remove chainer dependencies

data/util.py

@@ -121,7 +121,7 @@ def crop_bbox(
 
     This method is mainly used together with image cropping.
     This method translates the coordinates of bounding boxes like
-    :func:`~chainercv.transforms.translate_bbox`. In addition,
+    :func:`data.util.translate_bbox`. In addition,
     this function truncates the bounding boxes to fit within the cropped area.
     If a bounding box does not overlap with the cropped area,
     this bounding box will be removed.

data/voc_dataset.py

-# Mofidied work:
-# Original works by:
-# --------------------------------------------------------
-# Faster R-CNN implementation In ChainerCV
-# Copyright (c) 2017 Preferred Networks, Inc.
-# Licensed under The MIT License [see LICENSE for details]
-# https://github.com/chainer/chainercv
-# --------------------------------------------------------
-# Faster R-CNN implementation by Chainer
-# Copyright (c) 2016 Shunta Saito
-# Licensed under The MIT License [see LICENSE for details]
-# https://github.com/mitmul/chainer-faster-rcnn
-# --------------------------------------------------------
-# Faster R-CNN
-# Copyright (c) 2015 Microsoft
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Ross Girshick and Sean Bell
-# https://github.com/rbgirshick/py-faster-rcnn
-# ----------------------------------------------------
-
 import os
 import xml.etree.ElementTree as ET
 
@@ -51,7 +31,7 @@ class VOCBboxDataset:
     The labels are packed into a one dimensional tensor of shape :math:`(R,)`.
     :math:`R` is the number of bounding boxes in the image.
     The class name of the label :math:`l` is :math:`l` th element of
-    :obj:`chainercv.datasets.voc_bbox_label_names`.
+    :obj:`VOC_BBOX_LABEL_NAMES`.
 
     The array :obj:`difficult` is a one dimensional boolean array of shape
     :math:`(R,)`. :math:`R` is the number of bounding boxes in the image.

model/faster_rcnn.py

@@ -27,7 +27,7 @@ class FasterRCNN(nn.Module):
         in the RoIs and improve localizations.
 
     Each stage is carried out by one of the callable
-    :class:`chainer.Chain` objects :obj:`feature`, :obj:`rpn` and :obj:`head`.
+    :class:`torch.nn.Module` objects :obj:`feature`, :obj:`rpn` and :obj:`head`.
 
     There are two functions :meth:`predict` and :meth:`__call__` to conduct
     object detection.
@@ -49,10 +49,10 @@ class FasterRCNN(nn.Module):
         extractor (nn.Module): A module that takes a BCHW image
             array and returns feature maps.
         rpn (nn.Module): A module that has the same interface as
-            :class:`~chainercv.links.model.faster_rcnn.RegionProposalNetwork`.
+            :class:`model.region_proposal_network.RegionProposalNetwork`.
             Please refer to the documentation found there.
-        head (nn.Module): A callable that takes
-            a BCHW array, RoIs and batch indices for RoIs. This returns class
+        head (nn.Module): A module that takes
+            a BCHW variable, RoIs and batch indices for RoIs. This returns class
             dependent localization paramters and class scores.
         loc_normalize_mean (tuple of four floats): Mean values of
             localization estimates.
@@ -99,7 +99,7 @@ class FasterRCNN(nn.Module):
         the :math:`L` th class.
 
         Args:
-            x (~chainer.Variable): 4D image variable.
+            x (autograd.Variable): 4D image variable.
             scale (float): Amount of scaling applied to the raw image
                 during preprocessing.
 
@@ -261,6 +261,10 @@ class FasterRCNN(nn.Module):
         return bboxes, labels, scores
 
     def get_optimizer(self):
+        """
+        return optimizer, It could be overwriten if you want to specify 
+        special optimizer
+        """
         lr = opt.lr
         params = []
         for key, value in dict(self.named_parameters()).items():

model/faster_rcnn_vgg16.py

@@ -37,58 +37,17 @@ def decom_vgg16():
 
 class FasterRCNNVGG16(FasterRCNN):
     """Faster R-CNN based on VGG-16.
-
-    When you specify the path of a pre-trained chainer model serialized as
-    a :obj:`.npz` file in the constructor, this chain model automatically
-    initializes all the parameters with it.
-    When a string in prespecified set is provided, a pretrained model is
-    loaded from weights distributed on the Internet.
-    The list of pretrained models supported are as follows:
-
-    * :obj:`voc07`: Loads weights trained with the trainval split of \
-        PASCAL VOC2007 Detection Dataset.
-    * :obj:`imagenet`: Loads weights trained with ImageNet Classfication \
-        task for the feature extractor and the head modules. \
-        Weights that do not have a corresponding layer in VGG-16 \
-        will be randomly initialized.
-
     For descriptions on the interface of this model, please refer to
-    :class:`~chainercv.links.model.faster_rcnn.FasterRCNN`.
-
-    :class:`~chainercv.links.model.faster_rcnn.FasterRCNNVGG16`
-    supports finer control on random initializations of weights by arguments
-    :obj:`vgg_initialW`, :obj:`rpn_initialW`, :obj:`loc_initialW` and
-    :obj:`score_initialW`.
-    It accepts a callable that takes an array and edits its values.
-    If :obj:`None` is passed as an initializer, the default initializer is
-    used.
+    :class:`model.faster_rcnn.FasterRCNN`.
 
     Args:
         n_fg_class (int): The number of classes excluding the background.
-        pretrained_model (str): The destination of the pre-trained
-            chainer model serialized as a :obj:`.npz` file.
-            If this is one of the strings described
-            above, it automatically loads weights stored under a directory
-            :obj:`$CHAINER_DATASET_ROOT/pfnet/chainercv/models/`,
-            where :obj:`$CHAINER_DATASET_ROOT` is set as
-            :obj:`$HOME/.chainer/dataset` unless you specify another value
-            by modifying the environment variable.
-        min_size (int): A preprocessing paramter for :meth:`prepare`.
-        max_size (int): A preprocessing paramter for :meth:`prepare`.
         ratios (list of floats): This is ratios of width to height of
             the anchors.
         anchor_scales (list of numbers): This is areas of anchors.
             Those areas will be the product of the square of an element in
             :obj:`anchor_scales` and the original area of the reference
             window.
-        vgg_initialW (callable): Initializer for the layers corresponding to
-            the VGG-16 layers.
-        rpn_initialW (callable): Initializer for Region Proposal Network
-            layers.
-        loc_initialW (callable): Initializer for the localization head.
-        score_initialW (callable): Initializer for the score head.
-        proposal_creator_params (dict): Key valued paramters for
-            :class:`~chainercv.links.model.faster_rcnn.ProposalCreator`.
 
     """
 
@@ -128,14 +87,12 @@ class VGG16RoIHead(nn.Module):
     This class is used as a head for Faster R-CNN.
     This outputs class-wise localizations and classification based on feature
     maps in the given RoIs.
+    
     Args:
         n_class (int): The number of classes possibly including the background.
         roi_size (int): Height and width of the feature maps after RoI-pooling.
         spatial_scale (float): Scale of the roi is resized.
-        vgg_initialW (callable): Initializer for the layers corresponding to
-            the VGG-16 layers.
-        loc_initialW (callable): Initializer for the localization head.
-        score_initialW (callable): Initializer for the score head.
+        classifier (nn.Module): Two layer Linear ported from vgg16
 
     """
 

model/region_proposal_network.py

@@ -34,10 +34,10 @@ class RegionProposalNetwork(nn.Module):
             initialize weight.
             May also be a callable that takes an array and edits its values.
         proposal_creator_params (dict): Key valued paramters for
-            :class:`~chainercv.links.model.faster_rcnn.ProposalCreator`.
+            :class:`model.utils.creator_tools.ProposalCreator`.
 
     .. seealso::
-        :class:`~chainercv.links.model.faster_rcnn.ProposalCreator`
+        :class:`~model.utils.creator_tools.ProposalCreator`
 
     """
 
@@ -70,7 +70,7 @@ class RegionProposalNetwork(nn.Module):
         * :math:`A` is number of anchors assigned to each pixel.
 
         Args:
-            x (~chainer.Variable): The Features extracted from images.
+            x (~torch.autograd.Variable): The Features extracted from images.
                 Its shape is :math:`(N, C, H, W)`.
             img_size (tuple of ints): A tuple :obj:`height, width`,
                 which contains image size after scaling.
@@ -78,7 +78,7 @@ class RegionProposalNetwork(nn.Module):
                 reading them from files.
 
         Returns:
-            (~chainer.Variable, ~chainer.Variable, array, array, array):
+            (~torch.autograd.Variable, ~torch.autograd.Variable, array, array, array):
 
             This is a tuple of five following values.
 

model/utils/creator_tool.py

 import numpy as np
-from chainer import cuda
+import cupy
 
 from model.utils.bbox_tools import bbox2loc, bbox_iou, loc2bbox
 from model.utils.nms import non_maximum_suppression
@@ -419,9 +419,10 @@ class ProposalCreator:
         # Apply nms (e.g. threshold = 0.7).
         # Take after_nms_topN (e.g. 300).
 
-        # NOTE: somthing is wrong here!
+        # unNOTE: somthing is wrong here!
+        # TODO: remove cuda.to_gpu
         keep = non_maximum_suppression(
-            cuda.to_gpu(roi),
+            cp.ascontiguousarray(cp.asarray(a)),
             thresh=self.nms_thresh)
         if n_post_nms > 0:
             keep = keep[:n_post_nms]