fix error when detection output is null (#351)

* add inceptionv4 backbone/training settings
* add converted backbone, top-1 acc 80.08

.github/workflows/citest.yaml

@@ -39,7 +39,7 @@ concurrency:
 jobs:
   ut-torch180:
     # The type of runner that the job will run on
-    runs-on: [unittest-t4]
+    runs-on: [self-hosted]
     steps:
       - name: Checkout
         uses: actions/checkout@v2
@@ -64,8 +64,8 @@ jobs:
           # pip install -r requirements.txt
           #run test
           export CUDA_VISIBLE_DEVICES=7
-          source ~/workspace/anaconda2/etc/profile.d/conda.sh
-          conda activate easycv_torch1.8.0_py37
+          source ~/anaconda3/etc/profile.d/conda.sh
+          conda activate easycv_torch1.8.0
 
           # pip install pai-easycv
           pip uninstall -y pai-easycv
@@ -75,13 +75,16 @@ jobs:
           # move source code, ensure import easycv from site-package
           mv ./easycv ./easycv_src
 
-          PYTHONPATH=. python tests/run.py
+          PYTHONPATH=. python tests/run.py --skip_dir tests/test_toolkit/modelscope
+
+          conda activate easycv_torch1.8.0_py37
+          PYTHONPATH=. python tests/run.py --test_dir tests/test_toolkit/modelscope
 
 
 # blade test env will be updated! we do not support test with trt_efficient_nms
   ut-torch181-blade:
     # The type of runner that the job will run on
-    runs-on: [unittest-t4]
+    runs-on: [self-hosted]
     steps:
       - name: Checkout
         uses: actions/checkout@v2
@@ -107,7 +110,7 @@ jobs:
           # pip install -r requirements.txt
           #run test
           export CUDA_VISIBLE_DEVICES=6
-          source ~/workspace/anaconda2/etc/profile.d/conda.sh
+          source ~/anaconda3/etc/profile.d/conda.sh
           conda activate torch1.8.1_blade
           PYTHONPATH=. python tests/test_predictors/test_detector_blade.py
           PYTHONPATH=. python tests/test_apis/test_export_blade.py

README.md

@@ -248,7 +248,7 @@ Please refer to the following model zoo for more details.
 
 ## Data Hub
 
-EasyCV have collected dataset info for different senarios, making it easy for users to finetune or evaluate models in EasyCV model zoo.
+EasyCV have collected dataset info for different scenarios, making it easy for users to finetune or evaluate models in EasyCV model zoo.
 
 Please refer to [data_hub.md](docs/source/data_hub.md).
 

configs/classification/imagenet/common/dataset/imagenet_classification.py

@@ -153,7 +153,7 @@ eval_pipelines = [
         data=data['val'],
         dist_eval=True,
         evaluators=[
-            dict(type='ClsEvaluator', topk=(1, 5), class_list=class_list)
+            dict(type='ClsEvaluator', topk=(1, ), class_list=class_list)
         ],
     )
 ]

configs/classification/imagenet/inception/inceptionv3_b32x8_100e.py

@@ -4,16 +4,31 @@ num_classes = 1000
 # model settings
 model = dict(
     type='Classification',
-    backbone=dict(type='Inception3'),
-    head=dict(
-        type='ClsHead',
-        with_avg_pool=True,
-        in_channels=2048,
-        loss_config=dict(
-            type='CrossEntropyLossWithLabelSmooth',
-            label_smooth=0,
+    backbone=dict(type='Inception3', num_classes=1000),
+    head=[
+        dict(
+            type='ClsHead',
+            with_fc=False,
+            in_channels=2048,
+            loss_config=dict(
+                type='CrossEntropyLossWithLabelSmooth',
+                label_smooth=0,
+            ),
+            num_classes=num_classes,
+            input_feature_index=[1],
         ),
-        num_classes=num_classes))
+        dict(
+            type='ClsHead',
+            with_fc=False,
+            in_channels=768,
+            loss_config=dict(
+                type='CrossEntropyLossWithLabelSmooth',
+                label_smooth=0,
+            ),
+            num_classes=num_classes,
+            input_feature_index=[0],
+        )
+    ])
 
 class_list = [
     '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13',
@@ -196,3 +211,5 @@ log_config = dict(
     interval=10,
     hooks=[dict(type='TextLoggerHook'),
            dict(type='TensorboardLoggerHook')])
+
+export = dict(export_type='raw', export_neck=True)

configs/classification/imagenet/inception/inceptionv4_b32x8_100e.py

@@ -0,0 +1,33 @@
+_base_ = 'configs/classification/imagenet/inception/inceptionv3_b32x8_100e.py'
+
+num_classes = 1000
+# model settings
+model = dict(
+    type='Classification',
+    backbone=dict(type='Inception4', num_classes=num_classes),
+    head=[
+        dict(
+            type='ClsHead',
+            with_fc=False,
+            in_channels=1536,
+            loss_config=dict(
+                type='CrossEntropyLossWithLabelSmooth',
+                label_smooth=0,
+            ),
+            num_classes=num_classes,
+            input_feature_index=[1],
+        ),
+        dict(
+            type='ClsHead',
+            with_fc=False,
+            in_channels=768,
+            loss_config=dict(
+                type='CrossEntropyLossWithLabelSmooth',
+                label_smooth=0,
+            ),
+            num_classes=num_classes,
+            input_feature_index=[0],
+        )
+    ])
+
+img_norm_cfg = dict(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])

configs/classification/imagenet/inception/inceptionv4_b32x8_200e_rmsprop.py

@@ -0,0 +1,46 @@
+# A config with the optimization settings from https://arxiv.org/pdf/1602.07261
+# May run with 20 GPUs
+_base_ = 'configs/classification/imagenet/inception/inceptionv3_b32x8_100e.py'
+
+num_classes = 1000
+# model settings
+model = dict(
+    type='Classification',
+    backbone=dict(type='Inception4', num_classes=num_classes),
+    head=[
+        dict(
+            type='ClsHead',
+            with_fc=False,
+            in_channels=1536,
+            loss_config=dict(
+                type='CrossEntropyLossWithLabelSmooth',
+                label_smooth=0,
+            ),
+            num_classes=num_classes,
+            input_feature_index=[1],
+        ),
+        dict(
+            type='ClsHead',
+            with_fc=False,
+            in_channels=768,
+            loss_config=dict(
+                type='CrossEntropyLossWithLabelSmooth',
+                label_smooth=0,
+            ),
+            num_classes=num_classes,
+            input_feature_index=[0],
+        )
+    ])
+
+img_norm_cfg = dict(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+# optimizer
+optimizer = dict(
+    type='RMSprop', lr=0.045, momentum=0.9, weight_decay=0.9, eps=1.0)
+
+# learning policy
+lr_config = dict(policy='exp', gamma=0.96954)  # gamma**2 ~ 0.94
+checkpoint_config = dict(interval=10)
+
+# runtime settings
+total_epochs = 200

configs/classification/imagenet/mobilenet/mobilenetv2.py

@@ -13,7 +13,8 @@ model = dict(
             type='CrossEntropyLossWithLabelSmooth',
             label_smooth=0,
         ),
-        num_classes=num_classes))
+        num_classes=num_classes),
+    pretrained=True)
 
 # optimizer
 optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)
@@ -25,4 +26,4 @@ checkpoint_config = dict(interval=5)
 # runtime settings
 total_epochs = 100
 checkpoint_sync_export = True
-export = dict(export_neck=True)
+export = dict(export_type='raw', export_neck=True)

configs/classification/imagenet/resnet/imagenet_resnet50_jpg.py

@@ -6,3 +6,6 @@ model = dict(
         depth=50,
         out_indices=[4],  # 0: conv-1, x: stage-x
         norm_cfg=dict(type='BN')))
+
+checkpoint_sync_export = True
+export = dict(export_type='raw', export_neck=True)

configs/classification/imagenet/resnext/resnext50-32x4d_b32x8_100e_jpg.py

@@ -19,7 +19,8 @@ model = dict(
             type='CrossEntropyLossWithLabelSmooth',
             label_smooth=0,
         ),
-        num_classes=num_classes))
+        num_classes=num_classes),
+    pretrained=True)
 
 # optimizer
 optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.0001)
@@ -30,3 +31,4 @@ checkpoint_config = dict(interval=10)
 
 # runtime settings
 total_epochs = 100
+export = dict(export_type='raw', export_neck=True)

configs/config_templates/yolox_itag.py

@@ -49,14 +49,14 @@ img_norm_cfg = dict(
     mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
 
 train_pipeline = [
-    dict(type='MMMosaic', img_scale='${img_scale}', pad_val=114.0),
+    dict(type='MMMosaic', img_scale=tuple(img_scale), pad_val=114.0),
     dict(
         type='MMRandomAffine',
-        scaling_ratio_range='${scale_ratio}',
-        border=['-${img_scale}[0] // 2', '-${img_scale}[1] // 2']),
+        scaling_ratio_range=scale_ratio,
+        border=[img_scale[0] // 2, img_scale[1] // 2]),
     dict(
         type='MMMixUp',  # s m x l; tiny nano will detele
-        img_scale='${img_scale}',
+        img_scale=tuple(img_scale),
         ratio_range=(0.8, 1.6),
         pad_val=114.0),
     dict(
@@ -70,45 +70,43 @@ train_pipeline = [
     dict(type='MMPad', pad_to_square=True, pad_val=(114.0, 114.0, 114.0)),
     dict(
         type='MMNormalize',
-        mean='${img_norm_cfg.mean}',
-        std='${img_norm_cfg.std}',
-        to_rgb='${img_norm_cfg.to_rgb}'),
+        mean=img_norm_cfg['mean'],
+        std=img_norm_cfg['std'],
+        to_rgb=img_norm_cfg['to_rgb']),
     dict(type='DefaultFormatBundle'),
     dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
 ]
 test_pipeline = [
-    dict(type='MMResize', img_scale='${img_scale}', keep_ratio=True),
+    dict(type='MMResize', img_scale=img_scale, keep_ratio=True),
     dict(type='MMPad', pad_to_square=True, pad_val=(114.0, 114.0, 114.0)),
     dict(
         type='MMNormalize',
-        mean='${img_norm_cfg.mean}',
-        std='${img_norm_cfg.std}',
-        to_rgb='${img_norm_cfg.to_rgb}'),
+        mean=img_norm_cfg['mean'],
+        std=img_norm_cfg['std'],
+        to_rgb=img_norm_cfg['to_rgb']),
     dict(type='DefaultFormatBundle'),
     dict(type='Collect', keys=['img'])
 ]
 
+train_path = 'data/coco/train2017.manifest'
+val_path = 'data/coco/val2017.manifest'
+
+train_dataset = dict(
+    type='DetImagesMixDataset',
+    data_source=dict(type='DetSourcePAI', path=train_path, classes=CLASSES),
+    pipeline=train_pipeline,
+    dynamic_scale=tuple(img_scale))
+
+val_dataset = dict(
+    type='DetImagesMixDataset',
+    imgs_per_gpu=2,
+    data_source=dict(type='DetSourcePAI', path=val_path, classes=CLASSES),
+    pipeline=test_pipeline,
+    dynamic_scale=None,
+    label_padding=False)
+
 data = dict(
-    imgs_per_gpu=16,
-    workers_per_gpu=4,
-    train=dict(
-        type='DetImagesMixDataset',
-        data_source=dict(
-            type='DetSourcePAI',
-            path='data/coco/train2017.manifest',
-            classes='${CLASSES}'),
-        pipeline='${train_pipeline}',
-        dynamic_scale='${img_scale}'),
-    val=dict(
-        type='DetImagesMixDataset',
-        imgs_per_gpu=2,
-        data_source=dict(
-            type='DetSourcePAI',
-            path='data/coco/val2017.manifest',
-            classes='${CLASSES}'),
-        pipeline='${test_pipeline}',
-        dynamic_scale=None,
-        label_padding=False))
+    imgs_per_gpu=16, workers_per_gpu=4, train=train_dataset, val=val_dataset)
 
 # additional hooks
 interval = 10
@@ -120,14 +118,14 @@ custom_hooks = [
         priority=48),
     dict(
         type='SyncRandomSizeHook',
-        ratio_range='${random_size}',
-        img_scale='${img_scale}',
-        interval='${interval}',
+        ratio_range=random_size,
+        img_scale=img_scale,
+        interval=interval,
         priority=48),
     dict(
         type='SyncNormHook',
         num_last_epochs=15,
-        interval='${interval}',
+        interval=interval,
         priority=48)
 ]
 
@@ -135,23 +133,23 @@ custom_hooks = [
 vis_num = 20
 score_thr = 0.5
 eval_config = dict(
-    interval='${interval}',
+    interval=interval,
     gpu_collect=False,
     visualization_config=dict(
-        vis_num='${vis_num}',
-        score_thr='${score_thr}',
+        vis_num=vis_num,
+        score_thr=score_thr,
     )  # show by TensorboardLoggerHookV2
 )
 
 eval_pipelines = [
     dict(
         mode='test',
-        data='${data.val}',
+        data=val_dataset,
         evaluators=[dict(type='CocoDetectionEvaluator', classes=CLASSES)],
     )
 ]
 
-checkpoint_config = dict(interval='${interval}')
+checkpoint_config = dict(interval=interval)
 # optimizer
 # basic_lr_per_img = 0.01 / 64.0
 optimizer = dict(

configs/detection/fcos/coco_detection.py

@@ -102,6 +102,7 @@ eval_config = dict(interval=1, gpu_collect=False)
 eval_pipelines = [
     dict(
         mode='test',
+        data=data['val'],
         evaluators=[
             dict(type='CocoDetectionEvaluator', classes=CLASSES),
         ],

docs/source/quick_start.md

@@ -92,13 +92,13 @@
 
 ### Verification
 
-    Simple verification
+  Simple verification
 
-    ```python
-    from easycv.apis import *
-    ```
+```python
+from easycv.apis import *
+```
 
-    You can also verify your installation using following quick-start examples
+  You can also verify your installation using following quick-start examples
 
 
 ## Examples

easycv/__init__.py

@@ -1,4 +1,9 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
+# flake8: noqa
+# isort:skip_file
+import os
+os.environ['SETUPTOOLS_USE_DISTUTILS'] = 'stdlib'
+
 from .version import __version__, short_version
 
 __all__ = ['__version__', 'short_version']

easycv/apis/export.py

@@ -157,6 +157,52 @@ def _export_jit_and_blade(model, cfg, filename, dummy_inputs, fp16=False):
         torch.jit.save(blade_model, ofile)
 
 
+def _export_onnx_cls(model, model_config, cfg, filename, meta):
+    support_backbones = {
+        'ResNet': {
+            'depth': [50]
+        },
+        'MobileNetV2': {},
+        'Inception3': {},
+        'Inception4': {},
+        'ResNeXt': {
+            'depth': [50]
+        }
+    }
+    if model_config['backbone'].get('type', None) not in support_backbones:
+        tmp = ' '.join(support_backbones.keys())
+        info_str = f'Only support export onnx model for {tmp} now!'
+        raise ValueError(info_str)
+    configs = support_backbones[model_config['backbone'].get('type')]
+    for k, v in configs.items():
+        if v[0].__class__(model_config['backbone'].get(k, None)) not in v:
+            raise ValueError(
+                f"Unsupport config for {model_config['backbone'].get('type')}")
+
+    # save json config for test_pipline and class
+    with io.open(
+            filename +
+            '.config.json' if filename.endswith('onnx') else filename +
+            '.onnx.config.json', 'w') as ofile:
+        json.dump(meta, ofile)
+
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    model.eval()
+    model.to(device)
+    img_size = int(cfg.image_size2)
+    x_input = torch.randn((1, 3, img_size, img_size)).to(device)
+    torch.onnx.export(
+        model,
+        (x_input, 'onnx'),
+        filename if filename.endswith('onnx') else filename + '.onnx',
+        export_params=True,
+        opset_version=12,
+        do_constant_folding=True,
+        input_names=['input'],
+        output_names=['output'],
+    )
+
+
 def _export_cls(model, cfg, filename):
     """ export cls (cls & metric learning)model and preprocess config
 
@@ -170,6 +216,7 @@ def _export_cls(model, cfg, filename):
     else:
         export_cfg = dict(export_neck=False)
 
+    export_type = export_cfg.get('export_type', 'raw')
     export_neck = export_cfg.get('export_neck', True)
     label_map_path = cfg.get('label_map_path', None)
     class_list = None
@@ -232,9 +279,14 @@ def _export_cls(model, cfg, filename):
         if export_neck and (k.startswith('neck') or k.startswith('head')):
             state_dict[k] = v
 
-    checkpoint = dict(state_dict=state_dict, meta=meta, author='EasyCV')
-    with io.open(filename, 'wb') as ofile:
-        torch.save(checkpoint, ofile)
+    if export_type == 'raw':
+        checkpoint = dict(state_dict=state_dict, meta=meta, author='EasyCV')
+        with io.open(filename, 'wb') as ofile:
+            torch.save(checkpoint, ofile)
+    elif export_type == 'onnx':
+        _export_onnx_cls(model, model_config, cfg, filename, config)
+    else:
+        raise ValueError('Only support export onnx/raw model!')
 
 
 def _export_yolox(model, cfg, filename):
@@ -247,10 +299,10 @@ def _export_yolox(model, cfg, filename):
 
     if hasattr(cfg, 'export'):
         export_type = getattr(cfg.export, 'export_type', 'raw')
-        default_export_type_list = ['raw', 'jit', 'blade']
+        default_export_type_list = ['raw', 'jit', 'blade', 'onnx']
         if export_type not in default_export_type_list:
             logging.warning(
-                'YOLOX-PAI only supports the export type as  [raw,jit,blade], otherwise we use raw as default'
+                'YOLOX-PAI only supports the export type as  [raw,jit,blade,onnx], otherwise we use raw as default'
             )
             export_type = 'raw'
 
@@ -276,7 +328,7 @@ def _export_yolox(model, cfg, filename):
                 len(img_scale) == 2
             ), 'Export YoloX predictor config contains img_scale must be (int, int) tuple!'
 
-            input = 255 * torch.rand((batch_size, 3) + img_scale)
+            input = 255 * torch.rand((batch_size, 3) + tuple(img_scale))
 
             # assert use_trt_efficientnms only happens when static_opt=True
             if static_opt is not True:
@@ -355,6 +407,31 @@ def _export_yolox(model, cfg, filename):
 
                     json.dump(config, ofile)
 
+            if export_type == 'onnx':
+
+                with io.open(
+                        filename + '.config.json' if filename.endswith('onnx')
+                        else filename + '.onnx.config.json', 'w') as ofile:
+                    config = dict(
+                        model=cfg.model,
+                        export=cfg.export,
+                        test_pipeline=cfg.test_pipeline,
+                        classes=cfg.CLASSES)
+
+                    json.dump(config, ofile)
+
+                torch.onnx.export(
+                    model,
+                    input.to(device),
+                    filename if filename.endswith('onnx') else filename +
+                    '.onnx',
+                    export_params=True,
+                    opset_version=12,
+                    do_constant_folding=True,
+                    input_names=['input'],
+                    output_names=['output'],
+                )
+
             if export_type == 'jit':
                 with io.open(filename + '.jit', 'wb') as ofile:
                     torch.jit.save(yolox_trace, ofile)
@@ -650,6 +727,12 @@ def _export_pose_topdown(model, cfg, filename, fp16=False, dummy_inputs=None):
     model.to(device)
 
     if hasattr(cfg, 'export') and getattr(cfg.export, 'type', 'raw') == 'raw':
+        from mmcv.utils.path import is_filepath
+
+        if hasattr(cfg, 'dataset_info') and is_filepath(cfg.dataset_info):
+            dataset_info_cfg = Config.fromfile(cfg.dataset_info)
+            cfg.dataset_info = dataset_info_cfg._cfg_dict['dataset_info']
+
         return _export_common(model, cfg, filename)
 
     def _dummy_inputs(cfg):

easycv/core/evaluation/classification_eval.py

@@ -146,10 +146,14 @@ class ClsEvaluator(Evaluator):
                 matrix = confusion_matrix(
                     valid_true, valid_pred, labels=self.class_list)
 
-                print_log(
-                    'recall:{}\nprecision:{}\nattend:{}\nTP:{}\nFN:{}\nFP:{}\nTN:{}\nrecall/mean:{}\nprecision/mean:{}\nF1/mean:{}\nconfusion_matrix:{}\n'
-                    .format(recall, precision, attend, tp, fn, fp, tn,
-                            recall_mean, precision_mean, f1_mean, matrix))
+                # print_log(
+                #     'recall:{}\nprecision:{}\nattend:{}\nTP:{}\nFN:{}\nFP:{}\nTN:{}\nrecall/mean:{}\nprecision/mean:{}\nF1/mean:{}\nconfusion_matrix:{}\n'
+                #     .format(recall, precision, attend, tp, fn, fp, tn,
+                #             recall_mean, precision_mean, f1_mean, matrix))
+
+                eval_res[key] = \
+                    'recall:{}\nprecision:{}\nattend:{}\nTP:{}\nFN:{}\nFP:{}\nTN:{}\nrecall/mean:{}\nprecision/mean:{}\nF1/mean:{}\nconfusion_matrix:{}\n'\
+                    .format(recall, precision, attend, tp, fn, fp, tn, recall_mean, precision_mean, f1_mean, matrix.tolist())
 
         return eval_res
 

easycv/core/evaluation/custom_cocotools/cocoeval.py

@@ -467,8 +467,8 @@ class COCOeval:
                     fps = np.logical_and(
                         np.logical_not(dtm), np.logical_not(dtIg))
 
-                    tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float)
-                    fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float)
+                    tp_sum = np.cumsum(tps, axis=1).astype(dtype=np.float32)
+                    fp_sum = np.cumsum(fps, axis=1).astype(dtype=np.float32)
                     for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):
                         tp = np.array(tp)
                         fp = np.array(fp)

easycv/datasets/face/pipelines/face_keypoint_transform.py

@@ -252,7 +252,7 @@ class FaceKeypointsDataAugumentation:
             skin_factor_list = [0.6, 0.8, 1.0, 1.2, 1.4]
             skin_factor = np.random.choice(skin_factor_list)
             img_ycrcb_raw[:, :, 0:1] = np.clip(
-                img_ycrcb_raw[:, :, 0:1].astype(np.float) * skin_factor, 0,
+                img_ycrcb_raw[:, :, 0:1].astype(np.float32) * skin_factor, 0,
                 255).astype(np.uint8)
             img = cv2.cvtColor(img_ycrcb_raw, cv2.COLOR_YCR_CB2BGR)
 

easycv/datasets/loader/sampler.py

@@ -246,7 +246,7 @@ class DistributedSampler(_DistributedSampler):
                     l, size_per_label, replace=(len(l) <= size_per_label)))
         indices = np.array(indices)
         np.random.shuffle(indices)
-        indices = indices[:N].astype(np.int).tolist()
+        indices = indices[:N].astype(np.int64).tolist()
 
         # add extra samples to make it evenly divisible
         assert len(indices) <= self.total_size, \
@@ -438,7 +438,7 @@ class DistributedGivenIterationSampler(Sampler):
                     l, size_per_label, replace=(len(l) <= size_per_label)))
         indices = np.array(indices)
         np.random.shuffle(indices)
-        indices = indices[:N].astype(np.int)
+        indices = indices[:N].astype(np.int64)
         # repeat
         all_size = self.total_size * self.world_size
         indices = indices[:all_size]

easycv/datasets/segmentation/data_sources/cityscapes.py

@@ -110,16 +110,16 @@ class SegSourceCityscapes(SegSourceRaw):
                                         '')[:-len(self.img_suffix[0])]
             find_label_path = False
             for label_format in self.label_suffix:
-                lable_path = os.path.join(self.label_root,
+                label_path = os.path.join(self.label_root,
                                           img_name + label_format)
-                if io.exists(lable_path):
+                if io.exists(label_path):
                     find_label_path = True
-                    self.label_files.append(lable_path)
+                    self.label_files.append(label_path)
                     break
             if not find_label_path:
                 logging.warning(
                     'Not find label file %s for img: %s, skip the sample!' %
-                    (lable_path, img_path))
+                    (label_path, img_path))
                 self.img_files.remove(img_path)
 
         assert len(self.img_files) == len(self.label_files)

easycv/datasets/shared/pipelines/format.py

@@ -1,5 +1,5 @@
 # Copyright (c) OpenMMLab and Alibaba. All rights reserved.
-from collections import Sequence
+from collections.abc import Sequence
 
 import mmcv
 import numpy as np

easycv/datasets/video_recognition/pipelines/loading.py

@@ -161,7 +161,7 @@ class SampleFrames:
             ratio = (num_frames - ori_clip_len + 1.0) / self.num_clips
             clip_offsets = np.around(np.arange(self.num_clips) * ratio)
         else:
-            clip_offsets = np.zeros((self.num_clips, ), dtype=np.int)
+            clip_offsets = np.zeros((self.num_clips, ), dtype=np.int64)
 
         return clip_offsets
 
@@ -180,11 +180,11 @@ class SampleFrames:
         avg_interval = (num_frames - ori_clip_len + 1) / float(self.num_clips)
         if num_frames > ori_clip_len - 1:
             base_offsets = np.arange(self.num_clips) * avg_interval
-            clip_offsets = (base_offsets + avg_interval / 2.0).astype(np.int)
+            clip_offsets = (base_offsets + avg_interval / 2.0).astype(np.int64)
             if self.twice_sample:
                 clip_offsets = np.concatenate([clip_offsets, base_offsets])
         else:
-            clip_offsets = np.zeros((self.num_clips, ), dtype=np.int)
+            clip_offsets = np.zeros((self.num_clips, ), dtype=np.int64)
         return clip_offsets
 
     def _sample_clips(self, num_frames):
@@ -259,7 +259,7 @@ class SampleFrames:
             start_index = results['start_index']
             frame_inds = np.concatenate(frame_inds) + start_index
 
-        results['frame_inds'] = frame_inds.astype(np.int)
+        results['frame_inds'] = frame_inds.astype(np.int64)
         results['clip_len'] = self.clip_len
         results['frame_interval'] = self.frame_interval
         results['num_clips'] = self.num_clips

easycv/datasets/video_recognition/pipelines/pose_transform.py

@@ -34,7 +34,7 @@ class PaddingWithLoop:
         inds = np.arange(start, start + self.clip_len)
         inds = np.mod(inds, num_frames)
 
-        results['frame_inds'] = inds.astype(np.int)
+        results['frame_inds'] = inds.astype(np.int64)
         results['clip_len'] = self.clip_len
         results['frame_interval'] = None
         results['num_clips'] = self.num_clips

easycv/file/__init__.py

@@ -1,4 +1,4 @@
 from .file_io import IO
-from .utils import is_oss_path
+from .utils import get_oss_config, is_oss_path
 
 io = IO()

easycv/models/backbones/__init__.py

@@ -10,6 +10,7 @@ from .face_keypoint_backbone import FaceKeypointBackbone
 from .genet import PlainNet
 from .hrnet import HRNet
 from .inceptionv3 import Inception3
+from .inceptionv4 import Inception4
 from .lighthrnet import LiteHRNet
 from .mae_vit_transformer import *
 from .mit import MixVisionTransformer

easycv/models/backbones/efficientformer.py

@@ -1,15 +1,21 @@
 # Copyright (c) 2022 Snap Inc. All rights reserved.
 import itertools
 import os
+from distutils.version import LooseVersion
 
+import timm
 import torch
 import torch.nn as nn
 from timm.models.layers import DropPath, trunc_normal_
-from timm.models.layers.helpers import to_2tuple
 
 from ..modelzoo import efficientformer as model_urls
 from ..registry import BACKBONES
 
+if LooseVersion(timm.__version__) <= LooseVersion('0.8.2'):
+    from timm.models.layers.helpers import to_2tuple
+else:
+    from timm.layers.helpers import to_2tuple
+
 
 class Attention(torch.nn.Module):
 

easycv/models/backbones/inceptionv3.py

@@ -2,9 +2,6 @@
 r""" This model is taken from the official PyTorch model zoo.
      - torchvision.models.inception.py on 31th Aug, 2019
 """
-
-from collections import namedtuple
-
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -16,8 +13,6 @@ from ..registry import BACKBONES
 
 __all__ = ['Inception3']
 
-_InceptionOutputs = namedtuple('InceptionOutputs', ['logits', 'aux_logits'])
-
 
 @BACKBONES.register_module
 class Inception3(nn.Module):
@@ -113,6 +108,7 @@ class Inception3(nn.Module):
         # N x 768 x 17 x 17
         x = self.Mixed_6e(x)
         # N x 768 x 17 x 17
+        aux = None
         if self.training and self.aux_logits:
             aux = self.AuxLogits(x)
         # N x 768 x 17 x 17
@@ -132,10 +128,7 @@ class Inception3(nn.Module):
         if hasattr(self, 'fc'):
             x = self.fc(x)
 
-        # N x 1000 (num_classes)
-        if self.training and self.aux_logits and hasattr(self, 'fc'):
-            return [_InceptionOutputs(x, aux)]
-        return [x]
+        return [aux, x]
 
 
 class InceptionA(nn.Module):

easycv/models/backbones/inceptionv4.py

@@ -0,0 +1,393 @@
+from __future__ import absolute_import, division, print_function
+from collections import namedtuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import constant_init, kaiming_init
+from torch.nn.modules.batchnorm import _BatchNorm
+
+from ..modelzoo import inceptionv4 as model_urls
+from ..registry import BACKBONES
+
+__all__ = ['Inception4']
+
+
+class BasicConv2d(nn.Module):
+
+    def __init__(self,
+                 in_planes,
+                 out_planes,
+                 kernel_size,
+                 stride=1,
+                 padding=0):
+        super(BasicConv2d, self).__init__()
+        self.conv = nn.Conv2d(
+            in_planes,
+            out_planes,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            bias=False)  # verify bias false
+        self.bn = nn.BatchNorm2d(
+            out_planes,
+            eps=0.001,  # value found in tensorflow
+            momentum=0.1,  # default pytorch value
+            affine=True)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        x = self.relu(x)
+        return x
+
+
+class Mixed_3a(nn.Module):
+
+    def __init__(self):
+        super(Mixed_3a, self).__init__()
+        self.maxpool = nn.MaxPool2d(3, stride=2)
+        self.conv = BasicConv2d(64, 96, kernel_size=3, stride=2)
+
+    def forward(self, x):
+        x0 = self.maxpool(x)
+        x1 = self.conv(x)
+        out = torch.cat((x0, x1), 1)
+        return out
+
+
+class Mixed_4a(nn.Module):
+
+    def __init__(self):
+        super(Mixed_4a, self).__init__()
+
+        self.branch0 = nn.Sequential(
+            BasicConv2d(160, 64, kernel_size=1, stride=1),
+            BasicConv2d(64, 96, kernel_size=3, stride=1))
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(160, 64, kernel_size=1, stride=1),
+            BasicConv2d(64, 64, kernel_size=(1, 7), stride=1, padding=(0, 3)),
+            BasicConv2d(64, 64, kernel_size=(7, 1), stride=1, padding=(3, 0)),
+            BasicConv2d(64, 96, kernel_size=(3, 3), stride=1))
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        out = torch.cat((x0, x1), 1)
+        return out
+
+
+class Mixed_5a(nn.Module):
+
+    def __init__(self):
+        super(Mixed_5a, self).__init__()
+        self.conv = BasicConv2d(192, 192, kernel_size=3, stride=2)
+        self.maxpool = nn.MaxPool2d(3, stride=2)
+
+    def forward(self, x):
+        x0 = self.conv(x)
+        x1 = self.maxpool(x)
+        out = torch.cat((x0, x1), 1)
+        return out
+
+
+class Inception_A(nn.Module):
+
+    def __init__(self):
+        super(Inception_A, self).__init__()
+        self.branch0 = BasicConv2d(384, 96, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(384, 64, kernel_size=1, stride=1),
+            BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1))
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(384, 64, kernel_size=1, stride=1),
+            BasicConv2d(64, 96, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(96, 96, kernel_size=3, stride=1, padding=1))
+
+        self.branch3 = nn.Sequential(
+            nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
+            BasicConv2d(384, 96, kernel_size=1, stride=1))
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        x3 = self.branch3(x)
+        out = torch.cat((x0, x1, x2, x3), 1)
+        return out
+
+
+class Reduction_A(nn.Module):
+
+    def __init__(self):
+        super(Reduction_A, self).__init__()
+        self.branch0 = BasicConv2d(384, 384, kernel_size=3, stride=2)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(384, 192, kernel_size=1, stride=1),
+            BasicConv2d(192, 224, kernel_size=3, stride=1, padding=1),
+            BasicConv2d(224, 256, kernel_size=3, stride=2))
+
+        self.branch2 = nn.MaxPool2d(3, stride=2)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        out = torch.cat((x0, x1, x2), 1)
+        return out
+
+
+class Inception_B(nn.Module):
+
+    def __init__(self):
+        super(Inception_B, self).__init__()
+        self.branch0 = BasicConv2d(1024, 384, kernel_size=1, stride=1)
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(1024, 192, kernel_size=1, stride=1),
+            BasicConv2d(
+                192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)),
+            BasicConv2d(
+                224, 256, kernel_size=(7, 1), stride=1, padding=(3, 0)))
+
+        self.branch2 = nn.Sequential(
+            BasicConv2d(1024, 192, kernel_size=1, stride=1),
+            BasicConv2d(
+                192, 192, kernel_size=(7, 1), stride=1, padding=(3, 0)),
+            BasicConv2d(
+                192, 224, kernel_size=(1, 7), stride=1, padding=(0, 3)),
+            BasicConv2d(
+                224, 224, kernel_size=(7, 1), stride=1, padding=(3, 0)),
+            BasicConv2d(
+                224, 256, kernel_size=(1, 7), stride=1, padding=(0, 3)))
+
+        self.branch3 = nn.Sequential(
+            nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
+            BasicConv2d(1024, 128, kernel_size=1, stride=1))
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        x3 = self.branch3(x)
+        out = torch.cat((x0, x1, x2, x3), 1)
+        return out
+
+
+class Reduction_B(nn.Module):
+
+    def __init__(self):
+        super(Reduction_B, self).__init__()
+
+        self.branch0 = nn.Sequential(
+            BasicConv2d(1024, 192, kernel_size=1, stride=1),
+            BasicConv2d(192, 192, kernel_size=3, stride=2))
+
+        self.branch1 = nn.Sequential(
+            BasicConv2d(1024, 256, kernel_size=1, stride=1),
+            BasicConv2d(
+                256, 256, kernel_size=(1, 7), stride=1, padding=(0, 3)),
+            BasicConv2d(
+                256, 320, kernel_size=(7, 1), stride=1, padding=(3, 0)),
+            BasicConv2d(320, 320, kernel_size=3, stride=2))
+
+        self.branch2 = nn.MaxPool2d(3, stride=2)
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+        x1 = self.branch1(x)
+        x2 = self.branch2(x)
+        out = torch.cat((x0, x1, x2), 1)
+        return out
+
+
+class Inception_C(nn.Module):
+
+    def __init__(self):
+        super(Inception_C, self).__init__()
+
+        self.branch0 = BasicConv2d(1536, 256, kernel_size=1, stride=1)
+
+        self.branch1_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
+        self.branch1_1a = BasicConv2d(
+            384, 256, kernel_size=(1, 3), stride=1, padding=(0, 1))
+        self.branch1_1b = BasicConv2d(
+            384, 256, kernel_size=(3, 1), stride=1, padding=(1, 0))
+
+        self.branch2_0 = BasicConv2d(1536, 384, kernel_size=1, stride=1)
+        self.branch2_1 = BasicConv2d(
+            384, 448, kernel_size=(3, 1), stride=1, padding=(1, 0))
+        self.branch2_2 = BasicConv2d(
+            448, 512, kernel_size=(1, 3), stride=1, padding=(0, 1))
+        self.branch2_3a = BasicConv2d(
+            512, 256, kernel_size=(1, 3), stride=1, padding=(0, 1))
+        self.branch2_3b = BasicConv2d(
+            512, 256, kernel_size=(3, 1), stride=1, padding=(1, 0))
+
+        self.branch3 = nn.Sequential(
+            nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False),
+            BasicConv2d(1536, 256, kernel_size=1, stride=1))
+
+    def forward(self, x):
+        x0 = self.branch0(x)
+
+        x1_0 = self.branch1_0(x)
+        x1_1a = self.branch1_1a(x1_0)
+        x1_1b = self.branch1_1b(x1_0)
+        x1 = torch.cat((x1_1a, x1_1b), 1)
+
+        x2_0 = self.branch2_0(x)
+        x2_1 = self.branch2_1(x2_0)
+        x2_2 = self.branch2_2(x2_1)
+        x2_3a = self.branch2_3a(x2_2)
+        x2_3b = self.branch2_3b(x2_2)
+        x2 = torch.cat((x2_3a, x2_3b), 1)
+
+        x3 = self.branch3(x)
+
+        out = torch.cat((x0, x1, x2, x3), 1)
+        return out
+
+
+class InceptionAux(nn.Module):
+
+    def __init__(self, in_channels, num_classes):
+        super(InceptionAux, self).__init__()
+        self.conv0 = BasicConv2d(in_channels, 128, kernel_size=1)
+        self.conv1 = BasicConv2d(128, 768, kernel_size=5)
+        self.conv1.stddev = 0.01
+        self.fc = nn.Linear(768, num_classes)
+        self.fc.stddev = 0.001
+
+    def forward(self, x):
+        # N x 768 x 17 x 17
+        x = F.avg_pool2d(x, kernel_size=5, stride=3)
+        # N x 768 x 5 x 5
+        x = self.conv0(x)
+        # N x 128 x 5 x 5
+        x = self.conv1(x)
+        # N x 768 x 1 x 1
+        # Adaptive average pooling
+        x = F.adaptive_avg_pool2d(x, (1, 1))
+        # N x 768 x 1 x 1
+        x = torch.flatten(x, 1)
+        # N x 768
+        x = self.fc(x)
+        # N x 1000
+        return x
+
+
+# class BasicConv2d(nn.Module):
+
+#     def __init__(self, in_channels, out_channels, **kwargs):
+#         super(BasicConv2d, self).__init__()
+#         self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
+#         self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
+
+#     def forward(self, x):
+#         x = self.conv(x)
+#         x = self.bn(x)
+#         return F.relu(x, inplace=True)
+
+
+@BACKBONES.register_module
+class Inception4(nn.Module):
+    """InceptionV4 backbone.
+
+    Args:
+        num_classes (int): The num_classes of InceptionV4. An extra fc will be used if
+    """
+
+    def __init__(self,
+                 num_classes: int = 0,
+                 p_dropout=0.2,
+                 aux_logits: bool = True):
+        super(Inception4, self).__init__()
+        self.aux_logits = aux_logits
+        # Modules
+        self.features = nn.Sequential(
+            BasicConv2d(3, 32, kernel_size=3, stride=2),
+            BasicConv2d(32, 32, kernel_size=3, stride=1),
+            BasicConv2d(32, 64, kernel_size=3, stride=1, padding=1),
+            Mixed_3a(),
+            Mixed_4a(),
+            Mixed_5a(),
+            Inception_A(),
+            Inception_A(),
+            Inception_A(),
+            Inception_A(),
+            Reduction_A(),  # Mixed_6a
+            Inception_B(),
+            Inception_B(),
+            Inception_B(),
+            Inception_B(),
+            Inception_B(),
+            Inception_B(),
+            Inception_B(),  # Mixed_6h 1024 x 17 x 17
+            Reduction_B(),  # Mixed_7a
+            Inception_C(),
+            Inception_C(),
+            Inception_C())
+
+        if aux_logits:
+            self.AuxLogits = InceptionAux(1024, num_classes)
+
+        self.dropout = nn.Dropout(p_dropout)
+        self.last_linear = None
+        if num_classes > 0:
+            self.last_linear = nn.Linear(1536, num_classes)
+
+        self.default_pretrained_model_path = model_urls[
+            self.__class__.__name__]
+
+    @property
+    def fc(self):
+        return self.last_linear
+
+    def init_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
+                for m in self.modules():
+                    if isinstance(m, nn.Conv2d):
+                        kaiming_init(m, mode='fan_in', nonlinearity='relu')
+                    elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
+                        constant_init(m, 1)
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+    def logits(self, features):
+        x = F.adaptive_avg_pool2d(features, output_size=(1, 1))
+        # x = F.avg_pool2d(features, kernel_size=adaptiveAvgPoolWidth)
+        x = x.view(x.size(0), -1)  # B x 1536
+        x = self.fc(x)
+        # B x num_classes
+        return x
+
+    def forward(self, input: torch.Tensor):
+        """_summary_
+
+        Args:
+            input (torch.Tensor): A RGB image tensor with shape B x C x H x W
+
+        Returns:
+            torch.Tensor: A feature tensor or a logit tensor when num_classes is 0 (default)
+        """
+
+        if self.training and self.aux_logits:
+            x = self.features[:-4](input)
+            aux = self.AuxLogits(x)
+            x = self.features[-4:](x)
+        else:
+            x = self.features(input)
+            aux = None
+
+        if self.fc is not None:
+            x = self.logits(x)
+
+        return [aux, x]

easycv/models/classification/classification.py

@@ -151,6 +151,21 @@ class Classification(BaseModel):
         x = self.backbone(img)
         return x
 
+    @torch.jit.unused
+    def forward_onnx(self, img: torch.Tensor) -> Dict[str, torch.Tensor]:
+        """
+            forward_onnx means generate prob from image only support one neck  + one head
+        """
+        x = self.forward_backbone(img)  # tuple
+
+        # if self.neck_num > 0:
+        if hasattr(self, 'neck_0'):
+            x = self.neck_0([i for i in x])
+
+        out = self.head_0(x)[0].cpu()
+        out = self.activate_fn(out)
+        return out
+
     @torch.jit.unused
     def forward_train(self, img, gt_labels) -> Dict[str, torch.Tensor]:
         """
@@ -290,6 +305,9 @@ class Classification(BaseModel):
                 return self.forward_test_label(img, gt_labels)
             else:
                 return self.forward_test(img)
+        elif mode == 'onnx':
+            return self.forward_onnx(img)
+
         elif mode == 'extract':
             rd = self.forward_feature(img)
             rv = {}

easycv/models/detection/detectors/yolox/yolox.py

@@ -52,6 +52,22 @@ class YOLOX(BaseModel):
 
         assert model_type in self.param_map, f'invalid model_type for yolox {model_type}, valid ones are {list(self.param_map.keys())}'
 
+        if num_classes is not None:
+            # adapt to previous export model (before easycv0.6.0)
+            logging.warning(
+                'Warning: You are now attend to use an old YOLOX model before easycv0.6.0 with key num_classes'
+            )
+            head = dict(
+                type='YOLOXHead',
+                model_type=model_type,
+                num_classes=num_classes,
+            )
+
+        # the change of backbone/neck/head only support model_type as 's'
+        if model_type != 's':
+            head_type = head.get('type', None)
+            assert backbone == 'CSPDarknet' and neck_type == 'yolo' and neck_mode == 'all' and head_type == 'YOLOXHead', 'We only support the architecture modification for YOLOX-S.'
+
         self.pretrained = pretrained
 
         in_channels = [256, 512, 1024]
@@ -68,19 +84,14 @@ class YOLOX(BaseModel):
             asff_channel=asff_channel,
             use_att=use_att)
 
-        if num_classes is not None:
-            # adapt to previous export model (before easycv0.6.0)
-            logging.warning(
-                'Warning: You are now attend to use an old YOLOX model before easycv0.6.0 with key num_classes'
-            )
-            head = dict(
-                type='YOLOXHead',
-                model_type=model_type,
-                num_classes=num_classes,
-            )
-
         if head is not None:
             # head is None for YOLOX-edge to define a special head
+            # set and check model type in head as the same of yolox
+            head_model_type = head.get('model_type', None)
+            if head_model_type is None:
+                head['model_type'] = model_type
+            else:
+                assert model_type == head_model_type, 'Please provide the same model_type of YOLOX in config.'
             self.head = build_head(head)
             self.num_classes = self.head.num_classes
 

easycv/models/modelzoo.py

@@ -79,6 +79,12 @@ inceptionv3 = {
     'http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/pretrained_models/easycv/inceptionv3/inception_v3.pth',
 }
 
+inceptionv4 = {
+    # Inception v4 ported from http://data.lip6.fr/cadene/pretrainedmodels/inceptionv4-8e4777a0.pth
+    'Inception4':
+    'http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/pretrained_models/easycv/inceptionv4/inception_v4.pth',
+}
+
 genet = {
     'PlainNetnormal':
     'http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/pretrained_models/easycv/genet/GENet_normal.pth',

easycv/models/ocr/postprocess/db_postprocess.py

@@ -135,10 +135,10 @@ class DBPostProcess(object):
         '''
         h, w = bitmap.shape[:2]
         box = _box.copy()
-        xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int), 0, w - 1)
-        xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int), 0, w - 1)
-        ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int), 0, h - 1)
-        ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int), 0, h - 1)
+        xmin = np.clip(np.floor(box[:, 0].min()).astype(np.int32), 0, w - 1)
+        xmax = np.clip(np.ceil(box[:, 0].max()).astype(np.int32), 0, w - 1)
+        ymin = np.clip(np.floor(box[:, 1].min()).astype(np.int32), 0, h - 1)
+        ymax = np.clip(np.ceil(box[:, 1].max()).astype(np.int32), 0, h - 1)
 
         mask = np.zeros((ymax - ymin + 1, xmax - xmin + 1), dtype=np.uint8)
         box[:, 0] = box[:, 0] - xmin

easycv/models/utils/pos_embed.py

@@ -47,7 +47,7 @@ def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
     out: (M, D)
     """
     assert embed_dim % 2 == 0
-    omega = np.arange(embed_dim // 2, dtype=np.float)
+    omega = np.arange(embed_dim // 2, dtype=np.float32)
     omega /= embed_dim / 2.
     omega = 1. / 10000**omega  # (D/2,)
 

easycv/predictors/classifier.py

@@ -1,5 +1,7 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
+import glob
 import math
+import os
 
 import numpy as np
 import torch
@@ -7,11 +9,18 @@ from PIL import Image
 
 from easycv.file import io
 from easycv.framework.errors import ValueError
+from easycv.utils.checkpoint import load_checkpoint
 from easycv.utils.misc import deprecated
 from .base import InputProcessor, OutputProcessor, Predictor, PredictorV2
 from .builder import PREDICTORS
 
 
+# onnx specific
+def onnx_to_numpy(tensor):
+    return tensor.detach().cpu().numpy(
+    ) if tensor.requires_grad else tensor.cpu().numpy()
+
+
 class ClsInputProcessor(InputProcessor):
     """Process inputs for classification models.
 
@@ -146,6 +155,20 @@ class ClassificationPredictor(PredictorV2):
         self.pil_input = pil_input
         self.label_map_path = label_map_path
 
+        if model_path.endswith('onnx'):
+            self.model_type = 'onnx'
+            pwd_model = os.path.dirname(model_path)
+            raw_model = glob.glob(
+                os.path.join(pwd_model, '*.onnx.config.json'))
+            if len(raw_model) != 0:
+                config_file = raw_model[0]
+            else:
+                assert len(
+                    raw_model
+                ) == 0, 'Please have a file with the .onnx.config.json extension in your directory'
+        else:
+            self.model_type = 'raw'
+
         if self.pil_input:
             mode = 'RGB'
         super(ClassificationPredictor, self).__init__(
@@ -186,6 +209,41 @@ class ClassificationPredictor(PredictorV2):
 
         return ClsOutputProcessor(topk=self.topk, label_map=self.label_map)
 
+    def prepare_model(self):
+        """Build model from config file by default.
+        If the model is not loaded from a configuration file, e.g. torch jit model, you need to reimplement it.
+        """
+        if self.model_type == 'raw':
+            model = self._build_model()
+            model.to(self.device)
+            model.eval()
+            load_checkpoint(model, self.model_path, map_location='cpu')
+            return model
+        else:
+            import onnxruntime
+            if onnxruntime.get_device() == 'GPU':
+                onnx_model = onnxruntime.InferenceSession(
+                    self.model_path, providers=['CUDAExecutionProvider'])
+            else:
+                onnx_model = onnxruntime.InferenceSession(self.model_path)
+
+            return onnx_model
+
+    def model_forward(self, inputs):
+        """Model forward.
+        If you need refactor model forward, you need to reimplement it.
+        """
+        with torch.no_grad():
+            if self.model_type == 'raw':
+                outputs = self.model(**inputs, mode='test')
+            else:
+                outputs = self.model.run(None, {
+                    self.model.get_inputs()[0].name:
+                    onnx_to_numpy(inputs['img'])
+                })[0]
+                outputs = dict(prob=torch.from_numpy(outputs))
+        return outputs
+
 
 try:
     from easy_vision.python.inference.predictor import PredictorInterface

easycv/predictors/detector.py

@@ -23,6 +23,12 @@ except Exception:
     from .interface import PredictorInterface
 
 
+# 将张量转化为ndarray格式
+def onnx_to_numpy(tensor):
+    return tensor.detach().cpu().numpy(
+    ) if tensor.requires_grad else tensor.cpu().numpy()
+
+
 class DetInputProcessor(InputProcessor):
 
     def build_processor(self):
@@ -349,9 +355,11 @@ class YoloXPredictor(DetectionPredictor):
                 self.model_type = 'jit'
             elif model_path.endswith('blade'):
                 self.model_type = 'blade'
+            elif model_path.endswith('onnx'):
+                self.model_type = 'onnx'
             else:
                 self.model_type = 'raw'
-        assert self.model_type in ['raw', 'jit', 'blade']
+        assert self.model_type in ['raw', 'jit', 'blade', 'onnx']
 
         if self.model_type == 'blade' or self.use_trt_efficientnms:
             import torch_blade
@@ -381,8 +389,16 @@ class YoloXPredictor(DetectionPredictor):
 
     def _build_model(self):
         if self.model_type != 'raw':
-            with io.open(self.model_path, 'rb') as infile:
-                model = torch.jit.load(infile, self.device)
+            if self.model_type != 'onnx':
+                with io.open(self.model_path, 'rb') as infile:
+                    model = torch.jit.load(infile, self.device)
+            else:
+                import onnxruntime
+                if onnxruntime.get_device() == 'GPU':
+                    model = onnxruntime.InferenceSession(
+                        self.model_path, providers=['CUDAExecutionProvider'])
+                else:
+                    model = onnxruntime.InferenceSession(self.model_path)
         else:
             from easycv.utils.misc import reparameterize_models
             model = super()._build_model()
@@ -394,8 +410,9 @@ class YoloXPredictor(DetectionPredictor):
         If the model is not loaded from a configuration file, e.g. torch jit model, you need to reimplement it.
         """
         model = self._build_model()
-        model.to(self.device)
-        model.eval()
+        if self.model_type != 'onnx':
+            model.to(self.device)
+            model.eval()
         if self.model_type == 'raw':
             load_checkpoint(model, self.model_path, map_location='cpu')
         return model
@@ -406,7 +423,15 @@ class YoloXPredictor(DetectionPredictor):
         """
         if self.model_type != 'raw':
             with torch.no_grad():
-                outputs = self.model(inputs['img'])
+                if self.model_type != 'onnx':
+                    outputs = self.model(inputs['img'])
+                else:
+                    outputs = self.model.run(
+                        None, {
+                            self.model.get_inputs()[0].name:
+                            onnx_to_numpy(inputs['img'])
+                        })[0]
+                    outputs = torch.from_numpy(outputs)
                 outputs = {'results': outputs}  # convert to dict format
         else:
             outputs = super().model_forward(inputs)

easycv/predictors/pose_predictor.py

@@ -19,6 +19,8 @@ from easycv.utils.config_tools import mmcv_config_fromfile
 from easycv.utils.misc import deprecated
 from .base import InputProcessor, OutputProcessor, PredictorV2
 
+np.set_printoptions(suppress=True)
+
 
 def _box2cs(image_size, box):
     """This encodes bbox(x,y,w,h) into (center, scale)
@@ -222,11 +224,12 @@ class PoseTopDownInputProcessor(InputProcessor):
             bboxes = bboxes[valid_idx]
             person_results = [person_results[i] for i in valid_idx]
 
-        output_person_info = []
+        results = []
         for person_result in person_results:
-            box = person_result['bbox']  # x,y,x,y
-            box = [box[0], box[1], box[2] - box[0], box[3] - box[1]]  # x,y,w,h
-            center, scale = _box2cs(self.cfg.data_cfg['image_size'], box)
+            box = person_result['bbox']  # x,y,x,y,s
+            boxc = [box[0], box[1], box[2] - box[0],
+                    box[3] - box[1]]  # x,y,w,h
+            center, scale = _box2cs(self.cfg.data_cfg['image_size'], boxc)
             data = {
                 'image_id':
                 0,
@@ -264,11 +267,10 @@ class PoseTopDownInputProcessor(InputProcessor):
                 output['img_fields'],
             }
             box_id += 1
-            output_person_info.append(data)
+            data_processor = self.processor(data)
+            data_processor['bbox'] = box
+            results.append(data_processor)
 
-        results = []
-        for output in output_person_info:
-            results.append(self.processor(output))
         return results
 
     def __call__(self, inputs):
@@ -296,12 +298,7 @@ class PoseTopDownOutputProcessor(OutputProcessor):
     def __call__(self, inputs):
         output = {}
         output['keypoints'] = inputs['preds']
-        output['bbox'] = inputs['boxes']  # c1, c2, s1, s2, area, core
-
-        for i, bbox in enumerate(output['bbox']):
-            center, scale = bbox[:2], bbox[2:4]
-            output['bbox'][i][:4] = bbox_cs2xyxy(center, scale)
-        output['bbox'] = output['bbox'][:, [0, 1, 2, 3, 5]]
+        output['bbox'] = np.array(inputs['boxes'])  # x1, y1, x2, y2 score
 
         return output
 
@@ -403,6 +400,7 @@ class PoseTopDownPredictor(PredictorV2):
         return model
 
     def model_forward(self, inputs, return_heatmap=False):
+        boxes = inputs['bbox'].cpu().numpy()
         if self.model_type == 'raw':
             with torch.no_grad():
                 result = self.model(
@@ -423,6 +421,7 @@ class PoseTopDownPredictor(PredictorV2):
             result = decode_heatmap(output_heatmap, img_metas,
                                     self.cfg.model.test_cfg)
 
+        result['boxes'] = np.array(boxes)
         return result
 
     def get_input_processor(self):

easycv/predictors/segmentation.py

@@ -156,7 +156,7 @@ class Mask2formerOutputProcessor(OutputProcessor):
             ids = ids[legal_indices]
             labels = np.array([id % 1000 for id in ids], dtype=np.int64)
             segms = (pan_results[None] == ids[:, None, None])
-            masks = [it.astype(np.int) for it in segms]
+            masks = [it.astype(np.int32) for it in segms]
             labels_txt = np.array(self.classes)[labels].tolist()
 
             output['masks'] = masks

easycv/thirdparty/mot/bytetrack/byte_tracker.py

@@ -38,7 +38,7 @@ class STrack(BaseTrack):
     def __init__(self, tlwh, score):
 
         # wait activate
-        self._tlwh = np.asarray(tlwh, dtype=np.float)
+        self._tlwh = np.asarray(tlwh, dtype=np.float32)
         self.kalman_filter = None
         self.mean, self.covariance = None, None
         self.is_activated = False

easycv/thirdparty/mot/bytetrack/matching.py

@@ -86,15 +86,15 @@ def ious(atlbrs, btlbrs):
 
     :rtype ious np.ndarray
     """
-    ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float)
+    ious = np.zeros((len(atlbrs), len(btlbrs)), dtype=np.float32)
     if ious.size == 0:
         return ious
 
     from cython_bbox import bbox_overlaps as bbox_ious
     
     ious = bbox_ious(
-        np.ascontiguousarray(atlbrs, dtype=np.float),
-        np.ascontiguousarray(btlbrs, dtype=np.float))
+        np.ascontiguousarray(atlbrs, dtype=np.float32),
+        np.ascontiguousarray(btlbrs, dtype=np.float32))
 
     return ious
 
@@ -151,15 +151,15 @@ def embedding_distance(tracks, detections, metric='cosine'):
     :return: cost_matrix np.ndarray
     """
 
-    cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float)
+    cost_matrix = np.zeros((len(tracks), len(detections)), dtype=np.float32)
     if cost_matrix.size == 0:
         return cost_matrix
     det_features = np.asarray([track.curr_feat for track in detections],
-                              dtype=np.float)
+                              dtype=np.float32)
     #for i, track in enumerate(tracks):
     #cost_matrix[i, :] = np.maximum(0.0, cdist(track.smooth_feat.reshape(1,-1), det_features, metric))
     track_features = np.asarray([track.smooth_feat for track in tracks],
-                                dtype=np.float)
+                                dtype=np.float32)
     cost_matrix = np.maximum(0.0, cdist(track_features, det_features,
                                         metric))  # Nomalized features
     return cost_matrix

easycv/toolkit/modelscope/__init__.py

@@ -1,5 +1 @@
-import os
-
 from . import models, msdatasets, pipelines, trainers
-
-os.environ['SETUPTOOLS_USE_DISTUTILS'] = 'stdlib'

easycv/toolkit/modelscope/pipelines/detection_pipeline.py

@@ -48,9 +48,15 @@ class EasyCVDetectionPipeline(EasyCVPipeline):
         labels = []
         boxes = []
         for output in outputs:
-            for score, label, box in zip(output['detection_scores'],
-                                         output['detection_classes'],
-                                         output['detection_boxes']):
+            scores_list = output['detection_scores'] if output[
+                'detection_scores'] is not None else []
+            classes_list = output['detection_classes'] if output[
+                'detection_classes'] is not None else []
+            boxes_list = output['detection_boxes'] if output[
+                'detection_boxes'] is not None else []
+
+            for score, label, box in zip(scores_list, classes_list,
+                                         boxes_list):
                 scores.append(score)
                 labels.append(self.cfg.CLASSES[label])
                 boxes.append([b for b in box])

easycv/toolkit/modelscope/pipelines/segmentation_pipeline.py

@@ -36,7 +36,7 @@ class EasyCVSegmentationPipeline(EasyCVPipeline):
         legal_indices = ids != len(self.predict_op.CLASSES)  # for VOID label
         ids = ids[legal_indices]
         segms = (semantic_result[None] == ids[:, None, None])
-        masks = [it.astype(np.int) for it in segms]
+        masks = [it.astype(np.int32) for it in segms]
         labels_txt = np.array(self.predict_op.CLASSES)[ids].tolist()
 
         results = {

easycv/utils/config_tools.py

@@ -515,6 +515,10 @@ CONFIG_TEMPLATE_ZOO = {
     'configs/classification/imagenet/swint/imagenet_swin_tiny_patch4_window7_224_jpg.py',
     'CLASSIFICATION_M0BILENET':
     'configs/classification/imagenet/mobilenet/mobilenetv2.py',
+    'CLASSIFICATION_INCEPTIONV4':
+    'configs/classification/imagenet/inception/inceptionv4_b32x8_100e.py',
+    'CLASSIFICATION_INCEPTIONV3':
+    'configs/classification/imagenet/inception/inceptionv3_b32x8_100e.py',
 
     # metric learning
     'METRICLEARNING':

easycv/version.py

@@ -2,5 +2,5 @@
 # GENERATED VERSION FILE
 # TIME: Thu Nov  5 14:17:50 2020
 
-__version__ = '0.11.0'
-short_version = '0.11.0'
+__version__ = '0.11.7'
+short_version = '0.11.7'

requirements/runtime.txt

@@ -13,6 +13,7 @@ lmdb
 numba
 numpy
 nuscenes-devkit
+onnxruntime
 opencv-python
 oss2
 packaging
@@ -23,8 +24,9 @@ pycocotools
 pytorch_metric_learning>=0.9.89
 rapidfuzz
 scikit-image
+scikit-learn
 seaborn
-sklearn
+shapely==1.8.4
 tensorboard
 thop
 timm==0.5.4

tests/run.py

@@ -8,15 +8,26 @@ import unittest
 from fnmatch import fnmatch
 
 
-def gather_test_cases(test_dir, pattern, list_tests):
+def get_skip_file(skip_dir, pattern=None):
     case_list = []
+    if skip_dir:
+        for path in skip_dir:
+            for dirpath, dirnames, filenames in os.walk(path):
+                for file in filenames:
+                    if fnmatch(file, pattern):
+                        case_list.append(file)
+    return case_list
+
+
+def gather_test_cases(test_dir, pattern, list_tests, skip_dir):
+    case_list = []
+    skip_list = get_skip_file(skip_dir, pattern)
     for dirpath, dirnames, filenames in os.walk(test_dir):
         for file in filenames:
-            if fnmatch(file, pattern):
+            if fnmatch(file, pattern) and file not in skip_list:
                 case_list.append(file)
 
     test_suite = unittest.TestSuite()
-
     for case in case_list:
         test_case = unittest.defaultTestLoader.discover(
             start_dir=test_dir, pattern=case)
@@ -34,7 +45,8 @@ def gather_test_cases(test_dir, pattern, list_tests):
 def main(args):
     runner = unittest.TextTestRunner()
     test_suite = gather_test_cases(
-        os.path.abspath(args.test_dir), args.pattern, args.list_tests)
+        os.path.abspath(args.test_dir), args.pattern, args.list_tests,
+        args.skip_dir)
     if not args.list_tests:
         result = runner.run(test_suite)
         if len(result.failures) > 0 or len(result.errors) > 0:
@@ -49,5 +61,7 @@ if __name__ == '__main__':
         '--pattern', default='test_*.py', help='test file pattern')
     parser.add_argument(
         '--test_dir', default='tests', help='directory to be tested')
+    parser.add_argument(
+        '--skip_dir', nargs='+', required=False, help='it`s not run testcase')
     args = parser.parse_args()
     main(args)

tests/test_apis/test_export.py

@@ -116,6 +116,7 @@ class ModelExportTest(unittest.TestCase):
         cfg = mmcv_config_fromfile(config_file)
         cfg_options = {
             'model.backbone.norm_cfg.type': 'SyncBN',
+            'export.export_type': 'raw'
         }
         if cfg_options is not None:
             cfg.merge_from_dict(cfg_options)
@@ -210,6 +211,27 @@ class ModelExportTest(unittest.TestCase):
 
             self.assertTrue(os.path.exists(filename + '.jit'))
 
+    def test_export_resnet_onnx(self):
+
+        ckpt_path = PRETRAINED_MODEL_RESNET50
+
+        easycv_dir = os.path.dirname(easycv.__file__)
+
+        if os.path.exists(os.path.join(easycv_dir, 'configs')):
+            config_dir = os.path.join(easycv_dir, 'configs')
+        else:
+            config_dir = os.path.join(os.path.dirname(easycv_dir), 'configs')
+        config_file = os.path.join(
+            config_dir,
+            'classification/imagenet/resnet/imagenet_resnet50_jpg.py')
+
+        with tempfile.TemporaryDirectory() as tmpdir:
+            cfg = mmcv_config_fromfile(config_file)
+            cfg.export.export_type = 'onnx'
+            filename = os.path.join(tmpdir, 'imagenet_resnet50')
+            export(cfg, ckpt_path, filename)
+            self.assertTrue(os.path.exists(filename + '.onnx'))
+
 
 if __name__ == '__main__':
     unittest.main()

tests/test_core/evaluation/test_coco_tools.py

@@ -89,8 +89,8 @@ class CocoToolsTest(unittest.TestCase):
     def testExportGroundtruthToCOCO(self):
         image_ids = ['first', 'second']
         groundtruth_boxes = [
-            np.array([[100, 100, 200, 200]], np.float),
-            np.array([[50, 50, 100, 100]], np.float)
+            np.array([[100, 100, 200, 200]], np.float32),
+            np.array([[50, 50, 100, 100]], np.float32)
         ]
         groundtruth_classes = [
             np.array([1], np.int32),
@@ -126,12 +126,12 @@ class CocoToolsTest(unittest.TestCase):
     def testExportDetectionsToCOCO(self):
         image_ids = ['first', 'second']
         detections_boxes = [
-            np.array([[100, 100, 200, 200]], np.float),
-            np.array([[50, 50, 100, 100]], np.float)
+            np.array([[100, 100, 200, 200]], np.float32),
+            np.array([[50, 50, 100, 100]], np.float32)
         ]
         detections_scores = [
-            np.array([.8], np.float),
-            np.array([.7], np.float)
+            np.array([.8], np.float32),
+            np.array([.7], np.float32)
         ]
         detections_classes = [np.array([1], np.int32), np.array([1], np.int32)]
         categories = [{
@@ -152,7 +152,17 @@ class CocoToolsTest(unittest.TestCase):
             detections_classes,
             categories,
             output_path=output_path)
-        self.assertListEqual(result, self._detections_list)
+
+        self.assertEqual(len(result), len(detections_boxes))
+        self.assertEqual(len(detections_boxes), len(detections_boxes))
+
+        score_list = []
+        for i in range(len(detections_boxes)):
+            score = self._detections_list[i].pop('score')
+            score_list.append(score)
+            self.assertAlmostEqual(result[i].pop('score'), score)
+            self.assertDictEqual(result[i], self._detections_list[i])
+
         with io.open(output_path, 'r') as f:
             written_result = f.read()
             # The json output should have floats written to 4 digits of precision.
@@ -160,7 +170,10 @@ class CocoToolsTest(unittest.TestCase):
                                  re.MULTILINE)
             self.assertTrue(matcher.findall(written_result))
             written_result = json.loads(written_result)
-            self.assertAlmostEqual(result, written_result)
+            for i in range(len(result)):
+                self.assertAlmostEqual(written_result[i].pop('score'),
+                                       score_list[i])
+                self.assertDictEqual(result[i], written_result[i])
 
     def testExportSegmentsToCOCO(self):
         image_ids = ['first', 'second']
@@ -176,7 +189,10 @@ class CocoToolsTest(unittest.TestCase):
         for i, detection_mask in enumerate(detection_masks):
             detection_masks[i] = detection_mask[:, :, :, None]
 
-        detection_scores = [np.array([.8], np.float), np.array([.7], np.float)]
+        detection_scores = [
+            np.array([.8], np.float32),
+            np.array([.7], np.float32)
+        ]
         detection_classes = [np.array([1], np.int32), np.array([1], np.int32)]
 
         categories = [{
@@ -202,7 +218,12 @@ class CocoToolsTest(unittest.TestCase):
             written_result = json.loads(written_result)
             mask_load = mask.decode([written_result[0]['segmentation']])
             self.assertTrue(np.allclose(mask_load, detection_masks[0]))
-            self.assertAlmostEqual(result, written_result)
+            self.assertEqual(len(result), len(detection_masks))
+            self.assertEqual(len(written_result), len(detection_masks))
+            for i in range(len(detection_masks)):
+                self.assertAlmostEqual(result[i].pop('score'),
+                                       written_result[i].pop('score'))
+                self.assertDictEqual(result[i], written_result[i])
 
     def testExportKeypointsToCOCO(self):
         image_ids = ['first', 'second']
@@ -216,8 +237,8 @@ class CocoToolsTest(unittest.TestCase):
         ]
 
         detection_scores = [
-            np.array([.8, 0.2], np.float),
-            np.array([.7, 0.3], np.float)
+            np.array([.8, 0.2], np.float32),
+            np.array([.7, 0.3], np.float32)
         ]
         detection_classes = [
             np.array([1, 1], np.int32),
@@ -248,7 +269,12 @@ class CocoToolsTest(unittest.TestCase):
         with io.open(output_path, 'r') as f:
             written_result = f.read()
             written_result = json.loads(written_result)
-            self.assertAlmostEqual(result, written_result)
+            self.assertEqual(len(result), 4)
+            self.assertEqual(len(written_result), 4)
+            for i in range(4):
+                self.assertAlmostEqual(result[i].pop('score'),
+                                       written_result[i].pop('score'))
+                self.assertDictEqual(result[i], written_result[i])
 
     def testSingleImageDetectionBoxesExport(self):
         boxes = np.array([[0, 0, 1, 1], [0, 0, .5, .5], [.5, .5, 1, 1]],

tests/test_models/backbones/test_inceptionv4.py

@@ -0,0 +1,63 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import copy
+import random
+import unittest
+
+import numpy as np
+import torch
+
+from easycv.models import modelzoo
+from easycv.models.backbones import Inception4
+
+
+class InceptionV3Test(unittest.TestCase):
+
+    def setUp(self):
+        print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
+
+    def test_inceptionv3_withfc(self):
+        with torch.no_grad():
+            # input data
+            batch_size = random.randint(10, 30)
+            a = torch.rand(batch_size, 3, 299, 299).to('cuda')
+
+            num_classes = random.randint(10, 1000)
+            net = Inception4(
+                aux_logits=True, num_classes=num_classes).to('cuda')
+            net.init_weights()
+            net.train()
+
+            self.assertTrue(len(list(net(a)[-1].shape)) == 2)
+            self.assertTrue(len(list(net(a)[0].shape)) == 2)
+            self.assertTrue(net(a)[-1].size(1) == num_classes)
+            self.assertTrue(net(a)[-1].size(0) == batch_size)
+            self.assertTrue(net(a)[0].size(1) == num_classes)
+            self.assertTrue(net(a)[0].size(0) == batch_size)
+
+    def test_inceptionv3_withoutfc(self):
+        with torch.no_grad():
+            # input data
+            batch_size = random.randint(10, 30)
+            a = torch.rand(batch_size, 3, 299, 299).to('cuda')
+
+            net = Inception4(aux_logits=True, num_classes=0).to('cuda')
+            net.init_weights()
+            net.eval()
+
+            self.assertTrue(net(a)[-1].size(1) == 1536)
+            self.assertTrue(net(a)[-1].size(0) == batch_size)
+
+    def test_inceptionv3_load_modelzoo(self):
+        with torch.no_grad():
+            net = Inception4(aux_logits=True, num_classes=1000).to('cuda')
+            original_weight = net.features[0].conv.weight
+            original_weight = copy.deepcopy(original_weight.cpu().data.numpy())
+
+            net.init_weights()
+            load_weight = net.features[0].conv.weight.cpu().data.numpy()
+
+            self.assertFalse(np.allclose(original_weight, load_weight))
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/test_models/detection/yolox/test_yolox.py

@@ -40,9 +40,9 @@ class YOLOXTest(unittest.TestCase):
             }
             output = model(imgs, mode='train', **kwargs)
             self.assertEqual(output['img_h'].cpu().numpy(),
-                             np.array(640, dtype=np.float))
+                             np.array(640, dtype=np.float32))
             self.assertEqual(output['img_w'].cpu().numpy(),
-                             np.array(640, dtype=np.float))
+                             np.array(640, dtype=np.float32))
             self.assertEqual(output['total_loss'].shape, torch.Size([]))
             self.assertEqual(output['iou_l'].shape, torch.Size([]))
             self.assertEqual(output['conf_l'].shape, torch.Size([]))

tests/test_models/detection/yolox_edge/test_yolox_edge.py

@@ -45,9 +45,9 @@ class YOLOXEDGETest(unittest.TestCase):
             }
             output = model(imgs, mode='train', **kwargs)
             self.assertEqual(output['img_h'].cpu().numpy(),
-                             np.array(640, dtype=np.float))
+                             np.array(640, dtype=np.float32))
             self.assertEqual(output['img_w'].cpu().numpy(),
-                             np.array(640, dtype=np.float))
+                             np.array(640, dtype=np.float32))
             self.assertEqual(output['total_loss'].shape, torch.Size([]))
             self.assertEqual(output['iou_l'].shape, torch.Size([]))
             self.assertEqual(output['conf_l'].shape, torch.Size([]))

tests/test_predictors/test_classifier.py

@@ -11,7 +11,8 @@ import torch
 from easycv.predictors.classifier import ClassificationPredictor
 from easycv.utils.test_util import clean_up, get_tmp_dir
 from tests.ut_config import (PRETRAINED_MODEL_RESNET50_WITHOUTHEAD,
-                             IMAGENET_LABEL_TXT, TEST_IMAGES_DIR)
+                             IMAGENET_LABEL_TXT, TEST_IMAGES_DIR,
+                             PRETRAINED_MODEL_RESNET50_ONNX_WITHOUTHEAD)
 
 
 class ClassificationPredictorTest(unittest.TestCase):
@@ -33,6 +34,17 @@ class ClassificationPredictorTest(unittest.TestCase):
         self.assertListEqual(results['class_name'], ['"Persian cat",'])
         self.assertEqual(len(results['class_probs']), 1000)
 
+    def test_onnx_single(self):
+        checkpoint = PRETRAINED_MODEL_RESNET50_ONNX_WITHOUTHEAD
+        predict_op = ClassificationPredictor(model_path=checkpoint)
+
+        img_path = os.path.join(TEST_IMAGES_DIR, 'catb.jpg')
+
+        results = predict_op([img_path])[0]
+        self.assertListEqual(results['class'], [578])
+        self.assertListEqual(results['class_name'], ['gown'])
+        self.assertEqual(len(results['class_probs']), 1000)
+
     def test_batch(self):
         checkpoint = PRETRAINED_MODEL_RESNET50_WITHOUTHEAD
         config_file = 'configs/classification/imagenet/resnet/imagenet_resnet50_jpg.py'

tests/test_predictors/test_pose_predictor.py

@@ -54,10 +54,10 @@ class PoseTopDownPredictorTest(unittest.TestCase):
 
         assert_array_almost_equal(
             result0['bbox'],
-            np.array([[352.3085, 59.00325, 691.4247, 511.15814, 1.],
-                      [10.511196, 177.74883, 101.824326, 299.49966, 1.],
-                      [224.82036, 114.439865, 312.51306, 231.36348, 1.],
-                      [200.71407, 114.716736, 337.17535, 296.6651, 1.]],
+            np.array([[438.9, 59., 604.8, 511.2, 0.9],
+                      [10.5, 179.6, 101.8, 297.7, 0.9],
+                      [229.6, 114.4, 307.8, 231.4, 0.6],
+                      [229.4, 114.7, 308.5, 296.7, 0.6]],
                      dtype=np.float32),
             decimal=1)
         vis_result = predictor.show_result(img1, result0)
@@ -92,10 +92,10 @@ class PoseTopDownPredictorTest(unittest.TestCase):
 
         assert_array_almost_equal(
             result1['bbox'][:4],
-            np.array([[436.23096, 214.72766, 584.26013, 412.09985, 1.],
-                      [43.990044, 91.04126, 164.28406, 251.43329, 1.],
-                      [127.44148, 100.38604, 254.219, 269.42273, 1.],
-                      [190.08075, 117.31801, 311.22394, 278.8423, 1.]],
+            np.array([[470.6, 214.7, 549.9, 412.1, 0.9],
+                      [71.6, 91., 136.7, 251.4, 0.9],
+                      [159.7, 100.4, 221.9, 269.4, 0.9],
+                      [219.4, 117.3, 281.9, 278.8, 0.9]],
                      dtype=np.float32),
             decimal=1)
         vis_result = predictor.show_result(img2, result1)

tests/test_toolkit/modelscope/pipelines/test_panoptic_segmentation_pipeline.py

@@ -8,13 +8,11 @@ from modelscope.outputs import OutputKeys
 from modelscope.pipelines import pipeline
 from modelscope.utils.constant import Tasks
 from modelscope.utils.cv.image_utils import panoptic_seg_masks_to_image
-from modelscope.utils.demo_utils import DemoCompatibilityCheck
 from modelscope.utils.test_utils import test_level
 from tests.ut_config import BASE_LOCAL_PATH
 
 
-class EasyCVPanopticSegmentationPipelineTest(unittest.TestCase,
-                                             DemoCompatibilityCheck):
+class EasyCVPanopticSegmentationPipelineTest(unittest.TestCase):
     img_path = os.path.join(
         BASE_LOCAL_PATH, 'data/test_images/image_semantic_segmentation.jpg')
 
@@ -32,10 +30,6 @@ class EasyCVPanopticSegmentationPipelineTest(unittest.TestCase,
             cv2.imwrite(tmp_save_path, draw_img)
         print('print ' + self.model_id + ' success')
 
-    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
-    def test_demo_compatibility(self):
-        self.compatibility_check()
-
 
 if __name__ == '__main__':
     unittest.main()

tests/test_toolkit/modelscope/pipelines/test_segmentation_pipeline.py

@@ -9,14 +9,12 @@ from modelscope.outputs import OutputKeys
 from modelscope.pipelines import pipeline
 from modelscope.utils.constant import Tasks
 from modelscope.utils.cv.image_utils import semantic_seg_masks_to_image
-from modelscope.utils.demo_utils import DemoCompatibilityCheck
 from modelscope.utils.test_utils import test_level
 from PIL import Image
 from tests.ut_config import BASE_LOCAL_PATH
 
 
-class EasyCVSegmentationPipelineTest(unittest.TestCase,
-                                     DemoCompatibilityCheck):
+class EasyCVSegmentationPipelineTest(unittest.TestCase):
     img_path = os.path.join(BASE_LOCAL_PATH,
                             'data/test_images/image_segmentation.jpg')
 
@@ -82,10 +80,6 @@ class EasyCVSegmentationPipelineTest(unittest.TestCase,
         model_id = 'damo/cv_segformer-b5_image_semantic-segmentation_coco-stuff164k'
         self._internal_test_(model_id)
 
-    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
-    def test_demo_compatibility(self):
-        self.compatibility_check()
-
 
 if __name__ == '__main__':
     unittest.main()

tests/test_toolkit/modelscope/trainers/test_easycv_trainer_detection_dino.py

@@ -40,6 +40,7 @@ class EasyCVTrainerTestDetectionDino(unittest.TestCase):
             train_dataset=train_dataset,
             eval_dataset=eval_dataset,
             work_dir=tmp_dir,
+            use_fp16=True,
             cfg_options=cfg_options)
 
         trainer = build_trainer(trainer_name, kwargs)

tests/test_tools/test_classification_train.py

@@ -98,7 +98,9 @@ class ClassificationTrainTest(unittest.TestCase):
               (tmp_cfg_file, work_dir, args_str)
 
         logging.info('run command: %s' % cmd)
-        run_in_subprocess(cmd)
+        # run_in_subprocess(cmd)  # 管道缓冲区被写满，后面的写入请求都hang住了
+        import subprocess
+        subprocess.call(cmd, shell=True)
 
         output_files = io.listdir(work_dir)
         self.assertIn('epoch_1.pth', output_files)

tests/test_tools/test_export.py

@@ -0,0 +1,138 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import logging
+import os
+import sys
+import unittest
+
+import numpy as np
+import onnxruntime
+import torch
+
+from easycv.models import build_model
+from easycv.utils.checkpoint import load_checkpoint
+from easycv.utils.config_tools import mmcv_config_fromfile, rebuild_config
+from easycv.utils.test_util import run_in_subprocess
+
+sys.path.append(os.path.dirname(os.path.realpath(__file__)))
+logging.basicConfig(level=logging.INFO)
+
+WORK_DIRECTORY = 'work_dir3'
+
+BASIC_EXPORT_CONFIGS = {
+    'config_file': None,
+    'checkpoint': 'dummy',
+    'output_filename': f'{WORK_DIRECTORY}/test_out.pth',
+    'user_config_params': ['--export.export_type', 'onnx']
+}
+
+
+def build_cmd(export_configs, MODEL_TYPE) -> str:
+    base_cmd = 'python tools/export.py'
+    base_cmd += f" {export_configs['config_file']}"
+    base_cmd += f" {export_configs['checkpoint']}"
+    base_cmd += f" {export_configs['output_filename']}"
+    base_cmd += f' --model_type {MODEL_TYPE}'
+    user_params = ' '.join(export_configs['user_config_params'])
+    base_cmd += f' --user_config_params {user_params}'
+    return base_cmd
+
+
+class ExportTest(unittest.TestCase):
+    """In this unittest, we test the onnx export functionality of
+    some classification/detection models.
+    """
+
+    def setUp(self):
+        print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
+        os.makedirs(WORK_DIRECTORY, exist_ok=True)
+
+    def tearDown(self):
+        super().tearDown()
+
+    def run_test(self,
+                 CONFIG_FILE,
+                 MODEL_TYPE,
+                 img_size: int = 224,
+                 **override_configs):
+        configs = BASIC_EXPORT_CONFIGS.copy()
+        configs['config_file'] = CONFIG_FILE
+
+        configs.update(override_configs)
+
+        cmd = build_cmd(configs, MODEL_TYPE)
+        logging.info(f'Export with commands: {cmd}')
+        run_in_subprocess(cmd)
+
+        cfg = mmcv_config_fromfile(configs['config_file'])
+        cfg = rebuild_config(cfg, configs['user_config_params'])
+
+        if hasattr(cfg.model, 'pretrained'):
+            cfg.model.pretrained = False
+
+        torch_model = build_model(cfg.model).eval()
+        if 'checkpoint' in override_configs:
+            load_checkpoint(
+                torch_model,
+                override_configs['checkpoint'],
+                strict=False,
+                logger=logging.getLogger())
+        session = onnxruntime.InferenceSession(configs['output_filename'] +
+                                               '.onnx')
+        input_tensor = torch.randn((1, 3, img_size, img_size))
+
+        torch_output = torch_model(input_tensor, mode='test')['prob']
+
+        onnx_output = session.run(
+            [session.get_outputs()[0].name],
+            {session.get_inputs()[0].name: np.array(input_tensor)})
+        if isinstance(onnx_output, list):
+            onnx_output = onnx_output[0]
+
+        onnx_output = torch.tensor(onnx_output)
+
+        is_same_shape = torch_output.shape == onnx_output.shape
+
+        self.assertTrue(
+            is_same_shape,
+            f'The shapes of the two outputs are mismatch, got {torch_output.shape} and {onnx_output.shape}'
+        )
+        is_allclose = torch.allclose(torch_output, onnx_output)
+
+        torch_out_minmax = f'{float(torch_output.min())}~{float(torch_output.max())}'
+        onnx_out_minmax = f'{float(onnx_output.min())}~{float(onnx_output.max())}'
+
+        info_msg = f'got avg: {float(torch_output.mean())} and {float(onnx_output.mean())},'
+        info_msg += f' and range: {torch_out_minmax} and {onnx_out_minmax}'
+        self.assertTrue(
+            is_allclose,
+            f'The values between the two outputs are mismatch, {info_msg}')
+
+    def test_inceptionv3(self):
+        CONFIG_FILE = 'configs/classification/imagenet/inception/inceptionv3_b32x8_100e.py'
+        self.run_test(CONFIG_FILE, 'CLASSIFICATION_INCEPTIONV3', 299)
+
+    def test_inceptionv4(self):
+        CONFIG_FILE = 'configs/classification/imagenet/inception/inceptionv4_b32x8_100e.py'
+        self.run_test(CONFIG_FILE, 'CLASSIFICATION_INCEPTIONV4', 299)
+
+    def test_resnext50(self):
+        CONFIG_FILE = 'configs/classification/imagenet/resnext/imagenet_resnext50-32x4d_jpg.py'
+        self.run_test(
+            CONFIG_FILE,
+            'CLASSIFICATION_RESNEXT',
+            checkpoint=
+            'https://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/classification/resnext/resnext50-32x4d/epoch_100.pth'
+        )
+
+    def test_mobilenetv2(self):
+        CONFIG_FILE = 'configs/classification/imagenet/mobilenet/mobilenetv2.py'
+        self.run_test(
+            CONFIG_FILE,
+            'CLASSIFICATION_M0BILENET',
+            checkpoint=
+            'http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/pretrained_models/easycv/mobilenetv2/mobilenet_v2.pth'
+        )
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/test_tools/test_predict.py

@@ -13,7 +13,7 @@ from mmcv import Config
 from tests.ut_config import (PRETRAINED_MODEL_SEGFORMER,
                              PRETRAINED_MODEL_YOLOXS_EXPORT, TEST_IMAGES_DIR)
 
-from easycv.file import io
+from easycv.file import get_oss_config, io
 from easycv.utils.test_util import run_in_subprocess
 
 sys.path.append(os.path.dirname(os.path.realpath(__file__)))
@@ -75,6 +75,32 @@ class PredictTest(unittest.TestCase):
         model_path = PRETRAINED_MODEL_YOLOXS_EXPORT
         self._base_predict(model_type, model_path, dist=True)
 
+    def test_predict_oss_path(self):
+        model_type = 'YoloXPredictor'
+        model_path = PRETRAINED_MODEL_YOLOXS_EXPORT
+
+        os.environ['OSS_CONFIG_FILE'] = '~/.ossutilconfig.unittest'
+        oss_config = get_oss_config()
+        ak_id = oss_config['ak_id']
+        ak_secret = oss_config['ak_secret']
+        hosts = oss_config['hosts']
+        hosts = ','.join(_ for _ in hosts)
+        buckets = oss_config['buckets']
+        buckets = ','.join(_ for _ in buckets)
+
+        input_file = 'oss://pai-vision-data-hz/unittest/local_backup/easycv_nfs/data/test_images/http_image_list.txt'
+        output_file = tempfile.NamedTemporaryFile('w').name
+        cmd = f'PYTHONPATH=. python tools/predict.py \
+                    --input_file {input_file} \
+                    --output_file {output_file} \
+                    --model_type {model_type} \
+                    --model_path {model_path} \
+                    --oss_io_config ak_id={ak_id} ak_secret={ak_secret} hosts={hosts} buckets={buckets}'
+
+        logging.info('run command: %s' % cmd)
+        run_in_subprocess(cmd)
+        io.remove(output_file)
+
 
 if __name__ == '__main__':
     unittest.main()

tests/ut_config.py

@@ -179,6 +179,9 @@ PRETRAINED_MODEL_RESNET50 = os.path.join(
 PRETRAINED_MODEL_RESNET50_WITHOUTHEAD = os.path.join(
     BASE_LOCAL_PATH,
     'pretrained_models/classification/resnet/resnet50_withhead.pth')
+PRETRAINED_MODEL_RESNET50_ONNX_WITHOUTHEAD = os.path.join(
+    BASE_LOCAL_PATH,
+    'pretrained_models/classification/resnet/imagenet_resnet50.onnx')
 PRETRAINED_MODEL_FACEID = os.path.join(BASE_LOCAL_PATH,
                                        'pretrained_models/faceid')
 PRETRAINED_MODEL_YOLOXS_EXPORT = os.path.join(

tools/predict.py

@@ -10,8 +10,9 @@ import logging
 import os
 import threading
 import traceback
-
 import torch
+from mmcv import DictAction
+from easycv.file import io
 
 try:
     import easy_predict
@@ -115,6 +116,12 @@ def define_args():
         type=str,
         choices=[None, 'pytorch'],
         help='if assigned pytorch, should be used in gpu environment')
+    parser.add_argument(
+        '--oss_io_config',
+        nargs='+',
+        action=DictAction,
+        help='designer needs a oss of config to access the data')
+
     args = parser.parse_args()
     if 'LOCAL_RANK' not in os.environ:
         os.environ['LOCAL_RANK'] = str(args.local_rank)
@@ -255,7 +262,7 @@ def create_default_predictor_kwargs(model_dir):
     if config_path:
         return {'model_path': model_path, 'config_file': config_path}
     else:
-        return {'model_path': model_path}
+        return {'model_path': model_path, 'config_file': None}
 
 
 def create_predictor_kwargs(model_type, model_dir):
@@ -288,7 +295,7 @@ def replace_oss_with_local_path(ori_file, dst_file, bucket_prefix,
                                 local_prefix):
     bucket_prefix = bucket_prefix.rstrip('/') + '/'
     local_prefix = local_prefix.rstrip('/') + '/'
-    with open(ori_file, 'r') as infile:
+    with io.open(ori_file, 'r') as infile:
         with open(dst_file, 'w') as ofile:
             for l in infile:
                 if l.startswith('oss://'):
@@ -301,9 +308,26 @@ def build_and_run_file_io(args):
     rank, world_size = get_dist_info()
     worker_id = rank
 
-    input_oss_file_new_host = args.input_file + '.tmp%d' % worker_id
-    replace_oss_with_local_path(args.input_file, input_oss_file_new_host,
-                                args.oss_prefix, args.local_prefix)
+    # check oss_config and init oss io
+    if args.oss_io_config is not None:
+        io.access_oss(**args.oss_io_config)
+
+    # acquire the temporary save path
+    if args.output_file:
+        io.makedirs(os.path.dirname(args.output_file))
+        input_oss_file_new_host = os.path.join(
+            os.path.dirname(args.output_file),
+            os.path.basename(args.input_file + '.tmp%d' % worker_id))
+        replace_oss_with_local_path(args.input_file, input_oss_file_new_host,
+                                    args.oss_prefix, args.local_prefix)
+    else:
+        io.makedirs(args.output_dir)
+        input_oss_file_new_host = os.path.join(
+            args.output_dir,
+            os.path.basename(args.input_file + '.tmp%d' % worker_id))
+        replace_oss_with_local_path(args.input_file, input_oss_file_new_host,
+                                    args.oss_prefix, args.local_prefix)
+
     args.input_file = input_oss_file_new_host
     num_worker = world_size
     print(f'worker num {num_worker}')