「ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8」からtensorflow.jsの物体検知モデルを構築しています。モデルをトレーニングし、tensorflowjs_convertercli ツールで変換しました。モデルを jupyter で実行すると、次の出力が得られます。
しかし、モデルを tensorflow.js にロードすると、バウンディング ボックスが見つかりません。次のようなバウンディング ボックス テンソルに何かを吐き出します。
ほとんどランダム。これらのボックスには、クラスが関連付けられていません。最初は、コンバーターがいくつかの最適化を実行しているように見えるため、tfjs モデルの精度は低いと思いました。しかし、python ノートブックで精度を向上させた後、tfjs の精度は向上しませんでした。
tensorflowjs_converter最適化をオフにする方法があるようには見えません。本当？
モデルを tfjs で実行するために他にできることはありますか?
これが私のものpipeline_file.configです：
# SSD with Mobilenet v2 FPN-lite (go/fpn-lite) feature extractor, shared box
# predictor and focal loss (a mobile version of Retinanet).
# Retinanet: see Lin et al, https://arxiv.org/abs/1708.02002
# Trained on COCO, initialized from Imagenet classification checkpoint
# Train on TPU-8
#
# Achieves 28.2 mAP on COCO17 Val
model {
ssd {
inplace_batchnorm_update: true
freeze_batchnorm: false
num_classes: 6
box_coder {
faster_rcnn_box_coder {
y_scale: 10.0
x_scale: 10.0
height_scale: 5.0
width_scale: 5.0
}
}
matcher {
argmax_matcher {
matched_threshold: 0.5
unmatched_threshold: 0.5
ignore_thresholds: false
negatives_lower_than_unmatched: true
force_match_for_each_row: true
use_matmul_gather: true
}
}
similarity_calculator {
iou_similarity {
}
}
encode_background_as_zeros: true
anchor_generator {
multiscale_anchor_generator {
min_level: 3
max_level: 7
anchor_scale: 4.0
aspect_ratios: [1.0, 2.0, 0.5]
scales_per_octave: 2
}
}
image_resizer {
fixed_shape_resizer {
height: 640
width: 640
}
}
box_predictor {
weight_shared_convolutional_box_predictor {
depth: 128
class_prediction_bias_init: -4.6
conv_hyperparams {
activation: RELU_6,
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
random_normal_initializer {
stddev: 0.01
mean: 0.0
}
}
batch_norm {
scale: true,
decay: 0.997,
epsilon: 0.001,
}
}
num_layers_before_predictor: 4
share_prediction_tower: true
use_depthwise: true
kernel_size: 3
}
}
feature_extractor {
type: 'ssd_mobilenet_v2_fpn_keras'
use_depthwise: true
fpn {
min_level: 3
max_level: 7
additional_layer_depth: 128
}
min_depth: 16
depth_multiplier: 1.0
conv_hyperparams {
activation: RELU_6,
regularizer {
l2_regularizer {
weight: 0.00004
}
}
initializer {
random_normal_initializer {
stddev: 0.01
mean: 0.0
}
}
batch_norm {
scale: true,
decay: 0.997,
epsilon: 0.001,
}
}
override_base_feature_extractor_hyperparams: true
}
loss {
classification_loss {
weighted_sigmoid_focal {
alpha: 0.25
gamma: 2.0
}
}
localization_loss {
weighted_smooth_l1 {
}
}
classification_weight: 1.0
localization_weight: 1.0
}
normalize_loss_by_num_matches: true
normalize_loc_loss_by_codesize: true
post_processing {
batch_non_max_suppression {
score_threshold: 1e-8
iou_threshold: 0.6
max_detections_per_class: 100
max_total_detections: 100
}
score_converter: SIGMOID
}
}
}
train_config: {
fine_tune_checkpoint_version: V2
fine_tune_checkpoint: "/content/drive/MyDrive/nespresso_detection/models/research/deploy/ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/checkpoint/ckpt-0"
fine_tune_checkpoint_type: "detection"
batch_size: 16
sync_replicas: true
startup_delay_steps: 0
replicas_to_aggregate: 8
num_steps: 8000
data_augmentation_options {
random_horizontal_flip {
}
}
data_augmentation_options {
random_crop_image {
min_object_covered: 0.0
min_aspect_ratio: 0.75
max_aspect_ratio: 3.0
min_area: 0.75
max_area: 1.0
overlap_thresh: 0.0
}
}
optimizer {
momentum_optimizer: {
learning_rate: {
cosine_decay_learning_rate {
learning_rate_base: .08
total_steps: 50000
warmup_learning_rate: .026666
warmup_steps: 1000
}
}
momentum_optimizer_value: 0.9
}
use_moving_average: false
}
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
}
train_input_reader: {
label_map_path: "/content/drive/MyDrive/nespresso_detection/train/VertuoPlus_label_map.pbtxt"
tf_record_input_reader {
input_path: "/content/drive/MyDrive/nespresso_detection/train/VertuoPlus.tfrecord"
}
}
eval_config: {
metrics_set: "coco_detection_metrics"
use_moving_averages: false
}
eval_input_reader: {
label_map_path: "/content/drive/MyDrive/nespresso_detection/train/VertuoPlus_label_map.pbtxt"
shuffle: false
num_epochs: 1
tf_record_input_reader {
input_path: "/content/drive/MyDrive/nespresso_detection/valid/VertuoPlus.tfrecord"
}
}
model.jsonコンバーターが作成するファイルは次のとおりです。
{
"format": "graph-model",
"generatedBy": "2.4.0",
"convertedBy": "TensorFlow.js Converter v2.8.3",
"signature": {
"inputs": {
"input_tensor:0": {
"name": "input_tensor:0",
"dtype": "DT_UINT8",
"tensorShape": {
"dim": [
{
"size": "1"
},
{
"size": "-1"
},
{
"size": "-1"
},
{
"size": "3"
}
]
}
}
},
"outputs": {
"Identity_1:0": {
"name": "Identity_1:0",
"dtype": "DT_FLOAT",
"tensorShape": {
"dim": [
{
"size": "1"
},
{
"size": "100"
},
{
"size": "4"
}
]
}
},
"Identity_3:0": {
"name": "Identity_3:0",
"dtype": "DT_FLOAT",
"tensorShape": {
"dim": [
{
"size": "1"
},
{
"size": "100"
},
{
"size": "7"
}
]
}
},
"Identity_5:0": {
"name": "Identity_5:0",
"dtype": "DT_FLOAT",
"tensorShape": {
"dim": [
{
"size": "1"
}
]
}
},
"Identity:0": {
"name": "Identity:0",
"dtype": "DT_FLOAT",
"tensorShape": {
"dim": [
{
"size": "1"
},
{
"size": "100"
}
]
}
},
"Identity_7:0": {
"name": "Identity_7:0",
"dtype": "DT_FLOAT",
"tensorShape": {
"dim": [
{
"size": "1"
},
{
"size": "51150"
},
{
"size": "7"
}
]
}
},
"Identity_2:0": {
"name": "Identity_2:0",
"dtype": "DT_FLOAT",
"tensorShape": {
"dim": [
{
"size": "1"
},
{
"size": "100"
}
]
}
},
"Identity_4:0": {
"name": "Identity_4:0",
"dtype": "DT_FLOAT",
"tensorShape": {
"dim": [
{
"size": "1"
},
{
"size": "100"
}
]
}
},
"Identity_6:0": {
"name": "Identity_6:0",
"dtype": "DT_FLOAT",
"tensorShape": {
"dim": [
{
"size": "1"
},
{
"size": "51150"
},
{
"size": "4"
}
]
}
}
}
},
"modelTopology": {
"node": [
{
"name": "StatefulPartitionedCall/Postprocessor/BatchMultiClassNonMaxSuppression/PadOrClipBoxList/zeros_7",
"op": "Const",
"attr": {
"dtype": {
"type": "DT_INT32"
},
"value": {
"tensor": {
"dtype": "DT_INT32",
"tensorShape": {
"dim": [
{
"size": "1"
}
]
}
}
}
}
},
... to many nodes to list here ...
{
"name": "ConstantFolding/StatefulPartitionedCall/Postprocessor/BatchMultiClassNonMaxSuppression/stack_7_const_axis",
"shape": [],
"dtype": "int32"
}
]
}
]
}
そして私のコンバータースクリプト（価値があるため）：
!tensorflowjs_converter \
--input_format=tf_saved_model \
--output_format=tfjs_graph_model \
--signature_name=serving_default \
--saved_model_tags=serve \
./saved_model \
./tfjs