style(configs): put all config files together

put all config files into one place for easily control,
and add tools for put train_net.py which almost the same in
different projects

MODEL_ZOO.md

@@ -0,0 +1,31 @@
+# FastReID Model Zoo and Baselines
+
+## Introduction
+
+
+## Market1501 Baselines
+
+| Method | Pretrained | Rank@1 | mAP | mINP |
+| :---: | :---: | :---: |:---: | :---: |
+| BagTricks(R50) | ImageNet | 93.6% | 85.1% | 58.1% |
+| BagTricks(R50-ibn) | ImageNet | 94.8% | 87.3% | 63.5% |
+| AGW(R50) | ImageNet | 94.9% | 87.4% | 63.1% |
+| stronger baseline(R50-ibn) | ImageNet | 95.5% | 88.4% | 65.8% |
+
+## DukeMTMC Baseline
+
+| Method | Pretrained | Rank@1 | mAP | mINP |
+| :---: | :---: | :---: |:---: | :---: |
+| BagTricks(R50) | ImageNet | 86.1% | 75.9% | 38.7% |
+| BagTricks(R50-ibn) | ImageNet | 89.0% | 78.8% | 43.6% |
+| AGW(R50) | ImageNet | 88.9% | 79.1% | 43.2% |
+| stronger baseline(R50-ibn) | ImageNet | 91.3% | 81.6% | 47.6% |
+
+### MSMT17 Baseline
+
+| Method | Pretrained | Rank@1 | mAP | mINP |
+| :---: | :---: | :---: |:---: | :---: |
+| BagTricks(R50) | ImageNet | 70.4%  | 47.5% | 9.6% |
+| BagTricks(R50-ibn) | ImageNet | 76.9% | 55.0% | 13.5% |
+| AGW(R50) | ImageNet | 75.6% | 52.6% | 11.9% |
+| stronger baseline(R50-ibn) | ImageNet | 84.2% | 61.5% | 15.7% |

README.md

@@ -11,13 +11,12 @@ The designed architecture follows this guide [PyTorch-Project-Template](https://
 3. Install dependencies:
     - [pytorch 1.0.0+](https://pytorch.org/)
     - torchvision
-    - tensorboard
     - [yacs](https://github.com/rbgirshick/yacs)
 4. Prepare dataset
     Create a directory to store reid datasets under projects, for example
 
     ```bash
-    cd fast-reid/projects/StrongBaseline
+    cd fast-reid
     mkdir datasets
     ```
 
@@ -34,7 +33,7 @@ The designed architecture follows this guide [PyTorch-Project-Template](https://
 5. Prepare pretrained model.
     If you use origin ResNet, you do not need to do anything. But if you want to use ResNet_ibn, you need to download pretrain model in [here](https://drive.google.com/open?id=1thS2B8UOSBi_cJX6zRy6YYRwz_nVFI_S). And then you can put it in `~/.cache/torch/checkpoints` or anywhere you like.
 
-    Then you should set the pretrain model path in `configs/baseline_market1501.yml`.
+    Then you should set the pretrain model path in `configs/Base-bagtricks.yml`.
 
 6. compile with cython to accelerate evalution
 
@@ -44,28 +43,4 @@ The designed architecture follows this guide [PyTorch-Project-Template](https://
 
 ## Model Zoo and Baselines
 
-### Market1501 dataset
+We provide a large set of baseline results and trained models available for download in the [Fastreid Model Zoo](https://github.com/JDAI-CV/fast-reid/blob/master/MODEL_ZOO.md).
-
-| Method | Pretrained | Rank@1 | mAP | mINP |
-| :---: | :---: | :---: |:---: | :---: |
-| BagTricks | ImageNet | 93.6% | 85.1% | 58.1% |
-| BagTricks + Ibn-a | ImageNet | 94.8% | 87.3% | 63.5% |
-| AGW |  ImageNet | 94.9% | 87.4% | 63.1% |
-
-
-### DukeMTMC dataset
-
-| Method | Pretrained | Rank@1 | mAP | mINP |
-| :---: | :---: | :---: |:---: | :---: |
-| BagTricks | ImageNet | 86.1% | 75.9% | 38.7% |
-| BagTricks + Ibn-a | ImageNet | 89.0% | 78.8% | 43.6% |
-| AGW |  ImageNet | 88.9% | 79.1% | 43.2% |
-
-
-### MSMT17 dataset
-
-| Method | Pretrained | Rank@1 | mAP | mINP |
-| :---: | :---: | :---: |:---: | :---: |
-| BagTricks | ImageNet | 70.4%  | 47.5% | 9.6% |
-| BagTricks + Ibn-a | ImageNet | 76.9% | 55.0% | 13.5% |
-| AGW | ImageNet | 75.6% | 52.6% | 11.9% |

configs/Base-AGW.yml

@@ -0,0 +1,71 @@
+MODEL:
+  META_ARCHITECTURE: 'Baseline'
+
+  BACKBONE:
+    NAME: "build_resnet_backbone"
+    DEPTH: 50
+    LAST_STRIDE: 1
+    WITH_NL: True
+    PRETRAIN: True
+
+  HEADS:
+    NAME: "BNneckHead"
+    POOL_LAYER: "gempool"
+    CLS_LAYER: "linear"
+    NUM_CLASSES: 702
+
+  LOSSES:
+    NAME: ("CrossEntropyLoss", "TripletLoss")
+    CE:
+      EPSILON: 0.1
+      SCALE: 1.0
+
+    TRI:
+      MARGIN: 0.0
+      HARD_MINING: False
+      USE_COSINE_DIST: False
+      SCALE: 1.0
+
+DATASETS:
+  NAMES: ("DukeMTMC",)
+  TESTS: ("DukeMTMC",)
+
+INPUT:
+  SIZE_TRAIN: [256, 128]
+  SIZE_TEST: [256, 128]
+  REA:
+    ENABLED: True
+    PROB: 0.5
+    MEAN: [123.675, 116.28, 103.53]
+  DO_PAD: True
+
+DATALOADER:
+  PK_SAMPLER: True
+  NUM_INSTANCE: 4
+  NUM_WORKERS: 16
+
+SOLVER:
+  OPT: "Adam"
+  MAX_ITER: 18000
+  BASE_LR: 0.00035
+  BIAS_LR_FACTOR: 2.
+  WEIGHT_DECAY: 0.0005
+  WEIGHT_DECAY_BIAS: 0.0005
+  IMS_PER_BATCH: 64
+
+  STEPS: [8000, 14000]
+  GAMMA: 0.1
+
+  WARMUP_FACTOR: 0.01
+  WARMUP_ITERS: 2000
+
+  LOG_PERIOD: 200
+  CHECKPOINT_PERIOD: 6000
+
+TEST:
+  EVAL_PERIOD: 2000
+  IMS_PER_BATCH: 512
+
+CUDNN_BENCHMARK: True
+
+OUTPUT_DIR: "logs"

configs/Base-Strongerbaseline.yml

@@ -0,0 +1,84 @@
+MODEL:
+  META_ARCHITECTURE: 'Baseline'
+
+  OPEN_LAYERS: "heads"
+
+  BACKBONE:
+    NAME: "build_resnet_backbone"
+    DEPTH: 50
+    LAST_STRIDE: 1
+    WITH_IBN: False
+    WITH_NL: False
+    PRETRAIN: True
+
+  HEADS:
+    NAME: "BNneckHead"
+    CLS_LAYER: "circle"
+    POOL_LAYER: "gempool"
+    SCALE: 64
+    MARGIN: 0.35
+
+  LOSSES:
+    NAME: ("CrossEntropyLoss", "TripletLoss",)
+    CE:
+      EPSILON: 0.1
+      SCALE: 1.0
+
+    TRI:
+      MARGIN: 0.0
+      HARD_MINING: True
+      NORM_FEAT: False
+      USE_COSINE_DIST: False
+      SCALE: 1.0
+
+DATASETS:
+  NAMES: ("DukeMTMC",)
+  TESTS: ("DukeMTMC",)
+
+INPUT:
+  SIZE_TRAIN: [384, 128]
+  SIZE_TEST: [384, 128]
+  DO_AUTOAUG: True
+  REA:
+    ENABLED: True
+    PROB: 0.5
+    MEAN: [123.675, 116.28, 103.53]
+  DO_PAD: True
+
+DATALOADER:
+  PK_SAMPLER: True
+  NUM_INSTANCE: 16
+  NUM_WORKERS: 16
+
+SOLVER:
+  OPT: "Adam"
+  MAX_ITER: 18000
+  BASE_LR: 0.00035
+  BIAS_LR_FACTOR: 2.
+  WEIGHT_DECAY: 0.0005
+  WEIGHT_DECAY_BIAS: 0.0
+  IMS_PER_BATCH: 64
+
+  SCHED: "DelayedCosineAnnealingLR"
+
+  DELAY_ITERS: 2000
+  ETA_MIN_LR: 0.00000077
+
+  STEPS: [8000, 14000]
+  GAMMA: 0.1
+
+  WARMUP_FACTOR: 0.01
+  WARMUP_ITERS: 2000
+
+  FREEZE_ITERS: 2000
+
+  LOG_PERIOD: 200
+  CHECKPOINT_PERIOD: 6000
+
+TEST:
+  EVAL_PERIOD: 2000
+  IMS_PER_BATCH: 512
+
+CUDNN_BENCHMARK: True
+
+OUTPUT_DIR: "logs/dukemtmc/softmax"

configs/Base-bagtricks.yml

@@ -0,0 +1,73 @@
+MODEL:
+  META_ARCHITECTURE: 'Baseline'
+
+  OPEN_LAYERS: ""
+
+  BACKBONE:
+    NAME: "build_resnet_backbone"
+    DEPTH: 50
+    LAST_STRIDE: 1
+    WITH_IBN: False
+    PRETRAIN: True
+
+  HEADS:
+    NAME: "BNneckHead"
+    CLS_LAYER: "linear"
+
+  LOSSES:
+    NAME: ("CrossEntropyLoss", "TripletLoss")
+
+    CE:
+      EPSILON: 0.1
+      SCALE: 1.0
+
+    TRI:
+      MARGIN: 0.0
+      HARD_MINING: False
+      USE_COSINE_DIST: False
+      SCALE: 1.0
+
+DATASETS:
+  NAMES: ("DukeMTMC",)
+  TESTS: ("DukeMTMC",)
+
+INPUT:
+  SIZE_TRAIN: [384, 128]
+  SIZE_TEST: [384, 128]
+  REA:
+    ENABLED: True
+    PROB: 0.5
+    MEAN: [123.675, 116.28, 103.53]
+  DO_PAD: True
+
+
+DATALOADER:
+  PK_SAMPLER: True
+  NUM_INSTANCE: 4
+  NUM_WORKERS: 16
+
+SOLVER:
+  OPT: "Adam"
+  MAX_ITER: 18000
+  BASE_LR: 0.00035
+  BIAS_LR_FACTOR: 2.
+  WEIGHT_DECAY: 0.0005
+  WEIGHT_DECAY_BIAS: 0.
+  IMS_PER_BATCH: 128
+
+  STEPS: [8000, 14000]
+  GAMMA: 0.1
+
+  WARMUP_FACTOR: 0.01
+  WARMUP_ITERS: 2000
+
+  LOG_PERIOD: 200
+  CHECKPOINT_PERIOD: 2000
+
+TEST:
+  EVAL_PERIOD: 2000
+  IMS_PER_BATCH: 512
+
+CUDNN_BENCHMARK: True
+
+OUTPUT_DIR: "logs/dukemtmc/softmax"

configs/DukeMTMC/AGW.yml

@@ -0,0 +1,18 @@
+_BASE_: "../Base-AGW.yml"
+
+MODEL:
+  HEADS:
+    NUM_CLASSES: 702
+
+SOLVER:
+  MAX_ITER: 23000
+
+  STEPS: [10000, 18000]
+
+  WARMUP_ITERS: 2500
+
+DATASETS:
+  NAMES: ("DukeMTMC",)
+  TESTS: ("DukeMTMC",)
+
+OUTPUT_DIR: "logs/dukemtmc/agw"

configs/DukeMTMC/bagtricks.yml

@@ -0,0 +1,18 @@
+_BASE_: "../Base-bagtricks.yml"
+
+MODEL:
+  HEADS:
+    NUM_CLASSES: 702
+
+SOLVER:
+  MAX_ITER: 12500
+
+  STEPS: [5000, 9000]
+
+  WARMUP_ITERS: 1250
+
+DATASETS:
+  NAMES: ("DukeMTMC",)
+  TESTS: ("DukeMTMC",)
+
+OUTPUT_DIR: "logs/dukemtmc/resnet50_baseline"

configs/DukeMTMC/sbs.yml

@@ -0,0 +1,23 @@
+_BASE_: "../Base-Strongerbaseline.yml"
+
+MODEL:
+  BACKBONE:
+    NAME: "build_resnest_backbone"
+    WITH_IBN: False
+    WITH_NL: True
+    PRETRAIN_PATH: "/home/liaoxingyu2/lxy/.cache/torch/checkpoints/resnet50_ibn_a.pth.tar"
+
+  HEADS:
+    NUM_CLASSES: 702
+
+SOLVER:
+  MAX_ITER: 5
+  DELAY_ITERS: 8000
+  WARMUP_ITERS: 2600
+  FREEZE_ITERS: 2600
+
+DATASETS:
+  NAMES: ("DukeMTMC",)
+  TESTS: ("DukeMTMC",)
+
+OUTPUT_DIR: "logs/dukemtmc/test_prebn"

configs/MSMT17/AGW.yml

@@ -0,0 +1,22 @@
+_BASE_: "../Base-AGW.yml"
+
+MODEL:
+  HEADS:
+    NUM_CLASSES: 1041
+
+DATASETS:
+  NAMES: ("MSMT17",)
+  TESTS: ("MSMT17",)
+
+SOLVER:
+  MAX_ITER: 42000
+  STEPS: [19000, 33000]
+  WARMUP_ITERS: 4700
+
+  CHECKPOINT_PERIOD: 5000
+
+TEST:
+  EVAL_PERIOD: 5000
+
+OUTPUT_DIR: "logs/msmt17/agw"
+

configs/MSMT17/bagtricks.yml

@@ -0,0 +1,21 @@
+_BASE_: "../Base-bagtricks.yml"
+
+MODEL:
+  HEADS:
+    NUM_CLASSES: 1041
+
+DATASETS:
+  NAMES: ("MSMT17",)
+  TESTS: ("MSMT17",)
+
+SOLVER:
+  MAX_ITER: 42000
+  STEPS: [19000, 33000]
+  WARMUP_ITERS: 4700
+
+  CHECKPOINT_PERIOD: 5000
+
+TEST:
+  EVAL_PERIOD: 5000
+
+OUTPUT_DIR: "logs/msmt17/bagtricks"

configs/MSMT17/sbs.yml

@@ -0,0 +1,29 @@
+_BASE_: "../Base-Strongerbaseline.yml"
+
+MODEL:
+  BACKBONE:
+    NAME: "build_resnest_backbone"
+    WITH_IBN: False
+    WITH_NL: True
+    PRETRAIN_PATH: "/home/liaoxingyu2/lxy/.cache/torch/checkpoints/resnet50_ibn_a.pth.tar"
+
+  HEADS:
+    NUM_CLASSES: 1041
+
+DATASETS:
+  NAMES: ("MSMT17",)
+  TESTS: ("MSMT17",)
+
+
+SOLVER:
+  MAX_ITER: 29000
+  DELAY_ITERS: 14000
+  WARMUP_ITERS: 4700
+  FREEZE_ITERS: 4700
+
+  CHECKPOINT_PERIOD: 4000
+
+TEST:
+  EVAL_PERIOD: 4000
+
+OUTPUT_DIR: "logs/msmt17/resnest-nl-gem-circle_s64m0.35_loss-cos_delay-autoaug"

configs/Market1501/AGW.yml

@@ -0,0 +1,18 @@
+_BASE_: "../Base-AGW.yml"
+
+MODEL:
+  HEADS:
+    NUM_CLASSES: 751
+
+SOLVER:
+  MAX_ITER: 18000
+
+  STEPS: [8000, 14000]
+
+  WARMUP_ITERS: 2000
+
+DATASETS:
+  NAMES: ("Market1501",)
+  TESTS: ("Market1501",)
+
+OUTPUT_DIR: "logs/market1501/agw"

configs/Market1501/bagtricks.yml

@@ -0,0 +1,19 @@
+_BASE_: "../Base-bagtricks.yml"
+
+MODEL:
+  HEADS:
+    NUM_CLASSES: 751
+
+SOLVER:
+  MAX_ITER: 18000
+
+  STEPS: [8000, 14000]
+
+  WARMUP_ITERS: 2000
+
+DATASETS:
+  NAMES: ("Market1501",)
+  TESTS: ("Market1501",)
+
+
+OUTPUT_DIR: "logs/market1501/bagtricks"

configs/Market1501/sbs.yml

@@ -0,0 +1,26 @@
+_BASE_: "../Base-Strongerbaseline.yml"
+
+MODEL:
+  BACKBONE:
+    NAME: "build_resnest_backbone"
+    WITH_IBN: False
+    WITH_NL: True
+    PRETRAIN_PATH: "/home/liaoxingyu2/lxy/.cache/torch/checkpoints/resnet50_ibn_a.pth.tar"
+
+  HEADS:
+    NUM_CLASSES: 751
+
+SOLVER:
+  MAX_ITER: 16000
+  DELAY_ITERS: 8000
+  WARMUP_ITERS: 2600
+  FREEZE_ITERS: 2600
+
+DATASETS:
+  NAMES: ("Market1501",)
+  TESTS: ("Market1501",)
+
+TEST:
+  EVAL_PERIOD: 2000
+
+OUTPUT_DIR: "logs/market1501/resnest-nl-gem-circle_s64m0.35_loss-cos_delay-autoaug"

fastreid/export/demo.py

@@ -1,166 +0,0 @@
-# encoding: utf-8
-"""
-@author:  liaoxingyu
-@contact: sherlockliao01@gmail.com
-"""
-import torch.nn.functional as F
-from collections import defaultdict
-import argparse
-import json
-import os
-import sys
-import time
-
-import cv2
-import numpy as np
-import torch
-from torch.backends import cudnn
-from fastreid.modeling import build_model
-from fastreid.utils.checkpoint import Checkpointer
-from fastreid.config import get_cfg
-
-cudnn.benchmark = True
-
-
-class Reid(object):
-
-    def __init__(self, config_file):
-        cfg = get_cfg()
-        cfg.merge_from_file(config_file)
-        cfg.defrost()
-        cfg.MODEL.WEIGHTS = 'projects/bjzProject/logs/bjz/arcface_adam/model_final.pth'
-        model = build_model(cfg)
-        Checkpointer(model).resume_or_load(cfg.MODEL.WEIGHTS)
-
-        model.cuda()
-        model.eval()
-        self.model = model
-        # self.model = torch.jit.load("reid_model.pt")
-        # self.model.eval()
-        # self.model.cuda()
-
-        example = torch.rand(1, 3, 256, 128)
-        example = example.cuda()
-        traced_script_module = torch.jit.trace_module(model, {'inference': example})
-        traced_script_module.save("reid_feat_extractor.pt")
-
-    @classmethod
-    def preprocess(cls, img_path):
-        img = cv2.imread(img_path)
-        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
-        img = cv2.resize(img, (128, 256))
-        img = img / 255.0
-        img = (img - [0.485, 0.456, 0.406]) / [0.229, 0.224, 0.225]
-        img = img.transpose((2, 0, 1)).astype(np.float32)
-        img = img[np.newaxis, :, :, :]
-        data = torch.from_numpy(img).cuda().float()
-        return data
-
-    @torch.no_grad()
-    def demo(self, img_path):
-        data = self.preprocess(img_path)
-        output = self.model.inference(data)
-        feat = output.cpu().data.numpy()
-        return feat
-
-    # @torch.no_grad()
-    # def extract_feat(self, dataloader):
-    #     prefetcher = test_data_prefetcher(dataloader)
-    #     feats = []
-    #     labels = []
-    #     batch = prefetcher.next()
-    #     num_count = 0
-    #     while batch[0] is not None:
-    #         img, pid, camid = batch
-    #         feat = self.model(img)
-    #         feats.append(feat.cpu())
-    #         labels.extend(np.asarray(pid))
-    #
-    #         # if num_count > 2:
-    #             # break
-    #         batch = prefetcher.next()
-    #         # num_count += 1
-    #
-    #     feats = torch.cat(feats, dim=0)
-    #     id_feats = defaultdict(list)
-    #     for f, i in zip(feats, labels):
-    #         id_feats[i].append(f)
-    #     all_feats = []
-    #     label_names = []
-    #     for i in id_feats:
-    #         all_feats.append(torch.stack(id_feats[i], dim=0).mean(dim=0))
-    #         label_names.append(i)
-    #
-    #     label_names = np.asarray(label_names)
-    #     all_feats = torch.stack(all_feats, dim=0)  # (n, 2048)
-    #     all_feats = F.normalize(all_feats, p=2, dim=1)
-    #     np.save('feats.npy', all_feats.cpu())
-    #     np.save('labels.npy', label_names)
-    #     cos = torch.mm(all_feats, all_feats.t()).numpy()  # (n, n)
-    #     cos -= np.eye(all_feats.shape[0])
-    #     f = open('check_cross_folder_similarity.txt', 'w')
-    #     for i in range(len(label_names)):
-    #         sim_indx = np.argwhere(cos[i] > 0.5)[:, 0]
-    #         sim_name = label_names[sim_indx]
-    #         write_str = label_names[i] + ' '
-    #         # f.write(label_names[i]+'\t')
-    #         for n in sim_name:
-    #             write_str += (n + ' ')
-    #             # f.write(n+'\t')
-    #         f.write(write_str+'\n')
-    #
-    #
-    # def prepare_gt(self, json_file):
-    #     feat = []
-    #     label = []
-    #     with open(json_file, 'r') as f:
-    #         total = json.load(f)
-    #         for index in total:
-    #             label.append(index)
-    #             feat.append(np.array(total[index]))
-    #     time_label = [int(i[0:10]) for i in label]
-    #
-    #     return np.array(feat), np.array(label), np.array(time_label)
-
-    def compute_topk(self, k, feat, feats, label):
-
-        # num_gallery = feats.shape[0]
-        # new_feat = np.tile(feat,[num_gallery,1])
-        norm_feat = np.sqrt(np.sum(np.square(feat), axis=-1))
-        norm_feats = np.sqrt(np.sum(np.square(feats), axis=-1))
-        matrix = np.sum(np.multiply(feat, feats), axis=-1)
-        dist = matrix / np.multiply(norm_feat, norm_feats)
-        # print('feat:',feat.shape)
-        # print('feats:',feats.shape)
-        # print('label:',label.shape)
-        # print('dist:',dist.shape)
-
-        index = np.argsort(-dist)
-
-        # print('index:',index.shape)
-        result = []
-        for i in range(min(feats.shape[0], k)):
-            print(dist[index[i]])
-            result.append(label[index[i]])
-        return result
-
-
-if __name__ == '__main__':
-    reid_sys = Reid(config_file='../../projects/bjzProject/configs/bjz.yml')
-    img_path = '/export/home/lxy/beijingStationReID/reid_model/demo_imgs/003740_c5s2_1561733125170.000000.jpg'
-    feat = reid_sys.demo(img_path)
-    feat_extractor = torch.jit.load('reid_feat_extractor.pt')
-    data = reid_sys.preprocess(img_path)
-    feat2 = feat_extractor.inference(data)
-    from ipdb import set_trace; set_trace()
-    # imgs = os.listdir(img_path)
-    # feats = {}
-    # for i in range(len(imgs)):
-    # feat = reid.demo(os.path.join(img_path, imgs[i]))
-    # feats[imgs[i]] = feat
-    # feat = reid.demo(os.path.join(img_path, 'crop_img0.jpg'))
-    # out1 = feats['dog.jpg']
-    # out2 = feats['kobe2.jpg']
-    # innerProduct = np.dot(out1, out2.T)
-    # cosineSimilarity = innerProduct / (np.linalg.norm(out1, ord=2) * np.linalg.norm(out2, ord=2))
-    # print(f'cosine similarity is {cosineSimilarity[0][0]:.4f}')

fastreid/layers/splat.py

@@ -6,11 +6,12 @@
 
 import torch
 import torch.nn.functional as F
-from torch.nn import Conv2d, Module, ReLU
+from torch import nn
+from torch.nn import Conv2d, ReLU
 from torch.nn.modules.utils import _pair
 
 
-class SplAtConv2d(Module):
+class SplAtConv2d(nn.Module):
     """Split-Attention Conv2d
     """
 
@@ -36,13 +37,15 @@ class SplAtConv2d(Module):
             self.conv = Conv2d(in_channels, channels * radix, kernel_size, stride, padding, dilation,
                                groups=groups * radix, bias=bias, **kwargs)
         self.use_bn = norm_layer is not None
+        if self.use_bn:
             self.bn0 = norm_layer(channels * radix)
         self.relu = ReLU(inplace=True)
         self.fc1 = Conv2d(channels, inter_channels, 1, groups=self.cardinality)
+        if self.use_bn:
             self.bn1 = norm_layer(inter_channels)
         self.fc2 = Conv2d(inter_channels, channels * radix, 1, groups=self.cardinality)
-        if dropblock_prob > 0.0:
+
-            self.dropblock = DropBlock2D(dropblock_prob, 3)
+        self.rsoftmax = rSoftMax(radix, groups)
 
     def forward(self, x):
         x = self.conv(x)
@@ -52,9 +55,9 @@ class SplAtConv2d(Module):
             x = self.dropblock(x)
         x = self.relu(x)
 
-        batch, channel = x.shape[:2]
+        batch, rchannel = x.shape[:2]
         if self.radix > 1:
-            splited = torch.split(x, channel // self.radix, dim=1)
+            splited = torch.split(x, rchannel // self.radix, dim=1)
             gap = sum(splited)
         else:
             gap = x
@@ -65,15 +68,29 @@ class SplAtConv2d(Module):
             gap = self.bn1(gap)
         gap = self.relu(gap)
 
-        atten = self.fc2(gap).view((batch, self.radix, self.channels))
+        atten = self.fc2(gap)
-        if self.radix > 1:
+        atten = self.rsoftmax(atten).view(batch, -1, 1, 1)
-            atten = F.softmax(atten, dim=1).view(batch, -1, 1, 1)
-        else:
-            atten = F.sigmoid(atten, dim=1).view(batch, -1, 1, 1)
 
         if self.radix > 1:
-            atten = torch.split(atten, channel // self.radix, dim=1)
+            attens = torch.split(atten, rchannel // self.radix, dim=1)
-            out = sum([att * split for (att, split) in zip(atten, splited)])
+            out = sum([att * split for (att, split) in zip(attens, splited)])
         else:
             out = atten * x
         return out.contiguous()
+
+
+class rSoftMax(nn.Module):
+    def __init__(self, radix, cardinality):
+        super().__init__()
+        self.radix = radix
+        self.cardinality = cardinality
+
+    def forward(self, x):
+        batch = x.size(0)
+        if self.radix > 1:
+            x = x.view(batch, self.cardinality, self.radix, -1).transpose(1, 2)
+            x = F.softmax(x, dim=1)
+            x = x.reshape(batch, -1)
+        else:
+            x = torch.sigmoid(x)
+        return x

tools/export2tf.py

@@ -0,0 +1,48 @@
+# encoding: utf-8
+"""
+@author:  sherlock
+@contact: sherlockliao01@gmail.com
+"""
+
+import sys
+
+import torch
+sys.path.append('../..')
+from fastreid.config import get_cfg
+from fastreid.engine import default_argument_parser, default_setup
+from fastreid.modeling.meta_arch import build_model
+from fastreid.export.tensorflow_export import export_tf_reid_model
+from fastreid.export.tf_modeling import TfMetaArch
+
+
+def setup(args):
+    """
+    Create configs and perform basic setups.
+    """
+    cfg = get_cfg()
+    # cfg.merge_from_file(args.config_file)
+    cfg.merge_from_list(args.opts)
+    cfg.freeze()
+    default_setup(cfg, args)
+    return cfg
+
+
+if __name__ == "__main__":
+    args = default_argument_parser().parse_args()
+    print("Command Line Args:", args)
+    cfg = setup(args)
+    cfg.defrost()
+    cfg.MODEL.BACKBONE.NAME = "build_resnet_backbone"
+    cfg.MODEL.BACKBONE.DEPTH = 50
+    cfg.MODEL.BACKBONE.LAST_STRIDE = 1
+    # If use IBN block in backbone
+    cfg.MODEL.BACKBONE.WITH_IBN = False
+    cfg.MODEL.BACKBONE.PRETRAIN = False
+
+    from torchvision.models import resnet50
+    # model = TfMetaArch(cfg)
+    model = resnet50(pretrained=False)
+    # model.load_params_wo_fc(torch.load('logs/bjstation/res50_baseline_v0.4/ckpts/model_epoch80.pth'))
+    model.eval()
+    dummy_inputs = torch.randn(1, 3, 256, 128)
+    export_tf_reid_model(model, dummy_inputs, 'reid_tf.pb')