Interim applications update

Author: RouxJohannes

README.md

@@ -25,12 +25,12 @@ The [applications/](applications) folder contains example applications written i
 
 | Model | Domain | Type |Links |
 | ------- | ------- |------- | ------- |
-| ResNet  | Image Classifcation | Training & Inference | [TensorFlow 1](applications/tensorflow/cnns/) , [TensorFlow 2](applications/tensorflow/cnns/), [PyTorch](applications/pytorch/cnns/)|
+| ResNet  | Image Classifcation | Training & Inference | [TensorFlow 1](applications/tensorflow/cnns/) , [TensorFlow 2](applications/tensorflow2/classification/), [PyTorch](applications/pytorch/cnns/)|
 | ResNeXt  | Image Classifcation | Training & Inference | [TensorFlow 1](applications/tensorflow/cnns/) , [PopART (Inference)](applications/popart/resnext_inference)
 | EfficientNet | Image Classifcation | Training & Inference | [TensorFlow 1](applications/tensorflow/cnns/) , [PyTorch](applications/pytorch/cnns/)|
 | MobileNet | Image Classifcation | Inference | [TensorFlow 1](applications/tensorflow/cnns/inference) |
 | MobileNetv2 | Image Classifcation | Inference | [TensorFlow 1](applications/tensorflow/cnns/inference) |
-| MobileNetv3 | Image Classifcation | Training & Inference | [PyTorch](applications/tensorflow/cnns/) |
+| MobileNetv3 | Image Classifcation | Training & Inference | [PyTorch](applications/pytorch/cnns/) |
 | ViT(Vision Transformer) | Image Classifcation | Training| [PyTorch](applications/pytorch/vit) |
 | Yolov3 | Object Detection | Training & Inference | [TensorFlow 1](applications/tensorflow/detection/yolov3) |
 | Yolov4-P5 | Object Detection | Inference | [PyTorch](applications/pytorch/detection) |
@@ -41,7 +41,7 @@ The [applications/](applications) folder contains example applications written i
 | DeepVoice3 | TTS (TextToSpeech) | Training & Inference |[PopART](applications/popart/deep_voice) |
 | FastSpeech2 | TTS(TextToSpeech) | Training & Inference | [TensorFlow 2](applications/tensorflow2/fastspeech2/) |
 | Conformer | STT(SpeechToText) | Training & Inference | [PopART](applications/popart/conformer_asr) |
-| Conformer with Transformer | STT(SpeechToText) | Training & Inference | [TensorFlow 1](applications/tensorflow/conformer) |
+| Conformer with Transformer | STT(SpeechToText) | Training & Inference | [TensorFlow 1](applications/tensorflow/conformer) , [PyTorch](applications/pytorch/conformer) |
 | Transfomer Transducer | STT(SpeechToText) | Training & Inference | [PopART](applications/popart/transformer_transducer) |
 | TGN (Temporal Graph Network) | GNN | Training & Inference | [TensorFlow 1](applications/tensorflow/tgn/) |
 | MPNN (Message Passing Neural Networks) | GNN | Training & Inference | [TensorFlow 2](code_examples/tensorflow2/message_passing_neural_network) |
@@ -51,11 +51,12 @@ The [applications/](applications) folder contains example applications written i
 | RL Policy model | Reinforcement Learning | Training | [TensorFlow 1](applications/tensorflow/reinforcement_learning) |
 | MNIST RigL | Dynamic Sparsity | Training | [TensorFlow 1](applications/tensorflow/dynamic_sparsity/mnist_rigl) |
 | Autoregressive Language Modelling | Dynamic Sparsity | Training | [TensorFlow 1](applications/tensorflow/dynamic_sparsity/language_modelling) |
-| Sales forecasting | MLP (Multi-Layer Perceptron) | Training | [TensorFlow 1](applications/tensorflow/dynamic_sparsity/language_modelling) |
+| Sales forecasting | MLP (Multi-Layer Perceptron) | Training | [TensorFlow 1](applications/tensorflow/sales_forcasting/language_modelling) |
 | Contrastive Divergence VAE using MCMC methods  | Generative Model | Training | [TensorFlow 1](applications/tensorflow/contrastive_divergence_vae) |
 | Monte Carlo Ray Tracing  | Vision | Inference | [Poplar](applications/poplar/monte_carlo_ray_tracing) |
 
 
+
 ### Code examples
 
 The [code_examples/](code_examples) folder contains smaller models and code examples. See the READMEs in each folder for details.
@@ -82,3 +83,4 @@ December 2021:
     - GNN : TGN(TensorFlow1), MPNN(TensorFlow 2)
 
 
+

applications/popart/faster-rcnn/IPU/customized_ops.py

@@ -12,7 +12,10 @@
 def load_lib():
     global LIB_LOADED
     if not LIB_LOADED:
+        print(__file__)
+        print(os.path.dirname(__file__))
         basedir = os.path.abspath(os.path.dirname(__file__))
+        print(basedir)
         ctypes.cdll.LoadLibrary(
             os.path.join(basedir, "custom_ops/nms/build/libnms_ops.so"))
         ctypes.cdll.LoadLibrary(

applications/popart/faster-rcnn/tests/conftest.py

@@ -0,0 +1,11 @@
+# Copyright (c) 2021 Graphcore Ltd. All rights reserved.
+# Written by Hu Di
+
+import pytest
+import os
+import subprocess
+
+
+def pytest_sessionstart(session):
+    faster_rcnn_working_dic = os.path.join(os.path.dirname(__file__), '../')
+    subprocess.run(['make'], shell=True, cwd=faster_rcnn_working_dic)

applications/popart/faster-rcnn/tests/test_nms.py

@@ -4,7 +4,6 @@
 import numpy as np
 import sys
 import os
-import subprocess
 sys.path.append(os.path.join(os.path.dirname(__file__), '../'))
 sys.path.append(os.path.join(os.path.dirname(__file__), '../IPU'))
 from ipu_tensor import gcop
@@ -119,7 +118,5 @@ def py_cpu_nms(dets, scores, thresh):
 
 
 def test_nms():
-    faster_rcnn_working_dic = os.path.join(os.path.dirname(__file__), '../')
-    subprocess.run(['make'], shell=True, cwd=faster_rcnn_working_dic)
     for iou_thrd in [0.1, 0.3, 0.5, 0.7, 0.9]:
         helper_func(iou_thrd)

applications/popart/faster-rcnn/tests/test_roialign.py

@@ -3,7 +3,6 @@
 
 import sys
 import os
-import subprocess
 sys.path.append(os.path.join(os.path.dirname(__file__), '../'))
 sys.path.append(os.path.join(os.path.dirname(__file__), '../IPU'))
 from torchvision.ops import RoIAlign as torch_roialign
@@ -60,8 +59,6 @@ def my_test_func(feat, box, pt_on_output, output_shape, spatial_scale, aligned=T
 
 
 def test_roialign():
-    faster_rcnn_working_dic = os.path.join(os.path.dirname(__file__), '../')
-    subprocess.run(['make'], shell=True, cwd=faster_rcnn_working_dic)
     box_centers = np.random.rand(1, BOX_NUM, 2) * np.asarray(IMAGE_SIZE)
     box_whs = np.random.rand(
         1, BOX_NUM, 2) * (BOX_SIZE_RANGE[1] - BOX_SIZE_RANGE[0]) + BOX_SIZE_RANGE[0]
@@ -145,6 +142,6 @@ def clip_boxes(boxes, im_info):
     # local_spatial_scale = 1/16
     # local_result = my_test_func(local_feat,local_box,pt_on_output,output_shape,local_spatial_scale)
     np.testing.assert_allclose(
-        torch_result.detach().numpy(), pooled_feat.data[0], rtol=1e-2)
+        torch_result.detach().numpy(), pooled_feat.data[0], rtol=1e-2, atol=1e-5)
     np.testing.assert_allclose(
-        torch_feat.grad.detach().numpy(), input_features.grad, rtol=1e-3)
+        torch_feat.grad.detach().numpy(), input_features.grad, rtol=1e-3, atol=1e-6)

applications/popart/resnext_inference/requirements.txt

@@ -5,5 +5,5 @@ torch==1.7.0
 torchvision==0.8.1
 urllib3==1.26.6
 pretrainedmodels==0.7.4
-pytest==5.4.1
+pytest==6.2.5
 pytest-pythonpath==0.7.3

applications/poplar/monte_carlo_ray_tracing/tests/requirements.txt

@@ -1,4 +1,4 @@
-pytest
+pytest==6.2.5
 pytest-pythonpath
 pathlib
 

applications/pytorch/bert/README.md

@@ -236,22 +236,18 @@ for f in *.tfrecord; do python3 -m tfrecord.tools.tfrecord2idx $f `basename $f .
 
 ## Licensing
 
-The code presented here is licensed under the Apache License Version 2.0, see the LICENSE file in this directory.
+This application is licensed under Apache License 2.0.
+Please see the LICENSE file in this directory for full details of the license conditions.
 
-This directory includes derived work from the following:
+This directory contains derived work from:
+* BERT, https://github.com/google-research/bert (licensed under the Apache License, Version 2.0)
+* Hugging Face Transformers, https://github.com/huggingface/transformers (licensed under the Apache License, Version 2.0)
 
-BERT, https://github.com/google-research/bert
+See the headers in the source code for details.
 
-Copyright 2018 The Google AI Language Team Authors.
+The following files include code derived from https://github.com/huggingface/transformers which uses Apache License, Version 2.0:
+* bert_fused_attention.py
+* squad_data.py
 
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-      http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
+The following files include code derived from https://github.com/google-research/bert which uses Apache License, Version 2.0:
+* third_party/create_pretraining_data.py

applications/pytorch/bert/requirements.txt

@@ -4,7 +4,7 @@ tokenizers>=0.10.3
 scipy>=1.5.4
 pyyaml>=5.4.1
 wandb==0.12.1
-pytest>=6.2.4
+pytest==6.2.5
 pytest-pythonpath>=0.7.3
 tfrecord>=1.13
 filelock>=3.0.12

applications/pytorch/bert/squad_data.py

@@ -196,7 +196,9 @@ def postprocess_qa_predictions(examples, features, raw_predictions, n_best_size=
                     if start_index >= len(offset_mapping) \
                        or end_index >= len(offset_mapping) \
                        or offset_mapping[start_index] is None \
-                       or offset_mapping[end_index] is None:
+                       or offset_mapping[end_index] is None \
+                       or offset_mapping[start_index] == [] \
+                       or offset_mapping[end_index] == []:
                         continue
                     # Don't consider answers with a length that is either < 0 or > max_answer_length.
                     if end_index < start_index or end_index - start_index + 1 > max_answer_length: