class clientPeakFinder(clientAbstract.clientAbstract):
def __init__(self):
#Amount of events sent to PeakNet (For future, rayonix batchSize = 7 would be GPU memory efficient)
self.batchSize = 3
self.goodLikelihood = .003
#Limit of events iterated through in a run
self.eventLimit = self.batchSize * 10
#Minimum number of peaks to be found to calculate likelihood
self.goodNumPeaks = 10
#Minimum number of events to be found before peak finding on 1000 events of a run
self.minEvents = 1
#If the last run was a good run:
self.goodRun = False
#If batch size reached in the middle of a good run, have the worker return to this run
self.useLastRun = False
self.det = None
self.exp = None
self.runnum = None
self.eventNum = 0
#Step 1: Both Queen and Clients make their own Peaknet instances.
self.peaknet = Peaknet()
print("Made a peaknet instance!")
#Step 1a
logdir = "/reg/d/psdm/cxi/cxitut13/res/autosfx/tensorboard"
self.peaknet.set_writer(project_name=os.path.join(logdir,"test"))
# FIXME: fetch weights from mongoDB: peaknet.updateModel(model)
self.peaknet.loadCfg("/reg/neh/home/liponan/ai/pytorch-yolo2/cfg/newpeaksv10-asic.cfg")
print("Loaded arbitrary weights!")
#Step 2: Client loads arbitrary DN weights and connects to GPU
print("Connecting to GPU...")
self.peaknet.init_model()
#self.peaknet.model = torch.load("/reg/d/psdm/cxi/cxic0415/res/liponan/antfarm_backup/api_demo_psana_model_000086880")
self.peaknet.model.cuda()
print("Connected!")
# @Abstract method (this is the only required method for a plugin)
def algorithm(self, **kwargs):
""" Initialize the peakfinding algorithim with keyword
arguments given by the user, then run the peakfinding
algorithm on random sets of experiment runs
Arguments:
**kwargs -- a dictionary of arguments containing peakfinding parameters
"""
npxmin = kwargs["npix_min"]
npxmax = kwargs["npix_max"]
amaxthr = kwargs["amax_thr"]
atotthr = kwargs["atot_thr"]
sonmin = kwargs["son_min"]
alg = PyAlgos(mask = None, pbits = 0)
alg.set_peak_selection_pars(npix_min=npxmin, npix_max=npxmax, amax_thr=amaxthr, atot_thr=atotthr, son_min=sonmin) #(npix_min=2, npix_max=30, amax_thr=300, atot_thr=600, son_min=10)
self.run(alg, **kwargs)
def createDictionary(self, exp, runnum, event, peaks, likelihood):
"""Create a dictionary that holds the important information of events with crystals
Arguments:
exp -- experiment name
runnum -- run number
event -- event number
peaks -- number of peaks found
labels -- location of peaks
"""
post = {"Exp":exp,
"RunNum":runnum,
"Event":event,
"Peaks":peaks,
"Likelihood":likelihood}
return post
def bitwise_array(self, value):
""" Convert a numpy array to a form that can be sent through json.
Arguments:
value -- a numpy array that will be converted.
"""
if np.isscalar(value):
return value
val = np.asarray(value)
return [base64.b64encode(val), val.shape, val.dtype.str]
def calculate_likelihood(self, qPeaks):
""" Calculate the likelihood that an event is a crystal
Arguments:
qPeaks --
"""
nPeaks = int(qPeaks.shape[1])
selfD = distance.cdist(qPeaks.transpose(), qPeaks.transpose(), 'euclidean')
sortedSelfD = np.sort(selfD)
closestNeighborDist = sortedSelfD[:, 1]
meanClosestNeighborDist = np.median(closestNeighborDist)
closestPeaks = [None] * nPeaks
coords = qPeaks.transpose()
pairsFound = 0.
for ii in range(nPeaks):
index = np.where(selfD[ii, :] == closestNeighborDist[ii])
closestPeaks[ii] = coords[list(index[0]), :].copy()
p = coords[ii, :]
flip = 2 * p - closestPeaks[ii]
d = distance.cdist(coords, flip, 'euclidean')
sigma = closestNeighborDist[ii] / 4.
mu = 0.
bins = d
vals = np.exp(-(bins - mu) ** 2 / (2. * sigma ** 2))
weight = np.sum(vals)
pairsFound += weight
pairsFound = pairsFound / 2.
pairsFoundPerSpot = pairsFound / float(nPeaks)
return [meanClosestNeighborDist, pairsFoundPerSpot]
def getPeaks(self, d, alg, hdr, fmt, mask, times, env, run, j):
"""Finds peaks within an event, and returns the event information, peaks found, and hits found
Arguments:
d -- psana.Detector() of this experiment's detector
alg -- the algorithm used to find peaks
hdr -- Title row for printed chart of peaks found
fmt -- Locations of peaks found for printed chart
mask -- the detector mask
times -- all the events for this run
env -- ds.env()
run -- ds.runs().next(), the run information
j -- this event's number
"""
evt = run.event(times[j])
try:
nda = d.calib(evt) * mask
except TypeError:
nda = d.calib(evt)
if (nda is not None):
peaks = alg.peak_finder_v3r3(nda, rank=3, r0=3, dr=2, nsigm =10)
numPeaksFound = len(peaks)
return [evt, nda, peaks, numPeaksFound]
else:
return[None,None,None,None]
def getLikelihood(self, d, evt, peaks, numPeaksFound):
""" Returns the likeligood value for an event with 15 or more peaks
Arguments:
d -- psana.Detector() of this experiment's detector
evt -- ds.env()
peaks -- the peaks found for this event
numPeaksFound -- number of peaks found for this event
"""
if (numPeaksFound >= self.goodNumPeaks):
ix = d.indexes_x(evt)
iy = d.indexes_y(evt)
d.ipx, d.ipy = d.point_indexes(evt, pxy_um=(0, 0))
d.iX = np.array(ix, dtype=np.int64)
d.iY = np.array(iy, dtype=np.int64)
cenX = d.iX[np.array(peaks[:, 0], dtype=np.int64),
np.array(peaks[:, 1], dtype=np.int64),
np.array(peaks[:, 2], dtype=np.int64)] + 0.5
cenY = d.iY[np.array(peaks[:, 0], dtype=np.int64),
np.array(peaks[:, 1], dtype=np.int64),
np.array(peaks[:, 2], dtype=np.int64)] + 0.5
x = cenX - d.ipx
y = cenY - d.ipy
pixSize = float(d.pixel_size(evt))
detdis = np.mean(d.coords_z(evt)) * 1e-6 # metres
z = detdis / pixSize * np.ones(x.shape) # pixels
#ebeam = ebeamDet.get(evt)
#try:
# photonEnergy = ebeam.ebeamPhotonEnergy()
#except:
photonEnergy = 1
wavelength = 12.407002 / float(photonEnergy) # Angstrom
norm = np.sqrt(x ** 2 + y ** 2 + z ** 2)
qPeaks = (np.array([x, y, z]) / norm - np.array([[0.], [0.], [1.]])) / wavelength
[meanClosestNeighborDist, pairsFoundPerSpot] = self.calculate_likelihood(qPeaks)
return pairsFoundPerSpot
else:
return 0
def getStreaks(self, det, times, run, j):
"""Finds peaks within an event, and returns the event information, peaks found, and hits found
Arguments:
det -- psana.Detector() of this experiment's detector
times -- all the events for this run
run -- ds.runs().next(), the run information
j -- this event's number
"""
evt = run.event(times[j])
width = 300 # crop width
sigma = 1
smallObj = 15 # delete streaks if num pixels less than this
calib = det.calib(evt)
if calib is None:
return [None, None, None]
img = det.image(evt, calib)
# Edge pixels
edgePixels = np.zeros_like(calib)
for i in range(edgePixels.shape[0]):
edgePixels[i, 0, :] = 1
edgePixels[i, -1, :] = 1
edgePixels[i, :, 0] = 1
edgePixels[i, :, -1] = 1
imgEdges = det.image(evt, edgePixels)
# Crop centre of image
(ix, iy) = det.point_indexes(evt)
halfWidth = int(width // 2) # pixels
imgCrop = img[ix - halfWidth:ix + halfWidth, iy - halfWidth:iy + halfWidth]
imgEdges = imgEdges[ix - halfWidth:ix + halfWidth, iy - halfWidth:iy + halfWidth]
myInd = np.where(imgEdges == 1)
# Blur image
imgBlur = sg.convolve(imgCrop, np.ones((2, 2)), mode='same')
mean = imgBlur[imgBlur > 0].mean()
std = imgBlur[imgBlur > 0].std()
# Mask out pixels above 1 sigma
mask = imgBlur > mean + sigma * std
mask = mask.astype(int)
signalOnEdge = mask * imgEdges
mySigInd = np.where(signalOnEdge == 1)
mask[myInd[0].ravel(), myInd[1].ravel()] = 1
# Connected components
myLabel = label(mask, neighbors=4, connectivity=1, background=0)
# All pixels connected to edge pixels is masked out
myMask = np.ones_like(mask)
myParts = np.unique(myLabel[myInd])
for i in myParts:
myMask[np.where(myLabel == i)] = 0
# Delete edges
myMask[myInd] = 1
myMask[mySigInd] = 0
# Delete small objects
myMask = morphology.remove_small_objects(np.invert(myMask.astype('bool')), smallObj)
# Convert assembled to unassembled
wholeMask = np.zeros_like(img)
wholeMask[ix - halfWidth:ix + halfWidth, iy - halfWidth:iy + halfWidth] = myMask
calibMask = det.ndarray_from_image(evt, wholeMask)
streaks = []
for i in range(calib.shape[0]):
for j in regionprops(calibMask[i].astype('int')):
xmin, ymin, xmax, ymax = j.bbox
streaks.append([i, ymin, xmin, ymax - ymin, xmax - xmin])
return [evt, calib, streaks]
def evaluateExpRunDet(self, crawler, alg, peakDB, verbose):
""" Finds a random experiment run, finds peaks, and determines likelihood of crystal event. If an event is
likely to be a crystal, it will be used to train PeakNet. This function continues until the amount of
events found is equal to the batchSize.
return the list of peaks in good events, the list of corresponding images for the good events
"""
# List labels from good events
labels = []
# List of images of good event
imgs = []
# Number of peaks found during this function's call
totalNumPeaks = 0
# Number of events found during this function's call
numGoodEvents = 0
# Time when the client began working
clientBeginTime = time.time()
#How long the client has been running for
runTime = 0
while True:
#Time when the client began working on this event, used in print statement only
beginTimeForThisEvent = time.time()
# Until the amount of good events found is equal to the batchSize, keep finding experiments to find peaks on
if(len(labels) == self.batchSize):
self.useLastRun = True
if verbose > 2: print("### (outer) This run has some hits.")
break
#Keep working on the previous experiment/run
#if((self.useLastRun) and (self.exp is not None)): #TODO: dont redo information functions
# pass
#Use the crawler to fetch a random experiment+run+det
#else:
# self.exp, self.runnum, self.det, self.eventNum = crawler.next(self.eventNum)
#self.exp, self.runnum, self.det = ["cxif5315", 128, "DsaCsPad"] #A good run to use to quickly test if the client works
#eventInfo = self.getDetectorInformation(self.exp, self.runnum, self.det)
#self.d, self.hdr, self.fmt, self.mask, self.times, self.env, self.run, self.numEvents = eventInfo[:]
# if verbose > 0: print("\nExperiment: %s, Run: %s, Detector: %s, NumEvents: %d"%(self.exp, str(self.runnum), self.det, self.numEvents))
#Initialize the number of good events/hits for this event
# If less than 3 in the first 1000 events, this run is skipped
self.exp, self.runnum, self.det, self.eventNum = crawler.next(self.eventNum, self.useLastRun)
#Peak find for each event in an experiment+run
while (self.eventNum < crawler.numEvents):
if verbose > 0: print("eventNum: ", self.eventNum)
# Until the amount of good events found is equal to the batchSize, keep finding experiments to find peaks on
if(len(labels) == self.batchSize):
self.useLastRun = True
if verbose >= 0: print("### (inner) This run has some hits.")
break
#If the amount of good events found is less than minEvents before the eventLimit, then
#stop and try peak finding on a new experiment+run
if((self.eventNum >= self.eventLimit) and (crawler.numGoodEventsInThisRun < self.minEvents)):
self.useLastRun = False
if verbose >= 0: print("### This run is mostly misses. Skipping this run.")
break
if((self.eventNum >= self.eventLimit) and crawler.numGoodEventsInThisRun/float(self.eventNum) < 0.01):
self.useLastRun = False
if verbose >= 0: print("### Hit rate is too low. Skipping this run: ", crawler.numGoodEventsInThisRun/float(self.eventNum))
break
#Get Peak Info
evt, nda, peaks, numPeaksFound = self.getPeaks(crawler.d, alg, crawler.hdr, crawler.fmt, crawler.mask,
crawler.times, crawler.env, crawler.run, self.eventNum)
if verbose > 1: print("numPeaksFound: ", numPeaksFound)
#Get Likelihood
likelihood = self.getLikelihood(crawler.d, evt, peaks, numPeaksFound)
if verbose > 1: print("likelihood: ", likelihood, self.goodLikelihood)
if (likelihood >= self.goodLikelihood):
print("Crystal hit found: ", self.exp, self.runnum, self.eventNum, likelihood)
else:
print("Miss: ", self.exp, self.runnum, self.eventNum, likelihood)
# [[list of seg], [list of row], [list of col]], ... labelsForThisEvent[0:2][0] corresponds to one label
labelsForThisEvent = [np.array([]),np.array([]),np.array([]),np.array([]),np.array([]),np.array([])]
#If this event is a good event, save the labels to train PeakNet
if (likelihood >= self.goodLikelihood):
if verbose > 2: print crawler.hdr
cls = 0
height = 7
width = 7
for peak in peaks:
totalNumPeaks += 1 #number of peaks
seg,row,col,npix,amax,atot = peak[0:6]
labelsForThisEvent[0] = np.append(labelsForThisEvent[0], np.array([cls]), axis = 0) # class
labelsForThisEvent[1] = np.append(labelsForThisEvent[1], np.array([seg]), axis = 0) # seg
labelsForThisEvent[2] = np.append(labelsForThisEvent[2], np.array([row]), axis = 0) # row
labelsForThisEvent[3] = np.append(labelsForThisEvent[3], np.array([col]), axis=0) # col
labelsForThisEvent[4] = np.append(labelsForThisEvent[4], np.array([height]), axis=0) # height
labelsForThisEvent[5] = np.append(labelsForThisEvent[5], np.array([width]), axis=0) # width
if verbose > 2: print crawler.fmt % (cls, seg, row, col, height, width, npix, atot)
labels.append(labelsForThisEvent)
# Shape of imgs = (j <= (batchsize = 64), m = 32 tiles per nda, h = 185, w = 388)
imgs.append(nda)
#increase the skip condition value
crawler.numGoodEventsInThisRun += 1
#increase the database number of hits value
numGoodEvents += 1
#create a dictionary to be posted on the database
kwargs = self.createDictionary(self.exp, str(self.runnum), str(self.eventNum), numPeaksFound, likelihood) #, labelsForThisEvent)
peakDB.addExpRunEventPeaks(**kwargs) #TODO: MongoDB doesnt like the numpy array
self.eventNum += 1
if(self.eventNum >= crawler.numEvents):
self.useLastRun = False
#Time when the client ended working on this event, used in print statement only
endTimeForThisEvent = time.time()
if verbose > 1: print("This took %d seconds" % (endTimeForThisEvent-beginTimeForThisEvent))
#add total peaks and hits value to database when finished
#TODO: confirm that this updates the values and does not overwrite the values...
peakDB.addPeaksAndHits(totalNumPeaks, numGoodEvents)
#peakDB.printDatabase()
# update runTime
clientRunTime = time.time() - clientBeginTime
if verbose > 0: print("Peaks", totalNumPeaks, "Events", numGoodEvents)
if verbose > 1: print("Run time: ", clientRunTime)
return labels, imgs
def run(self, alg, **kwargs):
""" Runs the peakfinder, adds values to the database, trains peaknet, and reports model to the master
Arguments:
alg -- the peakfinding algorithm
kwargs -- peakfinding parameters, host and server name, client name
"""
socket = clientSocket(**kwargs)
peakDB = PeakDatabase(**kwargs) #create database to store good event info in
verbose = kwargs["verbose"]
# Crawler used to fetch a random experiment + run
myCrawler = Crawler()
counter = 0
jj = 0
outdir = "/reg/d/psdm/cxi/cxic0415/scratch/liponan/antfarm_backup" # save images for debugging
while(True):
# Randomly choose an experiment:run and look for crystal diffraction pattern and return with batchSize labels
labels, imgs = self.evaluateExpRunDet(myCrawler, alg, peakDB, verbose)
counter += 1
imgs = np.array(imgs)
#Step 3: Client tells queen it is ready
socket.push(["Ready", counter])
fname = os.path.join(outdir, kwargs["name"]+"_"+str(self.peaknet.model.seen) + ".pkl")
#Step 4: Client receives model from queen
val = socket.pull()
print("#### flag: ", val)
flag, model = val
#Step 5: Client updateModel(model from queen)
self.peaknet.updateModel(model, check=True)
self.peaknet.model.cuda()
fname = '/reg/neh/home/liponan/ai/peaknet4antfarm/val_and_test_cxic0415.json' # FIXME: don't hard code
df = None
if flag == 'train':
# Step 6: Client trains its Peaknet instance
numPanels = imgs[0].shape[0]
mini_batch_size = numPanels * self.batchSize
self.peaknet.train(imgs, labels, mini_batch_size=mini_batch_size, box_size=7, use_cuda=True,
writer=self.peaknet.writer, verbose=True)
if jj == 100:
self.peaknet.snapshot(imgs, labels, tag=kwargs["name"])
jj = 0
jj += 1
# Step 7: Client sends the new model to queen
#print("##### Grad: ", self.peaknet.getGrad())
socket.push(["Gradient", self.peaknet.getGrad(), mini_batch_size])
if kwargs["isFirstWorker"]==True:
if flag == 'validate':
print("######### validate")
# Read json
df = json_parser(fname, mode='validate', subset=False)
counter = 0
elif flag == 'validateSubset':
print("######### validateSubset")
# Read json
df = json_parser(fname, mode='validate', subset=True)
if flag.startswith('validate'):
pdl = Utils.psanaDataLoader(df, self.batchSize)
for i in range(pdl.numMacros):
valImgs, valLabels = pdl[i]
numPanels = valImgs[0].shape[0]
# Step 6: Validation
self.peaknet.validate(valImgs, valLabels, mini_batch_size=numPanels * self.batchSize, box_size=7,
use_cuda=True, writer=self.peaknet.writer, verbose=True)
class Trainer(object):
def __init__(self, params):
self.params = params
# get val list
self.get_val_list()
# get training list
self.get_train_list()
# set-up Peaknet
self.setup_peaknet()
self.grad = None
self.delta = 0
self.psana_ready = False
self.cxi_ready = False
self.writer = SummaryWriter("runs/" + params["project_name"])
def get_train_list(self):
if self.params["build_train_list"]:
self.df_train = get_train_df(
cxi_path="/reg/d/psdm/cxi/cxitut13/res/autosfx",
val_csv=
"/reg/d/psdm/cxi/cxic0415/res/liponan/peaknet4antfarm/df_val.csv",
test_csv=
"/reg/d/psdm/cxi/cxic0415/res/liponan/peaknet4antfarm/df_test.csv"
)
else:
self.df_train = pd.read_csv(
"/reg/d/psdm/cxi/cxic0415/res/liponan/peaknet4antfarm/df_train.csv",
index_col=0)
print("training list", len(self.df_train))
def get_val_list(self, n=1000):
self.df_val = pd.read_csv(
"/reg/d/psdm/cxi/cxic0415/res/liponan/peaknet4antfarm/df_val_events_1000.csv"
)
self.df_val = self.df_val.sort_values(by=["exp", "run", "event"])
print("validation list", len(self.df_val), "events")
self.df_val_runs = self.df_val[["exp", "run",
"path"]].drop_duplicates()
print("validation list", len(self.df_val_runs), "runs")
def setup_peaknet(self, model=None):
self.net = Peaknet()
self.net.loadCfg(
"/reg/neh/home/liponan/ai/pytorch-yolo2/cfg/newpeaksv10-asic.cfg")
if model is None:
self.net.init_model()
else:
self.net.model = model
self.net.model.cuda()
#self.net.set_writer(project_name=self.params["project_name"], parameters=self.params)
def get_grads(self):
return self.net.getGrad()
def validate(self):
print(
"=========================================== VAL ==========================================="
)
macro_batch_size = self.params["macro_batch_size"]
seen = 0
overall_recall = 0
# validation
for i in range(len(self.df_val_runs)):
exp, run, path = self.df_val_runs.iloc[i][["exp", "run", "path"]]
try:
ds = psana.DataSource("exp=" + exp + ":run=" + str(run) +
":idx")
det = psana.Detector('DscCsPad')
this_run = ds.runs().next()
times = this_run.times()
print(
"*********************** {}-{} OKAY ***********************"
.format(exp, run))
except:
print("{}-{} not avaiable".format(exp, run))
continue
sub_events = self.df_val.query(
"exp == '{}' and run == '{}'".format(exp, run))["event"]
# print(sub_events)
# print("path", path)
labels, eventIdxs = load_cxi_labels_yxhw(path, total_size=-1)
labels = [
labels[i] for i in range(len(labels))
if eventIdxs[i] in sub_events
]
eventIdxs = [
eventIdxs[i] for i in range(len(eventIdxs))
if eventIdxs[i] in sub_events
]
print("labels", len(labels), "eventIdxs", len(eventIdxs))
n_iters = int(np.ceil(len(labels) / float(macro_batch_size)))
print("# iterations", n_iters)
for j in range(n_iters):
idx_offset = j * macro_batch_size
if j == (n_iters - 1):
n = len(labels) - j * macro_batch_size
batch_imgs = psana_img_loader(eventIdxs, idx_offset, n,
det, this_run, times)
batch_labels = labels[(j * macro_batch_size):]
else:
n = macro_batch_size
batch_imgs = psana_img_loader(eventIdxs, idx_offset,
macro_batch_size, det,
this_run, times)
batch_labels = labels[j * macro_batch_size:(j + 1) *
macro_batch_size]
batch_imgs[batch_imgs < 0] = 0
batch_imgs = batch_imgs / batch_imgs.max()
my_recall = self.net.validate(
batch_imgs,
batch_labels,
mini_batch_size=macro_batch_size * 32)
print("my recall", my_recall)
overall_recall += n * my_recall
seen += n
overall_recall /= (1.0 * seen)
self.writer.add_scalar('recall_val', overall_recall,
self.net.model.seen)
print(
"----------------------------------------- END VAL -----------------------------------------"
)
def train(self):
# params
macro_batch_size = self.params["macro_batch_size"]
algo = self.params["optim"]
my_lr = self.params["lr"]
n_check = self.params["n_check"]
n_save = self.params["n_save"]
n_policy = self.params["n_policy"]
skip_trained = self.params["skip_trained"]
p_skip = self.params["p_skip"]
n_train_push = self.params["n_train_push"]
# training
#self.nets[0].set_writer(project_name=self.params["project_name"], parameters=self.params)
#self.nets[0].writer.add_scalar('lr', my_lr, self.nets[0].model.seen)
while not self.psana_ready:
self.exp, self.run, self.path = self.df_train.sample(1).iloc[0][[
"exp", "run", "path"
]]
#self.exp = "cxic0415"
#self.run = '91'
#self.path = "/reg/d/psdm/cxi/cxitut13/res/autosfx/cxic0415_0091.cxi"
#print(exp, run, path)
time.sleep(1)
try:
self.ds = psana.DataSource("exp=" + self.exp + ":run=" +
str(self.run) + ":idx")
#print(self.ds)
self.det = psana.Detector(
'DscCsPad') #FIXME: could be other CsPad?
#print(self.det)
self.this_run = self.ds.runs().next()
#print(self.this_run)
self.times = self.this_run.times()
print(
"*********************** {}-{} OKAY ***********************"
.format(self.exp, self.run))
except:
print("{}-{} not avaiable".format(self.exp, self.run))
continue
if skip_trained:
log_filename = os.path.join(
"/reg/d/psdm/cxi/cxic0415/res/liponan/peaknet4antfarm/train_log",
"{}_{}".format(exp, run))
if os.path.isfile(log_filename):
continue
else:
with open(log_filename, 'a'):
os.utime(log_filename, None)
self.psana_ready = True
self.j_iter = 0
print("end of psana test")
#self.net.writer.add_text("EXP-RUN", "{}-{}".format(exp, run), self.net.model.seen)
if not self.cxi_ready:
self.labels, self.eventIdxs = load_cxi_labels_yxhw(self.path,
total_size=-1)
print("labels", len(self.labels), "eventIdxs", len(self.eventIdxs))
self.n_iters = int(
np.floor(len(self.labels) / float(macro_batch_size)))
print("# iterations", self.n_iters)
self.net.set_optimizer(adagrad=(algo == "adagrad"), lr=my_lr)
for j in range(self.j_iter, self.j_iter + n_train_push): # was n_iters
self.delta = n_train_push
if self.j_iter == self.n_iters - 1:
self.psana_ready = False
self.cxi_ready = False
idx_offset = j * macro_batch_size
n = macro_batch_size
batch_imgs = psana_img_loader(self.eventIdxs, idx_offset,
macro_batch_size, self.det,
self.this_run, self.times)
batch_labels = self.labels[j * macro_batch_size:(j + 1) *
macro_batch_size]
self.net.set_optimizer(adagrad=(algo == "adagrad"), lr=my_lr)
batch_imgs[batch_imgs < 0] = 0
batch_imgs = batch_imgs / batch_imgs.max()
self.net.train(batch_imgs,
batch_labels,
mini_batch_size=macro_batch_size * 32)
self.grad = self.net.getGrad()
if self.net.model.seen % n_save == 0:
self.net.snapshot(batch_imgs,
batch_labels,
tag="antfarm_zmq_trainer")
print("snapshot saved")
if self.net.model.seen in n_policy:
my_lr /= 10.0
self.net.writer.add_scalar('lr', my_lr, self.net.model.seen)
self.j_iter += 1