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seg_solver.py
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seg_solver.py
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# pylint: skip-file
from __future__ import print_function
import numpy as np
import mxnet as mx
import time
import logging
from collections import namedtuple
from mxnet import optimizer as opt
from mxnet.optimizer import get_updater
from mxnet import metric
from pprint import pprint
from train.metric import MultiBoxMetric, CustomAccuracyMetric, DistanceAccuracyMetric
from utils import put_text
from dataset.cs_labels import labels as cs_labels
from evaluate.eval_metric import MApMetric
import cv2
from detect.nms import nms
import math, os, sys
outimgiter = 0
DEBUG = False
TIMING = False
short_class_name = {"traffic light":"t-light","traffic sign":"t-sign","person":"person",\
"rider":"rider","car":"car","truck":"truck","bus":"bus","train":"train",\
"motorcycle":"mbike","bicycle":"bike","vegetation":"tree"}
affine_matrix = mx.nd.array([[1, 0, 0],[0, 1, 0]],ctx=mx.gpu(0))
affine_matrix = mx.nd.reshape(affine_matrix, shape=(1, 6))
GRID = mx.nd.GridGenerator(data=affine_matrix, transform_type='affine', target_shape=(1024,2048))
def prob_upsampling(seg_prob, target_shape):
seg_prob = mx.nd.BilinearSampler(mx.nd.expand_dims(seg_prob,axis=0), GRID)
seg_resized = np.squeeze(mx.nd.argmax(seg_prob,axis=1).asnumpy()).astype(np.uint8)
return seg_resized
def get_seg_labels(shape):
annotation = np.zeros(shape,np.uint8)
from dataset.cs_labels import labels
from palette import get_palette
# palette = get_palette(256)
from dataset.cs_labels import labels as cs_labels
lut = np.zeros((256,3))
labels = []
for l in cs_labels:
if l.trainId<255 and l.trainId>=0:
labels.append((l.trainId,l.name,l.color))
palette = lut.flatten()
colors = np.array(palette).reshape((-1,3))
padding, blocksize, notes = 100, 15, 10
for idx,name,label in labels:
color = label
color = (color[2],color[1],color[0])
if idx<notes:
anchor = (idx*padding,0)
elif idx<notes*2:
anchor = ((idx-notes)*padding,blocksize)
cv2.rectangle(annotation, anchor, (anchor[0]+blocksize, anchor[1]+blocksize), color=color, thickness=-1)
fontFace = cv2.FONT_HERSHEY_PLAIN
fontScale = .8
# name = short_class_name[name] if name in short_class_name.keys() else name
cv2.putText(annotation, name, (anchor[0]+blocksize+1, anchor[1]+10), color=(255,255,255), \
fontFace=fontFace, fontScale=fontScale)
return annotation
def display_results(out_img,label_img,img,class_names):
# from utils import getpallete
# palette = getpallete(256)
from dataset.cs_labels import labels
lut = np.zeros((256,3))
for l in labels:
if l.trainId<255 and l.trainId>=0:
lut[l.trainId,:]=list(l.color)
palette = lut
det2seg = {0:6,1:7,2:11,3:12,4:13,5:14,6:15,7:16,8:17,9:18,}
if DEBUG:
print({"out_img":out_img.shape,"label_img":label_img.shape,"img":img.shape})
lut_reshaped = np.array(palette).astype(np.uint8).reshape((256,3))
lut_b = lut_reshaped[:,0]
lut_g = lut_reshaped[:,1]
lut_r = lut_reshaped[:,2]
# print np.vstack((lut_r[:10],lut_g[:10],lut_b[:10]))
# out_img = np.squeeze(self.executor.outputs[0].asnumpy().argmax(axis=1).astype(np.uint8))
out_img_r = cv2.LUT(out_img,lut_r)
out_img_g = cv2.LUT(out_img,lut_g)
out_img_b = cv2.LUT(out_img,lut_b)
out_img = cv2.merge((out_img_r,out_img_g,out_img_b))
# label_img = data[label_name].astype(np.uint8)
label_img = np.swapaxes(label_img, 1, 2)
label_img = np.swapaxes(label_img, 0, 2).astype(np.uint8)
label_img_r = cv2.LUT(label_img,lut_r)
label_img_g = cv2.LUT(label_img,lut_g)
label_img_b = cv2.LUT(label_img,lut_b)
label_img = cv2.merge((label_img_r,label_img_g,label_img_b))
# img = np.squeeze(data[data_name])
img = (img + np.array([123.68, 116.779, 103.939]).reshape((3,1,1))).astype(np.uint8)
img = np.swapaxes(img, 1, 2)
img = np.swapaxes(img, 0, 2).astype(np.uint8)
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
# detection result
# det_img = img.copy()
# dets = det[np.where(det[:,0]>=0),:].reshape((-1,7))
# if DEBUG:
# print(dets[:2,:])
# idx = nms(np.hstack((dets[:,2:6],dets[:,1:2])),.85)
# dets = dets[idx,:]
# idx = np.argsort(dets[:,6],axis=0)[::-1] ## draw nearest first !!
# dets = dets[idx,:]
h, w, ch = img.shape
fontScale = .8*(h/float(320))
thickness = 2 if h>320 else 1
# for idx in range(dets.shape[0]):
# # if dets[idx,1]<.15:
# # continue
# # bbox = [int(round(dets[idx,2]*w)),int(round(dets[idx,3]*h)), \
# # int(round(dets[idx,4]*w)),int(round(dets[idx,5]*h))]
# # color = palette[det2seg[int(dets[idx,0])],:]
# # cv2.rectangle(det_img, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color=(color[2],color[1],color[0]), thickness=thickness)
# # clsname = class_names[int(dets[idx,0])]
# # clsname_short = short_class_name[clsname]
# # text = "%s:%.0fm" % (clsname_short,dets[idx,6]*255.,)
# # text = "%.0fm" % (dets[idx,6]*255.,)
# # put_text(det_img, text, bbox, fontScale=fontScale)
# for box in gt_boxes.tolist():
# bbox = [int(round(box[1]*w)),int(round(box[2]*h)), \
# int(round(box[3]*w)),int(round(box[4]*h))]
# cv2.rectangle(img, (bbox[0], bbox[1]), (bbox[2], bbox[3]), color=(0,0,128), thickness=thickness)
# clsname = class_names[int(box[0])]
# clsname_short = short_class_name[clsname]
# text = "%s:%.0fm" % (clsname_short,box[5]*255.,)
# put_text(img, text, bbox, fontScale=fontScale)
if DEBUG:
print("img.shape,label_img.shape,out_img.shape", \
img.shape,label_img.shape,out_img.shape)
if img.shape[0]!=out_img.shape[0]:
out_img = cv2.resize(out_img,(img.shape[1],img.shape[0]),interpolation=cv2.INTER_NEAREST)
label_img = cv2.resize(label_img,(img.shape[1],img.shape[0]),interpolation=cv2.INTER_NEAREST)
if 1: # for training data with labels
displayimg = np.vstack((label_img,out_img))
else: # for evaluation_only is ture
displayimg = np.vstack((det_img,out_img))
seg_labels = get_seg_labels((30, displayimg.shape[1],3))
displayimg = np.vstack((displayimg,seg_labels))
if False: #displayimg.shape[0]>1000:
hh, ww, ch = displayimg.shape
displayimg_resized = cv2.resize(displayimg, (int(ww*.8),int(hh*.8)))
else:
displayimg_resized = displayimg
cv2.imshow('out_img',displayimg_resized);
# [exit(0) if (cv2.waitKey()&0xff)==27 else None]
# cv2.imwrite('tmp/out_img_%03d.png'%(outimgiter,),displayimg);
return displayimg
# Parameter to pass to batch_end_callback
BatchEndParam = namedtuple('BatchEndParams', ['epoch', 'nbatch', 'eval_metric'])
class SegTaskSolver(object):
def __init__(self, symbol, ctx=None,
begin_epoch=0, num_epoch=None,
arg_params=None, aux_params=None,
valid_metric=MApMetric(),
class_names=[],
optimizer='sgd', **kwargs):
self.symbol = symbol
if ctx is None:
ctx = mx.cpu(0)
self.ctx = ctx
self.begin_epoch = begin_epoch
self.num_epoch = num_epoch
self.arg_params = arg_params
self.aux_params = aux_params
self.valid_metric = valid_metric
self.class_names = class_names
self.optimizer = optimizer
self.evaluation_only = False
self.kwargs = kwargs.copy()
def fit(self, train_data, eval_data=None,
eval_metric='acc',
grad_req='write',
epoch_end_callback=None,
batch_end_callback=None,
kvstore='local',
logger=None):
global outimgiter
if logger is None:
logger = logging
logging.info('Start training with %s', str(self.ctx))
logging.info(str(self.kwargs))
batch_size = train_data.provide_data[0][1][0]
arg_shapes, out_shapes, aux_shapes = self.symbol.infer_shape( data=tuple(train_data.provide_data[0][1]))
arg_names = self.symbol.list_arguments()
out_names = self.symbol.list_outputs()
aux_names = self.symbol.list_auxiliary_states()
# pprint([(n,s) for n,s in zip(arg_names,arg_shapes)])
# pprint([(n,s) for n,s in zip(out_names,out_shapes)])
# pprint([(n,s) for n,s in zip(aux_names,aux_shapes)])
if grad_req != 'null':
self.grad_params = {}
for name, shape in zip(arg_names, arg_shapes):
if not (name.endswith('data') or name.endswith('label')):
self.grad_params[name] = mx.nd.zeros(shape, self.ctx)
else:
self.grad_params = None
self.aux_params = {k : mx.nd.zeros(s, self.ctx) for k, s in zip(aux_names, aux_shapes)}
data_name = train_data.provide_data[0][0]
label_name_det = train_data.provide_label[0][0]
label_name_seg = train_data.provide_label[1][0]
input_names = [data_name, label_name_det, label_name_seg]
print(train_data.provide_label)
print(os.environ["MXNET_CUDNN_AUTOTUNE_DEFAULT"])
self.optimizer = opt.create(self.optimizer, rescale_grad=(1.0/train_data.batch_size), **(self.kwargs))
self.updater = get_updater(self.optimizer)
eval_metric = CustomAccuracyMetric() # metric.create(eval_metric)
multibox_metric = MultiBoxMetric()
eval_metrics = metric.CompositeEvalMetric()
# eval_metrics.add(multibox_metric)
eval_metrics.add(eval_metric)
# begin training
for epoch in range(self.begin_epoch, self.num_epoch):
nbatch = 0
train_data.reset()
eval_metrics.reset()
logger.info('learning rate: '+str(self.optimizer.learning_rate))
for data,_ in train_data:
if self.evaluation_only:
break
nbatch += 1
label_shape_det = data.label[0].shape
label_shape_seg = data.label[1].shape
self.arg_params[data_name] = mx.nd.array(data.data[0], self.ctx)
self.arg_params[label_name_det] = mx.nd.array(data.label[0], self.ctx)
self.arg_params[label_name_seg] = mx.nd.array(data.label[1], self.ctx)
output_names = self.symbol.list_outputs()
###################### analyze shapes ####################
# pprint([(k,v.shape) for k,v in self.arg_params.items()])
self.executor = self.symbol.bind(self.ctx, self.arg_params,
args_grad=self.grad_params, grad_req=grad_req, aux_states=self.aux_params)
assert len(self.symbol.list_arguments()) == len(self.executor.grad_arrays)
update_dict = {name: nd for name, nd in zip(self.symbol.list_arguments(), \
self.executor.grad_arrays) if nd is not None}
output_dict = {}
output_buff = {}
for key, arr in zip(self.symbol.list_outputs(), self.executor.outputs):
output_dict[key] = arr
output_buff[key] = mx.nd.empty(arr.shape, ctx=mx.cpu())
# output_buff[key] = mx.nd.empty(arr.shape, ctx=self.ctx)
def stat_helper(name, array):
"""wrapper for executor callback"""
import ctypes
from mxnet.ndarray import NDArray
from mxnet.base import NDArrayHandle, py_str
array = ctypes.cast(array, NDArrayHandle)
if 0:
array = NDArray(array, writable=False).asnumpy()
print (name, array.shape, np.mean(array), np.std(array),
('%.1fms' % (float(time.time()-stat_helper.start_time)*1000)))
else:
array = NDArray(array, writable=False)
array.wait_to_read()
elapsed = float(time.time()-stat_helper.start_time)*1000.
if elapsed>5:
print (name, array.shape, ('%.1fms' % (elapsed,)))
stat_helper.start_time=time.time()
stat_helper.start_time=float(time.time())
# self.executor.set_monitor_callback(stat_helper)
tic = time.time()
self.executor.forward(is_train=True)
for key in output_dict:
output_dict[key].copyto(output_buff[key])
# exit(0) # for debugging forward pass only
self.executor.backward()
for key, arr in update_dict.items():
if key != "bigscore_weight":
self.updater(key, arr, self.arg_params[key])
for output in self.executor.outputs:
output.wait_to_read()
if TIMING:
print("%.0fms" % ((time.time()-tic)*1000.,))
output_dict = dict(zip(output_names, self.executor.outputs))
# pred_det_shape = output_dict["det_out_output"].shape
pred_seg_shape = output_dict["seg_out_output"].shape
# label_det = mx.nd.array(data.label[0].reshape((label_shape_det[0],
# label_shape_det[1]*label_shape_det[2])))
label_seg = mx.nd.array(data.label[1].reshape((label_shape_seg[0],
label_shape_seg[1]*label_shape_seg[2])))
# pred_det = mx.nd.array(output_buff["det_out_output"].reshape((pred_det_shape[0],
# pred_det_shape[1], pred_det_shape[2])))
pred_seg = mx.nd.array(output_buff["seg_out_output"].reshape((pred_seg_shape[0],
pred_seg_shape[1], pred_seg_shape[2]*pred_seg_shape[3])))
if DEBUG:
print(data.label[0].asnumpy()[0,:2,:])
if TIMING:
print("%.0fms" % ((time.time()-tic)*1000.,))
# eval_metrics.get_metric(0).update([mx.nd.zeros(output_buff["cls_prob_output"].shape),
# mx.nd.zeros(output_buff["loc_loss_output"].shape),label_det],
# [output_buff["cls_prob_output"], output_buff["loc_loss_output"],
# output_buff["cls_label_output"]])
eval_metrics.get_metric(0).update([label_seg.as_in_context(self.ctx)], [pred_seg.as_in_context(self.ctx)])
self.executor.outputs[0].wait_to_read()
##################### display results ##############################
# out_img = output_dict["seg_out_output"].asnumpy()
# out_det = output_dict["det_out_output"].asnumpy()
# for imgidx in range(out_img.shape[0]):
# res_img = np.squeeze(out_img[imgidx,:,:].argmax(axis=0).astype(np.uint8))
# label_img = data.label[1].asnumpy()[imgidx,:,:].astype(np.uint8)
# img = np.squeeze(data.data[0].asnumpy()[imgidx,:,:,:])
# det = out_det[imgidx,:,:]
# gt = label_det.asnumpy()[imgidx,:].reshape((-1,6))
# display_results(res_img,np.expand_dims(label_img,axis=0),img, self.class_names)
# [exit(0) if (cv2.waitKey()&0xff)==27 else None]
# outimgiter += 1
batch_end_params = BatchEndParam(epoch=epoch, nbatch=nbatch, eval_metric=eval_metrics)
batch_end_callback(batch_end_params)
if TIMING:
print("%.0fms" % ((time.time()-tic)*1000.,))
# exit(0) # for debugging only
##### save snapshot
if (not self.evaluation_only) and (epoch_end_callback is not None):
epoch_end_callback(epoch, self.symbol, self.arg_params, self.aux_params)
names, values = eval_metrics.get()
for name, value in zip(names,values):
logger.info(" --->Epoch[%d] Train-%s=%f", epoch, name, value)
# evaluation
if eval_data:
logger.info(" in eval process...")
nbatch = 0
# depth_metric = DistanceAccuracyMetric(class_names=self.class_names)
eval_data.reset()
eval_metrics.reset()
self.valid_metric.reset()
# depth_metric.reset()
timing_results = []
for data, fnames in eval_data:
nbatch += 1
# label_shape_det = data.label[0].shape
label_shape_seg = data.label[1].shape
self.arg_params[data_name] = mx.nd.array(data.data[0], self.ctx)
# self.arg_params[label_name_det] = mx.nd.array(data.label[0], self.ctx)
self.arg_params[label_name_seg] = mx.nd.array(data.label[1], self.ctx)
self.executor = self.symbol.bind(self.ctx, self.arg_params,
args_grad=self.grad_params, grad_req=grad_req, aux_states=self.aux_params)
output_names = self.symbol.list_outputs()
output_dict = dict(zip(output_names, self.executor.outputs))
cpu_output_array = mx.nd.zeros(output_dict["seg_out_output"].shape)
############## monitor status
# def stat_helper(name, array):
# """wrapper for executor callback"""
# import ctypes
# from mxnet.ndarray import NDArray
# from mxnet.base import NDArrayHandle, py_str
# array = ctypes.cast(array, NDArrayHandle)
# if 1:
# array = NDArray(array, writable=False).asnumpy()
# print (name, array.shape, np.mean(array), np.std(array),
# ('%.1fms' % (float(time.time()-stat_helper.start_time)*1000)))
# else:
# array = NDArray(array, writable=False)
# array.wait_to_read()
# elapsed = float(time.time()-stat_helper.start_time)*1000.
# if elapsed>5:
# print (name, array.shape, ('%.1fms' % (elapsed,)))
# stat_helper.start_time=time.time()
# stat_helper.start_time=float(time.time())
# self.executor.set_monitor_callback(stat_helper)
############## forward
tic = time.time()
self.executor.forward(is_train=True)
output_dict["seg_out_output"].wait_to_read()
timing_results.append((time.time()-tic)*1000.)
output_dict["seg_out_output"].copyto(cpu_output_array)
pred_shape = output_dict["seg_out_output"].shape
label = mx.nd.array(data.label[1].reshape((label_shape_seg[0], label_shape_seg[1]*label_shape_seg[2])))
output_dict["seg_out_output"].wait_to_read()
seg_out_output = output_dict["seg_out_output"].asnumpy()
# pred_det_shape = output_dict["det_out_output"].shape
pred_seg_shape = output_dict["seg_out_output"].shape
# label_det = mx.nd.array(data.label[0].reshape((label_shape_det[0], label_shape_det[1]*label_shape_det[2])))
label_seg = mx.nd.array(data.label[1].reshape((label_shape_seg[0], label_shape_seg[1]*label_shape_seg[2])),ctx=self.ctx)
# pred_det = mx.nd.array(output_dict["det_out_output"].reshape((pred_det_shape[0], pred_det_shape[1], pred_det_shape[2])))
pred_seg = mx.nd.array(output_dict["seg_out_output"].reshape((pred_seg_shape[0], pred_seg_shape[1], pred_seg_shape[2]*pred_seg_shape[3])),ctx=self.ctx)
#### remove invalid boxes
# out_dets = output_dict["det_out_output"].asnumpy()
# assert len(out_dets.shape)==3
# pred_det = np.zeros((batch_size, 200, 7), np.float32)-1.
# for idx, out_det in enumerate(out_dets):
# assert len(out_det.shape)==2
# out_det = np.expand_dims(out_det, axis=0)
# indices = np.where(out_det[:,:,0]>=0) # labeled as negative
# out_det = np.expand_dims(out_det[indices[0],indices[1],:],axis=0)
# indices = np.where(out_det[:,1]>.25) # higher confidence
# out_det = np.expand_dims(out_det[indices[0],indices[1],:],axis=0)
# pred_det[idx, :out_det.shape[1], :] = out_det
# del out_det
# pred_det = mx.nd.array(pred_det)
##### display results
if False: # self.evaluation_only:
out_img = output_dict["seg_out_output"]
out_img = mx.nd.split(out_img, axis=0, num_outputs=out_img.shape[0], squeeze_axis=0)
if not isinstance(out_img,list):
out_img = [out_img]
for imgidx in range(eval_data.batch_size):
### segmentation
seg_prob = out_img[imgidx]
seg_prob = mx.nd.array(np.squeeze(seg_prob.asnumpy(),axis=(0,)),ctx=self.ctx)
res_img = np.squeeze(seg_prob.asnumpy().argmax(axis=0).astype(np.uint8))
# res_img = np.squeeze(out_img[imgidx,:,:].argmax(axis=0).astype(np.uint8))
label_img = data.label[1].asnumpy()[imgidx,:,:].astype(np.uint8)
img = np.squeeze(data.data[0].asnumpy()[imgidx,:,:,:])
# det = pred_det.asnumpy()[imgidx,:,:]
### ground-truth
# gt = label_det.asnumpy()[imgidx,:].reshape((-1,6))
# save to results folder for evalutation
res_fname = fnames[imgidx].replace("SegmentationClass","results")
lut = np.zeros(256)
# lut[:19]=np.array([7,8,11,12,13,17,19,20,21,22,23,24,25,26,27,28,31,32,33])
lut[:20]=np.array([7,8,11,12,13,17,19,20,21,22,23,24,25,26,27,28,31,32,33,34])
seg_resized = prob_upsampling(seg_prob, target_shape=(1024,2048))
seg_resized2 = cv2.LUT(seg_resized,lut)
cv2.imwrite(res_fname, seg_resized2)
# display result
display_img = display_results(res_img,np.expand_dims(label_img,axis=0),img, self.class_names)
res_fname = fnames[imgidx].replace("SegmentationClass","Results").replace("labelIds","results")
if cv2.imwrite(res_fname, display_img):
print(res_fname,'saved.')
[exit(0) if (cv2.waitKey()&0xff)==27 else None]
outimgiter += 1
if self.evaluation_only:
continue
# eval_metrics.get_metric(0).update(None,
# [output_dict["cls_prob_output"], output_dict["loc_loss_output"],
# output_dict["cls_label_output"]])
eval_metrics.get_metric(0).update([label_seg], [pred_seg])
# self.valid_metric.update([mx.nd.slice_axis(data.label[0],axis=2,begin=0,end=5)], \
# [mx.nd.slice_axis(pred_det,axis=2,begin=0,end=6)])
# disparities = []
# for imgidx in range(batch_size):
# dispname = fnames[imgidx].replace("SegmentationClass","Disparity").replace("gtFine_labelTrainIds","disparity")
# disparities.append(cv2.imread(dispname,-1))
# assert disparities[0] is not None, dispname + " not found."
# depth_metric.update(mx.nd.array(disparities),[pred_det])
# det_metric = self.valid_metric
seg_metric = eval_metrics.get_metric(0)
# det_names, det_values = det_metric.get()
seg_name, seg_value = seg_metric.get()
# depth_names, depth_values = depth_metric.get()
print("\r %d/%d speed=%.1fms %.1f%% %s=%.1f" % \
(nbatch*eval_data.batch_size,eval_data.num_samples,
math.fsum(timing_results)/float(nbatch),
float(nbatch*eval_data.batch_size)*100./float(eval_data.num_samples),
# det_names[-1],det_values[-1]*100.,
seg_name,seg_value*100.,),end='\r')
names, values = eval_metrics.get()
for name, value in zip(names,values):
logger.info(' epoch[%d] Validation-%s=%f', epoch, name, value)
logger.info('----------------------------------------------')
print(' & '.join(names))
print(' & '.join(map(lambda v:'%.1f'%(v*100.,),values)))
# logger.info('----------------------------------------------')
# names, values = self.valid_metric.get()
# for name, value in zip(names,values):
# logger.info(' epoch[%d] Validation-%s=%f', epoch, name, value)
# logger.info('----------------------------------------------')
# print(' & '.join(names))
# print(' & '.join(map(lambda v:'%.1f'%(v*100.,),values)))
# logger.info('----------------------------------------------')
# names, values = depth_metric.get()
# for name, value in zip(names,values):
# logger.info(' epoch[%d] Validation-%s=%f', epoch, name, value)
# logger.info('----------------------------------------------')
# print(' & '.join(names))
# print(' & '.join(map(lambda v:'%.1f'%(v*100.,),values)))
logger.info('----------------------------------------------')
if self.evaluation_only:
exit(0) ## for debugging only