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--- a/experiments/toy_exp/dev/backbone.py
+++ /dev/null
@@ -1,218 +0,0 @@
-#!/usr/bin/env python
-# Copyright 2018 Division of Medical Image Computing, German Cancer Research Center (DKFZ).
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-import torch.nn as nn
-import torch.nn.functional as F
-import torch
-
-
-class FPN(nn.Module):
- """
- Feature Pyramid Network from https://arxiv.org/pdf/1612.03144.pdf with options for modifications.
- by default is constructed with Pyramid levels P2, P3, P4, P5.
- """
- def __init__(self, cf, conv, operate_stride1=False):
- """
- from configs:
- :param input_channels: number of channel dimensions in input data.
- :param start_filts: number of feature_maps in first layer. rest is scaled accordingly.
- :param out_channels: number of feature_maps for output_layers of all levels in decoder.
- :param conv: instance of custom conv class containing the dimension info.
- :param res_architecture: string deciding whether to use "resnet50" or "resnet101".
- :param operate_stride1: boolean flag. enables adding of Pyramid levels P1 (output stride 2) and P0 (output stride 1).
- :param norm: string specifying type of feature map normalization. If None, no normalization is applied.
- :param relu: string specifying type of nonlinearity. If None, no nonlinearity is applied.
- :param sixth_pooling: boolean flag. enables adding of Pyramid level P6.
- """
- super(FPN, self).__init__()
-
- self.start_filts = cf.start_filts
- start_filts = self.start_filts
- self.n_blocks = [3, 4, {"resnet50": 6, "resnet101": 23}[cf.res_architecture], 3]
- self.block = ResBlock
- self.block_expansion = 4
- self.operate_stride1 = operate_stride1
- self.sixth_pooling = cf.sixth_pooling
- self.dim = conv.dim
-
- if operate_stride1:
- self.C0 = nn.Sequential(conv(cf.n_channels, start_filts, ks=3, pad=1, norm=cf.norm, relu=cf.relu),
- conv(start_filts, start_filts, ks=3, pad=1, norm=cf.norm, relu=cf.relu))
-
- self.C1 = conv(start_filts, start_filts, ks=7, stride=(2, 2, 1) if conv.dim == 3 else 2, pad=3, norm=cf.norm, relu=cf.relu)
-
- else:
- self.C1 = conv(cf.n_channels, start_filts, ks=7, stride=(2, 2, 1) if conv.dim == 3 else 2, pad=3, norm=cf.norm, relu=cf.relu)
-
- start_filts_exp = start_filts * self.block_expansion
-
- C2_layers = []
- C2_layers.append(nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
- if conv.dim == 2 else nn.MaxPool3d(kernel_size=3, stride=(2, 2, 1), padding=1))
- C2_layers.append(self.block(start_filts, start_filts, conv=conv, stride=1, norm=cf.norm, relu=cf.relu,
- downsample=(start_filts, self.block_expansion, 1)))
- for i in range(1, self.n_blocks[0]):
- C2_layers.append(self.block(start_filts_exp, start_filts, conv=conv, norm=cf.norm, relu=cf.relu))
- self.C2 = nn.Sequential(*C2_layers)
-
- C3_layers = []
- C3_layers.append(self.block(start_filts_exp, start_filts * 2, conv=conv, stride=2, norm=cf.norm, relu=cf.relu,
- downsample=(start_filts_exp, 2, 2)))
- for i in range(1, self.n_blocks[1]):
- C3_layers.append(self.block(start_filts_exp * 2, start_filts * 2, conv=conv, norm=cf.norm, relu=cf.relu))
- self.C3 = nn.Sequential(*C3_layers)
-
- C4_layers = []
- C4_layers.append(self.block(
- start_filts_exp * 2, start_filts * 4, conv=conv, stride=2, norm=cf.norm, relu=cf.relu, downsample=(start_filts_exp * 2, 2, 2)))
- for i in range(1, self.n_blocks[2]):
- C4_layers.append(self.block(start_filts_exp * 4, start_filts * 4, conv=conv, norm=cf.norm, relu=cf.relu))
- self.C4 = nn.Sequential(*C4_layers)
-
- C5_layers = []
- C5_layers.append(self.block(
- start_filts_exp * 4, start_filts * 8, conv=conv, stride=2, norm=cf.norm, relu=cf.relu, downsample=(start_filts_exp * 4, 2, 2)))
- for i in range(1, self.n_blocks[3]):
- C5_layers.append(self.block(start_filts_exp * 8, start_filts * 8, conv=conv, norm=cf.norm, relu=cf.relu))
- self.C5 = nn.Sequential(*C5_layers)
-
- if self.sixth_pooling:
- C6_layers = []
- C6_layers.append(self.block(
- start_filts_exp * 8, start_filts * 16, conv=conv, stride=2, norm=cf.norm, relu=cf.relu, downsample=(start_filts_exp * 8, 2, 2)))
- for i in range(1, self.n_blocks[3]):
- C6_layers.append(self.block(start_filts_exp * 16, start_filts * 16, conv=conv, norm=cf.norm, relu=cf.relu))
- self.C6 = nn.Sequential(*C6_layers)
-
- if conv.dim == 2:
- self.P1_upsample = Interpolate(scale_factor=2, mode='bilinear')
- self.P2_upsample = Interpolate(scale_factor=2, mode='bilinear')
- else:
- self.P1_upsample = Interpolate(scale_factor=(2, 2, 1), mode='trilinear')
- self.P2_upsample = Interpolate(scale_factor=(2, 2, 1), mode='trilinear')
-
- self.out_channels = cf.end_filts
- self.P5_conv1 = conv(start_filts*32 + cf.n_latent_dims, self.out_channels, ks=1, stride=1, relu=None) #
- self.P4_conv1 = conv(start_filts*16, self.out_channels, ks=1, stride=1, relu=None)
- self.P3_conv1 = conv(start_filts*8, self.out_channels, ks=1, stride=1, relu=None)
- self.P2_conv1 = conv(start_filts*4, self.out_channels, ks=1, stride=1, relu=None)
- self.P1_conv1 = conv(start_filts, self.out_channels, ks=1, stride=1, relu=None)
-
- if operate_stride1:
- self.P0_conv1 = conv(start_filts, self.out_channels, ks=1, stride=1, relu=None)
- self.P0_conv2 = conv(self.out_channels, self.out_channels, ks=3, stride=1, pad=1, relu=None)
-
- self.P1_conv2 = conv(self.out_channels, self.out_channels, ks=3, stride=1, pad=1, relu=None)
- self.P2_conv2 = conv(self.out_channels, self.out_channels, ks=3, stride=1, pad=1, relu=None)
- self.P3_conv2 = conv(self.out_channels, self.out_channels, ks=3, stride=1, pad=1, relu=None)
- self.P4_conv2 = conv(self.out_channels, self.out_channels, ks=3, stride=1, pad=1, relu=None)
- self.P5_conv2 = conv(self.out_channels, self.out_channels, ks=3, stride=1, pad=1, relu=None)
-
- if self.sixth_pooling:
- self.P6_conv1 = conv(start_filts * 64, self.out_channels, ks=1, stride=1, relu=None)
- self.P6_conv2 = conv(self.out_channels, self.out_channels, ks=3, stride=1, pad=1, relu=None)
-
-
- def forward(self, x):
- """
- :param x: input image of shape (b, c, y, x, (z))
- :return: list of output feature maps per pyramid level, each with shape (b, c, y, x, (z)).
- """
- if self.operate_stride1:
- c0_out = self.C0(x)
- else:
- c0_out = x
-
- c1_out = self.C1(c0_out)
- c2_out = self.C2(c1_out)
- c3_out = self.C3(c2_out)
- c4_out = self.C4(c3_out)
- c5_out = self.C5(c4_out)
- if self.sixth_pooling:
- c6_out = self.C6(c5_out)
- p6_pre_out = self.P6_conv1(c6_out)
- p5_pre_out = self.P5_conv1(c5_out) + F.interpolate(p6_pre_out, scale_factor=2)
- else:
- p5_pre_out = self.P5_conv1(c5_out)
-
- p4_pre_out = self.P4_conv1(c4_out) + F.interpolate(p5_pre_out, scale_factor=2)
- p3_pre_out = self.P3_conv1(c3_out) + F.interpolate(p4_pre_out, scale_factor=2)
- p2_pre_out = self.P2_conv1(c2_out) + F.interpolate(p3_pre_out, scale_factor=2)
-
- # plot feature map shapes for debugging.
- # for ii in [c0_out, c1_out, c2_out, c3_out, c4_out, c5_out, c6_out]:
- # print ("encoder shapes:", ii.shape)
- #
- # for ii in [p6_out, p5_out, p4_out, p3_out, p2_out, p1_out]:
- # print("decoder shapes:", ii.shape)
-
- p2_out = self.P2_conv2(p2_pre_out)
- p3_out = self.P3_conv2(p3_pre_out)
- p4_out = self.P4_conv2(p4_pre_out)
- p5_out = self.P5_conv2(p5_pre_out)
- out_list = [p2_out, p3_out, p4_out, p5_out]
-
- if self.sixth_pooling:
- p6_out = self.P6_conv2(p6_pre_out)
- out_list.append(p6_out)
-
- if self.operate_stride1:
- p1_pre_out = self.P1_conv1(c1_out) + self.P2_upsample(p2_pre_out)
- p0_pre_out = self.P0_conv1(c0_out) + self.P1_upsample(p1_pre_out)
- # p1_out = self.P1_conv2(p1_pre_out) # usually not needed.
- p0_out = self.P0_conv2(p0_pre_out)
- out_list = [p0_out] + out_list
-
- return out_list
-
-
-
-class ResBlock(nn.Module):
-
- def __init__(self, start_filts, planes, conv, stride=1, downsample=None, norm=None, relu='relu'):
- super(ResBlock, self).__init__()
- self.conv1 = conv(start_filts, planes, ks=1, stride=stride, norm=norm, relu=relu)
- self.conv2 = conv(planes, planes, ks=3, pad=1, norm=norm, relu=relu)
- self.conv3 = conv(planes, planes * 4, ks=1, norm=norm, relu=None)
- self.relu = nn.ReLU(inplace=True) if relu == 'relu' else nn.LeakyReLU(inplace=True)
- if downsample is not None:
- self.downsample = conv(downsample[0], downsample[0] * downsample[1], ks=1, stride=downsample[2], norm=norm, relu=None)
- else:
- self.downsample = None
- self.stride = stride
-
- def forward(self, x):
- residual = x
- out = self.conv1(x)
- out = self.conv2(out)
- out = self.conv3(out)
- if self.downsample:
- residual = self.downsample(x)
- out += residual
- out = self.relu(out)
- return out
-
-
-class Interpolate(nn.Module):
- def __init__(self, scale_factor, mode):
- super(Interpolate, self).__init__()
- self.interp = nn.functional.interpolate
- self.scale_factor = scale_factor
- self.mode = mode
-
- def forward(self, x):
- x = self.interp(x, scale_factor=self.scale_factor, mode=self.mode, align_corners=False)
- return x
\ No newline at end of file
diff --git a/experiments/toy_exp/dev/configs.py b/experiments/toy_exp/dev/configs.py
deleted file mode 100644
index b2fd2c3..0000000
--- a/experiments/toy_exp/dev/configs.py
+++ /dev/null
@@ -1,335 +0,0 @@
-#!/usr/bin/env python
-# Copyright 2018 Division of Medical Image Computing, German Cancer Research Center (DKFZ).
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-import sys
-import os
-sys.path.append(os.path.dirname(os.path.realpath(__file__)))
-import numpy as np
-from default_configs import DefaultConfigs
-
-class configs(DefaultConfigs):
-
- def __init__(self, server_env=None):
-
- #########################
- # Preprocessing #
- #########################
-
- self.root_dir = '/path/to/raw/data'
- self.raw_data_dir = '{}/data_nrrd'.format(self.root_dir)
- self.pp_dir = '{}/pp_norm'.format(self.root_dir)
- self.target_spacing = (0.7, 0.7, 1.25)
-
- #########################
- # I/O #
- #########################
-
-
- # one out of [2, 3]. dimension the model operates in.
- self.dim = 3
-
- # one out of ['mrcnn', 'retina_net', 'retina_unet', 'detection_unet', 'ufrcnn', 'detection_unet'].
- self.model = 'mrcnn'
-
- DefaultConfigs.__init__(self, self.model, server_env, self.dim)
-
- # int [0 < dataset_size]. select n patients from dataset for prototyping. If None, all data is used.
- self.select_prototype_subset = None
-
- # path to preprocessed data.
- self.pp_name = 'lidc_preprocessed_for_G2'
- self.input_df_name = 'info_df.pickle'
- self.pp_data_path = '/mnt/HDD2TB/Documents/data/lidc/{}'.format(self.pp_name)
- self.pp_test_data_path = self.pp_data_path #change if test_data in separate folder.
-
- # settings for deployment in cloud.
- if server_env:
- # path to preprocessed data.
- self.pp_name = 'pp_fg_slices'
- self.crop_name = 'pp_fg_slices_packed'
- self.pp_data_path = '/path/to/preprocessed/data/{}/{}'.format(self.pp_name, self.crop_name)
- self.pp_test_data_path = self.pp_data_path
- self.select_prototype_subset = None
-
- #########################
- # Data Loader #
- #########################
-
- # select modalities from preprocessed data
- self.channels = [0]
- self.n_channels = len(self.channels)
-
- # patch_size to be used for training. pre_crop_size is the patch_size before data augmentation.
- self.pre_crop_size_2D = [300, 300]
- self.patch_size_2D = [288, 288]
- self.pre_crop_size_3D = [156, 156, 96]
- self.patch_size_3D = [128, 128, 64]
- self.patch_size = self.patch_size_2D if self.dim == 2 else self.patch_size_3D
- self.pre_crop_size = self.pre_crop_size_2D if self.dim == 2 else self.pre_crop_size_3D
-
- # ratio of free sampled batch elements before class balancing is triggered
- # (>0 to include "empty"/background patches.)
- self.batch_sample_slack = 0.2
-
- # set 2D network to operate in 3D images.
- self.merge_2D_to_3D_preds = True
-
- # feed +/- n neighbouring slices into channel dimension. set to None for no context.
- self.n_3D_context = None
- if self.n_3D_context is not None and self.dim == 2:
- self.n_channels *= (self.n_3D_context * 2 + 1)
-
-
- #########################
- # Architecture #
- #########################
-
- self.start_filts = 48 if self.dim == 2 else 18
- self.end_filts = self.start_filts * 4 if self.dim == 2 else self.start_filts * 2
- self.res_architecture = 'resnet50' # 'resnet101' , 'resnet50'
- self.norm = None # one of None, 'instance_norm', 'batch_norm'
- self.weight_decay = 0
-
- # one of 'xavier_uniform', 'xavier_normal', or 'kaiming_normal', None (=default = 'kaiming_uniform')
- self.weight_init = None
-
- #########################
- # Schedule / Selection #
- #########################
-
- self.num_epochs = 100
- self.num_train_batches = 200 if self.dim == 2 else 200
- self.batch_size = 20 if self.dim == 2 else 8
-
- self.do_validation = True
- # decide whether to validate on entire patient volumes (like testing) or sampled patches (like training)
- # the former is morge accurate, while the latter is faster (depending on volume size)
- self.val_mode = 'val_sampling' # one of 'val_sampling' , 'val_patient'
- if self.val_mode == 'val_patient':
- self.max_val_patients = 50 # if 'None' iterates over entire val_set once.
- if self.val_mode == 'val_sampling':
- self.num_val_batches = 50
-
- #########################
- # Testing / Plotting #
- #########################
-
- # set the top-n-epochs to be saved for temporal averaging in testing.
- self.save_n_models = 5
- self.test_n_epochs = 5
- # set a minimum epoch number for saving in case of instabilities in the first phase of training.
- self.min_save_thresh = 0 if self.dim == 2 else 0
-
- self.report_score_level = ['patient', 'rois'] # choose list from 'patient', 'rois'
- self.class_dict = {1: 'benign', 2: 'malignant'} # 0 is background.
- self.patient_class_of_interest = 2 # patient metrics are only plotted for one class.
- self.ap_match_ious = [0.1] # list of ious to be evaluated for ap-scoring.
-
- self.model_selection_criteria = ['malignant_ap', 'benign_ap'] # criteria to average over for saving epochs.
- self.min_det_thresh = 0.1 # minimum confidence value to select predictions for evaluation.
-
- # threshold for clustering predictions together (wcs = weighted cluster scoring).
- # needs to be >= the expected overlap of predictions coming from one model (typically NMS threshold).
- # if too high, preds of the same object are separate clusters.
- self.wcs_iou = 1e-5
-
- self.plot_prediction_histograms = True
- self.plot_stat_curves = False
-
- #########################
- # Data Augmentation #
- #########################
-
- self.da_kwargs={
- 'do_elastic_deform': True,
- 'alpha':(0., 1500.),
- 'sigma':(30., 50.),
- 'do_rotation':True,
- 'angle_x': (0., 2 * np.pi),
- 'angle_y': (0., 0),
- 'angle_z': (0., 0),
- 'do_scale': True,
- 'scale':(0.8, 1.1),
- 'random_crop':False,
- 'rand_crop_dist': (self.patch_size[0] / 2. - 3, self.patch_size[1] / 2. - 3),
- 'border_mode_data': 'constant',
- 'border_cval_data': 0,
- 'order_data': 1
- }
-
- if self.dim == 3:
- self.da_kwargs['do_elastic_deform'] = False
- self.da_kwargs['angle_x'] = (0, 0.0)
- self.da_kwargs['angle_y'] = (0, 0.0) #must be 0!!
- self.da_kwargs['angle_z'] = (0., 2 * np.pi)
-
-
- #########################
- # Add model specifics #
- #########################
-
- {'detection_unet': self.add_det_unet_configs,
- 'mrcnn': self.add_mrcnn_configs,
- 'ufrcnn': self.add_mrcnn_configs,
- 'retina_net': self.add_mrcnn_configs,
- 'retina_unet': self.add_mrcnn_configs,
- }[self.model]()
-
-
- def add_det_unet_configs(self):
-
- self.learning_rate = [1e-4] * self.num_epochs
-
- # aggregation from pixel perdiction to object scores (connected component). One of ['max', 'median']
- self.aggregation_operation = 'max'
-
- # max number of roi candidates to identify per batch element and class.
- self.n_roi_candidates = 10 if self.dim == 2 else 30
-
- # loss mode: either weighted cross entropy ('wce'), batch-wise dice loss ('dice), or the sum of both ('dice_wce')
- self.seg_loss_mode = 'dice_wce'
-
- # if <1, false positive predictions in foreground are penalized less.
- self.fp_dice_weight = 1 if self.dim == 2 else 1
-
- self.wce_weights = [1, 1, 1]
- self.detection_min_confidence = self.min_det_thresh
-
- # if 'True', loss distinguishes all classes, else only foreground vs. background (class agnostic).
- self.class_specific_seg_flag = True
- self.num_seg_classes = 3 if self.class_specific_seg_flag else 2
- self.head_classes = self.num_seg_classes
-
- def add_mrcnn_configs(self):
-
- # learning rate is a list with one entry per epoch.
- self.learning_rate = [1e-4] * self.num_epochs
-
- # disable the re-sampling of mask proposals to original size for speed-up.
- # since evaluation is detection-driven (box-matching) and not instance segmentation-driven (iou-matching),
- # mask-outputs are optional.
- self.return_masks_in_val = True
- self.return_masks_in_test = False
-
- # set number of proposal boxes to plot after each epoch.
- self.n_plot_rpn_props = 5 if self.dim == 2 else 30
-
- # number of classes for head networks: n_foreground_classes + 1 (background)
- self.head_classes = 3
-
- # seg_classes hier refers to the first stage classifier (RPN)
- self.num_seg_classes = 2 # foreground vs. background
-
- # feature map strides per pyramid level are inferred from architecture.
- self.backbone_strides = {'xy': [4, 8, 16, 32], 'z': [1, 2, 4, 8]}
-
- # anchor scales are chosen according to expected object sizes in data set. Default uses only one anchor scale
- # per pyramid level. (outer list are pyramid levels (corresponding to BACKBONE_STRIDES), inner list are scales per level.)
- self.rpn_anchor_scales = {'xy': [[8], [16], [32], [64]], 'z': [[2], [4], [8], [16]]}
-
- # choose which pyramid levels to extract features from: P2: 0, P3: 1, P4: 2, P5: 3.
- self.pyramid_levels = [0, 1, 2, 3]
-
- # number of feature maps in rpn. typically lowered in 3D to save gpu-memory.
- self.n_rpn_features = 512 if self.dim == 2 else 128
-
- # anchor ratios and strides per position in feature maps.
- self.rpn_anchor_ratios = [0.5, 1, 2]
- self.rpn_anchor_stride = 1
-
- # Threshold for first stage (RPN) non-maximum suppression (NMS): LOWER == HARDER SELECTION
- self.rpn_nms_threshold = 0.7 if self.dim == 2 else 0.7
-
- # loss sampling settings.
- self.rpn_train_anchors_per_image = 6 #per batch element
- self.train_rois_per_image = 6 #per batch element
- self.roi_positive_ratio = 0.5
- self.anchor_matching_iou = 0.7
-
- # factor of top-k candidates to draw from per negative sample (stochastic-hard-example-mining).
- # poolsize to draw top-k candidates from will be shem_poolsize * n_negative_samples.
- self.shem_poolsize = 10
-
- self.pool_size = (7, 7) if self.dim == 2 else (7, 7, 3)
- self.mask_pool_size = (14, 14) if self.dim == 2 else (14, 14, 5)
- self.mask_shape = (28, 28) if self.dim == 2 else (28, 28, 10)
-
- self.rpn_bbox_std_dev = np.array([0.1, 0.1, 0.1, 0.2, 0.2, 0.2])
- self.bbox_std_dev = np.array([0.1, 0.1, 0.1, 0.2, 0.2, 0.2])
- self.window = np.array([0, 0, self.patch_size[0], self.patch_size[1], 0, self.patch_size_3D[2]])
- self.scale = np.array([self.patch_size[0], self.patch_size[1], self.patch_size[0], self.patch_size[1],
- self.patch_size_3D[2], self.patch_size_3D[2]])
- if self.dim == 2:
- self.rpn_bbox_std_dev = self.rpn_bbox_std_dev[:4]
- self.bbox_std_dev = self.bbox_std_dev[:4]
- self.window = self.window[:4]
- self.scale = self.scale[:4]
-
- # pre-selection in proposal-layer (stage 1) for NMS-speedup. applied per batch element.
- self.pre_nms_limit = 3000 if self.dim == 2 else 6000
-
- # n_proposals to be selected after NMS per batch element. too high numbers blow up memory if "detect_while_training" is True,
- # since proposals of the entire batch are forwarded through second stage in as one "batch".
- self.roi_chunk_size = 2500 if self.dim == 2 else 600
- self.post_nms_rois_training = 500 if self.dim == 2 else 75
- self.post_nms_rois_inference = 500
-
- # Final selection of detections (refine_detections)
- self.model_max_instances_per_batch_element = 10 if self.dim == 2 else 30 # per batch element and class.
- self.detection_nms_threshold = 1e-5 # needs to be > 0, otherwise all predictions are one cluster.
- self.model_min_confidence = 0.1
-
- if self.dim == 2:
- self.backbone_shapes = np.array(
- [[int(np.ceil(self.patch_size[0] / stride)),
- int(np.ceil(self.patch_size[1] / stride))]
- for stride in self.backbone_strides['xy']])
- else:
- self.backbone_shapes = np.array(
- [[int(np.ceil(self.patch_size[0] / stride)),
- int(np.ceil(self.patch_size[1] / stride)),
- int(np.ceil(self.patch_size[2] / stride_z))]
- for stride, stride_z in zip(self.backbone_strides['xy'], self.backbone_strides['z']
- )])
-
- if self.model == 'ufrcnn':
- self.operate_stride1 = True
- self.class_specific_seg_flag = True
- self.num_seg_classes = 3 if self.class_specific_seg_flag else 2
- self.frcnn_mode = True
-
- if self.model == 'retina_net' or self.model == 'retina_unet' or self.model == 'prob_detector':
- # implement extra anchor-scales according to retina-net publication.
- self.rpn_anchor_scales['xy'] = [[ii[0], ii[0] * (2 ** (1 / 3)), ii[0] * (2 ** (2 / 3))] for ii in
- self.rpn_anchor_scales['xy']]
- self.rpn_anchor_scales['z'] = [[ii[0], ii[0] * (2 ** (1 / 3)), ii[0] * (2 ** (2 / 3))] for ii in
- self.rpn_anchor_scales['z']]
- self.n_anchors_per_pos = len(self.rpn_anchor_ratios) * 3
-
- self.n_rpn_features = 256 if self.dim == 2 else 64
-
- # pre-selection of detections for NMS-speedup. per entire batch.
- self.pre_nms_limit = 10000 if self.dim == 2 else 50000
-
- # anchor matching iou is lower than in Mask R-CNN according to https://arxiv.org/abs/1708.02002
- self.anchor_matching_iou = 0.5
-
- # if 'True', seg loss distinguishes all classes, else only foreground vs. background (class agnostic).
- self.num_seg_classes = 3 if self.class_specific_seg_flag else 2
-
- if self.model == 'retina_unet':
- self.operate_stride1 = True
-
diff --git a/experiments/toy_exp/dev/default_configs.py b/experiments/toy_exp/dev/default_configs.py
deleted file mode 100644
index 7abc68d..0000000
--- a/experiments/toy_exp/dev/default_configs.py
+++ /dev/null
@@ -1,140 +0,0 @@
-#!/usr/bin/env python
-# Copyright 2018 Division of Medical Image Computing, German Cancer Research Center (DKFZ).
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-"""Default Configurations script. Avoids changing configs of all experiments if general settings are to be changed."""
-
-import os
-
-class DefaultConfigs:
-
- def __init__(self, model, server_env=None, dim=2):
-
- #########################
- # I/O #
- #########################
-
- self.model = model
- self.dim = dim
- # int [0 < dataset_size]. select n patients from dataset for prototyping.
- self.select_prototype_subset = None
-
- # some default paths.
- self.backbone_path = 'models/backbone.py'
- self.source_dir = os.path.dirname(os.path.realpath(__file__)) #current dir.
- self.input_df_name = 'info_df.pickle'
- self.model_path = 'models/{}.py'.format(self.model)
-
- if server_env:
- self.source_dir = '/home/jaegerp/code/mamma_code/medicaldetectiontoolkit'
-
-
- #########################
- # Data Loader #
- #########################
-
- #random seed for fold_generator and batch_generator.
- self.seed = 0
-
- #number of threads for multithreaded batch generation.
- self.n_workers = 6
-
- # if True, segmentation losses learn all categories, else only foreground vs. background.
- self.class_specific_seg_flag = False
-
- #########################
- # Architecture #
- #########################
-
- self.weight_decay = 0.0
-
- # nonlinearity to be applied after convs with nonlinearity. one of 'relu' or 'leaky_relu'
- self.relu = 'relu'
-
- # if True initializes weights as specified in model script. else use default Pytorch init.
- self.custom_init = False
-
- # if True adds high-res decoder levels to feature pyramid: P1 + P0. (e.g. set to true in retina_unet configs)
- self.operate_stride1 = False
-
- #########################
- # Schedule #
- #########################
-
- # number of folds in cross validation.
- self.n_cv_splits = 5
-
-
- # number of probabilistic samples in validation.
- self.n_probabilistic_samples = None
-
- #########################
- # Testing / Plotting #
- #########################
-
- # perform mirroring at test time. (only XY. Z not done to not blow up predictions times).
- self.test_aug = True
-
- # if True, test data lies in a separate folder and is not part of the cross validation.
- self.hold_out_test_set = False
-
- # if hold_out_test_set provided, ensemble predictions over models of all trained cv-folds.
- self.ensemble_folds = False
-
- # color specifications for all box_types in prediction_plot.
- self.box_color_palette = {'det': 'b', 'gt': 'r', 'neg_class': 'purple',
- 'prop': 'w', 'pos_class': 'g', 'pos_anchor': 'c', 'neg_anchor': 'c'}
-
- # scan over confidence score in evaluation to optimize it on the validation set.
- self.scan_det_thresh = False
-
- # plots roc-curves / prc-curves in evaluation.
- self.plot_stat_curves = False
-
- # evaluates average precision per image and averages over images. instead computing one ap over data set.
- self.per_patient_ap = False
-
- # threshold for clustering 2D box predictions to 3D Cubes. Overlap is computed in XY.
- self.merge_3D_iou = 0.1
-
- # monitor any value from training.
- self.n_monitoring_figures = 1
- # dict to assign specific plot_values to monitor_figures > 0. {1: ['class_loss'], 2: ['kl_loss', 'kl_sigmas']}
- self.assign_values_to_extra_figure = {}
-
- # save predictions to csv file in experiment dir.
- self.save_preds_to_csv = True
-
- # select a maximum number of patient cases to test. number or "all" for all
- self.max_test_patients = "all"
-
- #########################
- # MRCNN #
- #########################
-
- # if True, mask loss is not applied. used for data sets, where no pixel-wise annotations are provided.
- self.frcnn_mode = False
-
- # if True, unmolds masks in Mask R-CNN to full-res for plotting/monitoring.
- self.return_masks_in_val = False
- self.return_masks_in_test = False # needed if doing instance segmentation. evaluation not yet implemented.
-
- # add P6 to Feature Pyramid Network.
- self.sixth_pooling = False
-
- # for probabilistic detection
- self.n_latent_dims = 0
-
-
diff --git a/experiments/toy_exp/dev/model.py b/experiments/toy_exp/dev/model.py
deleted file mode 100644
index ba792b5..0000000
--- a/experiments/toy_exp/dev/model.py
+++ /dev/null
@@ -1,1083 +0,0 @@
-#!/usr/bin/env python
-# Copyright 2018 Division of Medical Image Computing, German Cancer Research Center (DKFZ).
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-"""
-Parts are based on https://github.com/multimodallearning/pytorch-mask-rcnn
-published under MIT license.
-"""
-
-import utils.model_utils as mutils
-import utils.exp_utils as utils
-from cuda_functions.nms_2D.pth_nms import nms_gpu as nms_2D
-from cuda_functions.nms_3D.pth_nms import nms_gpu as nms_3D
-from cuda_functions.roi_align_2D.roi_align.crop_and_resize import CropAndResizeFunction as ra2D
-from cuda_functions.roi_align_3D.roi_align.crop_and_resize import CropAndResizeFunction as ra3D
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils
-
-
-############################################################
-# Networks on top of backbone
-############################################################
-
-class RPN(nn.Module):
- """
- Region Proposal Network.
- """
-
- def __init__(self, cf, conv):
-
- super(RPN, self).__init__()
- self.dim = conv.dim
-
- self.conv_shared = conv(cf.end_filts, cf.n_rpn_features, ks=3, stride=cf.rpn_anchor_stride, pad=1, relu=cf.relu)
- self.conv_class = conv(cf.n_rpn_features, 2 * len(cf.rpn_anchor_ratios), ks=1, stride=1, relu=None)
- self.conv_bbox = conv(cf.n_rpn_features, 2 * self.dim * len(cf.rpn_anchor_ratios), ks=1, stride=1, relu=None)
-
-
- def forward(self, x):
- """
- :param x: input feature maps (b, in_channels, y, x, (z))
- :return: rpn_class_logits (b, 2, n_anchors)
- :return: rpn_probs_logits (b, 2, n_anchors)
- :return: rpn_bbox (b, 2 * dim, n_anchors)
- """
-
- # Shared convolutional base of the RPN.
- x = self.conv_shared(x)
-
- # Anchor Score. (batch, anchors per location * 2, y, x, (z)).
- rpn_class_logits = self.conv_class(x)
- # Reshape to (batch, 2, anchors)
- axes = (0, 2, 3, 1) if self.dim == 2 else (0, 2, 3, 4, 1)
- rpn_class_logits = rpn_class_logits.permute(*axes)
- rpn_class_logits = rpn_class_logits.contiguous()
- rpn_class_logits = rpn_class_logits.view(x.size()[0], -1, 2)
-
- # Softmax on last dimension (fg vs. bg).
- rpn_probs = F.softmax(rpn_class_logits, dim=2)
-
- # Bounding box refinement. (batch, anchors_per_location * (y, x, (z), log(h), log(w), (log(d)), y, x, (z))
- rpn_bbox = self.conv_bbox(x)
-
- # Reshape to (batch, 2*dim, anchors)
- rpn_bbox = rpn_bbox.permute(*axes)
- rpn_bbox = rpn_bbox.contiguous()
- rpn_bbox = rpn_bbox.view(x.size()[0], -1, self.dim * 2)
-
- return [rpn_class_logits, rpn_probs, rpn_bbox]
-
-
-
-class Classifier(nn.Module):
- """
- Head network for classification and bounding box refinement. Performs RoiAlign, processes resulting features through a
- shared convolutional base and finally branches off the classifier- and regression head.
- """
- def __init__(self, cf, conv):
- super(Classifier, self).__init__()
-
- self.dim = conv.dim
- self.in_channels = cf.end_filts
- self.pool_size = cf.pool_size
- self.pyramid_levels = cf.pyramid_levels
- # instance_norm does not work with spatial dims (1, 1, (1))
- norm = cf.norm if cf.norm != 'instance_norm' else None
-
- self.conv1 = conv(cf.end_filts, cf.end_filts * 4, ks=self.pool_size, stride=1, norm=norm, relu=cf.relu)
- self.conv2 = conv(cf.end_filts * 4, cf.end_filts * 4, ks=1, stride=1, norm=norm, relu=cf.relu)
- self.linear_class = nn.Linear(cf.end_filts * 4, cf.head_classes)
- self.linear_bbox = nn.Linear(cf.end_filts * 4, cf.head_classes * 2 * self.dim)
-
- def forward(self, x, rois):
- """
- :param x: input feature maps (b, in_channels, y, x, (z))
- :param rois: normalized box coordinates as proposed by the RPN to be forwarded through
- the second stage (n_proposals, (y1, x1, y2, x2, (z1), (z2), batch_ix). Proposals of all batch elements
- have been merged to one vector, while the origin info has been stored for re-allocation.
- :return: mrcnn_class_logits (n_proposals, n_head_classes)
- :return: mrcnn_bbox (n_proposals, n_head_classes, 2 * dim) predicted corrections to be applied to proposals for refinement.
- """
- x = pyramid_roi_align(x, rois, self.pool_size, self.pyramid_levels, self.dim)
- x = self.conv1(x)
- x = self.conv2(x)
- x = x.view(-1, self.in_channels * 4)
- mrcnn_class_logits = self.linear_class(x)
- mrcnn_bbox = self.linear_bbox(x)
- mrcnn_bbox = mrcnn_bbox.view(mrcnn_bbox.size()[0], -1, self.dim * 2)
-
- return [mrcnn_class_logits, mrcnn_bbox]
-
-
-
-class Mask(nn.Module):
- """
- Head network for proposal-based mask segmentation. Performs RoiAlign, some convolutions and applies sigmoid on the
- output logits to allow for overlapping classes.
- """
- def __init__(self, cf, conv):
- super(Mask, self).__init__()
- self.pool_size = cf.mask_pool_size
- self.pyramid_levels = cf.pyramid_levels
- self.dim = conv.dim
- self.conv1 = conv(cf.end_filts, cf.end_filts, ks=3, stride=1, pad=1, norm=cf.norm, relu=cf.relu)
- self.conv2 = conv(cf.end_filts, cf.end_filts, ks=3, stride=1, pad=1, norm=cf.norm, relu=cf.relu)
- self.conv3 = conv(cf.end_filts, cf.end_filts, ks=3, stride=1, pad=1, norm=cf.norm, relu=cf.relu)
- self.conv4 = conv(cf.end_filts, cf.end_filts, ks=3, stride=1, pad=1, norm=cf.norm, relu=cf.relu)
- if conv.dim == 2:
- self.deconv = nn.ConvTranspose2d(cf.end_filts, cf.end_filts, kernel_size=2, stride=2)
- else:
- self.deconv = nn.ConvTranspose3d(cf.end_filts, cf.end_filts, kernel_size=2, stride=2)
-
- self.relu = nn.ReLU(inplace=True) if cf.relu == 'relu' else nn.LeakyReLU(inplace=True)
- self.conv5 = conv(cf.end_filts, cf.head_classes, ks=1, stride=1, relu=None)
- self.sigmoid = nn.Sigmoid()
-
- def forward(self, x, rois):
- """
- :param x: input feature maps (b, in_channels, y, x, (z))
- :param rois: normalized box coordinates as proposed by the RPN to be forwarded through
- the second stage (n_proposals, (y1, x1, y2, x2, (z1), (z2), batch_ix). Proposals of all batch elements
- have been merged to one vector, while the origin info has been stored for re-allocation.
- :return: x: masks (n_sampled_proposals (n_detections in inference), n_classes, y, x, (z))
- """
- x = pyramid_roi_align(x, rois, self.pool_size, self.pyramid_levels, self.dim)
- x = self.conv1(x)
- x = self.conv2(x)
- x = self.conv3(x)
- x = self.conv4(x)
- x = self.relu(self.deconv(x))
- x = self.conv5(x)
- x = self.sigmoid(x)
- return x
-
-
-############################################################
-# Loss Functions
-############################################################
-
-def compute_rpn_class_loss(rpn_match, rpn_class_logits, shem_poolsize):
- """
- :param rpn_match: (n_anchors). [-1, 0, 1] for negative, neutral, and positive matched anchors.
- :param rpn_class_logits: (n_anchors, 2). logits from RPN classifier.
- :param shem_poolsize: int. factor of top-k candidates to draw from per negative sample
- (stochastic-hard-example-mining).
- :return: loss: torch tensor
- :return: np_neg_ix: 1D array containing indices of the neg_roi_logits, which have been sampled for training.
- """
-
- # filter out neutral anchors.
- pos_indices = torch.nonzero(rpn_match == 1)
- neg_indices = torch.nonzero(rpn_match == -1)
-
- # loss for positive samples
- if 0 not in pos_indices.size():
- pos_indices = pos_indices.squeeze(1)
- roi_logits_pos = rpn_class_logits[pos_indices]
- pos_loss = F.cross_entropy(roi_logits_pos, torch.LongTensor([1] * pos_indices.shape[0]).cuda())
- else:
- pos_loss = torch.FloatTensor([0]).cuda()
-
- # loss for negative samples: draw hard negative examples (SHEM)
- # that match the number of positive samples, but at least 1.
- if 0 not in neg_indices.size():
- neg_indices = neg_indices.squeeze(1)
- roi_logits_neg = rpn_class_logits[neg_indices]
- negative_count = np.max((1, pos_indices.cpu().data.numpy().size))
- roi_probs_neg = F.softmax(roi_logits_neg, dim=1)
- neg_ix = mutils.shem(roi_probs_neg, negative_count, shem_poolsize)
- neg_loss = F.cross_entropy(roi_logits_neg[neg_ix], torch.LongTensor([0] * neg_ix.shape[0]).cuda())
- np_neg_ix = neg_ix.cpu().data.numpy()
- else:
- neg_loss = torch.FloatTensor([0]).cuda()
- np_neg_ix = np.array([]).astype('int32')
-
- loss = (pos_loss + neg_loss) / 2
- return loss, np_neg_ix
-
-
-def compute_rpn_bbox_loss(rpn_target_deltas, rpn_pred_deltas, rpn_match):
- """
- :param rpn_target_deltas: (b, n_positive_anchors, (dy, dx, (dz), log(dh), log(dw), (log(dd)))).
- Uses 0 padding to fill in unsed bbox deltas.
- :param rpn_pred_deltas: predicted deltas from RPN. (b, n_anchors, (dy, dx, (dz), log(dh), log(dw), (log(dd))))
- :param rpn_match: (n_anchors). [-1, 0, 1] for negative, neutral, and positive matched anchors.
- :return: loss: torch 1D tensor.
- """
- if 0 not in torch.nonzero(rpn_match == 1).size():
-
- indices = torch.nonzero(rpn_match == 1).squeeze(1)
- # Pick bbox deltas that contribute to the loss
- rpn_pred_deltas = rpn_pred_deltas[indices]
- # Trim target bounding box deltas to the same length as rpn_bbox.
- target_deltas = rpn_target_deltas[:rpn_pred_deltas.size()[0], :]
- # Smooth L1 loss
- loss = F.smooth_l1_loss(rpn_pred_deltas, target_deltas)
- else:
- loss = torch.FloatTensor([0]).cuda()
-
- return loss
-
-
-def compute_mrcnn_class_loss(target_class_ids, pred_class_logits):
- """
- :param target_class_ids: (n_sampled_rois) batch dimension was merged into roi dimension.
- :param pred_class_logits: (n_sampled_rois, n_classes)
- :return: loss: torch 1D tensor.
- """
- if 0 not in target_class_ids.size():
- loss = F.cross_entropy(pred_class_logits, target_class_ids.long())
- else:
- loss = torch.FloatTensor([0.]).cuda()
-
- return loss
-
-
-def compute_mrcnn_bbox_loss(mrcnn_target_deltas, mrcnn_pred_deltas, target_class_ids):
- """
- :param mrcnn_target_deltas: (n_sampled_rois, (dy, dx, (dz), log(dh), log(dw), (log(dh)))
- :param mrcnn_pred_deltas: (n_sampled_rois, n_classes, (dy, dx, (dz), log(dh), log(dw), (log(dh)))
- :param target_class_ids: (n_sampled_rois)
- :return: loss: torch 1D tensor.
- """
- if 0 not in torch.nonzero(target_class_ids > 0).size():
- positive_roi_ix = torch.nonzero(target_class_ids > 0)[:, 0]
- positive_roi_class_ids = target_class_ids[positive_roi_ix].long()
- target_bbox = mrcnn_target_deltas[positive_roi_ix, :].detach()
- pred_bbox = mrcnn_pred_deltas[positive_roi_ix, positive_roi_class_ids, :]
- loss = F.smooth_l1_loss(pred_bbox, target_bbox)
- else:
- loss = torch.FloatTensor([0]).cuda()
-
- return loss
-
-
-def compute_mrcnn_mask_loss(target_masks, pred_masks, target_class_ids):
- """
- :param target_masks: (n_sampled_rois, y, x, (z)) A float32 tensor of values 0 or 1. Uses zero padding to fill array.
- :param pred_masks: (n_sampled_rois, n_classes, y, x, (z)) float32 tensor with values between [0, 1].
- :param target_class_ids: (n_sampled_rois)
- :return: loss: torch 1D tensor.
- """
- if 0 not in torch.nonzero(target_class_ids > 0).size():
- # Only positive ROIs contribute to the loss. And only
- # the class specific mask of each ROI.
- positive_ix = torch.nonzero(target_class_ids > 0)[:, 0]
- positive_class_ids = target_class_ids[positive_ix].long()
- y_true = target_masks[positive_ix, :, :].detach()
- y_pred = pred_masks[positive_ix, positive_class_ids, :, :]
- loss = F.binary_cross_entropy(y_pred, y_true)
- else:
- loss = torch.FloatTensor([0]).cuda()
-
- return loss
-
-
-############################################################
-# Helper Layers
-############################################################
-
-def proposal_layer(rpn_pred_probs, rpn_pred_deltas, proposal_count, anchors, cf):
- """
- Receives anchor scores and selects a subset to pass as proposals
- to the second stage. Filtering is done based on anchor scores and
- non-max suppression to remove overlaps. It also applies bounding
- box refinment detals to anchors.
- :param rpn_pred_probs: (b, n_anchors, 2)
- :param rpn_pred_deltas: (b, n_anchors, (y, x, (z), log(h), log(w), (log(d))))
- :return: batch_normalized_boxes: Proposals in normalized coordinates
- (b, proposal_count, (y1, x1, y2, x2, (z1), (z2)))
- :return: batch_out_proposals: Box coords + RPN foreground scores
- for monitoring/plotting (b, proposal_count, (y1, x1, y2, x2, (z1), (z2), score))
- """
- batch_scores = rpn_pred_probs[:, :, 1]
- batch_deltas = rpn_pred_deltas
- batch_anchors = anchors
- batch_normalized_boxes = []
- batch_out_proposals = []
-
- # loop over batch dimension.
- for ix in range(batch_scores.shape[0]):
-
- scores = batch_scores[ix]
- deltas = batch_deltas[ix]
- anchors = batch_anchors.clone()
- # norm deltas
- std_dev = torch.from_numpy(cf.rpn_bbox_std_dev[None]).float().cuda()
- deltas = deltas * std_dev
-
- # improve performance by trimming to top anchors by score
- # and doing the rest on the smaller subset.
- pre_nms_limit = min(cf.pre_nms_limit, anchors.size()[0])
- scores, order = scores.sort(descending=True)
- order = order[:pre_nms_limit]
- scores = scores[:pre_nms_limit]
- deltas = deltas[order, :]
- anchors = anchors[order, :]
-
- # apply deltas to anchors to get refined anchors and filter with non-maximum surpression.
- if batch_deltas.shape[-1] == 4:
- boxes = mutils.apply_box_deltas_2D(anchors, deltas)
- boxes = mutils.clip_boxes_2D(boxes, cf.window)
- keep = nms_2D(torch.cat((boxes, scores.unsqueeze(1)), 1), cf.rpn_nms_threshold)
- norm = torch.from_numpy(cf.scale).float().cuda()
-
- else:
- boxes = mutils.apply_box_deltas_3D(anchors, deltas)
- boxes = mutils.clip_boxes_3D(boxes, cf.window)
- keep = nms_3D(torch.cat((boxes, scores.unsqueeze(1)), 1), cf.rpn_nms_threshold)
- norm = torch.from_numpy(cf.scale).float().cuda()
-
- keep = keep[:proposal_count]
- boxes = boxes[keep, :]
- rpn_scores = scores[keep][:, None]
-
- # pad missing boxes with 0.
- if boxes.shape[0] < proposal_count:
- n_pad_boxes = proposal_count - boxes.shape[0]
- zeros = torch.zeros([n_pad_boxes, boxes.shape[1]]).cuda()
- boxes = torch.cat([boxes, zeros], dim=0)
- zeros = torch.zeros([n_pad_boxes, rpn_scores.shape[1]]).cuda()
- rpn_scores = torch.cat([rpn_scores, zeros], dim=0)
-
- # concat box and score info for monitoring/plotting.
- batch_out_proposals.append(torch.cat((boxes, rpn_scores), 1).cpu().data.numpy())
- # normalize dimensions to range of 0 to 1.
- normalized_boxes = boxes / norm
- # add back batch dimension
- batch_normalized_boxes.append(normalized_boxes.unsqueeze(0))
-
- batch_normalized_boxes = torch.cat(batch_normalized_boxes)
- batch_out_proposals = np.array(batch_out_proposals)
- return batch_normalized_boxes, batch_out_proposals
-
-
-
-def pyramid_roi_align(feature_maps, rois, pool_size, pyramid_levels, dim):
- """
- Implements ROI Pooling on multiple levels of the feature pyramid.
- :param feature_maps: list of feature maps, each of shape (b, c, y, x , (z))
- :param rois: proposals (normalized coords.) as returned by RPN. contain info about original batch element allocation.
- (n_proposals, (y1, x1, y2, x2, (z1), (z2), batch_ixs)
- :param pool_size: list of poolsizes in dims: [x, y, (z)]
- :param pyramid_levels: list. [0, 1, 2, ...]
- :return: pooled: pooled feature map rois (n_proposals, c, poolsize_y, poolsize_x, (poolsize_z))
-
- Output:
- Pooled regions in the shape: [num_boxes, height, width, channels].
- The width and height are those specific in the pool_shape in the layer
- constructor.
- """
- boxes = rois[:, :dim*2]
- batch_ixs = rois[:, dim*2]
-
- # Assign each ROI to a level in the pyramid based on the ROI area.
- if dim == 2:
- y1, x1, y2, x2 = boxes.chunk(4, dim=1)
- else:
- y1, x1, y2, x2, z1, z2 = boxes.chunk(6, dim=1)
-
- h = y2 - y1
- w = x2 - x1
-
- # Equation 1 in https://arxiv.org/abs/1612.03144. Account for
- # the fact that our coordinates are normalized here.
- # divide sqrt(h*w) by 1 instead image_area.
- roi_level = (4 + mutils.log2(torch.sqrt(h*w))).round().int().clamp(pyramid_levels[0], pyramid_levels[-1])
- # if Pyramid contains additional level P6, adapt the roi_level assignemnt accordingly.
- if len(pyramid_levels) == 5:
- roi_level[h*w > 0.65] = 5
-
- # Loop through levels and apply ROI pooling to each.
- pooled = []
- box_to_level = []
- for level_ix, level in enumerate(pyramid_levels):
- ix = roi_level == level
- if not ix.any():
- continue
- ix = torch.nonzero(ix)[:, 0]
- level_boxes = boxes[ix, :]
- # re-assign rois to feature map of original batch element.
- ind = batch_ixs[ix].int()
-
- # Keep track of which box is mapped to which level
- box_to_level.append(ix)
-
- # Stop gradient propogation to ROI proposals
- level_boxes = level_boxes.detach()
-
- # Crop and Resize
- # From Mask R-CNN paper: "We sample four regular locations, so
- # that we can evaluate either max or average pooling. In fact,
- # interpolating only a single value at each bin center (without
- # pooling) is nearly as effective."
- #
- # Here we use the simplified approach of a single value per bin,
- # which is how is done in tf.crop_and_resize()
- #
- # Also fixed a bug from original implementation, reported in:
- # https://hackernoon.com/how-tensorflows-tf-image-resize-stole-60-days-of-my-life-aba5eb093f35
-
- if len(pool_size) == 2:
- pooled_features = ra2D(pool_size[0], pool_size[1], 0)(feature_maps[level_ix], level_boxes, ind)
- else:
- pooled_features = ra3D(pool_size[0], pool_size[1], pool_size[2], 0)(feature_maps[level_ix], level_boxes, ind)
-
- pooled.append(pooled_features)
-
-
- # Pack pooled features into one tensor
- pooled = torch.cat(pooled, dim=0)
-
- # Pack box_to_level mapping into one array and add another
- # column representing the order of pooled boxes
- box_to_level = torch.cat(box_to_level, dim=0)
-
- # Rearrange pooled features to match the order of the original boxes
- _, box_to_level = torch.sort(box_to_level)
- pooled = pooled[box_to_level, :, :]
-
- return pooled
-
-
-
-def detection_target_layer(batch_proposals, batch_mrcnn_class_scores, batch_gt_class_ids, batch_gt_boxes, batch_gt_masks, cf):
- """
- Subsamples proposals for mrcnn losses and generates targets. Sampling is done per batch element, seems to have positive
- effects on training, as opposed to sampling over entire batch. Negatives are sampled via stochastic-hard-example-mining
- (SHEM), where a number of negative proposals are drawn from larger pool of highest scoring proposals for stochasticity.
- Scoring is obtained here as the max over all foreground probabilities as returned by mrcnn_classifier (worked better than
- loss-based class balancing methods like "online-hard-example-mining" or "focal loss".)
-
- :param batch_proposals: (n_proposals, (y1, x1, y2, x2, (z1), (z2), batch_ixs).
- boxes as proposed by RPN. n_proposals here is determined by batch_size * POST_NMS_ROIS.
- :param batch_mrcnn_class_scores: (n_proposals, n_classes)
- :param batch_gt_class_ids: list over batch elements. Each element is a list over the corresponding roi target labels.
- :param batch_gt_boxes: list over batch elements. Each element is a list over the corresponding roi target coordinates.
- :param batch_gt_masks: list over batch elements. Each element is binary mask of shape (n_gt_rois, y, x, (z), c)
- :return: sample_indices: (n_sampled_rois) indices of sampled proposals to be used for loss functions.
- :return: target_class_ids: (n_sampled_rois)containing target class labels of sampled proposals.
- :return: target_deltas: (n_sampled_rois, 2 * dim) containing target deltas of sampled proposals for box refinement.
- :return: target_masks: (n_sampled_rois, y, x, (z)) containing target masks of sampled proposals.
- """
- # normalization of target coordinates
- if cf.dim == 2:
- h, w = cf.patch_size
- scale = torch.from_numpy(np.array([h, w, h, w])).float().cuda()
- else:
- h, w, z = cf.patch_size
- scale = torch.from_numpy(np.array([h, w, h, w, z, z])).float().cuda()
-
-
- positive_count = 0
- negative_count = 0
- sample_positive_indices = []
- sample_negative_indices = []
- sample_deltas = []
- sample_masks = []
- sample_class_ids = []
-
- # loop over batch and get positive and negative sample rois.
- for b in range(len(batch_gt_class_ids)):
-
- gt_class_ids = torch.from_numpy(batch_gt_class_ids[b]).int().cuda()
- gt_masks = torch.from_numpy(batch_gt_masks[b]).float().cuda()
- if np.any(batch_gt_class_ids[b] > 0): # skip roi selection for no gt images.
- gt_boxes = torch.from_numpy(batch_gt_boxes[b]).float().cuda() / scale
- else:
- gt_boxes = torch.FloatTensor().cuda()
-
- # get proposals and indices of current batch element.
- proposals = batch_proposals[batch_proposals[:, -1] == b][:, :-1]
- batch_element_indices = torch.nonzero(batch_proposals[:, -1] == b).squeeze(1)
-
- # Compute overlaps matrix [proposals, gt_boxes]
- if 0 not in gt_boxes.size():
- if gt_boxes.shape[1] == 4:
- overlaps = mutils.bbox_overlaps_2D(proposals, gt_boxes)
- else:
- overlaps = mutils.bbox_overlaps_3D(proposals, gt_boxes)
-
- # Determine postive and negative ROIs
- roi_iou_max = torch.max(overlaps, dim=1)[0]
- # 1. Positive ROIs are those with >= 0.5 IoU with a GT box
- positive_roi_bool = roi_iou_max >= (0.5 if cf.dim == 2 else 0.3)
- # 2. Negative ROIs are those with < 0.1 with every GT box.
- negative_roi_bool = roi_iou_max < (0.1 if cf.dim == 2 else 0.01)
- else:
- positive_roi_bool = torch.FloatTensor().cuda()
- negative_roi_bool = torch.from_numpy(np.array([1]*proposals.shape[0])).cuda()
-
- # Sample Positive ROIs
- if 0 not in torch.nonzero(positive_roi_bool).size():
- positive_indices = torch.nonzero(positive_roi_bool).squeeze(1)
- positive_samples = int(cf.train_rois_per_image * cf.roi_positive_ratio)
- rand_idx = torch.randperm(positive_indices.size()[0])
- rand_idx = rand_idx[:positive_samples].cuda()
- positive_indices = positive_indices[rand_idx]
- positive_samples = positive_indices.size()[0]
- positive_rois = proposals[positive_indices, :]
- # Assign positive ROIs to GT boxes.
- positive_overlaps = overlaps[positive_indices, :]
- roi_gt_box_assignment = torch.max(positive_overlaps, dim=1)[1]
- roi_gt_boxes = gt_boxes[roi_gt_box_assignment, :]
- roi_gt_class_ids = gt_class_ids[roi_gt_box_assignment]
-
- # Compute bbox refinement targets for positive ROIs
- deltas = mutils.box_refinement(positive_rois, roi_gt_boxes)
- std_dev = torch.from_numpy(cf.bbox_std_dev).float().cuda()
- deltas /= std_dev
-
- # Assign positive ROIs to GT masks
- roi_masks = gt_masks[roi_gt_box_assignment, :, :]
-
- # Compute mask targets
- boxes = positive_rois
- box_ids = torch.arange(roi_masks.size()[0]).int().cuda()
-
- if len(cf.mask_shape) == 2:
- masks = ra2D(cf.mask_shape[0], cf.mask_shape[1], 0)(roi_masks.unsqueeze(1), boxes, box_ids)
- else:
- masks = ra3D(cf.mask_shape[0], cf.mask_shape[1], cf.mask_shape[2], 0)(roi_masks.unsqueeze(1), boxes, box_ids)
-
- masks = masks.squeeze(1)
- # Threshold mask pixels at 0.5 to have GT masks be 0 or 1 to use with
- # binary cross entropy loss.
- masks = torch.round(masks)
-
- sample_positive_indices.append(batch_element_indices[positive_indices])
- sample_deltas.append(deltas)
- sample_masks.append(masks)
- sample_class_ids.append(roi_gt_class_ids)
- positive_count += positive_samples
- else:
- positive_samples = 0
-
- # Negative ROIs. Add enough to maintain positive:negative ratio, but at least 1. Sample via SHEM.
- if 0 not in torch.nonzero(negative_roi_bool).size():
- negative_indices = torch.nonzero(negative_roi_bool).squeeze(1)
- r = 1.0 / cf.roi_positive_ratio
- b_neg_count = np.max((int(r * positive_samples - positive_samples), 1))
- roi_probs_neg = batch_mrcnn_class_scores[batch_element_indices[negative_indices]]
- raw_sampled_indices = mutils.shem(roi_probs_neg, b_neg_count, cf.shem_poolsize)
- sample_negative_indices.append(batch_element_indices[negative_indices[raw_sampled_indices]])
- negative_count += raw_sampled_indices.size()[0]
-
- if len(sample_positive_indices) > 0:
- target_deltas = torch.cat(sample_deltas)
- target_masks = torch.cat(sample_masks)
- target_class_ids = torch.cat(sample_class_ids)
-
- # Pad target information with zeros for negative ROIs.
- if positive_count > 0 and negative_count > 0:
- sample_indices = torch.cat((torch.cat(sample_positive_indices), torch.cat(sample_negative_indices)), dim=0)
- zeros = torch.zeros(negative_count).int().cuda()
- target_class_ids = torch.cat([target_class_ids, zeros], dim=0)
- zeros = torch.zeros(negative_count, cf.dim * 2).cuda()
- target_deltas = torch.cat([target_deltas, zeros], dim=0)
- zeros = torch.zeros(negative_count, *cf.mask_shape).cuda()
- target_masks = torch.cat([target_masks, zeros], dim=0)
- elif positive_count > 0:
- sample_indices = torch.cat(sample_positive_indices)
- elif negative_count > 0:
- sample_indices = torch.cat(sample_negative_indices)
- zeros = torch.zeros(negative_count).int().cuda()
- target_class_ids = zeros
- zeros = torch.zeros(negative_count, cf.dim * 2).cuda()
- target_deltas = zeros
- zeros = torch.zeros(negative_count, *cf.mask_shape).cuda()
- target_masks = zeros
- else:
- sample_indices = torch.LongTensor().cuda()
- target_class_ids = torch.IntTensor().cuda()
- target_deltas = torch.FloatTensor().cuda()
- target_masks = torch.FloatTensor().cuda()
-
- return sample_indices, target_class_ids, target_deltas, target_masks
-
-
-############################################################
-# Output Handler
-############################################################
-
-def refine_detections(rois, probs, deltas, batch_ixs, cf):
- """
- Refine classified proposals, filter overlaps and return final detections.
-
- :param rois: (n_proposals, 2 * dim) normalized boxes as proposed by RPN. n_proposals = batch_size * POST_NMS_ROIS
- :param probs: (n_proposals, n_classes) softmax probabilities for all rois as predicted by mrcnn classifier.
- :param deltas: (n_proposals, n_classes, 2 * dim) box refinement deltas as predicted by mrcnn bbox regressor.
- :param batch_ixs: (n_proposals) batch element assignemnt info for re-allocation.
- :return: result: (n_final_detections, (y1, x1, y2, x2, (z1), (z2), batch_ix, pred_class_id, pred_score))
- """
- # class IDs per ROI. Since scores of all classes are of interest (not just max class), all are kept at this point.
- class_ids = []
- fg_classes = cf.head_classes - 1
- # repeat vectors to fill in predictions for all foreground classes.
- for ii in range(1, fg_classes + 1):
- class_ids += [ii] * rois.shape[0]
- class_ids = torch.from_numpy(np.array(class_ids)).cuda()
-
- rois = rois.repeat(fg_classes, 1)
- probs = probs.repeat(fg_classes, 1)
- deltas = deltas.repeat(fg_classes, 1, 1)
- batch_ixs = batch_ixs.repeat(fg_classes)
-
- # get class-specific scores and bounding box deltas
- idx = torch.arange(class_ids.size()[0]).long().cuda()
- class_scores = probs[idx, class_ids]
- deltas_specific = deltas[idx, class_ids]
- batch_ixs = batch_ixs[idx]
-
- # apply bounding box deltas. re-scale to image coordinates.
- std_dev = torch.from_numpy(np.reshape(cf.rpn_bbox_std_dev, [1, cf.dim * 2])).float().cuda()
- scale = torch.from_numpy(cf.scale).float().cuda()
- refined_rois = mutils.apply_box_deltas_2D(rois, deltas_specific * std_dev) * scale if cf.dim == 2 else \
- mutils.apply_box_deltas_3D(rois, deltas_specific * std_dev) * scale
-
- # round and cast to int since we're deadling with pixels now
- refined_rois = mutils.clip_to_window(cf.window, refined_rois)
- refined_rois = torch.round(refined_rois)
-
- # filter out low confidence boxes
- keep = idx
- keep_bool = (class_scores >= cf.model_min_confidence)
- if 0 not in torch.nonzero(keep_bool).size():
-
- score_keep = torch.nonzero(keep_bool)[:, 0]
- pre_nms_class_ids = class_ids[score_keep]
- pre_nms_rois = refined_rois[score_keep]
- pre_nms_scores = class_scores[score_keep]
- pre_nms_batch_ixs = batch_ixs[score_keep]
-
- for j, b in enumerate(mutils.unique1d(pre_nms_batch_ixs)):
-
- bixs = torch.nonzero(pre_nms_batch_ixs == b)[:, 0]
- bix_class_ids = pre_nms_class_ids[bixs]
- bix_rois = pre_nms_rois[bixs]
- bix_scores = pre_nms_scores[bixs]
-
- for i, class_id in enumerate(mutils.unique1d(bix_class_ids)):
-
- ixs = torch.nonzero(bix_class_ids == class_id)[:, 0]
- # nms expects boxes sorted by score.
- ix_rois = bix_rois[ixs]
- ix_scores = bix_scores[ixs]
- ix_scores, order = ix_scores.sort(descending=True)
- ix_rois = ix_rois[order, :]
-
- if cf.dim == 2:
- class_keep = nms_2D(torch.cat((ix_rois, ix_scores.unsqueeze(1)), dim=1), cf.detection_nms_threshold)
- else:
- class_keep = nms_3D(torch.cat((ix_rois, ix_scores.unsqueeze(1)), dim=1), cf.detection_nms_threshold)
-
- # map indices back.
- class_keep = keep[score_keep[bixs[ixs[order[class_keep]]]]]
- # merge indices over classes for current batch element
- b_keep = class_keep if i == 0 else mutils.unique1d(torch.cat((b_keep, class_keep)))
-
- # only keep top-k boxes of current batch-element
- top_ids = class_scores[b_keep].sort(descending=True)[1][:cf.model_max_instances_per_batch_element]
- b_keep = b_keep[top_ids]
-
- # merge indices over batch elements.
- batch_keep = b_keep if j == 0 else mutils.unique1d(torch.cat((batch_keep, b_keep)))
-
- keep = batch_keep
-
- else:
- keep = torch.tensor([0]).long().cuda()
-
- # arrange output
- result = torch.cat((refined_rois[keep],
- batch_ixs[keep].unsqueeze(1),
- class_ids[keep].unsqueeze(1).float(),
- class_scores[keep].unsqueeze(1)), dim=1)
-
- return result
-
-
-def get_results(cf, img_shape, detections, detection_masks, box_results_list=None, return_masks=True):
- """
- Restores batch dimension of merged detections, unmolds detections, creates and fills results dict.
- :param img_shape:
- :param detections: (n_final_detections, (y1, x1, y2, x2, (z1), (z2), batch_ix, pred_class_id, pred_score)
- :param detection_masks: (n_final_detections, n_classes, y, x, (z)) raw molded masks as returned by mask-head.
- :param box_results_list: None or list of output boxes for monitoring/plotting.
- each element is a list of boxes per batch element.
- :param return_masks: boolean. If True, full resolution masks are returned for all proposals (speed trade-off).
- :return: results_dict: dictionary with keys:
- 'boxes': list over batch elements. each batch element is a list of boxes. each box is a dictionary:
- [[{box_0}, ... {box_n}], [{box_0}, ... {box_n}], ...]
- 'seg_preds': pixel-wise class predictions (b, 1, y, x, (z)) with values [0, 1] only fg. vs. bg for now.
- class-specific return of masks will come with implementation of instance segmentation evaluation.
- """
- detections = detections.cpu().data.numpy()
- if cf.dim == 2:
- detection_masks = detection_masks.permute(0, 2, 3, 1).cpu().data.numpy()
- else:
- detection_masks = detection_masks.permute(0, 2, 3, 4, 1).cpu().data.numpy()
-
- # restore batch dimension of merged detections using the batch_ix info.
- batch_ixs = detections[:, cf.dim*2]
- detections = [detections[batch_ixs == ix] for ix in range(img_shape[0])]
- mrcnn_mask = [detection_masks[batch_ixs == ix] for ix in range(img_shape[0])]
-
- # for test_forward, where no previous list exists.
- if box_results_list is None:
- box_results_list = [[] for _ in range(img_shape[0])]
-
- seg_preds = []
- # loop over batch and unmold detections.
- for ix in range(img_shape[0]):
-
- if 0 not in detections[ix].shape:
- boxes = detections[ix][:, :2 * cf.dim].astype(np.int32)
- class_ids = detections[ix][:, 2 * cf.dim + 1].astype(np.int32)
- scores = detections[ix][:, 2 * cf.dim + 2]
- masks = mrcnn_mask[ix][np.arange(boxes.shape[0]), ..., class_ids]
-
- # Filter out detections with zero area. Often only happens in early
- # stages of training when the network weights are still a bit random.
- if cf.dim == 2:
- exclude_ix = np.where((boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) <= 0)[0]
- else:
- exclude_ix = np.where(
- (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 5] - boxes[:, 4]) <= 0)[0]
-
- if exclude_ix.shape[0] > 0:
- boxes = np.delete(boxes, exclude_ix, axis=0)
- class_ids = np.delete(class_ids, exclude_ix, axis=0)
- scores = np.delete(scores, exclude_ix, axis=0)
- masks = np.delete(masks, exclude_ix, axis=0)
-
- # Resize masks to original image size and set boundary threshold.
- full_masks = []
- permuted_image_shape = list(img_shape[2:]) + [img_shape[1]]
- if return_masks:
- for i in range(masks.shape[0]):
- # Convert neural network mask to full size mask.
- full_masks.append(mutils.unmold_mask_2D(masks[i], boxes[i], permuted_image_shape)
- if cf.dim == 2 else mutils.unmold_mask_3D(masks[i], boxes[i], permuted_image_shape))
- # if masks are returned, take max over binary full masks of all predictions in this image.
- # right now only binary masks for plotting/monitoring. for instance segmentation return all proposal maks.
- final_masks = np.max(np.array(full_masks), 0) if len(full_masks) > 0 else np.zeros(
- (*permuted_image_shape[:-1],))
-
- # add final perdictions to results.
- if 0 not in boxes.shape:
- for ix2, score in enumerate(scores):
- box_results_list[ix].append({'box_coords': boxes[ix2], 'box_score': score,
- 'box_type': 'det', 'box_pred_class_id': class_ids[ix2]})
- else:
- # pad with zero dummy masks.
- final_masks = np.zeros(img_shape[2:])
-
- seg_preds.append(final_masks)
-
- # create and fill results dictionary.
- results_dict = {'boxes': box_results_list,
- 'seg_preds': np.round(np.array(seg_preds))[:, np.newaxis].astype('uint8')}
-
- return results_dict
-
-
-############################################################
-# Mask R-CNN Class
-############################################################
-
-class net(nn.Module):
-
-
- def __init__(self, cf, logger):
-
- super(net, self).__init__()
- self.cf = cf
- self.logger = logger
- self.build()
-
- if self.cf.weight_init is not None:
- logger.info("using pytorch weight init of type {}".format(self.cf.weight_init))
- mutils.initialize_weights(self)
- else:
- logger.info("using default pytorch weight init")
-
-
- def build(self):
- """Build Mask R-CNN architecture."""
-
- # Image size must be dividable by 2 multiple times.
- h, w = self.cf.patch_size[:2]
- if h / 2**5 != int(h / 2**5) or w / 2**5 != int(w / 2**5):
- raise Exception("Image size must be dividable by 2 at least 5 times "
- "to avoid fractions when downscaling and upscaling."
- "For example, use 256, 320, 384, 448, 512, ... etc. ")
- if len(self.cf.patch_size) == 3:
- d = self.cf.patch_size[2]
- if d / 2**3 != int(d / 2**3):
- raise Exception("Image z dimension must be dividable by 2 at least 3 times "
- "to avoid fractions when downscaling and upscaling.")
-
-
-
- # instanciate abstract multi dimensional conv class and backbone class.
- conv = mutils.NDConvGenerator(self.cf.dim)
- backbone = utils.import_module('bbone', self.cf.backbone_path)
-
- # build Anchors, FPN, RPN, Classifier / Bbox-Regressor -head, Mask-head
- self.np_anchors = mutils.generate_pyramid_anchors(self.logger, self.cf)
- self.anchors = torch.from_numpy(self.np_anchors).float().cuda()
- self.fpn = backbone.FPN(self.cf, conv)
- self.rpn = RPN(self.cf, conv)
- self.classifier = Classifier(self.cf, conv)
- self.mask = Mask(self.cf, conv)
-
-
- def train_forward(self, batch, is_validation=False):
- """
- train method (also used for validation monitoring). wrapper around forward pass of network. prepares input data
- for processing, computes losses, and stores outputs in a dictionary.
- :param batch: dictionary containing 'data', 'seg', etc.
- :return: results_dict: dictionary with keys:
- 'boxes': list over batch elements. each batch element is a list of boxes. each box is a dictionary:
- [[{box_0}, ... {box_n}], [{box_0}, ... {box_n}], ...]
- 'seg_preds': pixel-wise class predictions (b, 1, y, x, (z)) with values [0, n_classes].
- 'monitor_values': dict of values to be monitored.
- """
- img = batch['data']
- gt_class_ids = batch['roi_labels']
- gt_boxes = batch['bb_target']
- axes = (0, 2, 3, 1) if self.cf.dim == 2 else (0, 2, 3, 4, 1)
- gt_masks = [np.transpose(batch['roi_masks'][ii], axes=axes) for ii in range(len(batch['roi_masks']))]
-
-
- img = torch.from_numpy(img).float().cuda()
- batch_rpn_class_loss = torch.FloatTensor([0]).cuda()
- batch_rpn_bbox_loss = torch.FloatTensor([0]).cuda()
-
- # list of output boxes for monitoring/plotting. each element is a list of boxes per batch element.
- box_results_list = [[] for _ in range(img.shape[0])]
-
- #forward passes. 1. general forward pass, where no activations are saved in second stage (for performance
- # monitoring and loss sampling). 2. second stage forward pass of sampled rois with stored activations for backprop.
- rpn_class_logits, rpn_pred_deltas, proposal_boxes, detections, detection_masks = self.forward(img)
- mrcnn_class_logits, mrcnn_pred_deltas, mrcnn_pred_mask, target_class_ids, mrcnn_target_deltas, target_mask, \
- sample_proposals = self.loss_samples_forward(gt_class_ids, gt_boxes, gt_masks)
-
- # loop over batch
- for b in range(img.shape[0]):
- if len(gt_boxes[b]) > 0:
-
- # add gt boxes to output list for monitoring.
- for ix in range(len(gt_boxes[b])):
- box_results_list[b].append({'box_coords': batch['bb_target'][b][ix],
- 'box_label': batch['roi_labels'][b][ix], 'box_type': 'gt'})
-
- # match gt boxes with anchors to generate targets for RPN losses.
- rpn_match, rpn_target_deltas = mutils.gt_anchor_matching(self.cf, self.np_anchors, gt_boxes[b])
-
- # add positive anchors used for loss to output list for monitoring.
- pos_anchors = mutils.clip_boxes_numpy(self.np_anchors[np.argwhere(rpn_match == 1)][:, 0], img.shape[2:])
- for p in pos_anchors:
- box_results_list[b].append({'box_coords': p, 'box_type': 'pos_anchor'})
-
- else:
- rpn_match = np.array([-1]*self.np_anchors.shape[0])
- rpn_target_deltas = np.array([0])
-
- rpn_match = torch.from_numpy(rpn_match).cuda()
- rpn_target_deltas = torch.from_numpy(rpn_target_deltas).float().cuda()
-
- # compute RPN losses.
- rpn_class_loss, neg_anchor_ix = compute_rpn_class_loss(rpn_match, rpn_class_logits[b], self.cf.shem_poolsize)
- rpn_bbox_loss = compute_rpn_bbox_loss(rpn_target_deltas, rpn_pred_deltas[b], rpn_match)
- batch_rpn_class_loss += rpn_class_loss / img.shape[0]
- batch_rpn_bbox_loss += rpn_bbox_loss / img.shape[0]
-
- # add negative anchors used for loss to output list for monitoring.
- neg_anchors = mutils.clip_boxes_numpy(self.np_anchors[np.argwhere(rpn_match == -1)][0, neg_anchor_ix], img.shape[2:])
- for n in neg_anchors:
- box_results_list[b].append({'box_coords': n, 'box_type': 'neg_anchor'})
-
- # add highest scoring proposals to output list for monitoring.
- rpn_proposals = proposal_boxes[b][proposal_boxes[b, :, -1].argsort()][::-1]
- for r in rpn_proposals[:self.cf.n_plot_rpn_props, :-1]:
- box_results_list[b].append({'box_coords': r, 'box_type': 'prop'})
-
- # add positive and negative roi samples used for mrcnn losses to output list for monitoring.
- if 0 not in sample_proposals.shape:
- rois = mutils.clip_to_window(self.cf.window, sample_proposals).cpu().data.numpy()
- for ix, r in enumerate(rois):
- box_results_list[int(r[-1])].append({'box_coords': r[:-1] * self.cf.scale,
- 'box_type': 'pos_class' if target_class_ids[ix] > 0 else 'neg_class'})
-
- batch_rpn_class_loss = batch_rpn_class_loss
- batch_rpn_bbox_loss = batch_rpn_bbox_loss
-
- # compute mrcnn losses.
- mrcnn_class_loss = compute_mrcnn_class_loss(target_class_ids, mrcnn_class_logits)
- mrcnn_bbox_loss = compute_mrcnn_bbox_loss(mrcnn_target_deltas, mrcnn_pred_deltas, target_class_ids)
-
- # mrcnn can be run without pixelwise annotations available (Faster R-CNN mode).
- # In this case, the mask_loss is taken out of training.
- if not self.cf.frcnn_mode:
- mrcnn_mask_loss = compute_mrcnn_mask_loss(target_mask, mrcnn_pred_mask, target_class_ids)
- else:
- mrcnn_mask_loss = torch.FloatTensor([0]).cuda()
-
- loss = batch_rpn_class_loss + batch_rpn_bbox_loss + mrcnn_class_loss + mrcnn_bbox_loss + mrcnn_mask_loss
-
- # monitor RPN performance: detection count = the number of correctly matched proposals per fg-class.
- dcount = [list(target_class_ids.cpu().data.numpy()).count(c) for c in np.arange(self.cf.head_classes)[1:]]
-
-
-
- # run unmolding of predictions for monitoring and merge all results to one dictionary.
- return_masks = self.cf.return_masks_in_val if is_validation else False
- results_dict = get_results(self.cf, img.shape, detections, detection_masks,
- box_results_list, return_masks=return_masks)
-
- results_dict['torch_loss'] = loss
- results_dict['monitor_values'] = {'loss': loss.item(), 'class_loss': mrcnn_class_loss.item()}
-
- results_dict['logger_string'] = \
- "loss: {0:.2f}, rpn_class: {1:.2f}, rpn_bbox: {2:.2f}, mrcnn_class: {3:.2f}, mrcnn_bbox: {4:.2f}, " \
- "mrcnn_mask: {5:.2f}, dcount {6}".format(loss.item(), batch_rpn_class_loss.item(),
- batch_rpn_bbox_loss.item(), mrcnn_class_loss.item(),
- mrcnn_bbox_loss.item(), mrcnn_mask_loss.item(), dcount)
-
- return results_dict
-
-
- def test_forward(self, batch, return_masks=True):
- """
- test method. wrapper around forward pass of network without usage of any ground truth information.
- prepares input data for processing and stores outputs in a dictionary.
- :param batch: dictionary containing 'data'
- :param return_masks: boolean. If True, full resolution masks are returned for all proposals (speed trade-off).
- :return: results_dict: dictionary with keys:
- 'boxes': list over batch elements. each batch element is a list of boxes. each box is a dictionary:
- [[{box_0}, ... {box_n}], [{box_0}, ... {box_n}], ...]
- 'seg_preds': pixel-wise class predictions (b, 1, y, x, (z)) with values [0, n_classes]
- """
- img = batch['data']
- img = torch.from_numpy(img).float().cuda()
- _, _, _, detections, detection_masks = self.forward(img)
- results_dict = get_results(self.cf, img.shape, detections, detection_masks, return_masks=return_masks)
- return results_dict
-
-
- def forward(self, img, is_training=True):
- """
- :param img: input images (b, c, y, x, (z)).
- :return: rpn_pred_logits: (b, n_anchors, 2)
- :return: rpn_pred_deltas: (b, n_anchors, (y, x, (z), log(h), log(w), (log(d))))
- :return: batch_proposal_boxes: (b, n_proposals, (y1, x1, y2, x2, (z1), (z2), batch_ix)) only for monitoring/plotting.
- :return: detections: (n_final_detections, (y1, x1, y2, x2, (z1), (z2), batch_ix, pred_class_id, pred_score)
- :return: detection_masks: (n_final_detections, n_classes, y, x, (z)) raw molded masks as returned by mask-head.
- """
- # extract features.
- fpn_outs = self.fpn(img)
- rpn_feature_maps = [fpn_outs[i] for i in self.cf.pyramid_levels]
- self.mrcnn_feature_maps = rpn_feature_maps
-
- # loop through pyramid layers and apply RPN.
- layer_outputs = [] # list of lists
- for p in rpn_feature_maps:
- layer_outputs.append(self.rpn(p))
-
- # concatenate layer outputs.
- # convert from list of lists of level outputs to list of lists of outputs across levels.
- # e.g. [[a1, b1, c1], [a2, b2, c2]] => [[a1, a2], [b1, b2], [c1, c2]]
- outputs = list(zip(*layer_outputs))
- outputs = [torch.cat(list(o), dim=1) for o in outputs]
- rpn_pred_logits, rpn_pred_probs, rpn_pred_deltas = outputs
-
- # generate proposals: apply predicted deltas to anchors and filter by foreground scores from RPN classifier.
- proposal_count = self.cf.post_nms_rois_training if is_training else self.cf.post_nms_rois_inference
- batch_rpn_rois, batch_proposal_boxes = proposal_layer(rpn_pred_probs, rpn_pred_deltas, proposal_count, self.anchors, self.cf)
-
- # merge batch dimension of proposals while storing allocation info in coordinate dimension.
- batch_ixs = torch.from_numpy(np.repeat(np.arange(batch_rpn_rois.shape[0]), batch_rpn_rois.shape[1])).float().cuda()
- rpn_rois = batch_rpn_rois.view(-1, batch_rpn_rois.shape[2])
- self.rpn_rois_batch_info = torch.cat((rpn_rois, batch_ixs.unsqueeze(1)), dim=1)
-
- # this is the first of two forward passes in the second stage, where no activations are stored for backprop.
- # here, all proposals are forwarded (with virtual_batch_size = batch_size * post_nms_rois.)
- # for inference/monitoring as well as sampling of rois for the loss functions.
- # processed in chunks of roi_chunk_size to re-adjust to gpu-memory.
- chunked_rpn_rois = self.rpn_rois_batch_info.split(self.cf.roi_chunk_size)
- class_logits_list, bboxes_list = [], []
- with torch.no_grad():
- for chunk in chunked_rpn_rois:
- chunk_class_logits, chunk_bboxes = self.classifier(self.mrcnn_feature_maps, chunk)
- class_logits_list.append(chunk_class_logits)
- bboxes_list.append(chunk_bboxes)
- batch_mrcnn_class_logits = torch.cat(class_logits_list, 0)
- batch_mrcnn_bbox = torch.cat(bboxes_list, 0)
- self.batch_mrcnn_class_scores = F.softmax(batch_mrcnn_class_logits, dim=1)
-
- # refine classified proposals, filter and return final detections.
- detections = refine_detections(rpn_rois, self.batch_mrcnn_class_scores, batch_mrcnn_bbox, batch_ixs, self.cf, )
-
- # forward remaining detections through mask-head to generate corresponding masks.
- scale = [img.shape[2]] * 4 + [img.shape[-1]] * 2
- scale = torch.from_numpy(np.array(scale[:self.cf.dim * 2] + [1])[None]).float().cuda()
-
-
- detection_boxes = detections[:, :self.cf.dim * 2 + 1] / scale
- with torch.no_grad():
- detection_masks = self.mask(self.mrcnn_feature_maps, detection_boxes)
-
- return [rpn_pred_logits, rpn_pred_deltas, batch_proposal_boxes, detections, detection_masks]
-
-
- def loss_samples_forward(self, batch_gt_class_ids, batch_gt_boxes, batch_gt_masks):
- """
- this is the second forward pass through the second stage (features from stage one are re-used).
- samples few rois in detection_target_layer and forwards only those for loss computation.
- :param batch_gt_class_ids: list over batch elements. Each element is a list over the corresponding roi target labels.
- :param batch_gt_boxes: list over batch elements. Each element is a list over the corresponding roi target coordinates.
- :param batch_gt_masks: list over batch elements. Each element is binary mask of shape (n_gt_rois, y, x, (z), c)
- :return: sample_logits: (n_sampled_rois, n_classes) predicted class scores.
- :return: sample_boxes: (n_sampled_rois, n_classes, 2 * dim) predicted corrections to be applied to proposals for refinement.
- :return: sample_mask: (n_sampled_rois, n_classes, y, x, (z)) predicted masks per class and proposal.
- :return: sample_target_class_ids: (n_sampled_rois) target class labels of sampled proposals.
- :return: sample_target_deltas: (n_sampled_rois, 2 * dim) target deltas of sampled proposals for box refinement.
- :return: sample_target_masks: (n_sampled_rois, y, x, (z)) target masks of sampled proposals.
- :return: sample_proposals: (n_sampled_rois, 2 * dim) RPN output for sampled proposals. only for monitoring/plotting.
- """
- # sample rois for loss and get corresponding targets for all Mask R-CNN head network losses.
- sample_ix, sample_target_class_ids, sample_target_deltas, sample_target_mask = \
- detection_target_layer(self.rpn_rois_batch_info, self.batch_mrcnn_class_scores,
- batch_gt_class_ids, batch_gt_boxes, batch_gt_masks, self.cf)
-
- # re-use feature maps and RPN output from first forward pass.
- sample_proposals = self.rpn_rois_batch_info[sample_ix]
- if 0 not in sample_proposals.size():
- sample_logits, sample_boxes = self.classifier(self.mrcnn_feature_maps, sample_proposals)
- sample_mask = self.mask(self.mrcnn_feature_maps, sample_proposals)
- else:
- sample_logits = torch.FloatTensor().cuda()
- sample_boxes = torch.FloatTensor().cuda()
- sample_mask = torch.FloatTensor().cuda()
-
- return [sample_logits, sample_boxes, sample_mask, sample_target_class_ids, sample_target_deltas,
- sample_target_mask, sample_proposals]
\ No newline at end of file
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