Add files using upload-large-folder tool

comprehensive_distillation_20251112_013111/experiments_summary.json

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+[]

exp_1762448560_9364_s96_f16_d3_m2_3_3_4/convert_openvino.sh

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+#!/bin/bash
+# Source OpenVINO environment
+source /opt/intel/openvino/bin/setupvars.sh
+
+# OpenVINO Model Optimizer script for ONNX model
+PYPATH=/opt/intel/openvino_2020.3.194/deployment_tools/model_optimizer/mo.py
+ONNX_MODEL=/home/mount/experiments/exp_1762448560_9364_s96_f16_d3_m2_3_3_4/onnx/model.onnx
+OUTPUT_DIR=/home/mount/experiments/exp_1762448560_9364_s96_f16_d3_m2_3_3_4/openvino
+
+echo "Converting ONNX model to OpenVINO IR format..."
+echo "Input model: $ONNX_MODEL"
+echo "Output directory: $OUTPUT_DIR"
+
+# Create output directory if it doesn't exist
+mkdir -p "$OUTPUT_DIR"
+
+python3 $PYPATH \
+    --input_model "$ONNX_MODEL" \
+    --data_type FP16 \
+    --input_shape "[1,8,96,96]" \
+    --mean_values "[0,0,0,0,0,0,0,0]" \
+    --scale_values "[1,1,1,1,1,1,1,1]" \
+    --progress \
+    --stream_output \
+    --output_dir "$OUTPUT_DIR" \
+    --model_name model
+
+echo "OpenVINO conversion completed!"
+echo "Generated files in $OUTPUT_DIR:"
+ls -la "$OUTPUT_DIR/"

exp_1762448560_9364_s96_f16_d3_m2_3_3_4/experiment.log

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+2025-11-06 18:02:40 - INFO - === Model Creation Phase ===
+2025-11-06 18:02:40 - INFO - Creating UNet model with config: {'input_size': 96, 'base_filters': 16, 'depth': 3, 'channel_multipliers': [2, 3, 3, 4], 'n_channels': 8, 'n_classes': 3}
+2025-11-06 18:02:40 - INFO - Model created successfully:
+2025-11-06 18:02:40 - INFO -   Depth: 3
+2025-11-06 18:02:40 - INFO -   Channels per level: [32, 48, 48, 64]
+2025-11-06 18:02:40 - INFO -   Total parameters: 194,195
+2025-11-06 18:02:40 - INFO -   Parameter memory: 0.74 MB
+2025-11-06 18:02:40 - INFO - === Detailed Architecture Analysis ===
+2025-11-06 18:02:40 - INFO - Input vector dimension: 73,728 (8 × 96 × 96)
+2025-11-06 18:02:40 - INFO - Component-wise parameter breakdown:
+2025-11-06 18:02:40 - INFO -   Encoder - encoders.0.channel_proj: 288 parameters
+2025-11-06 18:02:40 - INFO -   Encoder - encoders.0.convnext_block.dwconv: 1,600 parameters
+2025-11-06 18:02:40 - INFO -   Encoder - encoders.0.convnext_block.norm: 64 parameters
+2025-11-06 18:02:40 - INFO -   Encoder - encoders.0.convnext_block.pwconv1: 4,224 parameters
+2025-11-06 18:02:40 - INFO -   Encoder - encoders.0.convnext_block.pwconv2: 4,128 parameters
+2025-11-06 18:02:40 - INFO -   Encoder - encoders.1.channel_proj: 1,584 parameters
+2025-11-06 18:02:40 - INFO -   Encoder - encoders.1.convnext_block.dwconv: 2,400 parameters
+2025-11-06 18:02:40 - INFO -   Encoder - encoders.1.convnext_block.norm: 96 parameters
+2025-11-06 18:02:40 - INFO -   Encoder - encoders.1.convnext_block.pwconv1: 9,408 parameters
+2025-11-06 18:02:40 - INFO -   Encoder - encoders.1.convnext_block.pwconv2: 9,264 parameters
+2025-11-06 18:02:40 - INFO -   Encoder - encoders.2.convnext_block.dwconv: 2,400 parameters
+2025-11-06 18:02:40 - INFO -   Encoder - encoders.2.convnext_block.norm: 96 parameters
+2025-11-06 18:02:40 - INFO -   Encoder - encoders.2.convnext_block.pwconv1: 9,408 parameters
+2025-11-06 18:02:40 - INFO -   Encoder - encoders.2.convnext_block.pwconv2: 9,264 parameters
+2025-11-06 18:02:40 - INFO -   Bottleneck - bottleneck.channel_proj: 3,136 parameters
+2025-11-06 18:02:40 - INFO -   Bottleneck - bottleneck.convnext_block.dwconv: 3,200 parameters
+2025-11-06 18:02:40 - INFO -   Bottleneck - bottleneck.convnext_block.norm: 128 parameters
+2025-11-06 18:02:40 - INFO -   Bottleneck - bottleneck.convnext_block.pwconv1: 16,640 parameters
+2025-11-06 18:02:40 - INFO -   Bottleneck - bottleneck.convnext_block.pwconv2: 16,448 parameters
+2025-11-06 18:02:40 - INFO -   Decoder - upsamplers.0: 16,448 parameters
+2025-11-06 18:02:40 - INFO -   Decoder - upsamplers.1: 9,264 parameters
+2025-11-06 18:02:40 - INFO -   Decoder - upsamplers.2: 9,264 parameters
+2025-11-06 18:02:40 - INFO -   Decoder - decoders.0.channel_proj: 5,424 parameters
+2025-11-06 18:02:40 - INFO -   Decoder - decoders.0.convnext_block.dwconv: 2,400 parameters
+2025-11-06 18:02:40 - INFO -   Decoder - decoders.0.convnext_block.norm: 96 parameters
+2025-11-06 18:02:40 - INFO -   Decoder - decoders.0.convnext_block.pwconv1: 9,408 parameters
+2025-11-06 18:02:40 - INFO -   Decoder - decoders.0.convnext_block.pwconv2: 9,264 parameters
+2025-11-06 18:02:40 - INFO -   Decoder - decoders.1.channel_proj: 4,656 parameters
+2025-11-06 18:02:40 - INFO -   Decoder - decoders.1.convnext_block.dwconv: 2,400 parameters
+2025-11-06 18:02:40 - INFO -   Decoder - decoders.1.convnext_block.norm: 96 parameters
+2025-11-06 18:02:40 - INFO -   Decoder - decoders.1.convnext_block.pwconv1: 9,408 parameters
+2025-11-06 18:02:40 - INFO -   Decoder - decoders.1.convnext_block.pwconv2: 9,264 parameters
+2025-11-06 18:02:40 - INFO -   Decoder - decoders.2.channel_proj: 2,592 parameters
+2025-11-06 18:02:40 - INFO -   Decoder - decoders.2.convnext_block.dwconv: 1,600 parameters
+2025-11-06 18:02:40 - INFO -   Decoder - decoders.2.convnext_block.norm: 64 parameters
+2025-11-06 18:02:40 - INFO -   Decoder - decoders.2.convnext_block.pwconv1: 4,224 parameters
+2025-11-06 18:02:40 - INFO -   Decoder - decoders.2.convnext_block.pwconv2: 4,128 parameters
+2025-11-06 18:02:40 - INFO -   Other - final_conv: 99 parameters
+2025-11-06 18:02:40 - INFO - Parameter distribution summary:
+2025-11-06 18:02:40 - INFO -   Encoder parameters: 54,224 (28.0%)
+2025-11-06 18:02:40 - INFO -   Decoder parameters: 100,000 (51.6%)
+2025-11-06 18:02:40 - INFO -   Bottleneck parameters: 39,552 (20.4%)
+2025-11-06 18:02:40 - INFO -   Other parameters: 99 (0.1%)
+2025-11-06 18:02:40 - INFO - Latent space dimensions (feature maps at each level):
+2025-11-06 18:02:40 - INFO -   Level 0: 32 × 96 × 96 = 294,912 elements
+2025-11-06 18:02:40 - INFO -   Level 1: 48 × 48 × 48 = 110,592 elements
+2025-11-06 18:02:40 - INFO -   Level 2: 48 × 24 × 24 = 27,648 elements
+2025-11-06 18:02:40 - INFO -   Level 3: 64 × 12 × 12 = 9,216 elements
+2025-11-06 18:02:40 - INFO - Skip connection dimensions:
+2025-11-06 18:02:40 - INFO -   Skip 0: 32 × 96 × 96 = 294,912 elements
+2025-11-06 18:02:40 - INFO -   Skip 1: 48 × 48 × 48 = 110,592 elements
+2025-11-06 18:02:40 - INFO -   Skip 2: 48 × 24 × 24 = 27,648 elements
+2025-11-06 18:02:40 - INFO - Memory analysis:
+2025-11-06 18:02:40 - INFO -   Peak feature map memory (inference): 1.97 MB
+2025-11-06 18:02:40 - INFO -   Peak feature map memory (training): 3.94 MB (with gradients)
+2025-11-06 18:02:40 - INFO - Output vector dimension: 27,648 (3 × 96 × 96)
+2025-11-06 18:02:40 - INFO - PyTorch model saved to: /home/philab/Desktop/hydranet/experiments/exp_1762448560_9364_s96_f16_d3_m2_3_3_4/pytorch/model.pt
+2025-11-06 18:02:40 - INFO - === ONNX Conversion Phase ===
+2025-11-06 18:02:40 - INFO - === Model Export Diagnostics ===
+2025-11-06 18:02:40 - INFO - PyTorch version: 1.9.0+cu102
+2025-11-06 18:02:40 - INFO - Model parameters: 194,195
+2025-11-06 18:02:40 - INFO - Model memory: 0.74 MB
+2025-11-06 18:02:40 - INFO - Starting ONNX export with opset version 11
+2025-11-06 18:02:40 - INFO - Model input shape: torch.Size([1, 8, 96, 96])
+2025-11-06 18:02:40 - INFO - Model input dtype: torch.float32
+2025-11-06 18:02:40 - INFO - Forward pass successful. Output shape: torch.Size([1, 3, 96, 96])
+2025-11-06 18:02:40 - INFO - Output dtype: torch.float32
+2025-11-06 18:02:40 - INFO - Output value range: [-0.3959, 0.6202]
+2025-11-06 18:02:40 - INFO - Model successfully exported to /home/philab/Desktop/hydranet/experiments/exp_1762448560_9364_s96_f16_d3_m2_3_3_4/onnx/model.onnx
+2025-11-06 18:02:40 - INFO - ONNX model size: 0.75 MB
+2025-11-06 18:02:40 - INFO - Saved dummy input with shape (1, 8, 96, 96) to /home/philab/Desktop/hydranet/experiments/exp_1762448560_9364_s96_f16_d3_m2_3_3_4/onnx/sample_input.npy
+2025-11-06 18:02:40 - INFO - Input data type: float32
+2025-11-06 18:02:40 - INFO - Input value range: [-4.4606, 4.2294]
+2025-11-06 18:02:40 - INFO - === OpenVINO Conversion Phase ===
+2025-11-06 18:02:40 - INFO - Starting OpenVINO conversion in Docker container...
+2025-11-06 18:02:44 - INFO - OpenVINO conversion completed in 4.09 seconds
+2025-11-06 18:02:44 - INFO - OpenVINO model files created:
+2025-11-06 18:02:44 - INFO -   XML file: /home/philab/Desktop/hydranet/experiments/exp_1762448560_9364_s96_f16_d3_m2_3_3_4/openvino/model.xml (0.09 MB)
+2025-11-06 18:02:44 - INFO -   BIN file: /home/philab/Desktop/hydranet/experiments/exp_1762448560_9364_s96_f16_d3_m2_3_3_4/openvino/model.bin (0.37 MB)
+2025-11-06 18:02:44 - INFO - === Myriad Inference Phase ===
+2025-11-06 18:02:44 - INFO - Starting Myriad inference in Docker container...
+2025-11-06 18:02:47 - INFO - Myriad inference completed in 2.74 seconds
+2025-11-06 18:02:47 - INFO - Actual inference time: 0.132681 seconds
+2025-11-06 18:02:47 - INFO - ✅ Complete pipeline executed successfully!
+2025-11-06 18:02:47 - INFO - ✅ Experiment 11 completed successfully
+2025-11-06 18:02:47 - INFO -    Inference time: 0.132681s
+2025-11-06 18:02:47 - INFO - 
+=== Experiment 12/2475 ===
+2025-11-06 18:02:47 - INFO - Experiment ID: exp_1762448567_7023_s96_f18_d3_m1_1_1_1
+2025-11-06 18:02:47 - INFO - Experiment directory: /home/philab/Desktop/hydranet/experiments/exp_1762448567_7023_s96_f18_d3_m1_1_1_1

exp_1762448560_9364_s96_f16_d3_m2_3_3_4/model_info.json

@@ -0,0 +1,140 @@
+{
+  "config": {
+    "input_size": 96,
+    "base_filters": 16,
+    "depth": 3,
+    "channel_multipliers": [
+      2,
+      3,
+      3,
+      4
+    ],
+    "n_channels": 8,
+    "n_classes": 3
+  },
+  "model_info": {
+    "depth": 3,
+    "channels_per_level": [
+      32,
+      48,
+      48,
+      64
+    ],
+    "channel_multipliers": [
+      2,
+      3,
+      3,
+      4
+    ],
+    "total_parameters": 194195,
+    "trainable_parameters": 194195,
+    "model_size_mb": 0.7407951354980469
+  },
+  "architecture_stats": {
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+    "output_dimension": 27648,
+    "parameter_distribution": {
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+      "decoder_params": 100000,
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+      "Skip_1": {
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+      "peak_memory_training_mb": 3.9375,
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+  },
+  "timestamp": "2025-11-06T18:02:40.283930"
+}

exp_1762448560_9364_s96_f16_d3_m2_3_3_4/pipeline_results.json

@@ -0,0 +1,140 @@
+{
+  "experiment_id": "exp_1762448560_4658_s96_f16_d3_m2_3_3_4",
+  "config": {
+    "input_size": 96,
+    "base_filters": 16,
+    "depth": 3,
+    "channel_multipliers": [
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+      3,
+      3,
+      4
+    ],
+    "n_channels": 8,
+    "n_classes": 3
+  },
+  "start_time": "2025-11-06T18:02:40.277074",
+  "success": true,
+  "timings": {
+    "onnx_conversion": 0.28563704597763717,
+    "openvino_conversion": 4.088858654955402,
+    "inference_total": 2.74204633012414,
+    "inference_actual": 0.132681,
+    "total": 7.129755483940244
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+  "errors": [],
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+    "parameter_distribution": {
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+  },
+  "inference_results": {
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+    "total_time": 2.74204633012414,
+    "inference_time": 0.132681,
+    "stdout": "[setupvars.sh] OpenVINO environment initialized\nStarting Myriad inference...\nInput: /home/mount/experiments/exp_1762448560_9364_s96_f16_d3_m2_3_3_4/onnx/sample_input.npy\nModel XML: /home/mount/experiments/exp_1762448560_9364_s96_f16_d3_m2_3_3_4/openvino/model.xml\nModel BIN: /home/mount/experiments/exp_1762448560_9364_s96_f16_d3_m2_3_3_4/openvino/model.bin\n[OK] OpenVINO inference engine imported successfully\nDevice: MYRIAD\nModel XML: /home/mount/experiments/exp_1762448560_9364_s96_f16_d3_m2_3_3_4/openvino/model.xml\nModel BIN: /home/mount/experiments/exp_1762448560_9364_s96_f16_d3_m2_3_3_4/openvino/model.bin\nInput file: /home/mount/experiments/exp_1762448560_9364_s96_f16_d3_m2_3_3_4/onnx/sample_input.npy\nInitializing OpenVINO Runtime Core...\nAvailable devices:\n[E:] [BSL] found 0 ioexpander device\n  ['CPU', 'GNA', 'MYRIAD']\nLoading network...\nInput blob: input\nInput shape: [1, 8, 96, 96]\nOutput blob: Conv_106\nOutput shape: [1, 3, 96, 96]\nLoading network to MYRIAD...\nLoading input data...\nInput data shape: (1, 8, 96, 96)\nInput data type: float32\nRunning inference...\n[OK] Inference completed!\nInference time: 0.132681 seconds\nOutput shape: (1, 3, 96, 96)\nOutput dtype: float32\nOutput range: [-0.395752, 0.620117]\nMyriad inference completed!\n",
+    "stderr": ""
+  },
+  "end_time": "2025-11-06T18:02:47.406819"
+}

exp_1762448560_9364_s96_f16_d3_m2_3_3_4/run_inference.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+# Source OpenVINO environment
+source /opt/intel/openvino/bin/setupvars.sh
+
+PYPATH=/home/mount/scripts/simple_inference.py
+
+echo "Starting Myriad inference..."
+echo "Input: /home/mount/experiments/exp_1762448560_9364_s96_f16_d3_m2_3_3_4/onnx/sample_input.npy"
+echo "Model XML: /home/mount/experiments/exp_1762448560_9364_s96_f16_d3_m2_3_3_4/openvino/model.xml"
+echo "Model BIN: /home/mount/experiments/exp_1762448560_9364_s96_f16_d3_m2_3_3_4/openvino/model.bin"
+
+python3 $PYPATH \
+    --input_filepath /home/mount/experiments/exp_1762448560_9364_s96_f16_d3_m2_3_3_4/onnx/sample_input.npy \
+    --device_name MYRIAD \
+    --model_bin /home/mount/experiments/exp_1762448560_9364_s96_f16_d3_m2_3_3_4/openvino/model.bin \
+    --model_xml /home/mount/experiments/exp_1762448560_9364_s96_f16_d3_m2_3_3_4/openvino/model.xml \
+    --output /home/mount/experiments/exp_1762448560_9364_s96_f16_d3_m2_3_3_4/inference/
+
+echo "Myriad inference completed!"

exp_1762448567_7023_s96_f18_d3_m1_1_1_1/convert_openvino.sh

@@ -0,0 +1,30 @@
+#!/bin/bash
+# Source OpenVINO environment
+source /opt/intel/openvino/bin/setupvars.sh
+
+# OpenVINO Model Optimizer script for ONNX model
+PYPATH=/opt/intel/openvino_2020.3.194/deployment_tools/model_optimizer/mo.py
+ONNX_MODEL=/home/mount/experiments/exp_1762448567_7023_s96_f18_d3_m1_1_1_1/onnx/model.onnx
+OUTPUT_DIR=/home/mount/experiments/exp_1762448567_7023_s96_f18_d3_m1_1_1_1/openvino
+
+echo "Converting ONNX model to OpenVINO IR format..."
+echo "Input model: $ONNX_MODEL"
+echo "Output directory: $OUTPUT_DIR"
+
+# Create output directory if it doesn't exist
+mkdir -p "$OUTPUT_DIR"
+
+python3 $PYPATH \
+    --input_model "$ONNX_MODEL" \
+    --data_type FP16 \
+    --input_shape "[1,8,96,96]" \
+    --mean_values "[0,0,0,0,0,0,0,0]" \
+    --scale_values "[1,1,1,1,1,1,1,1]" \
+    --progress \
+    --stream_output \
+    --output_dir "$OUTPUT_DIR" \
+    --model_name model
+
+echo "OpenVINO conversion completed!"
+echo "Generated files in $OUTPUT_DIR:"
+ls -la "$OUTPUT_DIR/"

exp_1762448567_7023_s96_f18_d3_m1_1_1_1/experiment.log

@@ -0,0 +1,98 @@
+2025-11-06 18:02:47 - INFO - === Model Creation Phase ===
+2025-11-06 18:02:47 - INFO - Creating UNet model with config: {'input_size': 96, 'base_filters': 18, 'depth': 3, 'channel_multipliers': [1, 1, 1, 1], 'n_channels': 8, 'n_classes': 3}
+2025-11-06 18:02:47 - INFO - Model created successfully:
+2025-11-06 18:02:47 - INFO -   Depth: 3
+2025-11-06 18:02:47 - INFO -   Channels per level: [18, 18, 18, 18]
+2025-11-06 18:02:47 - INFO -   Total parameters: 31,611
+2025-11-06 18:02:47 - INFO -   Parameter memory: 0.12 MB
+2025-11-06 18:02:47 - INFO - === Detailed Architecture Analysis ===
+2025-11-06 18:02:47 - INFO - Input vector dimension: 73,728 (8 × 96 × 96)
+2025-11-06 18:02:47 - INFO - Component-wise parameter breakdown:
+2025-11-06 18:02:47 - INFO -   Encoder - encoders.0.channel_proj: 162 parameters
+2025-11-06 18:02:47 - INFO -   Encoder - encoders.0.convnext_block.dwconv: 900 parameters
+2025-11-06 18:02:47 - INFO -   Encoder - encoders.0.convnext_block.norm: 36 parameters
+2025-11-06 18:02:47 - INFO -   Encoder - encoders.0.convnext_block.pwconv1: 1,368 parameters
+2025-11-06 18:02:47 - INFO -   Encoder - encoders.0.convnext_block.pwconv2: 1,314 parameters
+2025-11-06 18:02:47 - INFO -   Encoder - encoders.1.convnext_block.dwconv: 900 parameters
+2025-11-06 18:02:47 - INFO -   Encoder - encoders.1.convnext_block.norm: 36 parameters
+2025-11-06 18:02:47 - INFO -   Encoder - encoders.1.convnext_block.pwconv1: 1,368 parameters
+2025-11-06 18:02:47 - INFO -   Encoder - encoders.1.convnext_block.pwconv2: 1,314 parameters
+2025-11-06 18:02:47 - INFO -   Encoder - encoders.2.convnext_block.dwconv: 900 parameters
+2025-11-06 18:02:47 - INFO -   Encoder - encoders.2.convnext_block.norm: 36 parameters
+2025-11-06 18:02:47 - INFO -   Encoder - encoders.2.convnext_block.pwconv1: 1,368 parameters
+2025-11-06 18:02:47 - INFO -   Encoder - encoders.2.convnext_block.pwconv2: 1,314 parameters
+2025-11-06 18:02:47 - INFO -   Bottleneck - bottleneck.convnext_block.dwconv: 900 parameters
+2025-11-06 18:02:47 - INFO -   Bottleneck - bottleneck.convnext_block.norm: 36 parameters
+2025-11-06 18:02:47 - INFO -   Bottleneck - bottleneck.convnext_block.pwconv1: 1,368 parameters
+2025-11-06 18:02:47 - INFO -   Bottleneck - bottleneck.convnext_block.pwconv2: 1,314 parameters
+2025-11-06 18:02:47 - INFO -   Decoder - upsamplers.0: 1,314 parameters
+2025-11-06 18:02:47 - INFO -   Decoder - upsamplers.1: 1,314 parameters
+2025-11-06 18:02:47 - INFO -   Decoder - upsamplers.2: 1,314 parameters
+2025-11-06 18:02:47 - INFO -   Decoder - decoders.0.channel_proj: 666 parameters
+2025-11-06 18:02:47 - INFO -   Decoder - decoders.0.convnext_block.dwconv: 900 parameters
+2025-11-06 18:02:47 - INFO -   Decoder - decoders.0.convnext_block.norm: 36 parameters
+2025-11-06 18:02:47 - INFO -   Decoder - decoders.0.convnext_block.pwconv1: 1,368 parameters
+2025-11-06 18:02:47 - INFO -   Decoder - decoders.0.convnext_block.pwconv2: 1,314 parameters
+2025-11-06 18:02:47 - INFO -   Decoder - decoders.1.channel_proj: 666 parameters
+2025-11-06 18:02:47 - INFO -   Decoder - decoders.1.convnext_block.dwconv: 900 parameters
+2025-11-06 18:02:47 - INFO -   Decoder - decoders.1.convnext_block.norm: 36 parameters
+2025-11-06 18:02:47 - INFO -   Decoder - decoders.1.convnext_block.pwconv1: 1,368 parameters
+2025-11-06 18:02:47 - INFO -   Decoder - decoders.1.convnext_block.pwconv2: 1,314 parameters
+2025-11-06 18:02:47 - INFO -   Decoder - decoders.2.channel_proj: 666 parameters
+2025-11-06 18:02:47 - INFO -   Decoder - decoders.2.convnext_block.dwconv: 900 parameters
+2025-11-06 18:02:47 - INFO -   Decoder - decoders.2.convnext_block.norm: 36 parameters
+2025-11-06 18:02:47 - INFO -   Decoder - decoders.2.convnext_block.pwconv1: 1,368 parameters
+2025-11-06 18:02:47 - INFO -   Decoder - decoders.2.convnext_block.pwconv2: 1,314 parameters
+2025-11-06 18:02:47 - INFO -   Other - final_conv: 57 parameters
+2025-11-06 18:02:47 - INFO - Parameter distribution summary:
+2025-11-06 18:02:47 - INFO -   Encoder parameters: 11,016 (35.0%)
+2025-11-06 18:02:47 - INFO -   Decoder parameters: 16,794 (53.3%)
+2025-11-06 18:02:47 - INFO -   Bottleneck parameters: 3,618 (11.5%)
+2025-11-06 18:02:47 - INFO -   Other parameters: 57 (0.2%)
+2025-11-06 18:02:47 - INFO - Latent space dimensions (feature maps at each level):
+2025-11-06 18:02:47 - INFO -   Level 0: 18 × 96 × 96 = 165,888 elements
+2025-11-06 18:02:47 - INFO -   Level 1: 18 × 48 × 48 = 41,472 elements
+2025-11-06 18:02:47 - INFO -   Level 2: 18 × 24 × 24 = 10,368 elements
+2025-11-06 18:02:47 - INFO -   Level 3: 18 × 12 × 12 = 2,592 elements
+2025-11-06 18:02:47 - INFO - Skip connection dimensions:
+2025-11-06 18:02:47 - INFO -   Skip 0: 18 × 96 × 96 = 165,888 elements
+2025-11-06 18:02:47 - INFO -   Skip 1: 18 × 48 × 48 = 41,472 elements
+2025-11-06 18:02:47 - INFO -   Skip 2: 18 × 24 × 24 = 10,368 elements
+2025-11-06 18:02:47 - INFO - Memory analysis:
+2025-11-06 18:02:47 - INFO -   Peak feature map memory (inference): 1.12 MB
+2025-11-06 18:02:47 - INFO -   Peak feature map memory (training): 2.24 MB (with gradients)
+2025-11-06 18:02:47 - INFO - Output vector dimension: 27,648 (3 × 96 × 96)
+2025-11-06 18:02:47 - INFO - PyTorch model saved to: /home/philab/Desktop/hydranet/experiments/exp_1762448567_7023_s96_f18_d3_m1_1_1_1/pytorch/model.pt
+2025-11-06 18:02:47 - INFO - === ONNX Conversion Phase ===
+2025-11-06 18:02:47 - INFO - === Model Export Diagnostics ===
+2025-11-06 18:02:47 - INFO - PyTorch version: 1.9.0+cu102
+2025-11-06 18:02:47 - INFO - Model parameters: 31,611
+2025-11-06 18:02:47 - INFO - Model memory: 0.12 MB
+2025-11-06 18:02:47 - INFO - Starting ONNX export with opset version 11
+2025-11-06 18:02:47 - INFO - Model input shape: torch.Size([1, 8, 96, 96])
+2025-11-06 18:02:47 - INFO - Model input dtype: torch.float32
+2025-11-06 18:02:47 - INFO - Forward pass successful. Output shape: torch.Size([1, 3, 96, 96])
+2025-11-06 18:02:47 - INFO - Output dtype: torch.float32
+2025-11-06 18:02:47 - INFO - Output value range: [-1.0509, 0.4186]
+2025-11-06 18:02:47 - INFO - Model successfully exported to /home/philab/Desktop/hydranet/experiments/exp_1762448567_7023_s96_f18_d3_m1_1_1_1/onnx/model.onnx
+2025-11-06 18:02:47 - INFO - ONNX model size: 0.13 MB
+2025-11-06 18:02:47 - INFO - Saved dummy input with shape (1, 8, 96, 96) to /home/philab/Desktop/hydranet/experiments/exp_1762448567_7023_s96_f18_d3_m1_1_1_1/onnx/sample_input.npy
+2025-11-06 18:02:47 - INFO - Input data type: float32
+2025-11-06 18:02:47 - INFO - Input value range: [-4.3423, 4.4816]
+2025-11-06 18:02:47 - INFO - === OpenVINO Conversion Phase ===
+2025-11-06 18:02:47 - INFO - Starting OpenVINO conversion in Docker container...
+2025-11-06 18:02:51 - INFO - OpenVINO conversion completed in 3.97 seconds
+2025-11-06 18:02:51 - INFO - OpenVINO model files created:
+2025-11-06 18:02:51 - INFO -   XML file: /home/philab/Desktop/hydranet/experiments/exp_1762448567_7023_s96_f18_d3_m1_1_1_1/openvino/model.xml (0.08 MB)
+2025-11-06 18:02:51 - INFO -   BIN file: /home/philab/Desktop/hydranet/experiments/exp_1762448567_7023_s96_f18_d3_m1_1_1_1/openvino/model.bin (0.06 MB)
+2025-11-06 18:02:51 - INFO - === Myriad Inference Phase ===
+2025-11-06 18:02:51 - INFO - Starting Myriad inference in Docker container...
+2025-11-06 18:02:54 - INFO - Myriad inference completed in 2.67 seconds
+2025-11-06 18:02:54 - INFO - Actual inference time: 0.118388 seconds
+2025-11-06 18:02:54 - INFO - ✅ Complete pipeline executed successfully!
+2025-11-06 18:02:54 - INFO - ✅ Experiment 12 completed successfully
+2025-11-06 18:02:54 - INFO -    Inference time: 0.118388s
+2025-11-06 18:02:54 - INFO - 
+=== Experiment 13/2475 ===
+2025-11-06 18:02:54 - INFO - Experiment ID: exp_1762448574_8263_s96_f18_d3_m2_2_2_2
+2025-11-06 18:02:54 - INFO - Experiment directory: /home/philab/Desktop/hydranet/experiments/exp_1762448574_8263_s96_f18_d3_m2_2_2_2

exp_1762448567_7023_s96_f18_d3_m1_1_1_1/model_info.json

@@ -0,0 +1,138 @@
+{
+  "config": {
+    "input_size": 96,
+    "base_filters": 18,
+    "depth": 3,
+    "channel_multipliers": [
+      1,
+      1,
+      1,
+      1
+    ],
+    "n_channels": 8,
+    "n_classes": 3
+  },
+  "model_info": {
+    "depth": 3,
+    "channels_per_level": [
+      18,
+      18,
+      18,
+      18
+    ],
+    "channel_multipliers": [
+      1,
+      1,
+      1,
+      1
+    ],
+    "total_parameters": 31611,
+    "trainable_parameters": 31611,
+    "model_size_mb": 0.12058639526367188
+  },
+  "architecture_stats": {
+    "input_dimension": 73728,
+    "output_dimension": 27648,
+    "parameter_distribution": {
+      "encoder_params": 11016,
+      "decoder_params": 16794,
+      "bottleneck_params": 3618,
+      "other_params": 57,
+      "total_params": 31485,
+      "encoder_percentage": 34.98808956646022,
+      "decoder_percentage": 53.33968556455455,
+      "bottleneck_percentage": 11.491186279180562,
+      "other_percentage": 0.1810385898046689
+    },
+    "component_breakdown": {
+      "Encoder - encoders.0.channel_proj": 162,
+      "Encoder - encoders.0.convnext_block.dwconv": 900,
+      "Encoder - encoders.0.convnext_block.norm": 36,
+      "Encoder - encoders.0.convnext_block.pwconv1": 1368,
+      "Encoder - encoders.0.convnext_block.pwconv2": 1314,
+      "Encoder - encoders.1.convnext_block.dwconv": 900,
+      "Encoder - encoders.1.convnext_block.norm": 36,
+      "Encoder - encoders.1.convnext_block.pwconv1": 1368,
+      "Encoder - encoders.1.convnext_block.pwconv2": 1314,
+      "Encoder - encoders.2.convnext_block.dwconv": 900,
+      "Encoder - encoders.2.convnext_block.norm": 36,
+      "Encoder - encoders.2.convnext_block.pwconv1": 1368,
+      "Encoder - encoders.2.convnext_block.pwconv2": 1314,
+      "Bottleneck - bottleneck.convnext_block.dwconv": 900,
+      "Bottleneck - bottleneck.convnext_block.norm": 36,
+      "Bottleneck - bottleneck.convnext_block.pwconv1": 1368,
+      "Bottleneck - bottleneck.convnext_block.pwconv2": 1314,
+      "Decoder - upsamplers.0": 1314,
+      "Decoder - upsamplers.1": 1314,
+      "Decoder - upsamplers.2": 1314,
+      "Decoder - decoders.0.channel_proj": 666,
+      "Decoder - decoders.0.convnext_block.dwconv": 900,
+      "Decoder - decoders.0.convnext_block.norm": 36,
+      "Decoder - decoders.0.convnext_block.pwconv1": 1368,
+      "Decoder - decoders.0.convnext_block.pwconv2": 1314,
+      "Decoder - decoders.1.channel_proj": 666,
+      "Decoder - decoders.1.convnext_block.dwconv": 900,
+      "Decoder - decoders.1.convnext_block.norm": 36,
+      "Decoder - decoders.1.convnext_block.pwconv1": 1368,
+      "Decoder - decoders.1.convnext_block.pwconv2": 1314,
+      "Decoder - decoders.2.channel_proj": 666,
+      "Decoder - decoders.2.convnext_block.dwconv": 900,
+      "Decoder - decoders.2.convnext_block.norm": 36,
+      "Decoder - decoders.2.convnext_block.pwconv1": 1368,
+      "Decoder - decoders.2.convnext_block.pwconv2": 1314,
+      "Other - final_conv": 57
+    },
+    "latent_dimensions": {
+      "Level_0": {
+        "channels": 18,
+        "height": 96,
+        "width": 96,
+        "total_elements": 165888
+      },
+      "Level_1": {
+        "channels": 18,
+        "height": 48,
+        "width": 48,
+        "total_elements": 41472
+      },
+      "Level_2": {
+        "channels": 18,
+        "height": 24,
+        "width": 24,
+        "total_elements": 10368
+      },
+      "Level_3": {
+        "channels": 18,
+        "height": 12,
+        "width": 12,
+        "total_elements": 2592
+      }
+    },
+    "skip_dimensions": {
+      "Skip_0": {
+        "channels": 18,
+        "height": 96,
+        "width": 96,
+        "total_elements": 165888
+      },
+      "Skip_1": {
+        "channels": 18,
+        "height": 48,
+        "width": 48,
+        "total_elements": 41472
+      },
+      "Skip_2": {
+        "channels": 18,
+        "height": 24,
+        "width": 24,
+        "total_elements": 10368
+      }
+    },
+    "memory_analysis": {
+      "peak_memory_inference_mb": 1.1217041015625,
+      "peak_memory_training_mb": 2.243408203125,
+      "peak_elements": 294048
+    }
+  },
+  "timestamp": "2025-11-06T18:02:47.415174"
+}

exp_1762448567_7023_s96_f18_d3_m1_1_1_1/pipeline_results.json

@@ -0,0 +1,138 @@
+{
+  "experiment_id": "exp_1762448567_2535_s96_f18_d3_m1_1_1_1",
+  "config": {
+    "input_size": 96,
+    "base_filters": 18,
+    "depth": 3,
+    "channel_multipliers": [
+      1,
+      1,
+      1,
+      1
+    ],
+    "n_channels": 8,
+    "n_classes": 3
+  },
+  "start_time": "2025-11-06T18:02:47.409086",
+  "success": true,
+  "timings": {
+    "onnx_conversion": 0.25858570099808276,
+    "openvino_conversion": 3.9692281188908964,
+    "inference_total": 2.66881703492254,
+    "inference_actual": 0.118388,
+    "total": 6.908043346134946
+  },
+  "errors": [],
+  "architecture_stats": {
+    "input_dimension": 73728,
+    "output_dimension": 27648,
+    "parameter_distribution": {
+      "encoder_params": 11016,
+      "decoder_params": 16794,
+      "bottleneck_params": 3618,
+      "other_params": 57,
+      "total_params": 31485,
+      "encoder_percentage": 34.98808956646022,
+      "decoder_percentage": 53.33968556455455,
+      "bottleneck_percentage": 11.491186279180562,
+      "other_percentage": 0.1810385898046689
+    },
+    "component_breakdown": {
+      "Encoder - encoders.0.channel_proj": 162,
+      "Encoder - encoders.0.convnext_block.dwconv": 900,
+      "Encoder - encoders.0.convnext_block.norm": 36,
+      "Encoder - encoders.0.convnext_block.pwconv1": 1368,
+      "Encoder - encoders.0.convnext_block.pwconv2": 1314,
+      "Encoder - encoders.1.convnext_block.dwconv": 900,
+      "Encoder - encoders.1.convnext_block.norm": 36,
+      "Encoder - encoders.1.convnext_block.pwconv1": 1368,
+      "Encoder - encoders.1.convnext_block.pwconv2": 1314,
+      "Encoder - encoders.2.convnext_block.dwconv": 900,
+      "Encoder - encoders.2.convnext_block.norm": 36,
+      "Encoder - encoders.2.convnext_block.pwconv1": 1368,
+      "Encoder - encoders.2.convnext_block.pwconv2": 1314,
+      "Bottleneck - bottleneck.convnext_block.dwconv": 900,
+      "Bottleneck - bottleneck.convnext_block.norm": 36,
+      "Bottleneck - bottleneck.convnext_block.pwconv1": 1368,
+      "Bottleneck - bottleneck.convnext_block.pwconv2": 1314,
+      "Decoder - upsamplers.0": 1314,
+      "Decoder - upsamplers.1": 1314,
+      "Decoder - upsamplers.2": 1314,
+      "Decoder - decoders.0.channel_proj": 666,
+      "Decoder - decoders.0.convnext_block.dwconv": 900,
+      "Decoder - decoders.0.convnext_block.norm": 36,
+      "Decoder - decoders.0.convnext_block.pwconv1": 1368,
+      "Decoder - decoders.0.convnext_block.pwconv2": 1314,
+      "Decoder - decoders.1.channel_proj": 666,
+      "Decoder - decoders.1.convnext_block.dwconv": 900,
+      "Decoder - decoders.1.convnext_block.norm": 36,
+      "Decoder - decoders.1.convnext_block.pwconv1": 1368,
+      "Decoder - decoders.1.convnext_block.pwconv2": 1314,
+      "Decoder - decoders.2.channel_proj": 666,
+      "Decoder - decoders.2.convnext_block.dwconv": 900,
+      "Decoder - decoders.2.convnext_block.norm": 36,
+      "Decoder - decoders.2.convnext_block.pwconv1": 1368,
+      "Decoder - decoders.2.convnext_block.pwconv2": 1314,
+      "Other - final_conv": 57
+    },
+    "latent_dimensions": {
+      "Level_0": {
+        "channels": 18,
+        "height": 96,
+        "width": 96,
+        "total_elements": 165888
+      },
+      "Level_1": {
+        "channels": 18,
+        "height": 48,
+        "width": 48,
+        "total_elements": 41472
+      },
+      "Level_2": {
+        "channels": 18,
+        "height": 24,
+        "width": 24,
+        "total_elements": 10368
+      },
+      "Level_3": {
+        "channels": 18,
+        "height": 12,
+        "width": 12,
+        "total_elements": 2592
+      }
+    },
+    "skip_dimensions": {
+      "Skip_0": {
+        "channels": 18,
+        "height": 96,
+        "width": 96,
+        "total_elements": 165888
+      },
+      "Skip_1": {
+        "channels": 18,
+        "height": 48,
+        "width": 48,
+        "total_elements": 41472
+      },
+      "Skip_2": {
+        "channels": 18,
+        "height": 24,
+        "width": 24,
+        "total_elements": 10368
+      }
+    },
+    "memory_analysis": {
+      "peak_memory_inference_mb": 1.1217041015625,
+      "peak_memory_training_mb": 2.243408203125,
+      "peak_elements": 294048
+    }
+  },
+  "inference_results": {
+    "success": true,
+    "total_time": 2.66881703492254,
+    "inference_time": 0.118388,
+    "stdout": "[setupvars.sh] OpenVINO environment initialized\nStarting Myriad inference...\nInput: /home/mount/experiments/exp_1762448567_7023_s96_f18_d3_m1_1_1_1/onnx/sample_input.npy\nModel XML: /home/mount/experiments/exp_1762448567_7023_s96_f18_d3_m1_1_1_1/openvino/model.xml\nModel BIN: /home/mount/experiments/exp_1762448567_7023_s96_f18_d3_m1_1_1_1/openvino/model.bin\n[OK] OpenVINO inference engine imported successfully\nDevice: MYRIAD\nModel XML: /home/mount/experiments/exp_1762448567_7023_s96_f18_d3_m1_1_1_1/openvino/model.xml\nModel BIN: /home/mount/experiments/exp_1762448567_7023_s96_f18_d3_m1_1_1_1/openvino/model.bin\nInput file: /home/mount/experiments/exp_1762448567_7023_s96_f18_d3_m1_1_1_1/onnx/sample_input.npy\nInitializing OpenVINO Runtime Core...\nAvailable devices:\n[E:] [BSL] found 0 ioexpander device\n  ['CPU', 'GNA', 'MYRIAD']\nLoading network...\nInput blob: input\nInput shape: [1, 8, 96, 96]\nOutput blob: Conv_104\nOutput shape: [1, 3, 96, 96]\nLoading network to MYRIAD...\nLoading input data...\nInput data shape: (1, 8, 96, 96)\nInput data type: float32\nRunning inference...\n[OK] Inference completed!\nInference time: 0.118388 seconds\nOutput shape: (1, 3, 96, 96)\nOutput dtype: float32\nOutput range: [-1.050781, 0.418945]\nMyriad inference completed!\n",
+    "stderr": ""
+  },
+  "end_time": "2025-11-06T18:02:54.317117"
+}

exp_1762448567_7023_s96_f18_d3_m1_1_1_1/run_inference.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+# Source OpenVINO environment
+source /opt/intel/openvino/bin/setupvars.sh
+
+PYPATH=/home/mount/scripts/simple_inference.py
+
+echo "Starting Myriad inference..."
+echo "Input: /home/mount/experiments/exp_1762448567_7023_s96_f18_d3_m1_1_1_1/onnx/sample_input.npy"
+echo "Model XML: /home/mount/experiments/exp_1762448567_7023_s96_f18_d3_m1_1_1_1/openvino/model.xml"
+echo "Model BIN: /home/mount/experiments/exp_1762448567_7023_s96_f18_d3_m1_1_1_1/openvino/model.bin"
+
+python3 $PYPATH \
+    --input_filepath /home/mount/experiments/exp_1762448567_7023_s96_f18_d3_m1_1_1_1/onnx/sample_input.npy \
+    --device_name MYRIAD \
+    --model_bin /home/mount/experiments/exp_1762448567_7023_s96_f18_d3_m1_1_1_1/openvino/model.bin \
+    --model_xml /home/mount/experiments/exp_1762448567_7023_s96_f18_d3_m1_1_1_1/openvino/model.xml \
+    --output /home/mount/experiments/exp_1762448567_7023_s96_f18_d3_m1_1_1_1/inference/
+
+echo "Myriad inference completed!"

exp_1762448652_9207_s96_f20_d3_m2_2_2_2/convert_openvino.sh

@@ -0,0 +1,30 @@
+#!/bin/bash
+# Source OpenVINO environment
+source /opt/intel/openvino/bin/setupvars.sh
+
+# OpenVINO Model Optimizer script for ONNX model
+PYPATH=/opt/intel/openvino_2020.3.194/deployment_tools/model_optimizer/mo.py
+ONNX_MODEL=/home/mount/experiments/exp_1762448652_9207_s96_f20_d3_m2_2_2_2/onnx/model.onnx
+OUTPUT_DIR=/home/mount/experiments/exp_1762448652_9207_s96_f20_d3_m2_2_2_2/openvino
+
+echo "Converting ONNX model to OpenVINO IR format..."
+echo "Input model: $ONNX_MODEL"
+echo "Output directory: $OUTPUT_DIR"
+
+# Create output directory if it doesn't exist
+mkdir -p "$OUTPUT_DIR"
+
+python3 $PYPATH \
+    --input_model "$ONNX_MODEL" \
+    --data_type FP16 \
+    --input_shape "[1,8,96,96]" \
+    --mean_values "[0,0,0,0,0,0,0,0]" \
+    --scale_values "[1,1,1,1,1,1,1,1]" \
+    --progress \
+    --stream_output \
+    --output_dir "$OUTPUT_DIR" \
+    --model_name model
+
+echo "OpenVINO conversion completed!"
+echo "Generated files in $OUTPUT_DIR:"
+ls -la "$OUTPUT_DIR/"

exp_1762448652_9207_s96_f20_d3_m2_2_2_2/experiment.log

@@ -0,0 +1,98 @@
+2025-11-06 18:04:12 - INFO - === Model Creation Phase ===
+2025-11-06 18:04:12 - INFO - Creating UNet model with config: {'input_size': 96, 'base_filters': 20, 'depth': 3, 'channel_multipliers': [2, 2, 2, 2], 'n_channels': 8, 'n_classes': 3}
+2025-11-06 18:04:12 - INFO - Model created successfully:
+2025-11-06 18:04:12 - INFO -   Depth: 3
+2025-11-06 18:04:12 - INFO -   Channels per level: [40, 40, 40, 40]
+2025-11-06 18:04:12 - INFO -   Total parameters: 135,363
+2025-11-06 18:04:12 - INFO -   Parameter memory: 0.52 MB
+2025-11-06 18:04:12 - INFO - === Detailed Architecture Analysis ===
+2025-11-06 18:04:12 - INFO - Input vector dimension: 73,728 (8 × 96 × 96)
+2025-11-06 18:04:12 - INFO - Component-wise parameter breakdown:
+2025-11-06 18:04:12 - INFO -   Encoder - encoders.0.channel_proj: 360 parameters
+2025-11-06 18:04:12 - INFO -   Encoder - encoders.0.convnext_block.dwconv: 2,000 parameters
+2025-11-06 18:04:12 - INFO -   Encoder - encoders.0.convnext_block.norm: 80 parameters
+2025-11-06 18:04:12 - INFO -   Encoder - encoders.0.convnext_block.pwconv1: 6,560 parameters
+2025-11-06 18:04:12 - INFO -   Encoder - encoders.0.convnext_block.pwconv2: 6,440 parameters
+2025-11-06 18:04:12 - INFO -   Encoder - encoders.1.convnext_block.dwconv: 2,000 parameters
+2025-11-06 18:04:12 - INFO -   Encoder - encoders.1.convnext_block.norm: 80 parameters
+2025-11-06 18:04:12 - INFO -   Encoder - encoders.1.convnext_block.pwconv1: 6,560 parameters
+2025-11-06 18:04:12 - INFO -   Encoder - encoders.1.convnext_block.pwconv2: 6,440 parameters
+2025-11-06 18:04:12 - INFO -   Encoder - encoders.2.convnext_block.dwconv: 2,000 parameters
+2025-11-06 18:04:12 - INFO -   Encoder - encoders.2.convnext_block.norm: 80 parameters
+2025-11-06 18:04:12 - INFO -   Encoder - encoders.2.convnext_block.pwconv1: 6,560 parameters
+2025-11-06 18:04:12 - INFO -   Encoder - encoders.2.convnext_block.pwconv2: 6,440 parameters
+2025-11-06 18:04:12 - INFO -   Bottleneck - bottleneck.convnext_block.dwconv: 2,000 parameters
+2025-11-06 18:04:12 - INFO -   Bottleneck - bottleneck.convnext_block.norm: 80 parameters
+2025-11-06 18:04:12 - INFO -   Bottleneck - bottleneck.convnext_block.pwconv1: 6,560 parameters
+2025-11-06 18:04:12 - INFO -   Bottleneck - bottleneck.convnext_block.pwconv2: 6,440 parameters
+2025-11-06 18:04:12 - INFO -   Decoder - upsamplers.0: 6,440 parameters
+2025-11-06 18:04:12 - INFO -   Decoder - upsamplers.1: 6,440 parameters
+2025-11-06 18:04:12 - INFO -   Decoder - upsamplers.2: 6,440 parameters
+2025-11-06 18:04:12 - INFO -   Decoder - decoders.0.channel_proj: 3,240 parameters
+2025-11-06 18:04:12 - INFO -   Decoder - decoders.0.convnext_block.dwconv: 2,000 parameters
+2025-11-06 18:04:12 - INFO -   Decoder - decoders.0.convnext_block.norm: 80 parameters
+2025-11-06 18:04:12 - INFO -   Decoder - decoders.0.convnext_block.pwconv1: 6,560 parameters
+2025-11-06 18:04:12 - INFO -   Decoder - decoders.0.convnext_block.pwconv2: 6,440 parameters
+2025-11-06 18:04:12 - INFO -   Decoder - decoders.1.channel_proj: 3,240 parameters
+2025-11-06 18:04:12 - INFO -   Decoder - decoders.1.convnext_block.dwconv: 2,000 parameters
+2025-11-06 18:04:12 - INFO -   Decoder - decoders.1.convnext_block.norm: 80 parameters
+2025-11-06 18:04:12 - INFO -   Decoder - decoders.1.convnext_block.pwconv1: 6,560 parameters
+2025-11-06 18:04:12 - INFO -   Decoder - decoders.1.convnext_block.pwconv2: 6,440 parameters
+2025-11-06 18:04:12 - INFO -   Decoder - decoders.2.channel_proj: 3,240 parameters
+2025-11-06 18:04:12 - INFO -   Decoder - decoders.2.convnext_block.dwconv: 2,000 parameters
+2025-11-06 18:04:12 - INFO -   Decoder - decoders.2.convnext_block.norm: 80 parameters
+2025-11-06 18:04:12 - INFO -   Decoder - decoders.2.convnext_block.pwconv1: 6,560 parameters
+2025-11-06 18:04:12 - INFO -   Decoder - decoders.2.convnext_block.pwconv2: 6,440 parameters
+2025-11-06 18:04:12 - INFO -   Other - final_conv: 123 parameters
+2025-11-06 18:04:12 - INFO - Parameter distribution summary:
+2025-11-06 18:04:12 - INFO -   Encoder parameters: 45,600 (33.8%)
+2025-11-06 18:04:12 - INFO -   Decoder parameters: 74,280 (55.0%)
+2025-11-06 18:04:12 - INFO -   Bottleneck parameters: 15,080 (11.2%)
+2025-11-06 18:04:12 - INFO -   Other parameters: 123 (0.1%)
+2025-11-06 18:04:12 - INFO - Latent space dimensions (feature maps at each level):
+2025-11-06 18:04:12 - INFO -   Level 0: 40 × 96 × 96 = 368,640 elements
+2025-11-06 18:04:12 - INFO -   Level 1: 40 × 48 × 48 = 92,160 elements
+2025-11-06 18:04:12 - INFO -   Level 2: 40 × 24 × 24 = 23,040 elements
+2025-11-06 18:04:12 - INFO -   Level 3: 40 × 12 × 12 = 5,760 elements
+2025-11-06 18:04:12 - INFO - Skip connection dimensions:
+2025-11-06 18:04:12 - INFO -   Skip 0: 40 × 96 × 96 = 368,640 elements
+2025-11-06 18:04:12 - INFO -   Skip 1: 40 × 48 × 48 = 92,160 elements
+2025-11-06 18:04:12 - INFO -   Skip 2: 40 × 24 × 24 = 23,040 elements
+2025-11-06 18:04:12 - INFO - Memory analysis:
+2025-11-06 18:04:12 - INFO -   Peak feature map memory (inference): 2.15 MB
+2025-11-06 18:04:12 - INFO -   Peak feature map memory (training): 4.30 MB (with gradients)
+2025-11-06 18:04:12 - INFO - Output vector dimension: 27,648 (3 × 96 × 96)
+2025-11-06 18:04:12 - INFO - PyTorch model saved to: /home/philab/Desktop/hydranet/experiments/exp_1762448652_9207_s96_f20_d3_m2_2_2_2/pytorch/model.pt
+2025-11-06 18:04:12 - INFO - === ONNX Conversion Phase ===
+2025-11-06 18:04:12 - INFO - === Model Export Diagnostics ===
+2025-11-06 18:04:12 - INFO - PyTorch version: 1.9.0+cu102
+2025-11-06 18:04:12 - INFO - Model parameters: 135,363
+2025-11-06 18:04:12 - INFO - Model memory: 0.52 MB
+2025-11-06 18:04:12 - INFO - Starting ONNX export with opset version 11
+2025-11-06 18:04:12 - INFO - Model input shape: torch.Size([1, 8, 96, 96])
+2025-11-06 18:04:12 - INFO - Model input dtype: torch.float32
+2025-11-06 18:04:12 - INFO - Forward pass successful. Output shape: torch.Size([1, 3, 96, 96])
+2025-11-06 18:04:12 - INFO - Output dtype: torch.float32
+2025-11-06 18:04:12 - INFO - Output value range: [-0.4622, 0.9376]
+2025-11-06 18:04:12 - INFO - Model successfully exported to /home/philab/Desktop/hydranet/experiments/exp_1762448652_9207_s96_f20_d3_m2_2_2_2/onnx/model.onnx
+2025-11-06 18:04:12 - INFO - ONNX model size: 0.52 MB
+2025-11-06 18:04:12 - INFO - Saved dummy input with shape (1, 8, 96, 96) to /home/philab/Desktop/hydranet/experiments/exp_1762448652_9207_s96_f20_d3_m2_2_2_2/onnx/sample_input.npy
+2025-11-06 18:04:12 - INFO - Input data type: float32
+2025-11-06 18:04:12 - INFO - Input value range: [-4.0005, 3.9783]
+2025-11-06 18:04:12 - INFO - === OpenVINO Conversion Phase ===
+2025-11-06 18:04:12 - INFO - Starting OpenVINO conversion in Docker container...
+2025-11-06 18:04:16 - INFO - OpenVINO conversion completed in 3.95 seconds
+2025-11-06 18:04:16 - INFO - OpenVINO model files created:
+2025-11-06 18:04:16 - INFO -   XML file: /home/philab/Desktop/hydranet/experiments/exp_1762448652_9207_s96_f20_d3_m2_2_2_2/openvino/model.xml (0.08 MB)
+2025-11-06 18:04:16 - INFO -   BIN file: /home/philab/Desktop/hydranet/experiments/exp_1762448652_9207_s96_f20_d3_m2_2_2_2/openvino/model.bin (0.26 MB)
+2025-11-06 18:04:16 - INFO - === Myriad Inference Phase ===
+2025-11-06 18:04:16 - INFO - Starting Myriad inference in Docker container...
+2025-11-06 18:04:19 - INFO - Myriad inference completed in 2.77 seconds
+2025-11-06 18:04:19 - INFO - Actual inference time: 0.148318 seconds
+2025-11-06 18:04:19 - INFO - ✅ Complete pipeline executed successfully!
+2025-11-06 18:04:19 - INFO - ✅ Experiment 24 completed successfully
+2025-11-06 18:04:19 - INFO -    Inference time: 0.148318s
+2025-11-06 18:04:19 - INFO - 
+=== Experiment 25/2475 ===
+2025-11-06 18:04:19 - INFO - Experiment ID: exp_1762448659_3550_s96_f20_d3_m1_2_2_2
+2025-11-06 18:04:19 - INFO - Experiment directory: /home/philab/Desktop/hydranet/experiments/exp_1762448659_3550_s96_f20_d3_m1_2_2_2

exp_1762448652_9207_s96_f20_d3_m2_2_2_2/model_info.json

@@ -0,0 +1,138 @@
+{
+  "config": {
+    "input_size": 96,
+    "base_filters": 20,
+    "depth": 3,
+    "channel_multipliers": [
+      2,
+      2,
+      2,
+      2
+    ],
+    "n_channels": 8,
+    "n_classes": 3
+  },
+  "model_info": {
+    "depth": 3,
+    "channels_per_level": [
+      40,
+      40,
+      40,
+      40
+    ],
+    "channel_multipliers": [
+      2,
+      2,
+      2,
+      2
+    ],
+    "total_parameters": 135363,
+    "trainable_parameters": 135363,
+    "model_size_mb": 0.5163688659667969
+  },
+  "architecture_stats": {
+    "input_dimension": 73728,
+    "output_dimension": 27648,
+    "parameter_distribution": {
+      "encoder_params": 45600,
+      "decoder_params": 74280,
+      "bottleneck_params": 15080,
+      "other_params": 123,
+      "total_params": 135083,
+      "encoder_percentage": 33.75702345965073,
+      "decoder_percentage": 54.988414530325805,
+      "bottleneck_percentage": 11.163506880954673,
+      "other_percentage": 0.09105512906879475
+    },
+    "component_breakdown": {
+      "Encoder - encoders.0.channel_proj": 360,
+      "Encoder - encoders.0.convnext_block.dwconv": 2000,
+      "Encoder - encoders.0.convnext_block.norm": 80,
+      "Encoder - encoders.0.convnext_block.pwconv1": 6560,
+      "Encoder - encoders.0.convnext_block.pwconv2": 6440,
+      "Encoder - encoders.1.convnext_block.dwconv": 2000,
+      "Encoder - encoders.1.convnext_block.norm": 80,
+      "Encoder - encoders.1.convnext_block.pwconv1": 6560,
+      "Encoder - encoders.1.convnext_block.pwconv2": 6440,
+      "Encoder - encoders.2.convnext_block.dwconv": 2000,
+      "Encoder - encoders.2.convnext_block.norm": 80,
+      "Encoder - encoders.2.convnext_block.pwconv1": 6560,
+      "Encoder - encoders.2.convnext_block.pwconv2": 6440,
+      "Bottleneck - bottleneck.convnext_block.dwconv": 2000,
+      "Bottleneck - bottleneck.convnext_block.norm": 80,
+      "Bottleneck - bottleneck.convnext_block.pwconv1": 6560,
+      "Bottleneck - bottleneck.convnext_block.pwconv2": 6440,
+      "Decoder - upsamplers.0": 6440,
+      "Decoder - upsamplers.1": 6440,
+      "Decoder - upsamplers.2": 6440,
+      "Decoder - decoders.0.channel_proj": 3240,
+      "Decoder - decoders.0.convnext_block.dwconv": 2000,
+      "Decoder - decoders.0.convnext_block.norm": 80,
+      "Decoder - decoders.0.convnext_block.pwconv1": 6560,
+      "Decoder - decoders.0.convnext_block.pwconv2": 6440,
+      "Decoder - decoders.1.channel_proj": 3240,
+      "Decoder - decoders.1.convnext_block.dwconv": 2000,
+      "Decoder - decoders.1.convnext_block.norm": 80,
+      "Decoder - decoders.1.convnext_block.pwconv1": 6560,
+      "Decoder - decoders.1.convnext_block.pwconv2": 6440,
+      "Decoder - decoders.2.channel_proj": 3240,
+      "Decoder - decoders.2.convnext_block.dwconv": 2000,
+      "Decoder - decoders.2.convnext_block.norm": 80,
+      "Decoder - decoders.2.convnext_block.pwconv1": 6560,
+      "Decoder - decoders.2.convnext_block.pwconv2": 6440,
+      "Other - final_conv": 123
+    },
+    "latent_dimensions": {
+      "Level_0": {
+        "channels": 40,
+        "height": 96,
+        "width": 96,
+        "total_elements": 368640
+      },
+      "Level_1": {
+        "channels": 40,
+        "height": 48,
+        "width": 48,
+        "total_elements": 92160
+      },
+      "Level_2": {
+        "channels": 40,
+        "height": 24,
+        "width": 24,
+        "total_elements": 23040
+      },
+      "Level_3": {
+        "channels": 40,
+        "height": 12,
+        "width": 12,
+        "total_elements": 5760
+      }
+    },
+    "skip_dimensions": {
+      "Skip_0": {
+        "channels": 40,
+        "height": 96,
+        "width": 96,
+        "total_elements": 368640
+      },
+      "Skip_1": {
+        "channels": 40,
+        "height": 48,
+        "width": 48,
+        "total_elements": 92160
+      },
+      "Skip_2": {
+        "channels": 40,
+        "height": 24,
+        "width": 24,
+        "total_elements": 23040
+      }
+    },
+    "memory_analysis": {
+      "peak_memory_inference_mb": 2.14892578125,
+      "peak_memory_training_mb": 4.2978515625,
+      "peak_elements": 563328
+    }
+  },
+  "timestamp": "2025-11-06T18:04:12.573784"
+}

exp_1762448652_9207_s96_f20_d3_m2_2_2_2/pipeline_results.json

@@ -0,0 +1,138 @@
+{
+  "experiment_id": "exp_1762448652_6643_s96_f20_d3_m2_2_2_2",
+  "config": {
+    "input_size": 96,
+    "base_filters": 20,
+    "depth": 3,
+    "channel_multipliers": [
+      2,
+      2,
+      2,
+      2
+    ],
+    "n_channels": 8,
+    "n_classes": 3
+  },
+  "start_time": "2025-11-06T18:04:12.566367",
+  "success": true,
+  "timings": {
+    "onnx_conversion": 0.26931651402264833,
+    "openvino_conversion": 3.9536154859233648,
+    "inference_total": 2.7693776809610426,
+    "inference_actual": 0.148318,
+    "total": 7.005332896020263
+  },
+  "errors": [],
+  "architecture_stats": {
+    "input_dimension": 73728,
+    "output_dimension": 27648,
+    "parameter_distribution": {
+      "encoder_params": 45600,
+      "decoder_params": 74280,
+      "bottleneck_params": 15080,
+      "other_params": 123,
+      "total_params": 135083,
+      "encoder_percentage": 33.75702345965073,
+      "decoder_percentage": 54.988414530325805,
+      "bottleneck_percentage": 11.163506880954673,
+      "other_percentage": 0.09105512906879475
+    },
+    "component_breakdown": {
+      "Encoder - encoders.0.channel_proj": 360,
+      "Encoder - encoders.0.convnext_block.dwconv": 2000,
+      "Encoder - encoders.0.convnext_block.norm": 80,
+      "Encoder - encoders.0.convnext_block.pwconv1": 6560,
+      "Encoder - encoders.0.convnext_block.pwconv2": 6440,
+      "Encoder - encoders.1.convnext_block.dwconv": 2000,
+      "Encoder - encoders.1.convnext_block.norm": 80,
+      "Encoder - encoders.1.convnext_block.pwconv1": 6560,
+      "Encoder - encoders.1.convnext_block.pwconv2": 6440,
+      "Encoder - encoders.2.convnext_block.dwconv": 2000,
+      "Encoder - encoders.2.convnext_block.norm": 80,
+      "Encoder - encoders.2.convnext_block.pwconv1": 6560,
+      "Encoder - encoders.2.convnext_block.pwconv2": 6440,
+      "Bottleneck - bottleneck.convnext_block.dwconv": 2000,
+      "Bottleneck - bottleneck.convnext_block.norm": 80,
+      "Bottleneck - bottleneck.convnext_block.pwconv1": 6560,
+      "Bottleneck - bottleneck.convnext_block.pwconv2": 6440,
+      "Decoder - upsamplers.0": 6440,
+      "Decoder - upsamplers.1": 6440,
+      "Decoder - upsamplers.2": 6440,
+      "Decoder - decoders.0.channel_proj": 3240,
+      "Decoder - decoders.0.convnext_block.dwconv": 2000,
+      "Decoder - decoders.0.convnext_block.norm": 80,
+      "Decoder - decoders.0.convnext_block.pwconv1": 6560,
+      "Decoder - decoders.0.convnext_block.pwconv2": 6440,
+      "Decoder - decoders.1.channel_proj": 3240,
+      "Decoder - decoders.1.convnext_block.dwconv": 2000,
+      "Decoder - decoders.1.convnext_block.norm": 80,
+      "Decoder - decoders.1.convnext_block.pwconv1": 6560,
+      "Decoder - decoders.1.convnext_block.pwconv2": 6440,
+      "Decoder - decoders.2.channel_proj": 3240,
+      "Decoder - decoders.2.convnext_block.dwconv": 2000,
+      "Decoder - decoders.2.convnext_block.norm": 80,
+      "Decoder - decoders.2.convnext_block.pwconv1": 6560,
+      "Decoder - decoders.2.convnext_block.pwconv2": 6440,
+      "Other - final_conv": 123
+    },
+    "latent_dimensions": {
+      "Level_0": {
+        "channels": 40,
+        "height": 96,
+        "width": 96,
+        "total_elements": 368640
+      },
+      "Level_1": {
+        "channels": 40,
+        "height": 48,
+        "width": 48,
+        "total_elements": 92160
+      },
+      "Level_2": {
+        "channels": 40,
+        "height": 24,
+        "width": 24,
+        "total_elements": 23040
+      },
+      "Level_3": {
+        "channels": 40,
+        "height": 12,
+        "width": 12,
+        "total_elements": 5760
+      }
+    },
+    "skip_dimensions": {
+      "Skip_0": {
+        "channels": 40,
+        "height": 96,
+        "width": 96,
+        "total_elements": 368640
+      },
+      "Skip_1": {
+        "channels": 40,
+        "height": 48,
+        "width": 48,
+        "total_elements": 92160
+      },
+      "Skip_2": {
+        "channels": 40,
+        "height": 24,
+        "width": 24,
+        "total_elements": 23040
+      }
+    },
+    "memory_analysis": {
+      "peak_memory_inference_mb": 2.14892578125,
+      "peak_memory_training_mb": 4.2978515625,
+      "peak_elements": 563328
+    }
+  },
+  "inference_results": {
+    "success": true,
+    "total_time": 2.7693776809610426,
+    "inference_time": 0.148318,
+    "stdout": "[setupvars.sh] OpenVINO environment initialized\nStarting Myriad inference...\nInput: /home/mount/experiments/exp_1762448652_9207_s96_f20_d3_m2_2_2_2/onnx/sample_input.npy\nModel XML: /home/mount/experiments/exp_1762448652_9207_s96_f20_d3_m2_2_2_2/openvino/model.xml\nModel BIN: /home/mount/experiments/exp_1762448652_9207_s96_f20_d3_m2_2_2_2/openvino/model.bin\n[OK] OpenVINO inference engine imported successfully\nDevice: MYRIAD\nModel XML: /home/mount/experiments/exp_1762448652_9207_s96_f20_d3_m2_2_2_2/openvino/model.xml\nModel BIN: /home/mount/experiments/exp_1762448652_9207_s96_f20_d3_m2_2_2_2/openvino/model.bin\nInput file: /home/mount/experiments/exp_1762448652_9207_s96_f20_d3_m2_2_2_2/onnx/sample_input.npy\nInitializing OpenVINO Runtime Core...\nAvailable devices:\n[E:] [BSL] found 0 ioexpander device\n  ['CPU', 'GNA', 'MYRIAD']\nLoading network...\nInput blob: input\nInput shape: [1, 8, 96, 96]\nOutput blob: Conv_104\nOutput shape: [1, 3, 96, 96]\nLoading network to MYRIAD...\nLoading input data...\nInput data shape: (1, 8, 96, 96)\nInput data type: float32\nRunning inference...\n[OK] Inference completed!\nInference time: 0.148318 seconds\nOutput shape: (1, 3, 96, 96)\nOutput dtype: float32\nOutput range: [-0.462646, 0.937988]\nMyriad inference completed!\n",
+    "stderr": ""
+  },
+  "end_time": "2025-11-06T18:04:19.571690"
+}

exp_1762448652_9207_s96_f20_d3_m2_2_2_2/run_inference.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+# Source OpenVINO environment
+source /opt/intel/openvino/bin/setupvars.sh
+
+PYPATH=/home/mount/scripts/simple_inference.py
+
+echo "Starting Myriad inference..."
+echo "Input: /home/mount/experiments/exp_1762448652_9207_s96_f20_d3_m2_2_2_2/onnx/sample_input.npy"
+echo "Model XML: /home/mount/experiments/exp_1762448652_9207_s96_f20_d3_m2_2_2_2/openvino/model.xml"
+echo "Model BIN: /home/mount/experiments/exp_1762448652_9207_s96_f20_d3_m2_2_2_2/openvino/model.bin"
+
+python3 $PYPATH \
+    --input_filepath /home/mount/experiments/exp_1762448652_9207_s96_f20_d3_m2_2_2_2/onnx/sample_input.npy \
+    --device_name MYRIAD \
+    --model_bin /home/mount/experiments/exp_1762448652_9207_s96_f20_d3_m2_2_2_2/openvino/model.bin \
+    --model_xml /home/mount/experiments/exp_1762448652_9207_s96_f20_d3_m2_2_2_2/openvino/model.xml \
+    --output /home/mount/experiments/exp_1762448652_9207_s96_f20_d3_m2_2_2_2/inference/
+
+echo "Myriad inference completed!"

exp_1762448723_5984_s96_f22_d3_m1_1_1_1/convert_openvino.sh

@@ -0,0 +1,30 @@
+#!/bin/bash
+# Source OpenVINO environment
+source /opt/intel/openvino/bin/setupvars.sh
+
+# OpenVINO Model Optimizer script for ONNX model
+PYPATH=/opt/intel/openvino_2020.3.194/deployment_tools/model_optimizer/mo.py
+ONNX_MODEL=/home/mount/experiments/exp_1762448723_5984_s96_f22_d3_m1_1_1_1/onnx/model.onnx
+OUTPUT_DIR=/home/mount/experiments/exp_1762448723_5984_s96_f22_d3_m1_1_1_1/openvino
+
+echo "Converting ONNX model to OpenVINO IR format..."
+echo "Input model: $ONNX_MODEL"
+echo "Output directory: $OUTPUT_DIR"
+
+# Create output directory if it doesn't exist
+mkdir -p "$OUTPUT_DIR"
+
+python3 $PYPATH \
+    --input_model "$ONNX_MODEL" \
+    --data_type FP16 \
+    --input_shape "[1,8,96,96]" \
+    --mean_values "[0,0,0,0,0,0,0,0]" \
+    --scale_values "[1,1,1,1,1,1,1,1]" \
+    --progress \
+    --stream_output \
+    --output_dir "$OUTPUT_DIR" \
+    --model_name model
+
+echo "OpenVINO conversion completed!"
+echo "Generated files in $OUTPUT_DIR:"
+ls -la "$OUTPUT_DIR/"

exp_1762448723_5984_s96_f22_d3_m1_1_1_1/experiment.log

@@ -0,0 +1,98 @@
+2025-11-06 18:05:23 - INFO - === Model Creation Phase ===
+2025-11-06 18:05:23 - INFO - Creating UNet model with config: {'input_size': 96, 'base_filters': 22, 'depth': 3, 'channel_multipliers': [1, 1, 1, 1], 'n_channels': 8, 'n_classes': 3}
+2025-11-06 18:05:23 - INFO - Model created successfully:
+2025-11-06 18:05:23 - INFO -   Depth: 3
+2025-11-06 18:05:23 - INFO -   Channels per level: [22, 22, 22, 22]
+2025-11-06 18:05:23 - INFO -   Total parameters: 45,147
+2025-11-06 18:05:23 - INFO -   Parameter memory: 0.17 MB
+2025-11-06 18:05:23 - INFO - === Detailed Architecture Analysis ===
+2025-11-06 18:05:23 - INFO - Input vector dimension: 73,728 (8 × 96 × 96)
+2025-11-06 18:05:23 - INFO - Component-wise parameter breakdown:
+2025-11-06 18:05:23 - INFO -   Encoder - encoders.0.channel_proj: 198 parameters
+2025-11-06 18:05:23 - INFO -   Encoder - encoders.0.convnext_block.dwconv: 1,100 parameters
+2025-11-06 18:05:23 - INFO -   Encoder - encoders.0.convnext_block.norm: 44 parameters
+2025-11-06 18:05:23 - INFO -   Encoder - encoders.0.convnext_block.pwconv1: 2,024 parameters
+2025-11-06 18:05:23 - INFO -   Encoder - encoders.0.convnext_block.pwconv2: 1,958 parameters
+2025-11-06 18:05:23 - INFO -   Encoder - encoders.1.convnext_block.dwconv: 1,100 parameters
+2025-11-06 18:05:23 - INFO -   Encoder - encoders.1.convnext_block.norm: 44 parameters
+2025-11-06 18:05:23 - INFO -   Encoder - encoders.1.convnext_block.pwconv1: 2,024 parameters
+2025-11-06 18:05:23 - INFO -   Encoder - encoders.1.convnext_block.pwconv2: 1,958 parameters
+2025-11-06 18:05:23 - INFO -   Encoder - encoders.2.convnext_block.dwconv: 1,100 parameters
+2025-11-06 18:05:23 - INFO -   Encoder - encoders.2.convnext_block.norm: 44 parameters
+2025-11-06 18:05:23 - INFO -   Encoder - encoders.2.convnext_block.pwconv1: 2,024 parameters
+2025-11-06 18:05:23 - INFO -   Encoder - encoders.2.convnext_block.pwconv2: 1,958 parameters
+2025-11-06 18:05:23 - INFO -   Bottleneck - bottleneck.convnext_block.dwconv: 1,100 parameters
+2025-11-06 18:05:23 - INFO -   Bottleneck - bottleneck.convnext_block.norm: 44 parameters
+2025-11-06 18:05:23 - INFO -   Bottleneck - bottleneck.convnext_block.pwconv1: 2,024 parameters
+2025-11-06 18:05:23 - INFO -   Bottleneck - bottleneck.convnext_block.pwconv2: 1,958 parameters
+2025-11-06 18:05:23 - INFO -   Decoder - upsamplers.0: 1,958 parameters
+2025-11-06 18:05:23 - INFO -   Decoder - upsamplers.1: 1,958 parameters
+2025-11-06 18:05:23 - INFO -   Decoder - upsamplers.2: 1,958 parameters
+2025-11-06 18:05:23 - INFO -   Decoder - decoders.0.channel_proj: 990 parameters
+2025-11-06 18:05:23 - INFO -   Decoder - decoders.0.convnext_block.dwconv: 1,100 parameters
+2025-11-06 18:05:23 - INFO -   Decoder - decoders.0.convnext_block.norm: 44 parameters
+2025-11-06 18:05:23 - INFO -   Decoder - decoders.0.convnext_block.pwconv1: 2,024 parameters
+2025-11-06 18:05:23 - INFO -   Decoder - decoders.0.convnext_block.pwconv2: 1,958 parameters
+2025-11-06 18:05:23 - INFO -   Decoder - decoders.1.channel_proj: 990 parameters
+2025-11-06 18:05:23 - INFO -   Decoder - decoders.1.convnext_block.dwconv: 1,100 parameters
+2025-11-06 18:05:23 - INFO -   Decoder - decoders.1.convnext_block.norm: 44 parameters
+2025-11-06 18:05:23 - INFO -   Decoder - decoders.1.convnext_block.pwconv1: 2,024 parameters
+2025-11-06 18:05:23 - INFO -   Decoder - decoders.1.convnext_block.pwconv2: 1,958 parameters
+2025-11-06 18:05:23 - INFO -   Decoder - decoders.2.channel_proj: 990 parameters
+2025-11-06 18:05:23 - INFO -   Decoder - decoders.2.convnext_block.dwconv: 1,100 parameters
+2025-11-06 18:05:23 - INFO -   Decoder - decoders.2.convnext_block.norm: 44 parameters
+2025-11-06 18:05:23 - INFO -   Decoder - decoders.2.convnext_block.pwconv1: 2,024 parameters
+2025-11-06 18:05:23 - INFO -   Decoder - decoders.2.convnext_block.pwconv2: 1,958 parameters
+2025-11-06 18:05:23 - INFO -   Other - final_conv: 69 parameters
+2025-11-06 18:05:23 - INFO - Parameter distribution summary:
+2025-11-06 18:05:23 - INFO -   Encoder parameters: 15,576 (34.6%)
+2025-11-06 18:05:23 - INFO -   Decoder parameters: 24,222 (53.8%)
+2025-11-06 18:05:23 - INFO -   Bottleneck parameters: 5,126 (11.4%)
+2025-11-06 18:05:23 - INFO -   Other parameters: 69 (0.2%)
+2025-11-06 18:05:23 - INFO - Latent space dimensions (feature maps at each level):
+2025-11-06 18:05:23 - INFO -   Level 0: 22 × 96 × 96 = 202,752 elements
+2025-11-06 18:05:23 - INFO -   Level 1: 22 × 48 × 48 = 50,688 elements
+2025-11-06 18:05:23 - INFO -   Level 2: 22 × 24 × 24 = 12,672 elements
+2025-11-06 18:05:23 - INFO -   Level 3: 22 × 12 × 12 = 3,168 elements
+2025-11-06 18:05:23 - INFO - Skip connection dimensions:
+2025-11-06 18:05:23 - INFO -   Skip 0: 22 × 96 × 96 = 202,752 elements
+2025-11-06 18:05:23 - INFO -   Skip 1: 22 × 48 × 48 = 50,688 elements
+2025-11-06 18:05:23 - INFO -   Skip 2: 22 × 24 × 24 = 12,672 elements
+2025-11-06 18:05:23 - INFO - Memory analysis:
+2025-11-06 18:05:23 - INFO -   Peak feature map memory (inference): 1.31 MB
+2025-11-06 18:05:23 - INFO -   Peak feature map memory (training): 2.62 MB (with gradients)
+2025-11-06 18:05:23 - INFO - Output vector dimension: 27,648 (3 × 96 × 96)
+2025-11-06 18:05:23 - INFO - PyTorch model saved to: /home/philab/Desktop/hydranet/experiments/exp_1762448723_5984_s96_f22_d3_m1_1_1_1/pytorch/model.pt
+2025-11-06 18:05:23 - INFO - === ONNX Conversion Phase ===
+2025-11-06 18:05:23 - INFO - === Model Export Diagnostics ===
+2025-11-06 18:05:23 - INFO - PyTorch version: 1.9.0+cu102
+2025-11-06 18:05:23 - INFO - Model parameters: 45,147
+2025-11-06 18:05:23 - INFO - Model memory: 0.17 MB
+2025-11-06 18:05:23 - INFO - Starting ONNX export with opset version 11
+2025-11-06 18:05:23 - INFO - Model input shape: torch.Size([1, 8, 96, 96])
+2025-11-06 18:05:23 - INFO - Model input dtype: torch.float32
+2025-11-06 18:05:23 - INFO - Forward pass successful. Output shape: torch.Size([1, 3, 96, 96])
+2025-11-06 18:05:23 - INFO - Output dtype: torch.float32
+2025-11-06 18:05:23 - INFO - Output value range: [-0.6694, 0.8579]
+2025-11-06 18:05:24 - INFO - Model successfully exported to /home/philab/Desktop/hydranet/experiments/exp_1762448723_5984_s96_f22_d3_m1_1_1_1/onnx/model.onnx
+2025-11-06 18:05:24 - INFO - ONNX model size: 0.18 MB
+2025-11-06 18:05:24 - INFO - Saved dummy input with shape (1, 8, 96, 96) to /home/philab/Desktop/hydranet/experiments/exp_1762448723_5984_s96_f22_d3_m1_1_1_1/onnx/sample_input.npy
+2025-11-06 18:05:24 - INFO - Input data type: float32
+2025-11-06 18:05:24 - INFO - Input value range: [-4.4875, 4.1861]
+2025-11-06 18:05:24 - INFO - === OpenVINO Conversion Phase ===
+2025-11-06 18:05:24 - INFO - Starting OpenVINO conversion in Docker container...
+2025-11-06 18:05:27 - INFO - OpenVINO conversion completed in 3.91 seconds
+2025-11-06 18:05:27 - INFO - OpenVINO model files created:
+2025-11-06 18:05:27 - INFO -   XML file: /home/philab/Desktop/hydranet/experiments/exp_1762448723_5984_s96_f22_d3_m1_1_1_1/openvino/model.xml (0.08 MB)
+2025-11-06 18:05:27 - INFO -   BIN file: /home/philab/Desktop/hydranet/experiments/exp_1762448723_5984_s96_f22_d3_m1_1_1_1/openvino/model.bin (0.09 MB)
+2025-11-06 18:05:27 - INFO - === Myriad Inference Phase ===
+2025-11-06 18:05:27 - INFO - Starting Myriad inference in Docker container...
+2025-11-06 18:05:30 - INFO - Myriad inference completed in 2.77 seconds
+2025-11-06 18:05:30 - INFO - Actual inference time: 0.175996 seconds
+2025-11-06 18:05:30 - INFO - ✅ Complete pipeline executed successfully!
+2025-11-06 18:05:30 - INFO - ✅ Experiment 34 completed successfully
+2025-11-06 18:05:30 - INFO -    Inference time: 0.175996s
+2025-11-06 18:05:30 - INFO - 
+=== Experiment 35/2475 ===
+2025-11-06 18:05:30 - INFO - Experiment ID: exp_1762448730_3552_s96_f22_d3_m2_2_2_2
+2025-11-06 18:05:30 - INFO - Experiment directory: /home/philab/Desktop/hydranet/experiments/exp_1762448730_3552_s96_f22_d3_m2_2_2_2

exp_1762448723_5984_s96_f22_d3_m1_1_1_1/model_info.json

@@ -0,0 +1,138 @@
+{
+  "config": {
+    "input_size": 96,
+    "base_filters": 22,
+    "depth": 3,
+    "channel_multipliers": [
+      1,
+      1,
+      1,
+      1
+    ],
+    "n_channels": 8,
+    "n_classes": 3
+  },
+  "model_info": {
+    "depth": 3,
+    "channels_per_level": [
+      22,
+      22,
+      22,
+      22
+    ],
+    "channel_multipliers": [
+      1,
+      1,
+      1,
+      1
+    ],
+    "total_parameters": 45147,
+    "trainable_parameters": 45147,
+    "model_size_mb": 0.17222213745117188
+  },
+  "architecture_stats": {
+    "input_dimension": 73728,
+    "output_dimension": 27648,
+    "parameter_distribution": {
+      "encoder_params": 15576,
+      "decoder_params": 24222,
+      "bottleneck_params": 5126,
+      "other_params": 69,
+      "total_params": 44993,
+      "encoder_percentage": 34.61871846731714,
+      "decoder_percentage": 53.83504100637877,
+      "bottleneck_percentage": 11.39288333740804,
+      "other_percentage": 0.1533571888960505
+    },
+    "component_breakdown": {
+      "Encoder - encoders.0.channel_proj": 198,
+      "Encoder - encoders.0.convnext_block.dwconv": 1100,
+      "Encoder - encoders.0.convnext_block.norm": 44,
+      "Encoder - encoders.0.convnext_block.pwconv1": 2024,
+      "Encoder - encoders.0.convnext_block.pwconv2": 1958,
+      "Encoder - encoders.1.convnext_block.dwconv": 1100,
+      "Encoder - encoders.1.convnext_block.norm": 44,
+      "Encoder - encoders.1.convnext_block.pwconv1": 2024,
+      "Encoder - encoders.1.convnext_block.pwconv2": 1958,
+      "Encoder - encoders.2.convnext_block.dwconv": 1100,
+      "Encoder - encoders.2.convnext_block.norm": 44,
+      "Encoder - encoders.2.convnext_block.pwconv1": 2024,
+      "Encoder - encoders.2.convnext_block.pwconv2": 1958,
+      "Bottleneck - bottleneck.convnext_block.dwconv": 1100,
+      "Bottleneck - bottleneck.convnext_block.norm": 44,
+      "Bottleneck - bottleneck.convnext_block.pwconv1": 2024,
+      "Bottleneck - bottleneck.convnext_block.pwconv2": 1958,
+      "Decoder - upsamplers.0": 1958,
+      "Decoder - upsamplers.1": 1958,
+      "Decoder - upsamplers.2": 1958,
+      "Decoder - decoders.0.channel_proj": 990,
+      "Decoder - decoders.0.convnext_block.dwconv": 1100,
+      "Decoder - decoders.0.convnext_block.norm": 44,
+      "Decoder - decoders.0.convnext_block.pwconv1": 2024,
+      "Decoder - decoders.0.convnext_block.pwconv2": 1958,
+      "Decoder - decoders.1.channel_proj": 990,
+      "Decoder - decoders.1.convnext_block.dwconv": 1100,
+      "Decoder - decoders.1.convnext_block.norm": 44,
+      "Decoder - decoders.1.convnext_block.pwconv1": 2024,
+      "Decoder - decoders.1.convnext_block.pwconv2": 1958,
+      "Decoder - decoders.2.channel_proj": 990,
+      "Decoder - decoders.2.convnext_block.dwconv": 1100,
+      "Decoder - decoders.2.convnext_block.norm": 44,
+      "Decoder - decoders.2.convnext_block.pwconv1": 2024,
+      "Decoder - decoders.2.convnext_block.pwconv2": 1958,
+      "Other - final_conv": 69
+    },
+    "latent_dimensions": {
+      "Level_0": {
+        "channels": 22,
+        "height": 96,
+        "width": 96,
+        "total_elements": 202752
+      },
+      "Level_1": {
+        "channels": 22,
+        "height": 48,
+        "width": 48,
+        "total_elements": 50688
+      },
+      "Level_2": {
+        "channels": 22,
+        "height": 24,
+        "width": 24,
+        "total_elements": 12672
+      },
+      "Level_3": {
+        "channels": 22,
+        "height": 12,
+        "width": 12,
+        "total_elements": 3168
+      }
+    },
+    "skip_dimensions": {
+      "Skip_0": {
+        "channels": 22,
+        "height": 96,
+        "width": 96,
+        "total_elements": 202752
+      },
+      "Skip_1": {
+        "channels": 22,
+        "height": 48,
+        "width": 48,
+        "total_elements": 50688
+      },
+      "Skip_2": {
+        "channels": 22,
+        "height": 24,
+        "width": 24,
+        "total_elements": 12672
+      }
+    },
+    "memory_analysis": {
+      "peak_memory_inference_mb": 1.3084716796875,
+      "peak_memory_training_mb": 2.616943359375,
+      "peak_elements": 343008
+    }
+  },
+  "timestamp": "2025-11-06T18:05:23.754547"
+}

exp_1762448723_5984_s96_f22_d3_m1_1_1_1/pipeline_results.json

@@ -0,0 +1,138 @@
+{
+  "experiment_id": "exp_1762448723_5692_s96_f22_d3_m1_1_1_1",
+  "config": {
+    "input_size": 96,
+    "base_filters": 22,
+    "depth": 3,
+    "channel_multipliers": [
+      1,
+      1,
+      1,
+      1
+    ],
+    "n_channels": 8,
+    "n_classes": 3
+  },
+  "start_time": "2025-11-06T18:05:23.748640",
+  "success": true,
+  "timings": {
+    "onnx_conversion": 0.25980374589562416,
+    "openvino_conversion": 3.912094983039424,
+    "inference_total": 2.765632274094969,
+    "inference_actual": 0.175996,
+    "total": 6.948178299935535
+  },
+  "errors": [],
+  "architecture_stats": {
+    "input_dimension": 73728,
+    "output_dimension": 27648,
+    "parameter_distribution": {
+      "encoder_params": 15576,
+      "decoder_params": 24222,
+      "bottleneck_params": 5126,
+      "other_params": 69,
+      "total_params": 44993,
+      "encoder_percentage": 34.61871846731714,
+      "decoder_percentage": 53.83504100637877,
+      "bottleneck_percentage": 11.39288333740804,
+      "other_percentage": 0.1533571888960505
+    },
+    "component_breakdown": {
+      "Encoder - encoders.0.channel_proj": 198,
+      "Encoder - encoders.0.convnext_block.dwconv": 1100,
+      "Encoder - encoders.0.convnext_block.norm": 44,
+      "Encoder - encoders.0.convnext_block.pwconv1": 2024,
+      "Encoder - encoders.0.convnext_block.pwconv2": 1958,
+      "Encoder - encoders.1.convnext_block.dwconv": 1100,
+      "Encoder - encoders.1.convnext_block.norm": 44,
+      "Encoder - encoders.1.convnext_block.pwconv1": 2024,
+      "Encoder - encoders.1.convnext_block.pwconv2": 1958,
+      "Encoder - encoders.2.convnext_block.dwconv": 1100,
+      "Encoder - encoders.2.convnext_block.norm": 44,
+      "Encoder - encoders.2.convnext_block.pwconv1": 2024,
+      "Encoder - encoders.2.convnext_block.pwconv2": 1958,
+      "Bottleneck - bottleneck.convnext_block.dwconv": 1100,
+      "Bottleneck - bottleneck.convnext_block.norm": 44,
+      "Bottleneck - bottleneck.convnext_block.pwconv1": 2024,
+      "Bottleneck - bottleneck.convnext_block.pwconv2": 1958,
+      "Decoder - upsamplers.0": 1958,
+      "Decoder - upsamplers.1": 1958,
+      "Decoder - upsamplers.2": 1958,
+      "Decoder - decoders.0.channel_proj": 990,
+      "Decoder - decoders.0.convnext_block.dwconv": 1100,
+      "Decoder - decoders.0.convnext_block.norm": 44,
+      "Decoder - decoders.0.convnext_block.pwconv1": 2024,
+      "Decoder - decoders.0.convnext_block.pwconv2": 1958,
+      "Decoder - decoders.1.channel_proj": 990,
+      "Decoder - decoders.1.convnext_block.dwconv": 1100,
+      "Decoder - decoders.1.convnext_block.norm": 44,
+      "Decoder - decoders.1.convnext_block.pwconv1": 2024,
+      "Decoder - decoders.1.convnext_block.pwconv2": 1958,
+      "Decoder - decoders.2.channel_proj": 990,
+      "Decoder - decoders.2.convnext_block.dwconv": 1100,
+      "Decoder - decoders.2.convnext_block.norm": 44,
+      "Decoder - decoders.2.convnext_block.pwconv1": 2024,
+      "Decoder - decoders.2.convnext_block.pwconv2": 1958,
+      "Other - final_conv": 69
+    },
+    "latent_dimensions": {
+      "Level_0": {
+        "channels": 22,
+        "height": 96,
+        "width": 96,
+        "total_elements": 202752
+      },
+      "Level_1": {
+        "channels": 22,
+        "height": 48,
+        "width": 48,
+        "total_elements": 50688
+      },
+      "Level_2": {
+        "channels": 22,
+        "height": 24,
+        "width": 24,
+        "total_elements": 12672
+      },
+      "Level_3": {
+        "channels": 22,
+        "height": 12,
+        "width": 12,
+        "total_elements": 3168
+      }
+    },
+    "skip_dimensions": {
+      "Skip_0": {
+        "channels": 22,
+        "height": 96,
+        "width": 96,
+        "total_elements": 202752
+      },
+      "Skip_1": {
+        "channels": 22,
+        "height": 48,
+        "width": 48,
+        "total_elements": 50688
+      },
+      "Skip_2": {
+        "channels": 22,
+        "height": 24,
+        "width": 24,
+        "total_elements": 12672
+      }
+    },
+    "memory_analysis": {
+      "peak_memory_inference_mb": 1.3084716796875,
+      "peak_memory_training_mb": 2.616943359375,
+      "peak_elements": 343008
+    }
+  },
+  "inference_results": {
+    "success": true,
+    "total_time": 2.765632274094969,
+    "inference_time": 0.175996,
+    "stdout": "[setupvars.sh] OpenVINO environment initialized\nStarting Myriad inference...\nInput: /home/mount/experiments/exp_1762448723_5984_s96_f22_d3_m1_1_1_1/onnx/sample_input.npy\nModel XML: /home/mount/experiments/exp_1762448723_5984_s96_f22_d3_m1_1_1_1/openvino/model.xml\nModel BIN: /home/mount/experiments/exp_1762448723_5984_s96_f22_d3_m1_1_1_1/openvino/model.bin\n[OK] OpenVINO inference engine imported successfully\nDevice: MYRIAD\nModel XML: /home/mount/experiments/exp_1762448723_5984_s96_f22_d3_m1_1_1_1/openvino/model.xml\nModel BIN: /home/mount/experiments/exp_1762448723_5984_s96_f22_d3_m1_1_1_1/openvino/model.bin\nInput file: /home/mount/experiments/exp_1762448723_5984_s96_f22_d3_m1_1_1_1/onnx/sample_input.npy\nInitializing OpenVINO Runtime Core...\nAvailable devices:\n[E:] [BSL] found 0 ioexpander device\n  ['CPU', 'GNA', 'MYRIAD']\nLoading network...\nInput blob: input\nInput shape: [1, 8, 96, 96]\nOutput blob: Conv_104\nOutput shape: [1, 3, 96, 96]\nLoading network to MYRIAD...\nLoading input data...\nInput data shape: (1, 8, 96, 96)\nInput data type: float32\nRunning inference...\n[OK] Inference completed!\nInference time: 0.175996 seconds\nOutput shape: (1, 3, 96, 96)\nOutput dtype: float32\nOutput range: [-0.668945, 0.857422]\nMyriad inference completed!\n",
+    "stderr": ""
+  },
+  "end_time": "2025-11-06T18:05:30.696806"
+}

exp_1762448723_5984_s96_f22_d3_m1_1_1_1/run_inference.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+# Source OpenVINO environment
+source /opt/intel/openvino/bin/setupvars.sh
+
+PYPATH=/home/mount/scripts/simple_inference.py
+
+echo "Starting Myriad inference..."
+echo "Input: /home/mount/experiments/exp_1762448723_5984_s96_f22_d3_m1_1_1_1/onnx/sample_input.npy"
+echo "Model XML: /home/mount/experiments/exp_1762448723_5984_s96_f22_d3_m1_1_1_1/openvino/model.xml"
+echo "Model BIN: /home/mount/experiments/exp_1762448723_5984_s96_f22_d3_m1_1_1_1/openvino/model.bin"
+
+python3 $PYPATH \
+    --input_filepath /home/mount/experiments/exp_1762448723_5984_s96_f22_d3_m1_1_1_1/onnx/sample_input.npy \
+    --device_name MYRIAD \
+    --model_bin /home/mount/experiments/exp_1762448723_5984_s96_f22_d3_m1_1_1_1/openvino/model.bin \
+    --model_xml /home/mount/experiments/exp_1762448723_5984_s96_f22_d3_m1_1_1_1/openvino/model.xml \
+    --output /home/mount/experiments/exp_1762448723_5984_s96_f22_d3_m1_1_1_1/inference/
+
+echo "Myriad inference completed!"

exp_1762448809_7498_s96_f24_d3_m2_2_2_2/convert_openvino.sh

@@ -0,0 +1,30 @@
+#!/bin/bash
+# Source OpenVINO environment
+source /opt/intel/openvino/bin/setupvars.sh
+
+# OpenVINO Model Optimizer script for ONNX model
+PYPATH=/opt/intel/openvino_2020.3.194/deployment_tools/model_optimizer/mo.py
+ONNX_MODEL=/home/mount/experiments/exp_1762448809_7498_s96_f24_d3_m2_2_2_2/onnx/model.onnx
+OUTPUT_DIR=/home/mount/experiments/exp_1762448809_7498_s96_f24_d3_m2_2_2_2/openvino
+
+echo "Converting ONNX model to OpenVINO IR format..."
+echo "Input model: $ONNX_MODEL"
+echo "Output directory: $OUTPUT_DIR"
+
+# Create output directory if it doesn't exist
+mkdir -p "$OUTPUT_DIR"
+
+python3 $PYPATH \
+    --input_model "$ONNX_MODEL" \
+    --data_type FP16 \
+    --input_shape "[1,8,96,96]" \
+    --mean_values "[0,0,0,0,0,0,0,0]" \
+    --scale_values "[1,1,1,1,1,1,1,1]" \
+    --progress \
+    --stream_output \
+    --output_dir "$OUTPUT_DIR" \
+    --model_name model
+
+echo "OpenVINO conversion completed!"
+echo "Generated files in $OUTPUT_DIR:"
+ls -la "$OUTPUT_DIR/"

exp_1762448809_7498_s96_f24_d3_m2_2_2_2/experiment.log

@@ -0,0 +1,98 @@
+2025-11-06 18:06:49 - INFO - === Model Creation Phase ===
+2025-11-06 18:06:49 - INFO - Creating UNet model with config: {'input_size': 96, 'base_filters': 24, 'depth': 3, 'channel_multipliers': [2, 2, 2, 2], 'n_channels': 8, 'n_classes': 3}
+2025-11-06 18:06:49 - INFO - Model created successfully:
+2025-11-06 18:06:49 - INFO -   Depth: 3
+2025-11-06 18:06:49 - INFO -   Channels per level: [48, 48, 48, 48]
+2025-11-06 18:06:49 - INFO -   Total parameters: 190,851
+2025-11-06 18:06:49 - INFO -   Parameter memory: 0.73 MB
+2025-11-06 18:06:49 - INFO - === Detailed Architecture Analysis ===
+2025-11-06 18:06:49 - INFO - Input vector dimension: 73,728 (8 × 96 × 96)
+2025-11-06 18:06:49 - INFO - Component-wise parameter breakdown:
+2025-11-06 18:06:49 - INFO -   Encoder - encoders.0.channel_proj: 432 parameters
+2025-11-06 18:06:49 - INFO -   Encoder - encoders.0.convnext_block.dwconv: 2,400 parameters
+2025-11-06 18:06:49 - INFO -   Encoder - encoders.0.convnext_block.norm: 96 parameters
+2025-11-06 18:06:49 - INFO -   Encoder - encoders.0.convnext_block.pwconv1: 9,408 parameters
+2025-11-06 18:06:49 - INFO -   Encoder - encoders.0.convnext_block.pwconv2: 9,264 parameters
+2025-11-06 18:06:49 - INFO -   Encoder - encoders.1.convnext_block.dwconv: 2,400 parameters
+2025-11-06 18:06:49 - INFO -   Encoder - encoders.1.convnext_block.norm: 96 parameters
+2025-11-06 18:06:49 - INFO -   Encoder - encoders.1.convnext_block.pwconv1: 9,408 parameters
+2025-11-06 18:06:49 - INFO -   Encoder - encoders.1.convnext_block.pwconv2: 9,264 parameters
+2025-11-06 18:06:49 - INFO -   Encoder - encoders.2.convnext_block.dwconv: 2,400 parameters
+2025-11-06 18:06:49 - INFO -   Encoder - encoders.2.convnext_block.norm: 96 parameters
+2025-11-06 18:06:49 - INFO -   Encoder - encoders.2.convnext_block.pwconv1: 9,408 parameters
+2025-11-06 18:06:49 - INFO -   Encoder - encoders.2.convnext_block.pwconv2: 9,264 parameters
+2025-11-06 18:06:49 - INFO -   Bottleneck - bottleneck.convnext_block.dwconv: 2,400 parameters
+2025-11-06 18:06:49 - INFO -   Bottleneck - bottleneck.convnext_block.norm: 96 parameters
+2025-11-06 18:06:49 - INFO -   Bottleneck - bottleneck.convnext_block.pwconv1: 9,408 parameters
+2025-11-06 18:06:49 - INFO -   Bottleneck - bottleneck.convnext_block.pwconv2: 9,264 parameters
+2025-11-06 18:06:49 - INFO -   Decoder - upsamplers.0: 9,264 parameters
+2025-11-06 18:06:49 - INFO -   Decoder - upsamplers.1: 9,264 parameters
+2025-11-06 18:06:49 - INFO -   Decoder - upsamplers.2: 9,264 parameters
+2025-11-06 18:06:49 - INFO -   Decoder - decoders.0.channel_proj: 4,656 parameters
+2025-11-06 18:06:49 - INFO -   Decoder - decoders.0.convnext_block.dwconv: 2,400 parameters
+2025-11-06 18:06:49 - INFO -   Decoder - decoders.0.convnext_block.norm: 96 parameters
+2025-11-06 18:06:49 - INFO -   Decoder - decoders.0.convnext_block.pwconv1: 9,408 parameters
+2025-11-06 18:06:49 - INFO -   Decoder - decoders.0.convnext_block.pwconv2: 9,264 parameters
+2025-11-06 18:06:49 - INFO -   Decoder - decoders.1.channel_proj: 4,656 parameters
+2025-11-06 18:06:49 - INFO -   Decoder - decoders.1.convnext_block.dwconv: 2,400 parameters
+2025-11-06 18:06:49 - INFO -   Decoder - decoders.1.convnext_block.norm: 96 parameters
+2025-11-06 18:06:49 - INFO -   Decoder - decoders.1.convnext_block.pwconv1: 9,408 parameters
+2025-11-06 18:06:49 - INFO -   Decoder - decoders.1.convnext_block.pwconv2: 9,264 parameters
+2025-11-06 18:06:49 - INFO -   Decoder - decoders.2.channel_proj: 4,656 parameters
+2025-11-06 18:06:49 - INFO -   Decoder - decoders.2.convnext_block.dwconv: 2,400 parameters
+2025-11-06 18:06:49 - INFO -   Decoder - decoders.2.convnext_block.norm: 96 parameters
+2025-11-06 18:06:49 - INFO -   Decoder - decoders.2.convnext_block.pwconv1: 9,408 parameters
+2025-11-06 18:06:49 - INFO -   Decoder - decoders.2.convnext_block.pwconv2: 9,264 parameters
+2025-11-06 18:06:49 - INFO -   Other - final_conv: 147 parameters
+2025-11-06 18:06:49 - INFO - Parameter distribution summary:
+2025-11-06 18:06:49 - INFO -   Encoder parameters: 63,936 (33.6%)
+2025-11-06 18:06:49 - INFO -   Decoder parameters: 105,264 (55.3%)
+2025-11-06 18:06:49 - INFO -   Bottleneck parameters: 21,168 (11.1%)
+2025-11-06 18:06:49 - INFO -   Other parameters: 147 (0.1%)
+2025-11-06 18:06:49 - INFO - Latent space dimensions (feature maps at each level):
+2025-11-06 18:06:49 - INFO -   Level 0: 48 × 96 × 96 = 442,368 elements
+2025-11-06 18:06:49 - INFO -   Level 1: 48 × 48 × 48 = 110,592 elements
+2025-11-06 18:06:49 - INFO -   Level 2: 48 × 24 × 24 = 27,648 elements
+2025-11-06 18:06:49 - INFO -   Level 3: 48 × 12 × 12 = 6,912 elements
+2025-11-06 18:06:49 - INFO - Skip connection dimensions:
+2025-11-06 18:06:49 - INFO -   Skip 0: 48 × 96 × 96 = 442,368 elements
+2025-11-06 18:06:49 - INFO -   Skip 1: 48 × 48 × 48 = 110,592 elements
+2025-11-06 18:06:49 - INFO -   Skip 2: 48 × 24 × 24 = 27,648 elements
+2025-11-06 18:06:49 - INFO - Memory analysis:
+2025-11-06 18:06:49 - INFO -   Peak feature map memory (inference): 2.52 MB
+2025-11-06 18:06:49 - INFO -   Peak feature map memory (training): 5.04 MB (with gradients)
+2025-11-06 18:06:49 - INFO - Output vector dimension: 27,648 (3 × 96 × 96)
+2025-11-06 18:06:49 - INFO - PyTorch model saved to: /home/philab/Desktop/hydranet/experiments/exp_1762448809_7498_s96_f24_d3_m2_2_2_2/pytorch/model.pt
+2025-11-06 18:06:49 - INFO - === ONNX Conversion Phase ===
+2025-11-06 18:06:49 - INFO - === Model Export Diagnostics ===
+2025-11-06 18:06:49 - INFO - PyTorch version: 1.9.0+cu102
+2025-11-06 18:06:49 - INFO - Model parameters: 190,851
+2025-11-06 18:06:49 - INFO - Model memory: 0.73 MB
+2025-11-06 18:06:49 - INFO - Starting ONNX export with opset version 11
+2025-11-06 18:06:49 - INFO - Model input shape: torch.Size([1, 8, 96, 96])
+2025-11-06 18:06:49 - INFO - Model input dtype: torch.float32
+2025-11-06 18:06:49 - INFO - Forward pass successful. Output shape: torch.Size([1, 3, 96, 96])
+2025-11-06 18:06:49 - INFO - Output dtype: torch.float32
+2025-11-06 18:06:49 - INFO - Output value range: [-0.4865, 0.6665]
+2025-11-06 18:06:49 - INFO - Model successfully exported to /home/philab/Desktop/hydranet/experiments/exp_1762448809_7498_s96_f24_d3_m2_2_2_2/onnx/model.onnx
+2025-11-06 18:06:49 - INFO - ONNX model size: 0.74 MB
+2025-11-06 18:06:49 - INFO - Saved dummy input with shape (1, 8, 96, 96) to /home/philab/Desktop/hydranet/experiments/exp_1762448809_7498_s96_f24_d3_m2_2_2_2/onnx/sample_input.npy
+2025-11-06 18:06:49 - INFO - Input data type: float32
+2025-11-06 18:06:49 - INFO - Input value range: [-4.5734, 4.4761]
+2025-11-06 18:06:49 - INFO - === OpenVINO Conversion Phase ===
+2025-11-06 18:06:49 - INFO - Starting OpenVINO conversion in Docker container...
+2025-11-06 18:06:53 - INFO - OpenVINO conversion completed in 4.00 seconds
+2025-11-06 18:06:53 - INFO - OpenVINO model files created:
+2025-11-06 18:06:53 - INFO -   XML file: /home/philab/Desktop/hydranet/experiments/exp_1762448809_7498_s96_f24_d3_m2_2_2_2/openvino/model.xml (0.08 MB)
+2025-11-06 18:06:53 - INFO -   BIN file: /home/philab/Desktop/hydranet/experiments/exp_1762448809_7498_s96_f24_d3_m2_2_2_2/openvino/model.bin (0.36 MB)
+2025-11-06 18:06:53 - INFO - === Myriad Inference Phase ===
+2025-11-06 18:06:53 - INFO - Starting Myriad inference in Docker container...
+2025-11-06 18:06:56 - INFO - Myriad inference completed in 2.77 seconds
+2025-11-06 18:06:56 - INFO - Actual inference time: 0.171749 seconds
+2025-11-06 18:06:56 - INFO - ✅ Complete pipeline executed successfully!
+2025-11-06 18:06:56 - INFO - ✅ Experiment 46 completed successfully
+2025-11-06 18:06:56 - INFO -    Inference time: 0.171749s
+2025-11-06 18:06:56 - INFO - 
+=== Experiment 47/2475 ===
+2025-11-06 18:06:56 - INFO - Experiment ID: exp_1762448816_2408_s96_f24_d3_m1_2_2_2
+2025-11-06 18:06:56 - INFO - Experiment directory: /home/philab/Desktop/hydranet/experiments/exp_1762448816_2408_s96_f24_d3_m1_2_2_2

exp_1762448809_7498_s96_f24_d3_m2_2_2_2/model_info.json

@@ -0,0 +1,138 @@
+{
+  "config": {
+    "input_size": 96,
+    "base_filters": 24,
+    "depth": 3,
+    "channel_multipliers": [
+      2,
+      2,
+      2,
+      2
+    ],
+    "n_channels": 8,
+    "n_classes": 3
+  },
+  "model_info": {
+    "depth": 3,
+    "channels_per_level": [
+      48,
+      48,
+      48,
+      48
+    ],
+    "channel_multipliers": [
+      2,
+      2,
+      2,
+      2
+    ],
+    "total_parameters": 190851,
+    "trainable_parameters": 190851,
+    "model_size_mb": 0.7280387878417969
+  },
+  "architecture_stats": {
+    "input_dimension": 73728,
+    "output_dimension": 27648,
+    "parameter_distribution": {
+      "encoder_params": 63936,
+      "decoder_params": 105264,
+      "bottleneck_params": 21168,
+      "other_params": 147,
+      "total_params": 190515,
+      "encoder_percentage": 33.55956223919377,
+      "decoder_percentage": 55.252342335249196,
+      "bottleneck_percentage": 11.110936146760098,
+      "other_percentage": 0.07715927879694512
+    },
+    "component_breakdown": {
+      "Encoder - encoders.0.channel_proj": 432,
+      "Encoder - encoders.0.convnext_block.dwconv": 2400,
+      "Encoder - encoders.0.convnext_block.norm": 96,
+      "Encoder - encoders.0.convnext_block.pwconv1": 9408,
+      "Encoder - encoders.0.convnext_block.pwconv2": 9264,
+      "Encoder - encoders.1.convnext_block.dwconv": 2400,
+      "Encoder - encoders.1.convnext_block.norm": 96,
+      "Encoder - encoders.1.convnext_block.pwconv1": 9408,
+      "Encoder - encoders.1.convnext_block.pwconv2": 9264,
+      "Encoder - encoders.2.convnext_block.dwconv": 2400,
+      "Encoder - encoders.2.convnext_block.norm": 96,
+      "Encoder - encoders.2.convnext_block.pwconv1": 9408,
+      "Encoder - encoders.2.convnext_block.pwconv2": 9264,
+      "Bottleneck - bottleneck.convnext_block.dwconv": 2400,
+      "Bottleneck - bottleneck.convnext_block.norm": 96,
+      "Bottleneck - bottleneck.convnext_block.pwconv1": 9408,
+      "Bottleneck - bottleneck.convnext_block.pwconv2": 9264,
+      "Decoder - upsamplers.0": 9264,
+      "Decoder - upsamplers.1": 9264,
+      "Decoder - upsamplers.2": 9264,
+      "Decoder - decoders.0.channel_proj": 4656,
+      "Decoder - decoders.0.convnext_block.dwconv": 2400,
+      "Decoder - decoders.0.convnext_block.norm": 96,
+      "Decoder - decoders.0.convnext_block.pwconv1": 9408,
+      "Decoder - decoders.0.convnext_block.pwconv2": 9264,
+      "Decoder - decoders.1.channel_proj": 4656,
+      "Decoder - decoders.1.convnext_block.dwconv": 2400,
+      "Decoder - decoders.1.convnext_block.norm": 96,
+      "Decoder - decoders.1.convnext_block.pwconv1": 9408,
+      "Decoder - decoders.1.convnext_block.pwconv2": 9264,
+      "Decoder - decoders.2.channel_proj": 4656,
+      "Decoder - decoders.2.convnext_block.dwconv": 2400,
+      "Decoder - decoders.2.convnext_block.norm": 96,
+      "Decoder - decoders.2.convnext_block.pwconv1": 9408,
+      "Decoder - decoders.2.convnext_block.pwconv2": 9264,
+      "Other - final_conv": 147
+    },
+    "latent_dimensions": {
+      "Level_0": {
+        "channels": 48,
+        "height": 96,
+        "width": 96,
+        "total_elements": 442368
+      },
+      "Level_1": {
+        "channels": 48,
+        "height": 48,
+        "width": 48,
+        "total_elements": 110592
+      },
+      "Level_2": {
+        "channels": 48,
+        "height": 24,
+        "width": 24,
+        "total_elements": 27648
+      },
+      "Level_3": {
+        "channels": 48,
+        "height": 12,
+        "width": 12,
+        "total_elements": 6912
+      }
+    },
+    "skip_dimensions": {
+      "Skip_0": {
+        "channels": 48,
+        "height": 96,
+        "width": 96,
+        "total_elements": 442368
+      },
+      "Skip_1": {
+        "channels": 48,
+        "height": 48,
+        "width": 48,
+        "total_elements": 110592
+      },
+      "Skip_2": {
+        "channels": 48,
+        "height": 24,
+        "width": 24,
+        "total_elements": 27648
+      }
+    },
+    "memory_analysis": {
+      "peak_memory_inference_mb": 2.5224609375,
+      "peak_memory_training_mb": 5.044921875,
+      "peak_elements": 661248
+    }
+  },
+  "timestamp": "2025-11-06T18:06:49.610394"
+}

exp_1762448809_7498_s96_f24_d3_m2_2_2_2/pipeline_results.json

@@ -0,0 +1,138 @@
+{
+  "experiment_id": "exp_1762448809_5070_s96_f24_d3_m2_2_2_2",
+  "config": {
+    "input_size": 96,
+    "base_filters": 24,
+    "depth": 3,
+    "channel_multipliers": [
+      2,
+      2,
+      2,
+      2
+    ],
+    "n_channels": 8,
+    "n_classes": 3
+  },
+  "start_time": "2025-11-06T18:06:49.604051",
+  "success": true,
+  "timings": {
+    "onnx_conversion": 0.2828185979742557,
+    "openvino_conversion": 3.9952829838730395,
+    "inference_total": 2.769099799916148,
+    "inference_actual": 0.171749,
+    "total": 7.058676311047748
+  },
+  "errors": [],
+  "architecture_stats": {
+    "input_dimension": 73728,
+    "output_dimension": 27648,
+    "parameter_distribution": {
+      "encoder_params": 63936,
+      "decoder_params": 105264,
+      "bottleneck_params": 21168,
+      "other_params": 147,
+      "total_params": 190515,
+      "encoder_percentage": 33.55956223919377,
+      "decoder_percentage": 55.252342335249196,
+      "bottleneck_percentage": 11.110936146760098,
+      "other_percentage": 0.07715927879694512
+    },
+    "component_breakdown": {
+      "Encoder - encoders.0.channel_proj": 432,
+      "Encoder - encoders.0.convnext_block.dwconv": 2400,
+      "Encoder - encoders.0.convnext_block.norm": 96,
+      "Encoder - encoders.0.convnext_block.pwconv1": 9408,
+      "Encoder - encoders.0.convnext_block.pwconv2": 9264,
+      "Encoder - encoders.1.convnext_block.dwconv": 2400,
+      "Encoder - encoders.1.convnext_block.norm": 96,
+      "Encoder - encoders.1.convnext_block.pwconv1": 9408,
+      "Encoder - encoders.1.convnext_block.pwconv2": 9264,
+      "Encoder - encoders.2.convnext_block.dwconv": 2400,
+      "Encoder - encoders.2.convnext_block.norm": 96,
+      "Encoder - encoders.2.convnext_block.pwconv1": 9408,
+      "Encoder - encoders.2.convnext_block.pwconv2": 9264,
+      "Bottleneck - bottleneck.convnext_block.dwconv": 2400,
+      "Bottleneck - bottleneck.convnext_block.norm": 96,
+      "Bottleneck - bottleneck.convnext_block.pwconv1": 9408,
+      "Bottleneck - bottleneck.convnext_block.pwconv2": 9264,
+      "Decoder - upsamplers.0": 9264,
+      "Decoder - upsamplers.1": 9264,
+      "Decoder - upsamplers.2": 9264,
+      "Decoder - decoders.0.channel_proj": 4656,
+      "Decoder - decoders.0.convnext_block.dwconv": 2400,
+      "Decoder - decoders.0.convnext_block.norm": 96,
+      "Decoder - decoders.0.convnext_block.pwconv1": 9408,
+      "Decoder - decoders.0.convnext_block.pwconv2": 9264,
+      "Decoder - decoders.1.channel_proj": 4656,
+      "Decoder - decoders.1.convnext_block.dwconv": 2400,
+      "Decoder - decoders.1.convnext_block.norm": 96,
+      "Decoder - decoders.1.convnext_block.pwconv1": 9408,
+      "Decoder - decoders.1.convnext_block.pwconv2": 9264,
+      "Decoder - decoders.2.channel_proj": 4656,
+      "Decoder - decoders.2.convnext_block.dwconv": 2400,
+      "Decoder - decoders.2.convnext_block.norm": 96,
+      "Decoder - decoders.2.convnext_block.pwconv1": 9408,
+      "Decoder - decoders.2.convnext_block.pwconv2": 9264,
+      "Other - final_conv": 147
+    },
+    "latent_dimensions": {
+      "Level_0": {
+        "channels": 48,
+        "height": 96,
+        "width": 96,
+        "total_elements": 442368
+      },
+      "Level_1": {
+        "channels": 48,
+        "height": 48,
+        "width": 48,
+        "total_elements": 110592
+      },
+      "Level_2": {
+        "channels": 48,
+        "height": 24,
+        "width": 24,
+        "total_elements": 27648
+      },
+      "Level_3": {
+        "channels": 48,
+        "height": 12,
+        "width": 12,
+        "total_elements": 6912
+      }
+    },
+    "skip_dimensions": {
+      "Skip_0": {
+        "channels": 48,
+        "height": 96,
+        "width": 96,
+        "total_elements": 442368
+      },
+      "Skip_1": {
+        "channels": 48,
+        "height": 48,
+        "width": 48,
+        "total_elements": 110592
+      },
+      "Skip_2": {
+        "channels": 48,
+        "height": 24,
+        "width": 24,
+        "total_elements": 27648
+      }
+    },
+    "memory_analysis": {
+      "peak_memory_inference_mb": 2.5224609375,
+      "peak_memory_training_mb": 5.044921875,
+      "peak_elements": 661248
+    }
+  },
+  "inference_results": {
+    "success": true,
+    "total_time": 2.769099799916148,
+    "inference_time": 0.171749,
+    "stdout": "[setupvars.sh] OpenVINO environment initialized\nStarting Myriad inference...\nInput: /home/mount/experiments/exp_1762448809_7498_s96_f24_d3_m2_2_2_2/onnx/sample_input.npy\nModel XML: /home/mount/experiments/exp_1762448809_7498_s96_f24_d3_m2_2_2_2/openvino/model.xml\nModel BIN: /home/mount/experiments/exp_1762448809_7498_s96_f24_d3_m2_2_2_2/openvino/model.bin\n[OK] OpenVINO inference engine imported successfully\nDevice: MYRIAD\nModel XML: /home/mount/experiments/exp_1762448809_7498_s96_f24_d3_m2_2_2_2/openvino/model.xml\nModel BIN: /home/mount/experiments/exp_1762448809_7498_s96_f24_d3_m2_2_2_2/openvino/model.bin\nInput file: /home/mount/experiments/exp_1762448809_7498_s96_f24_d3_m2_2_2_2/onnx/sample_input.npy\nInitializing OpenVINO Runtime Core...\nAvailable devices:\n[E:] [BSL] found 0 ioexpander device\n  ['CPU', 'GNA', 'MYRIAD']\nLoading network...\nInput blob: input\nInput shape: [1, 8, 96, 96]\nOutput blob: Conv_104\nOutput shape: [1, 3, 96, 96]\nLoading network to MYRIAD...\nLoading input data...\nInput data shape: (1, 8, 96, 96)\nInput data type: float32\nRunning inference...\n[OK] Inference completed!\nInference time: 0.171749 seconds\nOutput shape: (1, 3, 96, 96)\nOutput dtype: float32\nOutput range: [-0.486084, 0.666016]\nMyriad inference completed!\n",
+    "stderr": ""
+  },
+  "end_time": "2025-11-06T18:06:56.662717"
+}

exp_1762448809_7498_s96_f24_d3_m2_2_2_2/run_inference.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+# Source OpenVINO environment
+source /opt/intel/openvino/bin/setupvars.sh
+
+PYPATH=/home/mount/scripts/simple_inference.py
+
+echo "Starting Myriad inference..."
+echo "Input: /home/mount/experiments/exp_1762448809_7498_s96_f24_d3_m2_2_2_2/onnx/sample_input.npy"
+echo "Model XML: /home/mount/experiments/exp_1762448809_7498_s96_f24_d3_m2_2_2_2/openvino/model.xml"
+echo "Model BIN: /home/mount/experiments/exp_1762448809_7498_s96_f24_d3_m2_2_2_2/openvino/model.bin"
+
+python3 $PYPATH \
+    --input_filepath /home/mount/experiments/exp_1762448809_7498_s96_f24_d3_m2_2_2_2/onnx/sample_input.npy \
+    --device_name MYRIAD \
+    --model_bin /home/mount/experiments/exp_1762448809_7498_s96_f24_d3_m2_2_2_2/openvino/model.bin \
+    --model_xml /home/mount/experiments/exp_1762448809_7498_s96_f24_d3_m2_2_2_2/openvino/model.xml \
+    --output /home/mount/experiments/exp_1762448809_7498_s96_f24_d3_m2_2_2_2/inference/
+
+echo "Myriad inference completed!"

exp_1762448858_8082_s96_f24_d3_m1_2_3_4/convert_openvino.sh

@@ -0,0 +1,30 @@
+#!/bin/bash
+# Source OpenVINO environment
+source /opt/intel/openvino/bin/setupvars.sh
+
+# OpenVINO Model Optimizer script for ONNX model
+PYPATH=/opt/intel/openvino_2020.3.194/deployment_tools/model_optimizer/mo.py
+ONNX_MODEL=/home/mount/experiments/exp_1762448858_8082_s96_f24_d3_m1_2_3_4/onnx/model.onnx
+OUTPUT_DIR=/home/mount/experiments/exp_1762448858_8082_s96_f24_d3_m1_2_3_4/openvino
+
+echo "Converting ONNX model to OpenVINO IR format..."
+echo "Input model: $ONNX_MODEL"
+echo "Output directory: $OUTPUT_DIR"
+
+# Create output directory if it doesn't exist
+mkdir -p "$OUTPUT_DIR"
+
+python3 $PYPATH \
+    --input_model "$ONNX_MODEL" \
+    --data_type FP16 \
+    --input_shape "[1,8,96,96]" \
+    --mean_values "[0,0,0,0,0,0,0,0]" \
+    --scale_values "[1,1,1,1,1,1,1,1]" \
+    --progress \
+    --stream_output \
+    --output_dir "$OUTPUT_DIR" \
+    --model_name model
+
+echo "OpenVINO conversion completed!"
+echo "Generated files in $OUTPUT_DIR:"
+ls -la "$OUTPUT_DIR/"

exp_1762448858_8082_s96_f24_d3_m1_2_3_4/experiment.log

@@ -0,0 +1,101 @@
+2025-11-06 18:07:38 - INFO - === Model Creation Phase ===
+2025-11-06 18:07:38 - INFO - Creating UNet model with config: {'input_size': 96, 'base_filters': 24, 'depth': 3, 'channel_multipliers': [1, 2, 3, 4], 'n_channels': 8, 'n_classes': 3}
+2025-11-06 18:07:38 - INFO - Model created successfully:
+2025-11-06 18:07:38 - INFO -   Depth: 3
+2025-11-06 18:07:38 - INFO -   Channels per level: [24, 48, 72, 96]
+2025-11-06 18:07:38 - INFO -   Total parameters: 323,811
+2025-11-06 18:07:38 - INFO -   Parameter memory: 1.24 MB
+2025-11-06 18:07:38 - INFO - === Detailed Architecture Analysis ===
+2025-11-06 18:07:38 - INFO - Input vector dimension: 73,728 (8 × 96 × 96)
+2025-11-06 18:07:38 - INFO - Component-wise parameter breakdown:
+2025-11-06 18:07:38 - INFO -   Encoder - encoders.0.channel_proj: 216 parameters
+2025-11-06 18:07:38 - INFO -   Encoder - encoders.0.convnext_block.dwconv: 1,200 parameters
+2025-11-06 18:07:38 - INFO -   Encoder - encoders.0.convnext_block.norm: 48 parameters
+2025-11-06 18:07:38 - INFO -   Encoder - encoders.0.convnext_block.pwconv1: 2,400 parameters
+2025-11-06 18:07:38 - INFO -   Encoder - encoders.0.convnext_block.pwconv2: 2,328 parameters
+2025-11-06 18:07:38 - INFO -   Encoder - encoders.1.channel_proj: 1,200 parameters
+2025-11-06 18:07:38 - INFO -   Encoder - encoders.1.convnext_block.dwconv: 2,400 parameters
+2025-11-06 18:07:38 - INFO -   Encoder - encoders.1.convnext_block.norm: 96 parameters
+2025-11-06 18:07:38 - INFO -   Encoder - encoders.1.convnext_block.pwconv1: 9,408 parameters
+2025-11-06 18:07:38 - INFO -   Encoder - encoders.1.convnext_block.pwconv2: 9,264 parameters
+2025-11-06 18:07:38 - INFO -   Encoder - encoders.2.channel_proj: 3,528 parameters
+2025-11-06 18:07:38 - INFO -   Encoder - encoders.2.convnext_block.dwconv: 3,600 parameters
+2025-11-06 18:07:38 - INFO -   Encoder - encoders.2.convnext_block.norm: 144 parameters
+2025-11-06 18:07:38 - INFO -   Encoder - encoders.2.convnext_block.pwconv1: 21,024 parameters
+2025-11-06 18:07:38 - INFO -   Encoder - encoders.2.convnext_block.pwconv2: 20,808 parameters
+2025-11-06 18:07:38 - INFO -   Bottleneck - bottleneck.channel_proj: 7,008 parameters
+2025-11-06 18:07:38 - INFO -   Bottleneck - bottleneck.convnext_block.dwconv: 4,800 parameters
+2025-11-06 18:07:38 - INFO -   Bottleneck - bottleneck.convnext_block.norm: 192 parameters
+2025-11-06 18:07:38 - INFO -   Bottleneck - bottleneck.convnext_block.pwconv1: 37,248 parameters
+2025-11-06 18:07:38 - INFO -   Bottleneck - bottleneck.convnext_block.pwconv2: 36,960 parameters
+2025-11-06 18:07:38 - INFO -   Decoder - upsamplers.0: 36,960 parameters
+2025-11-06 18:07:38 - INFO -   Decoder - upsamplers.1: 20,808 parameters
+2025-11-06 18:07:38 - INFO -   Decoder - upsamplers.2: 9,264 parameters
+2025-11-06 18:07:38 - INFO -   Decoder - decoders.0.channel_proj: 12,168 parameters
+2025-11-06 18:07:38 - INFO -   Decoder - decoders.0.convnext_block.dwconv: 3,600 parameters
+2025-11-06 18:07:38 - INFO -   Decoder - decoders.0.convnext_block.norm: 144 parameters
+2025-11-06 18:07:38 - INFO -   Decoder - decoders.0.convnext_block.pwconv1: 21,024 parameters
+2025-11-06 18:07:38 - INFO -   Decoder - decoders.0.convnext_block.pwconv2: 20,808 parameters
+2025-11-06 18:07:38 - INFO -   Decoder - decoders.1.channel_proj: 5,808 parameters
+2025-11-06 18:07:38 - INFO -   Decoder - decoders.1.convnext_block.dwconv: 2,400 parameters
+2025-11-06 18:07:38 - INFO -   Decoder - decoders.1.convnext_block.norm: 96 parameters
+2025-11-06 18:07:38 - INFO -   Decoder - decoders.1.convnext_block.pwconv1: 9,408 parameters
+2025-11-06 18:07:38 - INFO -   Decoder - decoders.1.convnext_block.pwconv2: 9,264 parameters
+2025-11-06 18:07:38 - INFO -   Decoder - decoders.2.channel_proj: 1,752 parameters
+2025-11-06 18:07:38 - INFO -   Decoder - decoders.2.convnext_block.dwconv: 1,200 parameters
+2025-11-06 18:07:38 - INFO -   Decoder - decoders.2.convnext_block.norm: 48 parameters
+2025-11-06 18:07:38 - INFO -   Decoder - decoders.2.convnext_block.pwconv1: 2,400 parameters
+2025-11-06 18:07:38 - INFO -   Decoder - decoders.2.convnext_block.pwconv2: 2,328 parameters
+2025-11-06 18:07:38 - INFO -   Other - final_conv: 75 parameters
+2025-11-06 18:07:38 - INFO - Parameter distribution summary:
+2025-11-06 18:07:38 - INFO -   Encoder parameters: 77,664 (24.0%)
+2025-11-06 18:07:38 - INFO -   Decoder parameters: 159,480 (49.3%)
+2025-11-06 18:07:38 - INFO -   Bottleneck parameters: 86,208 (26.7%)
+2025-11-06 18:07:38 - INFO -   Other parameters: 75 (0.0%)
+2025-11-06 18:07:38 - INFO - Latent space dimensions (feature maps at each level):
+2025-11-06 18:07:38 - INFO -   Level 0: 24 × 96 × 96 = 221,184 elements
+2025-11-06 18:07:38 - INFO -   Level 1: 48 × 48 × 48 = 110,592 elements
+2025-11-06 18:07:38 - INFO -   Level 2: 72 × 24 × 24 = 41,472 elements
+2025-11-06 18:07:38 - INFO -   Level 3: 96 × 12 × 12 = 13,824 elements
+2025-11-06 18:07:38 - INFO - Skip connection dimensions:
+2025-11-06 18:07:38 - INFO -   Skip 0: 24 × 96 × 96 = 221,184 elements
+2025-11-06 18:07:38 - INFO -   Skip 1: 48 × 48 × 48 = 110,592 elements
+2025-11-06 18:07:38 - INFO -   Skip 2: 72 × 24 × 24 = 41,472 elements
+2025-11-06 18:07:38 - INFO - Memory analysis:
+2025-11-06 18:07:38 - INFO -   Peak feature map memory (inference): 1.76 MB
+2025-11-06 18:07:38 - INFO -   Peak feature map memory (training): 3.52 MB (with gradients)
+2025-11-06 18:07:38 - INFO - Output vector dimension: 27,648 (3 × 96 × 96)
+2025-11-06 18:07:38 - INFO - PyTorch model saved to: /home/philab/Desktop/hydranet/experiments/exp_1762448858_8082_s96_f24_d3_m1_2_3_4/pytorch/model.pt
+2025-11-06 18:07:38 - INFO - === ONNX Conversion Phase ===
+2025-11-06 18:07:38 - INFO - === Model Export Diagnostics ===
+2025-11-06 18:07:38 - INFO - PyTorch version: 1.9.0+cu102
+2025-11-06 18:07:38 - INFO - Model parameters: 323,811
+2025-11-06 18:07:38 - INFO - Model memory: 1.24 MB
+2025-11-06 18:07:38 - INFO - Starting ONNX export with opset version 11
+2025-11-06 18:07:38 - INFO - Model input shape: torch.Size([1, 8, 96, 96])
+2025-11-06 18:07:38 - INFO - Model input dtype: torch.float32
+2025-11-06 18:07:38 - INFO - Forward pass successful. Output shape: torch.Size([1, 3, 96, 96])
+2025-11-06 18:07:38 - INFO - Output dtype: torch.float32
+2025-11-06 18:07:38 - INFO - Output value range: [-0.4544, 0.5538]
+2025-11-06 18:07:39 - INFO - Model successfully exported to /home/philab/Desktop/hydranet/experiments/exp_1762448858_8082_s96_f24_d3_m1_2_3_4/onnx/model.onnx
+2025-11-06 18:07:39 - INFO - ONNX model size: 1.24 MB
+2025-11-06 18:07:39 - INFO - Saved dummy input with shape (1, 8, 96, 96) to /home/philab/Desktop/hydranet/experiments/exp_1762448858_8082_s96_f24_d3_m1_2_3_4/onnx/sample_input.npy
+2025-11-06 18:07:39 - INFO - Input data type: float32
+2025-11-06 18:07:39 - INFO - Input value range: [-4.4298, 4.3074]
+2025-11-06 18:07:39 - INFO - === OpenVINO Conversion Phase ===
+2025-11-06 18:07:39 - INFO - Starting OpenVINO conversion in Docker container...
+2025-11-06 18:07:43 - INFO - OpenVINO conversion completed in 4.16 seconds
+2025-11-06 18:07:43 - INFO - OpenVINO model files created:
+2025-11-06 18:07:43 - INFO -   XML file: /home/philab/Desktop/hydranet/experiments/exp_1762448858_8082_s96_f24_d3_m1_2_3_4/openvino/model.xml (0.09 MB)
+2025-11-06 18:07:43 - INFO -   BIN file: /home/philab/Desktop/hydranet/experiments/exp_1762448858_8082_s96_f24_d3_m1_2_3_4/openvino/model.bin (0.62 MB)
+2025-11-06 18:07:43 - INFO - === Myriad Inference Phase ===
+2025-11-06 18:07:43 - INFO - Starting Myriad inference in Docker container...
+2025-11-06 18:07:46 - INFO - Myriad inference completed in 2.71 seconds
+2025-11-06 18:07:46 - INFO - Actual inference time: 0.123546 seconds
+2025-11-06 18:07:46 - INFO - ✅ Complete pipeline executed successfully!
+2025-11-06 18:07:46 - INFO - ✅ Experiment 53 completed successfully
+2025-11-06 18:07:46 - INFO -    Inference time: 0.123546s
+2025-11-06 18:07:46 - INFO - 
+=== Experiment 54/2475 ===
+2025-11-06 18:07:46 - INFO - Experiment ID: exp_1762448866_1287_s96_f24_d3_m1_3_3_4
+2025-11-06 18:07:46 - INFO - Experiment directory: /home/philab/Desktop/hydranet/experiments/exp_1762448866_1287_s96_f24_d3_m1_3_3_4

exp_1762448858_8082_s96_f24_d3_m1_2_3_4/model_info.json

@@ -0,0 +1,141 @@
+{
+  "config": {
+    "input_size": 96,
+    "base_filters": 24,
+    "depth": 3,
+    "channel_multipliers": [
+      1,
+      2,
+      3,
+      4
+    ],
+    "n_channels": 8,
+    "n_classes": 3
+  },
+  "model_info": {
+    "depth": 3,
+    "channels_per_level": [
+      24,
+      48,
+      72,
+      96
+    ],
+    "channel_multipliers": [
+      1,
+      2,
+      3,
+      4
+    ],
+    "total_parameters": 323811,
+    "trainable_parameters": 323811,
+    "model_size_mb": 1.2352409362792969
+  },
+  "architecture_stats": {
+    "input_dimension": 73728,
+    "output_dimension": 27648,
+    "parameter_distribution": {
+      "encoder_params": 77664,
+      "decoder_params": 159480,
+      "bottleneck_params": 86208,
+      "other_params": 75,
+      "total_params": 323427,
+      "encoder_percentage": 24.01283751820349,
+      "decoder_percentage": 49.30942685675593,
+      "bottleneck_percentage": 26.654546466436013,
+      "other_percentage": 0.02318915860456919
+    },
+    "component_breakdown": {
+      "Encoder - encoders.0.channel_proj": 216,
+      "Encoder - encoders.0.convnext_block.dwconv": 1200,
+      "Encoder - encoders.0.convnext_block.norm": 48,
+      "Encoder - encoders.0.convnext_block.pwconv1": 2400,
+      "Encoder - encoders.0.convnext_block.pwconv2": 2328,
+      "Encoder - encoders.1.channel_proj": 1200,
+      "Encoder - encoders.1.convnext_block.dwconv": 2400,
+      "Encoder - encoders.1.convnext_block.norm": 96,
+      "Encoder - encoders.1.convnext_block.pwconv1": 9408,
+      "Encoder - encoders.1.convnext_block.pwconv2": 9264,
+      "Encoder - encoders.2.channel_proj": 3528,
+      "Encoder - encoders.2.convnext_block.dwconv": 3600,
+      "Encoder - encoders.2.convnext_block.norm": 144,
+      "Encoder - encoders.2.convnext_block.pwconv1": 21024,
+      "Encoder - encoders.2.convnext_block.pwconv2": 20808,
+      "Bottleneck - bottleneck.channel_proj": 7008,
+      "Bottleneck - bottleneck.convnext_block.dwconv": 4800,
+      "Bottleneck - bottleneck.convnext_block.norm": 192,
+      "Bottleneck - bottleneck.convnext_block.pwconv1": 37248,
+      "Bottleneck - bottleneck.convnext_block.pwconv2": 36960,
+      "Decoder - upsamplers.0": 36960,
+      "Decoder - upsamplers.1": 20808,
+      "Decoder - upsamplers.2": 9264,
+      "Decoder - decoders.0.channel_proj": 12168,
+      "Decoder - decoders.0.convnext_block.dwconv": 3600,
+      "Decoder - decoders.0.convnext_block.norm": 144,
+      "Decoder - decoders.0.convnext_block.pwconv1": 21024,
+      "Decoder - decoders.0.convnext_block.pwconv2": 20808,
+      "Decoder - decoders.1.channel_proj": 5808,
+      "Decoder - decoders.1.convnext_block.dwconv": 2400,
+      "Decoder - decoders.1.convnext_block.norm": 96,
+      "Decoder - decoders.1.convnext_block.pwconv1": 9408,
+      "Decoder - decoders.1.convnext_block.pwconv2": 9264,
+      "Decoder - decoders.2.channel_proj": 1752,
+      "Decoder - decoders.2.convnext_block.dwconv": 1200,
+      "Decoder - decoders.2.convnext_block.norm": 48,
+      "Decoder - decoders.2.convnext_block.pwconv1": 2400,
+      "Decoder - decoders.2.convnext_block.pwconv2": 2328,
+      "Other - final_conv": 75
+    },
+    "latent_dimensions": {
+      "Level_0": {
+        "channels": 24,
+        "height": 96,
+        "width": 96,
+        "total_elements": 221184
+      },
+      "Level_1": {
+        "channels": 48,
+        "height": 48,
+        "width": 48,
+        "total_elements": 110592
+      },
+      "Level_2": {
+        "channels": 72,
+        "height": 24,
+        "width": 24,
+        "total_elements": 41472
+      },
+      "Level_3": {
+        "channels": 96,
+        "height": 12,
+        "width": 12,
+        "total_elements": 13824
+      }
+    },
+    "skip_dimensions": {
+      "Skip_0": {
+        "channels": 24,
+        "height": 96,
+        "width": 96,
+        "total_elements": 221184
+      },
+      "Skip_1": {
+        "channels": 48,
+        "height": 48,
+        "width": 48,
+        "total_elements": 110592
+      },
+      "Skip_2": {
+        "channels": 72,
+        "height": 24,
+        "width": 24,
+        "total_elements": 41472
+      }
+    },
+    "memory_analysis": {
+      "peak_memory_inference_mb": 1.7578125,
+      "peak_memory_training_mb": 3.515625,
+      "peak_elements": 460800
+    }
+  },
+  "timestamp": "2025-11-06T18:07:38.908492"
+}

exp_1762448858_8082_s96_f24_d3_m1_2_3_4/pipeline_results.json

@@ -0,0 +1,141 @@
+{
+  "experiment_id": "exp_1762448858_7941_s96_f24_d3_m1_2_3_4",
+  "config": {
+    "input_size": 96,
+    "base_filters": 24,
+    "depth": 3,
+    "channel_multipliers": [
+      1,
+      2,
+      3,
+      4
+    ],
+    "n_channels": 8,
+    "n_classes": 3
+  },
+  "start_time": "2025-11-06T18:07:38.900499",
+  "success": true,
+  "timings": {
+    "onnx_conversion": 0.2930171131156385,
+    "openvino_conversion": 4.163721205899492,
+    "inference_total": 2.705671763047576,
+    "inference_actual": 0.123546,
+    "total": 7.187887142878026
+  },
+  "errors": [],
+  "architecture_stats": {
+    "input_dimension": 73728,
+    "output_dimension": 27648,
+    "parameter_distribution": {
+      "encoder_params": 77664,
+      "decoder_params": 159480,
+      "bottleneck_params": 86208,
+      "other_params": 75,
+      "total_params": 323427,
+      "encoder_percentage": 24.01283751820349,
+      "decoder_percentage": 49.30942685675593,
+      "bottleneck_percentage": 26.654546466436013,
+      "other_percentage": 0.02318915860456919
+    },
+    "component_breakdown": {
+      "Encoder - encoders.0.channel_proj": 216,
+      "Encoder - encoders.0.convnext_block.dwconv": 1200,
+      "Encoder - encoders.0.convnext_block.norm": 48,
+      "Encoder - encoders.0.convnext_block.pwconv1": 2400,
+      "Encoder - encoders.0.convnext_block.pwconv2": 2328,
+      "Encoder - encoders.1.channel_proj": 1200,
+      "Encoder - encoders.1.convnext_block.dwconv": 2400,
+      "Encoder - encoders.1.convnext_block.norm": 96,
+      "Encoder - encoders.1.convnext_block.pwconv1": 9408,
+      "Encoder - encoders.1.convnext_block.pwconv2": 9264,
+      "Encoder - encoders.2.channel_proj": 3528,
+      "Encoder - encoders.2.convnext_block.dwconv": 3600,
+      "Encoder - encoders.2.convnext_block.norm": 144,
+      "Encoder - encoders.2.convnext_block.pwconv1": 21024,
+      "Encoder - encoders.2.convnext_block.pwconv2": 20808,
+      "Bottleneck - bottleneck.channel_proj": 7008,
+      "Bottleneck - bottleneck.convnext_block.dwconv": 4800,
+      "Bottleneck - bottleneck.convnext_block.norm": 192,
+      "Bottleneck - bottleneck.convnext_block.pwconv1": 37248,
+      "Bottleneck - bottleneck.convnext_block.pwconv2": 36960,
+      "Decoder - upsamplers.0": 36960,
+      "Decoder - upsamplers.1": 20808,
+      "Decoder - upsamplers.2": 9264,
+      "Decoder - decoders.0.channel_proj": 12168,
+      "Decoder - decoders.0.convnext_block.dwconv": 3600,
+      "Decoder - decoders.0.convnext_block.norm": 144,
+      "Decoder - decoders.0.convnext_block.pwconv1": 21024,
+      "Decoder - decoders.0.convnext_block.pwconv2": 20808,
+      "Decoder - decoders.1.channel_proj": 5808,
+      "Decoder - decoders.1.convnext_block.dwconv": 2400,
+      "Decoder - decoders.1.convnext_block.norm": 96,
+      "Decoder - decoders.1.convnext_block.pwconv1": 9408,
+      "Decoder - decoders.1.convnext_block.pwconv2": 9264,
+      "Decoder - decoders.2.channel_proj": 1752,
+      "Decoder - decoders.2.convnext_block.dwconv": 1200,
+      "Decoder - decoders.2.convnext_block.norm": 48,
+      "Decoder - decoders.2.convnext_block.pwconv1": 2400,
+      "Decoder - decoders.2.convnext_block.pwconv2": 2328,
+      "Other - final_conv": 75
+    },
+    "latent_dimensions": {
+      "Level_0": {
+        "channels": 24,
+        "height": 96,
+        "width": 96,
+        "total_elements": 221184
+      },
+      "Level_1": {
+        "channels": 48,
+        "height": 48,
+        "width": 48,
+        "total_elements": 110592
+      },
+      "Level_2": {
+        "channels": 72,
+        "height": 24,
+        "width": 24,
+        "total_elements": 41472
+      },
+      "Level_3": {
+        "channels": 96,
+        "height": 12,
+        "width": 12,
+        "total_elements": 13824
+      }
+    },
+    "skip_dimensions": {
+      "Skip_0": {
+        "channels": 24,
+        "height": 96,
+        "width": 96,
+        "total_elements": 221184
+      },
+      "Skip_1": {
+        "channels": 48,
+        "height": 48,
+        "width": 48,
+        "total_elements": 110592
+      },
+      "Skip_2": {
+        "channels": 72,
+        "height": 24,
+        "width": 24,
+        "total_elements": 41472
+      }
+    },
+    "memory_analysis": {
+      "peak_memory_inference_mb": 1.7578125,
+      "peak_memory_training_mb": 3.515625,
+      "peak_elements": 460800
+    }
+  },
+  "inference_results": {
+    "success": true,
+    "total_time": 2.705671763047576,
+    "inference_time": 0.123546,
+    "stdout": "[setupvars.sh] OpenVINO environment initialized\nStarting Myriad inference...\nInput: /home/mount/experiments/exp_1762448858_8082_s96_f24_d3_m1_2_3_4/onnx/sample_input.npy\nModel XML: /home/mount/experiments/exp_1762448858_8082_s96_f24_d3_m1_2_3_4/openvino/model.xml\nModel BIN: /home/mount/experiments/exp_1762448858_8082_s96_f24_d3_m1_2_3_4/openvino/model.bin\n[OK] OpenVINO inference engine imported successfully\nDevice: MYRIAD\nModel XML: /home/mount/experiments/exp_1762448858_8082_s96_f24_d3_m1_2_3_4/openvino/model.xml\nModel BIN: /home/mount/experiments/exp_1762448858_8082_s96_f24_d3_m1_2_3_4/openvino/model.bin\nInput file: /home/mount/experiments/exp_1762448858_8082_s96_f24_d3_m1_2_3_4/onnx/sample_input.npy\nInitializing OpenVINO Runtime Core...\nAvailable devices:\n[E:] [BSL] found 0 ioexpander device\n  ['CPU', 'GNA', 'MYRIAD']\nLoading network...\nInput blob: input\nInput shape: [1, 8, 96, 96]\nOutput blob: Conv_107\nOutput shape: [1, 3, 96, 96]\nLoading network to MYRIAD...\nLoading input data...\nInput data shape: (1, 8, 96, 96)\nInput data type: float32\nRunning inference...\n[OK] Inference completed!\nInference time: 0.123546 seconds\nOutput shape: (1, 3, 96, 96)\nOutput dtype: float32\nOutput range: [-0.454834, 0.553711]\nMyriad inference completed!\n",
+    "stderr": ""
+  },
+  "end_time": "2025-11-06T18:07:46.088372"
+}

exp_1762448858_8082_s96_f24_d3_m1_2_3_4/run_inference.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+# Source OpenVINO environment
+source /opt/intel/openvino/bin/setupvars.sh
+
+PYPATH=/home/mount/scripts/simple_inference.py
+
+echo "Starting Myriad inference..."
+echo "Input: /home/mount/experiments/exp_1762448858_8082_s96_f24_d3_m1_2_3_4/onnx/sample_input.npy"
+echo "Model XML: /home/mount/experiments/exp_1762448858_8082_s96_f24_d3_m1_2_3_4/openvino/model.xml"
+echo "Model BIN: /home/mount/experiments/exp_1762448858_8082_s96_f24_d3_m1_2_3_4/openvino/model.bin"
+
+python3 $PYPATH \
+    --input_filepath /home/mount/experiments/exp_1762448858_8082_s96_f24_d3_m1_2_3_4/onnx/sample_input.npy \
+    --device_name MYRIAD \
+    --model_bin /home/mount/experiments/exp_1762448858_8082_s96_f24_d3_m1_2_3_4/openvino/model.bin \
+    --model_xml /home/mount/experiments/exp_1762448858_8082_s96_f24_d3_m1_2_3_4/openvino/model.xml \
+    --output /home/mount/experiments/exp_1762448858_8082_s96_f24_d3_m1_2_3_4/inference/
+
+echo "Myriad inference completed!"

exp_1762448908_9161_s96_f26_d3_m2_2_2_3/convert_openvino.sh

@@ -0,0 +1,30 @@
+#!/bin/bash
+# Source OpenVINO environment
+source /opt/intel/openvino/bin/setupvars.sh
+
+# OpenVINO Model Optimizer script for ONNX model
+PYPATH=/opt/intel/openvino_2020.3.194/deployment_tools/model_optimizer/mo.py
+ONNX_MODEL=/home/mount/experiments/exp_1762448908_9161_s96_f26_d3_m2_2_2_3/onnx/model.onnx
+OUTPUT_DIR=/home/mount/experiments/exp_1762448908_9161_s96_f26_d3_m2_2_2_3/openvino
+
+echo "Converting ONNX model to OpenVINO IR format..."
+echo "Input model: $ONNX_MODEL"
+echo "Output directory: $OUTPUT_DIR"
+
+# Create output directory if it doesn't exist
+mkdir -p "$OUTPUT_DIR"
+
+python3 $PYPATH \
+    --input_model "$ONNX_MODEL" \
+    --data_type FP16 \
+    --input_shape "[1,8,96,96]" \
+    --mean_values "[0,0,0,0,0,0,0,0]" \
+    --scale_values "[1,1,1,1,1,1,1,1]" \
+    --progress \
+    --stream_output \
+    --output_dir "$OUTPUT_DIR" \
+    --model_name model
+
+echo "OpenVINO conversion completed!"
+echo "Generated files in $OUTPUT_DIR:"
+ls -la "$OUTPUT_DIR/"

exp_1762448908_9161_s96_f26_d3_m2_2_2_3/experiment.log

@@ -0,0 +1,99 @@
+2025-11-06 18:08:28 - INFO - === Model Creation Phase ===
+2025-11-06 18:08:28 - INFO - Creating UNet model with config: {'input_size': 96, 'base_filters': 26, 'depth': 3, 'channel_multipliers': [2, 2, 2, 3], 'n_channels': 8, 'n_classes': 3}
+2025-11-06 18:08:28 - INFO - Model created successfully:
+2025-11-06 18:08:28 - INFO -   Depth: 3
+2025-11-06 18:08:28 - INFO -   Channels per level: [52, 52, 52, 78]
+2025-11-06 18:08:28 - INFO -   Total parameters: 269,727
+2025-11-06 18:08:28 - INFO -   Parameter memory: 1.03 MB
+2025-11-06 18:08:28 - INFO - === Detailed Architecture Analysis ===
+2025-11-06 18:08:28 - INFO - Input vector dimension: 73,728 (8 × 96 × 96)
+2025-11-06 18:08:28 - INFO - Component-wise parameter breakdown:
+2025-11-06 18:08:28 - INFO -   Encoder - encoders.0.channel_proj: 468 parameters
+2025-11-06 18:08:28 - INFO -   Encoder - encoders.0.convnext_block.dwconv: 2,600 parameters
+2025-11-06 18:08:28 - INFO -   Encoder - encoders.0.convnext_block.norm: 104 parameters
+2025-11-06 18:08:28 - INFO -   Encoder - encoders.0.convnext_block.pwconv1: 11,024 parameters
+2025-11-06 18:08:28 - INFO -   Encoder - encoders.0.convnext_block.pwconv2: 10,868 parameters
+2025-11-06 18:08:28 - INFO -   Encoder - encoders.1.convnext_block.dwconv: 2,600 parameters
+2025-11-06 18:08:28 - INFO -   Encoder - encoders.1.convnext_block.norm: 104 parameters
+2025-11-06 18:08:28 - INFO -   Encoder - encoders.1.convnext_block.pwconv1: 11,024 parameters
+2025-11-06 18:08:28 - INFO -   Encoder - encoders.1.convnext_block.pwconv2: 10,868 parameters
+2025-11-06 18:08:28 - INFO -   Encoder - encoders.2.convnext_block.dwconv: 2,600 parameters
+2025-11-06 18:08:28 - INFO -   Encoder - encoders.2.convnext_block.norm: 104 parameters
+2025-11-06 18:08:28 - INFO -   Encoder - encoders.2.convnext_block.pwconv1: 11,024 parameters
+2025-11-06 18:08:28 - INFO -   Encoder - encoders.2.convnext_block.pwconv2: 10,868 parameters
+2025-11-06 18:08:28 - INFO -   Bottleneck - bottleneck.channel_proj: 4,134 parameters
+2025-11-06 18:08:28 - INFO -   Bottleneck - bottleneck.convnext_block.dwconv: 3,900 parameters
+2025-11-06 18:08:28 - INFO -   Bottleneck - bottleneck.convnext_block.norm: 156 parameters
+2025-11-06 18:08:28 - INFO -   Bottleneck - bottleneck.convnext_block.pwconv1: 24,648 parameters
+2025-11-06 18:08:28 - INFO -   Bottleneck - bottleneck.convnext_block.pwconv2: 24,414 parameters
+2025-11-06 18:08:28 - INFO -   Decoder - upsamplers.0: 24,414 parameters
+2025-11-06 18:08:28 - INFO -   Decoder - upsamplers.1: 10,868 parameters
+2025-11-06 18:08:28 - INFO -   Decoder - upsamplers.2: 10,868 parameters
+2025-11-06 18:08:28 - INFO -   Decoder - decoders.0.channel_proj: 6,812 parameters
+2025-11-06 18:08:28 - INFO -   Decoder - decoders.0.convnext_block.dwconv: 2,600 parameters
+2025-11-06 18:08:28 - INFO -   Decoder - decoders.0.convnext_block.norm: 104 parameters
+2025-11-06 18:08:28 - INFO -   Decoder - decoders.0.convnext_block.pwconv1: 11,024 parameters
+2025-11-06 18:08:28 - INFO -   Decoder - decoders.0.convnext_block.pwconv2: 10,868 parameters
+2025-11-06 18:08:28 - INFO -   Decoder - decoders.1.channel_proj: 5,460 parameters
+2025-11-06 18:08:28 - INFO -   Decoder - decoders.1.convnext_block.dwconv: 2,600 parameters
+2025-11-06 18:08:28 - INFO -   Decoder - decoders.1.convnext_block.norm: 104 parameters
+2025-11-06 18:08:28 - INFO -   Decoder - decoders.1.convnext_block.pwconv1: 11,024 parameters
+2025-11-06 18:08:28 - INFO -   Decoder - decoders.1.convnext_block.pwconv2: 10,868 parameters
+2025-11-06 18:08:28 - INFO -   Decoder - decoders.2.channel_proj: 5,460 parameters
+2025-11-06 18:08:28 - INFO -   Decoder - decoders.2.convnext_block.dwconv: 2,600 parameters
+2025-11-06 18:08:28 - INFO -   Decoder - decoders.2.convnext_block.norm: 104 parameters
+2025-11-06 18:08:28 - INFO -   Decoder - decoders.2.convnext_block.pwconv1: 11,024 parameters
+2025-11-06 18:08:28 - INFO -   Decoder - decoders.2.convnext_block.pwconv2: 10,868 parameters
+2025-11-06 18:08:28 - INFO -   Other - final_conv: 159 parameters
+2025-11-06 18:08:28 - INFO - Parameter distribution summary:
+2025-11-06 18:08:28 - INFO -   Encoder parameters: 74,256 (27.6%)
+2025-11-06 18:08:28 - INFO -   Decoder parameters: 137,670 (51.1%)
+2025-11-06 18:08:28 - INFO -   Bottleneck parameters: 57,252 (21.3%)
+2025-11-06 18:08:28 - INFO -   Other parameters: 159 (0.1%)
+2025-11-06 18:08:28 - INFO - Latent space dimensions (feature maps at each level):
+2025-11-06 18:08:28 - INFO -   Level 0: 52 × 96 × 96 = 479,232 elements
+2025-11-06 18:08:28 - INFO -   Level 1: 52 × 48 × 48 = 119,808 elements
+2025-11-06 18:08:28 - INFO -   Level 2: 52 × 24 × 24 = 29,952 elements
+2025-11-06 18:08:28 - INFO -   Level 3: 78 × 12 × 12 = 11,232 elements
+2025-11-06 18:08:28 - INFO - Skip connection dimensions:
+2025-11-06 18:08:28 - INFO -   Skip 0: 52 × 96 × 96 = 479,232 elements
+2025-11-06 18:08:28 - INFO -   Skip 1: 52 × 48 × 48 = 119,808 elements
+2025-11-06 18:08:28 - INFO -   Skip 2: 52 × 24 × 24 = 29,952 elements
+2025-11-06 18:08:28 - INFO - Memory analysis:
+2025-11-06 18:08:28 - INFO -   Peak feature map memory (inference): 2.72 MB
+2025-11-06 18:08:28 - INFO -   Peak feature map memory (training): 5.45 MB (with gradients)
+2025-11-06 18:08:28 - INFO - Output vector dimension: 27,648 (3 × 96 × 96)
+2025-11-06 18:08:28 - INFO - PyTorch model saved to: /home/philab/Desktop/hydranet/experiments/exp_1762448908_9161_s96_f26_d3_m2_2_2_3/pytorch/model.pt
+2025-11-06 18:08:28 - INFO - === ONNX Conversion Phase ===
+2025-11-06 18:08:28 - INFO - === Model Export Diagnostics ===
+2025-11-06 18:08:28 - INFO - PyTorch version: 1.9.0+cu102
+2025-11-06 18:08:28 - INFO - Model parameters: 269,727
+2025-11-06 18:08:28 - INFO - Model memory: 1.03 MB
+2025-11-06 18:08:28 - INFO - Starting ONNX export with opset version 11
+2025-11-06 18:08:28 - INFO - Model input shape: torch.Size([1, 8, 96, 96])
+2025-11-06 18:08:28 - INFO - Model input dtype: torch.float32
+2025-11-06 18:08:28 - INFO - Forward pass successful. Output shape: torch.Size([1, 3, 96, 96])
+2025-11-06 18:08:28 - INFO - Output dtype: torch.float32
+2025-11-06 18:08:28 - INFO - Output value range: [-0.5734, 0.5689]
+2025-11-06 18:08:29 - INFO - Model successfully exported to /home/philab/Desktop/hydranet/experiments/exp_1762448908_9161_s96_f26_d3_m2_2_2_3/onnx/model.onnx
+2025-11-06 18:08:29 - INFO - ONNX model size: 1.04 MB
+2025-11-06 18:08:29 - INFO - Saved dummy input with shape (1, 8, 96, 96) to /home/philab/Desktop/hydranet/experiments/exp_1762448908_9161_s96_f26_d3_m2_2_2_3/onnx/sample_input.npy
+2025-11-06 18:08:29 - INFO - Input data type: float32
+2025-11-06 18:08:29 - INFO - Input value range: [-3.9566, 4.1282]
+2025-11-06 18:08:29 - INFO - === OpenVINO Conversion Phase ===
+2025-11-06 18:08:29 - INFO - Starting OpenVINO conversion in Docker container...
+2025-11-06 18:08:33 - INFO - OpenVINO conversion completed in 3.98 seconds
+2025-11-06 18:08:33 - INFO - OpenVINO model files created:
+2025-11-06 18:08:33 - INFO -   XML file: /home/philab/Desktop/hydranet/experiments/exp_1762448908_9161_s96_f26_d3_m2_2_2_3/openvino/model.xml (0.08 MB)
+2025-11-06 18:08:33 - INFO -   BIN file: /home/philab/Desktop/hydranet/experiments/exp_1762448908_9161_s96_f26_d3_m2_2_2_3/openvino/model.bin (0.51 MB)
+2025-11-06 18:08:33 - INFO - === Myriad Inference Phase ===
+2025-11-06 18:08:33 - INFO - Starting Myriad inference in Docker container...
+2025-11-06 18:08:36 - INFO - Myriad inference completed in 3.00 seconds
+2025-11-06 18:08:36 - INFO - Actual inference time: 0.351092 seconds
+2025-11-06 18:08:36 - INFO - ✅ Complete pipeline executed successfully!
+2025-11-06 18:08:36 - INFO - ✅ Experiment 60 completed successfully
+2025-11-06 18:08:36 - INFO -    Inference time: 0.351092s
+2025-11-06 18:08:36 - INFO - 
+=== Experiment 61/2475 ===
+2025-11-06 18:08:36 - INFO - Experiment ID: exp_1762448916_9432_s96_f26_d3_m1_2_3_3
+2025-11-06 18:08:36 - INFO - Experiment directory: /home/philab/Desktop/hydranet/experiments/exp_1762448916_9432_s96_f26_d3_m1_2_3_3

exp_1762448908_9161_s96_f26_d3_m2_2_2_3/model_info.json

@@ -0,0 +1,139 @@
+{
+  "config": {
+    "input_size": 96,
+    "base_filters": 26,
+    "depth": 3,
+    "channel_multipliers": [
+      2,
+      2,
+      2,
+      3
+    ],
+    "n_channels": 8,
+    "n_classes": 3
+  },
+  "model_info": {
+    "depth": 3,
+    "channels_per_level": [
+      52,
+      52,
+      52,
+      78
+    ],
+    "channel_multipliers": [
+      2,
+      2,
+      2,
+      3
+    ],
+    "total_parameters": 269727,
+    "trainable_parameters": 269727,
+    "model_size_mb": 1.0289268493652344
+  },
+  "architecture_stats": {
+    "input_dimension": 73728,
+    "output_dimension": 27648,
+    "parameter_distribution": {
+      "encoder_params": 74256,
+      "decoder_params": 137670,
+      "bottleneck_params": 57252,
+      "other_params": 159,
+      "total_params": 269337,
+      "encoder_percentage": 27.569921696610567,
+      "decoder_percentage": 51.11440314550173,
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+      "Bottleneck - bottleneck.convnext_block.pwconv1": 24648,
+      "Bottleneck - bottleneck.convnext_block.pwconv2": 24414,
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+      "Decoder - upsamplers.2": 10868,
+      "Decoder - decoders.0.channel_proj": 6812,
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+      "Decoder - decoders.0.convnext_block.norm": 104,
+      "Decoder - decoders.0.convnext_block.pwconv1": 11024,
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+      "Decoder - decoders.1.convnext_block.pwconv1": 11024,
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+      "Decoder - decoders.2.convnext_block.pwconv1": 11024,
+      "Decoder - decoders.2.convnext_block.pwconv2": 10868,
+      "Other - final_conv": 159
+    },
+    "latent_dimensions": {
+      "Level_0": {
+        "channels": 52,
+        "height": 96,
+        "width": 96,
+        "total_elements": 479232
+      },
+      "Level_1": {
+        "channels": 52,
+        "height": 48,
+        "width": 48,
+        "total_elements": 119808
+      },
+      "Level_2": {
+        "channels": 52,
+        "height": 24,
+        "width": 24,
+        "total_elements": 29952
+      },
+      "Level_3": {
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+        "height": 12,
+        "width": 12,
+        "total_elements": 11232
+      }
+    },
+    "skip_dimensions": {
+      "Skip_0": {
+        "channels": 52,
+        "height": 96,
+        "width": 96,
+        "total_elements": 479232
+      },
+      "Skip_1": {
+        "channels": 52,
+        "height": 48,
+        "width": 48,
+        "total_elements": 119808
+      },
+      "Skip_2": {
+        "channels": 52,
+        "height": 24,
+        "width": 24,
+        "total_elements": 29952
+      }
+    },
+    "memory_analysis": {
+      "peak_memory_inference_mb": 2.7235107421875,
+      "peak_memory_training_mb": 5.447021484375,
+      "peak_elements": 713952
+    }
+  },
+  "timestamp": "2025-11-06T18:08:28.837227"
+}

exp_1762448908_9161_s96_f26_d3_m2_2_2_3/pipeline_results.json

@@ -0,0 +1,139 @@
+{
+  "experiment_id": "exp_1762448908_7968_s96_f26_d3_m2_2_2_3",
+  "config": {
+    "input_size": 96,
+    "base_filters": 26,
+    "depth": 3,
+    "channel_multipliers": [
+      2,
+      2,
+      2,
+      3
+    ],
+    "n_channels": 8,
+    "n_classes": 3
+  },
+  "start_time": "2025-11-06T18:08:28.830729",
+  "success": true,
+  "timings": {
+    "onnx_conversion": 0.29142461507581174,
+    "openvino_conversion": 3.984111813828349,
+    "inference_total": 3.001626858022064,
+    "inference_actual": 0.351092,
+    "total": 7.291057776892558
+  },
+  "errors": [],
+  "architecture_stats": {
+    "input_dimension": 73728,
+    "output_dimension": 27648,
+    "parameter_distribution": {
+      "encoder_params": 74256,
+      "decoder_params": 137670,
+      "bottleneck_params": 57252,
+      "other_params": 159,
+      "total_params": 269337,
+      "encoder_percentage": 27.569921696610567,
+      "decoder_percentage": 51.11440314550173,
+      "bottleneck_percentage": 21.256641308101003,
+      "other_percentage": 0.05903384978669844
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+    "component_breakdown": {
+      "Encoder - encoders.0.channel_proj": 468,
+      "Encoder - encoders.0.convnext_block.dwconv": 2600,
+      "Encoder - encoders.0.convnext_block.norm": 104,
+      "Encoder - encoders.0.convnext_block.pwconv1": 11024,
+      "Encoder - encoders.0.convnext_block.pwconv2": 10868,
+      "Encoder - encoders.1.convnext_block.dwconv": 2600,
+      "Encoder - encoders.1.convnext_block.norm": 104,
+      "Encoder - encoders.1.convnext_block.pwconv1": 11024,
+      "Encoder - encoders.1.convnext_block.pwconv2": 10868,
+      "Encoder - encoders.2.convnext_block.dwconv": 2600,
+      "Encoder - encoders.2.convnext_block.norm": 104,
+      "Encoder - encoders.2.convnext_block.pwconv1": 11024,
+      "Encoder - encoders.2.convnext_block.pwconv2": 10868,
+      "Bottleneck - bottleneck.channel_proj": 4134,
+      "Bottleneck - bottleneck.convnext_block.dwconv": 3900,
+      "Bottleneck - bottleneck.convnext_block.norm": 156,
+      "Bottleneck - bottleneck.convnext_block.pwconv1": 24648,
+      "Bottleneck - bottleneck.convnext_block.pwconv2": 24414,
+      "Decoder - upsamplers.0": 24414,
+      "Decoder - upsamplers.1": 10868,
+      "Decoder - upsamplers.2": 10868,
+      "Decoder - decoders.0.channel_proj": 6812,
+      "Decoder - decoders.0.convnext_block.dwconv": 2600,
+      "Decoder - decoders.0.convnext_block.norm": 104,
+      "Decoder - decoders.0.convnext_block.pwconv1": 11024,
+      "Decoder - decoders.0.convnext_block.pwconv2": 10868,
+      "Decoder - decoders.1.channel_proj": 5460,
+      "Decoder - decoders.1.convnext_block.dwconv": 2600,
+      "Decoder - decoders.1.convnext_block.norm": 104,
+      "Decoder - decoders.1.convnext_block.pwconv1": 11024,
+      "Decoder - decoders.1.convnext_block.pwconv2": 10868,
+      "Decoder - decoders.2.channel_proj": 5460,
+      "Decoder - decoders.2.convnext_block.dwconv": 2600,
+      "Decoder - decoders.2.convnext_block.norm": 104,
+      "Decoder - decoders.2.convnext_block.pwconv1": 11024,
+      "Decoder - decoders.2.convnext_block.pwconv2": 10868,
+      "Other - final_conv": 159
+    },
+    "latent_dimensions": {
+      "Level_0": {
+        "channels": 52,
+        "height": 96,
+        "width": 96,
+        "total_elements": 479232
+      },
+      "Level_1": {
+        "channels": 52,
+        "height": 48,
+        "width": 48,
+        "total_elements": 119808
+      },
+      "Level_2": {
+        "channels": 52,
+        "height": 24,
+        "width": 24,
+        "total_elements": 29952
+      },
+      "Level_3": {
+        "channels": 78,
+        "height": 12,
+        "width": 12,
+        "total_elements": 11232
+      }
+    },
+    "skip_dimensions": {
+      "Skip_0": {
+        "channels": 52,
+        "height": 96,
+        "width": 96,
+        "total_elements": 479232
+      },
+      "Skip_1": {
+        "channels": 52,
+        "height": 48,
+        "width": 48,
+        "total_elements": 119808
+      },
+      "Skip_2": {
+        "channels": 52,
+        "height": 24,
+        "width": 24,
+        "total_elements": 29952
+      }
+    },
+    "memory_analysis": {
+      "peak_memory_inference_mb": 2.7235107421875,
+      "peak_memory_training_mb": 5.447021484375,
+      "peak_elements": 713952
+    }
+  },
+  "inference_results": {
+    "success": true,
+    "total_time": 3.001626858022064,
+    "inference_time": 0.351092,
+    "stdout": "[setupvars.sh] OpenVINO environment initialized\nStarting Myriad inference...\nInput: /home/mount/experiments/exp_1762448908_9161_s96_f26_d3_m2_2_2_3/onnx/sample_input.npy\nModel XML: /home/mount/experiments/exp_1762448908_9161_s96_f26_d3_m2_2_2_3/openvino/model.xml\nModel BIN: /home/mount/experiments/exp_1762448908_9161_s96_f26_d3_m2_2_2_3/openvino/model.bin\n[OK] OpenVINO inference engine imported successfully\nDevice: MYRIAD\nModel XML: /home/mount/experiments/exp_1762448908_9161_s96_f26_d3_m2_2_2_3/openvino/model.xml\nModel BIN: /home/mount/experiments/exp_1762448908_9161_s96_f26_d3_m2_2_2_3/openvino/model.bin\nInput file: /home/mount/experiments/exp_1762448908_9161_s96_f26_d3_m2_2_2_3/onnx/sample_input.npy\nInitializing OpenVINO Runtime Core...\nAvailable devices:\n[E:] [BSL] found 0 ioexpander device\n  ['CPU', 'GNA', 'MYRIAD']\nLoading network...\nInput blob: input\nInput shape: [1, 8, 96, 96]\nOutput blob: Conv_105\nOutput shape: [1, 3, 96, 96]\nLoading network to MYRIAD...\nLoading input data...\nInput data shape: (1, 8, 96, 96)\nInput data type: float32\nRunning inference...\n[OK] Inference completed!\nInference time: 0.351092 seconds\nOutput shape: (1, 3, 96, 96)\nOutput dtype: float32\nOutput range: [-0.573730, 0.568359]\nMyriad inference completed!\n",
+    "stderr": ""
+  },
+  "end_time": "2025-11-06T18:08:36.121777"
+}

exp_1762448908_9161_s96_f26_d3_m2_2_2_3/run_inference.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+# Source OpenVINO environment
+source /opt/intel/openvino/bin/setupvars.sh
+
+PYPATH=/home/mount/scripts/simple_inference.py
+
+echo "Starting Myriad inference..."
+echo "Input: /home/mount/experiments/exp_1762448908_9161_s96_f26_d3_m2_2_2_3/onnx/sample_input.npy"
+echo "Model XML: /home/mount/experiments/exp_1762448908_9161_s96_f26_d3_m2_2_2_3/openvino/model.xml"
+echo "Model BIN: /home/mount/experiments/exp_1762448908_9161_s96_f26_d3_m2_2_2_3/openvino/model.bin"
+
+python3 $PYPATH \
+    --input_filepath /home/mount/experiments/exp_1762448908_9161_s96_f26_d3_m2_2_2_3/onnx/sample_input.npy \
+    --device_name MYRIAD \
+    --model_bin /home/mount/experiments/exp_1762448908_9161_s96_f26_d3_m2_2_2_3/openvino/model.bin \
+    --model_xml /home/mount/experiments/exp_1762448908_9161_s96_f26_d3_m2_2_2_3/openvino/model.xml \
+    --output /home/mount/experiments/exp_1762448908_9161_s96_f26_d3_m2_2_2_3/inference/
+
+echo "Myriad inference completed!"

exp_1762449009_9308_s96_f28_d3_m1_2_2_3/convert_openvino.sh

@@ -0,0 +1,30 @@
+#!/bin/bash
+# Source OpenVINO environment
+source /opt/intel/openvino/bin/setupvars.sh
+
+# OpenVINO Model Optimizer script for ONNX model
+PYPATH=/opt/intel/openvino_2020.3.194/deployment_tools/model_optimizer/mo.py
+ONNX_MODEL=/home/mount/experiments/exp_1762449009_9308_s96_f28_d3_m1_2_2_3/onnx/model.onnx
+OUTPUT_DIR=/home/mount/experiments/exp_1762449009_9308_s96_f28_d3_m1_2_2_3/openvino
+
+echo "Converting ONNX model to OpenVINO IR format..."
+echo "Input model: $ONNX_MODEL"
+echo "Output directory: $OUTPUT_DIR"
+
+# Create output directory if it doesn't exist
+mkdir -p "$OUTPUT_DIR"
+
+python3 $PYPATH \
+    --input_model "$ONNX_MODEL" \
+    --data_type FP16 \
+    --input_shape "[1,8,96,96]" \
+    --mean_values "[0,0,0,0,0,0,0,0]" \
+    --scale_values "[1,1,1,1,1,1,1,1]" \
+    --progress \
+    --stream_output \
+    --output_dir "$OUTPUT_DIR" \
+    --model_name model
+
+echo "OpenVINO conversion completed!"
+echo "Generated files in $OUTPUT_DIR:"
+ls -la "$OUTPUT_DIR/"

exp_1762449009_9308_s96_f28_d3_m1_2_2_3/experiment.log

@@ -0,0 +1,100 @@
+2025-11-06 18:10:09 - INFO - === Model Creation Phase ===
+2025-11-06 18:10:09 - INFO - Creating UNet model with config: {'input_size': 96, 'base_filters': 28, 'depth': 3, 'channel_multipliers': [1, 2, 2, 3], 'n_channels': 8, 'n_classes': 3}
+2025-11-06 18:10:09 - INFO - Model created successfully:
+2025-11-06 18:10:09 - INFO -   Depth: 3
+2025-11-06 18:10:09 - INFO -   Channels per level: [28, 56, 56, 84]
+2025-11-06 18:10:09 - INFO -   Total parameters: 267,319
+2025-11-06 18:10:09 - INFO -   Parameter memory: 1.02 MB
+2025-11-06 18:10:09 - INFO - === Detailed Architecture Analysis ===
+2025-11-06 18:10:09 - INFO - Input vector dimension: 73,728 (8 × 96 × 96)
+2025-11-06 18:10:09 - INFO - Component-wise parameter breakdown:
+2025-11-06 18:10:09 - INFO -   Encoder - encoders.0.channel_proj: 252 parameters
+2025-11-06 18:10:09 - INFO -   Encoder - encoders.0.convnext_block.dwconv: 1,400 parameters
+2025-11-06 18:10:09 - INFO -   Encoder - encoders.0.convnext_block.norm: 56 parameters
+2025-11-06 18:10:09 - INFO -   Encoder - encoders.0.convnext_block.pwconv1: 3,248 parameters
+2025-11-06 18:10:09 - INFO -   Encoder - encoders.0.convnext_block.pwconv2: 3,164 parameters
+2025-11-06 18:10:09 - INFO -   Encoder - encoders.1.channel_proj: 1,624 parameters
+2025-11-06 18:10:09 - INFO -   Encoder - encoders.1.convnext_block.dwconv: 2,800 parameters
+2025-11-06 18:10:09 - INFO -   Encoder - encoders.1.convnext_block.norm: 112 parameters
+2025-11-06 18:10:09 - INFO -   Encoder - encoders.1.convnext_block.pwconv1: 12,768 parameters
+2025-11-06 18:10:09 - INFO -   Encoder - encoders.1.convnext_block.pwconv2: 12,600 parameters
+2025-11-06 18:10:09 - INFO -   Encoder - encoders.2.convnext_block.dwconv: 2,800 parameters
+2025-11-06 18:10:09 - INFO -   Encoder - encoders.2.convnext_block.norm: 112 parameters
+2025-11-06 18:10:09 - INFO -   Encoder - encoders.2.convnext_block.pwconv1: 12,768 parameters
+2025-11-06 18:10:09 - INFO -   Encoder - encoders.2.convnext_block.pwconv2: 12,600 parameters
+2025-11-06 18:10:09 - INFO -   Bottleneck - bottleneck.channel_proj: 4,788 parameters
+2025-11-06 18:10:09 - INFO -   Bottleneck - bottleneck.convnext_block.dwconv: 4,200 parameters
+2025-11-06 18:10:09 - INFO -   Bottleneck - bottleneck.convnext_block.norm: 168 parameters
+2025-11-06 18:10:09 - INFO -   Bottleneck - bottleneck.convnext_block.pwconv1: 28,560 parameters
+2025-11-06 18:10:09 - INFO -   Bottleneck - bottleneck.convnext_block.pwconv2: 28,308 parameters
+2025-11-06 18:10:09 - INFO -   Decoder - upsamplers.0: 28,308 parameters
+2025-11-06 18:10:09 - INFO -   Decoder - upsamplers.1: 12,600 parameters
+2025-11-06 18:10:09 - INFO -   Decoder - upsamplers.2: 12,600 parameters
+2025-11-06 18:10:09 - INFO -   Decoder - decoders.0.channel_proj: 7,896 parameters
+2025-11-06 18:10:09 - INFO -   Decoder - decoders.0.convnext_block.dwconv: 2,800 parameters
+2025-11-06 18:10:09 - INFO -   Decoder - decoders.0.convnext_block.norm: 112 parameters
+2025-11-06 18:10:09 - INFO -   Decoder - decoders.0.convnext_block.pwconv1: 12,768 parameters
+2025-11-06 18:10:09 - INFO -   Decoder - decoders.0.convnext_block.pwconv2: 12,600 parameters
+2025-11-06 18:10:09 - INFO -   Decoder - decoders.1.channel_proj: 6,328 parameters
+2025-11-06 18:10:09 - INFO -   Decoder - decoders.1.convnext_block.dwconv: 2,800 parameters
+2025-11-06 18:10:09 - INFO -   Decoder - decoders.1.convnext_block.norm: 112 parameters
+2025-11-06 18:10:09 - INFO -   Decoder - decoders.1.convnext_block.pwconv1: 12,768 parameters
+2025-11-06 18:10:09 - INFO -   Decoder - decoders.1.convnext_block.pwconv2: 12,600 parameters
+2025-11-06 18:10:09 - INFO -   Decoder - decoders.2.channel_proj: 2,380 parameters
+2025-11-06 18:10:09 - INFO -   Decoder - decoders.2.convnext_block.dwconv: 1,400 parameters
+2025-11-06 18:10:09 - INFO -   Decoder - decoders.2.convnext_block.norm: 56 parameters
+2025-11-06 18:10:09 - INFO -   Decoder - decoders.2.convnext_block.pwconv1: 3,248 parameters
+2025-11-06 18:10:09 - INFO -   Decoder - decoders.2.convnext_block.pwconv2: 3,164 parameters
+2025-11-06 18:10:09 - INFO -   Other - final_conv: 87 parameters
+2025-11-06 18:10:09 - INFO - Parameter distribution summary:
+2025-11-06 18:10:09 - INFO -   Encoder parameters: 66,304 (24.8%)
+2025-11-06 18:10:09 - INFO -   Decoder parameters: 134,540 (50.4%)
+2025-11-06 18:10:09 - INFO -   Bottleneck parameters: 66,024 (24.7%)
+2025-11-06 18:10:09 - INFO -   Other parameters: 87 (0.0%)
+2025-11-06 18:10:09 - INFO - Latent space dimensions (feature maps at each level):
+2025-11-06 18:10:09 - INFO -   Level 0: 28 × 96 × 96 = 258,048 elements
+2025-11-06 18:10:09 - INFO -   Level 1: 56 × 48 × 48 = 129,024 elements
+2025-11-06 18:10:09 - INFO -   Level 2: 56 × 24 × 24 = 32,256 elements
+2025-11-06 18:10:09 - INFO -   Level 3: 84 × 12 × 12 = 12,096 elements
+2025-11-06 18:10:09 - INFO - Skip connection dimensions:
+2025-11-06 18:10:09 - INFO -   Skip 0: 28 × 96 × 96 = 258,048 elements
+2025-11-06 18:10:09 - INFO -   Skip 1: 56 × 48 × 48 = 129,024 elements
+2025-11-06 18:10:09 - INFO -   Skip 2: 56 × 24 × 24 = 32,256 elements
+2025-11-06 18:10:09 - INFO - Memory analysis:
+2025-11-06 18:10:09 - INFO -   Peak feature map memory (inference): 1.93 MB
+2025-11-06 18:10:09 - INFO -   Peak feature map memory (training): 3.85 MB (with gradients)
+2025-11-06 18:10:09 - INFO - Output vector dimension: 27,648 (3 × 96 × 96)
+2025-11-06 18:10:09 - INFO - PyTorch model saved to: /home/philab/Desktop/hydranet/experiments/exp_1762449009_9308_s96_f28_d3_m1_2_2_3/pytorch/model.pt
+2025-11-06 18:10:09 - INFO - === ONNX Conversion Phase ===
+2025-11-06 18:10:09 - INFO - === Model Export Diagnostics ===
+2025-11-06 18:10:09 - INFO - PyTorch version: 1.9.0+cu102
+2025-11-06 18:10:09 - INFO - Model parameters: 267,319
+2025-11-06 18:10:09 - INFO - Model memory: 1.02 MB
+2025-11-06 18:10:09 - INFO - Starting ONNX export with opset version 11
+2025-11-06 18:10:09 - INFO - Model input shape: torch.Size([1, 8, 96, 96])
+2025-11-06 18:10:09 - INFO - Model input dtype: torch.float32
+2025-11-06 18:10:09 - INFO - Forward pass successful. Output shape: torch.Size([1, 3, 96, 96])
+2025-11-06 18:10:09 - INFO - Output dtype: torch.float32
+2025-11-06 18:10:09 - INFO - Output value range: [-0.6065, 0.7020]
+2025-11-06 18:10:10 - INFO - Model successfully exported to /home/philab/Desktop/hydranet/experiments/exp_1762449009_9308_s96_f28_d3_m1_2_2_3/onnx/model.onnx
+2025-11-06 18:10:10 - INFO - ONNX model size: 1.03 MB
+2025-11-06 18:10:10 - INFO - Saved dummy input with shape (1, 8, 96, 96) to /home/philab/Desktop/hydranet/experiments/exp_1762449009_9308_s96_f28_d3_m1_2_2_3/onnx/sample_input.npy
+2025-11-06 18:10:10 - INFO - Input data type: float32
+2025-11-06 18:10:10 - INFO - Input value range: [-3.8743, 4.3175]
+2025-11-06 18:10:10 - INFO - === OpenVINO Conversion Phase ===
+2025-11-06 18:10:10 - INFO - Starting OpenVINO conversion in Docker container...
+2025-11-06 18:10:14 - INFO - OpenVINO conversion completed in 4.10 seconds
+2025-11-06 18:10:14 - INFO - OpenVINO model files created:
+2025-11-06 18:10:14 - INFO -   XML file: /home/philab/Desktop/hydranet/experiments/exp_1762449009_9308_s96_f28_d3_m1_2_2_3/openvino/model.xml (0.09 MB)
+2025-11-06 18:10:14 - INFO -   BIN file: /home/philab/Desktop/hydranet/experiments/exp_1762449009_9308_s96_f28_d3_m1_2_2_3/openvino/model.bin (0.51 MB)
+2025-11-06 18:10:14 - INFO - === Myriad Inference Phase ===
+2025-11-06 18:10:14 - INFO - Starting Myriad inference in Docker container...
+2025-11-06 18:10:16 - INFO - Myriad inference completed in 2.81 seconds
+2025-11-06 18:10:16 - INFO - Actual inference time: 0.203952 seconds
+2025-11-06 18:10:16 - INFO - ✅ Complete pipeline executed successfully!
+2025-11-06 18:10:16 - INFO - ✅ Experiment 74 completed successfully
+2025-11-06 18:10:16 - INFO -    Inference time: 0.203952s
+2025-11-06 18:10:16 - INFO - 
+=== Experiment 75/2475 ===
+2025-11-06 18:10:16 - INFO - Experiment ID: exp_1762449016_3334_s96_f28_d3_m1_2_3_4
+2025-11-06 18:10:16 - INFO - Experiment directory: /home/philab/Desktop/hydranet/experiments/exp_1762449016_3334_s96_f28_d3_m1_2_3_4

exp_1762449009_9308_s96_f28_d3_m1_2_2_3/model_info.json

@@ -0,0 +1,140 @@
+{
+  "config": {
+    "input_size": 96,
+    "base_filters": 28,
+    "depth": 3,
+    "channel_multipliers": [
+      1,
+      2,
+      2,
+      3
+    ],
+    "n_channels": 8,
+    "n_classes": 3
+  },
+  "model_info": {
+    "depth": 3,
+    "channels_per_level": [
+      28,
+      56,
+      56,
+      84
+    ],
+    "channel_multipliers": [
+      1,
+      2,
+      2,
+      3
+    ],
+    "total_parameters": 267319,
+    "trainable_parameters": 267319,
+    "model_size_mb": 1.0197410583496094
+  },
+  "architecture_stats": {
+    "input_dimension": 73728,
+    "output_dimension": 27648,
+    "parameter_distribution": {
+      "encoder_params": 66304,
+      "decoder_params": 134540,
+      "bottleneck_params": 66024,
+      "other_params": 87,
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+      "encoder_percentage": 24.837144837144837,
+      "decoder_percentage": 50.39800715476391,
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+      "Encoder - encoders.1.convnext_block.dwconv": 2800,
+      "Encoder - encoders.1.convnext_block.norm": 112,
+      "Encoder - encoders.1.convnext_block.pwconv1": 12768,
+      "Encoder - encoders.1.convnext_block.pwconv2": 12600,
+      "Encoder - encoders.2.convnext_block.dwconv": 2800,
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+      "Encoder - encoders.2.convnext_block.pwconv1": 12768,
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+      "Bottleneck - bottleneck.convnext_block.dwconv": 4200,
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+      "Bottleneck - bottleneck.convnext_block.pwconv1": 28560,
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+      "Decoder - upsamplers.2": 12600,
+      "Decoder - decoders.0.channel_proj": 7896,
+      "Decoder - decoders.0.convnext_block.dwconv": 2800,
+      "Decoder - decoders.0.convnext_block.norm": 112,
+      "Decoder - decoders.0.convnext_block.pwconv1": 12768,
+      "Decoder - decoders.0.convnext_block.pwconv2": 12600,
+      "Decoder - decoders.1.channel_proj": 6328,
+      "Decoder - decoders.1.convnext_block.dwconv": 2800,
+      "Decoder - decoders.1.convnext_block.norm": 112,
+      "Decoder - decoders.1.convnext_block.pwconv1": 12768,
+      "Decoder - decoders.1.convnext_block.pwconv2": 12600,
+      "Decoder - decoders.2.channel_proj": 2380,
+      "Decoder - decoders.2.convnext_block.dwconv": 1400,
+      "Decoder - decoders.2.convnext_block.norm": 56,
+      "Decoder - decoders.2.convnext_block.pwconv1": 3248,
+      "Decoder - decoders.2.convnext_block.pwconv2": 3164,
+      "Other - final_conv": 87
+    },
+    "latent_dimensions": {
+      "Level_0": {
+        "channels": 28,
+        "height": 96,
+        "width": 96,
+        "total_elements": 258048
+      },
+      "Level_1": {
+        "channels": 56,
+        "height": 48,
+        "width": 48,
+        "total_elements": 129024
+      },
+      "Level_2": {
+        "channels": 56,
+        "height": 24,
+        "width": 24,
+        "total_elements": 32256
+      },
+      "Level_3": {
+        "channels": 84,
+        "height": 12,
+        "width": 12,
+        "total_elements": 12096
+      }
+    },
+    "skip_dimensions": {
+      "Skip_0": {
+        "channels": 28,
+        "height": 96,
+        "width": 96,
+        "total_elements": 258048
+      },
+      "Skip_1": {
+        "channels": 56,
+        "height": 48,
+        "width": 48,
+        "total_elements": 129024
+      },
+      "Skip_2": {
+        "channels": 56,
+        "height": 24,
+        "width": 24,
+        "total_elements": 32256
+      }
+    },
+    "memory_analysis": {
+      "peak_memory_inference_mb": 1.927001953125,
+      "peak_memory_training_mb": 3.85400390625,
+      "peak_elements": 505152
+    }
+  },
+  "timestamp": "2025-11-06T18:10:09.761818"
+}

exp_1762449009_9308_s96_f28_d3_m1_2_2_3/pipeline_results.json

@@ -0,0 +1,140 @@
+{
+  "experiment_id": "exp_1762449009_8812_s96_f28_d3_m1_2_2_3",
+  "config": {
+    "input_size": 96,
+    "base_filters": 28,
+    "depth": 3,
+    "channel_multipliers": [
+      1,
+      2,
+      2,
+      3
+    ],
+    "n_channels": 8,
+    "n_classes": 3
+  },
+  "start_time": "2025-11-06T18:10:09.754685",
+  "success": true,
+  "timings": {
+    "onnx_conversion": 0.2888925038278103,
+    "openvino_conversion": 4.100632845889777,
+    "inference_total": 2.8084738550242037,
+    "inference_actual": 0.203952,
+    "total": 7.21151207992807
+  },
+  "errors": [],
+  "architecture_stats": {
+    "input_dimension": 73728,
+    "output_dimension": 27648,
+    "parameter_distribution": {
+      "encoder_params": 66304,
+      "decoder_params": 134540,
+      "bottleneck_params": 66024,
+      "other_params": 87,
+      "total_params": 266955,
+      "encoder_percentage": 24.837144837144837,
+      "decoder_percentage": 50.39800715476391,
+      "bottleneck_percentage": 24.73225824577176,
+      "other_percentage": 0.03258976231949205
+    },
+    "component_breakdown": {
+      "Encoder - encoders.0.channel_proj": 252,
+      "Encoder - encoders.0.convnext_block.dwconv": 1400,
+      "Encoder - encoders.0.convnext_block.norm": 56,
+      "Encoder - encoders.0.convnext_block.pwconv1": 3248,
+      "Encoder - encoders.0.convnext_block.pwconv2": 3164,
+      "Encoder - encoders.1.channel_proj": 1624,
+      "Encoder - encoders.1.convnext_block.dwconv": 2800,
+      "Encoder - encoders.1.convnext_block.norm": 112,
+      "Encoder - encoders.1.convnext_block.pwconv1": 12768,
+      "Encoder - encoders.1.convnext_block.pwconv2": 12600,
+      "Encoder - encoders.2.convnext_block.dwconv": 2800,
+      "Encoder - encoders.2.convnext_block.norm": 112,
+      "Encoder - encoders.2.convnext_block.pwconv1": 12768,
+      "Encoder - encoders.2.convnext_block.pwconv2": 12600,
+      "Bottleneck - bottleneck.channel_proj": 4788,
+      "Bottleneck - bottleneck.convnext_block.dwconv": 4200,
+      "Bottleneck - bottleneck.convnext_block.norm": 168,
+      "Bottleneck - bottleneck.convnext_block.pwconv1": 28560,
+      "Bottleneck - bottleneck.convnext_block.pwconv2": 28308,
+      "Decoder - upsamplers.0": 28308,
+      "Decoder - upsamplers.1": 12600,
+      "Decoder - upsamplers.2": 12600,
+      "Decoder - decoders.0.channel_proj": 7896,
+      "Decoder - decoders.0.convnext_block.dwconv": 2800,
+      "Decoder - decoders.0.convnext_block.norm": 112,
+      "Decoder - decoders.0.convnext_block.pwconv1": 12768,
+      "Decoder - decoders.0.convnext_block.pwconv2": 12600,
+      "Decoder - decoders.1.channel_proj": 6328,
+      "Decoder - decoders.1.convnext_block.dwconv": 2800,
+      "Decoder - decoders.1.convnext_block.norm": 112,
+      "Decoder - decoders.1.convnext_block.pwconv1": 12768,
+      "Decoder - decoders.1.convnext_block.pwconv2": 12600,
+      "Decoder - decoders.2.channel_proj": 2380,
+      "Decoder - decoders.2.convnext_block.dwconv": 1400,
+      "Decoder - decoders.2.convnext_block.norm": 56,
+      "Decoder - decoders.2.convnext_block.pwconv1": 3248,
+      "Decoder - decoders.2.convnext_block.pwconv2": 3164,
+      "Other - final_conv": 87
+    },
+    "latent_dimensions": {
+      "Level_0": {
+        "channels": 28,
+        "height": 96,
+        "width": 96,
+        "total_elements": 258048
+      },
+      "Level_1": {
+        "channels": 56,
+        "height": 48,
+        "width": 48,
+        "total_elements": 129024
+      },
+      "Level_2": {
+        "channels": 56,
+        "height": 24,
+        "width": 24,
+        "total_elements": 32256
+      },
+      "Level_3": {
+        "channels": 84,
+        "height": 12,
+        "width": 12,
+        "total_elements": 12096
+      }
+    },
+    "skip_dimensions": {
+      "Skip_0": {
+        "channels": 28,
+        "height": 96,
+        "width": 96,
+        "total_elements": 258048
+      },
+      "Skip_1": {
+        "channels": 56,
+        "height": 48,
+        "width": 48,
+        "total_elements": 129024
+      },
+      "Skip_2": {
+        "channels": 56,
+        "height": 24,
+        "width": 24,
+        "total_elements": 32256
+      }
+    },
+    "memory_analysis": {
+      "peak_memory_inference_mb": 1.927001953125,
+      "peak_memory_training_mb": 3.85400390625,
+      "peak_elements": 505152
+    }
+  },
+  "inference_results": {
+    "success": true,
+    "total_time": 2.8084738550242037,
+    "inference_time": 0.203952,
+    "stdout": "[setupvars.sh] OpenVINO environment initialized\nStarting Myriad inference...\nInput: /home/mount/experiments/exp_1762449009_9308_s96_f28_d3_m1_2_2_3/onnx/sample_input.npy\nModel XML: /home/mount/experiments/exp_1762449009_9308_s96_f28_d3_m1_2_2_3/openvino/model.xml\nModel BIN: /home/mount/experiments/exp_1762449009_9308_s96_f28_d3_m1_2_2_3/openvino/model.bin\n[OK] OpenVINO inference engine imported successfully\nDevice: MYRIAD\nModel XML: /home/mount/experiments/exp_1762449009_9308_s96_f28_d3_m1_2_2_3/openvino/model.xml\nModel BIN: /home/mount/experiments/exp_1762449009_9308_s96_f28_d3_m1_2_2_3/openvino/model.bin\nInput file: /home/mount/experiments/exp_1762449009_9308_s96_f28_d3_m1_2_2_3/onnx/sample_input.npy\nInitializing OpenVINO Runtime Core...\nAvailable devices:\n[E:] [BSL] found 0 ioexpander device\n  ['CPU', 'GNA', 'MYRIAD']\nLoading network...\nInput blob: input\nInput shape: [1, 8, 96, 96]\nOutput blob: Conv_106\nOutput shape: [1, 3, 96, 96]\nLoading network to MYRIAD...\nLoading input data...\nInput data shape: (1, 8, 96, 96)\nInput data type: float32\nRunning inference...\n[OK] Inference completed!\nInference time: 0.203952 seconds\nOutput shape: (1, 3, 96, 96)\nOutput dtype: float32\nOutput range: [-0.605957, 0.702148]\nMyriad inference completed!\n",
+    "stderr": ""
+  },
+  "end_time": "2025-11-06T18:10:16.966186"
+}

exp_1762449009_9308_s96_f28_d3_m1_2_2_3/run_inference.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+# Source OpenVINO environment
+source /opt/intel/openvino/bin/setupvars.sh
+
+PYPATH=/home/mount/scripts/simple_inference.py
+
+echo "Starting Myriad inference..."
+echo "Input: /home/mount/experiments/exp_1762449009_9308_s96_f28_d3_m1_2_2_3/onnx/sample_input.npy"
+echo "Model XML: /home/mount/experiments/exp_1762449009_9308_s96_f28_d3_m1_2_2_3/openvino/model.xml"
+echo "Model BIN: /home/mount/experiments/exp_1762449009_9308_s96_f28_d3_m1_2_2_3/openvino/model.bin"
+
+python3 $PYPATH \
+    --input_filepath /home/mount/experiments/exp_1762449009_9308_s96_f28_d3_m1_2_2_3/onnx/sample_input.npy \
+    --device_name MYRIAD \
+    --model_bin /home/mount/experiments/exp_1762449009_9308_s96_f28_d3_m1_2_2_3/openvino/model.bin \
+    --model_xml /home/mount/experiments/exp_1762449009_9308_s96_f28_d3_m1_2_2_3/openvino/model.xml \
+    --output /home/mount/experiments/exp_1762449009_9308_s96_f28_d3_m1_2_2_3/inference/
+
+echo "Myriad inference completed!"

exp_1762449060_9675_s96_f30_d3_m1_2_2_3/convert_openvino.sh

@@ -0,0 +1,30 @@
+#!/bin/bash
+# Source OpenVINO environment
+source /opt/intel/openvino/bin/setupvars.sh
+
+# OpenVINO Model Optimizer script for ONNX model
+PYPATH=/opt/intel/openvino_2020.3.194/deployment_tools/model_optimizer/mo.py
+ONNX_MODEL=/home/mount/experiments/exp_1762449060_9675_s96_f30_d3_m1_2_2_3/onnx/model.onnx
+OUTPUT_DIR=/home/mount/experiments/exp_1762449060_9675_s96_f30_d3_m1_2_2_3/openvino
+
+echo "Converting ONNX model to OpenVINO IR format..."
+echo "Input model: $ONNX_MODEL"
+echo "Output directory: $OUTPUT_DIR"
+
+# Create output directory if it doesn't exist
+mkdir -p "$OUTPUT_DIR"
+
+python3 $PYPATH \
+    --input_model "$ONNX_MODEL" \
+    --data_type FP16 \
+    --input_shape "[1,8,96,96]" \
+    --mean_values "[0,0,0,0,0,0,0,0]" \
+    --scale_values "[1,1,1,1,1,1,1,1]" \
+    --progress \
+    --stream_output \
+    --output_dir "$OUTPUT_DIR" \
+    --model_name model
+
+echo "OpenVINO conversion completed!"
+echo "Generated files in $OUTPUT_DIR:"
+ls -la "$OUTPUT_DIR/"

exp_1762449060_9675_s96_f30_d3_m1_2_2_3/experiment.log

@@ -0,0 +1,100 @@
+2025-11-06 18:11:00 - INFO - === Model Creation Phase ===
+2025-11-06 18:11:00 - INFO - Creating UNet model with config: {'input_size': 96, 'base_filters': 30, 'depth': 3, 'channel_multipliers': [1, 2, 2, 3], 'n_channels': 8, 'n_classes': 3}
+2025-11-06 18:11:00 - INFO - Model created successfully:
+2025-11-06 18:11:00 - INFO -   Depth: 3
+2025-11-06 18:11:00 - INFO -   Channels per level: [30, 60, 60, 90]
+2025-11-06 18:11:00 - INFO -   Total parameters: 305,193
+2025-11-06 18:11:00 - INFO -   Parameter memory: 1.16 MB
+2025-11-06 18:11:00 - INFO - === Detailed Architecture Analysis ===
+2025-11-06 18:11:00 - INFO - Input vector dimension: 73,728 (8 × 96 × 96)
+2025-11-06 18:11:00 - INFO - Component-wise parameter breakdown:
+2025-11-06 18:11:00 - INFO -   Encoder - encoders.0.channel_proj: 270 parameters
+2025-11-06 18:11:00 - INFO -   Encoder - encoders.0.convnext_block.dwconv: 1,500 parameters
+2025-11-06 18:11:00 - INFO -   Encoder - encoders.0.convnext_block.norm: 60 parameters
+2025-11-06 18:11:00 - INFO -   Encoder - encoders.0.convnext_block.pwconv1: 3,720 parameters
+2025-11-06 18:11:00 - INFO -   Encoder - encoders.0.convnext_block.pwconv2: 3,630 parameters
+2025-11-06 18:11:00 - INFO -   Encoder - encoders.1.channel_proj: 1,860 parameters
+2025-11-06 18:11:00 - INFO -   Encoder - encoders.1.convnext_block.dwconv: 3,000 parameters
+2025-11-06 18:11:00 - INFO -   Encoder - encoders.1.convnext_block.norm: 120 parameters
+2025-11-06 18:11:00 - INFO -   Encoder - encoders.1.convnext_block.pwconv1: 14,640 parameters
+2025-11-06 18:11:00 - INFO -   Encoder - encoders.1.convnext_block.pwconv2: 14,460 parameters
+2025-11-06 18:11:00 - INFO -   Encoder - encoders.2.convnext_block.dwconv: 3,000 parameters
+2025-11-06 18:11:00 - INFO -   Encoder - encoders.2.convnext_block.norm: 120 parameters
+2025-11-06 18:11:00 - INFO -   Encoder - encoders.2.convnext_block.pwconv1: 14,640 parameters
+2025-11-06 18:11:00 - INFO -   Encoder - encoders.2.convnext_block.pwconv2: 14,460 parameters
+2025-11-06 18:11:00 - INFO -   Bottleneck - bottleneck.channel_proj: 5,490 parameters
+2025-11-06 18:11:00 - INFO -   Bottleneck - bottleneck.convnext_block.dwconv: 4,500 parameters
+2025-11-06 18:11:00 - INFO -   Bottleneck - bottleneck.convnext_block.norm: 180 parameters
+2025-11-06 18:11:00 - INFO -   Bottleneck - bottleneck.convnext_block.pwconv1: 32,760 parameters
+2025-11-06 18:11:00 - INFO -   Bottleneck - bottleneck.convnext_block.pwconv2: 32,490 parameters
+2025-11-06 18:11:00 - INFO -   Decoder - upsamplers.0: 32,490 parameters
+2025-11-06 18:11:00 - INFO -   Decoder - upsamplers.1: 14,460 parameters
+2025-11-06 18:11:00 - INFO -   Decoder - upsamplers.2: 14,460 parameters
+2025-11-06 18:11:00 - INFO -   Decoder - decoders.0.channel_proj: 9,060 parameters
+2025-11-06 18:11:00 - INFO -   Decoder - decoders.0.convnext_block.dwconv: 3,000 parameters
+2025-11-06 18:11:00 - INFO -   Decoder - decoders.0.convnext_block.norm: 120 parameters
+2025-11-06 18:11:00 - INFO -   Decoder - decoders.0.convnext_block.pwconv1: 14,640 parameters
+2025-11-06 18:11:00 - INFO -   Decoder - decoders.0.convnext_block.pwconv2: 14,460 parameters
+2025-11-06 18:11:00 - INFO -   Decoder - decoders.1.channel_proj: 7,260 parameters
+2025-11-06 18:11:00 - INFO -   Decoder - decoders.1.convnext_block.dwconv: 3,000 parameters
+2025-11-06 18:11:00 - INFO -   Decoder - decoders.1.convnext_block.norm: 120 parameters
+2025-11-06 18:11:00 - INFO -   Decoder - decoders.1.convnext_block.pwconv1: 14,640 parameters
+2025-11-06 18:11:00 - INFO -   Decoder - decoders.1.convnext_block.pwconv2: 14,460 parameters
+2025-11-06 18:11:00 - INFO -   Decoder - decoders.2.channel_proj: 2,730 parameters
+2025-11-06 18:11:00 - INFO -   Decoder - decoders.2.convnext_block.dwconv: 1,500 parameters
+2025-11-06 18:11:00 - INFO -   Decoder - decoders.2.convnext_block.norm: 60 parameters
+2025-11-06 18:11:00 - INFO -   Decoder - decoders.2.convnext_block.pwconv1: 3,720 parameters
+2025-11-06 18:11:00 - INFO -   Decoder - decoders.2.convnext_block.pwconv2: 3,630 parameters
+2025-11-06 18:11:00 - INFO -   Other - final_conv: 93 parameters
+2025-11-06 18:11:00 - INFO - Parameter distribution summary:
+2025-11-06 18:11:00 - INFO -   Encoder parameters: 75,480 (24.8%)
+2025-11-06 18:11:00 - INFO -   Decoder parameters: 153,810 (50.5%)
+2025-11-06 18:11:00 - INFO -   Bottleneck parameters: 75,420 (24.7%)
+2025-11-06 18:11:00 - INFO -   Other parameters: 93 (0.0%)
+2025-11-06 18:11:00 - INFO - Latent space dimensions (feature maps at each level):
+2025-11-06 18:11:00 - INFO -   Level 0: 30 × 96 × 96 = 276,480 elements
+2025-11-06 18:11:00 - INFO -   Level 1: 60 × 48 × 48 = 138,240 elements
+2025-11-06 18:11:00 - INFO -   Level 2: 60 × 24 × 24 = 34,560 elements
+2025-11-06 18:11:00 - INFO -   Level 3: 90 × 12 × 12 = 12,960 elements
+2025-11-06 18:11:00 - INFO - Skip connection dimensions:
+2025-11-06 18:11:00 - INFO -   Skip 0: 30 × 96 × 96 = 276,480 elements
+2025-11-06 18:11:00 - INFO -   Skip 1: 60 × 48 × 48 = 138,240 elements
+2025-11-06 18:11:00 - INFO -   Skip 2: 60 × 24 × 24 = 34,560 elements
+2025-11-06 18:11:00 - INFO - Memory analysis:
+2025-11-06 18:11:00 - INFO -   Peak feature map memory (inference): 2.04 MB
+2025-11-06 18:11:00 - INFO -   Peak feature map memory (training): 4.09 MB (with gradients)
+2025-11-06 18:11:00 - INFO - Output vector dimension: 27,648 (3 × 96 × 96)
+2025-11-06 18:11:00 - INFO - PyTorch model saved to: /home/philab/Desktop/hydranet/experiments/exp_1762449060_9675_s96_f30_d3_m1_2_2_3/pytorch/model.pt
+2025-11-06 18:11:00 - INFO - === ONNX Conversion Phase ===
+2025-11-06 18:11:00 - INFO - === Model Export Diagnostics ===
+2025-11-06 18:11:00 - INFO - PyTorch version: 1.9.0+cu102
+2025-11-06 18:11:00 - INFO - Model parameters: 305,193
+2025-11-06 18:11:00 - INFO - Model memory: 1.16 MB
+2025-11-06 18:11:00 - INFO - Starting ONNX export with opset version 11
+2025-11-06 18:11:00 - INFO - Model input shape: torch.Size([1, 8, 96, 96])
+2025-11-06 18:11:00 - INFO - Model input dtype: torch.float32
+2025-11-06 18:11:00 - INFO - Forward pass successful. Output shape: torch.Size([1, 3, 96, 96])
+2025-11-06 18:11:00 - INFO - Output dtype: torch.float32
+2025-11-06 18:11:00 - INFO - Output value range: [-0.6438, 0.5599]
+2025-11-06 18:11:00 - INFO - Model successfully exported to /home/philab/Desktop/hydranet/experiments/exp_1762449060_9675_s96_f30_d3_m1_2_2_3/onnx/model.onnx
+2025-11-06 18:11:00 - INFO - ONNX model size: 1.17 MB
+2025-11-06 18:11:00 - INFO - Saved dummy input with shape (1, 8, 96, 96) to /home/philab/Desktop/hydranet/experiments/exp_1762449060_9675_s96_f30_d3_m1_2_2_3/onnx/sample_input.npy
+2025-11-06 18:11:00 - INFO - Input data type: float32
+2025-11-06 18:11:00 - INFO - Input value range: [-4.3257, 4.2415]
+2025-11-06 18:11:00 - INFO - === OpenVINO Conversion Phase ===
+2025-11-06 18:11:00 - INFO - Starting OpenVINO conversion in Docker container...
+2025-11-06 18:11:04 - INFO - OpenVINO conversion completed in 4.08 seconds
+2025-11-06 18:11:04 - INFO - OpenVINO model files created:
+2025-11-06 18:11:04 - INFO -   XML file: /home/philab/Desktop/hydranet/experiments/exp_1762449060_9675_s96_f30_d3_m1_2_2_3/openvino/model.xml (0.09 MB)
+2025-11-06 18:11:04 - INFO -   BIN file: /home/philab/Desktop/hydranet/experiments/exp_1762449060_9675_s96_f30_d3_m1_2_2_3/openvino/model.bin (0.58 MB)
+2025-11-06 18:11:04 - INFO - === Myriad Inference Phase ===
+2025-11-06 18:11:04 - INFO - Starting Myriad inference in Docker container...
+2025-11-06 18:11:07 - INFO - Myriad inference completed in 2.90 seconds
+2025-11-06 18:11:07 - INFO - Actual inference time: 0.298507 seconds
+2025-11-06 18:11:07 - INFO - ✅ Complete pipeline executed successfully!
+2025-11-06 18:11:07 - INFO - ✅ Experiment 81 completed successfully
+2025-11-06 18:11:07 - INFO -    Inference time: 0.298507s
+2025-11-06 18:11:07 - INFO - 
+=== Experiment 82/2475 ===
+2025-11-06 18:11:07 - INFO - Experiment ID: exp_1762449067_7726_s96_f30_d3_m2_2_2_3
+2025-11-06 18:11:07 - INFO - Experiment directory: /home/philab/Desktop/hydranet/experiments/exp_1762449067_7726_s96_f30_d3_m2_2_2_3

exp_1762449060_9675_s96_f30_d3_m1_2_2_3/model_info.json

@@ -0,0 +1,140 @@
+{
+  "config": {
+    "input_size": 96,
+    "base_filters": 30,
+    "depth": 3,
+    "channel_multipliers": [
+      1,
+      2,
+      2,
+      3
+    ],
+    "n_channels": 8,
+    "n_classes": 3
+  },
+  "model_info": {
+    "depth": 3,
+    "channels_per_level": [
+      30,
+      60,
+      60,
+      90
+    ],
+    "channel_multipliers": [
+      1,
+      2,
+      2,
+      3
+    ],
+    "total_parameters": 305193,
+    "trainable_parameters": 305193,
+    "model_size_mb": 1.1642189025878906
+  },
+  "architecture_stats": {
+    "input_dimension": 73728,
+    "output_dimension": 27648,
+    "parameter_distribution": {
+      "encoder_params": 75480,
+      "decoder_params": 153810,
+      "bottleneck_params": 75420,
+      "other_params": 93,
+      "total_params": 304803,
+      "encoder_percentage": 24.76353579197055,
+      "decoder_percentage": 50.462101750967015,
+      "bottleneck_percentage": 24.743850946348953,
+      "other_percentage": 0.03051151071347723
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+    "component_breakdown": {
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+      "Encoder - encoders.0.convnext_block.dwconv": 1500,
+      "Encoder - encoders.0.convnext_block.norm": 60,
+      "Encoder - encoders.0.convnext_block.pwconv1": 3720,
+      "Encoder - encoders.0.convnext_block.pwconv2": 3630,
+      "Encoder - encoders.1.channel_proj": 1860,
+      "Encoder - encoders.1.convnext_block.dwconv": 3000,
+      "Encoder - encoders.1.convnext_block.norm": 120,
+      "Encoder - encoders.1.convnext_block.pwconv1": 14640,
+      "Encoder - encoders.1.convnext_block.pwconv2": 14460,
+      "Encoder - encoders.2.convnext_block.dwconv": 3000,
+      "Encoder - encoders.2.convnext_block.norm": 120,
+      "Encoder - encoders.2.convnext_block.pwconv1": 14640,
+      "Encoder - encoders.2.convnext_block.pwconv2": 14460,
+      "Bottleneck - bottleneck.channel_proj": 5490,
+      "Bottleneck - bottleneck.convnext_block.dwconv": 4500,
+      "Bottleneck - bottleneck.convnext_block.norm": 180,
+      "Bottleneck - bottleneck.convnext_block.pwconv1": 32760,
+      "Bottleneck - bottleneck.convnext_block.pwconv2": 32490,
+      "Decoder - upsamplers.0": 32490,
+      "Decoder - upsamplers.1": 14460,
+      "Decoder - upsamplers.2": 14460,
+      "Decoder - decoders.0.channel_proj": 9060,
+      "Decoder - decoders.0.convnext_block.dwconv": 3000,
+      "Decoder - decoders.0.convnext_block.norm": 120,
+      "Decoder - decoders.0.convnext_block.pwconv1": 14640,
+      "Decoder - decoders.0.convnext_block.pwconv2": 14460,
+      "Decoder - decoders.1.channel_proj": 7260,
+      "Decoder - decoders.1.convnext_block.dwconv": 3000,
+      "Decoder - decoders.1.convnext_block.norm": 120,
+      "Decoder - decoders.1.convnext_block.pwconv1": 14640,
+      "Decoder - decoders.1.convnext_block.pwconv2": 14460,
+      "Decoder - decoders.2.channel_proj": 2730,
+      "Decoder - decoders.2.convnext_block.dwconv": 1500,
+      "Decoder - decoders.2.convnext_block.norm": 60,
+      "Decoder - decoders.2.convnext_block.pwconv1": 3720,
+      "Decoder - decoders.2.convnext_block.pwconv2": 3630,
+      "Other - final_conv": 93
+    },
+    "latent_dimensions": {
+      "Level_0": {
+        "channels": 30,
+        "height": 96,
+        "width": 96,
+        "total_elements": 276480
+      },
+      "Level_1": {
+        "channels": 60,
+        "height": 48,
+        "width": 48,
+        "total_elements": 138240
+      },
+      "Level_2": {
+        "channels": 60,
+        "height": 24,
+        "width": 24,
+        "total_elements": 34560
+      },
+      "Level_3": {
+        "channels": 90,
+        "height": 12,
+        "width": 12,
+        "total_elements": 12960
+      }
+    },
+    "skip_dimensions": {
+      "Skip_0": {
+        "channels": 30,
+        "height": 96,
+        "width": 96,
+        "total_elements": 276480
+      },
+      "Skip_1": {
+        "channels": 60,
+        "height": 48,
+        "width": 48,
+        "total_elements": 138240
+      },
+      "Skip_2": {
+        "channels": 60,
+        "height": 24,
+        "width": 24,
+        "total_elements": 34560
+      }
+    },
+    "memory_analysis": {
+      "peak_memory_inference_mb": 2.0445556640625,
+      "peak_memory_training_mb": 4.089111328125,
+      "peak_elements": 535968
+    }
+  },
+  "timestamp": "2025-11-06T18:11:00.552144"
+}

exp_1762449060_9675_s96_f30_d3_m1_2_2_3/pipeline_results.json

@@ -0,0 +1,140 @@
+{
+  "experiment_id": "exp_1762449060_7398_s96_f30_d3_m1_2_2_3",
+  "config": {
+    "input_size": 96,
+    "base_filters": 30,
+    "depth": 3,
+    "channel_multipliers": [
+      1,
+      2,
+      2,
+      3
+    ],
+    "n_channels": 8,
+    "n_classes": 3
+  },
+  "start_time": "2025-11-06T18:11:00.543091",
+  "success": true,
+  "timings": {
+    "onnx_conversion": 0.295903658028692,
+    "openvino_conversion": 4.082625380018726,
+    "inference_total": 2.8962551350705326,
+    "inference_actual": 0.298507,
+    "total": 7.290062549058348
+  },
+  "errors": [],
+  "architecture_stats": {
+    "input_dimension": 73728,
+    "output_dimension": 27648,
+    "parameter_distribution": {
+      "encoder_params": 75480,
+      "decoder_params": 153810,
+      "bottleneck_params": 75420,
+      "other_params": 93,
+      "total_params": 304803,
+      "encoder_percentage": 24.76353579197055,
+      "decoder_percentage": 50.462101750967015,
+      "bottleneck_percentage": 24.743850946348953,
+      "other_percentage": 0.03051151071347723
+    },
+    "component_breakdown": {
+      "Encoder - encoders.0.channel_proj": 270,
+      "Encoder - encoders.0.convnext_block.dwconv": 1500,
+      "Encoder - encoders.0.convnext_block.norm": 60,
+      "Encoder - encoders.0.convnext_block.pwconv1": 3720,
+      "Encoder - encoders.0.convnext_block.pwconv2": 3630,
+      "Encoder - encoders.1.channel_proj": 1860,
+      "Encoder - encoders.1.convnext_block.dwconv": 3000,
+      "Encoder - encoders.1.convnext_block.norm": 120,
+      "Encoder - encoders.1.convnext_block.pwconv1": 14640,
+      "Encoder - encoders.1.convnext_block.pwconv2": 14460,
+      "Encoder - encoders.2.convnext_block.dwconv": 3000,
+      "Encoder - encoders.2.convnext_block.norm": 120,
+      "Encoder - encoders.2.convnext_block.pwconv1": 14640,
+      "Encoder - encoders.2.convnext_block.pwconv2": 14460,
+      "Bottleneck - bottleneck.channel_proj": 5490,
+      "Bottleneck - bottleneck.convnext_block.dwconv": 4500,
+      "Bottleneck - bottleneck.convnext_block.norm": 180,
+      "Bottleneck - bottleneck.convnext_block.pwconv1": 32760,
+      "Bottleneck - bottleneck.convnext_block.pwconv2": 32490,
+      "Decoder - upsamplers.0": 32490,
+      "Decoder - upsamplers.1": 14460,
+      "Decoder - upsamplers.2": 14460,
+      "Decoder - decoders.0.channel_proj": 9060,
+      "Decoder - decoders.0.convnext_block.dwconv": 3000,
+      "Decoder - decoders.0.convnext_block.norm": 120,
+      "Decoder - decoders.0.convnext_block.pwconv1": 14640,
+      "Decoder - decoders.0.convnext_block.pwconv2": 14460,
+      "Decoder - decoders.1.channel_proj": 7260,
+      "Decoder - decoders.1.convnext_block.dwconv": 3000,
+      "Decoder - decoders.1.convnext_block.norm": 120,
+      "Decoder - decoders.1.convnext_block.pwconv1": 14640,
+      "Decoder - decoders.1.convnext_block.pwconv2": 14460,
+      "Decoder - decoders.2.channel_proj": 2730,
+      "Decoder - decoders.2.convnext_block.dwconv": 1500,
+      "Decoder - decoders.2.convnext_block.norm": 60,
+      "Decoder - decoders.2.convnext_block.pwconv1": 3720,
+      "Decoder - decoders.2.convnext_block.pwconv2": 3630,
+      "Other - final_conv": 93
+    },
+    "latent_dimensions": {
+      "Level_0": {
+        "channels": 30,
+        "height": 96,
+        "width": 96,
+        "total_elements": 276480
+      },
+      "Level_1": {
+        "channels": 60,
+        "height": 48,
+        "width": 48,
+        "total_elements": 138240
+      },
+      "Level_2": {
+        "channels": 60,
+        "height": 24,
+        "width": 24,
+        "total_elements": 34560
+      },
+      "Level_3": {
+        "channels": 90,
+        "height": 12,
+        "width": 12,
+        "total_elements": 12960
+      }
+    },
+    "skip_dimensions": {
+      "Skip_0": {
+        "channels": 30,
+        "height": 96,
+        "width": 96,
+        "total_elements": 276480
+      },
+      "Skip_1": {
+        "channels": 60,
+        "height": 48,
+        "width": 48,
+        "total_elements": 138240
+      },
+      "Skip_2": {
+        "channels": 60,
+        "height": 24,
+        "width": 24,
+        "total_elements": 34560
+      }
+    },
+    "memory_analysis": {
+      "peak_memory_inference_mb": 2.0445556640625,
+      "peak_memory_training_mb": 4.089111328125,
+      "peak_elements": 535968
+    }
+  },
+  "inference_results": {
+    "success": true,
+    "total_time": 2.8962551350705326,
+    "inference_time": 0.298507,
+    "stdout": "[setupvars.sh] OpenVINO environment initialized\nStarting Myriad inference...\nInput: /home/mount/experiments/exp_1762449060_9675_s96_f30_d3_m1_2_2_3/onnx/sample_input.npy\nModel XML: /home/mount/experiments/exp_1762449060_9675_s96_f30_d3_m1_2_2_3/openvino/model.xml\nModel BIN: /home/mount/experiments/exp_1762449060_9675_s96_f30_d3_m1_2_2_3/openvino/model.bin\n[OK] OpenVINO inference engine imported successfully\nDevice: MYRIAD\nModel XML: /home/mount/experiments/exp_1762449060_9675_s96_f30_d3_m1_2_2_3/openvino/model.xml\nModel BIN: /home/mount/experiments/exp_1762449060_9675_s96_f30_d3_m1_2_2_3/openvino/model.bin\nInput file: /home/mount/experiments/exp_1762449060_9675_s96_f30_d3_m1_2_2_3/onnx/sample_input.npy\nInitializing OpenVINO Runtime Core...\nAvailable devices:\n[E:] [BSL] found 0 ioexpander device\n  ['CPU', 'GNA', 'MYRIAD']\nLoading network...\nInput blob: input\nInput shape: [1, 8, 96, 96]\nOutput blob: Conv_106\nOutput shape: [1, 3, 96, 96]\nLoading network to MYRIAD...\nLoading input data...\nInput data shape: (1, 8, 96, 96)\nInput data type: float32\nRunning inference...\n[OK] Inference completed!\nInference time: 0.298507 seconds\nOutput shape: (1, 3, 96, 96)\nOutput dtype: float32\nOutput range: [-0.643066, 0.559570]\nMyriad inference completed!\n",
+    "stderr": ""
+  },
+  "end_time": "2025-11-06T18:11:07.833143"
+}

exp_1762449060_9675_s96_f30_d3_m1_2_2_3/run_inference.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+# Source OpenVINO environment
+source /opt/intel/openvino/bin/setupvars.sh
+
+PYPATH=/home/mount/scripts/simple_inference.py
+
+echo "Starting Myriad inference..."
+echo "Input: /home/mount/experiments/exp_1762449060_9675_s96_f30_d3_m1_2_2_3/onnx/sample_input.npy"
+echo "Model XML: /home/mount/experiments/exp_1762449060_9675_s96_f30_d3_m1_2_2_3/openvino/model.xml"
+echo "Model BIN: /home/mount/experiments/exp_1762449060_9675_s96_f30_d3_m1_2_2_3/openvino/model.bin"
+
+python3 $PYPATH \
+    --input_filepath /home/mount/experiments/exp_1762449060_9675_s96_f30_d3_m1_2_2_3/onnx/sample_input.npy \
+    --device_name MYRIAD \
+    --model_bin /home/mount/experiments/exp_1762449060_9675_s96_f30_d3_m1_2_2_3/openvino/model.bin \
+    --model_xml /home/mount/experiments/exp_1762449060_9675_s96_f30_d3_m1_2_2_3/openvino/model.xml \
+    --output /home/mount/experiments/exp_1762449060_9675_s96_f30_d3_m1_2_2_3/inference/
+
+echo "Myriad inference completed!"

exp_1762449133_5525_s96_f32_d3_m1_2_2_2/convert_openvino.sh

@@ -0,0 +1,30 @@
+#!/bin/bash
+# Source OpenVINO environment
+source /opt/intel/openvino/bin/setupvars.sh
+
+# OpenVINO Model Optimizer script for ONNX model
+PYPATH=/opt/intel/openvino_2020.3.194/deployment_tools/model_optimizer/mo.py
+ONNX_MODEL=/home/mount/experiments/exp_1762449133_5525_s96_f32_d3_m1_2_2_2/onnx/model.onnx
+OUTPUT_DIR=/home/mount/experiments/exp_1762449133_5525_s96_f32_d3_m1_2_2_2/openvino
+
+echo "Converting ONNX model to OpenVINO IR format..."
+echo "Input model: $ONNX_MODEL"
+echo "Output directory: $OUTPUT_DIR"
+
+# Create output directory if it doesn't exist
+mkdir -p "$OUTPUT_DIR"
+
+python3 $PYPATH \
+    --input_model "$ONNX_MODEL" \
+    --data_type FP16 \
+    --input_shape "[1,8,96,96]" \
+    --mean_values "[0,0,0,0,0,0,0,0]" \
+    --scale_values "[1,1,1,1,1,1,1,1]" \
+    --progress \
+    --stream_output \
+    --output_dir "$OUTPUT_DIR" \
+    --model_name model
+
+echo "OpenVINO conversion completed!"
+echo "Generated files in $OUTPUT_DIR:"
+ls -la "$OUTPUT_DIR/"

exp_1762449133_5525_s96_f32_d3_m1_2_2_2/experiment.log

@@ -0,0 +1,99 @@
+2025-11-06 18:12:13 - INFO - === Model Creation Phase ===
+2025-11-06 18:12:13 - INFO - Creating UNet model with config: {'input_size': 96, 'base_filters': 32, 'depth': 3, 'channel_multipliers': [1, 2, 2, 2], 'n_channels': 8, 'n_classes': 3}
+2025-11-06 18:12:13 - INFO - Model created successfully:
+2025-11-06 18:12:13 - INFO -   Depth: 3
+2025-11-06 18:12:13 - INFO -   Channels per level: [32, 64, 64, 64]
+2025-11-06 18:12:13 - INFO -   Total parameters: 273,955
+2025-11-06 18:12:13 - INFO -   Parameter memory: 1.05 MB
+2025-11-06 18:12:13 - INFO - === Detailed Architecture Analysis ===
+2025-11-06 18:12:13 - INFO - Input vector dimension: 73,728 (8 × 96 × 96)
+2025-11-06 18:12:13 - INFO - Component-wise parameter breakdown:
+2025-11-06 18:12:13 - INFO -   Encoder - encoders.0.channel_proj: 288 parameters
+2025-11-06 18:12:13 - INFO -   Encoder - encoders.0.convnext_block.dwconv: 1,600 parameters
+2025-11-06 18:12:13 - INFO -   Encoder - encoders.0.convnext_block.norm: 64 parameters
+2025-11-06 18:12:13 - INFO -   Encoder - encoders.0.convnext_block.pwconv1: 4,224 parameters
+2025-11-06 18:12:13 - INFO -   Encoder - encoders.0.convnext_block.pwconv2: 4,128 parameters
+2025-11-06 18:12:13 - INFO -   Encoder - encoders.1.channel_proj: 2,112 parameters
+2025-11-06 18:12:13 - INFO -   Encoder - encoders.1.convnext_block.dwconv: 3,200 parameters
+2025-11-06 18:12:13 - INFO -   Encoder - encoders.1.convnext_block.norm: 128 parameters
+2025-11-06 18:12:13 - INFO -   Encoder - encoders.1.convnext_block.pwconv1: 16,640 parameters
+2025-11-06 18:12:13 - INFO -   Encoder - encoders.1.convnext_block.pwconv2: 16,448 parameters
+2025-11-06 18:12:13 - INFO -   Encoder - encoders.2.convnext_block.dwconv: 3,200 parameters
+2025-11-06 18:12:13 - INFO -   Encoder - encoders.2.convnext_block.norm: 128 parameters
+2025-11-06 18:12:13 - INFO -   Encoder - encoders.2.convnext_block.pwconv1: 16,640 parameters
+2025-11-06 18:12:13 - INFO -   Encoder - encoders.2.convnext_block.pwconv2: 16,448 parameters
+2025-11-06 18:12:13 - INFO -   Bottleneck - bottleneck.convnext_block.dwconv: 3,200 parameters
+2025-11-06 18:12:13 - INFO -   Bottleneck - bottleneck.convnext_block.norm: 128 parameters
+2025-11-06 18:12:13 - INFO -   Bottleneck - bottleneck.convnext_block.pwconv1: 16,640 parameters
+2025-11-06 18:12:13 - INFO -   Bottleneck - bottleneck.convnext_block.pwconv2: 16,448 parameters
+2025-11-06 18:12:13 - INFO -   Decoder - upsamplers.0: 16,448 parameters
+2025-11-06 18:12:13 - INFO -   Decoder - upsamplers.1: 16,448 parameters
+2025-11-06 18:12:13 - INFO -   Decoder - upsamplers.2: 16,448 parameters
+2025-11-06 18:12:13 - INFO -   Decoder - decoders.0.channel_proj: 8,256 parameters
+2025-11-06 18:12:13 - INFO -   Decoder - decoders.0.convnext_block.dwconv: 3,200 parameters
+2025-11-06 18:12:13 - INFO -   Decoder - decoders.0.convnext_block.norm: 128 parameters
+2025-11-06 18:12:13 - INFO -   Decoder - decoders.0.convnext_block.pwconv1: 16,640 parameters
+2025-11-06 18:12:13 - INFO -   Decoder - decoders.0.convnext_block.pwconv2: 16,448 parameters
+2025-11-06 18:12:13 - INFO -   Decoder - decoders.1.channel_proj: 8,256 parameters
+2025-11-06 18:12:13 - INFO -   Decoder - decoders.1.convnext_block.dwconv: 3,200 parameters
+2025-11-06 18:12:13 - INFO -   Decoder - decoders.1.convnext_block.norm: 128 parameters
+2025-11-06 18:12:13 - INFO -   Decoder - decoders.1.convnext_block.pwconv1: 16,640 parameters
+2025-11-06 18:12:13 - INFO -   Decoder - decoders.1.convnext_block.pwconv2: 16,448 parameters
+2025-11-06 18:12:13 - INFO -   Decoder - decoders.2.channel_proj: 3,104 parameters
+2025-11-06 18:12:13 - INFO -   Decoder - decoders.2.convnext_block.dwconv: 1,600 parameters
+2025-11-06 18:12:13 - INFO -   Decoder - decoders.2.convnext_block.norm: 64 parameters
+2025-11-06 18:12:13 - INFO -   Decoder - decoders.2.convnext_block.pwconv1: 4,224 parameters
+2025-11-06 18:12:13 - INFO -   Decoder - decoders.2.convnext_block.pwconv2: 4,128 parameters
+2025-11-06 18:12:13 - INFO -   Other - final_conv: 99 parameters
+2025-11-06 18:12:13 - INFO - Parameter distribution summary:
+2025-11-06 18:12:13 - INFO -   Encoder parameters: 85,248 (31.2%)
+2025-11-06 18:12:13 - INFO -   Decoder parameters: 151,808 (55.5%)
+2025-11-06 18:12:13 - INFO -   Bottleneck parameters: 36,416 (13.3%)
+2025-11-06 18:12:13 - INFO -   Other parameters: 99 (0.0%)
+2025-11-06 18:12:13 - INFO - Latent space dimensions (feature maps at each level):
+2025-11-06 18:12:13 - INFO -   Level 0: 32 × 96 × 96 = 294,912 elements
+2025-11-06 18:12:13 - INFO -   Level 1: 64 × 48 × 48 = 147,456 elements
+2025-11-06 18:12:13 - INFO -   Level 2: 64 × 24 × 24 = 36,864 elements
+2025-11-06 18:12:13 - INFO -   Level 3: 64 × 12 × 12 = 9,216 elements
+2025-11-06 18:12:13 - INFO - Skip connection dimensions:
+2025-11-06 18:12:13 - INFO -   Skip 0: 32 × 96 × 96 = 294,912 elements
+2025-11-06 18:12:13 - INFO -   Skip 1: 64 × 48 × 48 = 147,456 elements
+2025-11-06 18:12:13 - INFO -   Skip 2: 64 × 24 × 24 = 36,864 elements
+2025-11-06 18:12:13 - INFO - Memory analysis:
+2025-11-06 18:12:13 - INFO -   Peak feature map memory (inference): 2.14 MB
+2025-11-06 18:12:13 - INFO -   Peak feature map memory (training): 4.29 MB (with gradients)
+2025-11-06 18:12:13 - INFO - Output vector dimension: 27,648 (3 × 96 × 96)
+2025-11-06 18:12:13 - INFO - PyTorch model saved to: /home/philab/Desktop/hydranet/experiments/exp_1762449133_5525_s96_f32_d3_m1_2_2_2/pytorch/model.pt
+2025-11-06 18:12:13 - INFO - === ONNX Conversion Phase ===
+2025-11-06 18:12:13 - INFO - === Model Export Diagnostics ===
+2025-11-06 18:12:13 - INFO - PyTorch version: 1.9.0+cu102
+2025-11-06 18:12:13 - INFO - Model parameters: 273,955
+2025-11-06 18:12:13 - INFO - Model memory: 1.05 MB
+2025-11-06 18:12:13 - INFO - Starting ONNX export with opset version 11
+2025-11-06 18:12:13 - INFO - Model input shape: torch.Size([1, 8, 96, 96])
+2025-11-06 18:12:13 - INFO - Model input dtype: torch.float32
+2025-11-06 18:12:13 - INFO - Forward pass successful. Output shape: torch.Size([1, 3, 96, 96])
+2025-11-06 18:12:13 - INFO - Output dtype: torch.float32
+2025-11-06 18:12:13 - INFO - Output value range: [-0.6360, 0.3016]
+2025-11-06 18:12:13 - INFO - Model successfully exported to /home/philab/Desktop/hydranet/experiments/exp_1762449133_5525_s96_f32_d3_m1_2_2_2/onnx/model.onnx
+2025-11-06 18:12:13 - INFO - ONNX model size: 1.05 MB
+2025-11-06 18:12:13 - INFO - Saved dummy input with shape (1, 8, 96, 96) to /home/philab/Desktop/hydranet/experiments/exp_1762449133_5525_s96_f32_d3_m1_2_2_2/onnx/sample_input.npy
+2025-11-06 18:12:13 - INFO - Input data type: float32
+2025-11-06 18:12:13 - INFO - Input value range: [-4.9373, 4.2185]
+2025-11-06 18:12:13 - INFO - === OpenVINO Conversion Phase ===
+2025-11-06 18:12:13 - INFO - Starting OpenVINO conversion in Docker container...
+2025-11-06 18:12:17 - INFO - OpenVINO conversion completed in 4.02 seconds
+2025-11-06 18:12:17 - INFO - OpenVINO model files created:
+2025-11-06 18:12:17 - INFO -   XML file: /home/philab/Desktop/hydranet/experiments/exp_1762449133_5525_s96_f32_d3_m1_2_2_2/openvino/model.xml (0.08 MB)
+2025-11-06 18:12:17 - INFO -   BIN file: /home/philab/Desktop/hydranet/experiments/exp_1762449133_5525_s96_f32_d3_m1_2_2_2/openvino/model.bin (0.52 MB)
+2025-11-06 18:12:17 - INFO - === Myriad Inference Phase ===
+2025-11-06 18:12:17 - INFO - Starting Myriad inference in Docker container...
+2025-11-06 18:12:20 - INFO - Myriad inference completed in 2.74 seconds
+2025-11-06 18:12:20 - INFO - Actual inference time: 0.145800 seconds
+2025-11-06 18:12:20 - INFO - ✅ Complete pipeline executed successfully!
+2025-11-06 18:12:20 - INFO - ✅ Experiment 91 completed successfully
+2025-11-06 18:12:20 - INFO -    Inference time: 0.145800s
+2025-11-06 18:12:20 - INFO - 
+=== Experiment 92/2475 ===
+2025-11-06 18:12:20 - INFO - Experiment ID: exp_1762449140_4143_s96_f32_d3_m1_2_2_3
+2025-11-06 18:12:20 - INFO - Experiment directory: /home/philab/Desktop/hydranet/experiments/exp_1762449140_4143_s96_f32_d3_m1_2_2_3

exp_1762449133_5525_s96_f32_d3_m1_2_2_2/model_info.json

@@ -0,0 +1,139 @@
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+  "timestamp": "2025-11-06T18:12:13.447999"
+}

exp_1762449133_5525_s96_f32_d3_m1_2_2_2/pipeline_results.json

@@ -0,0 +1,139 @@
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+    "success": true,
+    "total_time": 2.742616229923442,
+    "inference_time": 0.1458,
+    "stdout": "[setupvars.sh] OpenVINO environment initialized\nStarting Myriad inference...\nInput: /home/mount/experiments/exp_1762449133_5525_s96_f32_d3_m1_2_2_2/onnx/sample_input.npy\nModel XML: /home/mount/experiments/exp_1762449133_5525_s96_f32_d3_m1_2_2_2/openvino/model.xml\nModel BIN: /home/mount/experiments/exp_1762449133_5525_s96_f32_d3_m1_2_2_2/openvino/model.bin\n[OK] OpenVINO inference engine imported successfully\nDevice: MYRIAD\nModel XML: /home/mount/experiments/exp_1762449133_5525_s96_f32_d3_m1_2_2_2/openvino/model.xml\nModel BIN: /home/mount/experiments/exp_1762449133_5525_s96_f32_d3_m1_2_2_2/openvino/model.bin\nInput file: /home/mount/experiments/exp_1762449133_5525_s96_f32_d3_m1_2_2_2/onnx/sample_input.npy\nInitializing OpenVINO Runtime Core...\nAvailable devices:\n[E:] [BSL] found 0 ioexpander device\n  ['CPU', 'GNA', 'MYRIAD']\nLoading network...\nInput blob: input\nInput shape: [1, 8, 96, 96]\nOutput blob: Conv_105\nOutput shape: [1, 3, 96, 96]\nLoading network to MYRIAD...\nLoading input data...\nInput data shape: (1, 8, 96, 96)\nInput data type: float32\nRunning inference...\n[OK] Inference completed!\nInference time: 0.145800 seconds\nOutput shape: (1, 3, 96, 96)\nOutput dtype: float32\nOutput range: [-0.636230, 0.301270]\nMyriad inference completed!\n",
+    "stderr": ""
+  },
+  "end_time": "2025-11-06T18:12:20.507575"
+}