constants.py

# Copyright 2018 Google. All Rights Reserved.
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# ==============================================================================
###############################################################################
# Copyright (C) 2020-2021 Habana Labs, Ltd. an Intel Company
###############################################################################
# Changes:
# - Removed unused constants
# - Added class label names
# - Removed obsolete todos
# - Formatted with autopep8
# - Removed __future__ imports

"""Central location for all constants related to MLPerf SSD."""

# ==============================================================================
# == Model =====================================================================
# ==============================================================================
IMAGE_SIZE = 300

NUM_CLASSES = 81  # Including "no class". Not all COCO classes are used.

# Note: Zero is special. (Background class) CLASS_INV_MAP[0] must be zero.
CLASS_INV_MAP = (
    0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21,
    22, 23, 24, 25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,
    44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
    64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87,
    88, 89, 90)
_MAP = {j: i for i, j in enumerate(CLASS_INV_MAP)}
CLASS_MAP = tuple(_MAP.get(i, -1) for i in range(max(CLASS_INV_MAP) + 1))

CLASS_LABEL = ['', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck',
               'boat', 'traffic light', 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat',
               'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella',
               'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat',
               'baseball glove', 'skateboard', 'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork',
               'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog',
               'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',
               'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
               'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush']

NUM_SSD_BOXES = 8732

RESNET_DEPTH = 34

"""SSD specific"""
MIN_LEVEL = 3
MAX_LEVEL = 8

FEATURE_SIZES = (38, 19, 10, 5, 3, 1)
STEPS = (8, 16, 32, 64, 100, 300)

# https://github.com/amdegroot/ssd.pytorch/blob/master/data/config.py
SCALES = (21, 45, 99, 153, 207, 261, 315)
ASPECT_RATIOS = ((2,), (2, 3), (2, 3), (2, 3), (2,), (2,))
NUM_DEFAULTS = (4, 6, 6, 6, 4, 4)
NUM_DEFAULTS_BY_LEVEL = {3: 4, 4: 6, 5: 6, 6: 6, 7: 4, 8: 4}
SCALE_XY = 0.1
SCALE_HW = 0.2
BOX_CODER_SCALES = (1 / SCALE_XY, 1 / SCALE_XY, 1 / SCALE_HW, 1 / SCALE_HW)
MATCH_THRESHOLD = 0.5

# https://discuss.pytorch.org/t/how-to-preprocess-input-for-pre-trained-networks/683
NORMALIZATION_MEAN = (0.485, 0.456, 0.406)
NORMALIZATION_STD = (0.229, 0.224, 0.225)

# SSD Cropping
NUM_CROP_PASSES = 50
CROP_MIN_IOU_CHOICES = (0, 0.1, 0.3, 0.5, 0.7, 0.9)
P_NO_CROP_PER_PASS = 1 / (len(CROP_MIN_IOU_CHOICES) + 1)

# Hard example mining
NEGS_PER_POSITIVE = 3

# Batch normalization
BATCH_NORM_DECAY = 0.9
BATCH_NORM_EPSILON = 1e-5


# ==============================================================================
# == Optimizer =================================================================
# ==============================================================================
MOMENTUM = 0.9
DEFAULT_BATCH_SIZE = 32.0

# ==============================================================================
# == Keys ======================================================================
# ==============================================================================
BOXES = "boxes"
CLASSES = "classes"
NUM_MATCHED_BOXES = "num_matched_boxes"
IMAGE = "image"
SOURCE_ID = "source_id"
RAW_SHAPE = "raw_shape"
IS_PADDED = "is_padded"

# ==============================================================================
# == Evaluation ================================================================
# ==============================================================================
EVAL_SAMPLES = 5000
MAX_NUM_EVAL_BOXES = 200
OVERLAP_CRITERIA = 0.5  # Used for nonmax supression
MIN_SCORE = 0.05  # Minimum score to be considered during evaluation.
DUMMY_SCORE = -1e5  # If no boxes are matched.