def main(args):
config = load_config(args.config)
check_channels(config)
check_classes(config)
assert torch.cuda.is_available(
), "No GPU support found. Check CUDA and NVidia Driver install."
assert torch.distributed.is_nccl_available(
), "No NCCL support found. Check your PyTorch install."
world_size = torch.cuda.device_count()
args.bs = args.bs if args.bs is not None else math.floor(os.cpu_count() /
world_size)
args.workers = args.workers if args.workers is not None else args.bs
palette, transparency = make_palette(
[classe["color"] for classe in config["classes"]])
args.cover = [
tile for tile in tiles_from_csv(os.path.expanduser(args.cover))
] if args.cover else None
args.out = os.path.expanduser(args.out)
log = Logs(os.path.join(args.out, "log"))
chkpt = torch.load(args.checkpoint, map_location=torch.device("cpu"))
log.log("abd predict on {} GPUs, with {} workers/GPU and {} tiles/batch".
format(world_size, args.workers, args.bs))
log.log("Model {} - UUID: {}".format(chkpt["nn"], chkpt["uuid"]))
log.log("---")
loader = load_module("abd_model.loaders.{}".format(
chkpt["loader"].lower()))
lock_file = os.path.abspath(os.path.join(args.out, str(uuid.uuid1())))
dataset = getattr(loader, chkpt["loader"])(
config,
chkpt["shape_in"][1:3],
args.dataset,
args.cover,
mode="predict",
metatiles=args.metatiles,
keep_borders=args.keep_borders,
)
mp.spawn(gpu_worker,
nprocs=world_size,
args=(world_size, lock_file, args, config, dataset, palette,
transparency))
if os.path.exists(lock_file):
os.remove(lock_file)
if not args.no_web_ui and dataset.cover:
template = "leaflet.html" if not args.web_ui_template else args.web_ui_template
base_url = args.web_ui_base_url if args.web_ui_base_url else "."
web_ui(args.out, base_url, dataset.cover, dataset.cover, "png",
template)
def __init__(self, metrics, classes, config=None):
self.config = config
self.classes = classes
self.metrics = []
for classe in classes:
self.metrics.append({metric: [] for metric in metrics})
self.modules = {
metric: load_module("abd_model.metrics." + metric)
for metric in metrics
}
self.n = 0
def __init__(self, shape_in, shape_out, encoder="resnet50", train_config=None):
super().__init__()
doc = "U-Net like encoder-decoder architecture with a ResNet, ResNext or WideResNet encoder.\n\n"
doc += " - https://arxiv.org/abs/1505.04597 - U-Net: Convolutional Networks for Biomedical Image Segmentation\n"
if encoder in ["resnet50", "resnet101", "resnet152"]:
doc += " - https://arxiv.org/abs/1512.03385 - Deep Residual Learning for Image Recognition\n"
elif encoder in ["resnext50_32x4d", "resnext101_32x8d"]:
doc += " - https://arxiv.org/pdf/1611.05431 - Aggregated Residual Transformations for DNN\n"
elif encoder in ["wide_resnet50_2", "wide_resnet101_2"]:
doc += " - https://arxiv.org/abs/1605.07146 - Wide Residual Networks\n"
else:
encoders = "resnet50, resnet101, resnet152, resnext50_32x4d, resnext101_32x8d, wide_resnet50_2, wide_resnet101_2"
assert False, "Albunet, expects as encoder: " + encoders
self.version = 2
self.doc_string = doc
num_filters = 32
num_channels = shape_in[0]
num_classes = shape_out[0]
assert num_channels, "Empty Channels"
assert num_classes, "Empty Classes"
try:
pretrained = train_config["pretrained"]
except:
pretrained = False
models = load_module("torchvision.models")
self.encoder = getattr(models, encoder)(pretrained=pretrained)
# https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
if num_channels != 3:
weights = nn.init.kaiming_normal_(torch.zeros((64, num_channels, 7, 7)), mode="fan_out", nonlinearity="relu")
if pretrained:
for c in range(min(num_channels, 3)):
weights.data[:, c, :, :] = self.encoder.conv1.weight.data[:, c, :, :]
self.encoder.conv1 = nn.Conv2d(num_channels, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.encoder.conv1.weight = nn.Parameter(weights)
self.center = DecoderBlock(2048, num_filters * 8)
self.dec0 = DecoderBlock(2048 + num_filters * 8, num_filters * 8)
self.dec1 = DecoderBlock(1024 + num_filters * 8, num_filters * 8)
self.dec2 = DecoderBlock(512 + num_filters * 8, num_filters * 2)
self.dec3 = DecoderBlock(256 + num_filters * 2, num_filters * 2 * 2)
self.dec4 = DecoderBlock(num_filters * 2 * 2, num_filters)
self.dec5 = ConvRelu(num_filters, num_filters)
self.final = nn.Conv2d(num_filters, num_classes, kernel_size=1)
def main(args):
try:
module = load_module("abd_model.osm.{}".format(args.type.lower()))
except:
sys.exit("ERROR: Unavailable --type {}".format(args.type))
if os.path.dirname(os.path.expanduser(args.out)):
os.makedirs(os.path.dirname(os.path.expanduser(args.out)), exist_ok=True)
osmium_handler = getattr(module, "{}Handler".format(args.type))()
print("abd extract {} from {} to {}".format(args.type, args.pbf, args.out), file=sys.stderr, flush=True)
print("\nNOTICE: could take time. Be patient...\n".format(args.type, args.pbf, args.out), file=sys.stderr, flush=True)
osmium_handler.apply_file(filename=os.path.expanduser(args.pbf), locations=True)
osmium_handler.save(os.path.expanduser(args.out))
def main(args):
if not args.masks or not args.labels:
assert args.mode != "list", "Parameters masks and labels are mandatories in list mode."
assert not (
args.min or args.max
), "Both --masks and --labels mandatory, for metric filtering."
if args.min or args.max:
config = load_config(args.config)
args.out = os.path.expanduser(args.out)
cover = [tile for tile in tiles_from_csv(os.path.expanduser(args.cover))
] if args.cover else None
args_minmax = set()
args.min = {(m[0], m[1]): m[2] for m in args.min} if args.min else dict()
args.max = {(m[0], m[1]): m[2] for m in args.max} if args.max else dict()
args_minmax.update(args.min.keys())
args_minmax.update(args.max.keys())
minmax = dict()
for mm in args_minmax:
mm_min = float(args.min[mm]) if mm in args.min else 0.0
mm_max = float(args.max[mm]) if mm in args.max else 1.0
assert mm_min < mm_max, "--min must be lower than --max, on {}".format(
mm)
minmax[mm] = {
"min":
mm_min,
"max":
mm_max,
"class_id": [
c for c, classe in enumerate(config["classes"])
if classe["title"] == mm[0]
][0],
"module":
load_module("abd_model.metrics." + mm[1]),
}
if not args.workers:
args.workers = os.cpu_count()
print("abd compare {} on CPU, with {} workers".format(
args.mode, args.workers),
file=sys.stderr,
flush=True)
if args.images:
tiles = [tile for tile in tiles_from_dir(args.images[0], cover=cover)]
assert len(tiles), "Empty images dir: {}".format(args.images[0])
for image in args.images[1:]:
assert sorted(tiles) == sorted([
tile for tile in tiles_from_dir(image, cover=cover)
]), "Unconsistent images dirs"
if args.labels and args.masks:
tiles_masks = [
tile for tile in tiles_from_dir(args.masks, cover=cover)
]
tiles_labels = [
tile for tile in tiles_from_dir(args.labels, cover=cover)
]
if args.images:
assert sorted(tiles) == sorted(tiles_masks) == sorted(
tiles_labels), "Unconsistent images/label/mask directories"
else:
assert len(tiles_masks), "Empty masks dir: {}".format(args.masks)
assert len(tiles_labels), "Empty labels dir: {}".format(
args.labels)
assert sorted(tiles_masks) == sorted(
tiles_labels), "Label and Mask directories are not consistent"
tiles = tiles_masks
tiles_list = []
tiles_compare = []
progress = tqdm(total=len(tiles), ascii=True, unit="tile")
log = False if args.mode == "list" else Logs(os.path.join(args.out, "log"))
with futures.ThreadPoolExecutor(args.workers) as executor:
def worker(tile):
x, y, z = list(map(str, tile))
if args.masks and args.labels:
label = np.array(
Image.open(
os.path.join(args.labels, z, x, "{}.png".format(y))))
mask = np.array(
Image.open(
os.path.join(args.masks, z, x, "{}.png".format(y))))
assert label.shape == mask.shape, "Inconsistent tiles (size or dimensions)"
metrics = dict()
for mm in minmax:
try:
metrics[mm] = getattr(minmax[mm]["module"], "get")(
torch.as_tensor(label, device="cpu"),
torch.as_tensor(mask, device="cpu"),
minmax[mm]["class_id"],
)
except:
progress.update()
return False, tile
if not (minmax[mm]["min"] <= metrics[mm] <=
minmax[mm]["max"]):
progress.update()
return True, tile
tiles_compare.append(tile)
if args.mode == "side":
for i, root in enumerate(args.images):
img = tile_image_from_file(tile_from_xyz(root, x, y, z)[1],
force_rgb=True)
if i == 0:
side = np.zeros(
(img.shape[0], img.shape[1] * len(args.images), 3))
side = np.swapaxes(side, 0,
1) if args.vertical else side
image_shape = img.shape
else:
assert image_shape[0:2] == img.shape[
0:2], "Unconsistent image size to compare"
if args.vertical:
side[i * image_shape[0]:(i + 1) *
image_shape[0], :, :] = img
else:
side[:, i * image_shape[0]:(i + 1) *
image_shape[0], :] = img
tile_image_to_file(args.out, tile, np.uint8(side))
elif args.mode == "stack":
for i, root in enumerate(args.images):
tile_image = tile_image_from_file(tile_from_xyz(
root, x, y, z)[1],
force_rgb=True)
if i == 0:
image_shape = tile_image.shape[0:2]
stack = tile_image / len(args.images)
else:
assert image_shape == tile_image.shape[
0:2], "Unconsistent image size to compare"
stack = stack + (tile_image / len(args.images))
tile_image_to_file(args.out, tile, np.uint8(stack))
elif args.mode == "list":
tiles_list.append([tile, metrics])
progress.update()
return True, tile
for ok, tile in executor.map(worker, tiles):
if not ok and log:
log.log("Warning: skipping. {}".format(str(tile)))
if args.mode == "list":
with open(args.out, mode="w") as out:
if args.geojson:
out.write('{"type":"FeatureCollection","features":[')
first = True
for tile_list in tiles_list:
tile, metrics = tile_list
x, y, z = list(map(str, tile))
if args.geojson:
prop = '"properties":{{"x":{},"y":{},"z":{}'.format(
x, y, z)
for metric in metrics:
prop += ',"{}":{:.3f}'.format(metric, metrics[metric])
geom = '"geometry":{}'.format(
json.dumps(feature(tile, precision=6)["geometry"]))
out.write('{}{{"type":"Feature",{},{}}}}}'.format(
"," if not first else "", geom, prop))
first = False
if not args.geojson:
out.write("{},{},{}".format(x, y, z))
for metric in metrics:
out.write("\t{:.3f}".format(metrics[metric]))
out.write(os.linesep)
if args.geojson:
out.write("]}")
out.close()
base_url = args.web_ui_base_url if args.web_ui_base_url else "."
if args.mode == "side" and not args.no_web_ui:
template = "compare.html" if not args.web_ui_template else args.web_ui_template
web_ui(args.out,
base_url,
tiles,
tiles_compare,
args.format,
template,
union_tiles=False)
if args.mode == "stack" and not args.no_web_ui:
template = "leaflet.html" if not args.web_ui_template else args.web_ui_template
tiles = [tile for tile in tiles_from_dir(args.images[0])]
web_ui(args.out, base_url, tiles, tiles_compare, args.format, template)
def main(args):
config = load_config(args.config)
args.cover = [
tile for tile in tiles_from_csv(os.path.expanduser(args.cover))
] if args.cover else None
if args.classes_weights:
try:
args.classes_weights = list(
map(float, args.classes_weights.split(",")))
except:
assert args.classes_weights == "auto", "invalid --classes_weights value"
args.classes_weights = compute_classes_weights(
args.dataset, config["classes"], args.cover, os.cpu_count())
else:
args.classes_weights = [
classe["weight"] for classe in config["classes"]
]
args.tiles_weights = ([(tile, weight) for tile, weight in tiles_from_csv(
os.path.expanduser(args.tiles_weights), extra_columns=True)]
if args.tiles_weights else None)
args.bs = args.bs if args.bs else config["train"]["bs"]
check_classes(config)
check_channels(config)
check_model(config)
assert torch.cuda.is_available(
), "No GPU support found. Check CUDA and NVidia Driver install."
assert torch.distributed.is_nccl_available(
), "No NCCL support found. Check your PyTorch install."
world_size = 1 # Hard Coded since eval MultiGPUs not yet implemented
args.workers = min(args.bs if not args.workers else args.workers,
math.floor(os.cpu_count() / world_size))
print("abd eval on 1 GPU, with {} workers, and {} tiles/batch".format(
args.workers, args.bs))
loader = load_module("abd_model.loaders.{}".format(
config["model"]["loader"].lower()))
assert os.path.isdir(os.path.expanduser(
args.dataset)), "--dataset path is not a directory"
dataset = getattr(loader,
config["model"]["loader"])(config, config["model"]["ts"],
args.dataset, args.cover,
args.tiles_weights, "eval")
assert len(dataset), "Empty or Invalid --dataset content"
shape_in = dataset.shape_in
shape_out = dataset.shape_out
print("DataSet Eval: {}".format(args.dataset))
print("\n--- Input tensor")
num_channel = 1 # 1-based numerotation
for channel in config["channels"]:
for band in channel["bands"]:
print("Channel {}:\t\t {} - (band:{})".format(
num_channel, channel["name"], band))
num_channel += 1
print("\n--- Output Classes ---")
for c, classe in enumerate(config["classes"]):
print("Class {}:\t\t {} ({:.2f})".format(c, classe["title"],
args.classes_weights[c]))
print("\n--- Model ---")
for hp in config["model"]:
print("{}{}".format(hp.ljust(25, " "), config["model"][hp]))
lock_file = os.path.abspath(os.path.join("/tmp", str(uuid.uuid1())))
mp.spawn(gpu_worker,
nprocs=world_size,
args=(world_size, lock_file, dataset, shape_in, shape_out, args,
config))
if os.path.exists(lock_file):
os.remove(lock_file)
def gpu_worker(rank, world_size, lock_file, dataset, shape_in, shape_out, args,
config):
dist.init_process_group(backend="nccl",
init_method="file://" + lock_file,
world_size=world_size,
rank=rank)
torch.cuda.set_device(rank)
torch.manual_seed(0)
loader = DataLoader(dataset,
batch_size=args.bs,
shuffle=False,
drop_last=True,
num_workers=args.workers)
nn_module = load_module("abd_model.nn.{}".format(
config["model"]["nn"].lower()))
nn = getattr(nn_module,
config["model"]["nn"])(shape_in, shape_out,
config["model"]["encoder"].lower(),
config["train"]).cuda(rank)
nn = DistributedDataParallel(nn,
device_ids=[rank],
find_unused_parameters=True)
if args.checkpoint:
chkpt = torch.load(os.path.expanduser(args.checkpoint),
map_location="cuda:{}".format(rank))
assert nn.module.version == chkpt[
"model_version"], "Model Version mismatch"
nn.load_state_dict(chkpt["state_dict"])
if rank == 0:
print("\n--- Using Checkpoint ---")
print("Path:\t\t {}".format(args.checkpoint))
print("UUID:\t\t {}".format(chkpt["uuid"]))
nn.eval()
with torch.no_grad():
args.metrics = args.metrics if args.metrics else config["train"][
"metrics"]
metrics = Metrics(args.metrics, config["classes"], config=config)
assert len(loader), "Empty or Inconsistent DataSet"
dataloader = tqdm(loader, desc="Eval", unit="Batch",
ascii=True) if rank == 0 else loader
for images, masks, tiles, tiles_weights in dataloader:
images = images.cuda(rank, non_blocking=True)
masks = masks.cuda(rank, non_blocking=True)
outputs = nn(images)
if rank == 0:
for mask, output in zip(masks, outputs):
metrics.add(mask, output)
if rank == 0:
print("\n{} μ\t σ".format(" ".ljust(25, " ")))
for c, classe in enumerate(config["classes"]):
if classe["weight"] != 0.0 and classe["color"] != "transparent":
for k, v in metrics.get()[c].items():
print("{}{:.3f}\t {:.3f}".format(
(classe["title"] + " " + k).ljust(25, " "), v["μ"],
v["σ"]))
dist.destroy_process_group()
def gpu_worker(rank, world_size, lock_file, dataset, shape_in, shape_out, args,
config):
log = Logs(os.path.join(args.out, "log")) if rank == 0 else None
dist.init_process_group(backend="nccl",
init_method="file://" + lock_file,
world_size=world_size,
rank=rank)
torch.cuda.set_device(rank)
torch.manual_seed(0)
bs = config["train"]["bs"]
sampler = torch.utils.data.distributed.DistributedSampler(
dataset, num_replicas=world_size, rank=rank)
loader = DataLoader(dataset,
batch_size=bs,
shuffle=False,
drop_last=True,
num_workers=args.workers,
sampler=sampler)
nn_module = load_module("abd_model.nn.{}".format(
config["model"]["nn"].lower()))
nn = getattr(nn_module,
config["model"]["nn"])(shape_in, shape_out,
config["model"]["encoder"].lower(),
config["train"]).cuda(rank)
nn = DistributedDataParallel(nn,
device_ids=[rank],
find_unused_parameters=True)
optimizer_params = {
key: value
for key, value in config["train"]["optimizer"].items() if key != "name"
}
optimizer = getattr(torch.optim, config["train"]["optimizer"]["name"])(
nn.parameters(), **optimizer_params)
if rank == 0:
log.log("\n--- Train ---")
for hp in config["train"]:
if hp == "da":
da = config["train"]["da"]["name"]
dap = config["train"]["da"]["p"]
log.log("{}{} ({:.2f})".format("da".ljust(25, " "), da, dap))
elif hp == "metrics":
log.log("{}{}".format(hp.ljust(25, " "),
set(config["train"][hp]))) # aesthetic
elif hp != "optimizer":
log.log("{}{}".format(hp.ljust(25, " "), config["train"][hp]))
log.log("{}{}".format("optimizer".ljust(25, " "),
config["train"]["optimizer"]["name"]))
for k, v in optimizer.state_dict()["param_groups"][0].items():
if k != "params":
log.log(" - {}{}".format(k.ljust(25 - 3, " "), v))
resume = 0
if args.checkpoint:
chkpt = torch.load(os.path.expanduser(args.checkpoint),
map_location="cuda:{}".format(rank))
assert nn.module.version == chkpt[
"model_version"], "Model Version mismatch"
nn.load_state_dict(chkpt["state_dict"])
if rank == 0:
log.log("\n--- Using Checkpoint ---")
log.log("Path:\t\t {}".format(args.checkpoint))
log.log("UUID:\t\t {}".format(chkpt["uuid"]))
if args.resume:
optimizer.load_state_dict(chkpt["optimizer"])
resume = chkpt["epoch"]
assert resume < args.epochs, "Epoch asked, already reached by the given checkpoint"
loss_module = load_module("abd_model.losses.{}".format(
config["train"]["loss"].lower()))
criterion = getattr(loss_module, config["train"]["loss"])().cuda(rank)
for epoch in range(resume + 1, args.epochs + 1): # 1-N based
if rank == 0:
log.log("\n---\nEpoch: {}/{}\n".format(epoch, args.epochs))
sampler.set_epoch(
epoch) # https://github.com/pytorch/pytorch/issues/31232
do_epoch(rank, loader, config, args.classes_weights, log, nn,
criterion, epoch, optimizer)
if rank == 0:
UUID = uuid.uuid1()
states = {
"uuid": UUID,
"model_version": nn.module.version,
"producer_name": "Neat-EO.pink",
"producer_version": abd.__version__,
"model_licence": "MIT",
"domain": "pink.Neat-EO", # reverse-DNS
"doc_string": nn.module.doc_string,
"shape_in": shape_in,
"shape_out": shape_out,
"state_dict": nn.state_dict(),
"epoch": epoch,
"nn": config["model"]["nn"],
"encoder": config["model"]["encoder"],
"optimizer": optimizer.state_dict(),
"loader": config["model"]["loader"],
}
checkpoint_path = os.path.join(
args.out, "checkpoint-{:05d}.pth".format(epoch))
if epoch == args.epochs or not (epoch % args.saving):
log.log("\n--- Saving Checkpoint ---")
log.log("Path:\t\t {}".format(checkpoint_path))
log.log("UUID:\t\t {}\n".format(UUID))
torch.save(states, checkpoint_path)
dist.barrier()
dist.destroy_process_group()
def main(args):
chkpt = torch.load(os.path.expanduser(args.checkpoint),
map_location=torch.device("cpu"))
assert chkpt, "Unable to load checkpoint {}".format(args.checkpoint)
if os.path.dirname(os.path.expanduser(args.out)):
os.makedirs(os.path.dirname(os.path.expanduser(args.out)),
exist_ok=True)
args.out = os.path.expanduser(args.out)
UUID = chkpt["uuid"] if "uuid" in chkpt else uuid.uuid1()
try:
nn_name = chkpt["nn"]
except:
assert args.nn, "--nn mandatory as not already in input .pth"
nn_name = args.nn
try:
encoder = chkpt["encoder"]
except:
assert args.encoder, "--encoder mandatory as not already in input .pth"
encoder = args.encoder
try:
loader = chkpt["loader"]
except:
assert args.loader, "--loader mandatory as not already in input .pth"
doc_string = args.doc_string
try:
doc_string = chkpt["doc_string"]
except:
assert args.doc_string, "--doc_string mandatory as not already in input .pth"
doc_string = args.doc_string
try:
shape_in = chkpt["shape_in"]
except:
assert args.shape_in, "--shape_in mandatory as not already in input .pth"
shape_in = tuple(map(int, args.shape_in.split(",")))
try:
shape_out = chkpt["shape_out"]
except:
assert args.shape_out, "--shape_out mandatory as not already in input .pth"
shape_out = tuple(map(int, args.shape_out.split(",")))
nn_module = load_module("abd_model.nn.{}".format(nn_name.lower()))
nn = getattr(nn_module, nn_name)(shape_in, shape_out,
encoder.lower()).to("cpu")
print("abd export model to {}".format(args.type), file=sys.stderr)
print("Model: {}".format(nn_name, file=sys.stderr))
print("UUID: {}".format(UUID, file=sys.stderr))
if args.type == "pth":
states = {
"uuid": UUID,
"model_version": None,
"producer_name": "abd_model",
"producer_version": abd.__version__,
"model_licence": "MIT",
"domain": "abd_model", # reverse-DNS
"doc_string": doc_string,
"shape_in": shape_in,
"shape_out": shape_out,
"state_dict": nn.state_dict(),
"epoch": 0,
"nn": nn_name,
"encoder": encoder,
"optimizer": None,
"loader": loader,
}
torch.save(states, args.out)
else:
try: # https://github.com/pytorch/pytorch/issues/9176
nn.module.state_dict(chkpt["state_dict"])
except AttributeError:
nn.state_dict(chkpt["state_dict"])
nn.eval()
batch = torch.rand(1, *shape_in)
if args.type == "onnx":
torch.onnx.export(
nn,
torch.autograd.Variable(batch),
args.out,
input_names=["input", "shape_in", "shape_out"],
output_names=["output"],
dynamic_axes={
"input": {
0: "num_batch"
},
"output": {
0: "num_batch"
}
},
)
if args.type == "jit":
torch.jit.trace(nn, batch).save(args.out)
def gpu_worker(rank, world_size, lock_file, args, config, dataset, palette,
transparency):
dist.init_process_group(backend="nccl",
init_method="file://" + lock_file,
world_size=world_size,
rank=rank)
torch.cuda.set_device(rank)
chkpt = torch.load(args.checkpoint, map_location=torch.device(rank))
nn_module = load_module("abd_model.nn.{}".format(chkpt["nn"].lower()))
nn = getattr(nn_module, chkpt["nn"])(chkpt["shape_in"], chkpt["shape_out"],
chkpt["encoder"].lower()).to(rank)
nn = DistributedDataParallel(nn,
device_ids=[rank],
find_unused_parameters=True)
chkpt = torch.load(os.path.expanduser(args.checkpoint),
map_location="cuda:{}".format(rank))
assert nn.module.version == chkpt[
"model_version"], "Model Version mismatch"
nn.load_state_dict(chkpt["state_dict"])
sampler = torch.utils.data.distributed.DistributedSampler(
dataset, num_replicas=world_size, rank=rank)
loader = DataLoader(dataset,
batch_size=args.bs,
shuffle=False,
num_workers=args.workers,
sampler=sampler)
assert len(loader), "Empty predict dataset directory. Check your path."
C, W, H = chkpt["shape_out"]
nn.eval()
with torch.no_grad():
dataloader = tqdm(loader, desc="Predict", unit="Batch/GPU",
ascii=True) if rank == 0 else loader
for images, tiles in dataloader:
if args.metatiles:
N = images.shape[0]
qs = int(W / 4)
hs = int(W / 2)
ts = int(W)
# fmt:off
probs = np.zeros((N, C, W, H), dtype=np.float)
probs[:, :, 0:hs, 0:hs] = nn(
images[:, :, 0:ts,
0:ts].to(rank)).data.cpu().numpy()[:, :, qs:-qs,
qs:-qs]
probs[:, :, 0:hs, hs:] = nn(
images[:, :, 0:ts,
hs:].to(rank)).data.cpu().numpy()[:, :, qs:-qs,
qs:-qs]
probs[:, :, hs:, 0:hs] = nn(
images[:, :, hs:,
0:ts].to(rank)).data.cpu().numpy()[:, :, qs:-qs,
qs:-qs]
probs[:, :, hs:, hs:] = nn(
images[:, :, hs:,
hs:].to(rank)).data.cpu().numpy()[:, :, qs:-qs,
qs:-qs]
# fmt:on
else:
probs = nn(images.to(rank)).data.cpu().numpy()
for tile, prob in zip(tiles, probs):
x, y, z = list(map(int, tile))
mask = np.zeros((W, H), dtype=np.uint8)
for c in range(C):
mask += np.around(prob[c, :, :]).astype(np.uint8) * c
tile_label_to_file(args.out, mercantile.Tile(x, y, z), palette,
transparency, mask)