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test_reffree.py
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test_reffree.py
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#!/usr/local/EMAN2/bin/python
from __future__ import print_function
#******************************************************************************
#*
#* GPU based reference free alignment
#*
#*
#* Author (C) Markus Stabrin 2019 ([email protected])
#* Fabian Schoenfeld 2019 ([email protected])
#* Thorsten Wagner 2019 ([email protected])
#* Tapu Shaikh 2019 ([email protected])
#* Adnan Ali 2019 ([email protected])
#* Luca Lusnig 2019 ([email protected])
#* Toshio Moriya 2019 ([email protected])
#* Pawel A.Penczek, 09/09/2006 ([email protected])
#*
#* Copyright (c) 2019 Max Planck Institute of Molecular Physiology
#* Copyright (c) 2000-2006 The University of Texas - Houston Medical School
#*
#* This program is free software: you can redistribute it and/or modify it
#* under the terms of the GNU General Public License as published by the Free
#* Software Foundation, either version 3 of the License, or (at your option) any
#* later version.
#*
#* This program is distributed in the hope that it will be useful, but WITHOUT
#* ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
#* FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
#*
#* You should have received a copy of the GNU General Public License along with
#* this program. If not, please visit: http://www.gnu.org/licenses/
#*
#******************************************************************************/
import os
import global_def
from global_def import *
from user_functions import *
from optparse import OptionParser
import sys
import sparx as spx
###############################################################
########################################################### GPU
import math
import numpy as np
import ctypes
from EMAN2 import EMNumPy
CUDA_PATH = os.path.join(os.path.dirname(__file__), "..", "cuda", "")
cu_module = ctypes.CDLL( CUDA_PATH + "gpu_aln_pack.so" )
float_ptr = ctypes.POINTER(ctypes.c_float)
cu_module.ref_free_alignment_2D_init.restype = ctypes.c_ulonglong
cu_module.pre_align_init.restype = ctypes.c_ulonglong
class Freezeable( object ):
def freeze( self ):
self._frozen = None
def __setattr__( self, name, value ):
if hasattr( self, '_frozen' )and not hasattr( self, name ):
raise AttributeError( "Error! Trying to add new attribute '%s' to frozen class '%s'!" % (name,self.__class__.__name__) )
object.__setattr__( self, name, value )
class AlignConfig( ctypes.Structure, Freezeable ):
_fields_ = [ # data param
("sbj_num", ctypes.c_uint), # number of subject images we want to align
("ref_num", ctypes.c_uint), # number of reference images we want to align the subjects to
("img_dim", ctypes.c_uint), # image dimension (in both x- and y-direction)
# polar sampling parameters
("ring_num", ctypes.c_uint), # number of rings when converting images to polar coordinates
("ring_len", ctypes.c_uint), # number of rings when converting images to polar coordinates
# shift parameters
("shift_step", ctypes.c_float), # step range when applying translational shifts
("shift_rng_x", ctypes.c_float), # translational shift range in x-direction
("shift_rng_y", ctypes.c_float) ] # translational shift range in y-direction
class AlignParam( ctypes.Structure, Freezeable ):
_fields_ = [ ("sbj_id", ctypes.c_int),
("ref_id", ctypes.c_int),
("shift_x", ctypes.c_float),
("shift_y", ctypes.c_float),
("angle", ctypes.c_float),
("mirror", ctypes.c_bool) ]
def __str__(self):
return "s_%d/r_%d::(%d,%d;%.2f)" % (self.sbj_id, self.ref_id, self.shift_x, self.shift_y, self.angle) \
+("[M]" if self.mirror else "")
aln_param_ptr = ctypes.POINTER(AlignParam)
def get_c_ptr_array( emdata_list ):
ptr_list = []
for img in emdata_list:
img_np = EMNumPy.em2numpy( img )
assert img_np.flags['C_CONTIGUOUS'] == True
assert img_np.dtype == np.float32
img_ptr = img_np.ctypes.data_as(float_ptr)
ptr_list.append(img_ptr)
return (float_ptr*len(emdata_list))(*ptr_list)
def print_gpu_info( cuda_device_id ):
cu_module.print_gpu_info( ctypes.c_int(cuda_device_id) )
########################################################### GPU
###############################################################
def ali2d_base_gpu_isac_CLEAN(
stack, outdir, maskfile=None, ir=1, ou=-1, rs=1, xr="4 2 1 1", yr="-1", ts="2 1 0.5 0.25",
nomirror = False, dst=0.0, center=-1, maxit=0, CTF=False, snr=1.0,
Fourvar=False, user_func_name="ref_ali2d", random_method = "", log = None,
number_of_proc = 1, myid = 0, main_node = 0, mpi_comm = None, write_headers = False,
mpi_gpu_proc=False, gpu_class_limit=0, cuda_device_occ=0.9):
"""
ISAC hardcoded defaults:
maskfile = None
ir = 1 # inner radius
rs = 1 # ring step
dst = 90.0
maxit = 14
snr = 1.0
Fourvar = False
user_func_name = "ref_ali2d"
random_method = ""
write_headers = False
PROFILE-GPU Toxin
alignment 52s
(..) 0s
resampling 56s <-- resampling an issue but already spread across all mpi_procs
sxheader 2s
PROFILE-CPU
alignment 169s
(..) 0
resampling 57 <--
sxheader 2
"""
#----------------------------------[ local imports ]
import os
import sys
import mpi
import math
import time
import alignment
import statistics
import fundamentals
import utilities as util
from filter import filt_ctf
from pixel_error import pixel_error_2D
import user_functions
from sp_applications import MPI_start_end
user_func = user_functions.factory[user_func_name]
# sanity check: gpu procs sound off
if not mpi_gpu_proc: return []
#----------------------------------[ setup ]
print( time.strftime("%Y-%m-%d %H:%M:%S :: ", time.localtime()) + "ali2d_base_gpu_isac_CLEAN() :: MPI proc["+str(myid)+"] run pre-alignment CPU setup" )
# mpi communicator sanity check
assert mpi_comm != None
# set alignment search ranges
yrng = xrng = util.get_input_from_string(xr)
step = util.get_input_from_string(ts)
# polar conversion (ring) parameters
first_ring = int(ir)
last_ring = int(ou)
rstep = int(rs)
# alignment iterations
if int(maxit)==0:
max_iter = 10
auto_stop = True
else:
max_iter = int(maxit)
auto_stop = False
# determine global index values of particles handled by this process
data = stack
total_nima = len(data)
total_nima = mpi.mpi_reduce(total_nima, 1, mpi.MPI_INT, mpi.MPI_SUM, 0, mpi_comm)
total_nima = mpi.mpi_bcast (total_nima, 1, mpi.MPI_INT, main_node, mpi_comm)[0]
list_of_particles = list(range(total_nima))
image_start, image_end = MPI_start_end(total_nima, number_of_proc, myid)
list_of_particles = list_of_particles[image_start:image_end] # list of global indices
nima = len(list_of_particles)
assert( nima == len(data) ) # sanity check
# read nx and broadcast to all nodes
# NOTE: nx is image size and images are assumed to be square
if myid == main_node:
nx = data[0].get_xsize()
else:
nx = 0
nx = util.bcast_number_to_all(nx, source_node=main_node, mpi_comm=mpi_comm)
if CTF:
phase_flip = True
else:
phase_flip = False
CTF = False # okay..?
# set default value for the last ring if none given
if last_ring == -1: last_ring = nx/2-2
# sanity check for last_ring value
if last_ring + max([max(xrng), max(yrng)]) > (nx-1) // 2:
ERROR( "Shift or radius is too large - particle crosses image boundary", "ali2d_MPI", 1 )
# mask (note: ISAC hardcodes default circular mask)
mask = util.model_circle(last_ring, nx, nx)
# image center
cny = cnx = nx/2+1
mode = "F"
ctf_2_sum = None
# create/reset alignment parameters
for im in range(nima):
data[im].set_attr( 'ID', list_of_particles[im] )
util.set_params2D( data[im], [0.0, 0.0, 0.0, 0, 1.0], 'xform.align2d' )
st = Util.infomask( data[im], mask, False )
data[im] -= st[0]
if phase_flip:
data[im] = filt_ctf(data[im], data[im].get_attr("ctf"), binary = True)
# precalculate rings & weights
numr = alignment.Numrinit( first_ring, last_ring, rstep, mode )
wr = alignment.ringwe( numr, mode )
# initialize data for the reference preparation function [the what now?]
if myid == main_node:
ref_data = [mask, center, None, None]
sx_sum = 0.0
sy_sum = 0.0
a0 = -1.0e22
recvcount = []
disp = []
for i in range(number_of_proc):
ib, ie = MPI_start_end(total_nima, number_of_proc, i)
recvcount.append(ie-ib)
if i == 0:
disp.append(0)
else:
disp.append(disp[i-1]+recvcount[i-1])
again = 1
total_iter = 0
cs = [0.0]*2
delta = 0.0
#----------------------------------[ gpu setup ]
print( time.strftime("%Y-%m-%d %H:%M:%S :: ", time.localtime()) + "ali2d_base_gpu_isac_CLEAN() :: MPI proc["+str(myid)+"] run pre-alignment GPU setup" )
# alignment parameters
aln_cfg = AlignConfig(
len(data), 1, # no. of images & averages
data[0].get_xsize(), # image size (images are always assumed to be square)
numr[-3], 256, # polar sampling params (no. of rings, no. of sample on ring)
step[0], xrng[0], xrng[0]) # shift params (step size & shift range in x/y dim)
aln_cfg.freeze()
# find largest batch size we can fit on the given card
gpu_batch_limit = 0
for split in [ 2**i for i in range(int(math.log(len(data),2))+1) ][::-1]:
aln_cfg.sbj_num = min( gpu_batch_limit + split, len(data) )
if cu_module.pre_align_size_check( ctypes.c_uint(len(data)), ctypes.byref(aln_cfg), ctypes.c_uint(myid), ctypes.c_float(cuda_device_occ), ctypes.c_bool(False) ) == True:
gpu_batch_limit += split
gpu_batch_limit = aln_cfg.sbj_num
print( time.strftime("%Y-%m-%d %H:%M:%S :: ", time.localtime()) + "ali2d_base_gpu_isac_CLEAN() :: GPU["+str(myid)+"] pre-alignment batch size: %d/%d" % (gpu_batch_limit, len(data)) )
# initialize gpu resources (returns location for our alignment parameters in CUDA unified memory)
gpu_aln_param = cu_module.pre_align_init( ctypes.c_uint(len(data)), ctypes.byref(aln_cfg), ctypes.c_uint(myid) )
gpu_aln_param = ctypes.cast( gpu_aln_param, aln_param_ptr ) # cast to allow Python-side r/w access
gpu_batch_count = len(data)/gpu_batch_limit if len(data)%gpu_batch_limit==0 else len(data)//gpu_batch_limit+1
print( time.strftime("%Y-%m-%d %H:%M:%S :: ", time.localtime()) + "ali2d_base_gpu_isac_CLEAN() :: GPU["+str(myid)+"] batch count:", gpu_batch_count )
# if the local stack fits on the gpu we only fetch the img data once before we loop
if( gpu_batch_count == 1 ):
cu_module.pre_align_fetch(
get_c_ptr_array(data),
ctypes.c_uint(len(data)),
ctypes.c_char_p("sbj_batch") )
#----------------------------------[ alignment ]
print( time.strftime("%Y-%m-%d %H:%M:%S :: ", time.localtime()) + "ali2d_base_gpu_isac_CLEAN() :: MPI proc["+str(myid)+"] start alignment iterations" )
N_step = 0
# set gpu search parameters
cu_module.reset_shifts( ctypes.c_float(xrng[N_step]), ctypes.c_float(step[N_step]) )
for Iter in range(max_iter):
total_iter += 1
#------------------------------------[ construct new average ]
# sum up the original images
ave1, ave2 = statistics.sum_oe( data, "a", CTF, EMData() ) # pass empty object to prevent calculation of ctf^2
util.reduce_EMData_to_root(ave1, myid, main_node, comm=mpi_comm) # sum_oe returns sum of [o]dd and [e]ven items
util.reduce_EMData_to_root(ave2, myid, main_node, comm=mpi_comm)
if myid == main_node:
# log iteration info
log.add("Iteration #%4d"%(total_iter))
msg = "X range = %5.2f Y range = %5.2f Step = %5.2f" % (xrng[N_step], yrng[N_step], step[N_step])
log.add(msg)
tavg = (ave1+ave2)/total_nima
if outdir:
tavg.write_image(os.path.join(outdir, "aqc.hdf"), total_iter-1)
frsc = statistics.fsc_mask(ave1, ave2, mask, 1.0, os.path.join(outdir, "resolution%03d"%(total_iter)))
else:
frsc = statistics.fsc_mask(ave1, ave2, mask, 1.0)
else: tavg = util.model_blank(nx, nx)
del ave1, ave2
# main node applies (optional) fourier transform and user function (center and filtering)
if myid == main_node:
# a0 should increase; stop algorithm when it decreases.
# However, the result will depend on filtration, so it is not quite right.
# moved it here, so it is for unfiltered average and thus hopefully makes more sense
a1 = tavg.cmp("dot", tavg, dict(negative=0, mask=ref_data[0]) )
msg = "Criterion %d = %15.8e"%(total_iter, a1)
log.add(msg)
ref_data[2] = tavg
ref_data[3] = frsc
# centering (default: average centering method)
if center == -1:
ref_data[1] = 0
tavg, cs = user_func(ref_data)
cs[0] = float(sx_sum)/total_nima
cs[1] = float(sy_sum)/total_nima
tavg = fundamentals.fshift(tavg, -cs[0], -cs[1])
msg = "Average center x = %10.3f Center y = %10.3f"%(cs[0], cs[1])
log.add(msg)
else:
if delta != 0.0:
cnt = ref_data[1]
ref_data[1] = 0
tavg, cs = user_func(ref_data)
if delta != 0.0:
ref_data[1] = cnt
# write the current filtered average
if outdir:
tavg.write_image(os.path.join(outdir, "aqf.hdf"), total_iter-1)
# update abort criterion
if a1 < a0:
if auto_stop:
again = 0
else:
a0 = a1
else:
tavg = util.model_blank(nx, nx)
cs = [0.0]*2
# check for abort
if auto_stop:
again = mpi.mpi_bcast(again, 1, mpi.MPI_INT, main_node, mpi_comm)
#------------------------------------[ alignment ]
# broadcast the newly constructed average to everyone
util.bcast_EMData_to_all(tavg, myid, main_node, comm=mpi_comm)
cs = mpi.mpi_bcast(cs, 2, mpi.MPI_FLOAT, main_node, mpi_comm)
cs = list(map(float, cs))
# backup last iteration's alignment parameters
old_ali_params = []
for im in range(nima):
alpha, sx, sy, mirror, scale = util.get_params2D(data[im])
old_ali_params.extend([alpha, sx, sy, mirror])
####################################### GPU
#"""
# transfer latest average to gpu
cu_module.pre_align_fetch(
get_c_ptr_array([tavg]),
ctypes.c_int(1),
ctypes.c_char_p("ref_batch") ) # NOTE: happens for each shift change
# FOR gpu_batch_i DO ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ >
for gpu_batch_idx in range(gpu_batch_count):
# determine next gpu batch and shove to the gpu (NOTE: if we
# only have a single batch, this already happened earlier)
if( gpu_batch_count > 1 ):
gpu_batch_start = gpu_batch_idx * gpu_batch_limit
gpu_batch_end = gpu_batch_start + gpu_batch_limit
if gpu_batch_end > len(data): gpu_batch_end = len(data)
cu_module.pre_align_fetch(
get_c_ptr_array( data[gpu_batch_start:gpu_batch_end] ),
ctypes.c_int( gpu_batch_end-gpu_batch_start ),
ctypes.c_char_p("sbj_batch") )
else:
gpu_batch_start = 0
gpu_batch_end = len(data)
# run the alignment on gpu
cu_module.pre_align_run( ctypes.c_int(gpu_batch_start), ctypes.c_int(gpu_batch_end) )
# print progress bar
gpu_calls_ttl = 1 * max_iter * gpu_batch_count
#gpu_calls_ttl = len(xrng) * max_iter * gpu_batch_count - 1
gpu_calls_cnt = N_step*max_iter*gpu_batch_count + Iter*gpu_batch_count + gpu_batch_idx
gpu_calls_prc = int( float(gpu_calls_cnt+1)/gpu_calls_ttl * 50.0 )
sys.stdout.write( "\r[PRE-ALIGN][GPU"+str(myid)+"][" + "="*gpu_calls_prc + "-"*(50-gpu_calls_prc) + "]~[%d/%d]~[%.2f%%]" % (gpu_calls_cnt+1, gpu_calls_ttl, (float(gpu_calls_cnt+1)/gpu_calls_ttl)*100.0) )
sys.stdout.flush()
if gpu_calls_cnt+1 == gpu_calls_ttl: print("")
# < ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ FOR gpu_batch_i DO
# convert alignment parameters to the format expected by
# other sphire functions and update EMData headers
sx_sum, sy_sum = 0.0, 0.0
for k, img in enumerate(data):
# this is usually done in ormq()
angle = gpu_aln_param[k].angle
sx_neg = -gpu_aln_param[k].shift_x
sy_neg = -gpu_aln_param[k].shift_y
c_ang = math.cos( math.radians(angle) )
s_ang = -math.sin( math.radians(angle) )
shift_x = sx_neg*c_ang - sy_neg*s_ang
shift_y = sx_neg*s_ang + sy_neg*c_ang
# this happens in ali2d_single_iter()
util.set_params2D( img, [angle, shift_x, shift_y, gpu_aln_param[k].mirror, 1.0], "xform.align2d" )
# this is what is returned by ali2d_single_iter()
if gpu_aln_param[k].mirror==0:
sx_sum += shift_x
else:
sx_sum -= shift_x
sy_sum += shift_y
sx_sum = mpi.mpi_reduce( sx_sum, 1, mpi.MPI_FLOAT, mpi.MPI_SUM, main_node, mpi_comm )
sy_sum = mpi.mpi_reduce( sy_sum, 1, mpi.MPI_FLOAT, mpi.MPI_SUM, main_node, mpi_comm )
#"""
###########################################
####################################### GPU
# for SHC
if random_method == "SHC":
nope = mpi_reduce(nope, 1, mpi.MPI_INT, mpi.MPI_SUM, main_node, mpi_comm)
nope = mpi_bcast (nope, 1, mpi.MPI_INT, main_node, mpi_comm)
pixel_error = 0.0
mirror_consistent = 0
pixel_error_list = [-1.0]*nima
for im in range(nima):
alpha, sx, sy, mirror, scale = util.get_params2D(data[im])
if old_ali_params[im*4+3] == mirror:
this_error = pixel_error_2D(old_ali_params[im*4:im*4+3], [alpha, sx, sy], last_ring)
pixel_error += this_error
pixel_error_list[im] = this_error
mirror_consistent += 1
del old_ali_params
mirror_consistent = mpi.mpi_reduce(mirror_consistent, 1, mpi.MPI_INT, mpi.MPI_SUM, main_node, mpi_comm)
pixel_error = mpi.mpi_reduce(pixel_error, 1, mpi.MPI_FLOAT, mpi.MPI_SUM, main_node, mpi_comm)
pixel_error_list = mpi.mpi_gatherv(pixel_error_list, nima, mpi.MPI_FLOAT, recvcount, disp, mpi.MPI_FLOAT, main_node, mpi_comm)
if myid == main_node and outdir:
util.drop_image(tavg, os.path.join(outdir, "aqfinal.hdf"))
# free gpu resources
cu_module.gpu_clear()
mpi.mpi_barrier(mpi_comm)
params = []
for im in range(nima):
alpha, sx, sy, mirror, scale = util.get_params2D(data[im])
params.append([alpha, sx, sy, mirror])
mpi.mpi_barrier(mpi_comm)
tmp = params[:]
tmp = spx.wrap_mpi_gatherv(tmp, main_node, mpi_comm)
if( myid == main_node ):
spx.write_text_row( tmp, os.path.join(outdir,"initial2Dparams.txt") )
del tmp
if myid == main_node:
log.add("Finished ali2d_base")
if myid==0: print( time.strftime("%Y-%m-%d %H:%M:%S :: ", time.localtime()) + "ali2d_base_gpu_isac_CLEAN() :: Alignment complete" )
return params
def ali2d_base(stack, outdir, maskfile=None, ir=1, ou=-1, rs=1, xr="4 2 1 1", yr="-1", ts="2 1 0.5 0.25", \
nomirror = False, dst=0.0, center=-1, maxit=0, CTF=False, snr=1.0, \
Fourvar=False, user_func_name="ref_ali2d", random_method = "", log = None, \
number_of_proc = 1, myid = 0, main_node = 0, mpi_comm = None, write_headers = False):
from sp_utilities import model_circle, model_blank, drop_image, get_image, get_input_from_string
from sp_utilities import reduce_EMData_to_root, bcast_EMData_to_all, send_attr_dict, file_type
from sp_utilities import bcast_number_to_all, bcast_list_to_all
from sp_statistics import fsc_mask, sum_oe, hist_list, varf2d_MPI
from sp_alignment import Numrinit, ringwe, ali2d_single_iter
from sp_pixel_error import pixel_error_2D
from numpy import reshape, shape
from sp_fundamentals import fshift, fft, rot_avg_table
from sp_utilities import get_params2D, set_params2D
from sp_utilities import wrap_mpi_gatherv
import os
import sys
from sp_applications import MPI_start_end
from mpi import mpi_init, mpi_comm_size, mpi_comm_rank, MPI_COMM_WORLD
from mpi import mpi_reduce, mpi_bcast, mpi_barrier, mpi_gatherv
from mpi import MPI_SUM, MPI_FLOAT, MPI_INT
import time
if log == None:
from sp_logger import Logger
log = Logger()
if mpi_comm == None:
mpi_comm = MPI_COMM_WORLD
# ftp = file_type(stack)
if myid == main_node:
import sp_global_def
sp_global_def.LOGFILE = os.path.join(outdir, sp_global_def.LOGFILE)
log.add("Start ali2d_MPI")
xrng = get_input_from_string(xr)
if yr == "-1": yrng = xrng
else : yrng = get_input_from_string(yr)
step = get_input_from_string(ts)
first_ring=int(ir); last_ring=int(ou); rstep=int(rs); max_iter=int(maxit);
if max_iter == 0:
max_iter = 10
auto_stop = True
else:
auto_stop = False
import types
if( type(stack) is bytes ):
if myid == main_node:
total_nima = EMUtil.get_image_count(stack)
else:
total_nima = 0
total_nima = bcast_number_to_all(total_nima)
list_of_particles = list(range(total_nima))
image_start, image_end = MPI_start_end(total_nima, number_of_proc, myid)
list_of_particles = list_of_particles[image_start:image_end]
nima = len(list_of_particles)
data = EMData.read_images(stack, list_of_particles)
else:
data = stack
total_nima = len(data)
total_nima = mpi_reduce(total_nima, 1, MPI_INT, MPI_SUM, 0, MPI_COMM_WORLD)
total_nima = mpi_bcast(total_nima, 1, MPI_INT, main_node, MPI_COMM_WORLD)[0]
list_of_particles = list(range(total_nima))
image_start, image_end = MPI_start_end(total_nima, number_of_proc, myid)
list_of_particles = list_of_particles[image_start:image_end]
nima = len(list_of_particles)
assert( nima == len(data))
# read nx and ctf_app (if CTF) and broadcast to all nodes
if myid == main_node:
nx = data[0].get_xsize()
else:
nx = 0
nx = bcast_number_to_all(nx, source_node = main_node)
phase_flip = False
CTF = False
# default value for the last ring
if last_ring == -1: last_ring = nx/2-2
if last_ring + max([max(xrng), max(yrng)]) > (nx-1) // 2:
ERROR('Shift or radius is too large - particle crosses image boundary', "ali2d_MPI", 1)
if myid == main_node:
# log.add("Input stack : %s"%(stack))
log.add("Number of images : %d"%(total_nima))
log.add("Output directory : %s"%(outdir))
log.add("Inner radius : %i"%(first_ring))
log.add("Outer radius : %i"%(last_ring))
log.add("Ring step : %i"%(rstep))
log.add("X search range : %s"%(xrng))
log.add("Y search range : %s"%(yrng))
log.add("Translational step : %s"%(step))
log.add("Disable checking mirror : %s"%(nomirror))
log.add("Discrete angle used : %d"%(dst))
log.add("Center type : %i"%(center))
log.add("Maximum iteration : %i"%(max_iter))
#log.add("Use Fourier variance : %s\n"%(Fourvar))
log.add("CTF correction : %s"%(CTF))
log.add("Phase flip : %s"%(phase_flip))
#log.add("Signal-to-Noise Ratio : %f\n"%(snr))
if auto_stop:
log.add("Stop iteration with : criterion")
else:
log.add("Stop iteration with : maxit")
import sp_user_functions
user_func = sp_user_functions.factory[user_func_name]
log.add("User function : %s"%(user_func_name))
log.add("Number of processors used : %d"%(number_of_proc))
if maskfile:
import types
if type(maskfile) is bytes:
if myid == main_node: log.add("Maskfile : %s"%(maskfile))
mask = get_image(maskfile)
else:
if myid == main_node: log.add("Maskfile : user provided in-core mask")
mask = maskfile
else:
if myid == main_node: log.add("Maskfile : default, a circle with radius %i"%(last_ring))
mask = model_circle(last_ring, nx, nx)
cnx = nx/2+1
cny = cnx
if random_method == "SCF": mode = "H"
else: mode = "F"
ctf_2_sum = None
for im in range(nima):
data[im].set_attr('ID', list_of_particles[im])
set_params2D(data[im], [0.0, 0.0, 0.0, 0, 1.0], 'xform.align2d')
st = Util.infomask(data[im], mask, False)
data[im] -= st[0]
if( random_method == "SHC" ): data[im].set_attr('previousmax',1.0e-23)
ctf_2_sum = None
# startup
numr = Numrinit(first_ring, last_ring, rstep, mode) #precalculate rings
wr = ringwe(numr, mode)
if myid == main_node:
# initialize data for the reference preparation function
ref_data = [mask, center, None, None]
sx_sum = 0.0
sy_sum = 0.0
a0 = -1.0e22
recvcount = []
disp = []
for i in range(number_of_proc):
ib, ie = MPI_start_end(total_nima, number_of_proc, i)
recvcount.append(ie-ib)
if i == 0:
disp.append(0)
else:
disp.append(disp[i-1]+recvcount[i-1])
N_step = 0 #only test first
again = 1
total_iter = 0
cs = [0.0]*2
delta = 0.0
if myid == main_node: print( time.strftime("%Y-%m-%d %H:%M:%S :: ", time.localtime()) + "ali2d_base() start alignment iterations" )
for Iter in range(max_iter):
total_iter += 1
ave1, ave2 = sum_oe(data, "a", CTF, EMData()) # pass empty object to prevent calculation of ctf^2
reduce_EMData_to_root(ave1, myid, main_node)
reduce_EMData_to_root(ave2, myid, main_node)
sys.stdout.flush()
if myid == main_node:
log.add("Iteration #%4d"%(total_iter))
msg = "X range = %5.2f Y range = %5.2f Step = %5.2f"%(xrng[N_step], yrng[N_step], step[N_step])
log.add(msg)
tavg = (ave1+ave2)/total_nima
if outdir:
tavg.write_image(os.path.join(outdir, "aqc.hdf"), total_iter-1)
frsc = fsc_mask(ave1, ave2, mask, 1.0, os.path.join(outdir, "resolution%03d"%(total_iter)))
else:
frsc = fsc_mask(ave1, ave2, mask, 1.0)
else:
tavg = model_blank(nx, nx)
del ave1, ave2
if Fourvar:
bcast_EMData_to_all(tavg, myid, main_node)
vav, rvar = varf2d_MPI(myid, data, tavg, mask, "a", CTF)
if myid == main_node:
if Fourvar:
tavg = fft(Util.divn_img(fft(tavg), vav))
vav_r = Util.pack_complex_to_real(vav)
if outdir:
vav_r.write_image(os.path.join(outdir, "varf.hdf"), total_iter-1)
# a0 should increase; stop algorithm when it decreases.
# However, the result will depend on filtration, so it is not quite right.
# moved it here, so it is for unfiltered average and thus hopefully makes more sense
a1 = tavg.cmp("dot", tavg, dict(negative = 0, mask = ref_data[0]))
msg = "Criterion %d = %15.8e"%(total_iter, a1)
log.add(msg)
ref_data[2] = tavg
ref_data[3] = frsc
# call user-supplied function to prepare reference image, i.e., center and filter it
if center == -1:
# When center = -1, which is by default, we use the average center method
ref_data[1] = 0
tavg, cs = user_func(ref_data)
cs[0] = float(sx_sum)/total_nima
cs[1] = float(sy_sum)/total_nima
tavg = fshift(tavg, -cs[0], -cs[1])
msg = "Average center x = %10.3f Center y = %10.3f"%(cs[0], cs[1])
log.add(msg)
else:
if delta != 0.0:
cnt = ref_data[1]
ref_data[1] = 0
tavg, cs = user_func(ref_data)
if delta != 0.0:
ref_data[1] = cnt
# write the current filtered average
if outdir:
tavg.write_image(os.path.join(outdir, "aqf.hdf"), total_iter-1)
if a1 < a0:
if auto_stop: again = 0
else: a0 = a1
else:
tavg = model_blank(nx, nx)
cs = [0.0]*2
if auto_stop:
again = mpi_bcast(again, 1, MPI_INT, main_node, mpi_comm)
if Fourvar: del vav
bcast_EMData_to_all(tavg, myid, main_node)
cs = mpi_bcast(cs, 2, MPI_FLOAT, main_node, mpi_comm)
cs = list(map(float, cs))
if total_iter != max_iter*len(xrng):
old_ali_params = []
for im in range(nima):
alpha, sx, sy, mirror, scale = get_params2D(data[im])
old_ali_params.extend([alpha, sx, sy, mirror])
if Iter%4 != 0 or total_iter > max_iter*len(xrng)-10: delta = 0.0
else: delta = dst
sx_sum, sy_sum, nope = ali2d_single_iter(data, numr, wr, cs, tavg, cnx, cny, \
xrng[N_step], yrng[N_step], step[N_step], \
nomirror=nomirror, mode=mode, CTF=CTF, delta=delta, \
random_method = random_method)
sx_sum = mpi_reduce(sx_sum, 1, MPI_FLOAT, MPI_SUM, main_node, mpi_comm)
sy_sum = mpi_reduce(sy_sum, 1, MPI_FLOAT, MPI_SUM, main_node, mpi_comm)
# for SHC
if random_method == "SHC":
nope = mpi_reduce(nope, 1, MPI_INT, MPI_SUM, main_node, mpi_comm)
nope = mpi_bcast(nope, 1, MPI_INT, main_node, mpi_comm)
pixel_error = 0.0
mirror_consistent = 0
pixel_error_list = [-1.0]*nima
for im in range(nima):
alpha, sx, sy, mirror, scale = get_params2D(data[im])
if old_ali_params[im*4+3] == mirror:
this_error = pixel_error_2D(old_ali_params[im*4:im*4+3], [alpha, sx, sy], last_ring)
pixel_error += this_error
pixel_error_list[im] = this_error
mirror_consistent += 1
del old_ali_params
mirror_consistent = mpi_reduce(mirror_consistent, 1, MPI_INT, MPI_SUM, main_node, mpi_comm)
pixel_error = mpi_reduce(pixel_error, 1, MPI_FLOAT, MPI_SUM, main_node, mpi_comm)
pixel_error_list = mpi_gatherv(pixel_error_list, nima, MPI_FLOAT, recvcount, disp, MPI_FLOAT, main_node, mpi_comm)
if myid == main_node:
log.add("Mirror consistency rate = %8.4f%%"%(float(mirror_consistent)/total_nima*100))
if mirror_consistent!=0:
log.add("Among the mirror-consistent images, average of pixel errors is %0.4f, and their distribution is:"%(float(pixel_error)/float(mirror_consistent)))
pixel_error_list = list(map(float, pixel_error_list))
for i in range(total_nima-1, -1, -1):
if pixel_error_list[i] < 0: del pixel_error_list[i]
region, hist = hist_list(pixel_error_list, 20)
for p in range(20):
log.add(" %14.2f: %6d"%(region[p], hist[p]))
log.add("\n\n")
if myid == main_node and outdir: drop_image(tavg, os.path.join(outdir, "aqfinal.hdf"))
if myid == main_node: print( time.strftime("%Y-%m-%d %H:%M:%S :: ", time.localtime()) + "ali2d_base() end alignment iterations" )
# write out headers and STOP, under MPI writing has to be done sequentially
mpi_barrier(mpi_comm)
params = []
for im in range(nima):
alpha, sx, sy, mirror, scale = get_params2D(data[im])
params.append([alpha, sx, sy, mirror])
mpi_barrier(mpi_comm)
tmp = params[:]
tmp = spx.wrap_mpi_gatherv(tmp, main_node, mpi_comm)
if( myid == main_node ):
spx.write_text_row( tmp, os.path.join(outdir,"initial2Dparams.txt") )
del tmp
if myid == main_node: log.add("Finished ali2d_base")
return params #, data
def mpi_assert( condition, msg ):
if not condition:
mpi_rank = mpi.mpi_comm_rank(mpi.MPI_COMM_WORLD)
print( "MPI PROC["+str(mpi_rank)+"] ASSERTION ERROR:", msg, file=sys.stderr)
sys.stderr.flush()
mpi.mpi_finalize()
sys.exit()
def main():
progname = os.path.basename(sys.argv[0])
usage = progname + " stack outdir <maskfile> --ir=inner_radius --ou=outer_radius --rs=ring_step --xr=x_range --yr=y_range --ts=translation_step --dst=delta --center=center --maxit=max_iteration --CTF --snr=SNR --Fourvar=Fourier_variance --Ng=group_number --Function=user_function_name --CUDA --GPUID --MPI"
parser = OptionParser(usage,version=SPARXVERSION)
parser.add_option("--ir", type="float", default=1, help="inner radius for rotational correlation > 0 (set to 1)")
parser.add_option("--ou", type="float", default=-1, help="outer radius for rotational correlation < nx/2-1 (set to the radius of the particle)")
parser.add_option("--rs", type="float", default=1, help="step between rings in rotational correlation > 0 (set to 1)" )
parser.add_option("--xr", type="string", default="4 2 1 1", help="range for translation search in x direction, search is +/xr ")
parser.add_option("--yr", type="string", default="-1", help="range for translation search in y direction, search is +/yr ")
parser.add_option("--ts", type="string", default="2 1 0.5 0.25",help="step of translation search in both directions")
parser.add_option("--nomirror", action="store_true", default=False, help="Disable checking mirror orientations of images (default False)")
parser.add_option("--dst", type="float", default=0.0, help="delta")
parser.add_option("--center", type="float", default=-1, help="-1.average center method; 0.not centered; 1.phase approximation; 2.cc with Gaussian function; 3.cc with donut-shaped image 4.cc with user-defined reference 5.cc with self-rotated average")
parser.add_option("--maxit", type="float", default=0, help="maximum number of iterations (0 means the maximum iterations is 10, but it will automatically stop should the criterion falls")
parser.add_option("--CTF", action="store_true", default=False, help="use CTF correction during alignment")
parser.add_option("--snr", type="float", default=1.0, help="signal-to-noise ratio of the data (set to 1.0)")
parser.add_option("--Fourvar", action="store_true", default=False, help="compute Fourier variance")
parser.add_option("--function", type="string", default="ref_ali2d", help="name of the reference preparation function (default ref_ali2d)")
parser.add_option("--gpu_devices", type="string", default="", help="Specify the GPUs to be used (e.g. --gpu_devices=0, or --gpu_devices=0,1 for one or two GPUs, respectively). Using \"$ nividia-smi\" in the terminal will print out what GPUs are available. [Default: None]" )
parser.add_option( "--gpu_info", action="store_true", default=False, help="Print detailed information about the selected GPUs. Use --gpu_devices to specify what GPUs you want to know about. NOTE: program will stop after printing this information, so don't use this parameter if you intend to actually process any data. [Default: False]" )
parser.add_option("--MPI", action="store_true", default=False, help="use MPI version ")
parser.add_option("--mode", type="string", default="F", help="Full or Half rings, default F")
parser.add_option("--randomize",action="store_true", default=False, help="randomize initial rotations (suboption of friedel, default False)")
parser.add_option("--orient", action="store_true", default=False, help="orient images such that the average is symmetric about x-axis, for layer lines (suboption of friedel, default False)")
parser.add_option("--random_method", type="string", default="", help="use SHC or SCF (default standard method)")
(options, args) = parser.parse_args()
if len(args) < 2 or len(args) > 3:
print("usage: " + usage)
print("Please run '" + progname + " -h' for detailed options")
else:
if args[1] == 'None': outdir = None
else: outdir = args[1]
if len(args) == 2: mask = None
else: mask = args[2]
if global_def.CACHE_DISABLE:
from utilities import disable_bdb_cache
disable_bdb_cache()
global_def.BATCH = True
if options.MPI:
from mpi import mpi_init, mpi_comm_size, mpi_comm_rank, MPI_COMM_WORLD
import mpi
sys.argv = mpi_init(len(sys.argv),sys.argv) # init and finalize are needed for each process
number_of_proc = mpi_comm_size(MPI_COMM_WORLD) # read from mpirun
myid = mpi_comm_rank(MPI_COMM_WORLD)
main_node = 0
if(myid == main_node):
import subprocess
from logger import Logger, BaseLogger_Files
# Create output directory
log = Logger(BaseLogger_Files())
log.prefix = os.path.join(outdir)
cmd = "mkdir "+log.prefix
outcome = subprocess.call(cmd, shell=True)
log.prefix += "/"
else:
outcome = 0
log = None
from utilities import bcast_number_to_all
outcome = bcast_number_to_all(outcome, source_node = main_node)
if(outcome == 1):
ERROR('Output directory exists, please change the name and restart the program', "ali2d_MPI", 1, myid)
params2d = ali2d_base(args[0], outdir, mask, options.ir, options.ou, options.rs, options.xr, options.yr, \
options.ts, options.nomirror, options.dst, \
options.center, options.maxit, options.CTF, options.snr, options.Fourvar, \
options.function, random_method = options.random_method, log = log, \
number_of_proc = number_of_proc, myid = myid, main_node = main_node, mpi_comm = MPI_COMM_WORLD,\
write_headers = True)
else:
import time
from mpi import mpi_init, mpi_comm_size, mpi_comm_rank, MPI_COMM_WORLD
import mpi
sys.argv = mpi_init(len(sys.argv),sys.argv)
global Blockdata
Blockdata = {}
Blockdata["nproc"] = mpi_comm_size(mpi.MPI_COMM_WORLD)
myid = mpi_comm_rank(MPI_COMM_WORLD)
main_node = 0
### GPU Related ###
Blockdata["shared_comm"] = mpi.mpi_comm_split_type(mpi.MPI_COMM_WORLD, mpi.MPI_COMM_TYPE_SHARED, 0, mpi.MPI_INFO_NULL)
Blockdata["myid_on_node"] = mpi.mpi_comm_rank(Blockdata["shared_comm"])
tmp_img = EMData() # this is just a placeholder EMData object that we'll re-use in a couple of loops
# get total number of images (nima) and broadcast
if(myid == main_node):
Blockdata["total_nima"] = global_def.EMUtil.get_image_count(args[0])
else:
Blockdata["total_nima"] = 0
Blockdata["total_nima"] = spx.bcast_number_to_all(Blockdata["total_nima"], source_node = main_node)
##################### [ System and GPU Memory check ] #####################
# map our GPU selection to list of available GPUs
global GPU_DEVICES
if options.gpu_devices != "":
os.environ["CUDA_VISIBLE_DEVICES"] = options.gpu_devices
options.gpu_devices = ",".join( map(str, range(len(options.gpu_devices.split(",")))) )
GPU_DEVICES = map( int, options.gpu_devices.split(",") )
if myid==0: print( time.strftime("%Y-%m-%d %H:%M:%S :: ", time.localtime()) + "main() :: Using CUDA devices", GPU_DEVICES )
else:
GPU_DEVICES = [0]
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
if myid==0:
print( "\nWarning: No GPU was specified. Using GPU [0] by default.")
print( " -> Program will crash if the selected GPU does not suffice." )
print( " -> Use \"$ nividia-smi\" in the terminal to see a list of available GPUs.\'\n" )
# sanity check: make sure each GPU can be assigned to an MPI process
mpi_assert( len(GPU_DEVICES) <= Blockdata["nproc"], "ERROR! Trying to use more GPUs (x"+str(len(GPU_DEVICES))+") than MPI are available (x"+str(Blockdata["nproc"])+")!" )
# GPU info
if options.gpu_info:
if myid==0:
import applications
for cuda_device_id in GPU_DEVICES:
print( "\n____[ CUDA device("+str(cuda_device_id)+") ]____" )
applications.print_gpu_info(cuda_device_id)
sys.stdout.flush()
mpi.mpi_finalize()
sys.exit()
global MPI_GPU_COMM
MPI_GPU_COMM = mpi.mpi_comm_split( mpi.MPI_COMM_WORLD, (Blockdata["myid_on_node"] in GPU_DEVICES), myid )
#------------------------------------------------------[ Memory check ]
# percentage of system memory we allow ourselves to occupy; we leave some
# in over to leave gpu isac and others some breathing room
sys_mem_use = 0.75
# we use a linux system call to get the RAM info we need
if "linux" in sys.platform:
if myid==0: print( time.strftime("%Y-%m-%d %H:%M:%S :: ", time.localtime()) + "main() :: Running memory check.." )
# get amount of available system RAM
sys_mem_avl = float( os.popen("free").readlines()[1].split()[6] ) / 1024 # mem size in MB (free returns kb)
if myid==0:
print( " "*32, "..avl sys mem (RAM): %.2f MB / %.2f GB" % (sys_mem_avl, sys_mem_avl/1024) )
print( " "*32, "..use sys mem (RAM): %.2f MB / %.2f GB" % (sys_mem_avl*sys_mem_use, (sys_mem_avl*sys_mem_use)/1024) )
# estimate memory requirement of raw data
tmp_img.read_image( args[0], 0 )
data_size_sub = (Blockdata["total_nima"]*tmp_img.get_xsize()*tmp_img.get_ysize()*4.) / 1024**2 # mem size in MB
if myid==0: print( " "*32, "..est mem req (raw data, %d %dx%d images): %.2f MB / %.2f GB" %
(Blockdata["total_nima"], tmp_img.get_xsize(), tmp_img.get_ysize(),
data_size_sub, data_size_sub/1024) )
# batch size of input reads per MPI proc