In converting a frozen graph to UFF file, the config script includes attributes for all custom plugins.
In config.py
import graphsurgeon as gs
import tensorflow as tf
Input = gs.create_node("Input",
op="Placeholder",
dtype=tf.float32,
shape=[1, 3, 640, 640])
PriorBox = gs.create_node("PriorBox",
numLayers=6,
minScale=0.2,
maxScale=0.95,
aspectRatios=[1.0, 2.0, 0.5, 3.0, 0.33],
layerVariances=[0.1,0.1,0.2,0.2],
featureMapShapes=[19, 10, 5, 3, 2, 1])
NMS = gs.create_node("NMS",
scoreThreshold=1e-8,
iouThreshold=0.6,
maxDetectionsPerClass=100,
maxTotalDetections=100,
numClasses=91,
scoreConverter="SIGMOID")
concat_priorbox = gs.create_node("concat_priorbox", dtype=tf.float32, axis=2)
concat_box_loc = gs.create_node("concat_box_loc")
concat_box_conf = gs.create_node("concat_box_conf")
namespace_plugin_map = {
"MultipleGridAnchorGenerator": PriorBox,
"Postprocessor": NMS,
"Preprocessor": Input,
"ToFloat": Input,
"image_tensor": Input,
"MultipleGridAnchorGenerator/Concatenate": concat_priorbox,
"concat": concat_box_loc,
"concat_1": concat_box_conf
}
def preprocess(dynamic_graph):
# Now create a new graph by collapsing namespaces
dynamic_graph.collapse_namespaces(namespace_plugin_map)
# Remove the outputs, so we just have a single output node (NMS).
dynamic_graph.remove(dynamic_graph.graph_outputs, remove_exclusive_dependencies=False)
How do I modify this conifg.py file when I want to convert different model’s frozen graph to UFF? I am trying to make this work for SSD_resnet50_640x640 and pipeline.config file is as follows:
model {
ssd {
num_classes: 90
image_resizer {
fixed_shape_resizer {
height: 640
width: 640
}
}
feature_extractor {
type: "ssd_resnet50_v1_fpn"
depth_multiplier: 1.0
min_depth: 16
conv_hyperparams {
regularizer {
l2_regularizer {
weight: 0.000399999989895
}
}
initializer {
truncated_normal_initializer {
mean: 0.0
stddev: 0.0299999993294
}
}
activation: RELU_6
batch_norm {
decay: 0.996999979019
scale: true
epsilon: 0.0010000000475
}
}
override_base_feature_extractor_hyperparams: true
}
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 {
}
}
box_predictor {
weight_shared_convolutional_box_predictor {
conv_hyperparams {
regularizer {
l2_regularizer {
weight: 0.000399999989895
}
}
initializer {
random_normal_initializer {
mean: 0.0
stddev: 0.00999999977648
}
}
activation: RELU_6
batch_norm {
decay: 0.996999979019
scale: true
epsilon: 0.0010000000475
}
}
depth: 256
num_layers_before_predictor: 4
kernel_size: 3
class_prediction_bias_init: -4.59999990463
}
}
anchor_generator {
multiscale_anchor_generator {
min_level: 3
max_level: 7
anchor_scale: 4.0
aspect_ratios: 1.0
aspect_ratios: 2.0
aspect_ratios: 0.5
scales_per_octave: 2
}
}
post_processing {
batch_non_max_suppression {
score_threshold: 0.300000011921
iou_threshold: 0.600000023842
max_detections_per_class: 100
max_total_detections: 100
}
score_converter: SIGMOID
}
normalize_loss_by_num_matches: true
loss {
localization_loss {
weighted_smooth_l1 {
}
}
classification_loss {
weighted_sigmoid_focal {
gamma: 2.0
alpha: 0.25
}
}
classification_weight: 1.0
localization_weight: 1.0
}
encode_background_as_zeros: true
normalize_loc_loss_by_codesize: true
inplace_batchnorm_update: true
freeze_batchnorm: false
}
}
train_config {
batch_size: 64
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
}
}
sync_replicas: true
optimizer {
momentum_optimizer {
learning_rate {
cosine_decay_learning_rate {
learning_rate_base: 0.0399999991059
total_steps: 25000
warmup_learning_rate: 0.0133330002427
warmup_steps: 2000
}
}
momentum_optimizer_value: 0.899999976158
}
use_moving_average: false
}
fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"
num_steps: 25000
startup_delay_steps: 0.0
replicas_to_aggregate: 8
max_number_of_boxes: 100
unpad_groundtruth_tensors: false
}
train_input_reader {
label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/mscoco_train.record-00000-of-00100"
}
}
eval_config {
num_examples: 8000
metrics_set: "coco_detection_metrics"
use_moving_averages: false
}
eval_input_reader {
label_map_path: "PATH_TO_BE_CONFIGURED/mscoco_label_map.pbtxt"
shuffle: false
num_readers: 1
tf_record_input_reader {
input_path: "PATH_TO_BE_CONFIGURED/mscoco_val.record-00000-of-00010"
}
}
On changing only the shape to [3, 640, 640] in config.py, I get an error saying ERROR: UFFParser: Parser error: Reshape_1: Reshape: Volume mismatch