{ "cells": [ { "cell_type": "markdown", "id": "4e20aa27", "metadata": {}, "source": [ "CT Training and Reconstructions with ODP\n", "========================================\n", "\n", "This example demonstrates the training of the unrolled optimization with\n", "deep priors (ODP) gradient descent architecture described in\n", " applied to a CT reconstruction problem.\n", "\n", "The source images are foam phantoms generated with xdesign.\n", "\n", "A class\n", "[scico.flax.ODPNet](../_autosummary/scico.flax.rst#scico.flax.ODPNet)\n", "implements the ODP architecture, which solves the optimization problem\n", "\n", "$$\\mathrm{argmin}_{\\mathbf{x}} \\; \\| A \\mathbf{x} - \\mathbf{y} \\|_2^2\n", "+ r(\\mathbf{x}) \\;,$$\n", "\n", "where $A$ is a tomographic projector, $\\mathbf{y}$ is a set of sinograms,\n", "$r$ is a regularizer and $\\mathbf{x}$ is the set of reconstructed images.\n", "The ODP, gradient descent architecture, abstracts the iterative solution\n", "by an unrolled network where each iteration corresponds to a different\n", "stage in the ODP network and updates the prediction by solving\n", "\n", "$$\\mathbf{x}^{k+1} = \\mathrm{argmin}_{\\mathbf{x}} \\; \\alpha_k \\| A\n", "\\mathbf{x} - \\mathbf{y} \\|_2^2 + \\frac{1}{2} \\| \\mathbf{x} -\n", "\\mathbf{x}^k - \\mathbf{x}^{k+1/2} \\|_2^2 \\;,$$\n", "\n", "which for the CT problem, using gradient descent, corresponds to\n", "\n", "$$\\mathbf{x}^{k+1} = \\mathbf{x}^k + \\mathbf{x}^{k+1/2} - \\alpha_k \\,\n", "A^T \\, (A \\mathbf{x}^k - \\mathbf{y}) \\;,$$\n", "\n", "where $k$ is the index of the stage (iteration), $\\mathbf{x}^k +\n", "\\mathbf{x}^{k+1/2} = \\mathrm{ResNet}(\\mathbf{x}^{k})$ is the\n", "regularization (implemented as a residual convolutional neural network),\n", "$\\mathbf{x}^k$ is the output of the previous stage and $\\alpha_k > 0$ is\n", "a learned stage-wise parameter weighting the contribution of the fidelity\n", "term. The output of the final stage is the set of reconstructed images." ] }, { "cell_type": "code", "execution_count": 1, "id": "35cc4973", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2023-11-15T01:06:45.390863Z", "iopub.status.busy": "2023-11-15T01:06:45.390595Z", "iopub.status.idle": "2023-11-15T01:06:50.897817Z", "shell.execute_reply": "2023-11-15T01:06:50.896298Z" } }, "outputs": [], "source": [ "# This scico project Jupyter notebook has been automatically modified\n", "# to install the dependencies required for running it on Google Colab.\n", "# If you encounter any problems in running it, please open an issue at\n", "# https://github.com/lanl/scico-data/issues\n", "\n", "!pip install 'scico[examples] @ git+https://github.com/lanl/scico'\n", "\n", "# This scico project Jupyter notebook has been automatically modified\n", "# to install the dependencies required for running it on Google Colab.\n", "# If you encounter any problems in running it, please open an issue at\n", "# https://github.com/lanl/scico-data/issues\n", "\n", "!pip install 'scico[examples] @ git+https://github.com/lanl/scico'\n", "\n", "# This scico project Jupyter notebook has been automatically modified\n", "# to install the dependencies required for running it on Google Colab.\n", "# If you encounter any problems in running it, please open an issue at\n", "# https://github.com/lanl/scico-data/issues\n", "\n", "!pip install 'scico[examples] @ git+https://github.com/lanl/scico\n'", "\n", "# This scico project Jupyter notebook has been automatically modified\n", "# to install the dependencies required for running it on Google Colab.\n", "# If you encounter any problems in running it, please open an issue at\n", "# https://github.com/lanl/scico-data/issues\n", "\n", "!pip install git+https://github.com/lanl/scico\n", "!pip install astra-toolbox xdesign\n", "\n", "# This scico project Jupyter notebook has been automatically modified\n", "# to install the dependencies required for running it on Google Colab.\n", "# If you encounter any problems in running it, please open an issue at\n", "# https://github.com/lanl/scico-data/issues\n", "\n", "!pip install git+https://github.com/lanl/scico\n", "!pip install astra-toolbox\n", "\n", "# This scico project Jupyter notebook has been automatically modified\n", "# to install the dependencies required for running it on Google Colab.\n", "# If you encounter any problems in running it, please open an issue at\n", "# https://github.com/lanl/scico-data/issues\n", "\n", "!pip install git+https://github.com/lanl/scico\n", "!pip install astra-toolbox\n", "\n", "# This scico project Jupyter notebook has been automatically modified\n", "# to install the dependencies required for running it on Google Colab.\n", "# If you encounter any problems in running it, please open an issue at\n", "# https://github.com/lanl/scico-data/issues\n", "\n", "!pip install git+https://github.com/lanl/scico\n", "!pip install astra-toolbox\n", "\n", "import os\n", "from functools import partial\n", "from time import time\n", "\n", "import numpy as np\n", "\n", "import jax\n", "\n", "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", "\n", "from scico import flax as sflax\n", "from scico import metric, plot\n", "from scico.flax.examples import load_ct_data\n", "from scico.flax.train.traversals import clip_positive, construct_traversal\n", "from scico.linop.xray.astra import XRayTransform\n", "plot.config_notebook_plotting()" ] }, { "cell_type": "markdown", "id": "f201031a", "metadata": {}, "source": [ "Prepare parallel processing. Set an arbitrary processor count (only\n", "applies if GPU is not available)." ] }, { "cell_type": "code", "execution_count": 2, "id": "32b0d8a5", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2023-11-15T01:06:50.900948Z", "iopub.status.busy": "2023-11-15T01:06:50.900728Z", "iopub.status.idle": "2023-11-15T01:06:50.905210Z", "shell.execute_reply": "2023-11-15T01:06:50.904599Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Platform: gpu\n" ] } ], "source": [ "os.environ[\"XLA_FLAGS\"] = \"--xla_force_host_platform_device_count=8\"\n", "platform = jax.lib.xla_bridge.get_backend().platform\n", "print(\"Platform: \", platform)" ] }, { "cell_type": "markdown", "id": "cfda0c97", "metadata": {}, "source": [ "Read data from cache or generate if not available." ] }, { "cell_type": "code", "execution_count": 3, "id": "79bc0196", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2023-11-15T01:06:50.908516Z", "iopub.status.busy": "2023-11-15T01:06:50.908305Z", "iopub.status.idle": "2023-11-15T01:06:51.604653Z", "shell.execute_reply": "2023-11-15T01:06:51.603602Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data read from path : ~/.cache/scico/examples/data\n", "Set --training-- : Size: 536\n", "Set --testing -- : Size: 64\n", "Data range --images -- : Min: 0.00, Max: 1.00\n", "Data range --sinogram-- : Min: 0.00, Max: 0.67\n", "Data range --FBP -- : Min: 0.00, Max: 1.00\n" ] } ], "source": [ "N = 256 # phantom size\n", "train_nimg = 536 # number of training images\n", "test_nimg = 64 # number of testing images\n", "nimg = train_nimg + test_nimg\n", "n_projection = 45 # CT views\n", "\n", "trdt, ttdt = load_ct_data(train_nimg, test_nimg, N, n_projection, verbose=True)" ] }, { "cell_type": "markdown", "id": "f765c54d", "metadata": {}, "source": [ "Build CT projection operator." ] }, { "cell_type": "code", "execution_count": 4, "id": "7fea1f6c", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2023-11-15T01:06:51.607383Z", "iopub.status.busy": "2023-11-15T01:06:51.607184Z", "iopub.status.idle": "2023-11-15T01:06:51.621748Z", "shell.execute_reply": "2023-11-15T01:06:51.620898Z" } }, "outputs": [], "source": [ "angles = np.linspace(0, np.pi, n_projection) # evenly spaced projection angles\n", "A = XRayTransform(\n", " input_shape=(N, N),\n", " detector_spacing=1,\n", " det_count=N,\n", " angles=angles,\n", ") # CT projection operator\n", "A = (1.0 / N) * A # normalized" ] }, { "cell_type": "markdown", "id": "f4b2b8c8", "metadata": {}, "source": [ "Build training and testing structures. Inputs are the sinograms and\n", "outpus are the original generated foams. Keep training and testing\n", "partitions." ] }, { "cell_type": "code", "execution_count": 5, "id": "55aabcdc", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2023-11-15T01:06:51.624003Z", "iopub.status.busy": "2023-11-15T01:06:51.623825Z", "iopub.status.idle": "2023-11-15T01:06:51.627901Z", "shell.execute_reply": "2023-11-15T01:06:51.627123Z" } }, "outputs": [], "source": [ "numtr = 320\n", "numtt = 32\n", "train_ds = {\"image\": trdt[\"sino\"][:numtr], \"label\": trdt[\"img\"][:numtr]}\n", "test_ds = {\"image\": ttdt[\"sino\"][:numtt], \"label\": ttdt[\"img\"][:numtt]}" ] }, { "cell_type": "markdown", "id": "2989ce3c", "metadata": {}, "source": [ "Define configuration dictionary for model and training loop.\n", "\n", "Parameters have been selected for demonstration purposes and relatively\n", "short training. The model depth is akin to the number of unrolled\n", "iterations in the MoDL model. The block depth controls the number of\n", "layers at each unrolled iteration. The number of filters is uniform\n", "throughout the iterations. The iterations used for the conjugate gradient\n", "(CG) solver can also be specified. Better performance may be obtained by\n", "increasing depth, block depth, number of filters, CG iterations, or\n", "training epochs, but may require longer training times." ] }, { "cell_type": "code", "execution_count": 6, "id": "214de4e4", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2023-11-15T01:06:51.631012Z", "iopub.status.busy": "2023-11-15T01:06:51.630765Z", "iopub.status.idle": "2023-11-15T01:06:51.635520Z", "shell.execute_reply": "2023-11-15T01:06:51.634711Z" } }, "outputs": [], "source": [ "# model configuration\n", "model_conf = {\n", " \"depth\": 8,\n", " \"num_filters\": 64,\n", " \"block_depth\": 6,\n", "}\n", "# training configuration\n", "train_conf: sflax.ConfigDict = {\n", " \"seed\": 1234,\n", " \"opt_type\": \"ADAM\",\n", " \"batch_size\": 16,\n", " \"num_epochs\": 200,\n", " \"base_learning_rate\": 1e-3,\n", " \"warmup_epochs\": 0,\n", " \"log_every_steps\": 160,\n", " \"log\": True,\n", " \"checkpointing\": True,\n", "}" ] }, { "cell_type": "markdown", "id": "3723b2ae", "metadata": {}, "source": [ "Construct functionality for making sure that the learned fidelity weight\n", "parameter is always positive." ] }, { "cell_type": "code", "execution_count": 7, "id": "60384e35", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2023-11-15T01:06:51.638581Z", "iopub.status.busy": "2023-11-15T01:06:51.638343Z", "iopub.status.idle": "2023-11-15T01:06:51.642340Z", "shell.execute_reply": "2023-11-15T01:06:51.641606Z" } }, "outputs": [], "source": [ "alphatrav = construct_traversal(\"alpha\") # select alpha parameters in model\n", "alphapost = partial(\n", " clip_positive, # apply this function\n", " traversal=alphatrav, # to alpha parameters in model\n", " minval=1e-3,\n", ")" ] }, { "cell_type": "markdown", "id": "90f3a903", "metadata": {}, "source": [ "Print configuration of distributed run." ] }, { "cell_type": "code", "execution_count": 8, "id": "a2012c61", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2023-11-15T01:06:51.645276Z", "iopub.status.busy": "2023-11-15T01:06:51.645055Z", "iopub.status.idle": "2023-11-15T01:06:51.649576Z", "shell.execute_reply": "2023-11-15T01:06:51.648738Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "JAX process: 0 / 1\n", "JAX local devices: [cuda(id=0), cuda(id=1), cuda(id=2), cuda(id=3), cuda(id=4), cuda(id=5), cuda(id=6), cuda(id=7)]\n" ] } ], "source": [ "print(f\"{'JAX process: '}{jax.process_index()}{' / '}{jax.process_count()}\")\n", "print(f\"{'JAX local devices: '}{jax.local_devices()}\")" ] }, { "cell_type": "markdown", "id": "4acb1398", "metadata": {}, "source": [ "Construct ODPNet model." ] }, { "cell_type": "code", "execution_count": 9, "id": "e66cf0f8", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2023-11-15T01:06:51.652473Z", "iopub.status.busy": "2023-11-15T01:06:51.652163Z", "iopub.status.idle": "2023-11-15T01:06:51.656538Z", "shell.execute_reply": "2023-11-15T01:06:51.655638Z" } }, "outputs": [], "source": [ "channels = train_ds[\"image\"].shape[-1]\n", "model = sflax.ODPNet(\n", " operator=A,\n", " depth=model_conf[\"depth\"],\n", " channels=channels,\n", " num_filters=model_conf[\"num_filters\"],\n", " block_depth=model_conf[\"block_depth\"],\n", " odp_block=sflax.inverse.ODPGrDescBlock,\n", " alpha_ini=1e-2,\n", ")" ] }, { "cell_type": "markdown", "id": "e86f9011", "metadata": {}, "source": [ "Run training loop." ] }, { "cell_type": "code", "execution_count": 10, "id": "dcbe0c9c", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2023-11-15T01:06:51.659994Z", "iopub.status.busy": "2023-11-15T01:06:51.659713Z", "iopub.status.idle": "2023-11-15T02:06:49.913556Z", "shell.execute_reply": "2023-11-15T02:06:49.911660Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Channels: 1, training signals: 320, testing signals: 32, signal size: 256\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "+---------------------------------------------------------+----------------+--------+-----------+--------+\n", "| Name | Shape | Size | Mean | Std |\n", "+---------------------------------------------------------+----------------+--------+-----------+--------+\n", "| ODPGrDescBlock_0/alpha | (1,) | 1 | 0.01 | 0.0 |\n", "| ODPGrDescBlock_0/resnet/BatchNorm_0/bias | (1,) | 1 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_0/resnet/BatchNorm_0/scale | (1,) | 1 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_0/resnet/ConvBNBlock_0/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_0/resnet/ConvBNBlock_0/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_0/resnet/ConvBNBlock_0/Conv_0/kernel | (3, 3, 1, 64) | 576 | -0.000308 | 0.0568 |\n", "| ODPGrDescBlock_0/resnet/ConvBNBlock_1/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_0/resnet/ConvBNBlock_1/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_0/resnet/ConvBNBlock_1/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | 2.38e-05 | 0.0416 |\n", "| ODPGrDescBlock_0/resnet/ConvBNBlock_2/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_0/resnet/ConvBNBlock_2/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_0/resnet/ConvBNBlock_2/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | 0.000402 | 0.0418 |\n", "| ODPGrDescBlock_0/resnet/ConvBNBlock_3/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_0/resnet/ConvBNBlock_3/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_0/resnet/ConvBNBlock_3/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | 0.000185 | 0.0416 |\n", "| ODPGrDescBlock_0/resnet/ConvBNBlock_4/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_0/resnet/ConvBNBlock_4/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_0/resnet/ConvBNBlock_4/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | -0.000101 | 0.0418 |\n", "| ODPGrDescBlock_0/resnet/Conv_0/kernel | (3, 3, 64, 1) | 576 | 0.00276 | 0.058 |\n", "| ODPGrDescBlock_1/alpha | (1,) | 1 | 0.005 | 0.0 |\n", "| ODPGrDescBlock_1/resnet/BatchNorm_0/bias | (1,) | 1 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_1/resnet/BatchNorm_0/scale | (1,) | 1 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_1/resnet/ConvBNBlock_0/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_1/resnet/ConvBNBlock_0/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_1/resnet/ConvBNBlock_0/Conv_0/kernel | (3, 3, 1, 64) | 576 | 0.00128 | 0.0583 |\n", "| ODPGrDescBlock_1/resnet/ConvBNBlock_1/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_1/resnet/ConvBNBlock_1/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_1/resnet/ConvBNBlock_1/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | -0.000226 | 0.0419 |\n", "| ODPGrDescBlock_1/resnet/ConvBNBlock_2/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_1/resnet/ConvBNBlock_2/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_1/resnet/ConvBNBlock_2/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | 0.00029 | 0.0415 |\n", "| ODPGrDescBlock_1/resnet/ConvBNBlock_3/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_1/resnet/ConvBNBlock_3/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_1/resnet/ConvBNBlock_3/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | -5.14e-05 | 0.0417 |\n", "| ODPGrDescBlock_1/resnet/ConvBNBlock_4/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_1/resnet/ConvBNBlock_4/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_1/resnet/ConvBNBlock_4/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | 0.000319 | 0.0416 |\n", "| ODPGrDescBlock_1/resnet/Conv_0/kernel | (3, 3, 64, 1) | 576 | 0.000334 | 0.0583 |\n", "| ODPGrDescBlock_2/alpha | (1,) | 1 | 0.0025 | 0.0 |\n", "| ODPGrDescBlock_2/resnet/BatchNorm_0/bias | (1,) | 1 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_2/resnet/BatchNorm_0/scale | (1,) | 1 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_2/resnet/ConvBNBlock_0/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_2/resnet/ConvBNBlock_0/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_2/resnet/ConvBNBlock_0/Conv_0/kernel | (3, 3, 1, 64) | 576 | -0.00119 | 0.0602 |\n", "| ODPGrDescBlock_2/resnet/ConvBNBlock_1/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_2/resnet/ConvBNBlock_1/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_2/resnet/ConvBNBlock_1/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | 0.000443 | 0.0415 |\n", "| ODPGrDescBlock_2/resnet/ConvBNBlock_2/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_2/resnet/ConvBNBlock_2/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_2/resnet/ConvBNBlock_2/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | 0.000163 | 0.0416 |\n", "| ODPGrDescBlock_2/resnet/ConvBNBlock_3/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_2/resnet/ConvBNBlock_3/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_2/resnet/ConvBNBlock_3/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | -0.0004 | 0.0417 |\n", "| ODPGrDescBlock_2/resnet/ConvBNBlock_4/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_2/resnet/ConvBNBlock_4/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_2/resnet/ConvBNBlock_4/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | 6.25e-05 | 0.0417 |\n", "| ODPGrDescBlock_2/resnet/Conv_0/kernel | (3, 3, 64, 1) | 576 | 0.000515 | 0.0586 |\n", "| ODPGrDescBlock_3/alpha | (1,) | 1 | 0.00125 | 0.0 |\n", "| ODPGrDescBlock_3/resnet/BatchNorm_0/bias | (1,) | 1 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_3/resnet/BatchNorm_0/scale | (1,) | 1 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_3/resnet/ConvBNBlock_0/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_3/resnet/ConvBNBlock_0/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_3/resnet/ConvBNBlock_0/Conv_0/kernel | (3, 3, 1, 64) | 576 | -0.00179 | 0.057 |\n", "| ODPGrDescBlock_3/resnet/ConvBNBlock_1/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_3/resnet/ConvBNBlock_1/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_3/resnet/ConvBNBlock_1/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | -3.06e-05 | 0.0417 |\n", "| ODPGrDescBlock_3/resnet/ConvBNBlock_2/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_3/resnet/ConvBNBlock_2/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_3/resnet/ConvBNBlock_2/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | 0.000153 | 0.0416 |\n", "| ODPGrDescBlock_3/resnet/ConvBNBlock_3/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_3/resnet/ConvBNBlock_3/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_3/resnet/ConvBNBlock_3/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | 0.000617 | 0.0418 |\n", "| ODPGrDescBlock_3/resnet/ConvBNBlock_4/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_3/resnet/ConvBNBlock_4/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_3/resnet/ConvBNBlock_4/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | -0.000218 | 0.0419 |\n", "| ODPGrDescBlock_3/resnet/Conv_0/kernel | (3, 3, 64, 1) | 576 | -0.000309 | 0.0575 |\n", "| ODPGrDescBlock_4/alpha | (1,) | 1 | 0.000625 | 0.0 |\n", "| ODPGrDescBlock_4/resnet/BatchNorm_0/bias | (1,) | 1 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_4/resnet/BatchNorm_0/scale | (1,) | 1 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_4/resnet/ConvBNBlock_0/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_4/resnet/ConvBNBlock_0/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_4/resnet/ConvBNBlock_0/Conv_0/kernel | (3, 3, 1, 64) | 576 | 0.00173 | 0.0585 |\n", "| ODPGrDescBlock_4/resnet/ConvBNBlock_1/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_4/resnet/ConvBNBlock_1/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_4/resnet/ConvBNBlock_1/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | -0.000285 | 0.0417 |\n", "| ODPGrDescBlock_4/resnet/ConvBNBlock_2/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_4/resnet/ConvBNBlock_2/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_4/resnet/ConvBNBlock_2/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | -0.000136 | 0.0416 |\n", "| ODPGrDescBlock_4/resnet/ConvBNBlock_3/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_4/resnet/ConvBNBlock_3/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_4/resnet/ConvBNBlock_3/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | 0.000243 | 0.0417 |\n", "| ODPGrDescBlock_4/resnet/ConvBNBlock_4/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_4/resnet/ConvBNBlock_4/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_4/resnet/ConvBNBlock_4/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | 0.000183 | 0.0417 |\n", "| ODPGrDescBlock_4/resnet/Conv_0/kernel | (3, 3, 64, 1) | 576 | 0.0033 | 0.0608 |\n", "| ODPGrDescBlock_5/alpha | (1,) | 1 | 0.000312 | 0.0 |\n", "| ODPGrDescBlock_5/resnet/BatchNorm_0/bias | (1,) | 1 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_5/resnet/BatchNorm_0/scale | (1,) | 1 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_5/resnet/ConvBNBlock_0/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_5/resnet/ConvBNBlock_0/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_5/resnet/ConvBNBlock_0/Conv_0/kernel | (3, 3, 1, 64) | 576 | 0.00178 | 0.0589 |\n", "| ODPGrDescBlock_5/resnet/ConvBNBlock_1/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_5/resnet/ConvBNBlock_1/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_5/resnet/ConvBNBlock_1/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | -0.000261 | 0.0417 |\n", "| ODPGrDescBlock_5/resnet/ConvBNBlock_2/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_5/resnet/ConvBNBlock_2/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_5/resnet/ConvBNBlock_2/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | 0.000299 | 0.0417 |\n", "| ODPGrDescBlock_5/resnet/ConvBNBlock_3/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_5/resnet/ConvBNBlock_3/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_5/resnet/ConvBNBlock_3/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | -0.000267 | 0.0418 |\n", "| ODPGrDescBlock_5/resnet/ConvBNBlock_4/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_5/resnet/ConvBNBlock_4/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_5/resnet/ConvBNBlock_4/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | 0.000261 | 0.0416 |\n", "| ODPGrDescBlock_5/resnet/Conv_0/kernel | (3, 3, 64, 1) | 576 | 0.00478 | 0.0608 |\n", "| ODPGrDescBlock_6/alpha | (1,) | 1 | 0.000156 | 0.0 |\n", "| ODPGrDescBlock_6/resnet/BatchNorm_0/bias | (1,) | 1 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_6/resnet/BatchNorm_0/scale | (1,) | 1 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_6/resnet/ConvBNBlock_0/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_6/resnet/ConvBNBlock_0/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_6/resnet/ConvBNBlock_0/Conv_0/kernel | (3, 3, 1, 64) | 576 | 0.000588 | 0.0576 |\n", "| ODPGrDescBlock_6/resnet/ConvBNBlock_1/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_6/resnet/ConvBNBlock_1/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_6/resnet/ConvBNBlock_1/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | 0.000272 | 0.0416 |\n", "| ODPGrDescBlock_6/resnet/ConvBNBlock_2/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_6/resnet/ConvBNBlock_2/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_6/resnet/ConvBNBlock_2/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | 6.59e-05 | 0.0417 |\n", "| ODPGrDescBlock_6/resnet/ConvBNBlock_3/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_6/resnet/ConvBNBlock_3/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_6/resnet/ConvBNBlock_3/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | 7.39e-05 | 0.0418 |\n", "| ODPGrDescBlock_6/resnet/ConvBNBlock_4/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_6/resnet/ConvBNBlock_4/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_6/resnet/ConvBNBlock_4/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | -5e-05 | 0.0418 |\n", "| ODPGrDescBlock_6/resnet/Conv_0/kernel | (3, 3, 64, 1) | 576 | -0.000458 | 0.0583 |\n", "| ODPGrDescBlock_7/alpha | (1,) | 1 | 7.81e-05 | 0.0 |\n", "| ODPGrDescBlock_7/resnet/BatchNorm_0/bias | (1,) | 1 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_7/resnet/BatchNorm_0/scale | (1,) | 1 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_7/resnet/ConvBNBlock_0/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_7/resnet/ConvBNBlock_0/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_7/resnet/ConvBNBlock_0/Conv_0/kernel | (3, 3, 1, 64) | 576 | 0.00317 | 0.0574 |\n", "| ODPGrDescBlock_7/resnet/ConvBNBlock_1/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_7/resnet/ConvBNBlock_1/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_7/resnet/ConvBNBlock_1/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | -0.000238 | 0.0415 |\n", "| ODPGrDescBlock_7/resnet/ConvBNBlock_2/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_7/resnet/ConvBNBlock_2/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_7/resnet/ConvBNBlock_2/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | 0.000153 | 0.0418 |\n", "| ODPGrDescBlock_7/resnet/ConvBNBlock_3/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_7/resnet/ConvBNBlock_3/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_7/resnet/ConvBNBlock_3/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | -3.74e-05 | 0.0417 |\n", "| ODPGrDescBlock_7/resnet/ConvBNBlock_4/BatchNorm_0/bias | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_7/resnet/ConvBNBlock_4/BatchNorm_0/scale | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_7/resnet/ConvBNBlock_4/Conv_0/kernel | (3, 3, 64, 64) | 36,864 | 0.000368 | 0.0418 |\n", "| ODPGrDescBlock_7/resnet/Conv_0/kernel | (3, 3, 64, 1) | 576 | -0.00299 | 0.0604 |\n", "+---------------------------------------------------------+----------------+--------+-----------+--------+\n", "Total: 1,194,008\n", "+--------------------------------------------------------+-------+------+------+-----+\n", "| Name | Shape | Size | Mean | Std |\n", "+--------------------------------------------------------+-------+------+------+-----+\n", "| ODPGrDescBlock_0/resnet/BatchNorm_0/mean | (1,) | 1 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_0/resnet/BatchNorm_0/var | (1,) | 1 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_0/resnet/ConvBNBlock_0/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_0/resnet/ConvBNBlock_0/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_0/resnet/ConvBNBlock_1/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_0/resnet/ConvBNBlock_1/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_0/resnet/ConvBNBlock_2/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_0/resnet/ConvBNBlock_2/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_0/resnet/ConvBNBlock_3/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_0/resnet/ConvBNBlock_3/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_0/resnet/ConvBNBlock_4/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_0/resnet/ConvBNBlock_4/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_1/resnet/BatchNorm_0/mean | (1,) | 1 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_1/resnet/BatchNorm_0/var | (1,) | 1 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_1/resnet/ConvBNBlock_0/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_1/resnet/ConvBNBlock_0/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_1/resnet/ConvBNBlock_1/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_1/resnet/ConvBNBlock_1/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_1/resnet/ConvBNBlock_2/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_1/resnet/ConvBNBlock_2/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_1/resnet/ConvBNBlock_3/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_1/resnet/ConvBNBlock_3/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_1/resnet/ConvBNBlock_4/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_1/resnet/ConvBNBlock_4/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_2/resnet/BatchNorm_0/mean | (1,) | 1 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_2/resnet/BatchNorm_0/var | (1,) | 1 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_2/resnet/ConvBNBlock_0/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_2/resnet/ConvBNBlock_0/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_2/resnet/ConvBNBlock_1/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_2/resnet/ConvBNBlock_1/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_2/resnet/ConvBNBlock_2/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_2/resnet/ConvBNBlock_2/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_2/resnet/ConvBNBlock_3/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_2/resnet/ConvBNBlock_3/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_2/resnet/ConvBNBlock_4/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_2/resnet/ConvBNBlock_4/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_3/resnet/BatchNorm_0/mean | (1,) | 1 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_3/resnet/BatchNorm_0/var | (1,) | 1 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_3/resnet/ConvBNBlock_0/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_3/resnet/ConvBNBlock_0/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_3/resnet/ConvBNBlock_1/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_3/resnet/ConvBNBlock_1/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_3/resnet/ConvBNBlock_2/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_3/resnet/ConvBNBlock_2/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_3/resnet/ConvBNBlock_3/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_3/resnet/ConvBNBlock_3/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_3/resnet/ConvBNBlock_4/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_3/resnet/ConvBNBlock_4/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_4/resnet/BatchNorm_0/mean | (1,) | 1 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_4/resnet/BatchNorm_0/var | (1,) | 1 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_4/resnet/ConvBNBlock_0/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_4/resnet/ConvBNBlock_0/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_4/resnet/ConvBNBlock_1/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_4/resnet/ConvBNBlock_1/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_4/resnet/ConvBNBlock_2/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_4/resnet/ConvBNBlock_2/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_4/resnet/ConvBNBlock_3/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_4/resnet/ConvBNBlock_3/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_4/resnet/ConvBNBlock_4/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_4/resnet/ConvBNBlock_4/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_5/resnet/BatchNorm_0/mean | (1,) | 1 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_5/resnet/BatchNorm_0/var | (1,) | 1 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_5/resnet/ConvBNBlock_0/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_5/resnet/ConvBNBlock_0/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_5/resnet/ConvBNBlock_1/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_5/resnet/ConvBNBlock_1/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_5/resnet/ConvBNBlock_2/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_5/resnet/ConvBNBlock_2/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_5/resnet/ConvBNBlock_3/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_5/resnet/ConvBNBlock_3/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_5/resnet/ConvBNBlock_4/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_5/resnet/ConvBNBlock_4/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_6/resnet/BatchNorm_0/mean | (1,) | 1 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_6/resnet/BatchNorm_0/var | (1,) | 1 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_6/resnet/ConvBNBlock_0/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_6/resnet/ConvBNBlock_0/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_6/resnet/ConvBNBlock_1/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_6/resnet/ConvBNBlock_1/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_6/resnet/ConvBNBlock_2/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_6/resnet/ConvBNBlock_2/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_6/resnet/ConvBNBlock_3/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_6/resnet/ConvBNBlock_3/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_6/resnet/ConvBNBlock_4/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_6/resnet/ConvBNBlock_4/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_7/resnet/BatchNorm_0/mean | (1,) | 1 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_7/resnet/BatchNorm_0/var | (1,) | 1 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_7/resnet/ConvBNBlock_0/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_7/resnet/ConvBNBlock_0/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_7/resnet/ConvBNBlock_1/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_7/resnet/ConvBNBlock_1/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_7/resnet/ConvBNBlock_2/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_7/resnet/ConvBNBlock_2/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_7/resnet/ConvBNBlock_3/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_7/resnet/ConvBNBlock_3/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "| ODPGrDescBlock_7/resnet/ConvBNBlock_4/BatchNorm_0/mean | (64,) | 64 | 0.0 | 0.0 |\n", "| ODPGrDescBlock_7/resnet/ConvBNBlock_4/BatchNorm_0/var | (64,) | 64 | 1.0 | 0.0 |\n", "+--------------------------------------------------------+-------+------+------+-----+\n", "Total: 5,136\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Initial compilation, this might take some minutes...\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Initial compilation completed.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Epoch Time Train_LR Train_Loss Train_SNR Eval_Loss Eval_SNR\n", "---------------------------------------------------------------------\n", " 7 1.61e+02 0.001000 0.052377 3.85 0.132320 -4.61\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 15 3.06e+02 0.001000 0.007551 7.91 0.126599 -4.42\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 23 4.47e+02 0.001000 0.005597 9.15 0.086118 -2.74\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 31 5.89e+02 0.001000 0.004938 9.69 0.081113 -2.48\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 39 7.30e+02 0.001000 0.004495 10.10 0.028649 2.04\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 47 8.73e+02 0.001000 0.004167 10.42 0.050053 -0.39\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 55 1.02e+03 0.001000 0.003868 10.75 0.011462 6.03\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 63 1.16e+03 0.001000 0.003687 10.96 0.004091 10.50\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 71 1.30e+03 0.001000 0.006230 10.08 21614.328125 -56.68\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 79 1.44e+03 0.001000 0.012242 6.12 0.019623 3.68\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 87 1.59e+03 0.001000 0.005031 9.62 0.041972 0.38\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 95 1.73e+03 0.001000 0.004204 10.39 0.012248 5.73\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 103 1.87e+03 0.001000 0.003774 10.86 0.005177 9.47\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 111 2.01e+03 0.001000 0.003523 11.15 0.004176 10.40\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 119 2.16e+03 0.001000 0.003324 11.41 0.003917 10.68\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 127 2.30e+03 0.001000 0.003194 11.58 0.004408 10.17\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 135 2.44e+03 0.001000 0.003065 11.76 0.003885 10.72\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 143 2.59e+03 0.001000 0.002968 11.90 0.004074 10.51\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 151 2.73e+03 0.001000 0.002864 12.05 0.003808 10.80\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 159 2.87e+03 0.001000 0.002801 12.15 0.003001 11.84\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 167 3.02e+03 0.001000 0.002711 12.29 0.002757 12.21\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 175 3.16e+03 0.001000 0.002627 12.43 0.002760 12.21\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 183 3.30e+03 0.001000 0.002596 12.48 0.004782 9.84\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 191 3.45e+03 0.001000 0.002539 12.58 0.003661 10.98\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " 199 3.59e+03 0.001000 0.002479 12.68 0.002647 12.39\n" ] } ], "source": [ "workdir = os.path.join(os.path.expanduser(\"~\"), \".cache\", \"scico\", \"examples\", \"odp_ct_out\")\n", "\n", "train_conf[\"workdir\"] = workdir\n", "train_conf[\"post_lst\"] = [alphapost]\n", "# Construct training object\n", "trainer = sflax.BasicFlaxTrainer(\n", " train_conf,\n", " model,\n", " train_ds,\n", " test_ds,\n", ")\n", "\n", "start_time = time()\n", "modvar, stats_object = trainer.train()\n", "time_train = time() - start_time" ] }, { "cell_type": "markdown", "id": "4470154a", "metadata": {}, "source": [ "Evaluate on testing data." ] }, { "cell_type": "code", "execution_count": 11, "id": "05bb2517", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2023-11-15T02:06:49.923022Z", "iopub.status.busy": "2023-11-15T02:06:49.922540Z", "iopub.status.idle": "2023-11-15T02:07:01.625979Z", "shell.execute_reply": "2023-11-15T02:07:01.624307Z" } }, "outputs": [], "source": [ "del train_ds[\"image\"]\n", "del train_ds[\"label\"]\n", "\n", "fmap = sflax.FlaxMap(model, modvar)\n", "del model, modvar\n", "\n", "maxn = numtt\n", "start_time = time()\n", "output = fmap(test_ds[\"image\"][:maxn])\n", "time_eval = time() - start_time\n", "output = np.clip(output, a_min=0, a_max=1.0)\n", "epochs = train_conf[\"num_epochs\"]" ] }, { "cell_type": "markdown", "id": "272367cd", "metadata": {}, "source": [ "Evaluate trained model in terms of reconstruction time and data fidelity." ] }, { "cell_type": "code", "execution_count": 12, "id": "563c15a6", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2023-11-15T02:07:01.633068Z", "iopub.status.busy": "2023-11-15T02:07:01.632587Z", "iopub.status.idle": "2023-11-15T02:07:03.128137Z", "shell.execute_reply": "2023-11-15T02:07:03.126623Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ODPNet training epochs: 200 time[s]: 3590.61\n", "ODPNet testing SNR: 12.40 dB PSNR: 22.78 dB time[s]: 11.57\n" ] } ], "source": [ "snr_eval = metric.snr(test_ds[\"label\"][:maxn], output)\n", "psnr_eval = metric.psnr(test_ds[\"label\"][:maxn], output)\n", "print(\n", " f\"{'ODPNet training':18s}{'epochs:':2s}{epochs:>5d}{'':21s}\"\n", " f\"{'time[s]:':10s}{time_train:>7.2f}\"\n", ")\n", "print(\n", " f\"{'ODPNet testing':18s}{'SNR:':5s}{snr_eval:>5.2f}{' dB'}{'':3s}\"\n", " f\"{'PSNR:':6s}{psnr_eval:>5.2f}{' dB'}{'':3s}{'time[s]:':10s}{time_eval:>7.2f}\"\n", ")" ] }, { "cell_type": "markdown", "id": "84a7939b", "metadata": {}, "source": [ "Plot comparison." ] }, { "cell_type": "code", "execution_count": 13, "id": "eaf54697", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2023-11-15T02:07:03.134642Z", "iopub.status.busy": "2023-11-15T02:07:03.134228Z", "iopub.status.idle": "2023-11-15T02:07:04.459859Z", "shell.execute_reply": "2023-11-15T02:07:04.459292Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "np.random.seed(123)\n", "indx = np.random.randint(0, high=maxn)\n", "\n", "fig, ax = plot.subplots(nrows=1, ncols=3, figsize=(15, 5))\n", "plot.imview(test_ds[\"label\"][indx, ..., 0], title=\"Ground truth\", cbar=None, fig=fig, ax=ax[0])\n", "plot.imview(\n", " test_ds[\"image\"][indx, ..., 0],\n", " title=\"Sinogram\",\n", " cbar=None,\n", " fig=fig,\n", " ax=ax[1],\n", ")\n", "plot.imview(\n", " output[indx, ..., 0],\n", " title=\"ODPNet Reconstruction\\nSNR: %.2f (dB), PSNR: %.2f\"\n", " % (\n", " metric.snr(test_ds[\"label\"][indx, ..., 0], output[indx, ..., 0]),\n", " metric.psnr(test_ds[\"label\"][indx, ..., 0], output[indx, ..., 0]),\n", " ),\n", " fig=fig,\n", " ax=ax[2],\n", ")\n", "divider = make_axes_locatable(ax[2])\n", "cax = divider.append_axes(\"right\", size=\"5%\", pad=0.2)\n", "fig.colorbar(ax[2].get_images()[0], cax=cax, label=\"arbitrary units\")\n", "fig.show()" ] }, { "cell_type": "markdown", "id": "b3274630", "metadata": {}, "source": [ "Plot convergence statistics. Statistics are generated only if a training\n", "cycle was done (i.e. if not reading final epoch results from checkpoint)." ] }, { "cell_type": "code", "execution_count": 14, "id": "7b1dd411", "metadata": { "collapsed": false, "execution": { "iopub.execute_input": "2023-11-15T02:07:04.475607Z", "iopub.status.busy": "2023-11-15T02:07:04.474873Z", "iopub.status.idle": "2023-11-15T02:07:05.206874Z", "shell.execute_reply": "2023-11-15T02:07:05.205846Z" } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "if stats_object is not None and len(stats_object.iterations) > 0:\n", " hist = stats_object.history(transpose=True)\n", " fig, ax = plot.subplots(nrows=1, ncols=2, figsize=(12, 5))\n", " plot.plot(\n", " np.vstack((hist.Train_Loss, hist.Eval_Loss)).T,\n", " x=hist.Epoch,\n", " ptyp=\"semilogy\",\n", " title=\"Loss function\",\n", " xlbl=\"Epoch\",\n", " ylbl=\"Loss value\",\n", " lgnd=(\"Train\", \"Test\"),\n", " fig=fig,\n", " ax=ax[0],\n", " )\n", " plot.plot(\n", " np.vstack((hist.Train_SNR, hist.Eval_SNR)).T,\n", " x=hist.Epoch,\n", " title=\"Metric\",\n", " xlbl=\"Epoch\",\n", " ylbl=\"SNR (dB)\",\n", " lgnd=(\"Train\", \"Test\"),\n", " fig=fig,\n", " ax=ax[1],\n", " )\n", " fig.show()" ] } ], "metadata": { "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.13" } }, "nbformat": 4, "nbformat_minor": 5 }