ketiswp/google-coral-DeepLabV3-MobileNetV2-0.5-PascalVOC-fp32-onnx
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Google Coral DeepLabV3 MobileNetV2 0.5 Pascal VOC FP32 ONNX
FP32 ONNX version of Google Coral DeepLabV3 MobileNetV2 0.5 Pascal VOC for semantic segmentation.
Model Files
Parameter Summary
Stored initializer elements includes weights, biases, quantization scales, zero-points, and other constant tensors. It is not a trainable-parameter count.
Original Model Inference
pip install huggingface_hub numpy tensorflow
import numpy as np
import tensorflow as tf
from huggingface_hub import hf_hub_download
repo_id = "ketiswp/google-coral-DeepLabV3-MobileNetV2-0.5-PascalVOC-fp32-onnx"
model_path = hf_hub_download(repo_id=repo_id, filename="source/model.pb")
graph_def = tf.compat.v1.GraphDef()
graph_def.ParseFromString(open(model_path, "rb").read())
graph = tf.Graph()
with graph.as_default():
tf.import_graph_def(graph_def, name="")
input_tensor = graph.get_tensor_by_name("ImageTensor:0")
output_tensor = graph.get_tensor_by_name("SemanticPredictions:0")
input_value = np.zeros((1, 513, 513, 3), dtype=input_tensor.dtype.as_numpy_dtype)
config = tf.compat.v1.ConfigProto(
intra_op_parallelism_threads=1,
inter_op_parallelism_threads=1,
device_count={"GPU": 0},
)
with tf.compat.v1.Session(graph=graph, config=config) as session:
output = session.run(output_tensor, feed_dict={input_tensor: input_value})
print(output.shape, output.dtype)Converted ONNX Inference
pip install huggingface_hub numpy onnxruntime
import numpy as np
import onnxruntime as ort
from huggingface_hub import hf_hub_download
repo_id = "ketiswp/google-coral-DeepLabV3-MobileNetV2-0.5-PascalVOC-fp32-onnx"
model_path = hf_hub_download(repo_id=repo_id, filename="model.onnx")
options = ort.SessionOptions()
options.intra_op_num_threads = 1
options.inter_op_num_threads = 1
options.execution_mode = ort.ExecutionMode.ORT_SEQUENTIAL
options.graph_optimization_level = ort.GraphOptimizationLevel.ORT_ENABLE_ALL
session = ort.InferenceSession(
model_path,
sess_options=options,
providers=["CPUExecutionProvider"],
)
dtype_by_ort_type = {
"tensor(float)": np.float32,
"tensor(double)": np.float64,
"tensor(float16)": np.float16,
"tensor(int64)": np.int64,
"tensor(int32)": np.int32,
"tensor(int16)": np.int16,
"tensor(int8)": np.int8,
"tensor(uint8)": np.uint8,
"tensor(bool)": np.bool_,
}
feeds = {}
for item in session.get_inputs():
shape = [dim if isinstance(dim, int) and dim > 0 else 1 for dim in item.shape]
feeds[item.name] = np.zeros(shape, dtype=dtype_by_ort_type[item.type])
outputs = session.run(None, feeds)
print([(item.name, value.shape, str(value.dtype))
for item, value in zip(session.get_outputs(), outputs)])Paired Model
Source
Project Validation
FP32/quantized comparison, conversion results, and reproduction code
This repository also includes the Netron graph, ONNX Dialect MLIR, and static MLIR dependency graph for this model variant.