mirror of https://github.com/JDAI-CV/fast-reid.git
liaoxingyu
parent
15e1729a27
commit
7e83d3175f
11
README.md
11
README.md
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@ -4,13 +4,14 @@ FastReID is a research platform that implements state-of-the-art re-identificati
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## What's New
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- [Oct 2020] Added the [Hyper-Parameter Optimization](https://github.com/JDAI-CV/fast-reid/tree/master/projects/HPOReID) based on fastreid. See `projects/HPOReID`.
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- [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`.
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- [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.
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- [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.
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- [Jan 2021] FastReID V1.0 has been released！🎉
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Support many tasks beyond reid, such image retrieval and face recognition. See [projects](https://github.com/JDAI-CV/fast-reid/tree/master/projects).
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- [Oct 2020] Added the [Hyper-Parameter Optimization](https://github.com/JDAI-CV/fast-reid/tree/master/projects/FastTune) based on fastreid. See `projects/FastTune`.
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- [Sep 2020] Added the [person attribute recognition](https://github.com/JDAI-CV/fast-reid/tree/master/projects/FastAttr) based on fastreid. See `projects/FastAttr`.
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- [Sep 2020] Automatic Mixed Precision training is supported with `apex`. Set `cfg.SOLVER.FP16_ENABLED=True` to switch it on.
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- [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.
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- [Aug 2020] ONNX/TensorRT converter is supported.
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- [Jul 2020] Distributed training with multiple GPUs, it trains much faster.
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- [Jul 2020] `MAX_ITER` in config means `epoch`, it will auto scale to maximum iterations.
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- 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.
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- 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.
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- Remove [ignite](https://github.com/pytorch/ignite)(a high-level library) dependency and powered by [PyTorch](https://pytorch.org/).
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@ -13,7 +13,6 @@ import sys
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from yacs.config import CfgNode as CfgNode
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# Global config object
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_C = CfgNode()
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@ -21,7 +20,6 @@ _C = CfgNode()
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# from core.config import cfg
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cfg = _C
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# ------------------------------------------------------------------------------------ #
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# Model options
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# ------------------------------------------------------------------------------------ #
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@ -39,7 +37,6 @@ _C.MODEL.NUM_CLASSES = 10
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# Loss function (see pycls/models/loss.py for options)
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_C.MODEL.LOSS_FUN = "cross_entropy"
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# ------------------------------------------------------------------------------------ #
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# ResNet options
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# ------------------------------------------------------------------------------------ #
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@ -57,7 +54,6 @@ _C.RESNET.WIDTH_PER_GROUP = 64
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# Apply stride to 1x1 conv (True -> MSRA; False -> fb.torch)
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_C.RESNET.STRIDE_1X1 = True
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# ------------------------------------------------------------------------------------ #
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# AnyNet options
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# ------------------------------------------------------------------------------------ #
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@ -93,7 +89,6 @@ _C.ANYNET.SE_ON = False
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# SE ratio
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_C.ANYNET.SE_R = 0.25
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# ------------------------------------------------------------------------------------ #
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# RegNet options
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# ------------------------------------------------------------------------------------ #
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@ -133,7 +128,6 @@ _C.REGNET.GROUP_W = 16
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# Bottleneck multiplier (bm = 1 / b from the paper)
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_C.REGNET.BOT_MUL = 1.0
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# ------------------------------------------------------------------------------------ #
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# EfficientNet options
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# ------------------------------------------------------------------------------------ #
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@ -169,7 +163,6 @@ _C.EN.DC_RATIO = 0.0
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# Dropout ratio
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_C.EN.DROPOUT_RATIO = 0.0
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# ------------------------------------------------------------------------------------ #
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# Batch norm options
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# ------------------------------------------------------------------------------------ #
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@ -192,7 +185,6 @@ _C.BN.ZERO_INIT_FINAL_GAMMA = False
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_C.BN.USE_CUSTOM_WEIGHT_DECAY = False
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_C.BN.CUSTOM_WEIGHT_DECAY = 0.0
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# ------------------------------------------------------------------------------------ #
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# Optimizer options
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# ------------------------------------------------------------------------------------ #
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@ -234,7 +226,6 @@ _C.OPTIM.WARMUP_FACTOR = 0.1
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# Gradually warm up the OPTIM.BASE_LR over this number of epochs
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_C.OPTIM.WARMUP_EPOCHS = 0
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# ------------------------------------------------------------------------------------ #
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# Training options
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# ------------------------------------------------------------------------------------ #
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@ -262,7 +253,6 @@ _C.TRAIN.AUTO_RESUME = True
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# Weights to start training from
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_C.TRAIN.WEIGHTS = ""
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# ------------------------------------------------------------------------------------ #
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# Testing options
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# ------------------------------------------------------------------------------------ #
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@ -281,7 +271,6 @@ _C.TEST.IM_SIZE = 256
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# Weights to use for testing
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_C.TEST.WEIGHTS = ""
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# ------------------------------------------------------------------------------------ #
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# Common train/test data loader options
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# ------------------------------------------------------------------------------------ #
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@ -293,7 +282,6 @@ _C.DATA_LOADER.NUM_WORKERS = 8
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# Load data to pinned host memory
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_C.DATA_LOADER.PIN_MEMORY = True
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# ------------------------------------------------------------------------------------ #
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# Memory options
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# ------------------------------------------------------------------------------------ #
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@ -302,7 +290,6 @@ _C.MEM = CfgNode()
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# Perform ReLU inplace
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_C.MEM.RELU_INPLACE = True
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# ------------------------------------------------------------------------------------ #
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# CUDNN options
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# ------------------------------------------------------------------------------------ #
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@ -313,7 +300,6 @@ _C.CUDNN = CfgNode()
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# in overall speedups when variable size inputs are used (e.g. COCO training)
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_C.CUDNN.BENCHMARK = True
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# ------------------------------------------------------------------------------------ #
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# Precise timing options
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# ------------------------------------------------------------------------------------ #
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@ -325,7 +311,6 @@ _C.PREC_TIME.WARMUP_ITER = 3
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# Number of iterations to compute avg time
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_C.PREC_TIME.NUM_ITER = 30
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# ------------------------------------------------------------------------------------ #
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# Misc options
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# ------------------------------------------------------------------------------------ #
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@ -359,7 +344,6 @@ _C.PORT_RANGE = [10000, 65000]
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# Models weights referred to by URL are downloaded to this local cache
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_C.DOWNLOAD_CACHE = "/tmp/pycls-download-cache"
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# ------------------------------------------------------------------------------------ #
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# Deprecated keys
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# ------------------------------------------------------------------------------------ #
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@ -369,7 +353,7 @@ _C.register_deprecated_key("PREC_TIME.ENABLED")
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_C.register_deprecated_key("PORT")
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def assert_and_infer_cfg(cache_urls=True):
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def assert_and_infer_cfg():
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"""Checks config values invariants."""
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err_str = "The first lr step must start at 0"
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assert not _C.OPTIM.STEPS or _C.OPTIM.STEPS[0] == 0, err_str
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@ -382,14 +366,6 @@ def assert_and_infer_cfg(cache_urls=True):
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assert _C.TEST.BATCH_SIZE % _C.NUM_GPUS == 0, err_str
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err_str = "Log destination '{}' not supported"
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assert _C.LOG_DEST in ["stdout", "file"], err_str.format(_C.LOG_DEST)
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if cache_urls:
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cache_cfg_urls()
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def cache_cfg_urls():
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"""Download URLs in config, cache them, and rewrite cfg to use cached file."""
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_C.TRAIN.WEIGHTS = cache_url(_C.TRAIN.WEIGHTS, _C.DOWNLOAD_CACHE)
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_C.TEST.WEIGHTS = cache_url(_C.TEST.WEIGHTS, _C.DOWNLOAD_CACHE)
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def dump_cfg():
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@ -417,4 +393,4 @@ def load_cfg_fom_args(description="Config file options."):
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sys.exit(1)
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args = parser.parse_args()
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_C.merge_from_file(args.cfg_file)
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_C.merge_from_list(args.opts)
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_C.merge_from_list(args.opts)
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@ -1,11 +1,12 @@
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from __future__ import absolute_import
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from . import caffe_pb2 as pb
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import numpy as np
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def pair_process(item,strict_one=True):
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if hasattr(item,'__iter__'):
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from . import caffe_pb2 as pb
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def pair_process(item, strict_one=True):
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if hasattr(item, '__iter__'):
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for i in item:
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if i!=item[0]:
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if i != item[0]:
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if strict_one:
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raise ValueError("number in item {} must be the same".format(item))
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else:
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@ -13,26 +14,28 @@ def pair_process(item,strict_one=True):
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return item[0]
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return item
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def pair_reduce(item):
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if hasattr(item,'__iter__'):
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if hasattr(item, '__iter__'):
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for i in item:
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if i!=item[0]:
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if i != item[0]:
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return item
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return [item[0]]
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return [item]
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class Layer_param():
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def __init__(self,name='',type='',top=(),bottom=()):
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self.param=pb.LayerParameter()
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self.name=self.param.name=name
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self.type=self.param.type=type
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self.top=self.param.top
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class Layer_param():
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def __init__(self, name='', type='', top=(), bottom=()):
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self.param = pb.LayerParameter()
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self.name = self.param.name = name
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self.type = self.param.type = type
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self.top = self.param.top
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self.top.extend(top)
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self.bottom=self.param.bottom
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self.bottom = self.param.bottom
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self.bottom.extend(bottom)
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def fc_param(self, num_output, weight_filler='xavier', bias_filler='constant',has_bias=True):
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def fc_param(self, num_output, weight_filler='xavier', bias_filler='constant', has_bias=True):
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if self.type != 'InnerProduct':
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raise TypeError('the layer type must be InnerProduct if you want set fc param')
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fc_param = pb.InnerProductParameter()
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@ -45,7 +48,7 @@ class Layer_param():
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def conv_param(self, num_output, kernel_size, stride=(1), pad=(0,),
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weight_filler_type='xavier', bias_filler_type='constant',
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bias_term=True, dilation=None,groups=None):
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bias_term=True, dilation=None, groups=None):
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"""
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add a conv_param layer if you spec the layer type "Convolution"
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Args:
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@ -56,80 +59,69 @@ class Layer_param():
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bias_filler_type: the bias filler type
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Returns:
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"""
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if self.type not in ['Convolution','Deconvolution']:
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if self.type not in ['Convolution', 'Deconvolution']:
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raise TypeError('the layer type must be Convolution or Deconvolution if you want set conv param')
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conv_param=pb.ConvolutionParameter()
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conv_param.num_output=num_output
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conv_param = pb.ConvolutionParameter()
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conv_param.num_output = num_output
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conv_param.kernel_size.extend(pair_reduce(kernel_size))
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conv_param.stride.extend(pair_reduce(stride))
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conv_param.pad.extend(pair_reduce(pad))
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conv_param.bias_term=bias_term
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conv_param.weight_filler.type=weight_filler_type
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conv_param.bias_term = bias_term
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conv_param.weight_filler.type = weight_filler_type
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if bias_term:
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conv_param.bias_filler.type = bias_filler_type
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if dilation:
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conv_param.dilation.extend(pair_reduce(dilation))
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if groups:
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conv_param.group=groups
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conv_param.group = groups
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self.param.convolution_param.CopyFrom(conv_param)
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def pool_param(self,type='MAX',kernel_size=2,stride=2,pad=None, ceil_mode = False):
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pool_param=pb.PoolingParameter()
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pool_param.pool=pool_param.PoolMethod.Value(type)
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pool_param.kernel_size=pair_process(kernel_size)
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pool_param.stride=pair_process(stride)
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pool_param.ceil_mode=ceil_mode
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def pool_param(self, type='MAX', kernel_size=2, stride=2, pad=None, ceil_mode=False):
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pool_param = pb.PoolingParameter()
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pool_param.pool = pool_param.PoolMethod.Value(type)
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pool_param.kernel_size = pair_process(kernel_size)
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pool_param.stride = pair_process(stride)
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pool_param.ceil_mode = ceil_mode
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if pad:
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if isinstance(pad,tuple):
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if isinstance(pad, tuple):
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pool_param.pad_h = pad[0]
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pool_param.pad_w = pad[1]
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else:
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pool_param.pad=pad
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pool_param.pad = pad
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self.param.pooling_param.CopyFrom(pool_param)
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def batch_norm_param(self,use_global_stats=0,moving_average_fraction=None,eps=None):
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bn_param=pb.BatchNormParameter()
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bn_param.use_global_stats=use_global_stats
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def batch_norm_param(self, use_global_stats=0, moving_average_fraction=None, eps=None):
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bn_param = pb.BatchNormParameter()
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bn_param.use_global_stats = use_global_stats
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if moving_average_fraction:
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bn_param.moving_average_fraction=moving_average_fraction
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bn_param.moving_average_fraction = moving_average_fraction
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if eps:
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bn_param.eps = eps
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self.param.batch_norm_param.CopyFrom(bn_param)
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# layer
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# {
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# name: "upsample_layer"
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# type: "Upsample"
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# bottom: "some_input_feature_map"
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# bottom: "some_input_pool_index"
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# top: "some_output"
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# upsample_param {
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# upsample_h: 224
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# upsample_w: 224
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# }
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# }
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def upsample_param(self,size=None, scale_factor=None):
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upsample_param=pb.UpsampleParameter()
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def upsample_param(self, size=None, scale_factor=None):
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upsample_param = pb.UpsampleParameter()
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if scale_factor:
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if isinstance(scale_factor,int):
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if isinstance(scale_factor, int):
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upsample_param.scale = scale_factor
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else:
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upsample_param.scale_h = scale_factor[0]
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upsample_param.scale_w = scale_factor[1]
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if size:
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if isinstance(size,int):
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if isinstance(size, int):
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upsample_param.upsample_h = size
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else:
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upsample_param.upsample_h = size[0]
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upsample_param.upsample_w = size[1]
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#upsample_param.upsample_h = size[0] * scale_factor
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#upsample_param.upsample_w = size[1] * scale_factor
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# upsample_param.upsample_h = size[0] * scale_factor
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# upsample_param.upsample_w = size[1] * scale_factor
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self.param.upsample_param.CopyFrom(upsample_param)
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def interp_param(self,size=None, scale_factor=None):
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interp_param=pb.InterpParameter()
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def interp_param(self, size=None, scale_factor=None):
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interp_param = pb.InterpParameter()
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if scale_factor:
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if isinstance(scale_factor,int):
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if isinstance(scale_factor, int):
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interp_param.zoom_factor = scale_factor
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if size:
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@ -138,7 +130,7 @@ class Layer_param():
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interp_param.width = size[1]
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self.param.interp_param.CopyFrom(interp_param)
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def add_data(self,*args):
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def add_data(self, *args):
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"""Args are data numpy array
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"""
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del self.param.blobs[:]
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@ -148,11 +140,12 @@ class Layer_param():
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new_blob.shape.dim.append(dim)
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new_blob.data.extend(data.flatten().astype(float))
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def set_params_by_dict(self,dic):
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def set_params_by_dict(self, dic):
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pass
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def copy_from(self,layer_param):
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def copy_from(self, layer_param):
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pass
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def set_enum(param,key,value):
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setattr(param,key,param.Value(value))
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def set_enum(param, key, value):
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setattr(param, key, param.Value(value))
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@ -1 +1,2 @@
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raise ImportError,'the nn_tools.Caffe.net is no longer used, please use nn_tools.Caffe.caffe_net'
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raise ImportError("the nn_tools.Caffe.net is no longer used, please use nn_tools.Caffe.caffe_net")
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