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panning_movi_e.py
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# Copyright 2024 The Kubric Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# pylint: disable=line-too-long, unexpected-keyword-arg
import dataclasses
import json
import logging
from typing import List, Dict, Union
from etils import epath
import imageio
import numpy as np
import png
import tensorflow as tf
import tensorflow_datasets.public_api as tfds
_DESCRIPTION = """
A simple rigid-body simulation with GSO objects and an HDRI background.
The scene consists of a dome (half-sphere) onto which a random HDRI is projected,
which acts as background, floor and lighting.
The scene contains between 10 and 20 random static objects, and between 1 and 3
dynamic objects (tossed onto the others).
The camera moves on a straight line with constant velocity, and the lookat point
follows a moving trajectory through the workspace center.
The starting point is sampled randomly in a half-sphere shell around the scene,
and from there the camera moves into a random direction with a random speed between 0 and 8.
This sampling process is repeated until a trajectory is found that starts and
ends within the specified half-sphere shell around the center of the scene.
The camera always points towards the origin.
Static objects are spawned without overlap in the region [(-7, -7, 0), (7, 7, 10)],
and are simulated to fall and settle before the first frame of the scene.
Dynamic objects are spawned without overlap in the region [(-5, -5, 1), (5, 5, 5)], and
initialized with a random velocity from the range [(-4, -4, 0), (4, 4, 0)]
minus the position of the object to bias their trajectory towards the center of
the scene.
The scene is simulated for 2 seconds, with the physical properties of the
objects kept at the default of friction=0.5, restitution=0.5 and density=1.0.
The dataset contains approx 10k videos rendered at 256x256 pixels and 12fps.
Each sample contains the following video-format data:
(s: sequence length, h: height, w: width)
- "video": (s, h, w, 3) [uint8]
The RGB frames.
- "segmentations": (s, h, w, 1) [uint8]
Instance segmentation as per-pixel object-id with background=0.
Note: because of this the instance IDs used here are one higher than their
corresponding index in sample["instances"].
- "depth": (s, h, w, 1) [uint16]
Distance of each pixel from the center of the camera.
(Note this is different from the z-value sometimes used, which measures the
distance to the camera *plane*.)
The values are stored as uint16 and span the range specified in
sample["metadata"]["depth_range"]. To convert them back to world-units
use:
minv, maxv = sample["metadata"]["depth_range"]
depth = sample["depth"] / 65535 * (maxv - minv) + minv
- "forward_flow": (s, h, w, 2) [uint16]
Forward optical flow in the form (delta_row, delta_column).
The values are stored as uint16 and span the range specified in
sample["metadata"]["forward_flow_range"]. To convert them back to pixels use:
minv, maxv = sample["metadata"]["forward_flow_range"]
depth = sample["forward_flow"] / 65535 * (maxv - minv) + minv
- "backward_flow": (s, h, w, 2) [uint16]
Backward optical flow in the form (delta_row, delta_column).
The values are stored as uint16 and span the range specified in
sample["metadata"]["backward_flow_range"]. To convert them back to pixels use:
minv, maxv = sample["metadata"]["backward_flow_range"]
depth = sample["backward_flow"] / 65535 * (maxv - minv) + minv
- "normal": (s, h, w, 3) [uint16]
Surface normals for each pixel in world coordinates.
- "object_coordinates": (s, h, w, 3) [uint16]
Object coordinates encode the position of each point relative to the objects
bounding box (i.e. back-left-top (X=Y=Z=1) corner is white,
while front-right-bottom (X=Y=Z=0) corner is black.)
Additionally there is rich instance-level information in sample["instances"]:
- "mass": [float32]
Mass of the object used for simulation.
- "friction": [float32]
Friction coefficient used for simulation.
- "restitution": [float32]
Restitution coefficient (bounciness) used for simulation.
- "positions": (s, 3) [float32]
Position of the object for each frame in world-coordinates.
- "quaternions": (s, 4) [float32]
Rotation of the object for each frame as quaternions.
- "velocities": (s, 3) [float32]
Velocity of the object for each frame.
- "angular_velocities": (s, 3) [float32]
Angular velocity of the object for each frame.
- "bboxes_3d": (s, 8, 3) [float32]
World-space corners of the 3D bounding box around the object.
- "image_positions": (s, 2) [float32]
Normalized (0, 1) image-space (2D) coordinates of the center of mass of the
object for each frame.
- "bboxes": (None, 4) [float32]
The normalized image-space (2D) coordinates of the bounding box
[ymin, xmin, ymax, xmax] for all the frames in which the object is visible
(as specified in bbox_frames).
- "bbox_frames": (None,) [int]
A list of all the frames the object is visible.
- "visibility": (s,) [uint16]
Visibility of the object in number of pixels for each frame (can be 0).
- "asset_id": [str] Asset id from Google Scanned Objects dataset.
- "category": ["Action Figures", "Bag", "Board Games",
"Bottles and Cans and Cups", "Camera", "Car Seat",
"Consumer Goods", "Hat", "Headphones", "Keyboard", "Legos",
"Media Cases", "Mouse", "None", "Shoe", "Stuffed Toys", "Toys"]
- "scale": float between 0.75 and 3.0
- "is_dynamic": bool indicating whether (at the start of the scene) the object
is sitting on the floor or is being tossed.
Information about the camera in sample["camera"]
(given for each frame eventhough the camera is static, so as to stay
consistent with other variants of the dataset):
- "focal_length": [float32]
- "sensor_width": [float32]
- "field_of_view": [float32]
- "positions": (s, 3) [float32]
- "quaternions": (s, 4) [float32]
And finally information about collision events in sample["events"]["collisions"]:
- "instances": (2,)[uint16]
Indices of the two instance between which the collision happened.
Note that collisions with the floor/background objects are marked with 65535
- "frame": tf.int32,
Frame in which the collision happenend.
- "force": tf.float32,
The force (strength) of the collision.
- "position": tfds.features.Tensor(shape=(3,), dtype=tf.float32),
Position of the collision event in 3D world coordinates.
- "image_position": tfds.features.Tensor(shape=(2,), dtype=tf.float32),
Position of the collision event projected onto normalized 2D image coordinates.
- "contact_normal": tfds.features.Tensor(shape=(3,), dtype=tf.float32),
The normal-vector of the contact (direction of the force).
"""
_CITATION = """\
@article{doersch2023tapir,
title={TAPIR: Tracking Any Point with per-frame Initialization
and temporal Refinement},
author={Doersch, Carl and Yang, Yi and Vecerik, Mel and Gokay, Dilara and
Gupta, Ankush and Aytar, Yusuf and Carreira, Joao and Zisserman, Andrew},
journal={ICCV},
year={2023}
}"""
@dataclasses.dataclass
class PanningMoviEConfig(tfds.core.BuilderConfig):
""""Configuration for Multi-Object Video (PanningMoviE) dataset."""
height: int = 256
width: int = 256
num_frames: int = 24
validation_ratio: float = 0.1
train_val_path: str = None
test_split_paths: Dict[str, str] = dataclasses.field(default_factory=dict)
class PanningMoviE(tfds.core.BeamBasedBuilder):
"""DatasetBuilder for Panning MOVi-E dataset."""
VERSION = tfds.core.Version("1.0.0")
RELEASE_NOTES = {
"1.0.0": "initial release",
}
BUILDER_CONFIGS = [
PanningMoviEConfig(
name="256x256",
description="Full resolution of 256x256",
height=256,
width=256,
validation_ratio=0.025,
train_val_path="gs://research-brain-kubric-xgcp/jobs/panning_movi_e_regen_100k/",
test_split_paths={
"test": "gs://research-brain-kubric-xgcp/jobs/panning_movi_e_test_regen_1k/",
}
),
]
def _info(self) -> tfds.core.DatasetInfo:
"""Returns the dataset metadata."""
h = self.builder_config.height
w = self.builder_config.width
s = self.builder_config.num_frames
def get_panning_movi_e_instance_features(seq_length: int):
features = get_instance_features(seq_length)
features.update({
"asset_id": tfds.features.Text(),
"category": tfds.features.ClassLabel(
names=["Action Figures", "Bag", "Board Games",
"Bottles and Cans and Cups", "Camera",
"Car Seat", "Consumer Goods", "Hat",
"Headphones", "Keyboard", "Legos",
"Media Cases", "Mouse", "None", "Shoe",
"Stuffed Toys", "Toys"]),
"scale": tf.float32,
"is_dynamic": tf.bool,
})
return features
return tfds.core.DatasetInfo(
builder=self,
description=_DESCRIPTION,
features=tfds.features.FeaturesDict({
"metadata": {
"video_name": tfds.features.Text(),
"width": tf.int32,
"height": tf.int32,
"num_frames": tf.int32,
"num_instances": tf.uint16,
"depth_range": tfds.features.Tensor(shape=(2,),
dtype=tf.float32),
"forward_flow_range": tfds.features.Tensor(shape=(2,),
dtype=tf.float32),
"backward_flow_range": tfds.features.Tensor(shape=(2,),
dtype=tf.float32),
},
"background": tfds.features.Text(),
"instances": tfds.features.Sequence(
feature=get_panning_movi_e_instance_features(seq_length=s)),
"camera": get_camera_features(s),
"events": get_events_features(),
# -----
"video": tfds.features.Video(shape=(s, h, w, 3)),
"segmentations": tfds.features.Sequence(
tfds.features.Image(shape=(h, w, 1), dtype=tf.uint8),
length=s),
"forward_flow": tfds.features.Sequence(
tfds.features.Tensor(shape=(h, w, 2), dtype=tf.uint16),
length=s),
"backward_flow": tfds.features.Sequence(
tfds.features.Tensor(shape=(h, w, 2), dtype=tf.uint16),
length=s),
"depth": tfds.features.Sequence(
tfds.features.Image(shape=(h, w, 1), dtype=tf.uint16),
length=s),
"normal": tfds.features.Video(shape=(s, h, w, 3), dtype=tf.uint16),
"object_coordinates": tfds.features.Video(shape=(s, h, w, 3),
dtype=tf.uint16),
}),
supervised_keys=None,
homepage="https://github.com/google-research/kubric",
citation=_CITATION)
def _split_generators(self, unused_dl_manager: tfds.download.DownloadManager):
"""Returns SplitGenerators."""
del unused_dl_manager
path = as_path(self.builder_config.train_val_path)
all_subdirs = [str(d) for d in path.iterdir()]
logging.info("Found %d sub-folders in master path: %s",
len(all_subdirs), path)
# shuffle
rng = np.random.RandomState(seed=42)
rng.shuffle(all_subdirs)
validation_ratio = self.builder_config.validation_ratio
validation_examples = max(1, round(len(all_subdirs) * validation_ratio))
training_examples = len(all_subdirs) - validation_examples
logging.info("Using %f of examples for validation for a total of %d",
validation_ratio, validation_examples)
logging.info("Using the other %d examples for training", training_examples)
splits = {
tfds.Split.TRAIN: self._generate_examples(all_subdirs[:training_examples]),
tfds.Split.VALIDATION: self._generate_examples(all_subdirs[training_examples:]),
}
for key, path in self.builder_config.test_split_paths.items():
path = as_path(path)
split_dirs = [d for d in path.iterdir()]
# sort the directories by their integer number
split_dirs = sorted(split_dirs, key=lambda x: int(x.name))
logging.info("Found %d sub-folders in '%s' path: %s",
len(split_dirs), key, path)
splits[key] = self._generate_examples([str(d) for d in split_dirs])
return splits
def _generate_examples(self, directories: List[str]):
"""Yields examples."""
target_size = (self.builder_config.height, self.builder_config.width)
def _process_example(video_dir):
key, result, metadata = load_scene_directory(video_dir, target_size)
# add MoviE-D specific instance information:
for i, obj in enumerate(result["instances"]):
obj["asset_id"] = asset_id_from_metadata(metadata["instances"][i])
obj["category"] = metadata["instances"][i]["category"]
obj["scale"] = metadata["instances"][i]["scale"]
obj["is_dynamic"] = metadata["instances"][i]["is_dynamic"]
return key, result
beam = tfds.core.lazy_imports.apache_beam
return (beam.Create(directories) |
beam.Filter(is_complete_dir) |
beam.Map(_process_example))
DEFAULT_LAYERS = ("rgba", "segmentation", "forward_flow", "backward_flow",
"depth", "normal", "object_coordinates")
def load_scene_directory(scene_dir, target_size, layers=DEFAULT_LAYERS):
scene_dir = as_path(scene_dir)
example_key = f"{scene_dir.name}"
with tf.io.gfile.GFile(str(scene_dir / "data_ranges.json"), "r") as fp:
data_ranges = json.load(fp)
with tf.io.gfile.GFile(str(scene_dir / "metadata.json"), "r") as fp:
metadata = json.load(fp)
with tf.io.gfile.GFile(str(scene_dir / "events.json"), "r") as fp:
events = json.load(fp)
num_frames = metadata["metadata"]["num_frames"]
result = {
"metadata": {
"video_name": example_key,
"width": target_size[1],
"height": target_size[0],
"num_frames": num_frames,
"num_instances": metadata["metadata"]["num_instances"],
},
"background": metadata["metadata"]["background"],
"instances": [format_instance_information(obj)
for obj in metadata["instances"]],
"camera": format_camera_information(metadata),
"events": format_events_information(events),
}
resolution = metadata["metadata"]["resolution"]
assert resolution[1] / target_size[0] == resolution[0] / target_size[1]
scale = resolution[1] / target_size[0]
assert scale == resolution[1] // target_size[0]
paths = {
key: [scene_dir / f"{key}_{f:05d}.png" for f in range(num_frames)]
for key in layers if key != "depth"
}
if "depth" in layers:
depth_paths = [scene_dir / f"depth_{f:05d}.tiff" for f in range(num_frames)]
depth_frames = np.array([
subsample_nearest_neighbor(read_tiff(frame_path), target_size)
for frame_path in depth_paths])
depth_min, depth_max = np.min(depth_frames), np.max(depth_frames)
result["depth"] = convert_float_to_uint16(depth_frames, depth_min, depth_max)
result["metadata"]["depth_range"] = [depth_min, depth_max]
if "forward_flow" in layers:
result["metadata"]["forward_flow_range"] = [
data_ranges["forward_flow"]["min"] / scale,
data_ranges["forward_flow"]["max"] / scale]
result["forward_flow"] = [
subsample_nearest_neighbor(read_png(frame_path)[..., :2],
target_size)
for frame_path in paths["forward_flow"]]
if "backward_flow" in layers:
result["metadata"]["backward_flow_range"] = [
data_ranges["backward_flow"]["min"] / scale,
data_ranges["backward_flow"]["max"] / scale]
result["backward_flow"] = [
subsample_nearest_neighbor(read_png(frame_path)[..., :2],
target_size)
for frame_path in paths["backward_flow"]]
for key in ["normal", "object_coordinates", "uv"]:
if key in layers:
result[key] = [
subsample_nearest_neighbor(read_png(frame_path),
target_size)
for frame_path in paths[key]]
if "segmentation" in layers:
# somehow we ended up calling this "segmentations" in TFDS and
# "segmentation" in kubric. So we have to treat it separately.
result["segmentations"] = [
subsample_nearest_neighbor(read_png(frame_path),
target_size)
for frame_path in paths["segmentation"]]
if "rgba" in layers:
result["video"] = [
subsample_avg(read_png(frame_path), target_size)[..., :3]
for frame_path in paths["rgba"]]
return example_key, result, metadata
def get_camera_features(seq_length):
return {
"focal_length": tf.float32,
"sensor_width": tf.float32,
"field_of_view": tf.float32,
"positions": tfds.features.Tensor(shape=(seq_length, 3),
dtype=tf.float32),
"quaternions": tfds.features.Tensor(shape=(seq_length, 4),
dtype=tf.float32),
}
def format_camera_information(metadata):
return {
"focal_length": metadata["camera"]["focal_length"],
"sensor_width": metadata["camera"]["sensor_width"],
"field_of_view": metadata["camera"]["field_of_view"],
"positions": np.array(metadata["camera"]["positions"], np.float32),
"quaternions": np.array(metadata["camera"]["quaternions"], np.float32),
}
def get_events_features():
return {
"collisions": tfds.features.Sequence({
"instances": tfds.features.Tensor(shape=(2,), dtype=tf.uint16),
"frame": tf.int32,
"force": tf.float32,
"position": tfds.features.Tensor(shape=(3,), dtype=tf.float32),
"image_position": tfds.features.Tensor(shape=(2,), dtype=tf.float32),
"contact_normal": tfds.features.Tensor(shape=(3,), dtype=tf.float32),
})
}
def format_events_information(events):
return {
"collisions": [{
"instances": np.array(c["instances"], dtype=np.uint16),
"frame": c["frame"],
"force": c["force"],
"position": np.array(c["position"], dtype=np.float32),
"image_position": np.array(c["image_position"], dtype=np.float32),
"contact_normal": np.array(c["contact_normal"], dtype=np.float32),
} for c in events["collisions"]],
}
def get_instance_features(seq_length: int):
return {
"mass": tf.float32,
"friction": tf.float32,
"restitution": tf.float32,
"positions": tfds.features.Tensor(shape=(seq_length, 3),
dtype=tf.float32),
"quaternions": tfds.features.Tensor(shape=(seq_length, 4),
dtype=tf.float32),
"velocities": tfds.features.Tensor(shape=(seq_length, 3),
dtype=tf.float32),
"angular_velocities": tfds.features.Tensor(shape=(seq_length, 3),
dtype=tf.float32),
"bboxes_3d": tfds.features.Tensor(shape=(seq_length, 8, 3),
dtype=tf.float32),
"image_positions": tfds.features.Tensor(shape=(seq_length, 2),
dtype=tf.float32),
"bboxes": tfds.features.Sequence(
tfds.features.BBoxFeature()),
"bbox_frames": tfds.features.Sequence(
tfds.features.Tensor(shape=(), dtype=tf.int32)),
"visibility": tfds.features.Tensor(shape=(seq_length,), dtype=tf.uint16),
}
def format_instance_information(obj):
return {
"mass": obj["mass"],
"friction": obj["friction"],
"restitution": obj["restitution"],
"positions": np.array(obj["positions"], np.float32),
"quaternions": np.array(obj["quaternions"], np.float32),
"velocities": np.array(obj["velocities"], np.float32),
"angular_velocities": np.array(obj["angular_velocities"], np.float32),
"bboxes_3d": np.array(obj["bboxes_3d"], np.float32),
"image_positions": np.array(obj["image_positions"], np.float32),
"bboxes": [tfds.features.BBox(*bbox) for bbox in obj["bboxes"]],
"bbox_frames": np.array(obj["bbox_frames"], dtype=np.uint16),
"visibility": np.array(obj["visibility"], dtype=np.uint16),
}
def subsample_nearest_neighbor(arr, size):
src_height, src_width, _ = arr.shape
dst_height, dst_width = size
height_step = src_height // dst_height
width_step = src_width // dst_width
assert height_step * dst_height == src_height
assert width_step * dst_width == src_width
height_offset = int(np.floor((height_step-1)/2))
width_offset = int(np.floor((width_step-1)/2))
subsampled = arr[height_offset::height_step, width_offset::width_step, :]
return subsampled
def convert_float_to_uint16(array, min_val, max_val):
return np.round((array - min_val) / (max_val - min_val) * 65535
).astype(np.uint16)
def subsample_avg(arr, size):
src_height, src_width, channels = arr.shape
dst_height, dst_width = size
height_bin = src_height // dst_height
width_bin = src_width // dst_width
return np.round(arr.reshape((dst_height, height_bin,
dst_width, width_bin,
channels)).mean(axis=(1, 3))).astype(np.uint8)
def is_complete_dir(video_dir, layers=DEFAULT_LAYERS):
video_dir = as_path(video_dir)
filenames = [d.name for d in video_dir.iterdir()]
if not ("data_ranges.json" in filenames and
"metadata.json" in filenames and
"events.json" in filenames):
return False
nr_frames_per_category = {
key: len([fn for fn in filenames if fn.startswith(key)])
for key in layers}
nr_expected_frames = nr_frames_per_category["rgba"]
if nr_expected_frames == 0:
return False
if not all(nr_frames == nr_expected_frames
for nr_frames in nr_frames_per_category.values()):
return False
return True
PathLike = Union[str, epath.Path]
def as_path(path: PathLike) -> epath.Path:
"""Convert str or pathlike object to epath.Path.
Instead of pathlib.Paths, we use the TFDS path because they transparently
support paths to GCS buckets such as "gs://kubric-public/GSO".
"""
return tfds.core.as_path(path)
def read_png(filename, rescale_range=None) -> np.ndarray:
filename = as_path(filename)
png_reader = png.Reader(bytes=filename.read_bytes())
width, height, pngdata, info = png_reader.read()
del png_reader
bitdepth = info["bitdepth"]
if bitdepth == 8:
dtype = np.uint8
elif bitdepth == 16:
dtype = np.uint16
else:
raise NotImplementedError(f"Unsupported bitdepth: {bitdepth}")
plane_count = info["planes"]
pngdata = np.vstack(list(map(dtype, pngdata)))
if rescale_range is not None:
minv, maxv = rescale_range
pngdata = pngdata / 2**bitdepth * (maxv - minv) + minv
return pngdata.reshape((height, width, plane_count))
def write_tiff(data: np.ndarray, filename: PathLike):
"""Save data as as tif image (which natively supports float values)."""
assert data.ndim == 3, data.shape
assert data.shape[2] in [1, 3, 4], "Must be grayscale, RGB, or RGBA"
img_as_bytes = imageio.imwrite("<bytes>", data, format="tiff")
filename = as_path(filename)
filename.write_bytes(img_as_bytes)
def read_tiff(filename: PathLike) -> np.ndarray:
filename = as_path(filename)
img = imageio.imread(filename.read_bytes(), format="tiff")
if img.ndim == 2:
img = img[:, :, None]
return img
def asset_id_from_metadata(meta):
asset_id_lookup = {
(20706, 'Shoe', '11pro SL TRX FG'): '11pro_SL_TRX_FG',
(8922, 'Consumer Goods', '2 of Jenga Clas'): '2_of_Jenga_Classic_Game',
(23766, 'Toys', '30 CONSTRUCTION'): '30_CONSTRUCTION_SET',
(14142, 'Consumer Goods', '3D Dollhouse Ha'): '3D_Dollhouse_Happy_Brother',
(10490, 'Toys', '3D Dollhouse La'): '3D_Dollhouse_Lamp',
(3202, 'Toys', '3D Dollhouse Re'): '3D_Dollhouse_Refrigerator',
(7978, 'Toys', '3D Dollhouse Si'): '3D_Dollhouse_Sink',
(15052, 'Toys', '3D Dollhouse So'): '3D_Dollhouse_Sofa',
(8876, 'Toys', '3D Dollhouse Sw'): '3D_Dollhouse_Swing',
(6446, 'Toys', '3D Dollhouse Ta'): '3D_Dollhouse_TablePurple',
(4750, 'None', '3M Antislip Sur'): '3M_Antislip_Surfacing_Light_Duty_White',
(11422, 'None', '3M Vinyl Tape, '): '3M_Vinyl_Tape_Green_1_x_36_yd',
(15358, 'Consumer Goods', '4.5oz RAMEKIN A'): '45oz_RAMEKIN_ASST_DEEP_COLORS',
(10728, 'Toys', '50 BLOCKS\nConta'): '50_BLOCKS',
(8362, 'Bottles and Cans and Cups', '5 HTP'): '5_HTP',
(26126, 'Toys', '60 CONSTRUCTION'): '60_CONSTRUCTION_SET',
(11224, 'Consumer Goods', 'ACE Coffee Mug,'): 'ACE_Coffee_Mug_Kristen_16_oz_cup',
(14692, 'Toys', 'ALPHABET A-Z (G'): 'ALPHABET_AZ_GRADIENT',
(13878, 'Toys', 'ALPHABET A-Z (G'): 'ALPHABET_AZ_GRADIENT_WQb1ufEycSj',
(23444, 'Shoe', 'AMBERLIGHT UP W'): 'AMBERLIGHT_UP_W',
(37508, 'Shoe', 'ASICS GEL-1140V'): 'ASICS_GEL1140V_WhiteBlackSilver',
(38246, 'Shoe', 'ASICS GEL-1140V'): 'ASICS_GEL1140V_WhiteRoyalSilver',
(45698, 'Shoe', 'ASICS GEL-Ace™ '): 'ASICS_GELAce_Pro_Pearl_WhitePink',
(39092, 'Shoe', 'ASICS GEL-Blur3'): 'ASICS_GELBlur33_20_GS_BlackWhiteSafety_Orange',
(30660, 'Shoe', 'ASICS GEL-Blur3'): 'ASICS_GELBlur33_20_GS_Flash_YellowHot_PunchSilver',
(33438, 'Shoe', 'ASICS GEL-Chall'): 'ASICS_GELChallenger_9_Royal_BlueWhiteBlack',
(44502, 'Shoe', 'ASICS GEL-Dirt '): 'ASICS_GELDirt_Dog_4_SunFlameBlack',
(34586, 'Shoe', 'ASICS GEL-Links'): 'ASICS_GELLinksmaster_WhiteCoffeeSand',
(31266, 'Shoe', 'ASICS GEL-Links'): 'ASICS_GELLinksmaster_WhiteRasberryGunmetal',
(34160, 'Shoe', 'ASICS GEL-Links'): 'ASICS_GELLinksmaster_WhiteSilverCarolina_Blue',
(36694, 'Shoe', 'ASICS GEL-Resol'): 'ASICS_GELResolution_5_Flash_YellowBlackSilver',
(26000, 'Shoe', 'ASICS GEL-Tour '): 'ASICS_GELTour_Lyte_WhiteOrchidSilver',
(21152, 'Shoe', 'ASICS Hyper-Roc'): 'ASICS_HyperRocketgirl_SP_5_WhiteMalibu_BlueBlack',
(20336, 'Toys', 'ASSORTED VEGETA'): 'ASSORTED_VEGETABLE_SET',
(45866, 'Shoe', 'Adrenaline GTS '): 'Adrenaline_GTS_13_Color_DrkDenimWhtBachlorBttnSlvr_Size_50_yfK40TNjq0V',
(48412, 'Shoe', 'Adrenaline GTS '): 'Adrenaline_GTS_13_Color_WhtObsdianBlckOlmpcSlvr_Size_70',
(5762, 'None', 'Air Hogs Wind F'): 'Air_Hogs_Wind_Flyers_Set_Airplane_Red',
(8126, 'Bottles and Cans and Cups', 'Allergen-Free J'): 'AllergenFree_JarroDophilus',
(11836, 'Consumer Goods', 'Android Figure,'): 'Android_Figure_Chrome',
(9698, 'Consumer Goods', 'Android Figure,'): 'Android_Figure_Orange',
(10618, 'Consumer Goods', 'Android Figure,'): 'Android_Figure_Panda',
(14214, 'Legos', 'Android Lego'): 'Android_Lego',
(2444, 'Media Cases', 'Animal Crossing'): 'Animal_Crossing_New_Leaf_Nintendo_3DS_Game',
(21978, 'Toys', 'Animal Planet F'): 'Animal_Planet_Foam_2Headed_Dragon',
(1690, 'Consumer Goods', 'Apples to Apple'): 'Apples_to_Apples_Kids_Edition',
(2820, 'Consumer Goods', 'Arm Hammer Diap'): 'Arm_Hammer_Diaper_Pail_Refills_12_Pack_MFWkmoweejt',
(21582, 'Consumer Goods', 'Aroma Stainless'): 'Aroma_Stainless_Steel_Milk_Frother_2_Cup',
(17458, 'None', 'Asus - 802.11ac'): 'Asus_80211ac_DualBand_Gigabit_Wireless_Router_RTAC68R',
(16504, 'None', 'Asus M5A78L-M/U'): 'Asus_M5A78LMUSB3_Motherboard_Micro_ATX_Socket_AM3',
(9370, 'None', 'Asus M5A99FX PR'): 'Asus_M5A99FX_PRO_R20_Motherboard_ATX_Socket_AM3',
(1708, 'None', 'Asus Sabertooth'): 'Asus_Sabertooth_990FX_20_Motherboard_ATX_Socket_AM3',
(3184, 'None', 'Asus Sabertooth'): 'Asus_Sabertooth_Z97_MARK_1_Motherboard_ATX_LGA1150_Socket',
(2838, 'None', 'Asus X99-Deluxe'): 'Asus_X99Deluxe_Motherboard_ATX_LGA2011v3_Socket',
(3592, 'None', 'Asus Z87-PRO Mo'): 'Asus_Z87PRO_Motherboard_ATX_LGA1150_Socket',
(5896, 'None', 'Asus Z97-AR LGA'): 'Asus_Z97AR_LGA_1150_Intel_ATX_Motherboard',
(34176, 'None', 'Asus Z97I-PLUS '): 'Asus_Z97IPLUS_Motherboard_Mini_ITX_LGA1150_Socket',
(18402, 'Toys', 'Avengers Gamma '): 'Avengers_Gamma_Green_Smash_Fists',
(10822, 'Action Figures', 'Avengers Thor'): 'Avengers_Thor_PLlrpYniaeB',
(30976, 'Shoe', 'Azure Snake Tie'): 'Azure_Snake_Tieks_Leather_Snake_Print_Ballet_Flats',
(17646, 'Toys', 'BABY CAR\nThis c'): 'BABY_CAR',
(14524, 'Toys', 'BAGEL WITH CHEE'): 'BAGEL_WITH_CHEESE',
(6782, 'Toys', 'BAKING UTENSILS'): 'BAKING_UTENSILS',
(11788, 'Toys', 'BALANCING CACTU'): 'BALANCING_CACTUS',
(6320, 'Toys', 'BATHROOM - CLAS'): 'BATHROOM_CLASSIC',
(6798, 'Toys', 'BATHROOM (FURNI'): 'BATHROOM_FURNITURE_SET_1',
(8436, 'Toys', 'BEDROOM - CLASS'): 'BEDROOM_CLASSIC',
(8872, 'Toys', 'BEDROOM - CLASS'): 'BEDROOM_CLASSIC_Gi22DjScTVS',
(10840, 'Toys', 'BEDROOM - NEO\nT'): 'BEDROOM_NEO',
(14542, 'None', 'B.I.A Cordon Bl'): 'BIA_Cordon_Bleu_White_Porcelain_Utensil_Holder_900028',
(16274, 'None', 'BIA Porcelain R'): 'BIA_Porcelain_Ramekin_With_Glazed_Rim_35_45_oz_cup',
(17104, 'Toys', 'BIRD RATTLE\nThi'): 'BIRD_RATTLE',
(16190, 'Toys', 'BRAILLE ALPHABE'): 'BRAILLE_ALPHABET_AZ',
(11676, 'Toys', 'BREAKFAST MENU\n'): 'BREAKFAST_MENU',
(11724, 'Toys', 'BUILD -A- ROBOT'): 'BUILD_A_ROBOT',
(12958, 'Toys', 'BUILD A ZOO\nHel'): 'BUILD_A_ZOO',
(12146, 'Toys', 'BUNNY RACER\nHav'): 'BUNNY_RACER',
(11316, 'Toys', 'BUNNY RATTLE\nLi'): 'BUNNY_RATTLE',
(10528, 'None', 'Baby Elements S'): 'Baby_Elements_Stacking_Cups',
(1464, 'Board Games', 'Balderdash Game'): 'Balderdash_Game',
(6770, 'Consumer Goods', 'Beetle Adventur'): 'Beetle_Adventure_Racing_Nintendo_64',
(8726, 'Bottles and Cans and Cups', 'Beta Glucan'): 'Beta_Glucan',
(4630, 'Consumer Goods', 'Beyonc? - Life '): 'Beyonc_Life_is_But_a_Dream_DVD',
(9344, 'Bottles and Cans and Cups', 'Bifidus Balance'): 'Bifidus_Balance_FOS',
(12514, 'Bag', 'Big Dot Aqua Pe'): 'Big_Dot_Aqua_Pencil_Case',
(11720, 'Bag', 'Big Dot Pink Pe'): 'Big_Dot_Pink_Pencil_Case',
(31182, 'Consumer Goods', 'Big O Sponges, '): 'Big_O_Sponges_Assorted_Cellulose_12_pack',
(6932, 'None', 'Black/Black Nin'): 'BlackBlack_Nintendo_3DSXL',
(30034, 'None', 'Black & Decker '): 'Black_Decker_CM2035B_12Cup_Thermal_Coffeemaker',
(47628, 'None', 'Black & Decker '): 'Black_Decker_Stainless_Steel_Toaster_4_Slice',
(4028, 'Consumer Goods', 'Black Elderberr'): 'Black_Elderberry_Syrup_54_oz_Gaia_Herbs',
(2036, 'Consumer Goods', 'Black Forest Fr'): 'Black_Forest_Fruit_Snacks_28_Pack_Grape',
(2544, 'Consumer Goods', 'Black Forest Fr'): 'Black_Forest_Fruit_Snacks_Juicy_Filled_Centers_10_pouches_9_oz_total',
(4658, 'None', 'Black and Decke'): 'Black_and_Decker_PBJ2000_FusionBlade_Blender_Jars',
(31474, 'None', 'Black and Decke'): 'Black_and_Decker_TR3500SD_2Slice_Toaster',
(8676, 'Bottles and Cans and Cups', 'Blackcurrant + '): 'Blackcurrant_Lutein',
(5932, 'None', 'Blue/Black Nint'): 'BlueBlack_Nintendo_3DSXL',
(6068, 'Media Cases', 'Blue Jasmine [I'): 'Blue_Jasmine_Includes_Digital_Copy_UltraViolet_DVD',
(9132, 'Bottles and Cans and Cups', 'Borage GLA-240+'): 'Borage_GLA240Gamma_Tocopherol',
(15502, 'None', 'Bradshaw Intern'): 'Bradshaw_International_11642_7_Qt_MP_Plastic_Bowl',
(13840, 'None', 'Breyer Horse Of'): 'Breyer_Horse_Of_The_Year_2015',
(3634, 'Consumer Goods', 'Brisk Iced Tea,'): 'Brisk_Iced_Tea_Lemon_12_12_fl_oz_355_ml_cans_144_fl_oz_426_lt',
(4782, 'Consumer Goods', 'Brother Ink Car'): 'Brother_Ink_Cartridge_Magenta_LC75M',
(5762, 'Consumer Goods', 'Brother LC 1053'): 'Brother_LC_1053PKS_Ink_Cartridge_CyanMagentaYellow_1pack',
(4816, 'Consumer Goods', 'Brother Printin'): 'Brother_Printing_Cartridge_PC501',
(11652, 'Toys', 'CARS-II'): 'CARSII',
(25266, 'Toys', 'CAR CARRIER TRA'): 'CAR_CARRIER_TRAIN',
(22108, 'Toys', 'CASTLE BLOCKS\nE'): 'CASTLE_BLOCKS',
(11022, 'Toys', 'CHICKEN NESTING'): 'CHICKEN_NESTING',
(12808, 'Toys', 'CHICKEN RACER\nH'): 'CHICKEN_RACER',
(16054, 'Toys', "CHILDREN'S ROOM"): 'CHILDRENS_ROOM_NEO',
(12712, 'Toys', 'CHILDREN BEDROO'): 'CHILDREN_BEDROOM_CLASSIC',
(11192, 'Toys', 'CITY TAXI & POL'): 'CITY_TAXI_POLICE_CAR',
(41832, 'Shoe', 'CLIMACOOL BOAT '): 'CLIMACOOL_BOAT_BREEZE_IE6CyqSaDwN',
(9562, 'Toys', 'COAST GUARD BOA'): 'COAST_GUARD_BOAT',
(21962, 'Toys', 'CONE SORTING\nLe'): 'CONE_SORTING',
(5406, 'Toys', 'CONE SORTING\nLe'): 'CONE_SORTING_kg5fbARBwts',
(14610, 'Toys', 'CREATIVE BLOCKS'): 'CREATIVE_BLOCKS_35_MM',
(23540, 'Shoe', 'California Navy'): 'California_Navy_Tieks_Italian_Leather_Ballet_Flats',
(8262, 'None', 'Calphalon Kitch'): 'Calphalon_Kitchen_Essentials_12_Cast_Iron_Fry_Pan_Black',
(6292, 'Consumer Goods', 'Canon 225/226 I'): 'Canon_225226_Ink_Cartridges_BlackColor_Cyan_Magenta_Yellow_6_count',
(6052, 'Consumer Goods', 'Canon Ink Cartr'): 'Canon_Ink_Cartridge_Green_6',
(5132, 'Consumer Goods', 'Canon Pixma Chr'): 'Canon_Pixma_Chromalife_100_Magenta_8',
(6536, 'Consumer Goods', 'Canon Pixma Ink'): 'Canon_Pixma_Ink_Cartridge_251_M',
(5612, 'Consumer Goods', 'Canon Pixma Ink'): 'Canon_Pixma_Ink_Cartridge_8',
(5722, 'Consumer Goods', 'Canon Pixma Ink'): 'Canon_Pixma_Ink_Cartridge_8_Green',
(6218, 'Consumer Goods', 'Canon Pixma Ink'): 'Canon_Pixma_Ink_Cartridge_8_Red',
(5756, 'Consumer Goods', 'Canon Pixma Ink'): 'Canon_Pixma_Ink_Cartridge_Cyan_251',
(42288, 'Shoe', 'Cascadia 8, Col'): 'Cascadia_8_Color_AquariusHibscsBearingSeaBlk_Size_50',
(7768, 'Consumer Goods', 'Central Garden '): 'Central_Garden_Flower_Pot_Goo_425',
(4436, 'None', 'Chef Style Roun'): 'Chef_Style_Round_Cake_Pan_9_inch_pan',
(9320, 'None', 'Chefmate 8" Fry'): 'Chefmate_8_Frypan',
(15226, 'Shoe', 'Chelsea Blk.Hee'): 'Chelsea_BlkHeelPMP_DwxLtZNxLZZ',
(7624, 'Shoe', 'Chelsea lo fl r'): 'Chelsea_lo_fl_rdheel_nQ0LPNF1oMw',
(7910, 'Shoe', 'Chelsea lo fl r'): 'Chelsea_lo_fl_rdheel_zAQrnhlEfw8',
(18510, 'Consumer Goods', 'Circo Fish Toot'): 'Circo_Fish_Toothbrush_Holder_14995988',
(28482, 'Shoe', 'ClimaCool Aerat'): 'ClimaCool_Aerate_2_W_Wide',
(10368, 'None', 'Clorox Premium '): 'Clorox_Premium_Choice_Gloves_SM_1_pair',
(12158, 'None', 'Closetmaid Prem'): 'Closetmaid_Premium_Fabric_Cube_Red',
(4450, 'Board Games', 'Clue Board Game'): 'Clue_Board_Game_Classic_Edition',
(10490, 'Bottles and Cans and Cups', 'Co-Q10'): 'CoQ10',
(9298, 'Bottles and Cans and Cups', 'Co-Q10'): 'CoQ10_BjTLbuRVt1t',
(10984, 'Bottles and Cans and Cups', 'Co-Q10'): 'CoQ10_wSSVoxVppVD',
(4484, 'None', 'Cole Hardware A'): 'Cole_Hardware_Antislip_Surfacing_Material_White',
(5650, 'None', 'Cole Hardware A'): 'Cole_Hardware_Antislip_Surfacing_White_2_x_60',
(10342, 'None', 'Cole Hardware B'): 'Cole_Hardware_Bowl_Scirocco_YellowBlue',
(34140, 'Consumer Goods', 'Cole Hardware B'): 'Cole_Hardware_Butter_Dish_Square_Red',
(10364, 'None', 'Cole Hardware D'): 'Cole_Hardware_Deep_Bowl_Good_Earth_1075',
(18694, 'None', 'Cole Hardware D'): 'Cole_Hardware_Dishtowel_Blue',
(17596, 'None', 'Cole Hardware D'): 'Cole_Hardware_Dishtowel_BlueWhite',
(21206, 'None', 'Cole Hardware D'): 'Cole_Hardware_Dishtowel_Multicolors',
(21290, 'None', 'Cole Hardware D'): 'Cole_Hardware_Dishtowel_Red',
(14942, 'None', 'Cole Hardware D'): 'Cole_Hardware_Dishtowel_Stripe',
(8752, 'None', 'Cole Hardware E'): 'Cole_Hardware_Electric_Pot_Assortment_55',
(7674, 'None', 'Cole Hardware E'): 'Cole_Hardware_Electric_Pot_Cabana_55',
(5820, 'None', 'Cole Hardware F'): 'Cole_Hardware_Flower_Pot_1025',
(7836, 'None', 'Cole Hardware H'): 'Cole_Hardware_Hammer_Black',
(6458, 'Consumer Goods', 'Cole Hardware M'): 'Cole_Hardware_Mini_Honey_Dipper',
(8716, 'None', 'Cole Hardware M'): 'Cole_Hardware_Mug_Classic_Blue',
(6834, 'None', 'Cole Hardware O'): 'Cole_Hardware_Orchid_Pot_85',
(4302, 'None', 'Cole Hardware P'): 'Cole_Hardware_Plant_Saucer_Brown_125',
(4126, 'None', 'Cole Hardware P'): 'Cole_Hardware_Plant_Saucer_Glazed_9',
(6796, 'None', 'Cole Hardware S'): 'Cole_Hardware_Saucer_Electric',
(5248, 'None', 'Cole Hardware S'): 'Cole_Hardware_Saucer_Glazed_6',
(8752, 'Consumer Goods', 'Cole Hardware S'): 'Cole_Hardware_School_Bell_Solid_Brass_38',
(26766, 'Shoe', 'Colton Wntr Chu'): 'Colton_Wntr_Chukka_y4jO0I8JQFW',
(2544, 'Board Games', 'Connect 4 Launc'): 'Connect_4_Launchers',
(28192, 'Consumer Goods', 'Cootie Game'): 'Cootie_Game',
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(30854, 'Shoe', 'Copperhead Snak'): 'Copperhead_Snake_Tieks_Brown_Snake_Print_Ballet_Flats',
(4612, 'None', 'Corningware CW '): 'Corningware_CW_by_Corningware_3qt_Oblong_Casserole_Dish_Blue',
(37954, 'Shoe', 'Court Attitude'): 'Court_Attitude',
(5610, 'None', 'Craftsman Grip '): 'Craftsman_Grip_Screwdriver_Phillips_Cushion',
(5558, 'None', 'Crayola Bonus 6'): 'Crayola_Bonus_64_Crayons',
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(25056, 'Shoe', 'Crazy 8\nFW-HIGH'): 'Crazy_8',
(39326, 'Shoe', 'Crazy Shadow 2'): 'Crazy_Shadow_2',
(23866, 'Shoe', 'Crazy Shadow 2'): 'Crazy_Shadow_2_oW4Jd10HFFr',
(17216, 'Shoe', 'Cream Tieks - I'): 'Cream_Tieks_Italian_Leather_Ballet_Flats',
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(10248, 'Consumer Goods', 'Crosley Alarm C'): 'Crosley_Alarm_Clock_Vintage_Metal',
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(55206, 'Hat', 'DPC tropical Tr'): 'DPC_tropical_Trends_Hat',
(18932, 'Shoe', 'DRAGON W'): 'DRAGON_W',
(30974, 'Shoe', 'D ROSE 4.5\nFW-H'): 'D_ROSE_45',
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(16358, 'Bag', 'Digital Camo Do'): 'Digital_Camo_Double_Decker_Lunch_Bag',
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(25548, 'None', 'Dixie 10 ounce '): 'Dixie_10_ounce_Bowls_35_ct',
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(35714, 'Shoe', 'ENFR MID (ENFOR'): 'ENFR_MID_ENFORCER',
(3812, 'Consumer Goods', 'Eat to Live: Th'): 'Eat_to_Live_The_Amazing_NutrientRich_Program_for_Fast_and_Sustained_Weight_Loss_Revised_Edition_Book',
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(4320, 'Consumer Goods', 'Epson LabelWork'): 'Epson_LabelWorks_LC4WBN9_Tape_reel_labels_047_x_295_Roll_Black_on_White',
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(3888, 'Consumer Goods', 'Epson UltraChro'): 'Epson_UltraChrome_T0548_Ink_Cartridge_Matte_Black_1pack',
(14708, 'Shoe', 'F10 TRX FG'): 'F10_TRX_FG_ssscuo9tGxb',
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(14248, 'Shoe', 'F5 TRX FG'): 'F5_TRX_FG',
(23734, 'Toys', 'FAIRY TALE BLOC'): 'FAIRY_TALE_BLOCKS',
(25032, 'Toys', 'FARM ANIMAL\nThe'): 'FARM_ANIMAL',
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(23904, 'Toys', 'FIRE ENGINE'): 'FIRE_ENGINE',
(20576, 'Toys', 'FIRE TRUCK\nThis'): 'FIRE_TRUCK',
(18240, 'Toys', 'FISHING GAME\nGo'): 'FISHING_GAME',
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(6278, 'Toys', 'FRACTION FUN\nFe'): 'FRACTION_FUN_n4h4qte23QR',
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(23218, 'Toys', 'FRUIT & VEGGIE '): 'FRUIT_VEGGIE_MEMO_GRADIENT',
(33206, 'Shoe', 'FYW ALTERNATION'): 'FYW_ALTERNATION',
(36812, 'Shoe', 'FYW DIVISION\nFW'): 'FYW_DIVISION',
(3272, 'Consumer Goods', 'Fem-Dophilus\nFe'): 'FemDophilus',
(6888, 'Media Cases', 'Final Fantasy X'): 'Final_Fantasy_XIV_A_Realm_Reborn_60Day_Subscription',
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(6770, 'None', 'Focus 8643 Lime'): 'Focus_8643_Lime_Squeezer_10x35x188_Enamelled_Aluminum_Light',
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(8370, 'Toys', 'GEOMETRIC SORTI'): 'GEOMETRIC_SORTING_BOARD_MNi4Rbuz9vj',
(18796, 'Shoe', 'GIRLS DECKHAND\n'): 'GIRLS_DECKHAND',
(15920, 'Toys', 'GRANDFATHER DOL'): 'GRANDFATHER_DOLL',
(15340, 'Toys', 'GRANDMOTHER'): 'GRANDMOTHER',
(9102, 'Bottles and Cans and Cups', 'Germanium GE-13'): 'Germanium_GE132',
(59036, 'Shoe', 'Ghost 6, Color:'): 'Ghost_6_Color_BlckWhtLavaSlvrCitrus_Size_80',
(57462, 'Shoe', 'Ghost 6, Color:'): 'Ghost_6_Color_MdngtDenmPomBrtePnkSlvBlk_Size_50',
(58118, 'Shoe', 'Ghost 6 GTX, Co'): 'Ghost_6_GTX_Color_AnthBlckSlvrFernSulphSprng_Size_80',
(12256, 'None', 'Gigabyte GA-78L'): 'Gigabyte_GA78LMTUSB3_50_Motherboard_Micro_ATX_Socket_AM3',
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(25798, 'None', 'Gigabyte GA-Z97'): 'Gigabyte_GAZ97XSLI_10_motherboard_ATX_LGA1150_Socket_Z97',
(36936, 'Shoe', 'Glycerin 11, Co'): 'Glycerin_11_Color_AqrsDrsdnBluBlkSlvShckOrng_Size_50',
(42844, 'Shoe', 'Glycerin 11, Co'): 'Glycerin_11_Color_BrllntBluSkydvrSlvrBlckWht_Size_80',
(22696, 'None', 'GoPro HERO3 Com'): 'GoPro_HERO3_Composite_Cable',
(5108, 'Consumer Goods', 'Google Cardboar'): 'Google_Cardboard_Original_package',
(23248, 'Shoe', 'Grand Prix'): 'Grand_Prix',
(12556, 'None', 'Granimals 20 Wo'): 'Granimals_20_Wooden_ABC_Blocks_Wagon',
(12112, 'None', 'Granimals 20 Wo'): 'Granimals_20_Wooden_ABC_Blocks_Wagon_85VdSftGsLi',
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(10310, 'None', 'Great Dinos Tri'): 'Great_Dinos_Triceratops_Toy',
(18656, 'Shoe', 'Great Jones Win'): 'Great_Jones_Wingtip',
(22786, 'Shoe', 'Great Jones Win'): 'Great_Jones_Wingtip_j5NV8GRnitM',
(22530, 'Shoe', 'Great Jones Win'): 'Great_Jones_Wingtip_kAqSg6EgG0I',
(18654, 'Shoe', 'Great Jones Win'): 'Great_Jones_Wingtip_wxH3dbtlvBC',
(8194, 'None', 'Grreat Choice D'): 'Grreat_Choice_Dog_Double_Dish_Plastic_Blue',
(8862, 'None', 'Grreatv Choice '): 'Grreatv_Choice_Dog_Bowl_Gray_Bones_Plastic_20_fl_oz_total',
(25030, 'Action Figures', 'Guardians of th'): 'Guardians_of_the_Galaxy_Galactic_Battlers_Rocket_Raccoon_Figure',
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(7254, 'Toys', 'HAMMER PEG\nA fo'): 'HAMMER_PEG',
(15304, 'Toys', 'HAPPY ENGINE\nHa'): 'HAPPY_ENGINE',
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(1652, 'None', 'HP 1800 Tablet,'): 'HP_1800_Tablet_8GB_7',
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(1502, 'Consumer Goods', 'Hasbro Cranium '): 'Hasbro_Cranium_Performance_and_Acting_Game',
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(4712, 'Board Games', 'Hasbro Trivial '): 'Hasbro_Trivial_Pursuit_Family_Edition_Game',
(17748, 'None', 'Heavy-Duty Flas'): 'HeavyDuty_Flashlight',
(13430, 'None', 'Hefty Waste Bas'): 'Hefty_Waste_Basket_Decorative_Bronze_85_liter',
(6462, 'Consumer Goods', 'Hey You, Pikach'): 'Hey_You_Pikachu_Nintendo_64',
(12072, 'Shoe', 'Hilary'): 'Hilary',
(43926, 'None', 'Home Fashions W'): 'Home_Fashions_Washcloth_Linen',
(48028, 'None', 'Home Fashions W'): 'Home_Fashions_Washcloth_Olive_Green',
(12128, 'Bag', 'Horse Dreams Pe'): 'Horse_Dreams_Pencil_Case',
(12696, 'Bag', 'Horses in Pink '): 'Horses_in_Pink_Pencil_Case',
(3506, 'Media Cases', 'House of Cards:'): 'House_of_Cards_The_Complete_First_Season_4_Discs_DVD',
(8834, 'Bottles and Cans and Cups', 'Hyaluronic Acid'): 'Hyaluronic_Acid',
(4036, 'Headphones', 'HyperX Cloud II'): 'HyperX_Cloud_II_Headset_Gun_Metal',
(3990, 'Headphones', 'HyperX Cloud II'): 'HyperX_Cloud_II_Headset_Red',
(5168, 'None', 'INTERNATIONAL P'): 'INTERNATIONAL_PAPER_Willamette_4_Brown_Bag_500Count',
(27334, 'Action Figures', 'Imaginext Castl'): 'Imaginext_Castle_Ogre',
(18272, 'None', 'In Green Compan'): 'In_Green_Company_Surface_Saver_Ring_10_Terra_Cotta',
(9522, 'Bottles and Cans and Cups', 'Inositol'): 'Inositol',
(9408, 'None', 'InterDesign Ove'): 'InterDesign_Over_Door',
(9094, 'Bottles and Cans and Cups', 'Iso-Rich Soy'): 'IsoRich_Soy',
(15856, 'None', 'J.A. Henckels I'): 'JA_Henckels_International_Premio_Cutlery_Block_Set_14Piece',
(6928, 'Consumer Goods', 'JBL Charge Spea'): 'JBL_Charge_Speaker_portable_wireless_wired_Green',
(34798, 'Shoe', 'JS WINGS 2.0 BL'): 'JS_WINGS_20_BLACK_FLAG',
(9760, 'Toys', 'JUICER SET\nHave'): 'JUICER_SET',
(18494, 'Toys', 'JUNGLE HEIGHT\nC'): 'JUNGLE_HEIGHT',
(30092, 'Bag', 'Jansport School'): 'Jansport_School_Backpack_Blue_Streak',
(8406, 'Bottles and Cans and Cups', 'Jarro-Dophilus+'): 'JarroDophilusFOS_Value_Size',
(7268, 'Consumer Goods', 'JarroSil, Activ'): 'JarroSil_Activated_Silicon',
(6446, 'Consumer Goods', 'JarroSil, Activ'): 'JarroSil_Activated_Silicon_5exdZHIeLAp',
(9234, 'Bottles and Cans and Cups', 'Jarrow Formulas'): 'Jarrow_Formulas_Glucosamine_Hci_Mega_1000_100_ct',
(6406, 'Bottles and Cans and Cups', 'Jarrow Glucosam'): 'Jarrow_Glucosamine_Chondroitin_Combination_120_Caps',
(7654, 'None', 'Jawbone UP24 Wi'): 'Jawbone_UP24_Wireless_Activity_Tracker_Pink_Coral_L',
(5512, 'Consumer Goods', 'Just For Men Mu'): 'Just_For_Men_Mustache_Beard_Brushin_Hair_Color_Gel_Kit_Jet_Black_M60',
(3612, 'Consumer Goods', 'Just For Men Mu'): 'Just_For_Men_Mustache_Beard_Brushin_Hair_Color_Gel_MediumDark_Brown_M40',
(4644, 'Consumer Goods', 'Just For Men Sh'): 'Just_For_Men_ShampooIn_Haircolor_Jet_Black_60',
(4056, 'Consumer Goods', 'Just For Men Sh'): 'Just_For_Men_ShampooIn_Haircolor_Light_Brown_25',
(3382, 'Consumer Goods', 'Just For Men Sh'): 'Just_For_Men_Shampoo_In_Haircolor_Darkest_Brown_50',
(4702, 'Media Cases', 'Justified: The '): 'Justified_The_Complete_Fourth_Season_3_Discs_DVD',