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Commit 4a39a0f7 authored by Shucai Xiao's avatar Shucai Xiao
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Merge branch 'develop' of github.com:ROCmSoftwarePlatform/AMDMIGraphX into add-conv_bn_add-test

parents 5564172e bb827865
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examples/python_bert_squad_example/BERT-Squad.ipynb → examples/nlp/python_bert_squad/BERT-Squad.ipynb 100644 → 100755
View file @ 4a39a0f7
......@@ -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 → examples/nlp/python_bert_squad/bert-squad-migraphx.py
View file @ 4a39a0f7
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/requirements_bertsquad.txt → examples/nlp/python_bert_squad/requirements_bertsquad.txt 100755 → 100644
View file @ 4a39a0f7
tensorflow==2.4.0
tensorflow==2.5.1
onnxruntime
tokenizers
\ No newline at end of file
examples/python_bert_squad_example/run_onnx_squad.py → examples/nlp/python_bert_squad/run_onnx_squad.py
View file @ 4a39a0f7
......@@ -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/vision/README.md 0 → 100644
View file @ 4a39a0f7
# Vision Inference Examples
- [CPP MNIST](./cpp_mnist)
- [Python Resnet50](./python_resnet50)
- [Python Super Resolution](./python_super_resolution)
- [Python NFNet](./python_nfnet)
- [Python U-Net](./python_unet)
- [Python 3D-UNet](./python_3dunet)
\ No newline at end of file
examples/cpp_api_inference/README.md → examples/vision/cpp_mnist/README.md 100644 → 100755
View file @ 4a39a0f7
......@@ -60,14 +60,14 @@ migraphx::quantize_int8(prog, targ, quant_opts);
## Compilation
Network graphs saved in e.g. ONNX or protobuf format are not target-specific. In order to run inference, we must compile the graph into a target-specific program.
Two options may be turned on (default for both is `false`) when compiling:
- `bool offload_copy`: For targets with offloaded memory (such as the gpu), this will insert instructions during compilation to copy the input parameters to the offloaded memory and to copy the final result from the offloaded memory back to main memory.
- `bool fast_math`: Optimize math functions to use faster approximate versions. There may be slight accuracy degredation when enabled.
Two options may be turned on when compiling:
- `set_offload_copy(bool value)`: For targets with offloaded memory (such as the gpu), this will insert instructions during compilation to copy the input parameters to the offloaded memory and to copy the final result from the offloaded memory back to main memory. Default value is `false` for offload_copy.
- `set_fast_math(bool value)`: Optimize math functions to use faster approximate versions. There may be slight accuracy degredation when enabled. Default value is `true` for fast_math.
The following snippet assumes `targ` has been set as "gpu", and will compile the program without the fast_math optimization.
```
migraphx_compile_options comp_opts;
comp_opts.offload_copy = true;
migraphx::compile_options comp_opts;
comp_opts.set_offload_copy();
prog.compile(targ, comp_opts);
```
......@@ -118,7 +118,7 @@ This directory contains everything that is needed to perform inference on an MNI
```
$ mkdir build
$ cd build
$ cmake ..
$ CXX=/opt/rocm/llvm/bin/clang++ cmake ..
$ make
```
There will now be an executable named `mnist_inference` in the `build` directory. This can be run with or without options. Executing without any options will produce the following output:
......
examples/cpp_api_inference/mnist_inference.cpp → examples/vision/cpp_mnist/mnist_inference.cpp 100644 → 100755
View file @ 4a39a0f7
......@@ -99,8 +99,8 @@ int main(int argc, char** argv)
if(GPU)
{
migraphx_compile_options comp_opts;
comp_opts.offload_copy = true;
migraphx::compile_options comp_opts;
comp_opts.set_offload_copy();
prog.compile(targ, comp_opts);
}
else
......@@ -122,10 +122,8 @@ int main(int argc, char** argv)
migraphx::program_parameters prog_params;
auto param_shapes = prog.get_parameter_shapes();
for(auto&& name : param_shapes.names())
{
prog_params.add(name, migraphx::argument(param_shapes[name], digit.data()));
}
auto input = param_shapes.names().front();
prog_params.add(input, migraphx::argument(param_shapes[input], digit.data()));
std::cout << "Model evaluating input..." << std::endl;
auto start = std::chrono::high_resolution_clock::now();
......
examples/vision/python_3dunet/3dunet_inference.ipynb 0 → 100755
View file @ 4a39a0f7
{
"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/vision/python_3dunet/README.md 0 → 100755
View file @ 4a39a0f7
# 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/vision/python_3dunet/visualization_utils.py 0 → 100755
View file @ 4a39a0f7
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/vision/python_nfnet/README.md 0 → 100755
View file @ 4a39a0f7
# NFNet Inference with MIGraphX
## NFNet
NFNet: Normalizer-Free Nets. An image recognition model that can be trained without batch normalization layers. It instead uses gradient clipping algorithm to provide same affects of BatchNorm.
<ins>**Summary:**</ins>
- SOTA on ImageNet (86.5% top-1 w/o extra data)
- Up to 8.7x faster to train than EfficientNets to a given accuracy
- Normalizer-free (no BatchNorm)
**Paper**: https://arxiv.org/pdf/2102.06171.pdf
**Colab notebook**: https://github.com/deepmind/deepmind-research/tree/master/nfnets
### Why not batch norm?
Batch normalization has three significant practical disadvantages:
1. It is an expensive computational primitive, which incurs memory overhead and significantly increases the time required to evaluate the gradient in some networks.
2. It introduces a discrepancy between the behavior of the model during training and at inference time, introducing hidden hyper-parameters that have to be tuned.
3. Last and most important point, batch normalization breaks the independence between training examples in the minibatch (batch size matters with batch norm, distributed training becomes extremely cumbersome).
Instead:
- Authors provide Adaptive Gradient Clipping (AGC), which clips gradients based on the unit-wise ratio of gradient norms to parameter norms, and they demonstrate that AGC allows them to train normalizer-free networks with larger batch sizes and stronger data augmentations.
- They design a family of Normalizer-Free ResNets, called NFNets, which set new state-of-the-art validation accuracies on ImageNet for a range of training latencies. Their NFNet-F1 model achieves similar accuracy to EfficientNet-B7 while being 8.7× faster to train, and their largest model sets a new overall state of the art without extra data of 86.5% top-1 accuracy.
- They show that NFNets achieve substantially higher validation accuracies than batch-normalized networks when fine-tuning on ImageNet after pre-training on a large private dataset of 300 million labelled images. Their best model achieves 89.2% top-1 accuracy after fine-tuning.
## Inference with MIGraphX using NFNet ONNX Model
There is no ONNX model released for NFNet, as of June 2021, however a PyTorch model is available at:
https://github.com/rwightman/pytorch-image-models.
We provide an in-house produced and optimized ONNX model, which can be parsed and compiled using MIGraphX for AMD GPUs. The ONNX model file can be fetched using the Jupyter notebook we provide.
### Requirements:
1) AMD GPU system with ROCm installed.
2) Jupyter notebook library.
### How to use NFNet for image recognition:
Please utilize the notebook example provided:
1) Install jupyter notebook to your environment if not already installed:
```
https://jupyter.org/install
```
2) Connect to your jupyter server and utilize `nfnet_inference.ipynb` notebook file.
### How to compare MIGraphX to ONNX Runtime for NFNet ONNX model:
First install requirements:
```
pip3 install -r requirements_nfnet.txt
```
On your terminal, invoke:
```
python3 ort_comparison.py
````
examples/vision/python_nfnet/nfnet_inference.ipynb 0 → 100755
View file @ 4a39a0f7
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# NFNet Inference with AMD MIGraphX\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Normalizer-Free ResNet is a new residual convolutional network providing new state-of-the-art Top-1 accuracy of 86.5% at ImageNet dataset. The most important feature of the model is removing batch normalization. Instead of batch normalization, it uses adaptive gradient clipping to provide same regularization effect of BatchNorm. <br> Details of this network: https://arxiv.org/abs/2102.06171\n",
"\n",
"In this notebook, we are showing: <br>\n",
"- How to optimize NFNet ONNX model with AMD MIGraphX.\n",
"- How to run inference on AMD GPU with the optimized ONNX model.\n",
"\n",
"The NFNet utilized in this example is the smallest NFNet version, F0: 71.5M parameters (83.6% top-1 accuracy on ImageNet)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Requirements"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!apt-get update\n",
"!apt-get install ffmpeg libsm6 libxext6 -y "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip3 install --upgrade pip\n",
"!pip3 install -r requirements_nfnet.txt"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import cv2\n",
"import json\n",
"from PIL import Image\n",
"import time\n",
"from os import path "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Importing AMD MIGraphX Python Module"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import migraphx"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create NFNet ONNX file\n",
"Following repository provides functionality to create NFNet ONNX file from PyTorch model."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!wget -nc https://www.dropbox.com/s/u4ga8zyxtppfzxc/dm_nfnet_f0.onnx"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Load ImageNet labels"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"with open('../python_api_inference/imagenet_simple_labels.json') as json_data:\n",
" labels = json.load(json_data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"## Load ONNX model using MIGraphX"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model = migraphx.parse_onnx(\"dm_nfnet_f0.onnx\")\n",
"model.compile(migraphx.get_target(\"gpu\"))\n",
"\n",
"print(model.get_parameter_names())\n",
"print(model.get_parameter_shapes())\n",
"print(model.get_output_shapes())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Functions for image processing"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def make_nxn(image, n):\n",
" height, width = image.shape[:2] \n",
" if height > width:\n",
" dif = height - width\n",
" bar = dif // 2 \n",
" square = image[(bar + (dif % 2)):(height - bar),:]\n",
" return cv2.resize(square, (n, n))\n",
" elif width > height:\n",
" dif = width - height\n",
" bar = dif // 2\n",
" square = image[:,(bar + (dif % 2)):(width - bar)]\n",
" return cv2.resize(square, (n, n))\n",
" else:\n",
" return cv2.resize(image, (n, n))\n",
" \n",
"def preprocess(img_data):\n",
" mean_vec = np.array([0.485, 0.456, 0.406])\n",
" stddev_vec = np.array([0.229, 0.224, 0.225])\n",
" norm_img_data = np.zeros(img_data.shape).astype('float32')\n",
" for i in range(img_data.shape[0]): \n",
" norm_img_data[i,:,:] = (img_data[i,:,:]/255 - mean_vec[i]) / stddev_vec[i]\n",
" return norm_img_data\n",
"\n",
"def input_process(frame, dim):\n",
" # Crop and resize original image\n",
" cropped = make_nxn(frame, dim)\n",
" # Convert from HWC to CHW\n",
" chw = cropped.transpose(2,0,1)\n",
" # Apply normalization\n",
" pp = preprocess(chw)\n",
" # Add singleton dimension (CHW to NCHW)\n",
" data = np.expand_dims(pp.astype('float32'),0)\n",
" return data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Download example image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Fetch example image: traffic light\n",
"!wget -nc http://farm5.static.flickr.com/4072/4462811418_8bc2bd42ca_z_d.jpg -O traffic_light.jpg\n",
"# Read the image\n",
"im = cv2.imread('traffic_light.jpg')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Process the read image to conform input requirements\n",
"data_input = input_process(im, 192)\n",
"\n",
"# 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: {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)]}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 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: {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)]}\")"
]
}
],
"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": 4
}
examples/vision/python_nfnet/ort_comparison.py 0 → 100755
View file @ 4a39a0f7
import numpy
import onnxruntime as rt
sess = rt.InferenceSession("dm_nfnet_f0.onnx")
input_name = sess.get_inputs()[0].name
print("input name", input_name)
input_shape = sess.get_inputs()[0].shape
print("input shape", input_shape)
input_type = sess.get_inputs()[0].type
print("input type", input_type)
output_name = sess.get_outputs()[0].name
print("output name", output_name)
output_shape = sess.get_outputs()[0].shape
print("output shape", output_shape)
output_type = sess.get_outputs()[0].type
print("output type", output_type)
x = numpy.random.random((1, 3, 192, 192))
x = x.astype(numpy.float32)
import migraphx
model = migraphx.parse_onnx("dm_nfnet_f0.onnx")
model.compile(migraphx.get_target("gpu"))
print(model.get_parameter_names())
print(model.get_parameter_shapes())
print(model.get_output_shapes())
result_migraphx = model.run({"inputs": x})
result_ort = sess.run([output_name], {input_name: x})
result_migraphx = result_migraphx[0].tolist()
for i in range(10):
x = numpy.random.random((1, 3, 192, 192))
x = x.astype(numpy.float32)
result_migraphx = model.run({"inputs": x})
result_ort = sess.run([output_name], {input_name: x})
try:
numpy.testing.assert_allclose(result_migraphx[0].tolist(),
result_ort[0][0],
rtol=1e-02)
print(f"Test #{i} completed.")
except AssertionError as e:
print(e)
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