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+
+# Created by https://www.gitignore.io/api/linux,python,pycharm
+# Edit at https://www.gitignore.io/?templates=linux,python,pycharm
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diff --git a/LICENSE b/LICENSE
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+BSD 3-Clause License
+
+Copyright (c) 2019, Jerome Rony
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/README.md b/README.md
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+## Requirements for the experiments
+
+ - scikit-learn
+ - pytorch >= 1.4
+ - sacred >= 0.8
+ - tqdm
+ - visdom_logger https://github.com/luizgh/visdom_logger
+ - faiss https://github.com/facebookresearch/faiss
+
+## Data management
+
+For In-Shop, you need to manually download the data from https://drive.google.com/drive/folders/0B7EVK8r0v71pVDZFQXRsMDZCX1E (at least the `img.zip` and `list_eval_partition.txt`), put them in `data/InShop` and extract `img.zip`.
+
+You can download and generate the `train.txt` and `test.txt` for every dataset using the `prepare_data.py` script with:
+```bash
+python prepare_data.py
+```
+This will download and prepare all the necessary data for _CUB200_, _Cars-196_ and _Stanford Online Products_.
+
+## Usage
+
+This repo uses `sacred` to manage the experiments.
+To run an experiment (e.g. on CUB200):
+
+```bash
+python experiment.py with dataset.cub
+```
+
+You can add an observer to save the metrics and files related to the expriment by adding `-F result_dir`:
+
+```bash
+python experiment.py -F result_dir with dataset.cub
+```
+
+## Reproducing the results of the paper
+
+CUB200
+```bash
+python experiment.py with dataset.cub model.resnet50 epochs=30 lr=0.02
+```
+
+CARS-196
+```bash
+python experiment.py with dataset.cars model.resnet50 epochs=100 lr=0.05 model.norm_layer=batch
+```
+
+Stanford Online Products
+```bash
+python experiment.py with dataset.sop model.resnet50 epochs=100 lr=0.01 momentum=0.9 nesterov=True model.norm_layer=batch
+```
+
+In-Shop
+```bash
+python experiment.py with dataset.inshop model.resnet50 epochs=100 lr=0.01 momentum=0.9 nesterov=True model.norm_layer=batch
+```
diff --git a/experiment.py b/experiment.py
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+import math
+import os
+from copy import deepcopy
+from functools import partial
+from pprint import pprint
+
+import sacred
+import torch
+import torch.nn as nn
+from sacred import SETTINGS
+from sacred.utils import apply_backspaces_and_linefeeds
+from torch.backends import cudnn
+from torch.optim import SGD, lr_scheduler
+from visdom_logger import VisdomLogger
+
+from models.ingredient import model_ingredient, get_model
+from utils import state_dict_to_cpu, SmoothCrossEntropy
+from utils.data.dataset_ingredient import data_ingredient, get_loaders
+from utils.training import train, evaluate
+
+ex = sacred.Experiment('Metric Learning', ingredients=[data_ingredient, model_ingredient])
+# Filter backspaces and linefeeds
+SETTINGS.CAPTURE_MODE = 'sys'
+ex.captured_out_filter = apply_backspaces_and_linefeeds
+
+
+@ex.config
+def config():
+    epochs = 20
+    lr = 0.02
+    momentum = 0.
+    nesterov = False
+    weight_decay = 5e-4
+    scheduler = 'warmcos'
+
+    visdom_port = None
+    visdom_freq = 20
+    cpu = False  # Force training on CPU
+    cudnn_flag = 'benchmark'
+    temp_dir = os.path.join('results', 'temp')
+
+    no_bias_decay = True
+    label_smoothing = 0.1
+    temperature = 1.
+
+
+@ex.capture
+def get_optimizer_scheduler(parameters, loader_length, epochs, lr, momentum, nesterov, weight_decay, scheduler,
+                            lr_step=None):
+    optimizer = SGD(parameters, lr=lr, momentum=momentum, weight_decay=weight_decay,
+                    nesterov=True if nesterov and momentum else False)
+    if epochs == 0:
+        scheduler = None
+    elif scheduler == 'cos':
+        scheduler = lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs * loader_length, eta_min=0)
+    elif scheduler == 'warmcos':
+        warm_cosine = lambda i: min((i + 1) / 100, (1 + math.cos(math.pi * i / (epochs * loader_length))) / 2)
+        scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=warm_cosine)
+    elif scheduler == 'step':
+        scheduler = lr_scheduler.StepLR(optimizer, lr_step * loader_length)
+    elif scheduler == 'warmstep':
+        warm_step = lambda i: min((i + 1) / 100, 1) * 0.1 ** (i // (lr_step * loader_length))
+        scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=warm_step)
+    else:
+        scheduler = lr_scheduler.StepLR(optimizer, epochs * loader_length)
+    return optimizer, scheduler
+
+
+@ex.automain
+def main(epochs, cpu, cudnn_flag, visdom_port, visdom_freq, temp_dir, seed, no_bias_decay, label_smoothing,
+         temperature):
+    device = torch.device('cuda:0' if torch.cuda.is_available() and not cpu else 'cpu')
+    callback = VisdomLogger(port=visdom_port) if visdom_port else None
+    if cudnn_flag == 'deterministic':
+        setattr(cudnn, cudnn_flag, True)
+
+    torch.manual_seed(seed)
+    loaders, recall_ks = get_loaders()
+
+    torch.manual_seed(seed)
+    model = get_model(num_classes=loaders.num_classes)
+    class_loss = SmoothCrossEntropy(epsilon=label_smoothing, temperature=temperature)
+
+    model.to(device)
+    if torch.cuda.device_count() > 1:
+        model = nn.DataParallel(model)
+    parameters = []
+    if no_bias_decay:
+        parameters.append({'params': [par for par in model.parameters() if par.dim() != 1]})
+        parameters.append({'params': [par for par in model.parameters() if par.dim() == 1], 'weight_decay': 0})
+    else:
+        parameters.append({'params': model.parameters()})
+    optimizer, scheduler = get_optimizer_scheduler(parameters=parameters, loader_length=len(loaders.train))
+
+    # setup partial function to simplify call
+    eval_function = partial(evaluate, model=model, recall=recall_ks, query_loader=loaders.query,
+                            gallery_loader=loaders.gallery)
+
+    # setup best validation logger
+    metrics = eval_function()
+    if callback is not None:
+        callback.scalars(['l2', 'cosine'], 0, [metrics.recall['l2'][1], metrics.recall['cosine'][1]],
+                         title='Val Recall@1')
+    pprint(metrics.recall)
+    best_val = (0, metrics.recall, deepcopy(model.state_dict()))
+
+    torch.manual_seed(seed)
+    for epoch in range(epochs):
+        if cudnn_flag == 'benchmark':
+            setattr(cudnn, cudnn_flag, True)
+
+        train(model=model, loader=loaders.train, class_loss=class_loss, optimizer=optimizer,
+              scheduler=scheduler, epoch=epoch, callback=callback, freq=visdom_freq, ex=ex)
+
+        # validation
+        if cudnn_flag == 'benchmark':
+            setattr(cudnn, cudnn_flag, False)
+        metrics = eval_function()
+        print('Validation [{:03d}]'.format(epoch)), pprint(metrics.recall)
+        ex.log_scalar('val.recall_l2@1', metrics.recall['l2'][1], step=epoch + 1)
+        ex.log_scalar('val.recall_cosine@1', metrics.recall['cosine'][1], step=epoch + 1)
+
+        if callback is not None:
+            callback.scalars(['l2', 'cosine'], epoch + 1,
+                             [metrics.recall['l2'][1], metrics.recall['cosine'][1]], title='Val Recall')
+
+        # save model dict if the chosen validation metric is better
+        if metrics.recall['cosine'][1] >= best_val[1]['cosine'][1]:
+            best_val = (epoch + 1, metrics.recall, deepcopy(model.state_dict()))
+
+    # logging
+    ex.info['recall'] = best_val[1]
+
+    # saving
+    save_name = os.path.join(temp_dir, '{}_{}.pt'.format(ex.current_run.config['model']['arch'],
+                                                         ex.current_run.config['dataset']['name']))
+    torch.save(state_dict_to_cpu(best_val[2]), save_name)
+    ex.add_artifact(save_name)
+
+    if callback is not None:
+        save_name = os.path.join(temp_dir, 'visdom_data.pt')
+        callback.save(save_name)
+        ex.add_artifact(save_name)
+
+    return best_val[1]['cosine'][1]
diff --git a/models/__init__.py b/models/__init__.py
diff --git a/models/architectures/__init__.py b/models/architectures/__init__.py
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+from .bn_inception import bninception
+from .resnet import resnet18, resnet34, resnet50, resnet101, resnet152, resnext50_32x4d, resnext101_32x8d, \
+    wide_resnet50_2, wide_resnet101_2
+
+__all__ = ['resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
+           'wide_resnet50_2', 'wide_resnet101_2', 'bninception']