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eval_multipro.py
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eval_multipro.py
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# System libs
import os
import argparse
from distutils.version import LooseVersion
from multiprocessing import Queue, Process
# Numerical libs
import numpy as np
import math
import torch
import torch.nn as nn
from torch.nn import functional as F
from scipy.io import loadmat
# from sklearn.metrics import confusion_matrix
# Our libs
from config import cfg
from dataset import ValDataset, imresize, b_imresize, patch_loader
from models import ModelBuilder, SegmentationModule, SegmentationModule_plain, SegmentationModule_fov_deform, FovSegmentationModule, DeformSegmentationModule
from utils import AverageMeter, colorEncode, accuracy, intersectionAndUnion, parse_devices, setup_logger, confusion_matrix, hd95
from criterion import OhemCrossEntropy, DiceCoeff, DiceLoss, FocalLoss
from lib.nn import user_scattered_collate, async_copy_to
from lib.utils import as_numpy
from PIL import Image
from tqdm import tqdm
colors = loadmat('data/color150.mat')['colors']
def visualize_result(data, pred, dir_result):
(img, seg, info) = data
# segmentation
seg_color = colorEncode(seg, colors)
# prediction
pred_color = colorEncode(pred, colors)
# aggregate images and save
im_vis = np.concatenate((img, seg_color, pred_color),
axis=1).astype(np.uint8)
img_name = info.split('/')[-1]
Image.fromarray(im_vis).save(os.path.join(dir_result, os.path.splitext(img_name)[0] + '_seg.png'))
def visualize_result_fov(data, foveated_expection, dir_result):
(img, F_Xlr, info) = data
# aggregate images and save
im_vis = np.concatenate((img, foveated_expection),
axis=1).astype(np.uint8)
img_name = info.split('/')[-1]
Image.fromarray(F_Xlr.astype(np.uint8),mode='L').save(os.path.join(dir_result, os.path.splitext(img_name)[0] + '_F_Xlr.png'))
Image.fromarray(im_vis).save(os.path.join(dir_result, os.path.splitext(img_name)[0] + '_fov_exp.png'))
def evaluate(segmentation_module, loader, cfg, gpu_id, result_queue, foveation_module=None):
segmentation_module.eval()
if cfg.MODEL.foveation:
foveation_module.eval()
patch_bank = list((float(cfg.VAL.expand_prediection_rate_patch)*np.array(cfg.MODEL.patch_bank)).astype(int))
# initialize a confusion matrix
confusion = np.zeros((cfg.DATASET.num_class, cfg.DATASET.num_class))
for batch_data in loader:
# process data
if batch_data is None:
continue
if cfg.VAL.batch_size > 1:
info_list = []
for b in range(len(batch_data)): # over all batches
info_list.append(batch_data[b]['info'])
if b == 0:
continue
if not cfg.VAL.test:
batch_data[0]['seg_label'] = torch.cat([batch_data[0]['seg_label'], batch_data[b]['seg_label']])
for s in range(len(batch_data[0]['img_data'])): # over all resized images
batch_data[0]['img_data'][s] = torch.cat([batch_data[0]['img_data'][s], batch_data[b]['img_data'][s]])
batch_data[0]['info'] = info_list
batch_data = batch_data[0]
if not cfg.VAL.test:
if cfg.VAL.batch_size > 1:
seg_label = as_numpy(batch_data['seg_label'])
else:
seg_label = as_numpy(batch_data['seg_label'][0])
img_resized_list = batch_data['img_data']
img_resized_list_unnorm = batch_data['img_data_unnorm']
if cfg.VAL.visualize and cfg.MODEL.foveation and cfg.VAL.foveated_expection:
foveated_expection = torch.zeros(batch_data['img_ori'].shape)
foveated_expection_temp = torch.cat([foveated_expection.unsqueeze(0), foveated_expection.unsqueeze(0)])
foveated_expection_weight = torch.zeros(foveated_expection_temp.shape[0:-1]) # 2,w,h
with torch.no_grad():
segSize = (seg_label.shape[-2], seg_label.shape[-1])
bs = img_resized_list[0].shape[0]
scores = torch.zeros(bs, cfg.DATASET.num_class, segSize[0], segSize[1])
scores_tmp = torch.zeros(bs, cfg.DATASET.num_class, segSize[0], segSize[1])
scores = async_copy_to(scores, gpu_id)
scores_tmp = async_copy_to(scores_tmp, gpu_id)
if cfg.VAL.max_score:
scores_tmp_2 = torch.cat([scores_tmp.unsqueeze(0), scores_tmp.unsqueeze(0)])
scores_tmp_2 = async_copy_to(scores_tmp_2, gpu_id)
if cfg.VAL.approx_pred_Fxlr_by_ensemble or cfg.VAL.F_Xlr_low_scale != 0:
fov_map_scale_temp = cfg.MODEL.fov_map_scale
if cfg.VAL.approx_pred_Fxlr_by_ensemble:
scores_ensemble = torch.zeros(1, cfg.DATASET.num_class, segSize[0], segSize[1])
scores_ensemble = async_copy_to(scores_ensemble, gpu_id)
approx_pred_Fxlr_iter = len(patch_bank)
# create fake feed_dict
feed_dict = batch_data.copy()
feed_dict['img_data'] = img_resized_list[0]
feed_dict['img_data_unnorm'] = img_resized_list_unnorm[0]
del feed_dict['img_ori']
del feed_dict['info']
feed_dict = async_copy_to(feed_dict, gpu_id)
# get F_Xlr at original high resolution fov_map_scale # b,d,w,h
X = feed_dict['img_data'] # NOTE only support test image = 1
fov_map_scale = cfg.MODEL.fov_map_scale
X_lr = b_imresize(X, (round(X.shape[2]/fov_map_scale), round(X.shape[3]/(fov_map_scale*cfg.MODEL.patch_ap))), interp='bilinear')
feed_dict['cor_info'] = (tuple([0]), tuple([0]))
if cfg.VAL.visualize:
patch_data, F_Xlr, Y_patch_cord, X_patches_cords, X_patches_unnorm = foveation_module(feed_dict, train_mode=False)
else:
patch_data, F_Xlr, Y_patch_cord = foveation_module(feed_dict, train_mode=False)
# patch_data, F_Xlr, Y_patch_cord = foveation_module(feed_dict, train_mode=False)
F_Xlr_ori = F_Xlr.clone()
print(F_Xlr.size())
# scale F_Xlr to size of score b,d,W,H
if cfg.VAL.approx_pred_Fxlr_by_ensemble:
F_Xlr_scale = b_imresize(F_Xlr, (segSize[0], segSize[1]), interp='nearest')
if cfg.VAL.F_Xlr_low_scale != 0:
# print('!!!!!!!!!!!!!!!!!!!!!!!!!!!Fist detect F_Xlr_low_scale')
F_Xlr_low_res = b_imresize(F_Xlr, (round(X.shape[2]/cfg.VAL.F_Xlr_low_scale), round(X.shape[3]/(cfg.VAL.F_Xlr_low_scale*cfg.MODEL.patch_ap))), interp='bilinear')
cfg.MODEL.fov_map_scale = cfg.VAL.F_Xlr_low_scale
approx_pred_Fxlr_iter = 1
# print('cfg.VAL.F_Xlr_low_scale:', cfg.VAL.F_Xlr_low_scale)
else:
approx_pred_Fxlr_iter = 1
for pred_iter in range(approx_pred_Fxlr_iter):
if cfg.VAL.approx_pred_Fxlr_by_ensemble:
cfg.MODEL.fov_map_scale = patch_bank[0]
cfg.MODEL.one_hot_patch = [0]*len(patch_bank)
cfg.MODEL.one_hot_patch[pred_iter] = 1
for idx in range(len(img_resized_list)):
feed_dict = batch_data.copy()
feed_dict['img_data'] = img_resized_list[idx]
feed_dict['img_data_unnorm'] = img_resized_list_unnorm[idx]
if cfg.VAL.F_Xlr_low_scale != 0:
# print('!!!!!!!!!!!!!!!!!!!!!!!!!!!ADD')
feed_dict['F_Xlr_low_res'] = F_Xlr_low_res
# print('F_Xlr_low_res_size:', feed_dict['F_Xlr_low_res'].size())
del feed_dict['img_ori']
del feed_dict['info']
feed_dict = async_copy_to(feed_dict, gpu_id)
# Foveation
if cfg.MODEL.foveation:
X, Y = feed_dict['img_data'], feed_dict['seg_label']
X_unnorm = feed_dict['img_data_unnorm']
with torch.no_grad():
patch_segSize = (patch_bank[0], patch_bank[0]*cfg.MODEL.patch_ap)
patch_scores = torch.zeros(cfg.VAL.batch_size, cfg.DATASET.num_class, patch_segSize[0], patch_segSize[1])
patch_scores = async_copy_to(patch_scores, gpu_id)
fov_map_scale = cfg.MODEL.fov_map_scale
# NOTE: although here we use batch imresize yet in practical batch size for X = 1
X_lr = b_imresize(X, (round(X.shape[2]/fov_map_scale), round(X.shape[3]/(fov_map_scale*cfg.MODEL.patch_ap))), interp='bilinear')
# foveation (crop as you go)
if cfg.VAL.F_Xlr_only:
feed_dict['cor_info'] = (tuple([0]), tuple([0]))
patch_data, F_Xlr, Y_patch_cord = foveation_module(feed_dict, train_mode=False)
else:
if cfg.VAL.F_Xlr_acc_map_only:
Xlr_miou_map = torch.zeros(X_lr.shape[2], X_lr.shape[3])
Xlr_loss_map = torch.zeros(X_lr.shape[2], X_lr.shape[3])
print('Percentage of fov_location finished:')
pbar_X_lr = tqdm(total=X_lr.shape[2])
for xi in range(X_lr.shape[2]):
for yi in range(X_lr.shape[3]):
# feed_dict['cor_info'] = (xi, yi)
feed_dict['cor_info'] = (tuple([xi]), tuple([yi]))
if cfg.VAL.visualize:
patch_data, F_Xlr, Y_patch_cord, X_patches_cords, X_patches_unnorm = foveation_module(feed_dict, train_mode=False)
else:
patch_data, F_Xlr, Y_patch_cord = foveation_module(feed_dict, train_mode=False)
# TODO: foveation (pre_cropped available)
if cfg.VAL.F_Xlr_acc_map_only:
patch_scores, patch_loss = segmentation_module(patch_data, segSize=patch_segSize, F_Xlr_acc_map=cfg.VAL.F_Xlr_acc_map_only)
_, patch_pred = torch.max(patch_scores, dim=1)
# w,h
patch_pred = as_numpy(patch_pred.squeeze(0).cpu())
# calculate accuracy and SEND THEM TO MASTER
# acc, pix = accuracy(pred, seg_label)
if 'CITYSCAPES' in cfg.DATASET.root_dataset:
intersection, union, area_lab = intersectionAndUnion(patch_pred, patch_data['seg_label'].squeeze(0).cpu(), cfg.DATASET.num_class, ignore_index=20-1)
else:
if cfg.DATASET.ignore_index != -2:
intersection, union, area_lab = intersectionAndUnion(patch_pred, patch_data['seg_label'].squeeze(0).cpu(), cfg.DATASET.num_class, ignore_index=cfg.DATASET.ignore_index)
else:
intersection, union, area_lab = intersectionAndUnion(patch_pred, patch_data['seg_label'].squeeze(0).cpu(), cfg.DATASET.num_class)
patch_iou = intersection.sum() / union.sum()
Xlr_miou_map[xi,yi] = patch_iou
Xlr_loss_map[xi,yi] = patch_loss
continue
else:
patch_scores = segmentation_module(patch_data, segSize=patch_segSize)
cx_Y, cy_Y, patch_size_Y, p_y_w, p_y_h = Y_patch_cord
if cfg.MODEL.fov_padding:
# p_y = max(patch_bank[0], patch_bank[0]*cfg.MODEL.patch_ap)
scores_tmp_pad = torch.zeros(scores_tmp.shape)
scores_tmp_pad = F.pad(scores_tmp_pad, (p_y_w,p_y_w,p_y_h,p_y_h))
scores_tmp_pad = async_copy_to(scores_tmp_pad, gpu_id)
# print('scores_tmp_pad shape: ', scores_tmp_pad.shape)
patch_size_Y_x = patch_size_Y
patch_size_Y_y = patch_size_Y*cfg.MODEL.patch_ap
if not cfg.VAL.max_score:
if cfg.MODEL.fov_padding:
scores_tmp_pad = scores_tmp_pad*0
scores_tmp_pad[:, :, cx_Y:cx_Y+patch_size_Y_x, cy_Y:cy_Y+patch_size_Y_y] = patch_scores.clone()
scores_tmp = torch.add(scores_tmp, scores_tmp_pad[:, :, p_y_h:scores_tmp_pad.shape[2]-p_y_h, p_y_w:scores_tmp_pad.shape[3]-p_y_w])
else:
scores_tmp[:, :, cx_Y:cx_Y+patch_size_Y_x, cy_Y:cy_Y+patch_size_Y_y] = torch.add(scores_tmp[:, :, cx_Y:cx_Y+patch_size_Y_x, cy_Y:cy_Y+patch_size_Y_y], patch_scores)
else:
if cfg.MODEL.fov_padding:
scores_tmp_pad = scores_tmp_pad*0
scores_tmp_pad[:, :, cx_Y:cx_Y+patch_size_Y_x, cy_Y:cy_Y+patch_size_Y_y] = patch_scores
scores_tmp_2[1] = scores_tmp_pad[:, :, p_y_h:scores_tmp_pad.shape[2]-p_y_h, p_y_w:scores_tmp_pad.shape[3]-p_y_w]
else:
scores_tmp_2[1, :, :, cx_Y:cx_Y+patch_size_Y_x, cy_Y:cy_Y+patch_size_Y_y] = patch_scores
max_class_scores_tmp_2_0, _ = torch.max(scores_tmp_2[0], dim=1)
max_class_scores_tmp_2_1, _ = torch.max(scores_tmp_2[1], dim=1)
# 2,B,W,H, B=1
max_class_scores_tmp_2 = torch.cat([max_class_scores_tmp_2_0.unsqueeze(0), max_class_scores_tmp_2_1.unsqueeze(0)])
# get patch idx of max(max(score))
# patch_idx_by_score.shape = B,W,H; B=1
_, patch_idx_by_score = torch.max(max_class_scores_tmp_2, dim=0)
scores_tmp_2_patch_idx = patch_idx_by_score.unsqueeze(1).unsqueeze(0).expand(scores_tmp_2.shape)
scores_tmp_2[0] = scores_tmp_2.gather(0, scores_tmp_2_patch_idx)[0]
scores_tmp_2[1] = torch.zeros(scores_tmp_2[0].shape)
if cfg.VAL.visualize:
if cfg.VAL.central_crop:
cx_0, cy_0, patch_size_0, p_y_w, p_y_h = Y_patch_cord
if cfg.VAL.hard_max_fov:
weight_s, max_s = torch.max(F_Xlr[0,:,xi,yi], dim=0)
if cfg.MODEL.hard_fov or cfg.MODEL.categorical:
max_s = 0
cx, cy, patch_size, p_w, p_h = X_patches_cords[max_s]
X_patch = b_imresize(X_patches_unnorm[:,max_s,:,:,:], (patch_size, patch_size), interp='nearest')
X_patch = X_patch[0]
print('X_patch_shape: ', X_patch.shape)
# c,w,h
weighed_patch = X_patch.permute(1,2,0).cpu()
# w,h
patch_weight = weight_s.unsqueeze(-1).expand(*weighed_patch.shape[0:-1])
else: # soft fov - max_score=False mode not currently supported
cx_w, cy_w, patch_size_w = 0, 0, 0
for i in range(len(X_patches_cords)):
cx, cy, patch_size, p_w, p_h = X_patches_cords[i]
w = F_Xlr[0,i,xi,yi]
cx_w += w*cx
cy_w += w*cy
patch_size_w += w*patch_size
cx, cy, patch_size = int(cx_w), int(cy_w), int(patch_size_w)
# patch_size = int(torch.sum(F_Xlr[0,:,xi,yi] * torch.FloatTensor(cfg.MODEL.patch_bank)))
if cfg.MODEL.fov_padding:
fov_map_scale = cfg.MODEL.fov_map_scale
# p = patch_size
cx_p = xi*fov_map_scale + patch_size_Y//2 - patch_size//2 + p_h
cy_p = yi*(fov_map_scale*cfg.MODEL.patch_ap) + patch_size_Y//2 - patch_size//2 + p_w
X_unnorm_pad = F.pad(X_unnorm, (p_w,p_w,p_h,p_h))
crop_patch = X_unnorm_pad[:, :, cx_p:cx_p+patch_size, cy_p:cy_p+patch_size]
else:
crop_patch = X_unnorm[:, :, cx:cx+patch_size, cy:cy+patch_size]
X_patch = b_imresize(crop_patch, (patch_size_0,patch_size_0), interp='bilinear')
X_patch = b_imresize(X_patch, (patch_size, patch_size), interp='nearest')
X_patch = X_patch[0]
print('X_patch_shape: ', X_patch.shape)
# c,w,h
weighed_patch = X_patch.permute(1,2,0).cpu()
if cfg.VAL.foveated_expection:
if cfg.MODEL.fov_padding:
fov_map_scale = cfg.MODEL.fov_map_scale
# p = patch_size
cx_p = xi*fov_map_scale + patch_size_Y//2 - patch_size//2 + p_h
cy_p = yi*(fov_map_scale*cfg.MODEL.patch_ap) + patch_size_Y//2 - patch_size//2 + p_w
# C,W,H
foveated_expection_temp_pad = torch.zeros(foveated_expection_temp.shape[3],foveated_expection_temp.shape[1],foveated_expection_temp.shape[2])
foveated_expection_temp_pad = F.pad(foveated_expection_temp_pad, (p_w,p_w,p_h,p_h))
# print('foveated_expection_temp_pad:', foveated_expection_temp_pad.shape)
# print('cx_p, cy_p, patch_size:', cx_p, cy_p, patch_size)
# W,H,C
foveated_expection_temp_pad = foveated_expection_temp_pad.permute(1,2,0)
foveated_expection_temp_pad[cx_p:cx_p+patch_size, cy_p:cy_p+patch_size, :] = weighed_patch
foveated_expection_temp[1] = foveated_expection_temp_pad[p_h:-p_h, p_w:-p_w, :]
if cfg.VAL.central_crop:
# p_y = max(patch_bank[0], patch_bank[0]*cfg.MODEL.patch_ap)
foveated_expection_temp_pad_y = foveated_expection_temp[1].clone() # W,H,C
foveated_expection_temp_pad_y = foveated_expection_temp_pad_y.permute(2,0,1) # C,W,H
foveated_expection_temp_pad_y = F.pad(foveated_expection_temp_pad_y, (p_y_w,p_y_w,p_y_h,p_y_h))
foveated_expection_temp_temp = foveated_expection_temp_pad_y[:, cx_0:cx_0+patch_size_0, cy_0:cy_0+patch_size_0].clone()
foveated_expection_temp_pad_y = foveated_expection_temp_pad_y*0
foveated_expection_temp_pad_y[:, cx_0:cx_0+patch_size_0, cy_0:cy_0+patch_size_0] = foveated_expection_temp_temp
foveated_expection_temp_pad_y = foveated_expection_temp_pad_y.permute(1,2,0) # W,H,C
foveated_expection_temp[1] = foveated_expection_temp_pad_y[p_y_h:foveated_expection_temp_pad_y.shape[0]-p_y_h, p_y_w:foveated_expection_temp_pad_y.shape[1]-p_y_w, :]
print('max: ', torch.max(foveated_expection_temp_temp))
print('min: ', torch.min(foveated_expection_temp_temp))
# if torch.min(foveated_expection_temp_temp) == 0:
# print(foveated_expection_temp_temp)
# raise Exception('weighted patch may wrong')
if cfg.VAL.hard_max_fov:
# W,H
foveated_expection_weight_pad = torch.zeros(foveated_expection_temp_pad.shape[0:-1])
foveated_expection_weight_pad[cx_p:cx_p+patch_size, cy_p:cy_p+patch_size] = patch_weight
foveated_expection_weight[1] = foveated_expection_weight_pad[p_h:-p_h, p_w:-p_w]
else:
# W,H,C
foveated_expection_temp[1, cx:cx+patch_size, cy:cy+patch_size, :] = weighed_patch
if cfg.VAL.hard_max_fov:
# W,H
foveated_expection_weight[1, cx:cx+patch_size, cy:cy+patch_size] = patch_weight
if cfg.VAL.hard_max_fov:
foveated_expection_weight[0], max_w_idx = torch.max(foveated_expection_weight, dim=0)
if not cfg.VAL.max_score:
max_w_idx = max_w_idx.unsqueeze(0).unsqueeze(-1).expand(*foveated_expection_temp.shape)
# max_w_idx_w = max_w_idx.unsqueeze(0).expand(*foveated_expection_weight.shape)
foveated_expection = foveated_expection_temp.gather(0, max_w_idx)[0]
else:
max_s_idx = patch_idx_by_score.unsqueeze(-1).expand(*foveated_expection_temp.shape).cpu()
foveated_expection = foveated_expection_temp.gather(0, max_s_idx)[0]
# foveated_expection_weight[0] = foveated_expection_weight.gather(0, max_w_idx_w).squeeze(0)
foveated_expection_temp[0] = foveated_expection
foveated_expection_temp[1] = torch.zeros(foveated_expection_temp[0].shape)
if cfg.VAL.hard_max_fov:
foveated_expection_weight[1] = torch.zeros(foveated_expection_weight[0].shape)
pbar_X_lr.update(1)
# print('{}/{} foveate points, xi={}, yi={}\n'.format(xi*X_lr.shape[3]+yi, X_lr.shape[2]*X_lr.shape[3], xi, yi))
if cfg.VAL.max_score:
scores_tmp = scores_tmp_2[0]
# print('F_Xlr: ', F_Xlr.shape)
# print(F_Xlr)
# non foveation mode
else:
# forward pass
scores_tmp, deformed_score, y_sampled = segmentation_module(feed_dict, segSize=segSize)
if scores_tmp.shape != scores.shape:
print('scores_tmp shape: {}\n'.format(scores_tmp.shape))
print('scores shape: {}\n'.format(scores.shape))
scores = scores + scores_tmp / len(cfg.DATASET.imgSizes)
y_sampled = as_numpy(y_sampled.squeeze(0))
if cfg.VAL.approx_pred_Fxlr_by_ensemble:
scores_ensemble = scores_ensemble + scores * F_Xlr_scale[:,pred_iter,:,:]
if cfg.VAL.approx_pred_Fxlr_by_ensemble:
scores = scores_ensemble
cfg.MODEL.fov_map_scale = fov_map_scale_temp
if cfg.VAL.F_Xlr_low_scale != 0:
cfg.MODEL.fov_map_scale = fov_map_scale_temp
F_Xlr = F_Xlr_ori
if cfg.VAL.ensemble:
if not os.path.isdir(os.path.join(cfg.DIR, "{}result_{}".format(cfg.VAL.rename_eval_folder, cfg.VAL.checkpoint), 'scores')):
os.makedirs(os.path.join(cfg.DIR, "{}result_{}".format(cfg.VAL.rename_eval_folder, cfg.VAL.checkpoint), 'scores'))
np.save(os.path.join(cfg.DIR, "{}result_{}".format(cfg.VAL.rename_eval_folder, cfg.VAL.checkpoint), 'scores', batch_data['info'].split('/')[-1]), scores.cpu())
if cfg.VAL.F_Xlr_acc_map_only:
if not os.path.isdir(os.path.join(cfg.DIR, "{}result_{}".format(cfg.VAL.rename_eval_folder, cfg.VAL.checkpoint), 'Xlr_miou_map')):
os.makedirs(os.path.join(cfg.DIR, "{}result_{}".format(cfg.VAL.rename_eval_folder, cfg.VAL.checkpoint), 'Xlr_miou_map'))
np.save(os.path.join(cfg.DIR, "{}result_{}".format(cfg.VAL.rename_eval_folder, cfg.VAL.checkpoint), 'Xlr_miou_map', batch_data['info'].split('/')[-1]), Xlr_miou_map.cpu())
if not os.path.isdir(os.path.join(cfg.DIR, "{}result_{}".format(cfg.VAL.rename_eval_folder, cfg.VAL.checkpoint), 'Xlr_loss_map')):
os.makedirs(os.path.join(cfg.DIR, "{}result_{}".format(cfg.VAL.rename_eval_folder, cfg.VAL.checkpoint), 'Xlr_loss_map'))
np.save(os.path.join(cfg.DIR, "{}result_{}".format(cfg.VAL.rename_eval_folder, cfg.VAL.checkpoint), 'Xlr_loss_map', batch_data['info'].split('/')[-1]), Xlr_loss_map.cpu())
_, pred = torch.max(scores, dim=1)
# w,h
pred = as_numpy(pred.squeeze(0).cpu())
# print('pred shape: {}\n'.format(scores.shape))
_, pred_deformed = torch.max(deformed_score, dim=1)
pred_deformed = as_numpy(pred_deformed.squeeze(0).cpu())
# calculate accuracy and SEND THEM TO MASTER
acc, pix = accuracy(pred, seg_label)
acc_deformed, pix_deformed = accuracy(pred_deformed, y_sampled)
if 'CITYSCAPES' in cfg.DATASET.root_dataset:
intersection, union, area_lab = intersectionAndUnion(pred, seg_label, cfg.DATASET.num_class, ignore_index=20-1)
intersection_deformed, union_deformed, area_lab_deformed = intersectionAndUnion(pred_deformed, y_sampled, cfg.DATASET.num_class, ignore_index=20-1)
else:
if cfg.DATASET.ignore_index != -2:
intersection, union, area_lab = intersectionAndUnion(pred, seg_label, cfg.DATASET.num_class, ignore_index=cfg.DATASET.ignore_index)
intersection_deformed, union_deformed, area_lab_deformed = intersectionAndUnion(pred_deformed, y_sampled, cfg.DATASET.num_class, ignore_index=cfg.DATASET.ignore_index)
else:
intersection, union, area_lab = intersectionAndUnion(pred, seg_label, cfg.DATASET.num_class)
intersection_deformed, union_deformed, area_lab_deformed = intersectionAndUnion(pred_deformed, y_sampled, cfg.DATASET.num_class)
# calculate Hausdorff distance
h_dists = []
if cfg.VAL.hd95:
if 'CITYSCAPES' in cfg.DATASET.root_dataset:
ig_class = 19
else:
ig_class = cfg.DATASET.ignore_index
for cur_class in range(cfg.DATASET.num_class):
if cur_class == ig_class:
h_dists.append(np.nan)
continue
pred_c = pred.copy()
seg_label_c = seg_label.copy()
mask_pred_b = pred_c != cur_class
mask_pred_f = pred_c == cur_class
pred_c[mask_pred_b] = 0
pred_c[mask_pred_f] = 1
mask_seg_label_b = seg_label_c != cur_class
mask_seg_label_f = seg_label_c == cur_class
seg_label_c[mask_seg_label_b] = 0
seg_label_c[mask_seg_label_f] = 1
if (pred_c == 1).sum() > 1 and (seg_label_c == 1).sum() > 1:
dist_ = hd95(pred_c, seg_label_c)
h_dists.append(dist_)
else:
h_dists.append(np.nan)
if 'CITYSCAPES' in cfg.DATASET.root_dataset:
confusion += confusion_matrix(seg_label, pred, seg_label.shape, cfg.DATASET.num_class, ignore=20-1)
else:
if cfg.DATASET.ignore_index != -2:
confusion += confusion_matrix(seg_label, pred, seg_label.shape, cfg.DATASET.num_class, ignore=cfg.DATASET.ignore_index)
else:
confusion += confusion_matrix(seg_label, pred, seg_label.shape, cfg.DATASET.num_class)
if cfg.MODEL.foveation:
if cfg.MODEL.gumbel_softmax:
F_Xlr = F_Xlr.exp()
F_Xlr_cp = F_Xlr.clone()
F_Xlr_score = as_numpy(F_Xlr.clone().cpu())
patch_bank_F_Xlr = torch.tensor(patch_bank).to(F_Xlr.device)
F_Xlr = patch_bank_F_Xlr.unsqueeze(-1).unsqueeze(-1).float()*(F_Xlr.squeeze(0)).float()
F_Xlr = as_numpy(F_Xlr.cpu())
# t,b,d,w,h
F_Xlr = np.sum(F_Xlr,axis=0)
F_Xlr = np.expand_dims(F_Xlr,axis=0)
if cfg.VAL.all_F_Xlr_time:
F_Xlr_info = (F_Xlr, batch_data['info'].split('/')[-1].split('.')[0], F_Xlr_score)
result_queue.put_nowait((acc, pix, intersection, union, confusion, area_lab, F_Xlr_info, h_dists))
else:
result_queue.put_nowait((acc, pix, intersection, union, confusion, area_lab, F_Xlr, F_Xlr_score, h_dists))
else:
# result_queue.put_nowait((acc, pix, intersection, union, confusion, area_lab))
result_queue.put_nowait((acc, pix, intersection, union, confusion, area_lab, acc_deformed, pix_deformed, intersection_deformed, union_deformed))
# visualization
if cfg.VAL.visualize:
visualize_result(
(batch_data['img_ori'], seg_label, batch_data['info']),
pred,
os.path.join(cfg.DIR, "{}result_{}".format(cfg.VAL.rename_eval_folder, cfg.VAL.checkpoint))
)
if cfg.MODEL.foveation and cfg.VAL.foveated_expection:
foveated_expection = as_numpy(foveated_expection.cpu())
# b,d,w,h
F_Xlr = F_Xlr_cp
F_Xlr = (F_Xlr-torch.min(F_Xlr))/(torch.max(F_Xlr)-torch.min(F_Xlr))
F_Xlr = b_imresize((1-F_Xlr), (segSize[0], segSize[1]), interp='nearest')
# b,d,w,h -> d,w,h -> w,h,d
F_Xlr = as_numpy(F_Xlr.squeeze(0).permute(1,2,0).cpu())
for idx in range(F_Xlr.shape[2]):
F_Xlr[:,:,idx] = F_Xlr[:,:,idx]*(255//F_Xlr.shape[2]*idx)
F_Xlr = np.sum(F_Xlr,axis=2)
visualize_result_fov(
(batch_data['img_ori'], F_Xlr, batch_data['info']),
foveated_expection*255,
os.path.join(cfg.DIR, "{}result_{}".format(cfg.VAL.rename_eval_folder, cfg.VAL.checkpoint))
)
def evaluate_train(segmentation_module, loader, cfg, gpu_id, result_queue, foveation_module=None):
segmentation_module.eval()
if cfg.MODEL.foveation:
foveation_module.eval()
patch_bank = list((float(cfg.VAL.expand_prediection_rate_patch)*np.array(cfg.MODEL.patch_bank)).astype(int))
# initialize a confusion matrix
confusion = np.zeros((cfg.DATASET.num_class, cfg.DATASET.num_class))
for batch_data in loader:
# process data
if batch_data is None:
continue
batch_data = batch_data[0]
seg_label = as_numpy(batch_data['seg_label'][0])
img_resized_list = batch_data['img_data']
img_resized_list_unnorm = batch_data['img_data_unnorm']
# note for foveation resize not applied, i.e. both seg_label and img_data are at original size
if cfg.VAL.visualize and cfg.MODEL.foveation:
foveated_expection = torch.zeros(batch_data['img_ori'].shape)
foveated_expection_temp = torch.cat([foveated_expection.unsqueeze(0), foveated_expection.unsqueeze(0)])
foveated_expection_weight = torch.zeros(foveated_expection_temp.shape[0:-1]) # 2,w,h
with torch.no_grad():
segSize = (seg_label.shape[0], seg_label.shape[1])
scores = torch.zeros(cfg.VAL.batch_size, cfg.DATASET.num_class, segSize[0], segSize[1])
scores_tmp = torch.zeros(1, cfg.DATASET.num_class, segSize[0], segSize[1])
if cfg.VAL.max_score:
scores_tmp_2 = torch.cat([scores_tmp.unsqueeze(0), scores_tmp.unsqueeze(0)])
# scores_tmp_2 = async_copy_to(scores_tmp_2, gpu_id)
for idx in range(len(img_resized_list)):
feed_dict = batch_data.copy()
feed_dict['img_data'] = img_resized_list[idx]
feed_dict['img_data_unnorm'] = img_resized_list_unnorm[idx]
del feed_dict['img_ori']
del feed_dict['info']
# Foveation
if cfg.MODEL.foveation:
X, Y = feed_dict['img_data'], feed_dict['seg_label']
X_unnorm = feed_dict['img_data_unnorm']
with torch.no_grad():
patch_segSize = (patch_bank[0], patch_bank[0]*cfg.MODEL.patch_ap)
patch_scores = torch.zeros(cfg.VAL.batch_size, cfg.DATASET.num_class, patch_segSize[0], patch_segSize[1])
fov_map_scale = cfg.MODEL.fov_map_scale
# NOTE: although here we use batch imresize yet in practical batch size for X = 1
X_lr = b_imresize(X, (round(X.shape[2]/fov_map_scale), round(X.shape[3]/(fov_map_scale*cfg.MODEL.patch_ap))), interp='bilinear')
# foveation (crop as you go)
pbar_X_lr = tqdm(total=X_lr.shape[2])
for xi in range(X_lr.shape[2]):
for yi in range(X_lr.shape[3]):
# feed_dict['cor_info'] = (xi, yi)
feed_dict['cor_info'] = (tuple([xi]), tuple([yi]))
if cfg.VAL.visualize:
patch_data, F_Xlr, Y_patch_cord, X_patches_cords, X_patches_unnorm = foveation_module(feed_dict, train_mode=False)
else:
patch_data, F_Xlr, Y_patch_cord = foveation_module(feed_dict, train_mode=False)
patch_scores = segmentation_module(patch_data, segSize=patch_segSize)
cx_Y, cy_Y, patch_size_Y, p_y_w, p_y_h = Y_patch_cord
if cfg.MODEL.fov_padding:
# p_y = max(patch_bank[0], patch_bank[0]*cfg.MODEL.patch_ap)
scores_tmp_pad = torch.zeros(scores_tmp.shape)
scores_tmp_pad = F.pad(scores_tmp_pad, (p_y_w,p_y_w,p_y_h,p_y_h))
patch_size_Y_x = patch_size_Y
patch_size_Y_y = patch_size_Y*cfg.MODEL.patch_ap
if not cfg.VAL.max_score:
if cfg.MODEL.fov_padding:
scores_tmp_pad = scores_tmp_pad*0
scores_tmp_pad[:, :, cx_Y:cx_Y+patch_size_Y_x, cy_Y:cy_Y+patch_size_Y_y] = patch_scores.clone()
scores_tmp = torch.add(scores_tmp, scores_tmp_pad[:, :, p_y_h:scores_tmp_pad.shape[2]-p_y_h, p_y_w:scores_tmp_pad.shape[3]-p_y_w])
else:
scores_tmp[:, :, cx_Y:cx_Y+patch_size_Y_x, cy_Y:cy_Y+patch_size_Y_y] = torch.add(scores_tmp[:, :, cx_Y:cx_Y+patch_size_Y_x, cy_Y:cy_Y+patch_size_Y_y], patch_scores)
else:
if cfg.MODEL.fov_padding:
scores_tmp_pad = scores_tmp_pad*0
scores_tmp_pad[:, :, cx_Y:cx_Y+patch_size_Y_x, cy_Y:cy_Y+patch_size_Y_y] = patch_scores
scores_tmp_2[1] = scores_tmp_pad[:, :, p_y_h:scores_tmp_pad.shape[2]-p_y_h, p_y_w:scores_tmp_pad.shape[3]-p_y_w]
else:
scores_tmp_2[1, :, :, cx_Y:cx_Y+patch_size_Y_x, cy_Y:cy_Y+patch_size_Y_y] = patch_scores
max_class_scores_tmp_2_0, _ = torch.max(scores_tmp_2[0], dim=1)
max_class_scores_tmp_2_1, _ = torch.max(scores_tmp_2[1], dim=1)
# 2,B,W,H, B=1
max_class_scores_tmp_2 = torch.cat([max_class_scores_tmp_2_0.unsqueeze(0), max_class_scores_tmp_2_1.unsqueeze(0)])
_, patch_idx_by_score = torch.max(max_class_scores_tmp_2, dim=0)
scores_tmp_2_patch_idx = patch_idx_by_score.unsqueeze(1).unsqueeze(0).expand(scores_tmp_2.shape)
scores_tmp_2[0] = scores_tmp_2.gather(0, scores_tmp_2_patch_idx)[0]
scores_tmp_2[1] = torch.zeros(scores_tmp_2[0].shape)
if cfg.VAL.visualize:
if cfg.VAL.central_crop:
cx_0, cy_0, patch_size_0, p_y_w, p_y_h = Y_patch_cord
if cfg.VAL.hard_max_fov:
weight_s, max_s = torch.max(F_Xlr[0,:,xi,yi], dim=0)
cx, cy, patch_size, p_w, p_h = X_patches_cords[max_s]
X_patch = b_imresize(X_patches_unnorm[:,max_s,:,:,:], (patch_size, patch_size), interp='nearest')
X_patch = X_patch[0]
print('X_patch_shape: ', X_patch.shape)
# c,w,h
weighed_patch = X_patch.permute(1,2,0).cpu()
# w,h
patch_weight = weight_s.unsqueeze(-1).expand(*weighed_patch.shape[0:-1])
else: # soft fov - max_score=False mode not currently supported
cx_w, cy_w, patch_size_w = 0, 0, 0
for i in range(len(X_patches_cords)):
cx, cy, patch_size, p_w, p_h = X_patches_cords[i]
w = F_Xlr[0,i,xi,yi]
cx_w += w*cx
cy_w += w*cy
patch_size_w += w*patch_size
cx, cy, patch_size = int(cx_w), int(cy_w), int(patch_size_w)
# patch_size = int(torch.sum(F_Xlr[0,:,xi,yi] * torch.FloatTensor(cfg.MODEL.patch_bank)))
if cfg.MODEL.fov_padding:
fov_map_scale = cfg.MODEL.fov_map_scale
# p = patch_size
cx_p = xi*fov_map_scale + patch_size_Y//2 - patch_size//2 + p_h
cy_p = yi*(fov_map_scale*cfg.MODEL.patch_ap) + patch_size_Y//2 - patch_size//2 + p_w
X_unnorm_pad = F.pad(X_unnorm, (p_w,p_w,p_h,p_h))
crop_patch = X_unnorm_pad[:, :, cx_p:cx_p+patch_size, cy_p:cy_p+patch_size]
else:
crop_patch = X_unnorm[:, :, cx:cx+patch_size, cy:cy+patch_size]
X_patch = b_imresize(crop_patch, (patch_size_0,patch_size_0), interp='bilinear')
X_patch = b_imresize(X_patch, (patch_size, patch_size), interp='nearest')
X_patch = X_patch[0]
print('X_patch_shape: ', X_patch.shape)
# c,w,h
weighed_patch = X_patch.permute(1,2,0).cpu()
if cfg.MODEL.fov_padding:
fov_map_scale = cfg.MODEL.fov_map_scale
# p = patch_size
cx_p = xi*fov_map_scale + patch_size_Y//2 - patch_size//2 + p_h
cy_p = yi*(fov_map_scale*cfg.MODEL.patch_ap) + patch_size_Y//2 - patch_size//2 + p_w
# C,W,H
foveated_expection_temp_pad = torch.zeros(foveated_expection_temp.shape[3],foveated_expection_temp.shape[1],foveated_expection_temp.shape[2])
foveated_expection_temp_pad = F.pad(foveated_expection_temp_pad, (p_w,p_w,p_h,p_h))
# W,H,C
foveated_expection_temp_pad = foveated_expection_temp_pad.permute(1,2,0)
foveated_expection_temp_pad[cx_p:cx_p+patch_size, cy_p:cy_p+patch_size, :] = weighed_patch
foveated_expection_temp[1] = foveated_expection_temp_pad[p_h:-p_h, p_w:-p_w, :]
if cfg.VAL.central_crop:
# p_y = max(patch_bank[0], patch_bank[0]*cfg.MODEL.patch_ap)
foveated_expection_temp_pad_y = foveated_expection_temp[1].clone() # W,H,C
foveated_expection_temp_pad_y = foveated_expection_temp_pad_y.permute(2,0,1) # C,W,H
foveated_expection_temp_pad_y = F.pad(foveated_expection_temp_pad_y, (p_y_w,p_y_w,p_y_h,p_y_h))
foveated_expection_temp_temp = foveated_expection_temp_pad_y[:, cx_0:cx_0+patch_size_0, cy_0:cy_0+patch_size_0].clone()
foveated_expection_temp_pad_y = foveated_expection_temp_pad_y*0
foveated_expection_temp_pad_y[:, cx_0:cx_0+patch_size_0, cy_0:cy_0+patch_size_0] = foveated_expection_temp_temp
foveated_expection_temp_pad_y = foveated_expection_temp_pad_y.permute(1,2,0) # W,H,C
foveated_expection_temp[1] = foveated_expection_temp_pad_y[p_y_h:foveated_expection_temp_pad_y.shape[0]-p_y_h, p_y_w:foveated_expection_temp_pad_y.shape[1]-p_y_w, :]
print('max: ', torch.max(foveated_expection_temp_temp))
print('min: ', torch.min(foveated_expection_temp_temp))
if cfg.VAL.hard_max_fov:
# W,H
foveated_expection_weight_pad = torch.zeros(foveated_expection_temp_pad.shape[0:-1])
foveated_expection_weight_pad[cx_p:cx_p+patch_size, cy_p:cy_p+patch_size] = patch_weight
foveated_expection_weight[1] = foveated_expection_weight_pad[p_h:-p_h, p_w:-p_w]
else:
# W,H,C
foveated_expection_temp[1, cx:cx+patch_size, cy:cy+patch_size, :] = weighed_patch
if cfg.VAL.hard_max_fov:
# W,H
foveated_expection_weight[1, cx:cx+patch_size, cy:cy+patch_size] = patch_weight
if cfg.VAL.hard_max_fov:
foveated_expection_weight[0], max_w_idx = torch.max(foveated_expection_weight, dim=0)
if not cfg.VAL.max_score:
max_w_idx = max_w_idx.unsqueeze(0).unsqueeze(-1).expand(*foveated_expection_temp.shape)
# max_w_idx_w = max_w_idx.unsqueeze(0).expand(*foveated_expection_weight.shape)
foveated_expection = foveated_expection_temp.gather(0, max_w_idx)[0]
else:
max_s_idx = patch_idx_by_score.unsqueeze(-1).expand(*foveated_expection_temp.shape).cpu()
foveated_expection = foveated_expection_temp.gather(0, max_s_idx)[0]
# foveated_expection_weight[0] = foveated_expection_weight.gather(0, max_w_idx_w).squeeze(0)
foveated_expection_temp[0] = foveated_expection
foveated_expection_temp[1] = torch.zeros(foveated_expection_temp[0].shape)
if cfg.VAL.hard_max_fov:
foveated_expection_weight[1] = torch.zeros(foveated_expection_weight[0].shape)
# print('{}/{} foveate points, xi={}, yi={}'.format(xi*X_lr.shape[3]+yi, X_lr.shape[2]*X_lr.shape[3], xi, yi))
pbar_X_lr.update(1)
if cfg.VAL.max_score:
scores_tmp = scores_tmp_2[0]
else:
# forward pass
scores_tmp = segmentation_module(feed_dict, segSize=segSize)
scores = scores + scores_tmp / len(cfg.DATASET.imgSizes)
_, pred = torch.max(scores, dim=1)
# w,h
pred = as_numpy(pred.squeeze(0).cpu())
# calculate accuracy and SEND THEM TO MASTER
acc, pix = accuracy(pred, seg_label)
if 'CITYSCAPES' in cfg.DATASET.root_dataset:
intersection, union, area_lab = intersectionAndUnion(pred, seg_label, cfg.DATASET.num_class, ignore_index=20-1)
else:
if cfg.DATASET.ignore_index != -2:
intersection, union, area_lab = intersectionAndUnion(pred, seg_label, cfg.DATASET.num_class, ignore_index=cfg.DATASET.ignore_index)
else:
intersection, union, area_lab = intersectionAndUnion(pred, seg_label, cfg.DATASET.num_class)
# flatten for confusion confusion_matrix
pred_flatten = pred.flatten()
seg_label_flatten = seg_label.flatten()
# calculate the confusion matrix and add to the accumulated matrix
if 'CITYSCAPES' in cfg.DATASET.root_dataset:
confusion += confusion_matrix(seg_label, pred, seg_label.shape, cfg.DATASET.num_class, ignore=20-1)
else:
if cfg.DATASET.ignore_index != -2:
confusion += confusion_matrix(seg_label, pred, seg_label.shape, cfg.DATASET.num_class, ignore=cfg.DATASET.ignore_index)
else:
confusion += confusion_matrix(seg_label, pred, seg_label.shape, cfg.DATASET.num_class)
if cfg.MODEL.foveation:
patch_bank_F_Xlr = torch.tensor(patch_bank).to(F_Xlr.device)
F_Xlr = patch_bank_F_Xlr.unsqueeze(-1).unsqueeze(-1).float()*(F_Xlr.squeeze(0)).float()
F_Xlr = as_numpy(F_Xlr.cpu())
# t,b,d,w,h
F_Xlr = np.sum(F_Xlr,axis=0)
F_Xlr = np.expand_dims(F_Xlr,axis=0)
if cfg.VAL.all_F_Xlr_time:
F_Xlr_info = (F_Xlr, batch_data['info'].split('/')[-1].split('.')[0])
result_queue.put_nowait((acc, pix, intersection, union, confusion, area_lab, F_Xlr_info))
else:
result_queue.put_nowait((acc, pix, intersection, union, confusion, area_lab, F_Xlr))
else:
result_queue.put_nowait((acc, pix, intersection, union, confusion, area_lab))
# visualization
if cfg.VAL.visualize:
# seg_label = convert_label(label=seg_label, inverse=True)
visualize_result(
(batch_data['img_ori'], seg_label, batch_data['info']),
pred,
os.path.join(cfg.DIR, "{}result_{}".format(cfg.VAL.rename_eval_folder, cfg.VAL.checkpoint))
)
if cfg.MODEL.foveation:
# if not cfg.VAL.hard_max_fov:
# foveated_expection = foveated_expection / overlap_count
foveated_expection = as_numpy(foveated_expection.cpu())
# b,d,w,h
F_Xlr = (F_Xlr-torch.min(F_Xlr))/(torch.max(F_Xlr)-torch.min(F_Xlr))
F_Xlr = b_imresize((1-F_Xlr), (segSize[0], segSize[1]), interp='nearest')
# b,d,w,h -> d,w,h -> w,h,d
F_Xlr = as_numpy(F_Xlr.squeeze(0).permute(1,2,0).cpu())
for idx in range(F_Xlr.shape[2]):
F_Xlr[:,:,idx] = F_Xlr[:,:,idx]*(255//F_Xlr.shape[2]*idx)
F_Xlr = np.sum(F_Xlr,axis=2)
visualize_result_fov(
(batch_data['img_ori'], F_Xlr, batch_data['info']),
foveated_expection*255,
os.path.join(cfg.DIR, "{}result_{}".format(cfg.VAL.rename_eval_folder, cfg.VAL.checkpoint))
)
def worker_train(cfg, gpu_id, start_idx, end_idx, result_queue):
# torch.cuda.set_device(gpu_id)
# Dataset and Loader
dataset_val = ValDataset(
cfg.DATASET.root_dataset,
cfg.DATASET.list_val,
cfg.DATASET,
cfg,
start_idx=start_idx, end_idx=end_idx)
loader_val = torch.utils.data.DataLoader(
dataset_val,
batch_size=cfg.VAL.batch_size,
shuffle=False,
collate_fn=user_scattered_collate,
num_workers=2)
# Network Builders
net_encoder = ModelBuilder.build_encoder(
arch=cfg.MODEL.arch_encoder.lower(),
fc_dim=cfg.MODEL.fc_dim,
weights=cfg.MODEL.weights_encoder)
net_decoder = ModelBuilder.build_decoder(
arch=cfg.MODEL.arch_decoder.lower(),
fc_dim=cfg.MODEL.fc_dim,
num_class=cfg.DATASET.num_class,
weights=cfg.MODEL.weights_decoder,
use_softmax=True)
if cfg.MODEL.foveation:
net_foveater = ModelBuilder.build_foveater(
in_channel=cfg.MODEL.in_dim,
out_channel=len(cfg.MODEL.patch_bank),
weights=cfg.MODEL.weights_foveater,
cfg=cfg)
if 'CITYSCAPES' in cfg.DATASET.root_dataset:
if cfg.TRAIN.loss_fun == 'NLLLoss':
crit = nn.NLLLoss(ignore_index=19)
else:
crit = nn.CrossEntropyLoss(ignore_index=20-1)
elif 'Digest' in cfg.DATASET.root_dataset:
crit = nn.CrossEntropyLoss(ignore_index=-2)
elif cfg.TRAIN.loss_fun == 'FocalLoss' and 'DeepGlob' in cfg.DATASET.root_dataset:
crit = FocalLoss(gamma=6, ignore_label=cfg.DATASET.ignore_index)
else:
if cfg.TRAIN.loss_fun == 'NLLLoss':
if cfg.DATASET.ignore_index != -2:
crit = nn.NLLLoss(ignore_index=cfg.DATASET.ignore_index)
else:
crit = nn.NLLLoss(ignore_index=-2)
else:
if cfg.DATASET.ignore_index != -2:
crit = nn.CrossEntropyLoss(ignore_index=cfg.DATASET.ignore_index)
else:
crit = nn.CrossEntropyLoss(ignore_index=-2)
segmentation_module = SegmentationModule(net_encoder, net_decoder, crit, cfg)
if cfg.MODEL.foveation:
foveation_module = FovSegmentationModule(net_foveater, cfg)
if cfg.MODEL.foveation:
foveation_module = FovSegmentationModule(net_foveater, cfg)
# Main loop
if cfg.MODEL.foveation:
evaluate(segmentation_module, loader_val, cfg, gpu_id, result_queue, foveation_module)
else:
evaluate(segmentation_module, loader_val, cfg, gpu_id, result_queue)
def worker_deform(cfg, gpu_id, start_idx, end_idx, result_queue):
torch.cuda.set_device(gpu_id)
# Dataset and Loader
dataset_val = ValDataset(
cfg.DATASET.root_dataset,
cfg.DATASET.list_val,
cfg.DATASET,
cfg,
start_idx=start_idx, end_idx=end_idx)
loader_val = torch.utils.data.DataLoader(
dataset_val,
batch_size=cfg.VAL.batch_size,
shuffle=False,
collate_fn=user_scattered_collate,
num_workers=2)
# Network Builders
net_encoder = ModelBuilder.build_encoder(
arch=cfg.MODEL.arch_encoder.lower(),
fc_dim=cfg.MODEL.fc_dim,
weights=cfg.MODEL.weights_encoder)
net_decoder = ModelBuilder.build_decoder(
arch=cfg.MODEL.arch_decoder.lower(),
fc_dim=cfg.MODEL.fc_dim,
num_class=cfg.DATASET.num_class,
weights=cfg.MODEL.weights_decoder,
use_softmax=True)
if cfg.MODEL.fov_deform:
net_saliency = ModelBuilder.build_net_saliency(
cfg=cfg,
weights=cfg.MODEL.weights_net_saliency)
net_compress = ModelBuilder.build_net_compress(
cfg=cfg,
weights=cfg.MODEL.weights_net_compress)
if cfg.MODEL.foveation:
net_foveater = ModelBuilder.build_foveater(
in_channel=cfg.MODEL.in_dim,
out_channel=len(cfg.MODEL.patch_bank),
weights=cfg.MODEL.weights_foveater,
cfg=cfg)
if 'CITYSCAPES' in cfg.DATASET.root_dataset:
if cfg.TRAIN.loss_fun == 'NLLLoss':
crit = nn.NLLLoss(ignore_index=19)
else:
crit = nn.CrossEntropyLoss(ignore_index=19)
elif 'Digest' in cfg.DATASET.root_dataset:
if cfg.TRAIN.loss_fun == 'NLLLoss':
crit = nn.NLLLoss(ignore_index=-2)
else:
crit = nn.CrossEntropyLoss(ignore_index=-2)
elif cfg.TRAIN.loss_fun == 'FocalLoss' and 'DeepGlob' in cfg.DATASET.root_dataset:
crit = FocalLoss(gamma=6, ignore_label=cfg.DATASET.ignore_index)
else:
if cfg.TRAIN.loss_fun == 'NLLLoss':
if cfg.DATASET.ignore_index != -2:
crit = nn.NLLLoss(ignore_index=cfg.DATASET.ignore_index)
else:
crit = nn.NLLLoss(ignore_index=-2)
else:
if cfg.DATASET.ignore_index != -2:
crit = nn.CrossEntropyLoss(ignore_index=cfg.DATASET.ignore_index)
else:
crit = nn.CrossEntropyLoss(ignore_index=-2)
if cfg.MODEL.fov_deform:
segmentation_module = DeformSegmentationModule(net_encoder, net_decoder, net_saliency, net_compress, crit, cfg)
else:
segmentation_module = SegmentationModule(net_encoder, net_decoder, crit, cfg)
if cfg.MODEL.foveation:
foveation_module = FovSegmentationModule(net_foveater, cfg)
segmentation_module.cuda()
if cfg.MODEL.foveation:
foveation_module.cuda()
# if not os.path.exists(os.path.join(cfg.DIR, 'network_summary.txt')):
f = open(os.path.join(cfg.DIR, 'network_summary.txt'), 'w')
if cfg.MODEL.foveation:
print(foveation_module, file = f)
print(segmentation_module, file = f)
if cfg.MODEL.foveation:
foveation_module = FovSegmentationModule(net_foveater, cfg)
total_fov = sum([param.nelement() for param in foveation_module.parameters()])
print('Number of FoveationModule params: %.2fM \n' % (total_fov / 1e6))
total = sum([param.nelement() for param in segmentation_module.parameters()])
f.write('Number of SegmentationModule params: %.2fM \n' % (total / 1e6))
# Main loop
if cfg.MODEL.foveation:
evaluate(segmentation_module, loader_val, cfg, gpu_id, result_queue, foveation_module)
else:
evaluate(segmentation_module, loader_val, cfg, gpu_id, result_queue)
f.write('Max memory allocated: %.2fM' % (torch.cuda.max_memory_allocated() / 1e6))
f.close()
def worker(cfg, gpu_id, start_idx, end_idx, result_queue):
torch.cuda.set_device(gpu_id)
# Dataset and Loader
dataset_val = ValDataset(
cfg.DATASET.root_dataset,
cfg.DATASET.list_val,
cfg.DATASET,
cfg,
start_idx=start_idx, end_idx=end_idx)
loader_val = torch.utils.data.DataLoader(
dataset_val,
batch_size=cfg.VAL.batch_size,
shuffle=False,
collate_fn=user_scattered_collate,
num_workers=2)
# Network Builders
net_encoder = ModelBuilder.build_encoder(
arch=cfg.MODEL.arch_encoder.lower(),
fc_dim=cfg.MODEL.fc_dim,
weights=cfg.MODEL.weights_encoder)
net_decoder = ModelBuilder.build_decoder(
arch=cfg.MODEL.arch_decoder.lower(),
fc_dim=cfg.MODEL.fc_dim,
num_class=cfg.DATASET.num_class,
weights=cfg.MODEL.weights_decoder,
use_softmax=True)
if cfg.MODEL.foveation:
net_foveater = ModelBuilder.build_foveater(
in_channel=cfg.MODEL.in_dim,
out_channel=len(cfg.MODEL.patch_bank),
weights=cfg.MODEL.weights_foveater,
cfg=cfg)
if 'CITYSCAPES' in cfg.DATASET.root_dataset:
if cfg.TRAIN.loss_fun == 'NLLLoss':
crit = nn.NLLLoss(ignore_index=19)
else:
crit = nn.CrossEntropyLoss(ignore_index=19)
elif 'Digest' in cfg.DATASET.root_dataset:
if cfg.TRAIN.loss_fun == 'NLLLoss':
crit = nn.NLLLoss(ignore_index=-2)
else:
crit = nn.CrossEntropyLoss(ignore_index=-2)
elif cfg.TRAIN.loss_fun == 'FocalLoss' and 'DeepGlob' in cfg.DATASET.root_dataset:
crit = FocalLoss(gamma=6, ignore_label=cfg.DATASET.ignore_index)
else:
if cfg.TRAIN.loss_fun == 'NLLLoss':
if cfg.DATASET.ignore_index != -2:
crit = nn.NLLLoss(ignore_index=cfg.DATASET.ignore_index)
else:
crit = nn.NLLLoss(ignore_index=-2)
else:
if cfg.DATASET.ignore_index != -2:
crit = nn.CrossEntropyLoss(ignore_index=cfg.DATASET.ignore_index)
else:
crit = nn.CrossEntropyLoss(ignore_index=-2)
segmentation_module = SegmentationModule(net_encoder, net_decoder, crit, cfg)
if cfg.MODEL.foveation:
foveation_module = FovSegmentationModule(net_foveater, cfg)
segmentation_module.cuda()
if cfg.MODEL.foveation:
foveation_module.cuda()
# if not os.path.exists(os.path.join(cfg.DIR, 'network_summary.txt')):
f = open(os.path.join(cfg.DIR, 'network_summary.txt'), 'w')
if cfg.MODEL.foveation:
print(foveation_module, file = f)
print(segmentation_module, file = f)
if cfg.MODEL.foveation:
foveation_module = FovSegmentationModule(net_foveater, cfg)
total_fov = sum([param.nelement() for param in foveation_module.parameters()])
print('Number of FoveationModule params: %.2fM \n' % (total_fov / 1e6))
total = sum([param.nelement() for param in segmentation_module.parameters()])