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update README.md
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Summary: add information about fastreid V1.0
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@L1aoXingyu
L1aoXingyu committed Jan 18, 2021
1 parent 15e1729 commit 7e83d31
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11 changes: 6 additions & 5 deletions README.md
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Expand Up @@ -4,13 +4,14 @@ FastReID is a research platform that implements state-of-the-art re-identificati

## What's New

- [Oct 2020] Added the [Hyper-Parameter Optimization](https://github.com/JDAI-CV/fast-reid/tree/master/projects/HPOReID) based on fastreid. See `projects/HPOReID`.
- [Sep 2020] Added the [person attribute recognition](https://github.com/JDAI-CV/fast-reid/tree/master/projects/attribute_recognition) based on fastreid. See `projects/attribute_recognition`.
- [Sep 2020] Automatic Mixed Precision training is supported with pytorch1.6 built-in `torch.cuda.amp`. Set `cfg.SOLVER.AMP_ENABLED=True` to switch it on.
- [Aug 2020] [Model Distillation](https://github.com/JDAI-CV/fast-reid/tree/master/projects/DistillReID) is supported, thanks for [guan'an wang](https://github.com/wangguanan)'s contribution.
- [Jan 2021] FastReID V1.0 has been released！🎉
Support many tasks beyond reid, such image retrieval and face recognition. See [projects](https://github.com/JDAI-CV/fast-reid/tree/master/projects).
- [Oct 2020] Added the [Hyper-Parameter Optimization](https://github.com/JDAI-CV/fast-reid/tree/master/projects/FastTune) based on fastreid. See `projects/FastTune`.
- [Sep 2020] Added the [person attribute recognition](https://github.com/JDAI-CV/fast-reid/tree/master/projects/FastAttr) based on fastreid. See `projects/FastAttr`.
- [Sep 2020] Automatic Mixed Precision training is supported with `apex`. Set `cfg.SOLVER.FP16_ENABLED=True` to switch it on.
- [Aug 2020] [Model Distillation](https://github.com/JDAI-CV/fast-reid/tree/master/projects/FastDistill) is supported, thanks for [guan'an wang](https://github.com/wangguanan)'s contribution.
- [Aug 2020] ONNX/TensorRT converter is supported.
- [Jul 2020] Distributed training with multiple GPUs, it trains much faster.
- [Jul 2020] `MAX_ITER` in config means `epoch`, it will auto scale to maximum iterations.
- Includes more features such as circle loss, abundant visualization methods and evaluation metrics, SoTA results on conventional, cross-domain, partial and vehicle re-id, testing on multi-datasets simultaneously, etc.
- Can be used as a library to support [different projects](https://github.com/JDAI-CV/fast-reid/tree/master/projects) on top of it. We'll open source more research projects in this way.
- Remove [ignite](https://github.com/pytorch/ignite)(a high-level library) dependency and powered by [PyTorch](https://pytorch.org/).
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28 changes: 2 additions & 26 deletions fastreid/modeling/backbones/regnet/config.py
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Expand Up @@ -13,15 +13,13 @@

from yacs.config import CfgNode as CfgNode


# Global config object
_C = CfgNode()

# Example usage:
# from core.config import cfg
cfg = _C


# ------------------------------------------------------------------------------------ #
# Model options
# ------------------------------------------------------------------------------------ #
Expand All @@ -39,7 +37,6 @@
# Loss function (see pycls/models/loss.py for options)
_C.MODEL.LOSS_FUN = "cross_entropy"


# ------------------------------------------------------------------------------------ #
# ResNet options
# ------------------------------------------------------------------------------------ #
Expand All @@ -57,7 +54,6 @@
# Apply stride to 1x1 conv (True -> MSRA; False -> fb.torch)
_C.RESNET.STRIDE_1X1 = True


# ------------------------------------------------------------------------------------ #
# AnyNet options
# ------------------------------------------------------------------------------------ #
Expand Down Expand Up @@ -93,7 +89,6 @@
# SE ratio
_C.ANYNET.SE_R = 0.25


# ------------------------------------------------------------------------------------ #
# RegNet options
# ------------------------------------------------------------------------------------ #
Expand Down Expand Up @@ -133,7 +128,6 @@
# Bottleneck multiplier (bm = 1 / b from the paper)
_C.REGNET.BOT_MUL = 1.0


# ------------------------------------------------------------------------------------ #
# EfficientNet options
# ------------------------------------------------------------------------------------ #
Expand Down Expand Up @@ -169,7 +163,6 @@
# Dropout ratio
_C.EN.DROPOUT_RATIO = 0.0


# ------------------------------------------------------------------------------------ #
# Batch norm options
# ------------------------------------------------------------------------------------ #
Expand All @@ -192,7 +185,6 @@
_C.BN.USE_CUSTOM_WEIGHT_DECAY = False
_C.BN.CUSTOM_WEIGHT_DECAY = 0.0


# ------------------------------------------------------------------------------------ #
# Optimizer options
# ------------------------------------------------------------------------------------ #
Expand Down Expand Up @@ -234,7 +226,6 @@
# Gradually warm up the OPTIM.BASE_LR over this number of epochs
_C.OPTIM.WARMUP_EPOCHS = 0


# ------------------------------------------------------------------------------------ #
# Training options
# ------------------------------------------------------------------------------------ #
Expand Down Expand Up @@ -262,7 +253,6 @@
# Weights to start training from
_C.TRAIN.WEIGHTS = ""


# ------------------------------------------------------------------------------------ #
# Testing options
# ------------------------------------------------------------------------------------ #
Expand All @@ -281,7 +271,6 @@
# Weights to use for testing
_C.TEST.WEIGHTS = ""


# ------------------------------------------------------------------------------------ #
# Common train/test data loader options
# ------------------------------------------------------------------------------------ #
Expand All @@ -293,7 +282,6 @@
# Load data to pinned host memory
_C.DATA_LOADER.PIN_MEMORY = True


# ------------------------------------------------------------------------------------ #
# Memory options
# ------------------------------------------------------------------------------------ #
Expand All @@ -302,7 +290,6 @@
# Perform ReLU inplace
_C.MEM.RELU_INPLACE = True


# ------------------------------------------------------------------------------------ #
# CUDNN options
# ------------------------------------------------------------------------------------ #
Expand All @@ -313,7 +300,6 @@
# in overall speedups when variable size inputs are used (e.g. COCO training)
_C.CUDNN.BENCHMARK = True


# ------------------------------------------------------------------------------------ #
# Precise timing options
# ------------------------------------------------------------------------------------ #
Expand All @@ -325,7 +311,6 @@
# Number of iterations to compute avg time
_C.PREC_TIME.NUM_ITER = 30


# ------------------------------------------------------------------------------------ #
# Misc options
# ------------------------------------------------------------------------------------ #
Expand Down Expand Up @@ -359,7 +344,6 @@
# Models weights referred to by URL are downloaded to this local cache
_C.DOWNLOAD_CACHE = "/tmp/pycls-download-cache"


# ------------------------------------------------------------------------------------ #
# Deprecated keys
# ------------------------------------------------------------------------------------ #
Expand All @@ -369,7 +353,7 @@
_C.register_deprecated_key("PORT")


def assert_and_infer_cfg(cache_urls=True):
def assert_and_infer_cfg():
"""Checks config values invariants."""
err_str = "The first lr step must start at 0"
assert not _C.OPTIM.STEPS or _C.OPTIM.STEPS[0] == 0, err_str
Expand All @@ -382,14 +366,6 @@ def assert_and_infer_cfg(cache_urls=True):
assert _C.TEST.BATCH_SIZE % _C.NUM_GPUS == 0, err_str
err_str = "Log destination '{}' not supported"
assert _C.LOG_DEST in ["stdout", "file"], err_str.format(_C.LOG_DEST)
if cache_urls:
cache_cfg_urls()


def cache_cfg_urls():
"""Download URLs in config, cache them, and rewrite cfg to use cached file."""
_C.TRAIN.WEIGHTS = cache_url(_C.TRAIN.WEIGHTS, _C.DOWNLOAD_CACHE)
_C.TEST.WEIGHTS = cache_url(_C.TEST.WEIGHTS, _C.DOWNLOAD_CACHE)


def dump_cfg():
Expand Down Expand Up @@ -417,4 +393,4 @@ def load_cfg_fom_args(description="Config file options."):
sys.exit(1)
args = parser.parse_args()
_C.merge_from_file(args.cfg_file)
_C.merge_from_list(args.opts)
_C.merge_from_list(args.opts)
109 changes: 51 additions & 58 deletions tools/deploy/Caffe/layer_param.py
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@@ -1,38 +1,41 @@
from __future__ import absolute_import

from . import caffe_pb2 as pb
import numpy as np

def pair_process(item,strict_one=True):
if hasattr(item,'__iter__'):

def pair_process(item, strict_one=True):
if hasattr(item, '__iter__'):
for i in item:
if i!=item[0]:
if i != item[0]:
if strict_one:
raise ValueError("number in item {} must be the same".format(item))
else:
print("IMPORTANT WARNING: number in item {} must be the same".format(item))
return item[0]
return item


def pair_reduce(item):
if hasattr(item,'__iter__'):
if hasattr(item, '__iter__'):
for i in item:
if i!=item[0]:
if i != item[0]:
return item
return [item[0]]
return [item]


class Layer_param():
def __init__(self,name='',type='',top=(),bottom=()):
self.param=pb.LayerParameter()
self.name=self.param.name=name
self.type=self.param.type=type
def __init__(self, name='', type='', top=(), bottom=()):
self.param = pb.LayerParameter()
self.name = self.param.name = name
self.type = self.param.type = type

self.top=self.param.top
self.top = self.param.top
self.top.extend(top)
self.bottom=self.param.bottom
self.bottom = self.param.bottom
self.bottom.extend(bottom)

def fc_param(self, num_output, weight_filler='xavier', bias_filler='constant',has_bias=True):
def fc_param(self, num_output, weight_filler='xavier', bias_filler='constant', has_bias=True):
if self.type != 'InnerProduct':
raise TypeError('the layer type must be InnerProduct if you want set fc param')
fc_param = pb.InnerProductParameter()
Expand All @@ -45,7 +48,7 @@ def fc_param(self, num_output, weight_filler='xavier', bias_filler='constant',ha

def conv_param(self, num_output, kernel_size, stride=(1), pad=(0,),
weight_filler_type='xavier', bias_filler_type='constant',
bias_term=True, dilation=None,groups=None):
bias_term=True, dilation=None, groups=None):
"""
add a conv_param layer if you spec the layer type "Convolution"
Args:
Expand All @@ -56,80 +59,69 @@ def conv_param(self, num_output, kernel_size, stride=(1), pad=(0,),
bias_filler_type: the bias filler type
Returns:
"""
if self.type not in ['Convolution','Deconvolution']:
if self.type not in ['Convolution', 'Deconvolution']:
raise TypeError('the layer type must be Convolution or Deconvolution if you want set conv param')
conv_param=pb.ConvolutionParameter()
conv_param.num_output=num_output
conv_param = pb.ConvolutionParameter()
conv_param.num_output = num_output
conv_param.kernel_size.extend(pair_reduce(kernel_size))
conv_param.stride.extend(pair_reduce(stride))
conv_param.pad.extend(pair_reduce(pad))
conv_param.bias_term=bias_term
conv_param.weight_filler.type=weight_filler_type
conv_param.bias_term = bias_term
conv_param.weight_filler.type = weight_filler_type
if bias_term:
conv_param.bias_filler.type = bias_filler_type
if dilation:
conv_param.dilation.extend(pair_reduce(dilation))
if groups:
conv_param.group=groups
conv_param.group = groups
self.param.convolution_param.CopyFrom(conv_param)

def pool_param(self,type='MAX',kernel_size=2,stride=2,pad=None, ceil_mode = False):
pool_param=pb.PoolingParameter()
pool_param.pool=pool_param.PoolMethod.Value(type)
pool_param.kernel_size=pair_process(kernel_size)
pool_param.stride=pair_process(stride)
pool_param.ceil_mode=ceil_mode
def pool_param(self, type='MAX', kernel_size=2, stride=2, pad=None, ceil_mode=False):
pool_param = pb.PoolingParameter()
pool_param.pool = pool_param.PoolMethod.Value(type)
pool_param.kernel_size = pair_process(kernel_size)
pool_param.stride = pair_process(stride)
pool_param.ceil_mode = ceil_mode
if pad:
if isinstance(pad,tuple):
if isinstance(pad, tuple):
pool_param.pad_h = pad[0]
pool_param.pad_w = pad[1]
else:
pool_param.pad=pad
pool_param.pad = pad
self.param.pooling_param.CopyFrom(pool_param)

def batch_norm_param(self,use_global_stats=0,moving_average_fraction=None,eps=None):
bn_param=pb.BatchNormParameter()
bn_param.use_global_stats=use_global_stats
def batch_norm_param(self, use_global_stats=0, moving_average_fraction=None, eps=None):
bn_param = pb.BatchNormParameter()
bn_param.use_global_stats = use_global_stats
if moving_average_fraction:
bn_param.moving_average_fraction=moving_average_fraction
bn_param.moving_average_fraction = moving_average_fraction
if eps:
bn_param.eps = eps
self.param.batch_norm_param.CopyFrom(bn_param)

# layer
# {
# name: "upsample_layer"
# type: "Upsample"
# bottom: "some_input_feature_map"
# bottom: "some_input_pool_index"
# top: "some_output"
# upsample_param {
# upsample_h: 224
# upsample_w: 224
# }
# }
def upsample_param(self,size=None, scale_factor=None):
upsample_param=pb.UpsampleParameter()
def upsample_param(self, size=None, scale_factor=None):
upsample_param = pb.UpsampleParameter()
if scale_factor:
if isinstance(scale_factor,int):
if isinstance(scale_factor, int):
upsample_param.scale = scale_factor
else:
upsample_param.scale_h = scale_factor[0]
upsample_param.scale_w = scale_factor[1]

if size:
if isinstance(size,int):
if isinstance(size, int):
upsample_param.upsample_h = size
else:
upsample_param.upsample_h = size[0]
upsample_param.upsample_w = size[1]
#upsample_param.upsample_h = size[0] * scale_factor
#upsample_param.upsample_w = size[1] * scale_factor
# upsample_param.upsample_h = size[0] * scale_factor
# upsample_param.upsample_w = size[1] * scale_factor
self.param.upsample_param.CopyFrom(upsample_param)
def interp_param(self,size=None, scale_factor=None):
interp_param=pb.InterpParameter()

def interp_param(self, size=None, scale_factor=None):
interp_param = pb.InterpParameter()
if scale_factor:
if isinstance(scale_factor,int):
if isinstance(scale_factor, int):
interp_param.zoom_factor = scale_factor

if size:
Expand All @@ -138,7 +130,7 @@ def interp_param(self,size=None, scale_factor=None):
interp_param.width = size[1]
self.param.interp_param.CopyFrom(interp_param)

def add_data(self,*args):
def add_data(self, *args):
"""Args are data numpy array
"""
del self.param.blobs[:]
Expand All @@ -148,11 +140,12 @@ def add_data(self,*args):
new_blob.shape.dim.append(dim)
new_blob.data.extend(data.flatten().astype(float))

def set_params_by_dict(self,dic):
def set_params_by_dict(self, dic):
pass

def copy_from(self,layer_param):
def copy_from(self, layer_param):
pass

def set_enum(param,key,value):
setattr(param,key,param.Value(value))

def set_enum(param, key, value):
setattr(param, key, param.Value(value))
3 changes: 2 additions & 1 deletion tools/deploy/Caffe/net.py
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@@ -1 +1,2 @@
raise ImportError,'the nn_tools.Caffe.net is no longer used, please use nn_tools.Caffe.caffe_net'
raise ImportError("the nn_tools.Caffe.net is no longer used, please use nn_tools.Caffe.caffe_net")

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