "[object " + name + "]" ] = name.toLowerCase(); +} ); + +function isArrayLike( obj ) { + + // Support: real iOS 8.2 only (not reproducible in simulator) + // `in` check used to prevent JIT error (gh-2145) + // hasOwn isn't used here due to false negatives + // regarding Nodelist length in IE + var length = !!obj && "length" in obj && obj.length, + type = jQuery.type( obj ); + + if ( type === "function" || jQuery.isWindow( obj ) ) { + return false; + } + + return type === "array" || length === 0 || + typeof length === "number" && length > 0 && ( length - 1 ) in obj; +} +var Sizzle = +/*! + * Sizzle CSS Selector Engine v2.3.0 + * https://sizzlejs.com/ + * + * Copyright jQuery Foundation and other contributors + * Released under the MIT license + * http://jquery.org/license + * + * Date: 2016-01-04 + */ +(function( window ) { + +var i, + support, + Expr, + getText, + isXML, + tokenize, + compile, + select, + outermostContext, + sortInput, + hasDuplicate, + + // Local document vars + setDocument, + document, + docElem, + documentIsHTML, + rbuggyQSA, + rbuggyMatches, + matches, + contains, + + // Instance-specific data + expando = "sizzle" + 1 * new Date(), + preferredDoc = window.document, + dirruns = 0, + done = 0, + classCache = createCache(), + tokenCache = createCache(), + compilerCache = createCache(), + sortOrder = function( a, b ) { + if ( a === b ) { + hasDuplicate = true; + } + return 0; + }, + + // Instance methods + hasOwn = ({}).hasOwnProperty, + arr = [], + pop = arr.pop, + push_native = arr.push, + push = arr.push, + slice = arr.slice, + // Use a stripped-down indexOf as it's faster than native + // https://jsperf.com/thor-indexof-vs-for/5 + indexOf = function( list, elem ) { + var i = 0, + len = list.length; + for ( ; i < len; i++ ) { + if ( list[i] === elem ) { + return i; + } + } + return -1; + }, + + booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped", + + // Regular expressions + + // http://www.w3.org/TR/css3-selectors/#whitespace + whitespace = "[\\x20\\t\\r\\n\\f]", + + // http://www.w3.org/TR/CSS21/syndata.html#value-def-identifier + identifier = "(?:\\\\.|[\\w-]|[^\0-\\xa0])+", + + // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors + attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + + // Operator (capture 2) + "*([*^$|!~]?=)" + whitespace + + // "Attribute values must be CSS identifiers [capture 5] or strings [capture 3 or capture 4]" + "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + whitespace + + "*\\]", + + pseudos = ":(" + identifier + ")(?:\\((" + + // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: + // 1. quoted (capture 3; capture 4 or capture 5) + "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + + // 2. simple (capture 6) + "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + + // 3. anything else (capture 2) + ".*" + + ")\\)|)", + + // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter + rwhitespace = new RegExp( whitespace + "+", "g" ), + rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + whitespace + "+$", "g" ), + + rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), + rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + "*" ), + + rattributeQuotes = new RegExp( "=" + whitespace + "*([^\\]'\"]*?)" + whitespace + "*\\]", "g" ), + + rpseudo = new RegExp( pseudos ), + ridentifier = new RegExp( "^" + identifier + "$" ), + + matchExpr = { + "ID": new RegExp( "^#(" + identifier + ")" ), + "CLASS": new RegExp( "^\\.(" + identifier + ")" ), + "TAG": new RegExp( "^(" + identifier + "|[*])" ), + "ATTR": new RegExp( "^" + attributes ), + "PSEUDO": new RegExp( "^" + pseudos ), + "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + whitespace + + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + whitespace + + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), + "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), + // For use in libraries implementing .is() + // We use this for POS matching in `select` + "needsContext": new RegExp( "^" + whitespace + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + + whitespace + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) + }, + + rinputs = /^(?:input|select|textarea|button)$/i, + rheader = /^h\d$/i, + + rnative = /^[^{]+\{\s*\[native \w/, + + // Easily-parseable/retrievable ID or TAG or CLASS selectors + rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, + + rsibling = /[+~]/, + + // CSS escapes + // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters + runescape = new RegExp( "\\\\([\\da-f]{1,6}" + whitespace + "?|(" + whitespace + ")|.)", "ig" ), + funescape = function( _, escaped, escapedWhitespace ) { + var high = "0x" + escaped - 0x10000; + // NaN means non-codepoint + // Support: Firefox<24 + // Workaround erroneous numeric interpretation of +"0x" + return high !== high || escapedWhitespace ? + escaped : + high < 0 ? + // BMP codepoint + String.fromCharCode( high + 0x10000 ) : + // Supplemental Plane codepoint (surrogate pair) + String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); + }, + + // CSS string/identifier serialization + // https://drafts.csswg.org/cssom/#common-serializing-idioms + rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\x80-\uFFFF\w-]/g, + fcssescape = function( ch, asCodePoint ) { + if ( asCodePoint ) { + + // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER + if ( ch === "\0" ) { + return "\uFFFD"; + } + + // Control characters and (dependent upon position) numbers get escaped as code points + return ch.slice( 0, -1 ) + "\\" + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; + } + + // Other potentially-special ASCII characters get backslash-escaped + return "\\" + ch; + }, + + // Used for iframes + // See setDocument() + // Removing the function wrapper causes a "Permission Denied" + // error in IE + unloadHandler = function() { + setDocument(); + }, + + disabledAncestor = addCombinator( + function( elem ) { + return elem.disabled === true; + }, + { dir: "parentNode", next: "legend" } + ); + +// Optimize for push.apply( _, NodeList ) +try { + push.apply( + (arr = slice.call( preferredDoc.childNodes )), + preferredDoc.childNodes + ); + // Support: Android<4.0 + // Detect silently failing push.apply + arr[ preferredDoc.childNodes.length ].nodeType; +} catch ( e ) { + push = { apply: arr.length ? + + // Leverage slice if possible + function( target, els ) { + push_native.apply( target, slice.call(els) ); + } : + + // Support: IE<9 + // Otherwise append directly + function( target, els ) { + var j = target.length, + i = 0; + // Can't trust NodeList.length + while ( (target[j++] = els[i++]) ) {} + target.length = j - 1; + } + }; +} + +function Sizzle( selector, context, results, seed ) { + var m, i, elem, nid, match, groups, newSelector, + newContext = context && context.ownerDocument, + + // nodeType defaults to 9, since context defaults to document + nodeType = context ? context.nodeType : 9; + + results = results || []; + + // Return early from calls with invalid selector or context + if ( typeof selector !== "string" || !selector || + nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { + + return results; + } + + // Try to shortcut find operations (as opposed to filters) in HTML documents + if ( !seed ) { + + if ( ( context ? context.ownerDocument || context : preferredDoc ) !== document ) { + setDocument( context ); + } + context = context || document; + + if ( documentIsHTML ) { + + // If the selector is sufficiently simple, try using a "get*By*" DOM method + // (excepting DocumentFragment context, where the methods don't exist) + if ( nodeType !== 11 && (match = rquickExpr.exec( selector )) ) { + + // ID selector + if ( (m = match[1]) ) { + + // Document context + if ( nodeType === 9 ) { + if ( (elem = context.getElementById( m )) ) { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( elem.id === m ) { + results.push( elem ); + return results; + } + } else { + return results; + } + + // Element context + } else { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( newContext && (elem = newContext.getElementById( m )) && + contains( context, elem ) && + elem.id === m ) { + + results.push( elem ); + return results; + } + } + + // Type selector + } else if ( match[2] ) { + push.apply( results, context.getElementsByTagName( selector ) ); + return results; + + // Class selector + } else if ( (m = match[3]) && support.getElementsByClassName && + context.getElementsByClassName ) { + + push.apply( results, context.getElementsByClassName( m ) ); + return results; + } + } + + // Take advantage of querySelectorAll + if ( support.qsa && + !compilerCache[ selector + " " ] && + (!rbuggyQSA || !rbuggyQSA.test( selector )) ) { + + if ( nodeType !== 1 ) { + newContext = context; + newSelector = selector; + + // qSA looks outside Element context, which is not what we want + // Thanks to Andrew Dupont for this workaround technique + // Support: IE <=8 + // Exclude object elements + } else if ( context.nodeName.toLowerCase() !== "object" ) { + + // Capture the context ID, setting it first if necessary + if ( (nid = context.getAttribute( "id" )) ) { + nid = nid.replace( rcssescape, fcssescape ); + } else { + context.setAttribute( "id", (nid = expando) ); + } + + // Prefix every selector in the list + groups = tokenize( selector ); + i = groups.length; + while ( i-- ) { + groups[i] = "#" + nid + " " + toSelector( groups[i] ); + } + newSelector = groups.join( "," ); + + // Expand context for sibling selectors + newContext = rsibling.test( selector ) && testContext( context.parentNode ) || + context; + } + + if ( newSelector ) { + try { + push.apply( results, + newContext.querySelectorAll( newSelector ) + ); + return results; + } catch ( qsaError ) { + } finally { + if ( nid === expando ) { + context.removeAttribute( "id" ); + } + } + } + } + } + } + + // All others + return select( selector.replace( rtrim, "$1" ), context, results, seed ); +} + +/** + * Create key-value caches of limited size + * @returns {function(string, object)} Returns the Object data after storing it on itself with + * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) + * deleting the oldest entry + */ +function createCache() { + var keys = []; + + function cache( key, value ) { + // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) + if ( keys.push( key + " " ) > Expr.cacheLength ) { + // Only keep the most recent entries + delete cache[ keys.shift() ]; + } + return (cache[ key + " " ] = value); + } + return cache; +} + +/** + * Mark a function for special use by Sizzle + * @param {Function} fn The function to mark + */ +function markFunction( fn ) { + fn[ expando ] = true; + return fn; +} + +/** + * Support testing using an element + * @param {Function} fn Passed the created element and returns a boolean result + */ +function assert( fn ) { + var el = document.createElement("fieldset"); + + try { + return !!fn( el ); + } catch (e) { + return false; + } finally { + // Remove from its parent by default + if ( el.parentNode ) { + el.parentNode.removeChild( el ); + } + // release memory in IE + el = null; + } +} + +/** + * Adds the same handler for all of the specified attrs + * @param {String} attrs Pipe-separated list of attributes + * @param {Function} handler The method that will be applied + */ +function addHandle( attrs, handler ) { + var arr = attrs.split("|"), + i = arr.length; + + while ( i-- ) { + Expr.attrHandle[ arr[i] ] = handler; + } +} + +/** + * Checks document order of two siblings + * @param {Element} a + * @param {Element} b + * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b + */ +function siblingCheck( a, b ) { + var cur = b && a, + diff = cur && a.nodeType === 1 && b.nodeType === 1 && + a.sourceIndex - b.sourceIndex; + + // Use IE sourceIndex if available on both nodes + if ( diff ) { + return diff; + } + + // Check if b follows a + if ( cur ) { + while ( (cur = cur.nextSibling) ) { + if ( cur === b ) { + return -1; + } + } + } + + return a ? 1 : -1; +} + +/** + * Returns a function to use in pseudos for input types + * @param {String} type + */ +function createInputPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for buttons + * @param {String} type + */ +function createButtonPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return (name === "input" || name === "button") && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for :enabled/:disabled + * @param {Boolean} disabled true for :disabled; false for :enabled + */ +function createDisabledPseudo( disabled ) { + // Known :disabled false positives: + // IE: *[disabled]:not(button, input, select, textarea, optgroup, option, menuitem, fieldset) + // not IE: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable + return function( elem ) { + + // Check form elements and option elements for explicit disabling + return "label" in elem && elem.disabled === disabled || + "form" in elem && elem.disabled === disabled || + + // Check non-disabled form elements for fieldset[disabled] ancestors + "form" in elem && elem.disabled === false && ( + // Support: IE6-11+ + // Ancestry is covered for us + elem.isDisabled === disabled || + + // Otherwise, assume any non-