def train_until(**kwargs): if tf.train.latest_checkpoint(kwargs['output_folder']): trained_until = int( tf.train.latest_checkpoint(kwargs['output_folder']).split('_')[-1]) else: trained_until = 0 if trained_until >= kwargs['max_iteration']: return anchor = gp.ArrayKey('ANCHOR') raw = gp.ArrayKey('RAW') raw_cropped = gp.ArrayKey('RAW_CROPPED') gt_threeclass = gp.ArrayKey('GT_THREECLASS') loss_weights_threeclass = gp.ArrayKey('LOSS_WEIGHTS_THREECLASS') pred_threeclass = gp.ArrayKey('PRED_THREECLASS') pred_threeclass_gradients = gp.ArrayKey('PRED_THREECLASS_GRADIENTS') with open( os.path.join(kwargs['output_folder'], kwargs['name'] + '_config.json'), 'r') as f: net_config = json.load(f) with open( os.path.join(kwargs['output_folder'], kwargs['name'] + '_names.json'), 'r') as f: net_names = json.load(f) voxel_size = gp.Coordinate(kwargs['voxel_size']) input_shape_world = gp.Coordinate(net_config['input_shape']) * voxel_size output_shape_world = gp.Coordinate(net_config['output_shape']) * voxel_size # formulate the request for what a batch should (at least) contain request = gp.BatchRequest() request.add(raw, input_shape_world) request.add(raw_cropped, output_shape_world) request.add(gt_threeclass, output_shape_world) request.add(anchor, output_shape_world) request.add(loss_weights_threeclass, output_shape_world) # when we make a snapshot for inspection (see below), we also want to # request the predicted affinities and gradients of the loss wrt the # affinities snapshot_request = gp.BatchRequest() snapshot_request.add(raw_cropped, output_shape_world) snapshot_request.add(gt_threeclass, output_shape_world) snapshot_request.add(pred_threeclass, output_shape_world) # snapshot_request.add(pred_threeclass_gradients, output_shape_world) if kwargs['input_format'] != "hdf" and kwargs['input_format'] != "zarr": raise NotImplementedError("train node for {} not implemented".format( kwargs['input_format'])) fls = [] shapes = [] for f in kwargs['data_files']: fls.append(os.path.splitext(f)[0]) if kwargs['input_format'] == "hdf": vol = h5py.File(f, 'r')['volumes/raw'] elif kwargs['input_format'] == "zarr": vol = zarr.open(f, 'r')['volumes/raw'] print(f, vol.shape, vol.dtype) shapes.append(vol.shape) if vol.dtype != np.float32: print("please convert to float32") ln = len(fls) print("first 5 files: ", fls[0:4]) # padR = 46 # padGT = 32 if kwargs['input_format'] == "hdf": sourceNode = gp.Hdf5Source elif kwargs['input_format'] == "zarr": sourceNode = gp.ZarrSource augmentation = kwargs['augmentation'] pipeline = ( tuple( # read batches from the HDF5 file sourceNode( fls[t] + "." + kwargs['input_format'], datasets={ raw: 'volumes/raw', gt_threeclass: 'volumes/gt_threeclass', anchor: 'volumes/gt_threeclass', }, array_specs={ raw: gp.ArraySpec(interpolatable=True), gt_threeclass: gp.ArraySpec(interpolatable=False), anchor: gp.ArraySpec(interpolatable=False) } ) + gp.MergeProvider() + gp.Pad(raw, None) + gp.Pad(gt_threeclass, None) + gp.Pad(anchor, gp.Coordinate((2,2,2))) # chose a random location for each requested batch + gp.RandomLocation() for t in range(ln) ) + # chose a random source (i.e., sample) from the above gp.RandomProvider() + # elastically deform the batch (gp.ElasticAugment( augmentation['elastic']['control_point_spacing'], augmentation['elastic']['jitter_sigma'], [augmentation['elastic']['rotation_min']*np.pi/180.0, augmentation['elastic']['rotation_max']*np.pi/180.0], subsample=augmentation['elastic'].get('subsample', 1)) \ if augmentation.get('elastic') is not None else NoOp()) + # apply transpose and mirror augmentations gp.SimpleAugment(mirror_only=augmentation['simple'].get("mirror"), transpose_only=augmentation['simple'].get("transpose")) + # # scale and shift the intensity of the raw array gp.IntensityAugment( raw, scale_min=augmentation['intensity']['scale'][0], scale_max=augmentation['intensity']['scale'][1], shift_min=augmentation['intensity']['shift'][0], shift_max=augmentation['intensity']['shift'][1], z_section_wise=False) + # grow a boundary between labels # TODO: check # gp.GrowBoundary( # gt_threeclass, # steps=1, # only_xy=False) + gp.BalanceLabels( gt_threeclass, loss_weights_threeclass, num_classes=3) + # pre-cache batches from the point upstream gp.PreCache( cache_size=kwargs['cache_size'], num_workers=kwargs['num_workers']) + # perform one training iteration for each passing batch (here we use # the tensor names earlier stored in train_net.config) gp.tensorflow.Train( os.path.join(kwargs['output_folder'], kwargs['name']), optimizer=net_names['optimizer'], summary=net_names['summaries'], log_dir=kwargs['output_folder'], loss=net_names['loss'], inputs={ net_names['raw']: raw, net_names['anchor']: anchor, net_names['gt_threeclass']: gt_threeclass, net_names['loss_weights_threeclass']: loss_weights_threeclass }, outputs={ net_names['pred_threeclass']: pred_threeclass, net_names['raw_cropped']: raw_cropped, }, gradients={ net_names['pred_threeclass']: pred_threeclass_gradients, }, save_every=kwargs['checkpoints']) + # save the passing batch as an HDF5 file for inspection gp.Snapshot( { raw: '/volumes/raw', raw_cropped: 'volumes/raw_cropped', gt_threeclass: '/volumes/gt_threeclass', pred_threeclass: '/volumes/pred_threeclass', }, output_dir=os.path.join(kwargs['output_folder'], 'snapshots'), output_filename='batch_{iteration}.hdf', every=kwargs['snapshots'], additional_request=snapshot_request, compression_type='gzip') + # show a summary of time spend in each node every 10 iterations gp.PrintProfilingStats(every=kwargs['profiling']) ) ######### # TRAIN # ######### print("Starting training...") with gp.build(pipeline): print(pipeline) for i in range(trained_until, kwargs['max_iteration']): # print("request", request) start = time.time() pipeline.request_batch(request) time_of_iteration = time.time() - start logger.info("Batch: iteration=%d, time=%f", i, time_of_iteration) # exit() print("Training finished")
validate = False: raw = gp.ArrayKey('raw') gt = gp.ArrayKey('ground_truth') files = os.listdir(input_path) files = [os.path.join(input_path,f) for f in files ] pipeline =( tuple ( gp.ZarrSource( files[t], # the zarr container {raw: 'raw', gt : 'ground_truth'}, # which dataset to associate to the array key {raw: gp.ArraySpec(interpolatable=True,dtype=np.dtype('float32'),voxel_size=voxel_shape), gt: gp.ArraySpec(interpolatable=True,dtype=np.dtype('float32'),voxel_size=voxel_shape)} # meta-information ) + gp.RandomLocation() for t in range(len(files)) ) + gp.RandomProvider() # +gp.Stack(batch_size) ) input_size = gp.Coordinate(input_shape) output_size = gp.Coordinate(output_shape) request = gp.BatchRequest() request.add(raw,input_size) request.add(gt,input_size) diff = input_shape[1] - output_shape[1] diff = int(diff/2) max_p = input_shape[1]-diff different_shape = diff > 0 if different_shape: print('Difference padding: {}'.format(diff)) with gp.build(pipeline):
def train(until): model = SpineUNet() loss = torch.nn.BCELoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) input_size = (8, 96, 96) raw = gp.ArrayKey('RAW') labels = gp.ArrayKey('LABELS') affs = gp.ArrayKey('AFFS') affs_predicted = gp.ArrayKey('AFFS_PREDICTED') pipeline = ( ( gp.ZarrSource( 'data/20200201.zarr', { raw: 'train/sample1/raw', labels: 'train/sample1/labels' }), gp.ZarrSource( 'data/20200201.zarr', { raw: 'train/sample2/raw', labels: 'train/sample2/labels' }), gp.ZarrSource( 'data/20200201.zarr', { raw: 'train/sample3/raw', labels: 'train/sample3/labels' }) ) + gp.RandomProvider() + gp.Normalize(raw) + gp.RandomLocation() + gp.SimpleAugment(transpose_only=(1, 2)) + gp.ElasticAugment((2, 10, 10), (0.0, 0.5, 0.5), [0, math.pi]) + gp.AddAffinities( [(1, 0, 0), (0, 1, 0), (0, 0, 1)], labels, affs) + gp.Normalize(affs, factor=1.0) + #gp.PreCache(num_workers=1) + # raw: (d, h, w) # affs: (3, d, h, w) gp.Stack(1) + # raw: (1, d, h, w) # affs: (1, 3, d, h, w) AddChannelDim(raw) + # raw: (1, 1, d, h, w) # affs: (1, 3, d, h, w) gp_torch.Train( model, loss, optimizer, inputs={'x': raw}, outputs={0: affs_predicted}, loss_inputs={0: affs_predicted, 1: affs}, save_every=10000) + RemoveChannelDim(raw) + RemoveChannelDim(raw) + RemoveChannelDim(affs) + RemoveChannelDim(affs_predicted) + # raw: (d, h, w) # affs: (3, d, h, w) # affs_predicted: (3, d, h, w) gp.Snapshot( { raw: 'raw', labels: 'labels', affs: 'affs', affs_predicted: 'affs_predicted' }, every=500, output_filename='iteration_{iteration}.hdf') ) request = gp.BatchRequest() request.add(raw, input_size) request.add(labels, input_size) request.add(affs, input_size) request.add(affs_predicted, input_size) with gp.build(pipeline): for i in range(until): pipeline.request_batch(request)
def train_until(**kwargs): print("cuda visibile devices", os.environ["CUDA_VISIBLE_DEVICES"]) if tf.train.latest_checkpoint(kwargs['output_folder']): trained_until = int( tf.train.latest_checkpoint(kwargs['output_folder']).split('_')[-1]) else: trained_until = 0 if trained_until >= kwargs['max_iteration']: return anchor = gp.ArrayKey('ANCHOR') raw = gp.ArrayKey('RAW') raw_cropped = gp.ArrayKey('RAW_CROPPED') gt_labels = gp.ArrayKey('GT_LABELS') gt_affs = gp.ArrayKey('GT_AFFS') pred_affs = gp.ArrayKey('PRED_AFFS') pred_affs_gradients = gp.ArrayKey('PRED_AFFS_GRADIENTS') with open( os.path.join(kwargs['output_folder'], kwargs['name'] + '_config.json'), 'r') as f: net_config = json.load(f) with open( os.path.join(kwargs['output_folder'], kwargs['name'] + '_names.json'), 'r') as f: net_names = json.load(f) voxel_size = gp.Coordinate(kwargs['voxel_size']) input_shape_world = gp.Coordinate(net_config['input_shape']) * voxel_size output_shape_world = gp.Coordinate(net_config['output_shape']) * voxel_size # formulate the request for what a batch should (at least) contain request = gp.BatchRequest() # when we make a snapshot for inspection (see below), we also want to # request the predicted affinities and gradients of the loss wrt the # affinities snapshot_request = gp.BatchRequest() snapshot_request.add(raw_cropped, output_shape_world) snapshot_request.add(pred_affs, output_shape_world) snapshot_request.add(gt_affs, output_shape_world) if kwargs['input_format'] != "hdf" and kwargs['input_format'] != "zarr": raise NotImplementedError("train node for %s not implemented yet", kwargs['input_format']) fls = [] for f in kwargs['data_files']: fls.append(os.path.splitext(f)[0]) ln = len(fls) print("first 5 files: ", fls[0:4]) if kwargs['input_format'] == "hdf": sourceNode = gp.Hdf5Source elif kwargs['input_format'] == "zarr": sourceNode = gp.ZarrSource neighborhood = [] psH = np.array(kwargs['patchshape']) // 2 for i in range(-psH[0], psH[0] + 1, kwargs['patchstride'][0]): for j in range(-psH[1], psH[1] + 1, kwargs['patchstride'][1]): for k in range(-psH[2], psH[2] + 1, kwargs['patchstride'][2]): neighborhood.append([i, j, k]) datasets = { raw: 'volumes/raw', gt_labels: 'volumes/gt_labels', anchor: 'volumes/gt_fgbg', } input_specs = { raw: gp.ArraySpec(roi=gp.Roi((0, ) * len(input_shape_world), input_shape_world), interpolatable=True, dtype=np.float32), gt_labels: gp.ArraySpec(roi=gp.Roi((0, ) * len(output_shape_world), output_shape_world), interpolatable=False, dtype=np.uint16), anchor: gp.ArraySpec(roi=gp.Roi((0, ) * len(output_shape_world), output_shape_world), interpolatable=False, dtype=np.uint8), gt_affs: gp.ArraySpec(roi=gp.Roi((0, ) * len(output_shape_world), output_shape_world), interpolatable=False, dtype=np.uint8) } inputs = { net_names['raw']: raw, net_names['gt_affs']: gt_affs, net_names['anchor']: anchor, } outputs = { net_names['pred_affs']: pred_affs, net_names['raw_cropped']: raw_cropped, } snapshot = { raw_cropped: 'volumes/raw_cropped', gt_affs: '/volumes/gt_affs', pred_affs: '/volumes/pred_affs', } optimizer_args = None if kwargs['auto_mixed_precision']: optimizer_args = (kwargs['optimizer'], { 'args': kwargs['args'], 'kwargs': kwargs['kwargs'] }) augmentation = kwargs['augmentation'] pipeline = ( tuple( sourceNode( fls[t] + "." + kwargs['input_format'], datasets=datasets, # array_specs=array_specs ) + gp.Pad(raw, None) + gp.Pad(gt_labels, None) # chose a random location for each requested batch + gp.RandomLocation() for t in range(ln) ) + # chose a random source (i.e., sample) from the above gp.RandomProvider() + # elastically deform the batch gp.ElasticAugment( augmentation['elastic']['control_point_spacing'], augmentation['elastic']['jitter_sigma'], [augmentation['elastic']['rotation_min']*np.pi/180.0, augmentation['elastic']['rotation_max']*np.pi/180.0], subsample=4) + # apply transpose and mirror augmentations gp.SimpleAugment(mirror_only=augmentation['simple'].get("mirror"), transpose_only=augmentation['simple'].get("transpose")) + # scale and shift the intensity of the raw array gp.IntensityAugment( raw, scale_min=augmentation['intensity']['scale'][0], scale_max=augmentation['intensity']['scale'][1], shift_min=augmentation['intensity']['shift'][0], shift_max=augmentation['intensity']['shift'][1], z_section_wise=False) + # grow a boundary between labels gp.GrowBoundary( gt_labels, steps=1, only_xy=False) + # convert labels into affinities between voxels gp.AddAffinities( neighborhood, gt_labels, gt_affs) + # create a weight array that balances positive and negative samples in # the affinity array # gp.BalanceLabels( # gt_affs, # loss_weights_affs) + # pre-cache batches from the point upstream gp.PreCache( cache_size=kwargs['cache_size'], num_workers=kwargs['num_workers']) + # pre-fetch batches from the point upstream (gp.tensorflow.TFData() \ if kwargs.get('use_tf_data') else NoOp()) + # perform one training iteration for each passing batch (here we use # the tensor names earlier stored in train_net.config) gp.tensorflow.Train( os.path.join(kwargs['output_folder'], kwargs['name']), optimizer=net_names['optimizer'], summary=net_names['summaries'], log_dir=kwargs['output_folder'], loss=net_names['loss'], inputs=inputs, outputs=outputs, array_specs=input_specs, gradients={ net_names['pred_affs']: pred_affs_gradients, }, auto_mixed_precision=kwargs['auto_mixed_precision'], optimizer_args=optimizer_args, use_tf_data=kwargs['use_tf_data'], save_every=kwargs['checkpoints'], snapshot_every=kwargs['snapshots']) + # save the passing batch as an HDF5 file for inspection gp.Snapshot( snapshot, output_dir=os.path.join(kwargs['output_folder'], 'snapshots'), output_filename='batch_{iteration}.hdf', every=kwargs['snapshots'], additional_request=snapshot_request, compression_type='gzip') + # show a summary of time spend in each node every 10 iterations gp.PrintProfilingStats(every=kwargs['profiling']) ) ######### # TRAIN # ######### print("Starting training...") try: with gp.build(pipeline): print(pipeline) for i in range(trained_until, kwargs['max_iteration']): start = time.time() pipeline.request_batch(request) time_of_iteration = time.time() - start logger.info("Batch: iteration=%d, time=%f", i, time_of_iteration) # exit() except KeyboardInterrupt: sys.exit() print("Training finished")
def train_until(**kwargs): print("cuda visibile devices", os.environ["CUDA_VISIBLE_DEVICES"]) if tf.train.latest_checkpoint(kwargs['output_folder']): trained_until = int( tf.train.latest_checkpoint(kwargs['output_folder']).split('_')[-1]) else: trained_until = 0 if trained_until >= kwargs['max_iteration']: return raw = gp.ArrayKey('RAW') raw_cropped = gp.ArrayKey('RAW_CROPPED') gt_labels = gp.ArrayKey('GT_LABELS') gt_instances = gp.ArrayKey('GT_INSTANCES') gt_affs = gp.ArrayKey('GT_AFFS') gt_numinst = gp.ArrayKey('GT_NUMINST') gt_sample_mask = gp.ArrayKey('GT_SAMPLE_MASK') pred_affs = gp.ArrayKey('PRED_AFFS') pred_affs_gradients = gp.ArrayKey('PRED_AFFS_GRADIENTS') pred_numinst = gp.ArrayKey('PRED_NUMINST') with open(os.path.join(kwargs['output_folder'], kwargs['name'] + '_config.json'), 'r') as f: net_config = json.load(f) with open(os.path.join(kwargs['output_folder'], kwargs['name'] + '_names.json'), 'r') as f: net_names = json.load(f) voxel_size = gp.Coordinate(kwargs['voxel_size']) input_shape_world = gp.Coordinate(net_config['input_shape'])*voxel_size output_shape_world = gp.Coordinate(net_config['output_shape'])*voxel_size context = gp.Coordinate(input_shape_world - output_shape_world) / 2 # formulate the request for what a batch should (at least) contain request = gp.BatchRequest() request.add(raw, input_shape_world) request.add(raw_cropped, output_shape_world) request.add(gt_labels, output_shape_world) request.add(gt_instances, output_shape_world) request.add(gt_sample_mask, output_shape_world) request.add(gt_affs, output_shape_world) if kwargs['overlapping_inst']: request.add(gt_numinst, output_shape_world) # request.add(loss_weights_affs, output_shape_world) # when we make a snapshot for inspection (see below), we also want to # request the predicted affinities and gradients of the loss wrt the # affinities snapshot_request = gp.BatchRequest() snapshot_request.add(raw_cropped, output_shape_world) snapshot_request.add(pred_affs, output_shape_world) if kwargs['overlapping_inst']: snapshot_request.add(pred_numinst, output_shape_world) # snapshot_request.add(pred_affs_gradients, output_shape_world) if kwargs['input_format'] != "hdf" and kwargs['input_format'] != "zarr": raise NotImplementedError("train node for %s not implemented yet", kwargs['input_format']) raw_key = kwargs.get('raw_key', 'volumes/raw') print('raw key: ', raw_key) fls = [] shapes = [] for f in kwargs['data_files']: fls.append(os.path.splitext(f)[0]) if kwargs['input_format'] == "hdf": vol = h5py.File(f, 'r')[raw_key] elif kwargs['input_format'] == "zarr": vol = zarr.open(f, 'r')[raw_key] # print(f, vol.shape, vol.dtype) shapes.append(vol.shape) if vol.dtype != np.float32: print("please convert to float32") ln = len(fls) print("first 5 files: ", fls[0:4]) if kwargs['input_format'] == "hdf": sourceNode = gp.Hdf5Source elif kwargs['input_format'] == "zarr": sourceNode = gp.ZarrSource neighborhood = [] psH = np.array(kwargs['patchshape'])//2 for i in range(-psH[1], psH[1]+1, kwargs['patchstride'][1]): for j in range(-psH[2], psH[2]+1, kwargs['patchstride'][2]): neighborhood.append([i,j]) datasets = { raw: raw_key, gt_labels: 'volumes/gt_labels', gt_instances: 'volumes/gt_instances' } array_specs = { raw: gp.ArraySpec(interpolatable=True), gt_labels: gp.ArraySpec(interpolatable=False), gt_instances: gp.ArraySpec(interpolatable=False) } inputs = { net_names['raw']: raw, net_names['gt_affs']: gt_affs, # net_names['loss_weights_affs']: loss_weights_affs, } outputs = { net_names['pred_affs']: pred_affs, net_names['raw_cropped']: raw_cropped, } snapshot = { raw: '/volumes/raw', raw_cropped: 'volumes/raw_cropped', gt_affs: '/volumes/gt_affs', pred_affs: '/volumes/pred_affs', pred_affs_gradients: '/volumes/pred_affs_gradients', } if kwargs['overlapping_inst']: datasets[gt_numinst] = 'volumes/gt_numinst' array_specs[gt_numinst] = gp.ArraySpec(interpolatable=False) inputs[net_names['gt_numinst']] = gt_numinst outputs[net_names['pred_numinst']] = pred_numinst snapshot[gt_numinst] = '/volumes/gt_numinst' snapshot[pred_numinst] = '/volumes/pred_numinst' augmentation = kwargs['augmentation'] sampling = kwargs['sampling'] source_fg = tuple( sourceNode( fls[t] + "." + kwargs['input_format'], datasets=datasets, array_specs=array_specs ) + gp.Pad(raw, context) + # chose a random location for each requested batch nl.CountOverlap(gt_labels, gt_sample_mask, maxnuminst=1) + gp.RandomLocation( min_masked=sampling['min_masked'], mask=gt_sample_mask ) for t in range(ln) ) source_fg += gp.RandomProvider() source_overlap = tuple( sourceNode( fls[t] + "." + kwargs['input_format'], datasets=datasets, array_specs=array_specs ) + gp.Pad(raw, context) + # chose a random location for each requested batch nl.MaskCloseDistanceToOverlap( gt_labels, gt_sample_mask, sampling['overlap_min_dist'], sampling['overlap_max_dist'] ) + gp.RandomLocation( min_masked=sampling['min_masked_overlap'], mask=gt_sample_mask ) for t in range(ln) ) source_overlap += gp.RandomProvider() pipeline = ( (source_fg, source_overlap) + # chose a random source (i.e., sample) from the above gp.RandomProvider(probabilities=[sampling['probability_fg'], sampling['probability_overlap']]) + # elastically deform the batch gp.ElasticAugment( augmentation['elastic']['control_point_spacing'], augmentation['elastic']['jitter_sigma'], [augmentation['elastic']['rotation_min']*np.pi/180.0, augmentation['elastic']['rotation_max']*np.pi/180.0]) + # apply transpose and mirror augmentations gp.SimpleAugment( mirror_only=augmentation['simple'].get("mirror"), transpose_only=augmentation['simple'].get("transpose")) + # # scale and shift the intensity of the raw array gp.IntensityAugment( raw, scale_min=augmentation['intensity']['scale'][0], scale_max=augmentation['intensity']['scale'][1], shift_min=augmentation['intensity']['shift'][0], shift_max=augmentation['intensity']['shift'][1], z_section_wise=False) + gp.IntensityScaleShift(raw, 2, -1) + # convert labels into affinities between voxels nl.AddAffinities( neighborhood, gt_labels, gt_affs, multiple_labels=kwargs['overlapping_inst']) + # pre-cache batches from the point upstream gp.PreCache( cache_size=kwargs['cache_size'], num_workers=kwargs['num_workers']) + # perform one training iteration for each passing batch (here we use # the tensor names earlier stored in train_net.config) gp.tensorflow.Train( os.path.join(kwargs['output_folder'], kwargs['name']), optimizer=net_names['optimizer'], summary=net_names['summaries'], log_dir=kwargs['output_folder'], loss=net_names['loss'], inputs=inputs, outputs=outputs, gradients={ net_names['pred_affs']: pred_affs_gradients, }, save_every=kwargs['checkpoints']) + # save the passing batch as an HDF5 file for inspection gp.Snapshot( snapshot, output_dir=os.path.join(kwargs['output_folder'], 'snapshots'), output_filename='batch_{iteration}.hdf', every=kwargs['snapshots'], additional_request=snapshot_request, compression_type='gzip') + # show a summary of time spend in each node every 10 iterations gp.PrintProfilingStats(every=kwargs['profiling']) ) ######### # TRAIN # ######### print("Starting training...") with gp.build(pipeline): print(pipeline) for i in range(trained_until, kwargs['max_iteration']): # print("request", request) start = time.time() pipeline.request_batch(request) time_of_iteration = time.time() - start logger.info( "Batch: iteration=%d, time=%f", i, time_of_iteration) # exit() print("Training finished")
def train(iterations): ################## # DECLARE ARRAYS # ################## # raw intensities raw = gp.ArrayKey('RAW') # objects labelled with unique IDs gt_labels = gp.ArrayKey('LABELS') # array of per-voxel affinities to direct neighbors gt_affs = gp.ArrayKey('AFFINITIES') # weights to use to balance the loss loss_weights = gp.ArrayKey('LOSS_WEIGHTS') # the predicted affinities pred_affs = gp.ArrayKey('PRED_AFFS') # the gredient of the loss wrt to the predicted affinities pred_affs_gradients = gp.ArrayKey('PRED_AFFS_GRADIENTS') #################### # DECLARE REQUESTS # #################### with open('train_net_config.json', 'r') as f: net_config = json.load(f) # get the input and output size in world units (nm, in this case) voxel_size = gp.Coordinate((8, 8, 8)) input_size = gp.Coordinate(net_config['input_shape']) * voxel_size output_size = gp.Coordinate(net_config['output_shape']) * voxel_size # formulate the request for what a batch should (at least) contain request = gp.BatchRequest() request.add(raw, input_size) request.add(gt_affs, output_size) request.add(loss_weights, output_size) # when we make a snapshot for inspection (see below), we also want to # request the predicted affinities and gradients of the loss wrt the # affinities snapshot_request = gp.BatchRequest() snapshot_request[pred_affs] = request[gt_affs] snapshot_request[pred_affs_gradients] = request[gt_affs] ############################## # ASSEMBLE TRAINING PIPELINE # ############################## pipeline = ( # a tuple of sources, one for each sample (A, B, and C) provided by the # CREMI challenge tuple( # read batches from the HDF5 file gp.Hdf5Source(os.path.join(data_dir, 'fib.hdf'), datasets={ raw: 'volumes/raw', gt_labels: 'volumes/labels/neuron_ids' }) + # convert raw to float in [0, 1] gp.Normalize(raw) + # chose a random location for each requested batch gp.RandomLocation()) + # chose a random source (i.e., sample) from the above gp.RandomProvider() + # elastically deform the batch gp.ElasticAugment([8, 8, 8], [0, 2, 2], [0, math.pi / 2.0], prob_slip=0.05, prob_shift=0.05, max_misalign=25) + # apply transpose and mirror augmentations gp.SimpleAugment(transpose_only=[1, 2]) + # scale and shift the intensity of the raw array gp.IntensityAugment(raw, scale_min=0.9, scale_max=1.1, shift_min=-0.1, shift_max=0.1, z_section_wise=True) + # grow a boundary between labels gp.GrowBoundary(gt_labels, steps=3, only_xy=True) + # convert labels into affinities between voxels gp.AddAffinities([[-1, 0, 0], [0, -1, 0], [0, 0, -1]], gt_labels, gt_affs) + # create a weight array that balances positive and negative samples in # the affinity array gp.BalanceLabels(gt_affs, loss_weights) + # pre-cache batches from the point upstream gp.PreCache(cache_size=10, num_workers=5) + # perform one training iteration for each passing batch (here we use # the tensor names earlier stored in train_net.config) gp.tensorflow.Train( 'train_net', net_config['optimizer'], net_config['loss'], inputs={ net_config['raw']: raw, net_config['gt_affs']: gt_affs, net_config['loss_weights']: loss_weights }, outputs={net_config['pred_affs']: pred_affs}, gradients={net_config['pred_affs']: pred_affs_gradients}, save_every=10000) + # save the passing batch as an HDF5 file for inspection gp.Snapshot( { raw: '/volumes/raw', gt_labels: '/volumes/labels/neuron_ids', gt_affs: '/volumes/labels/affs', pred_affs: '/volumes/pred_affs', pred_affs_gradients: '/volumes/pred_affs_gradients' }, output_dir='snapshots', output_filename='batch_{iteration}.hdf', every=1000, additional_request=snapshot_request, compression_type='gzip') + # show a summary of time spend in each node every 10 iterations gp.PrintProfilingStats(every=1000)) ######### # TRAIN # ######### print("Training for", iterations, "iterations") with gp.build(pipeline): for i in range(iterations): pipeline.request_batch(request) print("Finished")
def train_simple_pipeline(n_iterations, setup_config, mknet_tensor_names, loss_tensor_names): input_shape = gp.Coordinate(setup_config["INPUT_SHAPE"]) output_shape = gp.Coordinate(setup_config["OUTPUT_SHAPE"]) voxel_size = gp.Coordinate(setup_config["VOXEL_SIZE"]) num_iterations = setup_config["NUM_ITERATIONS"] cache_size = setup_config["CACHE_SIZE"] num_workers = setup_config["NUM_WORKERS"] snapshot_every = setup_config["SNAPSHOT_EVERY"] checkpoint_every = setup_config["CHECKPOINT_EVERY"] profile_every = setup_config["PROFILE_EVERY"] seperate_by = setup_config["SEPERATE_BY"] gap_crossing_dist = setup_config["GAP_CROSSING_DIST"] match_distance_threshold = setup_config["MATCH_DISTANCE_THRESHOLD"] point_balance_radius = setup_config["POINT_BALANCE_RADIUS"] neuron_radius = setup_config["NEURON_RADIUS"] samples_path = Path(setup_config["SAMPLES_PATH"]) mongo_url = setup_config["MONGO_URL"] input_size = input_shape * voxel_size output_size = output_shape * voxel_size # voxels have size ~= 1 micron on z axis # use this value to scale anything that depends on world unit distance micron_scale = voxel_size[0] seperate_distance = (np.array(seperate_by)).tolist() # array keys for data sources raw = gp.ArrayKey("RAW") consensus = gp.PointsKey("CONSENSUS") skeletonization = gp.PointsKey("SKELETONIZATION") matched = gp.PointsKey("MATCHED") labels = gp.ArrayKey("LABELS") labels_fg = gp.ArrayKey("LABELS_FG") labels_fg_bin = gp.ArrayKey("LABELS_FG_BIN") loss_weights = gp.ArrayKey("LOSS_WEIGHTS") # tensorflow tensors gt_fg = gp.ArrayKey("GT_FG") fg_pred = gp.ArrayKey("FG_PRED") embedding = gp.ArrayKey("EMBEDDING") fg = gp.ArrayKey("FG") maxima = gp.ArrayKey("MAXIMA") gradient_embedding = gp.ArrayKey("GRADIENT_EMBEDDING") gradient_fg = gp.ArrayKey("GRADIENT_FG") emst = gp.ArrayKey("EMST") edges_u = gp.ArrayKey("EDGES_U") edges_v = gp.ArrayKey("EDGES_V") ratio_pos = gp.ArrayKey("RATIO_POS") ratio_neg = gp.ArrayKey("RATIO_NEG") dist = gp.ArrayKey("DIST") num_pos_pairs = gp.ArrayKey("NUM_POS") num_neg_pairs = gp.ArrayKey("NUM_NEG") # add request request = gp.BatchRequest() request.add(labels_fg, output_size) request.add(labels_fg_bin, output_size) request.add(loss_weights, output_size) request.add(raw, input_size) request.add(labels, input_size) request.add(matched, input_size) request.add(skeletonization, input_size) request.add(consensus, input_size) # add snapshot request snapshot_request = gp.BatchRequest() request.add(labels_fg, output_size) # tensorflow requests # snapshot_request.add(raw, input_size) # input_size request for positioning # snapshot_request.add(embedding, output_size, voxel_size=voxel_size) # snapshot_request.add(fg, output_size, voxel_size=voxel_size) # snapshot_request.add(gt_fg, output_size, voxel_size=voxel_size) # snapshot_request.add(fg_pred, output_size, voxel_size=voxel_size) # snapshot_request.add(maxima, output_size, voxel_size=voxel_size) # snapshot_request.add(gradient_embedding, output_size, voxel_size=voxel_size) # snapshot_request.add(gradient_fg, output_size, voxel_size=voxel_size) # snapshot_request[emst] = gp.ArraySpec() # snapshot_request[edges_u] = gp.ArraySpec() # snapshot_request[edges_v] = gp.ArraySpec() # snapshot_request[ratio_pos] = gp.ArraySpec() # snapshot_request[ratio_neg] = gp.ArraySpec() # snapshot_request[dist] = gp.ArraySpec() # snapshot_request[num_pos_pairs] = gp.ArraySpec() # snapshot_request[num_neg_pairs] = gp.ArraySpec() data_sources = tuple( ( gp.N5Source( filename=str((sample / "fluorescence-near-consensus.n5").absolute()), datasets={raw: "volume"}, array_specs={ raw: gp.ArraySpec(interpolatable=True, voxel_size=voxel_size, dtype=np.uint16) }, ), gp.DaisyGraphProvider( f"mouselight-{sample.name}-consensus", mongo_url, points=[consensus], directed=True, node_attrs=[], edge_attrs=[], ), gp.DaisyGraphProvider( f"mouselight-{sample.name}-skeletonization", mongo_url, points=[skeletonization], directed=False, node_attrs=[], edge_attrs=[], ), ) + gp.MergeProvider() + gp.RandomLocation( ensure_nonempty=consensus, ensure_centered=True, point_balance_radius=point_balance_radius * micron_scale, ) + TopologicalMatcher( skeletonization, consensus, matched, failures=Path("matching_failures_slow"), match_distance_threshold=match_distance_threshold * micron_scale, max_gap_crossing=gap_crossing_dist * micron_scale, try_complete=False, use_gurobi=True, ) + RejectIfEmpty(matched) + RasterizeSkeleton( points=matched, array=labels, array_spec=gp.ArraySpec( interpolatable=False, voxel_size=voxel_size, dtype=np.uint32), ) + GrowLabels(labels, radii=[neuron_radius * micron_scale]) # TODO: Do these need to be scaled by world units? + gp.ElasticAugment( [40, 10, 10], [0.25, 1, 1], [0, math.pi / 2.0], subsample=4, use_fast_points_transform=True, recompute_missing_points=False, ) # + gp.SimpleAugment(mirror_only=[1, 2], transpose_only=[1, 2]) + gp.Normalize(raw) + gp.IntensityAugment(raw, 0.9, 1.1, -0.001, 0.001) for sample in samples_path.iterdir() if sample.name in ("2018-07-02", "2018-08-01")) pipeline = ( data_sources + gp.RandomProvider() + Crop(labels, labels_fg) + BinarizeGt(labels_fg, labels_fg_bin) + gp.BalanceLabels(labels_fg_bin, loss_weights) + gp.PreCache(cache_size=cache_size, num_workers=num_workers) + gp.tensorflow.Train( "train_net", optimizer=create_custom_loss(mknet_tensor_names, setup_config), loss=None, inputs={ mknet_tensor_names["loss_weights"]: loss_weights, mknet_tensor_names["raw"]: raw, mknet_tensor_names["gt_labels"]: labels_fg, }, outputs={ mknet_tensor_names["embedding"]: embedding, mknet_tensor_names["fg"]: fg, loss_tensor_names["fg_pred"]: fg_pred, loss_tensor_names["maxima"]: maxima, loss_tensor_names["gt_fg"]: gt_fg, loss_tensor_names["emst"]: emst, loss_tensor_names["edges_u"]: edges_u, loss_tensor_names["edges_v"]: edges_v, loss_tensor_names["ratio_pos"]: ratio_pos, loss_tensor_names["ratio_neg"]: ratio_neg, loss_tensor_names["dist"]: dist, loss_tensor_names["num_pos_pairs"]: num_pos_pairs, loss_tensor_names["num_neg_pairs"]: num_neg_pairs, }, gradients={ mknet_tensor_names["embedding"]: gradient_embedding, mknet_tensor_names["fg"]: gradient_fg, }, save_every=checkpoint_every, summary="Merge/MergeSummary:0", log_dir="tensorflow_logs", ) + gp.PrintProfilingStats(every=profile_every) + gp.Snapshot( additional_request=snapshot_request, output_filename="snapshot_{}_{}.hdf".format( int(np.min(seperate_distance)), "{id}"), dataset_names={ # raw data raw: "volumes/raw", # labeled data labels: "volumes/labels", # trees skeletonization: "points/skeletonization", consensus: "points/consensus", matched: "points/matched", # output volumes embedding: "volumes/embedding", fg: "volumes/fg", maxima: "volumes/maxima", gt_fg: "volumes/gt_fg", fg_pred: "volumes/fg_pred", gradient_embedding: "volumes/gradient_embedding", gradient_fg: "volumes/gradient_fg", # output trees emst: "emst", edges_u: "edges_u", edges_v: "edges_v", # output debug data ratio_pos: "ratio_pos", ratio_neg: "ratio_neg", dist: "dist", num_pos_pairs: "num_pos_pairs", num_neg_pairs: "num_neg_pairs", loss_weights: "volumes/loss_weights", }, every=snapshot_every, )) with gp.build(pipeline): for _ in range(num_iterations): pipeline.request_batch(request)
def train_until(max_iteration): in_channels = 1 num_fmaps = 12 fmap_inc_factors = 6 downsample_factors = [(1, 3, 3), (1, 3, 3), (3, 3, 3)] unet = UNet(in_channels, num_fmaps, fmap_inc_factors, downsample_factors, constant_upsample=True) model = Convolve(unet, 12, 1) loss = torch.nn.MSELoss() optimizer = torch.optim.Adam(model.parameters(), lr=1e-6) # start of gunpowder part: raw = gp.ArrayKey('RAW') points = gp.GraphKey('POINTS') groundtruth = gp.ArrayKey('RASTER') prediction = gp.ArrayKey('PRED_POINT') grad = gp.ArrayKey('GRADIENT') voxel_size = gp.Coordinate((40, 4, 4)) input_shape = (96, 430, 430) output_shape = (60, 162, 162) input_size = gp.Coordinate(input_shape) * voxel_size output_size = gp.Coordinate(output_shape) * voxel_size request = gp.BatchRequest() request.add(raw, input_size) request.add(points, output_size) request.add(groundtruth, output_size) request.add(prediction, output_size) request.add(grad, output_size) pos_sources = tuple( gp.ZarrSource(filename, {raw: 'volumes/raw'}, {raw: gp.ArraySpec(interpolatable=True)}) + AddCenterPoint(points, raw) + gp.Pad(raw, None) + gp.RandomLocation(ensure_nonempty=points) for filename in pos_samples) + gp.RandomProvider() neg_sources = tuple( gp.ZarrSource(filename, {raw: 'volumes/raw'}, {raw: gp.ArraySpec(interpolatable=True)}) + AddNoPoint(points, raw) + gp.RandomLocation() for filename in neg_samples) + gp.RandomProvider() data_sources = (pos_sources, neg_sources) data_sources += gp.RandomProvider(probabilities=[0.9, 0.1]) data_sources += gp.Normalize(raw) train_pipeline = data_sources train_pipeline += gp.ElasticAugment(control_point_spacing=[4, 40, 40], jitter_sigma=[0, 2, 2], rotation_interval=[0, math.pi / 2.0], prob_slip=0.05, prob_shift=0.05, max_misalign=10, subsample=8) train_pipeline += gp.SimpleAugment(transpose_only=[1, 2]) train_pipeline += gp.IntensityAugment(raw, 0.9, 1.1, -0.1, 0.1, \ z_section_wise=True) train_pipeline += gp.RasterizePoints( points, groundtruth, array_spec=gp.ArraySpec(voxel_size=voxel_size), settings=gp.RasterizationSettings(radius=(100, 100, 100), mode='peak')) train_pipeline += gp.PreCache(cache_size=40, num_workers=10) train_pipeline += Reshape(raw, (1, 1) + input_shape) train_pipeline += Reshape(groundtruth, (1, 1) + output_shape) train_pipeline += gp_torch.Train(model=model, loss=loss, optimizer=optimizer, inputs={'x': raw}, outputs={0: prediction}, loss_inputs={ 0: prediction, 1: groundtruth }, gradients={0: grad}, save_every=1000, log_dir='log') train_pipeline += Reshape(raw, input_shape) train_pipeline += Reshape(groundtruth, output_shape) train_pipeline += Reshape(prediction, output_shape) train_pipeline += Reshape(grad, output_shape) train_pipeline += gp.Snapshot( { raw: 'volumes/raw', groundtruth: 'volumes/groundtruth', prediction: 'volumes/prediction', grad: 'volumes/gradient' }, every=500, output_filename='test_{iteration}.hdf') train_pipeline += gp.PrintProfilingStats(every=10) with gp.build(train_pipeline): for i in range(max_iteration): train_pipeline.request_batch(request)
def train_until(max_iteration): # get the latest checkpoint if tf.train.latest_checkpoint('.'): trained_until = int(tf.train.latest_checkpoint('.').split('_')[-1]) else: trained_until = 0 if trained_until >= max_iteration: return # array keys for fused volume raw = gp.ArrayKey('RAW') labels = gp.ArrayKey('LABELS') labels_fg = gp.ArrayKey('LABELS_FG') # array keys for base volume raw_base = gp.ArrayKey('RAW_BASE') labels_base = gp.ArrayKey('LABELS_BASE') swc_base = gp.PointsKey('SWC_BASE') swc_center_base = gp.PointsKey('SWC_CENTER_BASE') # array keys for add volume raw_add = gp.ArrayKey('RAW_ADD') labels_add = gp.ArrayKey('LABELS_ADD') swc_add = gp.PointsKey('SWC_ADD') swc_center_add = gp.PointsKey('SWC_CENTER_ADD') # output data fg = gp.ArrayKey('FG') gradient_fg = gp.ArrayKey('GRADIENT_FG') loss_weights = gp.ArrayKey('LOSS_WEIGHTS') voxel_size = gp.Coordinate((3, 3, 3)) input_size = gp.Coordinate(net_config['input_shape']) * voxel_size output_size = gp.Coordinate(net_config['output_shape']) * voxel_size # add request request = gp.BatchRequest() request.add(raw, input_size) request.add(labels, output_size) request.add(labels_fg, output_size) request.add(loss_weights, output_size) request.add(swc_center_base, output_size) request.add(swc_base, input_size) request.add(swc_center_add, output_size) request.add(swc_add, input_size) # add snapshot request snapshot_request = gp.BatchRequest() snapshot_request.add(fg, output_size) snapshot_request.add(labels_fg, output_size) snapshot_request.add(gradient_fg, output_size) snapshot_request.add(raw_base, input_size) snapshot_request.add(raw_add, input_size) snapshot_request.add(labels_base, input_size) snapshot_request.add(labels_add, input_size) # data source for "base" volume data_sources_base = tuple() data_sources_base += tuple( (gp.Hdf5Source(file, datasets={ raw_base: '/volume', }, array_specs={ raw_base: gp.ArraySpec(interpolatable=True, voxel_size=voxel_size, dtype=np.uint16), }, channels_first=False), SwcSource(filename=file, dataset='/reconstruction', points=(swc_center_base, swc_base), scale=voxel_size)) + gp.MergeProvider() + gp.RandomLocation(ensure_nonempty=swc_center_base) + RasterizeSkeleton( points=swc_base, array=labels_base, array_spec=gp.ArraySpec( interpolatable=False, voxel_size=voxel_size, dtype=np.uint32), iteration=10) for file in files) data_sources_base += gp.RandomProvider() # data source for "add" volume data_sources_add = tuple() data_sources_add += tuple( (gp.Hdf5Source(file, datasets={ raw_add: '/volume', }, array_specs={ raw_add: gp.ArraySpec(interpolatable=True, voxel_size=voxel_size, dtype=np.uint16), }, channels_first=False), SwcSource(filename=file, dataset='/reconstruction', points=(swc_center_add, swc_add), scale=voxel_size)) + gp.MergeProvider() + gp.RandomLocation(ensure_nonempty=swc_center_add) + RasterizeSkeleton( points=swc_add, array=labels_add, array_spec=gp.ArraySpec( interpolatable=False, voxel_size=voxel_size, dtype=np.uint32), iteration=1) for file in files) data_sources_add += gp.RandomProvider() data_sources = tuple([data_sources_base, data_sources_add ]) + gp.MergeProvider() pipeline = ( data_sources + FusionAugment(raw_base, raw_add, labels_base, labels_add, raw, labels, blend_mode='labels_mask', blend_smoothness=10, num_blended_objects=0) + # augment gp.ElasticAugment([10, 10, 10], [1, 1, 1], [0, math.pi / 2.0], subsample=8) + gp.SimpleAugment(mirror_only=[2], transpose_only=[]) + gp.Normalize(raw) + gp.IntensityAugment(raw, 0.9, 1.1, -0.001, 0.001) + BinarizeGt(labels, labels_fg) + gp.BalanceLabels(labels_fg, loss_weights) + # train gp.PreCache(cache_size=40, num_workers=10) + gp.tensorflow.Train('./train_net', optimizer=net_names['optimizer'], loss=net_names['loss'], inputs={ net_names['raw']: raw, net_names['labels_fg']: labels_fg, net_names['loss_weights']: loss_weights, }, outputs={ net_names['fg']: fg, }, gradients={ net_names['fg']: gradient_fg, }, save_every=100) + # visualize gp.Snapshot(output_filename='snapshot_{iteration}.hdf', dataset_names={ raw: 'volumes/raw', raw_base: 'volumes/raw_base', raw_add: 'volumes/raw_add', labels: 'volumes/labels', labels_base: 'volumes/labels_base', labels_add: 'volumes/labels_add', fg: 'volumes/fg', labels_fg: 'volumes/labels_fg', gradient_fg: 'volumes/gradient_fg', }, additional_request=snapshot_request, every=10) + gp.PrintProfilingStats(every=100)) with gp.build(pipeline): print("Starting training...") for i in range(max_iteration - trained_until): pipeline.request_batch(request)
def get_mouselight_data_sources(setup_config: Dict[str, Any], source_samples: List[str], locations=False): # Source Paths and accessibility raw_n5 = setup_config["RAW_N5"] mongo_url = setup_config["MONGO_URL"] samples_path = Path(setup_config["SAMPLES_PATH"]) # specified_locations = setup_config.get("SPECIFIED_LOCATIONS") # Graph matching parameters point_balance_radius = setup_config["POINT_BALANCE_RADIUS"] matching_failures_dir = setup_config["MATCHING_FAILURES_DIR"] matching_failures_dir = (matching_failures_dir if matching_failures_dir is None else Path(matching_failures_dir)) # Data Properties voxel_size = gp.Coordinate(setup_config["VOXEL_SIZE"]) output_shape = gp.Coordinate(setup_config["OUTPUT_SHAPE"]) output_size = output_shape * voxel_size micron_scale = voxel_size[0] distance_attr = setup_config["DISTANCE_ATTRIBUTE"] target_distance = float(setup_config["MIN_DIST_TO_FALLBACK"]) max_nonempty_points = int(setup_config["MAX_RANDOM_LOCATION_POINTS"]) mongo_db_template = setup_config["MONGO_DB_TEMPLATE"] matched_source = setup_config.get("MATCHED_SOURCE", "matched") # New array keys # Note: These are intended to be requested with size input_size raw = ArrayKey("RAW") matched = gp.PointsKey("MATCHED") nonempty_placeholder = gp.PointsKey("NONEMPTY") labels = ArrayKey("LABELS") ensure_nonempty = nonempty_placeholder node_offset = { sample.name: (daisy.persistence.MongoDbGraphProvider( mongo_db_template.format(sample=sample.name, source="skeletonization"), mongo_url, ).num_nodes(None) + 1) for sample in samples_path.iterdir() if sample.name in source_samples } # if specified_locations is not None: # centers = pickle.load(open(specified_locations, "rb")) # random = gp.SpecifiedLocation # kwargs = {"locations": centers, "choose_randomly": True} # logger.info(f"Using specified locations from {specified_locations}") # elif locations: # random = RandomLocations # kwargs = { # "ensure_nonempty": ensure_nonempty, # "ensure_centered": True, # "point_balance_radius": point_balance_radius * micron_scale, # "loc": gp.ArrayKey("RANDOM_LOCATION"), # } # else: random = RandomLocation kwargs = { "ensure_nonempty": ensure_nonempty, "ensure_centered": True, "point_balance_radius": point_balance_radius * micron_scale, } data_sources = (tuple( ( gp.ZarrSource( filename=str((sample / raw_n5).absolute()), datasets={raw: "volume-rechunked"}, array_specs={ raw: gp.ArraySpec(interpolatable=True, voxel_size=voxel_size, dtype=np.uint16) }, ), DaisyGraphProvider( mongo_db_template.format(sample=sample.name, source=matched_source), mongo_url, points=[matched], directed=True, node_attrs=[], edge_attrs=[], ), FilteredDaisyGraphProvider( mongo_db_template.format(sample=sample.name, source=matched_source), mongo_url, points=[nonempty_placeholder], directed=True, node_attrs=["distance_to_fallback"], edge_attrs=[], num_nodes=max_nonempty_points, dist_attribute=distance_attr, min_dist=target_distance, ), ) + gp.MergeProvider() + random(**kwargs) + gp.Normalize(raw) + FilterComponents( matched, node_offset[sample.name], centroid_size=output_size) + RasterizeSkeleton( points=matched, array=labels, array_spec=gp.ArraySpec( interpolatable=False, voxel_size=voxel_size, dtype=np.int64), ) for sample in samples_path.iterdir() if sample.name in source_samples) + gp.RandomProvider()) return (data_sources, raw, labels, nonempty_placeholder, matched)
def train_until(max_iteration): # get the latest checkpoint if tf.train.latest_checkpoint("."): trained_until = int(tf.train.latest_checkpoint(".").split("_")[-1]) else: trained_until = 0 if trained_until >= max_iteration: return # array keys for data sources raw = gp.ArrayKey("RAW") swcs = gp.PointsKey("SWCS") labels = gp.ArrayKey("LABELS") # array keys for base volume raw_base = gp.ArrayKey("RAW_BASE") labels_base = gp.ArrayKey("LABELS_BASE") swc_base = gp.PointsKey("SWC_BASE") # array keys for add volume raw_add = gp.ArrayKey("RAW_ADD") labels_add = gp.ArrayKey("LABELS_ADD") swc_add = gp.PointsKey("SWC_ADD") # array keys for fused volume raw_fused = gp.ArrayKey("RAW_FUSED") labels_fused = gp.ArrayKey("LABELS_FUSED") swc_fused = gp.PointsKey("SWC_FUSED") # output data fg = gp.ArrayKey("FG") labels_fg = gp.ArrayKey("LABELS_FG") labels_fg_bin = gp.ArrayKey("LABELS_FG_BIN") gradient_fg = gp.ArrayKey("GRADIENT_FG") loss_weights = gp.ArrayKey("LOSS_WEIGHTS") voxel_size = gp.Coordinate((10, 3, 3)) input_size = gp.Coordinate(net_config["input_shape"]) * voxel_size output_size = gp.Coordinate(net_config["output_shape"]) * voxel_size # add request request = gp.BatchRequest() request.add(raw_fused, input_size) request.add(labels_fused, input_size) request.add(swc_fused, input_size) request.add(labels_fg, output_size) request.add(labels_fg_bin, output_size) request.add(loss_weights, output_size) # add snapshot request # request.add(fg, output_size) # request.add(labels_fg, output_size) request.add(gradient_fg, output_size) request.add(raw_base, input_size) request.add(raw_add, input_size) request.add(labels_base, input_size) request.add(labels_add, input_size) request.add(swc_base, input_size) request.add(swc_add, input_size) data_sources = tuple( ( gp.N5Source( filename=str( ( filename / "consensus-neurons-with-machine-centerpoints-labelled-as-swcs-carved.n5" ).absolute() ), datasets={raw: "volume"}, array_specs={ raw: gp.ArraySpec( interpolatable=True, voxel_size=voxel_size, dtype=np.uint16 ) }, ), MouselightSwcFileSource( filename=str( ( filename / "consensus-neurons-with-machine-centerpoints-labelled-as-swcs" ).absolute() ), points=(swcs,), scale=voxel_size, transpose=(2, 1, 0), transform_file=str((filename / "transform.txt").absolute()), ignore_human_nodes=True ), ) + gp.MergeProvider() + gp.RandomLocation( ensure_nonempty=swcs, ensure_centered=True ) + RasterizeSkeleton( points=swcs, array=labels, array_spec=gp.ArraySpec( interpolatable=False, voxel_size=voxel_size, dtype=np.uint32 ), ) + GrowLabels(labels, radius=10) # augment + gp.ElasticAugment( [40, 10, 10], [0.25, 1, 1], [0, math.pi / 2.0], subsample=4, ) + gp.SimpleAugment(mirror_only=[1, 2], transpose_only=[1, 2]) + gp.Normalize(raw) + gp.IntensityAugment(raw, 0.9, 1.1, -0.001, 0.001) for filename in Path(sample_dir).iterdir() if "2018-08-01" in filename.name # or "2018-07-02" in filename.name ) pipeline = ( data_sources + gp.RandomProvider() + GetNeuronPair( swcs, raw, labels, (swc_base, swc_add), (raw_base, raw_add), (labels_base, labels_add), seperate_by=150, shift_attempts=50, request_attempts=10, ) + FusionAugment( raw_base, raw_add, labels_base, labels_add, swc_base, swc_add, raw_fused, labels_fused, swc_fused, blend_mode="labels_mask", blend_smoothness=10, num_blended_objects=0, ) + Crop(labels_fused, labels_fg) + BinarizeGt(labels_fg, labels_fg_bin) + gp.BalanceLabels(labels_fg_bin, loss_weights) # train + gp.PreCache(cache_size=40, num_workers=10) + gp.tensorflow.Train( "./train_net", optimizer=net_names["optimizer"], loss=net_names["loss"], inputs={ net_names["raw"]: raw_fused, net_names["labels_fg"]: labels_fg_bin, net_names["loss_weights"]: loss_weights, }, outputs={net_names["fg"]: fg}, gradients={net_names["fg"]: gradient_fg}, save_every=100000, ) + gp.Snapshot( output_filename="snapshot_{iteration}.hdf", dataset_names={ raw_fused: "volumes/raw_fused", raw_base: "volumes/raw_base", raw_add: "volumes/raw_add", labels_fused: "volumes/labels_fused", labels_base: "volumes/labels_base", labels_add: "volumes/labels_add", labels_fg_bin: "volumes/labels_fg_bin", fg: "volumes/pred_fg", gradient_fg: "volumes/gradient_fg", }, every=100, ) + gp.PrintProfilingStats(every=10) ) with gp.build(pipeline): logging.info("Starting training...") for i in range(max_iteration - trained_until): logging.info("requesting batch {}".format(i)) batch = pipeline.request_batch(request) """
def train_until(max_iteration): # get the latest checkpoint if tf.train.latest_checkpoint("."): trained_until = int(tf.train.latest_checkpoint(".").split("_")[-1]) else: trained_until = 0 if trained_until >= max_iteration: return # array keys for fused volume raw = gp.ArrayKey("RAW") labels = gp.ArrayKey("LABELS") labels_fg = gp.ArrayKey("LABELS_FG") # array keys for base volume raw_base = gp.ArrayKey("RAW_BASE") labels_base = gp.ArrayKey("LABELS_BASE") swc_base = gp.PointsKey("SWC_BASE") swc_center_base = gp.PointsKey("SWC_CENTER_BASE") # array keys for add volume raw_add = gp.ArrayKey("RAW_ADD") labels_add = gp.ArrayKey("LABELS_ADD") swc_add = gp.PointsKey("SWC_ADD") swc_center_add = gp.PointsKey("SWC_CENTER_ADD") # output data fg = gp.ArrayKey("FG") gradient_fg = gp.ArrayKey("GRADIENT_FG") loss_weights = gp.ArrayKey("LOSS_WEIGHTS") voxel_size = gp.Coordinate((4, 1, 1)) input_size = gp.Coordinate(net_config["input_shape"]) * voxel_size output_size = gp.Coordinate(net_config["output_shape"]) * voxel_size # add request request = gp.BatchRequest() request.add(raw, input_size) request.add(labels, output_size) request.add(labels_fg, output_size) request.add(loss_weights, output_size) request.add(swc_center_base, output_size) request.add(swc_center_add, output_size) # add snapshot request snapshot_request = gp.BatchRequest() snapshot_request.add(fg, output_size) snapshot_request.add(labels_fg, output_size) snapshot_request.add(gradient_fg, output_size) snapshot_request.add(raw_base, input_size) snapshot_request.add(raw_add, input_size) snapshot_request.add(labels_base, input_size) snapshot_request.add(labels_add, input_size) # data source for "base" volume data_sources_base = tuple( ( gp.Hdf5Source( filename, datasets={raw_base: "/volume"}, array_specs={ raw_base: gp.ArraySpec(interpolatable=True, voxel_size=voxel_size, dtype=np.uint16) }, channels_first=False, ), SwcSource( filename=filename, dataset="/reconstruction", points=(swc_center_base, swc_base), scale=voxel_size, ), ) + gp.MergeProvider() + gp.RandomLocation(ensure_nonempty=swc_center_base) + RasterizeSkeleton( points=swc_base, array=labels_base, array_spec=gp.ArraySpec( interpolatable=False, voxel_size=voxel_size, dtype=np.uint32), radius=5.0, ) for filename in files) # data source for "add" volume data_sources_add = tuple( ( gp.Hdf5Source( file, datasets={raw_add: "/volume"}, array_specs={ raw_add: gp.ArraySpec(interpolatable=True, voxel_size=voxel_size, dtype=np.uint16) }, channels_first=False, ), SwcSource( filename=file, dataset="/reconstruction", points=(swc_center_add, swc_add), scale=voxel_size, ), ) + gp.MergeProvider() + gp.RandomLocation(ensure_nonempty=swc_center_add) + RasterizeSkeleton( points=swc_add, array=labels_add, array_spec=gp.ArraySpec( interpolatable=False, voxel_size=voxel_size, dtype=np.uint32), radius=5.0, ) for file in files) data_sources = ( (data_sources_base + gp.RandomProvider()), (data_sources_add + gp.RandomProvider()), ) + gp.MergeProvider() pipeline = ( data_sources + FusionAugment( raw_base, raw_add, labels_base, labels_add, raw, labels, blend_mode="labels_mask", blend_smoothness=10, num_blended_objects=0, ) + # augment gp.ElasticAugment([40, 10, 10], [0.25, 1, 1], [0, math.pi / 2.0], subsample=4) + gp.SimpleAugment(mirror_only=[1, 2], transpose_only=[1, 2]) + gp.Normalize(raw) + gp.IntensityAugment(raw, 0.9, 1.1, -0.001, 0.001) + BinarizeGt(labels, labels_fg) + gp.BalanceLabels(labels_fg, loss_weights) + # train gp.PreCache(cache_size=40, num_workers=10) + gp.tensorflow.Train( "./train_net", optimizer=net_names["optimizer"], loss=net_names["loss"], inputs={ net_names["raw"]: raw, net_names["labels_fg"]: labels_fg, net_names["loss_weights"]: loss_weights, }, outputs={net_names["fg"]: fg}, gradients={net_names["fg"]: gradient_fg}, save_every=100000, ) + # visualize gp.Snapshot( output_filename="snapshot_{iteration}.hdf", dataset_names={ raw: "volumes/raw", raw_base: "volumes/raw_base", raw_add: "volumes/raw_add", labels: "volumes/labels", labels_base: "volumes/labels_base", labels_add: "volumes/labels_add", fg: "volumes/fg", labels_fg: "volumes/labels_fg", gradient_fg: "volumes/gradient_fg", }, additional_request=snapshot_request, every=100, ) + gp.PrintProfilingStats(every=100)) with gp.build(pipeline): print("Starting training...") for i in range(max_iteration - trained_until): pipeline.request_batch(request)
def train_until(max_iteration, name='train_net', output_folder='.', clip_max=2000): # get the latest checkpoint if tf.train.latest_checkpoint(output_folder): trained_until = int(tf.train.latest_checkpoint(output_folder).split('_')[-1]) else: trained_until = 0 if trained_until >= max_iteration: return with open(os.path.join(output_folder, name + '_config.json'), 'r') as f: net_config = json.load(f) with open(os.path.join(output_folder, name + '_names.json'), 'r') as f: net_names = json.load(f) # array keys raw = gp.ArrayKey('RAW') gt_instances = gp.ArrayKey('GT_INSTANCES') gt_mask = gp.ArrayKey('GT_MASK') pred_mask = gp.ArrayKey('PRED_MASK') #loss_weights = gp.ArrayKey('LOSS_WEIGHTS') loss_gradients = gp.ArrayKey('LOSS_GRADIENTS') # array keys for base and add volume raw_base = gp.ArrayKey('RAW_BASE') gt_instances_base = gp.ArrayKey('GT_INSTANCES_BASE') gt_mask_base = gp.ArrayKey('GT_MASK_BASE') raw_add = gp.ArrayKey('RAW_ADD') gt_instances_add = gp.ArrayKey('GT_INSTANCES_ADD') gt_mask_add = gp.ArrayKey('GT_MASK_ADD') voxel_size = gp.Coordinate((1, 1, 1)) input_shape = gp.Coordinate(net_config['input_shape']) output_shape = gp.Coordinate(net_config['output_shape']) context = gp.Coordinate(input_shape - output_shape) / 2 request = gp.BatchRequest() request.add(raw, input_shape) request.add(gt_instances, output_shape) request.add(gt_mask, output_shape) #request.add(loss_weights, output_shape) request.add(raw_base, input_shape) request.add(raw_add, input_shape) request.add(gt_mask_base, output_shape) request.add(gt_mask_add, output_shape) snapshot_request = gp.BatchRequest() snapshot_request.add(raw, input_shape) #snapshot_request.add(raw_base, input_shape) #snapshot_request.add(raw_add, input_shape) snapshot_request.add(gt_mask, output_shape) #snapshot_request.add(gt_mask_base, output_shape) #snapshot_request.add(gt_mask_add, output_shape) snapshot_request.add(pred_mask, output_shape) snapshot_request.add(loss_gradients, output_shape) # specify data source # data source for base volume data_sources_base = tuple() for data_file in data_files: current_path = os.path.join(data_dir, data_file) with h5py.File(current_path, 'r') as f: data_sources_base += tuple( gp.Hdf5Source( current_path, datasets={ raw_base: sample + '/raw', gt_instances_base: sample + '/gt', gt_mask_base: sample + '/fg', }, array_specs={ raw_base: gp.ArraySpec(interpolatable=True, dtype=np.uint16, voxel_size=voxel_size), gt_instances_base: gp.ArraySpec(interpolatable=False, dtype=np.uint16, voxel_size=voxel_size), gt_mask_base: gp.ArraySpec(interpolatable=False, dtype=np.bool, voxel_size=voxel_size), } ) + Convert(gt_mask_base, np.uint8) + gp.Pad(raw_base, context) + gp.Pad(gt_instances_base, context) + gp.Pad(gt_mask_base, context) + gp.RandomLocation(min_masked=0.005, mask=gt_mask_base) #gp.Reject(gt_mask_base, min_masked=0.005, reject_probability=1.) for sample in f) data_sources_base += gp.RandomProvider() # data source for add volume data_sources_add = tuple() for data_file in data_files: current_path = os.path.join(data_dir, data_file) with h5py.File(current_path, 'r') as f: data_sources_add += tuple( gp.Hdf5Source( current_path, datasets={ raw_add: sample + '/raw', gt_instances_add: sample + '/gt', gt_mask_add: sample + '/fg', }, array_specs={ raw_add: gp.ArraySpec(interpolatable=True, dtype=np.uint16, voxel_size=voxel_size), gt_instances_add: gp.ArraySpec(interpolatable=False, dtype=np.uint16, voxel_size=voxel_size), gt_mask_add: gp.ArraySpec(interpolatable=False, dtype=np.bool, voxel_size=voxel_size), } ) + Convert(gt_mask_add, np.uint8) + gp.Pad(raw_add, context) + gp.Pad(gt_instances_add, context) + gp.Pad(gt_mask_add, context) + gp.RandomLocation() + gp.Reject(gt_mask_add, min_masked=0.005, reject_probability=0.95) for sample in f) data_sources_add += gp.RandomProvider() data_sources = tuple([data_sources_base, data_sources_add]) + gp.MergeProvider() pipeline = ( data_sources + nl.FusionAugment( raw_base, raw_add, gt_instances_base, gt_instances_add, raw, gt_instances, blend_mode='labels_mask', blend_smoothness=5, num_blended_objects=0 ) + BinarizeLabels(gt_instances, gt_mask) + nl.Clip(raw, 0, clip_max) + gp.Normalize(raw, factor=1.0/clip_max) + gp.ElasticAugment( control_point_spacing=[20, 20, 20], jitter_sigma=[1, 1, 1], rotation_interval=[0, math.pi/2.0], subsample=4) + gp.SimpleAugment(mirror_only=[1, 2], transpose_only=[1, 2]) + gp.IntensityAugment(raw, 0.9, 1.1, -0.1, 0.1) + gp.IntensityScaleShift(raw, 2, -1) + #gp.BalanceLabels(gt_mask, loss_weights) + # train gp.PreCache( cache_size=40, num_workers=10) + gp.tensorflow.Train( os.path.join(output_folder, name), optimizer=net_names['optimizer'], loss=net_names['loss'], inputs={ net_names['raw']: raw, net_names['gt']: gt_mask, #net_names['loss_weights']: loss_weights, }, outputs={ net_names['pred']: pred_mask, }, gradients={ net_names['output']: loss_gradients, }, save_every=5000) + # visualize gp.Snapshot({ raw: 'volumes/raw', pred_mask: 'volumes/pred_mask', gt_mask: 'volumes/gt_mask', #loss_weights: 'volumes/loss_weights', loss_gradients: 'volumes/loss_gradients', }, output_filename=os.path.join(output_folder, 'snapshots', 'batch_{iteration}.hdf'), additional_request=snapshot_request, every=2500) + gp.PrintProfilingStats(every=1000) ) with gp.build(pipeline): print("Starting training...") for i in range(max_iteration - trained_until): pipeline.request_batch(request)
def train_until(**kwargs): if tf.train.latest_checkpoint(kwargs['output_folder']): trained_until = int( tf.train.latest_checkpoint(kwargs['output_folder']).split('_')[-1]) else: trained_until = 0 if trained_until >= kwargs['max_iteration']: return anchor = gp.ArrayKey('ANCHOR') raw = gp.ArrayKey('RAW') raw_cropped = gp.ArrayKey('RAW_CROPPED') points = gp.PointsKey('POINTS') gt_cp = gp.ArrayKey('GT_CP') pred_cp = gp.ArrayKey('PRED_CP') pred_cp_gradients = gp.ArrayKey('PRED_CP_GRADIENTS') with open( os.path.join(kwargs['output_folder'], kwargs['name'] + '_config.json'), 'r') as f: net_config = json.load(f) with open( os.path.join(kwargs['output_folder'], kwargs['name'] + '_names.json'), 'r') as f: net_names = json.load(f) voxel_size = gp.Coordinate(kwargs['voxel_size']) input_shape_world = gp.Coordinate(net_config['input_shape']) * voxel_size output_shape_world = gp.Coordinate(net_config['output_shape']) * voxel_size # formulate the request for what a batch should (at least) contain request = gp.BatchRequest() request.add(raw, input_shape_world) request.add(raw_cropped, output_shape_world) request.add(gt_cp, output_shape_world) request.add(anchor, output_shape_world) # when we make a snapshot for inspection (see below), we also want to # request the predicted affinities and gradients of the loss wrt the # affinities snapshot_request = gp.BatchRequest() snapshot_request.add(raw_cropped, output_shape_world) snapshot_request.add(gt_cp, output_shape_world) snapshot_request.add(pred_cp, output_shape_world) # snapshot_request.add(pred_cp_gradients, output_shape_world) if kwargs['input_format'] != "hdf" and kwargs['input_format'] != "zarr": raise NotImplementedError("train node for %s not implemented yet", kwargs['input_format']) fls = [] shapes = [] mn = [] mx = [] for f in kwargs['data_files']: fls.append(os.path.splitext(f)[0]) if kwargs['input_format'] == "hdf": vol = h5py.File(f, 'r')['volumes/raw'] elif kwargs['input_format'] == "zarr": vol = zarr.open(f, 'r')['volumes/raw'] print(f, vol.shape, vol.dtype) shapes.append(vol.shape) mn.append(np.min(vol)) mx.append(np.max(vol)) if vol.dtype != np.float32: print("please convert to float32") ln = len(fls) print("first 5 files: ", fls[0:4]) if kwargs['input_format'] == "hdf": sourceNode = gp.Hdf5Source elif kwargs['input_format'] == "zarr": sourceNode = gp.ZarrSource augmentation = kwargs['augmentation'] sources = tuple( (sourceNode(fls[t] + "." + kwargs['input_format'], datasets={ raw: 'volumes/raw', anchor: 'volumes/gt_fgbg', }, array_specs={ raw: gp.ArraySpec(interpolatable=True), anchor: gp.ArraySpec(interpolatable=False) }), gp.CsvIDPointsSource(fls[t] + ".csv", points, points_spec=gp.PointsSpec( roi=gp.Roi(gp.Coordinate(( 0, 0, 0)), gp.Coordinate(shapes[t]))))) + gp.MergeProvider() # + Clip(raw, mn=mn[t], mx=mx[t]) # + NormalizeMinMax(raw, mn=mn[t], mx=mx[t]) + gp.Pad(raw, None) + gp.Pad(points, None) # chose a random location for each requested batch + gp.RandomLocation() for t in range(ln)) pipeline = ( sources + # chose a random source (i.e., sample) from the above gp.RandomProvider() + # elastically deform the batch (gp.ElasticAugment( augmentation['elastic']['control_point_spacing'], augmentation['elastic']['jitter_sigma'], [augmentation['elastic']['rotation_min']*np.pi/180.0, augmentation['elastic']['rotation_max']*np.pi/180.0], subsample=augmentation['elastic'].get('subsample', 1)) \ if augmentation.get('elastic') is not None else NoOp()) + # apply transpose and mirror augmentations gp.SimpleAugment(mirror_only=augmentation['simple'].get("mirror"), transpose_only=augmentation['simple'].get("transpose")) + # (gp.SimpleAugment( # mirror_only=augmentation['simple'].get("mirror"), # transpose_only=augmentation['simple'].get("transpose")) \ # if augmentation.get('simple') is not None and \ # augmentation.get('simple') != {} else NoOp()) + # # scale and shift the intensity of the raw array (gp.IntensityAugment( raw, scale_min=augmentation['intensity']['scale'][0], scale_max=augmentation['intensity']['scale'][1], shift_min=augmentation['intensity']['shift'][0], shift_max=augmentation['intensity']['shift'][1], z_section_wise=False) \ if augmentation.get('intensity') is not None and \ augmentation.get('intensity') != {} else NoOp()) + gp.RasterizePoints( points, gt_cp, array_spec=gp.ArraySpec(voxel_size=voxel_size), settings=gp.RasterizationSettings( radius=(2, 2, 2), mode='peak')) + # pre-cache batches from the point upstream gp.PreCache( cache_size=kwargs['cache_size'], num_workers=kwargs['num_workers']) + # perform one training iteration for each passing batch (here we use # the tensor names earlier stored in train_net.config) gp.tensorflow.Train( os.path.join(kwargs['output_folder'], kwargs['name']), optimizer=net_names['optimizer'], summary=net_names['summaries'], log_dir=kwargs['output_folder'], loss=net_names['loss'], inputs={ net_names['raw']: raw, net_names['gt_cp']: gt_cp, net_names['anchor']: anchor, }, outputs={ net_names['pred_cp']: pred_cp, net_names['raw_cropped']: raw_cropped, }, gradients={ # net_names['pred_cp']: pred_cp_gradients, }, save_every=kwargs['checkpoints']) + # save the passing batch as an HDF5 file for inspection gp.Snapshot( { raw: '/volumes/raw', raw_cropped: 'volumes/raw_cropped', gt_cp: '/volumes/gt_cp', pred_cp: '/volumes/pred_cp', # pred_cp_gradients: '/volumes/pred_cp_gradients', }, output_dir=os.path.join(kwargs['output_folder'], 'snapshots'), output_filename='batch_{iteration}.hdf', every=kwargs['snapshots'], additional_request=snapshot_request, compression_type='gzip') + # show a summary of time spend in each node every 10 iterations gp.PrintProfilingStats(every=kwargs['profiling']) ) ######### # TRAIN # ######### print("Starting training...") with gp.build(pipeline): print(pipeline) for i in range(trained_until, kwargs['max_iteration']): # print("request", request) start = time.time() pipeline.request_batch(request) time_of_iteration = time.time() - start logger.info("Batch: iteration=%d, time=%f", i, time_of_iteration) # exit() print("Training finished")
def batch__aug_data_generator(input_path, batch_size=12, voxel_shape=[1, 1, 1], input_shape=[240, 240, 4], output_shape=[240, 240, 4], without_background=False, mix_output=False, validate=False, seq=None): raw = gp.ArrayKey('raw') gt = gp.ArrayKey('ground_truth') files = os.listdir(input_path) files = [os.path.join(input_path, f) for f in files] pipeline = ( tuple( gp.ZarrSource( files[t], # the zarr container { raw: 'raw', gt: 'ground_truth' }, # which dataset to associate to the array key { raw: gp.ArraySpec(interpolatable=True, dtype=np.dtype('float32'), voxel_size=voxel_shape), gt: gp.ArraySpec(interpolatable=True, dtype=np.dtype('float32'), voxel_size=voxel_shape) } # meta-information ) + gp.RandomLocation() for t in range(len(files))) + gp.RandomProvider() # +gp.Stack(batch_size) ) input_size = gp.Coordinate(input_shape) output_size = gp.Coordinate(output_shape) request = gp.BatchRequest() request.add(raw, input_size) request.add(gt, input_size) diff = input_shape[1] - output_shape[1] diff = int(diff / 2) max_p = input_shape[1] - diff different_shape = diff > 0 if different_shape: print('Difference padding: {}'.format(diff)) with gp.build(pipeline): while 1: b = 0 imgs = [] masks = [] while b < batch_size: valid = False batch = pipeline.request_batch(request) if validate: valid = validate_mask(batch[gt].data) else: valid = True while (valid == False): batch = pipeline.request_batch(request) valid = validate_mask(batch[gt].data) im = batch[raw].data out = batch[gt].data # if different_shape: # out = out[diff:max_p,diff:max_p,:] if without_background: out = out[:, :, 1:4] if mix_output: out = out.argmax(axis=3).astype(float) imgs.append(im) masks.append(out) b = b + 1 imgs = np.asarray(imgs) masks = np.asarray(masks) if seq is not None: imgs, masks = augmentation(imgs, masks, seq) if different_shape: out = [] for m in masks: out.append(m[diff:max_p, diff:max_p, :]) masks = np.asarray(out) yield imgs, masks
def train_distance_pipeline(n_iterations, setup_config, mknet_tensor_names, loss_tensor_names): input_shape = gp.Coordinate(setup_config["INPUT_SHAPE"]) output_shape = gp.Coordinate(setup_config["OUTPUT_SHAPE"]) voxel_size = gp.Coordinate(setup_config["VOXEL_SIZE"]) num_iterations = setup_config["NUM_ITERATIONS"] cache_size = setup_config["CACHE_SIZE"] num_workers = setup_config["NUM_WORKERS"] snapshot_every = setup_config["SNAPSHOT_EVERY"] checkpoint_every = setup_config["CHECKPOINT_EVERY"] profile_every = setup_config["PROFILE_EVERY"] seperate_by = setup_config["SEPERATE_BY"] gap_crossing_dist = setup_config["GAP_CROSSING_DIST"] match_distance_threshold = setup_config["MATCH_DISTANCE_THRESHOLD"] point_balance_radius = setup_config["POINT_BALANCE_RADIUS"] max_label_dist = setup_config["MAX_LABEL_DIST"] samples_path = Path(setup_config["SAMPLES_PATH"]) mongo_url = setup_config["MONGO_URL"] input_size = input_shape * voxel_size output_size = output_shape * voxel_size # voxels have size ~= 1 micron on z axis # use this value to scale anything that depends on world unit distance micron_scale = voxel_size[0] seperate_distance = (np.array(seperate_by)).tolist() # array keys for data sources raw = gp.ArrayKey("RAW") consensus = gp.PointsKey("CONSENSUS") skeletonization = gp.PointsKey("SKELETONIZATION") matched = gp.PointsKey("MATCHED") labels = gp.ArrayKey("LABELS") dist = gp.ArrayKey("DIST") dist_mask = gp.ArrayKey("DIST_MASK") dist_cropped = gp.ArrayKey("DIST_CROPPED") loss_weights = gp.ArrayKey("LOSS_WEIGHTS") # tensorflow tensors fg_dist = gp.ArrayKey("FG_DIST") gradient_fg = gp.ArrayKey("GRADIENT_FG") # add request request = gp.BatchRequest() request.add(dist_mask, output_size) request.add(dist_cropped, output_size) request.add(raw, input_size) request.add(labels, input_size) request.add(dist, input_size) request.add(matched, input_size) request.add(skeletonization, input_size) request.add(consensus, input_size) request.add(loss_weights, output_size) # add snapshot request snapshot_request = gp.BatchRequest() # tensorflow requests snapshot_request.add(raw, input_size) # input_size request for positioning snapshot_request.add(gradient_fg, output_size, voxel_size=voxel_size) snapshot_request.add(fg_dist, output_size, voxel_size=voxel_size) data_sources = tuple( ( gp.N5Source( filename=str((sample / "fluorescence-near-consensus.n5").absolute()), datasets={raw: "volume"}, array_specs={ raw: gp.ArraySpec(interpolatable=True, voxel_size=voxel_size, dtype=np.uint16) }, ), gp.DaisyGraphProvider( f"mouselight-{sample.name}-consensus", mongo_url, points=[consensus], directed=True, node_attrs=[], edge_attrs=[], ), gp.DaisyGraphProvider( f"mouselight-{sample.name}-skeletonization", mongo_url, points=[skeletonization], directed=False, node_attrs=[], edge_attrs=[], ), ) + gp.MergeProvider() + gp.RandomLocation( ensure_nonempty=consensus, ensure_centered=True, point_balance_radius=point_balance_radius * micron_scale, ) + TopologicalMatcher( skeletonization, consensus, matched, failures=Path("matching_failures_slow"), match_distance_threshold=match_distance_threshold * micron_scale, max_gap_crossing=gap_crossing_dist * micron_scale, try_complete=False, use_gurobi=True, ) + RejectIfEmpty(matched, center_size=output_size) + RasterizeSkeleton( points=matched, array=labels, array_spec=gp.ArraySpec( interpolatable=False, voxel_size=voxel_size, dtype=np.uint32), ) + gp.contrib.nodes.add_distance.AddDistance( labels, dist, dist_mask, max_distance=max_label_dist * micron_scale) + gp.contrib.nodes. tanh_saturate.TanhSaturate(dist, scale=micron_scale, offset=1) + ThresholdMask(dist, loss_weights, 1e-4) # TODO: Do these need to be scaled by world units? + gp.ElasticAugment( [40, 10, 10], [0.25, 1, 1], [0, math.pi / 2.0], subsample=4, use_fast_points_transform=True, recompute_missing_points=False, ) # + gp.SimpleAugment(mirror_only=[1, 2], transpose_only=[1, 2]) + gp.Normalize(raw) + gp.IntensityAugment(raw, 0.9, 1.1, -0.001, 0.001) for sample in samples_path.iterdir() if sample.name in ("2018-07-02", "2018-08-01")) pipeline = ( data_sources + gp.RandomProvider() + Crop(dist, dist_cropped) # + gp.PreCache(cache_size=cache_size, num_workers=num_workers) + gp.tensorflow.Train( "train_net_foreground", optimizer=mknet_tensor_names["optimizer"], loss=mknet_tensor_names["fg_loss"], inputs={ mknet_tensor_names["raw"]: raw, mknet_tensor_names["gt_distances"]: dist_cropped, mknet_tensor_names["loss_weights"]: loss_weights, }, outputs={mknet_tensor_names["fg_pred"]: fg_dist}, gradients={mknet_tensor_names["fg_pred"]: gradient_fg}, save_every=checkpoint_every, # summary=mknet_tensor_names["summaries"], log_dir="tensorflow_logs", ) + gp.PrintProfilingStats(every=profile_every) + gp.Snapshot( additional_request=snapshot_request, output_filename="snapshot_{}_{}.hdf".format( int(np.min(seperate_distance)), "{id}"), dataset_names={ # raw data raw: "volumes/raw", labels: "volumes/labels", # labeled data dist_cropped: "volumes/dist", # trees skeletonization: "points/skeletonization", consensus: "points/consensus", matched: "points/matched", # output volumes fg_dist: "volumes/fg_dist", gradient_fg: "volumes/gradient_fg", # output debug data dist_mask: "volumes/dist_mask", loss_weights: "volumes/loss_weights" }, every=snapshot_every, )) with gp.build(pipeline): for _ in range(num_iterations): pipeline.request_batch(request)
points=swcs, array=labels, array_spec=gp.ArraySpec( interpolatable=False, voxel_size=voxel_size, dtype=np.uint32), ) + GrowLabels(labels, radius=20) # augment + gp.ElasticAugment( [40, 10, 10], [0.25, 1, 1], [0, math.pi / 2.0], subsample=4) + gp.SimpleAugment( mirror_only=[1, 2], transpose_only=[1, 2]) + gp.Normalize(raw) + gp.IntensityAugment(raw, 0.9, 1.1, -0.001, 0.001) for filename in path_to_data.iterdir() if "2018-07-02" in filename.name) pipeline = (data_sources + gp.RandomProvider() + GetNeuronPair( swcs, raw, labels, (swc_base, swc_add), (raw_base, raw_add), (labels_base, labels_add), seperate_by=SEPERATE_DISTANCE, shift_attempts=50, request_attempts=10, ) + FusionAugment( raw_base, raw_add, labels_base, labels_add, swc_base,
def build_batch_provider(self, datasets, model, task, snapshot_container=None): input_shape = Coordinate(model.input_shape) output_shape = Coordinate(model.output_shape) # get voxel sizes raw_voxel_size = datasets[0].raw.voxel_size prediction_voxel_size = model.scale(raw_voxel_size) # define input and output size: # switch to world units input_size = raw_voxel_size * input_shape output_size = prediction_voxel_size * output_shape # padding of groundtruth/mask gt_mask_padding = output_size + task.predictor.padding(prediction_voxel_size) # define keys: raw_key = gp.ArrayKey("RAW") gt_key = gp.ArrayKey("GT") mask_key = gp.ArrayKey("MASK") target_key = gp.ArrayKey("TARGET") weight_key = gp.ArrayKey("WEIGHT") # Get source nodes dataset_sources = [] for dataset in datasets: raw_source = DaCapoArraySource(dataset.raw, raw_key) raw_source += gp.Pad(raw_key, None, 0) gt_source = DaCapoArraySource(dataset.gt, gt_key) gt_source += gp.Pad(gt_key, gt_mask_padding, 0) if dataset.mask is not None: mask_source = DaCapoArraySource(dataset.mask, mask_key) else: # Always provide a mask. By default it is simply an array # of ones with the same shape/roi as gt. Avoids making us # specially handle no mask case and allows padding of the # ground truth without worrying about training on incorrect # data. mask_source = DaCapoArraySource(OnesArray.like(dataset.gt), mask_key) mask_source += gp.Pad(mask_key, gt_mask_padding, 0) array_sources = [raw_source, gt_source, mask_source] dataset_source = ( tuple(array_sources) + gp.MergeProvider() + gp.RandomLocation() ) dataset_sources.append(dataset_source) pipeline = tuple(dataset_sources) + gp.RandomProvider() for augment in self.augments: pipeline += augment.node(raw_key, gt_key, mask_key) pipeline += gp.Reject(mask_key, min_masked=self.min_masked) # Add predictor nodes to pipeline pipeline += DaCapoTargetFilter( task.predictor, gt_key=gt_key, target_key=target_key, weights_key=weight_key, mask_key=mask_key, ) # Trainer attributes: if self.num_data_fetchers > 1: pipeline += gp.PreCache(num_workers=self.num_data_fetchers) # stack to create a batch dimension pipeline += gp.Stack(self.batch_size) # print profiling stats pipeline += gp.PrintProfilingStats(every=self.print_profiling) # generate request for all necessary inputs to training request = gp.BatchRequest() request.add(raw_key, input_size) request.add(target_key, output_size) request.add(weight_key, output_size) # request additional keys for snapshots request.add(gt_key, output_size) request.add(mask_key, output_size) self._request = request self._pipeline = pipeline self._raw_key = raw_key self._gt_key = gt_key self._mask_key = mask_key self._weight_key = weight_key self._target_key = target_key self._loss = task.loss self.snapshot_container = snapshot_container
def build_pipeline(parameter, augment=True): voxel_size = gp.Coordinate(parameter['voxel_size']) # Array Specifications. raw = gp.ArrayKey('RAW') gt_neurons = gp.ArrayKey('GT_NEURONS') gt_postpre_vectors = gp.ArrayKey('GT_POSTPRE_VECTORS') gt_post_indicator = gp.ArrayKey('GT_POST_INDICATOR') post_loss_weight = gp.ArrayKey('POST_LOSS_WEIGHT') vectors_mask = gp.ArrayKey('VECTORS_MASK') pred_postpre_vectors = gp.ArrayKey('PRED_POSTPRE_VECTORS') pred_post_indicator = gp.ArrayKey('PRED_POST_INDICATOR') grad_syn_indicator = gp.ArrayKey('GRAD_SYN_INDICATOR') grad_partner_vectors = gp.ArrayKey('GRAD_PARTNER_VECTORS') # Points specifications dummypostsyn = gp.PointsKey('DUMMYPOSTSYN') postsyn = gp.PointsKey('POSTSYN') presyn = gp.PointsKey('PRESYN') trg_context = 140 # AddPartnerVectorMap context in nm - pre-post distance with open('train_net_config.json', 'r') as f: net_config = json.load(f) input_size = gp.Coordinate(net_config['input_shape']) * voxel_size output_size = gp.Coordinate(net_config['output_shape']) * voxel_size request = gp.BatchRequest() request.add(raw, input_size) request.add(gt_neurons, output_size) request.add(gt_postpre_vectors, output_size) request.add(gt_post_indicator, output_size) request.add(post_loss_weight, output_size) request.add(vectors_mask, output_size) request.add(dummypostsyn, output_size) for (key, request_spec) in request.items(): print(key) print(request_spec.roi) request_spec.roi.contains(request_spec.roi) # slkfdms snapshot_request = gp.BatchRequest({ pred_post_indicator: request[gt_postpre_vectors], pred_postpre_vectors: request[gt_postpre_vectors], grad_syn_indicator: request[gt_postpre_vectors], grad_partner_vectors: request[gt_postpre_vectors], vectors_mask: request[gt_postpre_vectors] }) postsyn_rastersetting = gp.RasterizationSettings( radius=parameter['blob_radius'], mask=gt_neurons, mode=parameter['blob_mode']) pipeline = tuple([ create_source(sample, raw, presyn, postsyn, dummypostsyn, parameter, gt_neurons) for sample in samples ]) pipeline += gp.RandomProvider() if augment: pipeline += gp.ElasticAugment([4, 40, 40], [0, 2, 2], [0, math.pi / 2.0], prob_slip=0.05, prob_shift=0.05, max_misalign=10, subsample=8) pipeline += gp.SimpleAugment(transpose_only=[1, 2], mirror_only=[1, 2]) pipeline += gp.IntensityAugment(raw, 0.9, 1.1, -0.1, 0.1, z_section_wise=True) pipeline += gp.IntensityScaleShift(raw, 2, -1) pipeline += gp.RasterizePoints( postsyn, gt_post_indicator, gp.ArraySpec(voxel_size=voxel_size, dtype=np.int32), postsyn_rastersetting) spec = gp.ArraySpec(voxel_size=voxel_size) pipeline += AddPartnerVectorMap( src_points=postsyn, trg_points=presyn, array=gt_postpre_vectors, radius=parameter['d_blob_radius'], trg_context=trg_context, # enlarge array_spec=spec, mask=gt_neurons, pointmask=vectors_mask) pipeline += gp.BalanceLabels(labels=gt_post_indicator, scales=post_loss_weight, slab=(-1, -1, -1), clipmin=parameter['cliprange'][0], clipmax=parameter['cliprange'][1]) if parameter['d_scale'] != 1: pipeline += gp.IntensityScaleShift(gt_postpre_vectors, scale=parameter['d_scale'], shift=0) pipeline += gp.PreCache(cache_size=40, num_workers=10) pipeline += gp.tensorflow.Train( './train_net', optimizer=net_config['optimizer'], loss=net_config['loss'], summary=net_config['summary'], log_dir='./tensorboard/', save_every=30000, # 10000 log_every=100, inputs={ net_config['raw']: raw, net_config['gt_partner_vectors']: gt_postpre_vectors, net_config['gt_syn_indicator']: gt_post_indicator, net_config['vectors_mask']: vectors_mask, # Loss weights --> mask net_config['indicator_weight']: post_loss_weight, # Loss weights }, outputs={ net_config['pred_partner_vectors']: pred_postpre_vectors, net_config['pred_syn_indicator']: pred_post_indicator, }, gradients={ net_config['pred_partner_vectors']: grad_partner_vectors, net_config['pred_syn_indicator']: grad_syn_indicator, }, ) # Visualize. pipeline += gp.IntensityScaleShift(raw, 0.5, 0.5) pipeline += gp.Snapshot( { raw: 'volumes/raw', gt_neurons: 'volumes/labels/neuron_ids', gt_post_indicator: 'volumes/gt_post_indicator', gt_postpre_vectors: 'volumes/gt_postpre_vectors', pred_postpre_vectors: 'volumes/pred_postpre_vectors', pred_post_indicator: 'volumes/pred_post_indicator', post_loss_weight: 'volumes/post_loss_weight', grad_syn_indicator: 'volumes/post_indicator_gradients', grad_partner_vectors: 'volumes/partner_vectors_gradients', vectors_mask: 'volumes/vectors_mask' }, every=1000, output_filename='batch_{iteration}.hdf', compression_type='gzip', additional_request=snapshot_request) pipeline += gp.PrintProfilingStats(every=100) print("Starting training...") max_iteration = parameter['max_iteration'] with gp.build(pipeline) as b: for i in range(max_iteration): b.request_batch(request)
def random_point_pairs_pipeline(model, loss, optimizer, dataset, augmentation_parameters, point_density, out_dir, normalize_factor=None, checkpoint_interval=5000, snapshot_interval=5000): raw_0 = gp.ArrayKey('RAW_0') points_0 = gp.GraphKey('POINTS_0') locations_0 = gp.ArrayKey('LOCATIONS_0') emb_0 = gp.ArrayKey('EMBEDDING_0') raw_1 = gp.ArrayKey('RAW_1') points_1 = gp.GraphKey('POINTS_1') locations_1 = gp.ArrayKey('LOCATIONS_1') emb_1 = gp.ArrayKey('EMBEDDING_1') # TODO parse this key from somewhere key = 'train/raw/0' data = daisy.open_ds(dataset.filename, key) source_roi = gp.Roi(data.roi.get_offset(), data.roi.get_shape()) voxel_size = gp.Coordinate(data.voxel_size) emb_voxel_size = voxel_size # Get in and out shape in_shape = gp.Coordinate(model.in_shape) out_shape = gp.Coordinate(model.out_shape) logger.info(f"source roi: {source_roi}") logger.info(f"in_shape: {in_shape}") logger.info(f"out_shape: {out_shape}") logger.info(f"voxel_size: {voxel_size}") request = gp.BatchRequest() request.add(raw_0, in_shape) request.add(raw_1, in_shape) request.add(points_0, out_shape) request.add(points_1, out_shape) request[locations_0] = gp.ArraySpec(nonspatial=True) request[locations_1] = gp.ArraySpec(nonspatial=True) snapshot_request = gp.BatchRequest() snapshot_request[emb_0] = gp.ArraySpec(roi=request[points_0].roi) snapshot_request[emb_1] = gp.ArraySpec(roi=request[points_1].roi) # Let's hardcode this for now # TODO read actual number from zarr file keys n_samples = 447 batch_size = 1 dim = 2 padding = (100, 100) sources = [] for i in range(n_samples): ds_key = f'train/raw/{i}' image_sources = tuple( gp.ZarrSource( dataset.filename, {raw: ds_key}, {raw: gp.ArraySpec(interpolatable=True, voxel_size=(1, 1))}) + gp.Pad(raw, None) for raw in [raw_0, raw_1]) random_point_generator = RandomPointGenerator(density=point_density, repetitions=2) point_sources = tuple( (RandomPointSource(points_0, dim, random_point_generator=random_point_generator), RandomPointSource(points_1, dim, random_point_generator=random_point_generator))) # TODO: get augmentation parameters from some config file! points_and_image_sources = tuple( (img_source, point_source) + gp.MergeProvider() + \ gp.SimpleAugment() + \ gp.ElasticAugment( spatial_dims=2, control_point_spacing=(10, 10), jitter_sigma=(0.0, 0.0), rotation_interval=(0, math.pi/2)) + \ gp.IntensityAugment(r, scale_min=0.8, scale_max=1.2, shift_min=-0.2, shift_max=0.2, clip=False) + \ gp.NoiseAugment(r, var=0.01, clip=False) for r, img_source, point_source in zip([raw_0, raw_1], image_sources, point_sources)) sample_source = points_and_image_sources + gp.MergeProvider() data = daisy.open_ds(dataset.filename, ds_key) source_roi = gp.Roi(data.roi.get_offset(), data.roi.get_shape()) sample_source += gp.Crop(raw_0, source_roi) sample_source += gp.Crop(raw_1, source_roi) sample_source += gp.Pad(raw_0, padding) sample_source += gp.Pad(raw_1, padding) sample_source += gp.RandomLocation() sources.append(sample_source) sources = tuple(sources) pipeline = sources + gp.RandomProvider() pipeline += gp.Unsqueeze([raw_0, raw_1]) pipeline += PrepareBatch(raw_0, raw_1, points_0, points_1, locations_0, locations_1) # How does prepare batch relate to Stack????? pipeline += RejectArray(ensure_nonempty=locations_1) pipeline += RejectArray(ensure_nonempty=locations_0) # batch content # raw_0: (1, h, w) # raw_1: (1, h, w) # locations_0: (n, 2) # locations_1: (n, 2) pipeline += gp.Stack(batch_size) # batch content # raw_0: (b, 1, h, w) # raw_1: (b, 1, h, w) # locations_0: (b, n, 2) # locations_1: (b, n, 2) pipeline += gp.PreCache(num_workers=10) pipeline += gp.torch.Train( model, loss, optimizer, inputs={ 'raw_0': raw_0, 'raw_1': raw_1 }, loss_inputs={ 'emb_0': emb_0, 'emb_1': emb_1, 'locations_0': locations_0, 'locations_1': locations_1 }, outputs={ 2: emb_0, 3: emb_1 }, array_specs={ emb_0: gp.ArraySpec(voxel_size=emb_voxel_size), emb_1: gp.ArraySpec(voxel_size=emb_voxel_size) }, checkpoint_basename=os.path.join(out_dir, 'model'), save_every=checkpoint_interval) pipeline += gp.Snapshot( { raw_0: 'raw_0', raw_1: 'raw_1', emb_0: 'emb_0', emb_1: 'emb_1', # locations_0 : 'locations_0', # locations_1 : 'locations_1', }, every=snapshot_interval, additional_request=snapshot_request) return pipeline, request
def train_until(max_iteration, name='train_net', output_folder='.', clip_max=2000): # get the latest checkpoint if tf.train.latest_checkpoint(output_folder): trained_until = int(tf.train.latest_checkpoint(output_folder).split('_')[-1]) else: trained_until = 0 if trained_until >= max_iteration: return with open(os.path.join(output_folder, name + '_config.json'), 'r') as f: net_config = json.load(f) with open(os.path.join(output_folder, name + '_names.json'), 'r') as f: net_names = json.load(f) # array keys raw = gp.ArrayKey('RAW') gt_mask = gp.ArrayKey('GT_MASK') gt_dt = gp.ArrayKey('GT_DT') pred_dt = gp.ArrayKey('PRED_DT') loss_gradient = gp.ArrayKey('LOSS_GRADIENT') voxel_size = gp.Coordinate((1, 1, 1)) input_shape = gp.Coordinate(net_config['input_shape']) output_shape = gp.Coordinate(net_config['output_shape']) context = gp.Coordinate(input_shape - output_shape) / 2 request = gp.BatchRequest() request.add(raw, input_shape) request.add(gt_mask, output_shape) request.add(gt_dt, output_shape) snapshot_request = gp.BatchRequest() snapshot_request.add(raw, input_shape) snapshot_request.add(gt_mask, output_shape) snapshot_request.add(gt_dt, output_shape) snapshot_request.add(pred_dt, output_shape) snapshot_request.add(loss_gradient, output_shape) # specify data source data_sources = tuple() for data_file in data_files: current_path = os.path.join(data_dir, data_file) with h5py.File(current_path, 'r') as f: data_sources += tuple( gp.Hdf5Source( current_path, datasets={ raw: sample + '/raw', gt_mask: sample + '/fg' }, array_specs={ raw: gp.ArraySpec(interpolatable=True, dtype=np.uint16, voxel_size=voxel_size), gt_mask: gp.ArraySpec(interpolatable=False, dtype=np.bool, voxel_size=voxel_size), } ) + Convert(gt_mask, np.uint8) + gp.Pad(raw, context) + gp.Pad(gt_mask, context) + gp.RandomLocation() for sample in f) pipeline = ( data_sources + gp.RandomProvider() + gp.Reject(gt_mask, min_masked=0.005, reject_probability=1.) + DistanceTransform(gt_mask, gt_dt, 3) + nl.Clip(raw, 0, clip_max) + gp.Normalize(raw, factor=1.0/clip_max) + gp.ElasticAugment( control_point_spacing=[20, 20, 20], jitter_sigma=[1, 1, 1], rotation_interval=[0, math.pi/2.0], subsample=4) + gp.SimpleAugment(mirror_only=[1,2], transpose_only=[1,2]) + gp.IntensityAugment(raw, 0.9, 1.1, -0.1, 0.1) + gp.IntensityScaleShift(raw, 2,-1) + # train gp.PreCache( cache_size=40, num_workers=5) + gp.tensorflow.Train( os.path.join(output_folder, name), optimizer=net_names['optimizer'], loss=net_names['loss'], inputs={ net_names['raw']: raw, net_names['gt_dt']: gt_dt, }, outputs={ net_names['pred_dt']: pred_dt, }, gradients={ net_names['pred_dt']: loss_gradient, }, save_every=5000) + # visualize gp.Snapshot({ raw: 'volumes/raw', gt_mask: 'volumes/gt_mask', gt_dt: 'volumes/gt_dt', pred_dt: 'volumes/pred_dt', loss_gradient: 'volumes/gradient', }, output_filename=os.path.join(output_folder, 'snapshots', 'batch_{iteration}.hdf'), additional_request=snapshot_request, every=2000) + gp.PrintProfilingStats(every=500) ) with gp.build(pipeline): print("Starting training...") for i in range(max_iteration - trained_until): pipeline.request_batch(request)
def train_until(max_iteration): # get the latest checkpoint if tf.train.latest_checkpoint("."): trained_until = int(tf.train.latest_checkpoint(".").split("_")[-1]) else: trained_until = 0 if trained_until >= max_iteration: return # array keys for data sources raw = gp.ArrayKey("RAW") swcs = gp.PointsKey("SWCS") voxel_size = gp.Coordinate((10, 3, 3)) input_size = gp.Coordinate(net_config["input_shape"]) * voxel_size * 2 # add request request = gp.BatchRequest() request.add(raw, input_size) request.add(swcs, input_size) data_sources = tuple(( gp.N5Source( filename=str(( filename / "consensus-neurons-with-machine-centerpoints-labelled-as-swcs-carved.n5" ).absolute()), datasets={raw: "volume"}, array_specs={ raw: gp.ArraySpec(interpolatable=True, voxel_size=voxel_size, dtype=np.uint16) }, ), MouselightSwcFileSource( filename=str(( filename / "consensus-neurons-with-machine-centerpoints-labelled-as-swcs/G-002.swc" ).absolute()), points=(swcs, ), scale=voxel_size, transpose=(2, 1, 0), transform_file=str((filename / "transform.txt").absolute()), ), ) + gp.MergeProvider() + gp.RandomLocation(ensure_nonempty=swcs, ensure_centered=True) for filename in Path(sample_dir).iterdir() if "2018-08-01" in filename.name) pipeline = data_sources + gp.RandomProvider() with gp.build(pipeline): print("Starting training...") for i in range(max_iteration - trained_until): batch = pipeline.request_batch(request) vis_points_with_array(batch[raw].data, points_to_graph(batch[swcs].data), np.array(voxel_size))