update README.md

Summary: add information about fastreid V1.0

Author: L1aoXingyu

README.md

@@ -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/).

fastreid/modeling/backbones/regnet/config.py

@@ -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
 # ------------------------------------------------------------------------------------ #
@@ -39,7 +37,6 @@
 # Loss function (see pycls/models/loss.py for options)
 _C.MODEL.LOSS_FUN = "cross_entropy"
 
-
 # ------------------------------------------------------------------------------------ #
 # ResNet options
 # ------------------------------------------------------------------------------------ #
@@ -57,7 +54,6 @@
 # Apply stride to 1x1 conv (True -> MSRA; False -> fb.torch)
 _C.RESNET.STRIDE_1X1 = True
 
-
 # ------------------------------------------------------------------------------------ #
 # AnyNet options
 # ------------------------------------------------------------------------------------ #
@@ -93,7 +89,6 @@
 # SE ratio
 _C.ANYNET.SE_R = 0.25
 
-
 # ------------------------------------------------------------------------------------ #
 # RegNet options
 # ------------------------------------------------------------------------------------ #
@@ -133,7 +128,6 @@
 # Bottleneck multiplier (bm = 1 / b from the paper)
 _C.REGNET.BOT_MUL = 1.0
 
-
 # ------------------------------------------------------------------------------------ #
 # EfficientNet options
 # ------------------------------------------------------------------------------------ #
@@ -169,7 +163,6 @@
 # Dropout ratio
 _C.EN.DROPOUT_RATIO = 0.0
 
-
 # ------------------------------------------------------------------------------------ #
 # Batch norm options
 # ------------------------------------------------------------------------------------ #
@@ -192,7 +185,6 @@
 _C.BN.USE_CUSTOM_WEIGHT_DECAY = False
 _C.BN.CUSTOM_WEIGHT_DECAY = 0.0
 
-
 # ------------------------------------------------------------------------------------ #
 # Optimizer options
 # ------------------------------------------------------------------------------------ #
@@ -234,7 +226,6 @@
 # Gradually warm up the OPTIM.BASE_LR over this number of epochs
 _C.OPTIM.WARMUP_EPOCHS = 0
 
-
 # ------------------------------------------------------------------------------------ #
 # Training options
 # ------------------------------------------------------------------------------------ #
@@ -262,7 +253,6 @@
 # Weights to start training from
 _C.TRAIN.WEIGHTS = ""
 
-
 # ------------------------------------------------------------------------------------ #
 # Testing options
 # ------------------------------------------------------------------------------------ #
@@ -281,7 +271,6 @@
 # Weights to use for testing
 _C.TEST.WEIGHTS = ""
 
-
 # ------------------------------------------------------------------------------------ #
 # Common train/test data loader options
 # ------------------------------------------------------------------------------------ #
@@ -293,7 +282,6 @@
 # Load data to pinned host memory
 _C.DATA_LOADER.PIN_MEMORY = True
 
-
 # ------------------------------------------------------------------------------------ #
 # Memory options
 # ------------------------------------------------------------------------------------ #
@@ -302,7 +290,6 @@
 # Perform ReLU inplace
 _C.MEM.RELU_INPLACE = True
 
-
 # ------------------------------------------------------------------------------------ #
 # CUDNN options
 # ------------------------------------------------------------------------------------ #
@@ -313,7 +300,6 @@
 # in overall speedups when variable size inputs are used (e.g. COCO training)
 _C.CUDNN.BENCHMARK = True
 
-
 # ------------------------------------------------------------------------------------ #
 # Precise timing options
 # ------------------------------------------------------------------------------------ #
@@ -325,7 +311,6 @@
 # Number of iterations to compute avg time
 _C.PREC_TIME.NUM_ITER = 30
 
-
 # ------------------------------------------------------------------------------------ #
 # Misc options
 # ------------------------------------------------------------------------------------ #
@@ -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
 # ------------------------------------------------------------------------------------ #
@@ -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
@@ -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():
@@ -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)

tools/deploy/Caffe/layer_param.py

@@ -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()
@@ -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:
@@ -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:
@@ -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[:]
@@ -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))

tools/deploy/Caffe/net.py

@@ -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")
+