update facenet readme-hy

facenet/facenet/README-hy.md

+## .pb模型转换
+- python ./tools/pb_frozen_del_phase_train.py
+
+## 转onnx格式
+- bash ./tools/tf_2_onnx.sh
+
+## 转migraphx模型
+- bash ./tools/onnx_2_mxr.sh
+
+## 模型推理
+- bash ./inference.sh
+
+## ana.sh
+- bash ana.sh
+
+## post.sh
+- bash post.sh
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facenet/facenet/tools/check_pb.py

+import tensorflow as tf
+from tensorflow.python.platform import gfile
+
+pb_path = '/home/sunzhq/workspace/yidong-infer/facenet/facenet/models_m/facenet-tmp/20180408-102900.pb'
+
+with tf.io.gfile.GFile(pb_path, 'rb') as f:
+    graph_def = tf.compat.v1.GraphDef()
+    graph_def.ParseFromString(f.read())
+
+with tf.Graph().as_default() as graph:
+    tf.import_graph_def(graph_def, name='')
+
+ops = graph.get_operations()
+
+# --- 查找 Identity 操作，特别关注可能代表最终输出的 ---
+print("--- Identity Nodes (Potential Model Outputs) ---")
+identity_ops = [op for op in ops if op.type == "Identity"]
+
+# 存储映射到最终输出的 Identity 信息
+final_outputs = []
+
+for op in identity_ops:
+    input_to_identity = op.inputs[0]
+    producer_op = input_to_identity.op
+
+    # 尝试判断是否是模型的主要输出（例如，名称包含 embedding，或者 producer op 是常见的输出层类型）
+    # In Facenet, the final layer before normalization might be called 'embeddings' or similar.
+    # Common final ops could be MatMul, Add, BatchMatMul, etc., followed by normalization like L2Norm or similar.
+    # Let's look for the most likely candidate based on name patterns common in Facenet models.
+
+    # Look for a name pattern often used for the final embedding vector
+    if ('embedding' in input_to_identity.name.lower() or 
+        'bottleneck' in input_to_identity.name.lower() or
+        producer_op.type in ['MatMul', 'Add', 'BatchMatMulV2']): # Common final computation types
+        final_outputs.append({
+            'identity_name': op.name,
+            'final_output_name': input_to_identity.name,
+            'producer_name': producer_op.name,
+            'type': producer_op.type,
+            'shape': input_to_identity.shape,
+            'dtype': input_to_identity.dtype
+        })
+
+# Print only the potentially relevant final outputs
+if final_outputs:
+    print("Potential Model Output Candidates:")
+    for out_info in final_outputs:
+        print(f"  Identity Op: {out_info['identity_name']}")
+        print(f"    Maps Output From: {out_info['producer_name']} (Type: {out_info['type']})")
+        print(f"    Final Output Name: {out_info['final_output_name']}")
+        print(f"    Shape: {out_info['shape']}, Dtype: {out_info['dtype']}")
+        print("  ---")
+else:
+    print("No obvious final output Identity nodes found matching common patterns.")
+
+# --- Alternative: Check for common output layer names directly (if not mapped by Identity) ---
+# This is less likely if Identity is used, but worth a quick check
+print("\n--- Checking for Common Embedding Layer Names ---")
+potential_embedding_names = [
+    'embeddings', 'Bottleneck/BatchNorm/Reshape_1', 'prelogits', 'InceptionResnetV1/Bottleneck/Identity'
+    # Add more potential names based on the specific model architecture if needed
+]
+# Add a suffix like :0 if necessary, TensorFlow usually adds this implicitly for the first output
+potential_embedding_names_with_suffix = [name + ':0' for name in potential_embedding_names]
+
+found_embedding_directly = False
+for op in ops:
+    if op.name in potential_embedding_names or op.name + ':0' in potential_embedding_names_with_suffix:
+        print(f"Found potential embedding node directly: {op.name}")
+        for output in op.outputs:
+             print(f"  Output: {output.name}, Shape: {output.shape}, Dtype: {output.dtype}")
+        found_embedding_directly = True
+        break # Assume the first match is the one
+
+if not found_embedding_directly:
+    print("  No direct match found for common embedding names.")
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