[to #42322933] fix seg4demo

Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/10189886

modelscope/models/cv/image_panoptic_segmentation/panseg_model.py

@@ -1,3 +1,4 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
 import os.path as osp
 
 import torch
@@ -49,6 +50,4 @@ class SwinLPanopticSegmentation(TorchModel):
         return results
 
     def forward(self, Inputs):
-        import pdb
-        pdb.set_trace()
         return self.model(**Inputs)

modelscope/models/cv/image_semantic_segmentation/pan_merge/__init__.py

@@ -1 +1,2 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
 from .maskformer_semantic_head import MaskFormerSemanticHead

modelscope/models/cv/image_semantic_segmentation/pan_merge/maskformer_semantic_head.py

@@ -1,3 +1,4 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
 import torch
 import torch.nn.functional as F
 from mmdet.models.builder import HEADS

modelscope/models/cv/image_semantic_segmentation/semantic_seg_model.py

@@ -1,3 +1,4 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
 import os.path as osp
 
 import numpy as np

modelscope/models/cv/image_semantic_segmentation/vit_adapter/__init__.py

@@ -1,3 +1,5 @@
+# The implementation is adopted from VitAdapter,
+# made publicly available under the Apache License at https://github.com/czczup/ViT-Adapter.git
 from .models import backbone, decode_heads, segmentors
 from .utils import (ResizeToMultiple, add_prefix, build_pixel_sampler,
                     seg_resize)

modelscope/models/cv/image_semantic_segmentation/vit_adapter/models/__init__.py

@@ -1,3 +1,5 @@
+# The implementation is adopted from VitAdapter,
+# made publicly available under the Apache License at https://github.com/czczup/ViT-Adapter.git
 from .backbone import BASEBEiT, BEiTAdapter
 from .decode_heads import Mask2FormerHeadFromMMSeg
 from .segmentors import EncoderDecoderMask2Former

modelscope/models/cv/image_semantic_segmentation/vit_adapter/models/backbone/__init__.py

@@ -1,3 +1,5 @@
+# The implementation is adopted from VitAdapter,
+# made publicly available under the Apache License at https://github.com/czczup/ViT-Adapter.git
 from .base import BASEBEiT
 from .beit_adapter import BEiTAdapter
 

modelscope/models/cv/image_semantic_segmentation/vit_adapter/models/backbone/adapter_modules.py

@@ -1,6 +1,5 @@
-# The implementation refers to the VitAdapter
-# available at
-# https://github.com/czczup/ViT-Adapter.git
+# The implementation is adopted from VitAdapter,
+# made publicly available under the Apache License at https://github.com/czczup/ViT-Adapter.git
 
 import logging
 from functools import partial
@@ -417,7 +416,7 @@ class SpatialPriorModule(nn.Module):
         self.stem = nn.Sequential(*[
             nn.Conv2d(
                 3, inplanes, kernel_size=3, stride=2, padding=1, bias=False),
-            nn.SyncBatchNorm(inplanes),
+            nn.BatchNorm2d(inplanes),
             nn.ReLU(inplace=True),
             nn.Conv2d(
                 inplanes,
@@ -426,7 +425,7 @@ class SpatialPriorModule(nn.Module):
                 stride=1,
                 padding=1,
                 bias=False),
-            nn.SyncBatchNorm(inplanes),
+            nn.BatchNorm2d(inplanes),
             nn.ReLU(inplace=True),
             nn.Conv2d(
                 inplanes,
@@ -435,7 +434,7 @@ class SpatialPriorModule(nn.Module):
                 stride=1,
                 padding=1,
                 bias=False),
-            nn.SyncBatchNorm(inplanes),
+            nn.BatchNorm2d(inplanes),
             nn.ReLU(inplace=True),
             nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
         ])
@@ -447,7 +446,7 @@ class SpatialPriorModule(nn.Module):
                 stride=2,
                 padding=1,
                 bias=False),
-            nn.SyncBatchNorm(2 * inplanes),
+            nn.BatchNorm2d(2 * inplanes),
             nn.ReLU(inplace=True)
         ])
         self.conv3 = nn.Sequential(*[
@@ -458,7 +457,7 @@ class SpatialPriorModule(nn.Module):
                 stride=2,
                 padding=1,
                 bias=False),
-            nn.SyncBatchNorm(4 * inplanes),
+            nn.BatchNorm2d(4 * inplanes),
             nn.ReLU(inplace=True)
         ])
         self.conv4 = nn.Sequential(*[
@@ -469,7 +468,7 @@ class SpatialPriorModule(nn.Module):
                 stride=2,
                 padding=1,
                 bias=False),
-            nn.SyncBatchNorm(4 * inplanes),
+            nn.BatchNorm2d(4 * inplanes),
             nn.ReLU(inplace=True)
         ])
         self.fc1 = nn.Conv2d(

modelscope/models/cv/image_semantic_segmentation/vit_adapter/models/backbone/base/__init__.py

@@ -1,3 +1,5 @@
+# The implementation is adopted from VitAdapter,
+# made publicly available under the Apache License at https://github.com/czczup/ViT-Adapter.git
 from .beit import BASEBEiT
 
 __all__ = ['BASEBEiT']

modelscope/models/cv/image_semantic_segmentation/vit_adapter/models/backbone/base/beit.py

@@ -1,7 +1,5 @@
-# BEIT: BERT Pre-Training of Image Transformers (https://arxiv.org/abs/2106.08254)
-# Github source: https://github.com/microsoft/unilm/tree/master/beit
-# This implementation refers to
-# https://github.com/czczup/ViT-Adapter.git
+# The implementation is adopted from VitAdapter,
+# made publicly available under the Apache License at https://github.com/czczup/ViT-Adapter.git
 import math
 from functools import partial
 

modelscope/models/cv/image_semantic_segmentation/vit_adapter/models/backbone/beit_adapter.py

@@ -1,6 +1,5 @@
-# The implementation refers to the VitAdapter
-# available at
-# https://github.com/czczup/ViT-Adapter.git
+# The implementation is adopted from VitAdapter,
+# made publicly available under the Apache License at https://github.com/czczup/ViT-Adapter.git
 import logging
 import math
 
@@ -69,10 +68,10 @@ class BEiTAdapter(BASEBEiT):
         ])
 
         self.up = nn.ConvTranspose2d(embed_dim, embed_dim, 2, 2)
-        self.norm1 = nn.SyncBatchNorm(embed_dim)
-        self.norm2 = nn.SyncBatchNorm(embed_dim)
-        self.norm3 = nn.SyncBatchNorm(embed_dim)
-        self.norm4 = nn.SyncBatchNorm(embed_dim)
+        self.norm1 = nn.BatchNorm2d(embed_dim)
+        self.norm2 = nn.BatchNorm2d(embed_dim)
+        self.norm3 = nn.BatchNorm2d(embed_dim)
+        self.norm4 = nn.BatchNorm2d(embed_dim)
 
         self.up.apply(self._init_weights)
         self.spm.apply(self._init_weights)

modelscope/models/cv/image_semantic_segmentation/vit_adapter/models/decode_heads/__init__.py

@@ -1,3 +1,5 @@
+# The implementation is adopted from VitAdapter,
+# made publicly available under the Apache License at https://github.com/czczup/ViT-Adapter.git
 from .mask2former_head_from_mmseg import Mask2FormerHeadFromMMSeg
 
 __all__ = ['Mask2FormerHeadFromMMSeg']

modelscope/models/cv/image_semantic_segmentation/vit_adapter/models/decode_heads/base_decode_head.py

@@ -1,6 +1,5 @@
-# The implementation refers to the VitAdapter
-# available at
-# https://github.com/czczup/ViT-Adapter.git
+# The implementation is adopted from VitAdapter,
+# made publicly available under the Apache License at https://github.com/czczup/ViT-Adapter.git
 from abc import ABCMeta, abstractmethod
 
 import torch

modelscope/models/cv/image_semantic_segmentation/vit_adapter/models/decode_heads/mask2former_head_from_mmseg.py

@@ -1,6 +1,5 @@
-# The implementation refers to the VitAdapter
-# available at
-# https://github.com/czczup/ViT-Adapter.git
+# The implementation is adopted from VitAdapter,
+# made publicly available under the Apache License at https://github.com/czczup/ViT-Adapter.git
 
 import copy
 

modelscope/models/cv/image_semantic_segmentation/vit_adapter/models/segmentors/__init__.py

@@ -1,3 +1,5 @@
+# The implementation is adopted from VitAdapter,
+# made publicly available under the Apache License at https://github.com/czczup/ViT-Adapter.git
 from .encoder_decoder_mask2former import EncoderDecoderMask2Former
 
 __all__ = ['EncoderDecoderMask2Former']

modelscope/models/cv/image_semantic_segmentation/vit_adapter/models/segmentors/base_segmentor.py

@@ -1,6 +1,5 @@
-# The implementation refers to the VitAdapter
-# available at
-# https://github.com/czczup/ViT-Adapter.git
+# The implementation is adopted from VitAdapter,
+# made publicly available under the Apache License at https://github.com/czczup/ViT-Adapter.git
 import warnings
 from abc import ABCMeta, abstractmethod
 from collections import OrderedDict

modelscope/models/cv/image_semantic_segmentation/vit_adapter/models/segmentors/encoder_decoder_mask2former.py

@@ -1,6 +1,5 @@
-# The implementation refers to the VitAdapter
-# available at
-# https://github.com/czczup/ViT-Adapter.git
+# The implementation is adopted from VitAdapter,
+# made publicly available under the Apache License at https://github.com/czczup/ViT-Adapter.git
 import torch
 import torch.nn as nn
 import torch.nn.functional as F

modelscope/models/cv/image_semantic_segmentation/vit_adapter/utils/__init__.py

@@ -1,3 +1,5 @@
+# The implementation is adopted from VitAdapter,
+# made publicly available under the Apache License at https://github.com/czczup/ViT-Adapter.git
 from .builder import build_pixel_sampler
 from .data_process_func import ResizeToMultiple
 from .seg_func import add_prefix, seg_resize

modelscope/models/cv/image_semantic_segmentation/vit_adapter/utils/builder.py

@@ -1,6 +1,5 @@
-# The implementation refers to the VitAdapter
-# available at
-# https://github.com/czczup/ViT-Adapter.git
+# The implementation is adopted from VitAdapter,
+# made publicly available under the Apache License at https://github.com/czczup/ViT-Adapter.git
 from mmcv.utils import Registry, build_from_cfg
 
 PIXEL_SAMPLERS = Registry('pixel sampler')

modelscope/models/cv/image_semantic_segmentation/vit_adapter/utils/seg_func.py

@@ -1,6 +1,5 @@
-# The implementation refers to the VitAdapter
-# available at
-# https://github.com/czczup/ViT-Adapter.git
+# The implementation is adopted from VitAdapter,
+# made publicly available under the Apache License at https://github.com/czczup/ViT-Adapter.git
 
 import warnings
 

modelscope/pipelines/cv/image_panoptic_segmentation_pipeline.py

@@ -4,11 +4,13 @@ from typing import Any, Dict, Union
 import cv2
 import numpy as np
 import PIL
+import torch
 
 from modelscope.metainfo import Pipelines
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Input, Pipeline
 from modelscope.pipelines.builder import PIPELINES
+from modelscope.preprocessors import load_image
 from modelscope.utils.constant import Tasks
 from modelscope.utils.logger import get_logger
 
@@ -39,28 +41,24 @@ class ImagePanopticSegmentationPipeline(Pipeline):
         # build the data pipeline
 
         if isinstance(input, str):
-            # input is str, file names, pipeline loadimagefromfile
-            # collect data
-            data = dict(img_info=dict(filename=input), img_prefix=None)
+            cfg.data.test.pipeline[0].type = 'LoadImageFromWebcam'
+            img = np.array(load_image(input))
+            img = img[:, :, ::-1]  # convert to bgr
         elif isinstance(input, PIL.Image.Image):
             cfg.data.test.pipeline[0].type = 'LoadImageFromWebcam'
             img = np.array(input.convert('RGB'))
-            # collect data
-            data = dict(img=img)
         elif isinstance(input, np.ndarray):
             cfg.data.test.pipeline[0].type = 'LoadImageFromWebcam'
             if len(input.shape) == 2:
                 img = cv2.cvtColor(input, cv2.COLOR_GRAY2BGR)
             else:
                 img = input
-            img = img[:, :, ::-1]  # in rgb order
-            # collect data
-            data = dict(img=img)
-
         else:
             raise TypeError(f'input should be either str, PIL.Image,'
                             f' np.array, but got {type(input)}')
 
+        # collect data
+        data = dict(img=img)
         cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline)
         test_pipeline = Compose(cfg.data.test.pipeline)
 

modelscope/pipelines/cv/image_semantic_segmentation_pipeline.py

@@ -10,6 +10,7 @@ from modelscope.metainfo import Pipelines
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines.base import Input, Model, Pipeline
 from modelscope.pipelines.builder import PIPELINES
+from modelscope.preprocessors import load_image
 from modelscope.utils.constant import Tasks
 from modelscope.utils.logger import get_logger
 
@@ -40,28 +41,24 @@ class ImageSemanticSegmentationPipeline(Pipeline):
         # build the data pipeline
 
         if isinstance(input, str):
-            # input is str, file names, pipeline loadimagefromfile
-            # collect data
-            data = dict(img_info=dict(filename=input), img_prefix=None)
+            cfg.data.test.pipeline[0].type = 'LoadImageFromWebcam'
+            img = np.array(load_image(input))
+            img = img[:, :, ::-1]  # convert to bgr
         elif isinstance(input, PIL.Image.Image):  # BGR
             cfg.data.test.pipeline[0].type = 'LoadImageFromWebcam'
             img = np.array(input)[:, :, ::-1]
-            # collect data
-            data = dict(img=img)
         elif isinstance(input, np.ndarray):
             cfg.data.test.pipeline[0].type = 'LoadImageFromWebcam'
             if len(input.shape) == 2:
                 img = cv2.cvtColor(input, cv2.COLOR_GRAY2BGR)
             else:
                 img = input
-            # collect data
-            data = dict(img=img)
-
         else:
             raise TypeError(f'input should be either str, PIL.Image,'
                             f' np.array, but got {type(input)}')
 
-        # data = dict(img=input)
+        # collect data
+        data = dict(img=img)
         cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline)
         test_pipeline = Compose(cfg.data.test.pipeline)
 
@@ -80,11 +77,9 @@ class ImageSemanticSegmentationPipeline(Pipeline):
 
     def forward(self, input: Dict[str, Any]) -> Dict[str, Any]:
         results = self.model.inference(input)
-
         return results
 
     def postprocess(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
-
         results = self.model.postprocess(inputs)
         outputs = {
             OutputKeys.MASKS: results[OutputKeys.MASKS],