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"vscode:/vscode.git/clone" did not exist on "21b3922e35529dfbf9213365d7d37756a59f8e0e"
Commit a4f8d30b authored by Paul's avatar Paul
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Merge branch 'develop' into layout-nhwc

parents d12efcd2 a8d86615
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Showing with 1058 additions and 38 deletions
.github/workflows/ci.yaml
View file @ a4f8d30b
......@@ -131,6 +131,22 @@ jobs:
find . -iname '*.py' \
| grep -v 'build/' \
| xargs -n 1 -P 1 -I{} -t sh -c 'yapf {} | diff - {}'
pyflakes:
runs-on: ubuntu-18.04
steps:
- uses: actions/checkout@v2
- name: Set up Python
uses: actions/setup-python@v2
with:
python-version: 3.6
- name: Install pyflakes
run: pip install pyflakes==2.3.1
- name: Run pyflakes
run: pyflakes examples/ tools/ src/ test/ doc/
linux:
runs-on: ${{ matrix.os }}
......@@ -209,7 +225,7 @@ jobs:
rbuild build -d cget -s gh -t check \
-DCMAKE_BUILD_TYPE=${{matrix.configuration}} \
-DMIGRAPHX_ENABLE_PYTHON=${{matrix.configuration == 'release' && 'On' || 'Off'}} \
-DCMAKE_CXX_FLAGS_DEBUG="-g1 -Os -fdebug-prefix-map=$PWD=. -fdebug-types-section -fno-omit-frame-pointer ${{matrix.os != 'ubuntu-16.04' && '-fsanitize-address-use-after-scope' || ''}} -fsanitize=undefined,address -fno-sanitize-recover=undefined,address" \
-DCMAKE_CXX_FLAGS_DEBUG="-g1 -Os -fdebug-prefix-map=$PWD=. -fdebug-types-section -fno-omit-frame-pointer -fsanitize=undefined -fno-sanitize-recover=undefined" \
-DCMAKE_CXX_FLAGS_CODECOV="-g1 -Og -fdebug-prefix-map=$PWD=. -fdebug-types-section -fprofile-arcs -ftest-coverage -fno-omit-frame-pointer" \
-DCMAKE_EXE_LINKER_FLAGS='-fuse-ld=gold' \
-DCMAKE_SHARED_LINKER_FLAGS='-fuse-ld=gold'
......
Jenkinsfile
View file @ a4f8d30b
......@@ -15,11 +15,13 @@ def rocmtestnode(Map conf) {
def cmd = """
env
ulimit -c unlimited
echo "leak:dnnl::impl::malloc" > suppressions.txt
export LSAN_OPTIONS="suppressions=\$(pwd)/suppressions.txt"
rm -rf build
mkdir build
cd build
CXX=${compiler} CXXFLAGS='-Werror -Wno-fallback' cmake -DCMAKE_C_COMPILER_LAUNCHER=ccache -DCMAKE_CXX_COMPILER_LAUNCHER=ccache ${flags} ..
CTEST_PARALLEL_LEVEL=32 make -j\$(nproc) generate all doc package check VERBOSE=1
make -j\$(nproc) generate all doc package check VERBOSE=1
"""
echo cmd
sh cmd
......@@ -73,6 +75,8 @@ def rocmnodename(name) {
node_name = "${rocmtest_name} && fiji";
} else if(name == "vega") {
node_name = "${rocmtest_name} && vega";
} else if(name == "nogpu") {
return rocmtest_name;
}
return node_name
}
......@@ -100,6 +104,12 @@ rocmtest clang_debug: rocmnode('vega') { cmake_build ->
def debug_flags = "-g -O2 -fsanitize=${sanitizers} -fno-sanitize-recover=${sanitizers}"
cmake_build("/opt/rocm/llvm/bin/clang++", "-DCMAKE_BUILD_TYPE=debug -DMIGRAPHX_ENABLE_PYTHON=Off -DMIGRAPHX_ENABLE_MLIR=On -DCMAKE_CXX_FLAGS_DEBUG='${debug_flags}'")
}
}, clang_asan: rocmnode('nogpu') { cmake_build ->
stage('Clang ASAN') {
def sanitizers = "undefined,address"
def debug_flags = "-g -O2 -fno-omit-frame-pointer -fsanitize=${sanitizers} -fno-sanitize-recover=${sanitizers}"
cmake_build("/opt/rocm/llvm/bin/clang++", "-DCMAKE_BUILD_TYPE=debug -DMIGRAPHX_ENABLE_PYTHON=Off -DMIGRAPHX_ENABLE_GPU=Off -DMIGRAPHX_ENABLE_CPU=On -DCMAKE_CXX_FLAGS_DEBUG='${debug_flags}'")
}
}
def onnxnode(name, body) {
......
examples/README.md 100644 → 100755
View file @ a4f8d30b
......@@ -10,8 +10,9 @@ This directory contains examples of common use cases for MIGraphX.
- [Exporting Frozen Graphs in TF2](./export_frozen_graph_tf2)
- [MIGraphX Docker Container](./migraphx_docker)
- [MIGraphX Driver](./migraphx_driver)
- [Python Resnet50 Inference](./python_api_inference)
- [Python BERT SQuAD Inference](./python_bert_squad_example)
- [Python Resnet50](./python_api_inference)
- [Python BERT-SQuAD](./python_bert_squad_example)
- [Python Super Resolution](./python_super_resolution)
- [Python NFNet Inference](./python_nfnet_inference)
- [Python NFNet](./python_nfnet_inference)
- [Python U-Net](./python_unet)
- [Python 3D-UNet](./python_3dunet)
examples/export_frozen_graph_tf2/example.ipynb
View file @ a4f8d30b
......@@ -148,13 +148,6 @@
"\n",
"print(process.stdout)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
......@@ -172,8 +165,7 @@
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
"pygments_lexer": "ipython3"
}
},
"nbformat": 4,
......
examples/python_3dunet/3dunet_inference.ipynb 0 → 100755
View file @ a4f8d30b
{
"cells": [
{
"cell_type": "markdown",
"id": "fee8cfa5",
"metadata": {},
"source": [
"# 3D-UNet Example with MIGraphX\n",
"References:<br>\n",
"https://github.com/naomifridman/Unet_Brain_tumor_segmentation"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bb22bcc4",
"metadata": {},
"outputs": [],
"source": [
"import migraphx\n",
"from PIL import Image\n",
"import numpy as np\n",
"import os\n",
"import SimpleITK as sitk"
]
},
{
"cell_type": "markdown",
"id": "cb973c63",
"metadata": {},
"source": [
"## Fetch U-NET ONNX Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1928662c",
"metadata": {},
"outputs": [],
"source": [
"!wget -nc https://zenodo.org/record/3928973/files/224_224_160.onnx"
]
},
{
"cell_type": "markdown",
"id": "1a64a616",
"metadata": {},
"source": [
"## Load ONNX Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "53928a98",
"metadata": {},
"outputs": [],
"source": [
"model = migraphx.parse_onnx(\"224_224_160.onnx\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "27e8587f",
"metadata": {},
"outputs": [],
"source": [
"model.compile(migraphx.get_target(\"gpu\"))"
]
},
{
"cell_type": "markdown",
"id": "2f6014a4",
"metadata": {},
"source": [
"## Print model parameters"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9e73728c",
"metadata": {},
"outputs": [],
"source": [
"print(model.get_parameter_names())\n",
"print(model.get_parameter_shapes())\n",
"print(model.get_output_shapes())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a4cac52e",
"metadata": {},
"outputs": [],
"source": [
"img_type=['FLAIR', 'T1','T1CE', 'T2']\n",
"label_type_shrt = ['background', 'necrotic',\n",
" 'edema', 'enhancing']\n",
"label_type = ['background', 'necrotic and non-enhancing tumor', 'edema', 'enhancing tumor']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b65f9297",
"metadata": {},
"outputs": [],
"source": [
"red_multiplier = [1, 0.2, 0.2]\n",
"green_multiplier = [0.35,0.75,0.25]\n",
"blue_multiplier = [0,0.5,1.]#[0,0.25,0.9]\n",
"yellow_multiplier = [1,1,0.25]\n",
"brown_miltiplier = [40./255, 26./255, 13./255]\n",
"my_colors=[blue_multiplier, yellow_multiplier, brown_miltiplier]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0e175ac5",
"metadata": {},
"outputs": [],
"source": [
"from importlib import reload # Python 3.4+ only."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "530e4f97",
"metadata": {},
"outputs": [],
"source": [
"import visualization_utils as vu\n",
"from visualization_utils import show_label_on_image4\n",
"reload(vu)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "865c46a2",
"metadata": {},
"outputs": [],
"source": [
"def show_img_label(img, lbl, modality = 0):\n",
" \n",
" if (len(lbl.shape)> 2):\n",
" lbl[0,0,3]=1 # for uniqe colors in plot\n",
" lbl = lbl_from_cat(lbl)\n",
" vu.show_n_images([img[:,:,modality],lbl, show_label_on_image4(img[:,:,modality],lbl)],\n",
" titles = [img_type[modality], 'Label', 'Label on '+ img_type[modality]]);\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1e926482",
"metadata": {},
"outputs": [],
"source": [
"def read_img_sitk(img):\n",
" inputImage = sitk.ReadImage( img )\n",
" inputImage = sitk.Cast( inputImage, sitk.sitkFloat32 )\n",
" image = sitk.GetArrayFromImage(inputImage)\n",
" return image"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0b620138",
"metadata": {},
"outputs": [],
"source": [
"# ima files are of the form\n",
"# BraTS19_TCIA04_192_1_flair.nii.gz \n",
"# BraTS19_TCIA04_192_1_t1.nii.gz \n",
"# BraTS19_TCIA04_192_1_t2.nii.gz\n",
"# BraTS19_TCIA04_192_1_seg.nii.gz \n",
"# BraTS19_TCIA04_192_1_t1ce.nii.gz\n",
"\n",
"def read_image_into_numpy(dirpath):\n",
" \n",
" img_id = os.path.basename(dirpath)\n",
" np_image=np.zeros((4, 160, 224, 224), dtype=np.float32)\n",
" \n",
" ## Flair\n",
" flair_img = os.path.join(dirpath, img_id+'_flair.nii.gz')\n",
" if (not os.path.isfile(flair_img)):\n",
" print(flair_img,' not found aborting')\n",
" return None\n",
" np_image[0] = read_img_sitk(flair_img)\n",
" \n",
" ## T1\n",
" t1_nb4_img = os.path.join(dirpath, img_id+'_t1_nb4.nii.gz')\n",
" if (not os.path.isfile(t1_nb4_img)):\n",
" #print(t1_nb4_img,' not found')\n",
" t1_img = os.path.join(dirpath, img_id+'_t1.nii.gz')\n",
" if (not os.path.isfile(t1_img)):\n",
" print(t1_img,' not found aborting')\n",
" return None\n",
" np_image[1] = read_img_sitk(t1_img)\n",
" else:\n",
" np_image[1] = read_img_sitk(t1_nb4_img) \n",
" \n",
" ## T1CE\n",
" t1ce_nb4_img = os.path.join(dirpath, img_id+'_t1ce_nb4.nii.gz')\n",
" if (not os.path.isfile(t1ce_nb4_img)):\n",
" #print(t1ce_nb4_img,' not found')\n",
" t1ce_img = os.path.join(dirpath, img_id+'_t1ce.nii.gz')\n",
" if (not os.path.isfile(t1ce_img)):\n",
" print(t1ce_img,' not found aborting')\n",
" return None\n",
" np_image[2] = read_img_sitk(t1ce_img)\n",
" else:\n",
" np_image[2] = read_img_sitk(t1ce_nb4_img) \n",
" \n",
" \n",
" ## T2\n",
" t2_img = os.path.join(dirpath, img_id+'_t2.nii.gz')\n",
" if (not os.path.isfile(t2_img)):\n",
" print(t2_img,' not found aborting')\n",
" return None\n",
" np_image[3] = read_img_sitk(t2_img)\n",
"\n",
" return np_image"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2fb66f17",
"metadata": {},
"outputs": [],
"source": [
"def read_label_into_numpy(dirpath):\n",
" \n",
" img_id = os.path.basename(dirpath)\n",
" np_image=np.zeros((160, 224, 224), dtype=np.int)\n",
" \n",
" ## label\n",
" label_img = os.path.join(dirpath, img_id+'_seg.nii.gz')\n",
" if (not os.path.isfile(label_img)):\n",
" print(label_img,' not found aborting')\n",
" return None\n",
" np_image = read_img_sitk(label_img).astype(int)\n",
"\n",
" return np_image"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "558d47b9",
"metadata": {},
"outputs": [],
"source": [
"def bbox2_3D(img):\n",
"\n",
" r = np.any(img, axis=(1, 2))\n",
" c = np.any(img, axis=(0, 2))\n",
" z = np.any(img, axis=(0, 1))\n",
"\n",
" rmin, rmax = np.where(r)[0][[0, -1]]\n",
" cmin, cmax = np.where(c)[0][[0, -1]]\n",
" zmin, zmax = np.where(z)[0][[0, -1]]\n",
"\n",
" return [rmin, rmax, cmin, cmax, zmin, zmax]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1405e186",
"metadata": {},
"outputs": [],
"source": [
"def lbl_from_cat(cat_lbl):\n",
" \n",
" lbl=0\n",
" if (len(cat_lbl.shape)==3):\n",
" for i in range(1,4):\n",
" lbl = lbl + cat_lbl[:,:,i]*i\n",
" elif (len(cat_lbl.shape)==4):\n",
" for i in range(1,4):\n",
" lbl = lbl + cat_lbl[:,:,:,i]*i\n",
" else:\n",
" print('Error in lbl_from_cat', cat_lbl.shape)\n",
" return None\n",
" return lbl"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "24eb472f",
"metadata": {},
"outputs": [],
"source": [
"def show_label(lbl):\n",
" vu.show_n_images([lbl[:,:,k] for k in range(4)]+[lbl_from_cat(lbl)],\n",
" titles = label_type_shrt + ['Label'])\n",
"\n",
"def show_pred_im_label(im, lb, pred):\n",
" \n",
" vu.show_n_images([im[:,:,1], lb[:,:], \n",
" show_label_on_image4(im[:,:,1], lb[:,:]),\n",
" show_label_on_image4(im[:,:,1], pred[:,:])],\n",
" titles=['Flair', 'Label', 'Label on T1', 'Prediction on Flair'])\n",
"\n",
"def show_pred_im(im, pred):\n",
" \n",
" vu.show_n_images([im[:,:,1], \n",
" im[:,:,0],pred,\n",
" show_label_on_image4(im[:,:,1], pred[:,:])],\n",
" titles=['Flair','T1', 'Pred', 'Prediction on Flair'])"
]
},
{
"cell_type": "markdown",
"id": "d15f788b",
"metadata": {},
"source": [
"Multiple image inputs:\n",
"- Native (T1)\n",
"- Post-contrast T1-weighted (T1Gd)\n",
"- T2-weighted (T2)\n",
"- T2 Fluid Attenuated Inversion Recovery (T2-FLAIR)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7a7aad87",
"metadata": {},
"outputs": [],
"source": [
"# Resize input images\n",
"from scipy.ndimage import zoom\n",
"\n",
"def resize(img, shape, mode='constant', orig_shape=(155, 240, 240)):\n",
" \"\"\"\n",
" Wrapper for scipy.ndimage.zoom suited for MRI images.\n",
" \"\"\"\n",
" assert len(shape) == 3, \"Can not have more than 3 dimensions\"\n",
" factors = (\n",
" shape[0]/orig_shape[0],\n",
" shape[1]/orig_shape[1], \n",
" shape[2]/orig_shape[2]\n",
" )\n",
" \n",
" # Resize to the given shape\n",
" return zoom(img, factors, mode=mode)\n",
"\n",
"def preprocess_label(img, out_shape=None, mode='nearest'):\n",
" \"\"\"\n",
" Separates out the 3 labels from the segmentation provided, namely:\n",
" GD-enhancing tumor (ET — label 4), the peritumoral edema (ED — label 2))\n",
" and the necrotic and non-enhancing tumor core (NCR/NET — label 1)\n",
" \"\"\"\n",
" ncr = img == 1 # Necrotic and Non-Enhancing Tumor (NCR/NET)\n",
" \n",
" ed = img == 2 # Peritumoral Edema (ED)\n",
" et = img == 4 # GD-enhancing Tumor (ET)\n",
" \n",
" if out_shape is not None:\n",
" ncr = resize(ncr, out_shape, mode=mode)\n",
" ed = resize(ed, out_shape, mode=mode)\n",
" et = resize(et, out_shape, mode=mode)\n",
" return np.array([ncr, ed, et], dtype=np.uint8)\n",
"\n",
"hgg_path = \"/code/AMDMIGraphX/bratsdata/MICCAI_BraTS_2019_Data_Training/HGG\"\n",
"np_image=np.zeros((4, 160, 224, 224), dtype=np.float32)\n",
"tmp = read_img_sitk('%s/BraTS19_TMC_30014_1/BraTS19_TMC_30014_1_flair.nii.gz'%hgg_path)\n",
"tmp = resize(tmp, [160,224,224])\n",
"mean = tmp.mean()\n",
"std = tmp.std()\n",
"np_image[0] = (tmp - mean) / std\n",
"\n",
"tmp = read_img_sitk('%s/BraTS19_TMC_30014_1/BraTS19_TMC_30014_1_t1.nii.gz'%hgg_path)\n",
"tmp = resize(tmp, [160,224,224])\n",
"mean = tmp.mean()\n",
"std = tmp.std()\n",
"np_image[1] = (tmp - mean) / std\n",
"\n",
"tmp = read_img_sitk('%s/BraTS19_TMC_30014_1/BraTS19_TMC_30014_1_t1ce.nii.gz'%hgg_path)\n",
"tmp = resize(tmp, [160,224,224])\n",
"mean = tmp.mean()\n",
"std = tmp.std()\n",
"np_image[2] = (tmp - mean) / std\n",
"\n",
"tmp = read_img_sitk('%s/BraTS19_TMC_30014_1/BraTS19_TMC_30014_1_t2.nii.gz'%hgg_path)\n",
"tmp = resize(tmp, [160,224,224])\n",
"mean = tmp.mean()\n",
"std = tmp.std()\n",
"np_image[3] = (tmp - mean) / std\n",
"\n",
"print(np_image.shape)\n",
"np_image_tmp = np_image.copy()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d7e5b3c6",
"metadata": {},
"outputs": [],
"source": [
"vu.show_n_images(np_image[:,100,:,:], titles=img_type)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "19117da5",
"metadata": {},
"outputs": [],
"source": [
"np_lbl=np.zeros((160, 224, 224), dtype=np.int)\n",
"tmp = read_img_sitk('/code/AMDMIGraphX/bratsdata/MICCAI_BraTS_2019_Data_Training/HGG/BraTS19_TMC_30014_1/BraTS19_TMC_30014_1_seg.nii.gz').astype(int)\n",
"tmp = resize(tmp, [160,224,224])\n",
"print(tmp.shape)\n",
"np_lbl = tmp.astype(int)\n",
"print(np_lbl.shape)\n",
"\n",
"print(np_image.shape)\n",
"\n",
"img1 = vu.show_label_on_image4(np_image[1,100,:,:], np_lbl[100])\n",
"img2 = vu.show_label_on_image(np_image[1,100,:,:], np_lbl[100])\n",
"vu.show_n_images([img1,img2,np_image[0,100]])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "facdea15",
"metadata": {},
"outputs": [],
"source": [
"def get_pred(img, threshold=0.5):\n",
" out_img=img.copy()\n",
" out_img=np.where(out_img>threshold, 1,0)\n",
" return out_img\n",
"\n",
"def prediction_from_probabily_3D(img):\n",
" \n",
" int_image = get_pred(img)\n",
" return lbl_from_cat(int_image)\n",
"\n",
"def get_prediction_for_batch(pred_batch, threshold=0.5):\n",
" \n",
" out_batch = np.zeros((pred_batch.shape[0], 224, 224),dtype=np.int)\n",
" \n",
" for j in range(pred_batch.shape[0]):\n",
" pred = get_prediction(pred_batch[j])\n",
" if (pred.sum()>0):\n",
" print(j, np.unique(pred , return_counts=True))\n",
" out_batch[j] = lbl_from_cat(get_prediction(pred_batch[j]))\n",
" return out_batch\n",
"\n",
"def get_label_from_pred_batch(labels_batch):\n",
" \n",
" batch = np.zeros((labels_batch.shape[0], 224, 224), np.uint8)\n",
" \n",
" for j in range(labels_batch.shape[0]):\n",
" batch[j]=get_pred(labels_batch[j,:,:,0])+\\\n",
" get_pred(labels_batch[j,:,:,1])*2+\\\n",
" get_pred(labels_batch[j,:,:,2])*4\n",
"\n",
" return batch\n",
"\n",
"def predict_3D_img_prob(np_file):\n",
" \n",
" np_img = np.load(np_file)\n",
" for_pred_img = np.zeros((160, 224, 224, 4), np.float32)\n",
"\n",
" # Normalize image\n",
" for_pred_img = normalize_3D_image(np_img)\n",
"\n",
" mdl_pred_img = model.predict(for_pred_img)\n",
"\n",
" #pred_label = prediction_from_probabily_3D(mdl_pred_img)\n",
"\n",
" return mdl_pred_img\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7f7fe7ee",
"metadata": {},
"outputs": [],
"source": [
"#Remember the MIGraphX model inputs\n",
"print(model.get_parameter_names())\n",
"print(model.get_parameter_shapes())\n",
"\n",
"np_image = np_image.transpose((0,2,3,1))\n",
"\n",
"print(np_image.shape)\n",
"print(np_image.strides)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dfc47b53",
"metadata": {},
"outputs": [],
"source": [
"def normalize_3D_image(img):\n",
" for z in range(img.shape[0]):\n",
" for k in range(4):\n",
" if (img[z,:,:,k].max()>0):\n",
" img[z,:,:,k] /= img[z,:,:,k].max()\n",
" return img"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f990cb50",
"metadata": {},
"outputs": [],
"source": [
"print(np_image_tmp.shape)\n",
"np_image_tmp = np_image_tmp.transpose((1,2,3,0))\n",
"print(np_image_tmp.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "24c3736d",
"metadata": {},
"outputs": [],
"source": [
"np_image = np.expand_dims(np_image, 0)\n",
"print(np_image.shape)\n",
"print(np_image.strides)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1aac6285",
"metadata": {},
"outputs": [],
"source": [
"input_im = np.zeros((1,4,224,224,160),dtype='float32')\n",
"np.lib.stride_tricks.as_strided(input_im, shape=np_image.shape, strides=input_im.strides)[:] = np_image #getting correct stride\n",
"print(input_im.strides)\n",
"print(input_im.shape)\n",
"\n",
"#input_im = normalize_3D_image(input_im)\n",
"\n",
"print(input_im.strides)\n",
"print(input_im.shape)\n",
"\n",
"result = model.run({\n",
" \"input\": input_im\n",
" })"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5848b63d",
"metadata": {},
"outputs": [],
"source": [
"output = np.array(result[0])\n",
"print(output.shape)\n",
"output = output[0]\n",
"print(output.shape)\n",
"output = output.transpose((3,1,2,0))\n",
"print(output.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ab77f7e9",
"metadata": {},
"outputs": [],
"source": [
"out = prediction_from_probabily_3D(output)\n",
"print(np_image_tmp.shape)\n",
"print(np_lbl.shape)\n",
"print(out.shape)\n",
"print(np.unique(out))\n",
"ind=[100]\n",
"for i in ind:\n",
" show_label(output[i])\n",
" show_label(get_pred(output[i]))\n",
" show_pred_im_label(np_image_tmp[i], np_lbl[i], out[i])"
]
},
{
"cell_type": "markdown",
"id": "d2862d81",
"metadata": {},
"source": [
"The possible prediction discrepancy is due to the not-perfect resizing 3D input image, as BRATS dataset has 3D images of size 160x240x240, meanwhile the ONNX model utilized here requires 155x224x224. This example is representative for how to utilize MIGraphX for such an application. All data processing should follow and match the model requirements otherwise. "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
examples/python_3dunet/README.md 0 → 100755
View file @ a4f8d30b
# 3D-Unet Inference with AMD MIGraphX
This example applies image segmentation to 3D images using AMD MIGraphX on a given AMD GPU.
## How to:
1) User will need to have access to the BRATS dataset. Please follow https://www.med.upenn.edu/cbica/brats2019/data.html for how to get access to the dataset.
2) Follow the provided notebook `3dunet_inference.ipynb`.
examples/python_3dunet/visualization_utils.py 0 → 100755
View file @ a4f8d30b
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import matplotlib.pylab as pylab
import numpy as np
params = {
'legend.fontsize': 'x-large',
'figure.figsize': (6, 5),
'axes.labelsize': 'x-large',
'axes.titlesize': 'x-large',
'xtick.labelsize': 'x-large',
'ytick.labelsize': 'x-large'
}
pylab.rcParams.update(params)
#-----------------------------------------------------------
def show_n_images(imgs, titles=None, enlarge=20, cmap='jet'):
plt.set_cmap(cmap)
n = len(imgs)
gs1 = gridspec.GridSpec(1, n)
fig1 = plt.figure()
# create a figure with the default size
fig1.set_size_inches(enlarge, 2 * enlarge)
for i in range(n):
ax1 = fig1.add_subplot(gs1[i])
ax1.imshow(imgs[i], interpolation='none')
if (titles is not None):
ax1.set_title(titles[i])
ax1.set_ylim(ax1.get_ylim()[::-1])
plt.show()
#--------------------------------------------------------------
from skimage import color, img_as_float
from skimage.exposure import adjust_gamma
# Creates an image of original brain with segmentation overlay
def show_label_on_image(test_img, test_lbl):
label_im = test_lbl
ones = np.argwhere(label_im == 1)
twos = np.argwhere(label_im == 2)
threes = np.argwhere(label_im == 3)
fours = np.argwhere(label_im == 4)
gray_img = img_as_float(test_img / test_img.max())
# adjust gamma of image
# print(color.gray2rgb(gray_img))
image = adjust_gamma(np.abs(color.gray2rgb(gray_img)), 0.45)
#sliced_image = image.copy()
green_multiplier = [0.35, 0.75, 0.25]
blue_multiplier = [0, 0.5, 1.] #[0,0.25,0.9]
yellow_multiplier = [1, 1, 0.25]
brown_miltiplier = [40. / 255, 26. / 255, 13. / 255]
# change colors of segmented classes
for i in range(len(ones)):
image[ones[i][0]][ones[i][1]] = blue_multiplier
for i in range(len(twos)):
image[twos[i][0]][twos[i][1]] = yellow_multiplier
for i in range(len(threes)):
image[threes[i][0]][threes[i][1]] = brown_miltiplier #blue_multiplier
for i in range(len(fours)):
image[fours[i][0]][fours[i][1]] = green_multiplier #yellow_multiplier
return image
#-------------------------------------------------------------------------------------
def show_label_on_image4(test_img, label_im):
alpha = 0.8
img = img_as_float(test_img / test_img.max())
rows, cols = img.shape
# Construct a colour image to superimpose
color_mask = np.zeros((rows, cols, 3))
green_multiplier = [0.35, 0.75, 0.25]
blue_multiplier = [0, 0.25, 0.9]
yellow_multiplier = [1, 1, 0.25]
brown_miltiplier = [40. / 255, 26. / 255, 13. / 255]
color_mask[label_im == 1] = blue_multiplier #[1, 0, 0] # Red block
color_mask[label_im == 2] = yellow_multiplier #[0, 1, 0] # Green block
color_mask[label_im == 3] = brown_miltiplier #[0, 0, 1] # Blue block
color_mask[label_im == 4] = green_multiplier #[0, 1, 1] # Blue block
# Construct RGB version of grey-level image
img_color = np.dstack((img, img, img))
# Convert the input image and color mask to Hue Saturation Value (HSV)
# colorspace
img_hsv = color.rgb2hsv(img_color)
color_mask_hsv = color.rgb2hsv(color_mask)
# Replace the hue and saturation of the original image
# with that of the color mask
img_hsv[..., 0] = color_mask_hsv[..., 0]
img_hsv[..., 1] = color_mask_hsv[..., 1] * alpha
img_masked = color.hsv2rgb(img_hsv)
return img_masked
#------------------------------------------------------------------------------
examples/python_api_inference/resnet50_inference.ipynb
View file @ a4f8d30b
......@@ -339,8 +339,7 @@
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.9"
"pygments_lexer": "ipython3"
}
},
"nbformat": 4,
......
examples/python_bert_squad_example/BERT-Squad.ipynb
View file @ a4f8d30b
......@@ -273,8 +273,7 @@
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.9"
"pygments_lexer": "ipython3"
}
},
"nbformat": 4,
......
examples/python_bert_squad_example/bert-squad-migraphx.py
View file @ a4f8d30b
import numpy as np
import json
import time
import os.path
from os import path
import sys
import tokenizers
from run_onnx_squad import *
import collections
from run_onnx_squad import read_squad_examples, write_predictions, convert_examples_to_features
import migraphx
RawResult = collections.namedtuple("RawResult",
["unique_id", "start_logits", "end_logits"])
#######################################
input_file = 'inputs_amd.json'
with open(input_file) as json_file:
......
examples/python_bert_squad_example/run_onnx_squad.py
View file @ a4f8d30b
......@@ -29,7 +29,6 @@ python onnx_squad.py --model $SQUAD_MODEL/squad.onnx \
import argparse
import collections
import json
import logging
import math
import os
import sys
......@@ -145,8 +144,6 @@ def convert_examples_to_features(examples, tokenizer, max_seq_length,
tok_to_orig_index.append(i)
all_doc_tokens.append(sub_token)
tok_start_position = None
tok_end_position = None
# The -3 accounts for [CLS], [SEP] and [SEP]
max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
......@@ -567,7 +564,7 @@ def main():
sess_options = onnxrt.SessionOptions()
sess_options.session_log_verbosity_level = args.log
tokenizer = BertWordPieceTokenizer(vocab_file)
tokenizer = BertWordPieceTokenizer(args.vocab_file)
eval_examples = read_squad_examples(input_file=args.predict_file)
input_ids, input_mask, segment_ids, extra_data = \
......
examples/python_nfnet_inference/nfnet_inference.ipynb
View file @ a4f8d30b
......@@ -213,7 +213,7 @@
"# Run the model\n",
"start = time.time()\n",
"results = model.run({'inputs':data_input}) # Your first inference would take longer than the following ones.\n",
"print(f\"Time inference took: {100*(time.time() - start):.2f}ms\")\n",
"print(f\"Time inference took: {1000*(time.time() - start):.2f}ms\")\n",
"# Extract the index of the top prediction\n",
"res_npa = np.array(results[0])\n",
"print(f\"\\nResult: {labels[np.argmax(res_npa)]}\")"
......@@ -228,7 +228,7 @@
"# Run the model again, first one would take long\n",
"start = time.time()\n",
"results = model.run({'inputs':data_input}) # Your first inference would take longer than the following ones.\n",
"print(f\"Time inference took: {100*(time.time() - start):.2f}ms\")\n",
"print(f\"Time inference took: {1000*(time.time() - start):.2f}ms\")\n",
"# Extract the index of the top prediction\n",
"res_npa = np.array(results[0])\n",
"print(f\"\\nResult: {labels[np.argmax(res_npa)]}\")"
......@@ -250,8 +250,7 @@
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.9"
"pygments_lexer": "ipython3"
}
},
"nbformat": 4,
......
examples/python_super_resolution/Run_Super_Resolution_Model.ipynb
View file @ a4f8d30b
......@@ -210,8 +210,7 @@
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.9"
"pygments_lexer": "ipython3"
}
},
"nbformat": 4,
......
examples/python_unet/README.md 0 → 100755
View file @ a4f8d30b
# U-Net Image Segmentation Inference with AMD MIGraphX
This examples provides a simple example for utilizing U-Net ONNX model for image segmentation, using AMD MIGraphX graph optimization engine for fast inference.
## How-to
Please utilize the notebook given `unet_inference.ipynb`.
## Model Details
ONNX model utilized in this example can be found [here](https://www.dropbox.com/s/3ntkhyk30x05uuv/unet_13_256.onnx).
\ No newline at end of file
examples/python_unet/requirements.txt 0 → 100644
View file @ a4f8d30b
numpy
matplotlib
\ No newline at end of file
examples/python_unet/unet_inference.ipynb 0 → 100644
View file @ a4f8d30b
{
"cells": [
{
"cell_type": "markdown",
"id": "cd7a3990",
"metadata": {},
"source": [
"## Import MIGraphX Python Library"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3930d7b8",
"metadata": {},
"outputs": [],
"source": [
"import migraphx\n",
"from PIL import Image\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"id": "b350c333",
"metadata": {},
"source": [
"## Fetch U-NET ONNX Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "02a7b7de",
"metadata": {},
"outputs": [],
"source": [
"!wget -nc https://www.dropbox.com/s/3ntkhyk30x05uuv/unet_13_256.onnx"
]
},
{
"cell_type": "markdown",
"id": "a6cfe6e9",
"metadata": {},
"source": [
"## Load ONNX Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e05a13dc",
"metadata": {},
"outputs": [],
"source": [
"model = migraphx.parse_onnx(\"unet_13_256.onnx\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "52c67023",
"metadata": {},
"outputs": [],
"source": [
"model.compile(migraphx.get_target(\"gpu\"))"
]
},
{
"cell_type": "markdown",
"id": "80edb6f1",
"metadata": {},
"source": [
"## Print model parameters"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fd5c3269",
"metadata": {},
"outputs": [],
"source": [
"print(model.get_parameter_names())\n",
"print(model.get_parameter_shapes())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "47f956c7",
"metadata": {},
"outputs": [],
"source": [
"def preprocess(pil_img, newW, newH):\n",
" w, h = pil_img.size\n",
" assert newW > 0 and newH > 0, 'Scale is too small'\n",
" pil_img = pil_img.resize((newW, newH))\n",
"\n",
" img_nd = np.array(pil_img)\n",
"\n",
" if len(img_nd.shape) == 2:\n",
" img_nd = np.expand_dims(img_nd, axis=2)\n",
"\n",
" # HWC to CHW\n",
" img_print = pil_img\n",
" img_trans = img_nd.transpose((2, 0, 1))\n",
" if img_trans.max() > 1:\n",
" img_trans = img_trans / 255\n",
" \n",
" img_trans = np.expand_dims(img_trans, 0)\n",
"\n",
" return img_trans, img_print\n",
"\n",
"def plot_img_and_mask(img, mask):\n",
" classes = mask.shape[0] if len(mask.shape) > 3 else 1\n",
" print(classes)\n",
" fig, ax = plt.subplots(1, classes + 1)\n",
" ax[0].set_title('Input image')\n",
" ax[0].imshow(img)\n",
" if classes > 1:\n",
" for i in range(classes):\n",
" ax[i+1].set_title(f'Output mask (class {i+1})')\n",
" ax[i+1].imshow(mask[:, :, i])\n",
" else:\n",
" ax[1].set_title(f'Output mask')\n",
" ax[1].imshow(mask[0,0])\n",
" plt.xticks([]), plt.yticks([])\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "389ddc4d",
"metadata": {},
"outputs": [],
"source": [
"img = Image.open(\"./car1.jpeg\")\n",
"img, imPrint = preprocess(img, 256, 256)\n",
"input_im = np.zeros((1,3,256,256),dtype='float32') \n",
"np.lib.stride_tricks.as_strided(input_im, shape=img.shape, strides=input_im.strides)[:] = img #getting correct stride\n",
"print(input_im.strides)\n",
"print(input_im.shape)\n",
"imPrint.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9de6f2a7",
"metadata": {},
"outputs": [],
"source": [
"mask = model.run({'inputs':input_im}) # Your first inference would take longer than the following ones.\n",
"output_mask = np.array(mask[0])\n",
"print(output_mask.shape)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "acbd68e3",
"metadata": {},
"outputs": [],
"source": [
"def sigmoid(x):\n",
" return 1 / (1 + np.exp(-x))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "58e3062c",
"metadata": {},
"outputs": [],
"source": [
"probs = sigmoid(output_mask)\n",
"full_mask = probs > 0.996\n",
"plot_img_and_mask(imPrint, full_mask)"
]
},
{
"cell_type": "markdown",
"id": "6126df0b",
"metadata": {},
"source": [
"<b>NOTE:</b> The model weights utilized here are trained by using car images with plain backgrounds. The imperfect result on a \"real-world\" image as shown above is expected. To get a better result fine-tuning the model on a dataset of real-world examples is recommended. "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
src/CMakeLists.txt
View file @ a4f8d30b
......@@ -152,6 +152,7 @@ register_migraphx_ops(
round
rsqrt
scalar
scatter
sigmoid
sign
sinh
......
src/convert_to_json.cpp
View file @ a4f8d30b
......@@ -6,6 +6,7 @@
#include <migraphx/errors.hpp>
#include <migraphx/ranges.hpp>
#include <migraphx/convert_to_json.hpp>
#include <migraphx/stringutils.hpp>
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
......@@ -77,6 +78,18 @@ std::vector<token> json_tokenize(const std::string& s)
return ++start;
});
// Line comments
lexers.push_back([](const char* start, const char* end) {
if(*start == '#')
start++;
else if((start + 1) < end and start[0] == '/' and start[1] == '/')
start += 2;
else
return start;
return std::find_if(start, end, [&](char c) { return c == '\n'; });
});
// Whitespace
lexers.push_back(lex_while(&isspace));
// Punctation
......@@ -98,6 +111,8 @@ std::string convert_to_json(const std::string& str)
for(auto& token : tokens)
{
std::string s(token.first, token.second);
if(starts_with(s, "#") or starts_with(s, "//"))
continue;
if(std::isalpha(s.front()) != 0 and
not contains({"null", "nan", "true", "false", "inf"}, s))
{
......
src/file_buffer.cpp 100644 → 100755
View file @ a4f8d30b
......@@ -6,7 +6,8 @@
namespace migraphx {
inline namespace MIGRAPHX_INLINE_NS {
std::vector<char> read_buffer(const std::string& filename)
template <class T>
T generic_read_file(const std::string& filename)
{
std::ifstream is(filename, std::ios::binary | std::ios::ate);
std::streamsize size = is.tellg();
......@@ -14,12 +15,22 @@ std::vector<char> read_buffer(const std::string& filename)
MIGRAPHX_THROW("Invalid size for: " + filename);
is.seekg(0, std::ios::beg);
std::vector<char> buffer(size);
if(!is.read(buffer.data(), size))
T buffer(size, 0);
if(!is.read(&buffer[0], size))
MIGRAPHX_THROW("Error reading file: " + filename);
return buffer;
}
std::vector<char> read_buffer(const std::string& filename)
{
return generic_read_file<std::vector<char>>(filename);
}
std::string read_string(const std::string& filename)
{
return generic_read_file<std::string>(filename);
}
void write_buffer(const std::string& filename, const char* buffer, std::size_t size)
{
std::ofstream os(filename);
......
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