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!8570 add TinyNet-A, B, D, E to model_zoo

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From: @wangrao124
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pull/8570/MERGE
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92
model_zoo/research/cv/tinynet/README.md
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@ -5,14 +5,12 @@
- [Dataset](#dataset) - [Dataset](#dataset)
- [Environment Requirements](#environment-requirements) - [Environment Requirements](#environment-requirements)
- [Script Description](#script-description) - [Script Description](#script-description)
- [Script and Sample Code](#script-and-sample-code) - [Script and Sample Code](#script-and-sample-code)
- [Training Process](#training-process) - [Training Process](#training-process)
- [Evaluation Process](#evaluation-process) - [Evaluation Process](#evaluation-process)
- [Evaluation](#evaluation)
- [Model Description](#model-description) - [Model Description](#model-description)
- [Performance](#performance) - [Performance](#performance)
- [Training Performance](#evaluation-performance) - [Evaluation Performance](#evaluation-performance)
- [Inference Performance](#evaluation-performance)
- [Description of Random Situation](#description-of-random-situation) - [Description of Random Situation](#description-of-random-situation)
- [ModelZoo Homepage](#modelzoo-homepage) - [ModelZoo Homepage](#modelzoo-homepage)
@ -22,7 +20,6 @@ TinyNets are a series of lightweight models obtained by twisting resolution, dep
[Paper](https://arxiv.org/abs/2010.14819): Kai Han, Yunhe Wang, Qiulin Zhang, Wei Zhang, Chunjing Xu, Tong Zhang. Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets. In NeurIPS 2020. [Paper](https://arxiv.org/abs/2010.14819): Kai Han, Yunhe Wang, Qiulin Zhang, Wei Zhang, Chunjing Xu, Tong Zhang. Model Rubik's Cube: Twisting Resolution, Depth and Width for TinyNets. In NeurIPS 2020.
Note: We have only released TinyNet-C for now, and will release other TinyNets soon.
# [Model architecture](#contents) # [Model architecture](#contents)
The overall network architecture of TinyNet is show below: The overall network architecture of TinyNet is show below:
@ -33,53 +30,56 @@ The overall network architecture of TinyNet is show below:
Dataset used: [ImageNet 2012](http://image-net.org/challenges/LSVRC/2012/) Dataset used: [ImageNet 2012](http://image-net.org/challenges/LSVRC/2012/)
- Dataset size: - Dataset size:
- Train: 1.2 million images in 1,000 classes - Train: 1.2 million images in 1,000 classes
- Test: 50,000 validation images in 1,000 classes - Test: 50,000 validation images in 1,000 classes
- Data format: RGB images. - Data format: RGB images.
- Note: Data will be processed in src/dataset/dataset.py - Note: Data will be processed in src/dataset/dataset.py
# [Environment Requirements](#contents) # [Environment Requirements](#contents)
- Hardware (GPU) - Hardware (GPU)
- Framework - Framework
- [MindSpore](https://www.mindspore.cn/install/en) - [MindSpore](https://www.mindspore.cn/install/en)
- For more information, please check the resources below： - For more information, please check the resources below:
- [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html) - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/en/master/index.html)
- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html) - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
# [Script description](#contents) # [Script Description](#contents)
## [Script and sample code](#contents) ## [Script and Sample Code](#contents)
``` ```markdown
.tinynet .tinynet
├── Readme.md # descriptions about tinynet ├── README.md # descriptions about tinynet
├── script ├── script
│ ├── eval.sh # evaluation script │ ├── eval.sh # evaluation script
│ ├── train_1p_gpu.sh # training script on single GPU │ ├── train_1p_gpu.sh # training script on single GPU
│ └── train_distributed_gpu.sh # distributed training script on multiple GPUs │ └── train_distributed_gpu.sh # distributed training script on multiple GPUs
├── src ├── src
│ ├── callback.py # loss and checkpoint callbacks │ ├── callback.py # loss, ema, and checkpoint callbacks
│ ├── dataset.py # data processing │ ├── dataset.py # data preprocessing
│ ├── loss.py # label-smoothing cross-entropy loss function │ ├── loss.py # label-smoothing cross-entropy loss function
│ ├── tinynet.py # tinynet architecture │ ├── tinynet.py # tinynet architecture
│ └── utils.py # utility functions │ └── utils.py # utility functions
├── eval.py # evaluation interface ├── eval.py # evaluation interface
└── train.py # training interface └── train.py # training interface
``` ```
## [Training process](#contents)
### Launch ### [Training process](#contents)
``` #### Launch
```bash
# training on single GPU # training on single GPU
sh train_1p_gpu.sh sh train_1p_gpu.sh
# training on multiple GPUs, the number after -n indicates how many GPUs will be used for training # training on multiple GPUs, the number after -n indicates how many GPUs will be used for training
sh train_distributed_gpu.sh -n 8 sh train_distributed_gpu.sh -n 8
``` ```
Inside train.sh, there are hyperparameters that can be adjusted during training, for example: Inside train.sh, there are hyperparameters that can be adjusted during training, for example:
```
```python
--model tinynet_c model to be used for training --model tinynet_c model to be used for training
--drop 0.2 dropout rate --drop 0.2 dropout rate
--drop-connect 0 drop connect rate --drop-connect 0 drop connect rate
@ -88,51 +88,55 @@ Inside train.sh, there are hyperparameters that can be adjusted during training,
--lr 0.048 learning rate --lr 0.048 learning rate
--batch-size 128 batch size --batch-size 128 batch size
--decay-epochs 2.4 learning rate decays every 2.4 epoch --decay-epochs 2.4 learning rate decays every 2.4 epoch
--warmup-lr 1e-6 warm up learning rate --warmup-lr 1e-6 warm up learning rate
--warmup-epochs 3 learning rate warm up epoch --warmup-epochs 3 learning rate warm up epoch
--decay-rate 0.97 learning rate decay rate --decay-rate 0.97 learning rate decay rate
--ema-decay 0.9999 decay factor for model weights moving average --ema-decay 0.9999 decay factor for model weights moving average
--weight-decay 1e-5 optimizer's weight decay --weight-decay 1e-5 optimizer's weight decay
--epochs 450 number of epochs to be trained --epochs 450 number of epochs to be trained
--ckpt_save_epoch 1 checkpoint saving interval --ckpt_save_epoch 1 checkpoint saving interval
--workers 8 number of processes for loading data --workers 8 number of processes for loading data
--amp_level O0 training auto-mixed precision --amp_level O0 training auto-mixed precision
--opt rmsprop optimizers, currently we support SGD and RMSProp --opt rmsprop optimizers, currently we support SGD and RMSProp
--data_path /path_to_ImageNet/ --data_path /path_to_ImageNet/
--GPU using GPU for training --GPU using GPU for training
--dataset_sink using sink mode --dataset_sink using sink mode
``` ```
The config above was used to train tinynets on ImageNet (change drop-connect to 0.2 for training tinynet-b)
The config above was used to train tinynets on ImageNet (change drop-connect to 0.1 for training tinynet_b)
> checkpoints will be saved in the ./device_{rank_id} folder (single GPU) > checkpoints will be saved in the ./device_{rank_id} folder (single GPU)
or ./device_parallel folder (multiple GPUs) or ./device_parallel folder (multiple GPUs)
## [Eval process](#contents) ### [Evaluation Process](#contents)
### Launch #### Launch
``` ```bash
# infer example # infer example
sh eval.sh sh eval.sh
``` ```
Inside the eval.sh, there are configs that can be adjusted during inference, for example: Inside the eval.sh, there are configs that can be adjusted during inference, for example:
```
--num-classes 1000 ```python
--batch-size 128 --num-classes 1000
--workers 8 --batch-size 128
--data_path /path_to_ImageNet/ --workers 8
--GPU --data_path /path_to_ImageNet/
--ckpt /path_to_EMA_checkpoint/ --GPU
--ckpt /path_to_EMA_checkpoint/
--dataset_sink > tinynet_c_eval.log 2>&1 & --dataset_sink > tinynet_c_eval.log 2>&1 &
``` ```
> checkpoint can be produced in training process. > checkpoint can be produced in training process.
# [Model Description](#contents) # [Model Description](#contents)
## [Performance](#contents) ## [Performance](#contents)
#### Evaluation Performance ### Evaluation Performance
| Model | FLOPs | Latency* | ImageNet Top-1 | | Model | FLOPs | Latency* | ImageNet Top-1 |
| ------------------- | ----- | -------- | -------------- | | ------------------- | ----- | -------- | -------------- |
@ -149,6 +153,6 @@ Inside the eval.sh, there are configs that can be adjusted during inference, for
We set the seed inside dataset.py. We also use random seed in train.py. We set the seed inside dataset.py. We also use random seed in train.py.
# [Model Zoo Homepage](#contents) # [ModelZoo Homepage](#contents)
Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo). Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).

32
model_zoo/research/cv/tinynet/src/callback.py
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@ -36,7 +36,7 @@ def load_nparray_into_net(net, array_dict):
for _, param in net.parameters_and_names(): for _, param in net.parameters_and_names():
if param.name in array_dict: if param.name in array_dict:
new_param = array_dict[param.name] new_param = array_dict[param.name]
param.set_data(Parameter(new_param.copy(), name=param.name)) param.set_data(Parameter(Tensor(deepcopy(new_param)), name=param.name))
else: else:
param_not_load.append(param.name) param_not_load.append(param.name)
return param_not_load return param_not_load
@ -48,8 +48,8 @@ class EmaEvalCallBack(Callback):
the end of training epoch. the end of training epoch.
Args: Args:
model: Mindspore model instance. network: tinynet network instance.
ema_network: step-wise exponential moving average for ema_network. ema_network: step-wise exponential moving average of network.
eval_dataset: the evaluation daatset. eval_dataset: the evaluation daatset.
decay (float): ema decay. decay (float): ema decay.
save_epoch (int): defines how often to save checkpoint. save_epoch (int): defines how often to save checkpoint.
@ -57,9 +57,9 @@ class EmaEvalCallBack(Callback):
start_epoch (int): which epoch to start/resume training. start_epoch (int): which epoch to start/resume training.
""" """
def __init__(self, model, ema_network, eval_dataset, loss_fn, decay=0.999, def __init__(self, network, ema_network, eval_dataset, loss_fn, decay=0.999,
save_epoch=1, dataset_sink_mode=True, start_epoch=0): save_epoch=1, dataset_sink_mode=True, start_epoch=0):
self.model = model self.network = network
self.ema_network = ema_network self.ema_network = ema_network
self.eval_dataset = eval_dataset self.eval_dataset = eval_dataset
self.loss_fn = loss_fn self.loss_fn = loss_fn
@ -80,14 +80,12 @@ class EmaEvalCallBack(Callback):
def begin(self, run_context): def begin(self, run_context):
"""Initialize the EMA parameters """ """Initialize the EMA parameters """
cb_params = run_context.original_args() for _, param in self.network.parameters_and_names():
for _, param in cb_params.network.parameters_and_names():
self.shadow[param.name] = deepcopy(param.data.asnumpy()) self.shadow[param.name] = deepcopy(param.data.asnumpy())
def step_end(self, run_context): def step_end(self, run_context):
"""Update the EMA parameters""" """Update the EMA parameters"""
cb_params = run_context.original_args() for _, param in self.network.parameters_and_names():
for _, param in cb_params.network.parameters_and_names():
new_average = (1.0 - self.decay) * param.data.asnumpy().copy() + \ new_average = (1.0 - self.decay) * param.data.asnumpy().copy() + \
self.decay * self.shadow[param.name] self.decay * self.shadow[param.name]
self.shadow[param.name] = new_average self.shadow[param.name] = new_average
@ -98,24 +96,20 @@ class EmaEvalCallBack(Callback):
cur_epoch = cb_params.cur_epoch_num + self._start_epoch - 1 cur_epoch = cb_params.cur_epoch_num + self._start_epoch - 1
save_ckpt = (cur_epoch % self.save_epoch == 0) save_ckpt = (cur_epoch % self.save_epoch == 0)
acc = self.model.eval(
self.eval_dataset, dataset_sink_mode=self.dataset_sink_mode)
print("Model Accuracy:", acc)
load_nparray_into_net(self.ema_network, self.shadow) load_nparray_into_net(self.ema_network, self.shadow)
self.ema_network.set_train(False) model = Model(self.network, loss_fn=self.loss_fn, metrics=self.eval_metrics)
model_ema = Model(self.ema_network, loss_fn=self.loss_fn, model_ema = Model(self.ema_network, loss_fn=self.loss_fn,
metrics=self.eval_metrics) metrics=self.eval_metrics)
acc = model.eval(
self.eval_dataset, dataset_sink_mode=self.dataset_sink_mode)
ema_acc = model_ema.eval( ema_acc = model_ema.eval(
self.eval_dataset, dataset_sink_mode=self.dataset_sink_mode) self.eval_dataset, dataset_sink_mode=self.dataset_sink_mode)
print("Model Accuracy:", acc)
print("EMA-Model Accuracy:", ema_acc) print("EMA-Model Accuracy:", ema_acc)
self.ema_accuracy[cur_epoch] = ema_acc["Top1-Acc"]
output = [{"name": k, "data": Tensor(v)} output = [{"name": k, "data": Tensor(v)}
for k, v in self.shadow.items()] for k, v in self.shadow.items()]
self.ema_accuracy[cur_epoch] = ema_acc["Top1-Acc"]
if self.best_ema_accuracy < ema_acc["Top1-Acc"]: if self.best_ema_accuracy < ema_acc["Top1-Acc"]:
self.best_ema_accuracy = ema_acc["Top1-Acc"] self.best_ema_accuracy = ema_acc["Top1-Acc"]
self.best_ema_epoch = cur_epoch self.best_ema_epoch = cur_epoch

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model_zoo/research/cv/tinynet/src/dataset.py
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@ -65,12 +65,12 @@ def create_dataset(batch_size, train_data_url='', workers=8, distributed=False,
contrast=adjust_range, contrast=adjust_range,
saturation=adjust_range) saturation=adjust_range)
to_tensor = py_vision.ToTensor() to_tensor = py_vision.ToTensor()
nromlize_op = py_vision.Normalize( normalize_op = py_vision.Normalize(
IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD) IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
# assemble all the transforms # assemble all the transforms
image_ops = py_transforms.Compose([decode_op, random_resize_crop_bicubic, image_ops = py_transforms.Compose([decode_op, random_resize_crop_bicubic,
random_horizontal_flip_op, random_color_jitter_op, to_tensor, nromlize_op]) random_horizontal_flip_op, random_color_jitter_op, to_tensor, normalize_op])
rank_id = get_rank() if distributed else 0 rank_id = get_rank() if distributed else 0
rank_size = get_group_size() if distributed else 1 rank_size = get_group_size() if distributed else 1
@ -125,11 +125,11 @@ def create_dataset_val(batch_size=128, val_data_url='', workers=8, distributed=F
resize_op = py_vision.Resize(size=scale_size, interpolation=Inter.BICUBIC) resize_op = py_vision.Resize(size=scale_size, interpolation=Inter.BICUBIC)
center_crop = py_vision.CenterCrop(size=input_size) center_crop = py_vision.CenterCrop(size=input_size)
to_tensor = py_vision.ToTensor() to_tensor = py_vision.ToTensor()
nromlize_op = py_vision.Normalize( normalize_op = py_vision.Normalize(
IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD) IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD)
image_ops = py_transforms.Compose([decode_op, resize_op, center_crop, image_ops = py_transforms.Compose([decode_op, resize_op, center_crop,
to_tensor, nromlize_op]) to_tensor, normalize_op])
dataset = dataset.map(input_columns=["label"], operations=type_cast_op, dataset = dataset.map(input_columns=["label"], operations=type_cast_op,
num_parallel_workers=workers) num_parallel_workers=workers)

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model_zoo/research/cv/tinynet/src/tinynet.py
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@ -18,10 +18,12 @@ import re
from copy import deepcopy from copy import deepcopy
import mindspore.nn as nn import mindspore.nn as nn
import mindspore.common.dtype as mstype
from mindspore.ops import operations as P from mindspore.ops import operations as P
from mindspore.common.initializer import Normal, Zero, One, initializer, Uniform from mindspore.common.initializer import Normal, Zero, One, initializer, Uniform
from mindspore import context, ms_function from mindspore import context, ms_function
from mindspore.common.parameter import Parameter from mindspore.common.parameter import Parameter
from mindspore import Tensor
# Imagenet constant values # Imagenet constant values
IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406) IMAGENET_DEFAULT_MEAN = (0.485, 0.456, 0.406)
@ -29,12 +31,14 @@ IMAGENET_DEFAULT_STD = (0.229, 0.224, 0.225)
# model structure configurations for TinyNets, values are # model structure configurations for TinyNets, values are
# (resolution multiplier, channel multiplier, depth multiplier) # (resolution multiplier, channel multiplier, depth multiplier)
# only tinynet-c is availiable for now, we will release other tinynet
# models soon
# codes are inspired and partially adapted from # codes are inspired and partially adapted from
# https://github.com/rwightman/gen-efficientnet-pytorch # https://github.com/rwightman/gen-efficientnet-pytorch
TINYNET_CFG = {"c": (0.825, 0.54, 0.85)} TINYNET_CFG = {"a": (0.86, 1.0, 1.2),
"b": (0.84, 0.75, 1.1),
"c": (0.825, 0.54, 0.85),
"d": (0.68, 0.54, 0.695),
"e": (0.475, 0.51, 0.60)}
relu = P.ReLU() relu = P.ReLU()
sigmoid = P.Sigmoid() sigmoid = P.Sigmoid()
@ -524,13 +528,15 @@ class DropConnect(nn.Cell):
self.dtype = P.DType() self.dtype = P.DType()
self.keep_prob = 1 - drop_connect_rate self.keep_prob = 1 - drop_connect_rate
self.dropout = P.Dropout(keep_prob=self.keep_prob) self.dropout = P.Dropout(keep_prob=self.keep_prob)
self.keep_prob_tensor = Tensor(self.keep_prob, dtype=mstype.float32)
def construct(self, x): def construct(self, x):
shape = self.shape(x) shape = self.shape(x)
dtype = self.dtype(x) dtype = self.dtype(x)
ones_tensor = P.Fill()(dtype, (shape[0], 1, 1, 1), 1) ones_tensor = P.Fill()(dtype, (shape[0], 1, 1, 1), 1)
_, mask_ = self.dropout(ones_tensor) _, mask = self.dropout(ones_tensor)
x = x * mask_ x = x * mask
x = x / self.keep_prob_tensor
return x return x

2
model_zoo/research/cv/tinynet/train.py
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@ -227,7 +227,7 @@ def main():
net_ema.set_train(False) net_ema.set_train(False)
assert args.ema_decay > 0, "EMA should be used in tinynet training." assert args.ema_decay > 0, "EMA should be used in tinynet training."
ema_cb = EmaEvalCallBack(model=model, ema_cb = EmaEvalCallBack(network=net,
ema_network=net_ema, ema_network=net_ema,
loss_fn=loss, loss_fn=loss,
eval_dataset=val_dataset, eval_dataset=val_dataset,

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