def cfg(data_config):
model_config = { # Load pretrained model for testing
"model_name": "DenseDepth",
"model_params": {
"existing": os.path.join("models", "nyu.h5"),
},
"model_state_dict_fn": None
}
ckpt_file = None # Keep as None
save_outputs = True
seed = 95290421 # changing seed does not impact evaluation
small_run = 0
dataset_type = "test"
entry = None
# print(data_config.keys())
output_dir = os.path.join(
"results",
data_config["data_name"], # e.g. nyu_depth_v2
"{}_{}".format(dataset_type, small_run),
model_config["model_name"]) # e.g. DORN_nyu_nohints
safe_makedir(output_dir)
ex.observers.append(
FileStorageObserver.create(os.path.join(output_dir, "runs")))
cuda_device = "0" # The gpu index to run on. Should be a string
os.environ["CUDA_VISIBLE_DEVICES"] = cuda_device
# print("after: {}".format(os.environ["CUDA_VISIBLE_DEVICES"]))
if ckpt_file is not None:
model_update, _, _ = load_checkpoint(ckpt_file)
model_config.update(model_update)
del model_update, _ # So sacred doesn't collect them.
def cfg(data_config, spad_config):
model_config = { # Load pretrained model for testing
"model_name": "SinkhornOpt",
"model_params": {
"sgd_iters": 300,
"sinkhorn_iters": 40,
"sigma": 0.5,
"lam": 2e1,
"kde_eps": 1e-4,
"sinkhorn_eps": 1e-7,
"dc_eps": 1e-5,
"remove_dc": spad_config["dc_count"] > 0.,
"use_intensity": spad_config["use_intensity"],
"use_squared_falloff": spad_config["use_squared_falloff"],
"lr": 1e5,
"sid_bins": data_config["sid_bins"],
"offset": data_config["offset"],
"min_depth": data_config["min_depth"],
"max_depth": data_config["max_depth"],
"alpha": data_config["alpha"],
"beta": data_config["beta"],
},
"model_state_dict_fn": None # Keep as None
}
ckpt_file = None # Keep as None
save_outputs = True
seed = 95290421
small_run = 0
entry = None
pdict = model_config["model_params"]
comment = "_".join(["sgd_iters_{}".format(pdict["sgd_iters"]),
"sinkhorn_iters_{}".format(pdict["sinkhorn_iters"]),
"sigma_{}".format(pdict["sigma"]),
"lam_{}".format(pdict["lam"]),
"kde_eps_{}".format(pdict["kde_eps"]),
"sinkhorn_eps_{}".format(pdict["sinkhorn_eps"]),
])
del pdict
# print(data_config.keys())
fullcomment = comment + "_" + spad_config["spad_comment"]
output_dir = os.path.join("results",
data_config["data_name"], # e.g. nyu_depth_v2
"test_{}".format(small_run),
model_config["model_name"]) # e.g. DORN_nyu_nohints
if fullcomment is not "":
output_dir = os.path.join(output_dir, fullcomment)
safe_makedir(output_dir)
ex.observers.append(FileStorageObserver.create(os.path.join(output_dir, "runs")))
# Devices are for pytorch.
cuda_device = "0" # The gpu index to run on. Should be a string
os.environ["CUDA_VISIBLE_DEVICES"] = tf_cuda_device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("using device: {} (CUDA_VISIBLE_DEVICES = {})".format(device,
os.environ["CUDA_VISIBLE_DEVICES"]))
if ckpt_file is not None:
model_update, _, _ = load_checkpoint(ckpt_file)
model_config.update(model_update)
del model_update, _ # So sacred doesn't collect them.
def cfg(data_config):
model_config = { # Load pretrained model for testing
"model_name": "DORN_nyu_hints_Unet",
"model_params": {
"hints_len":
68,
"spad_weight":
1.,
"in_channels":
3,
"in_height":
257,
"in_width":
353,
"sid_bins":
data_config["sid_bins"],
"offset":
data_config["offset"],
"min_depth":
data_config["min_depth"],
"max_depth":
data_config["max_depth"],
"alpha":
data_config["alpha"],
"beta":
data_config["beta"],
"frozen":
True,
"pretrained":
True,
"state_dict_file":
os.path.join("models", "torch_params_nyuv2_BGR.pth.tar"),
},
"model_state_dict_fn": None
}
ckpt_file = "checkpoints/Mar15/04-10-54_DORN_nyu_hints_nyu_depth_v2/checkpoint_epoch_9_name_fixed.pth.tar"
# ckpt_file = None # Bayesian hints eval
dataset_type = "val"
save_outputs = True
output_dir = os.path.join(
"results",
data_config["data_name"], # e.g. nyu_depth_v2
model_config["model_name"], # e.g. DORN_nyu_nohints
dataset_type)
seed = 95290421
small_run = False
cuda_device = "0" # The gpu index to run on. Should be a string
os.environ["CUDA_VISIBLE_DEVICES"] = cuda_device
# print("after: {}".format(os.environ["CUDA_VISIBLE_DEVICES"]))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("using device: {} (CUDA_VISIBLE_DEVICES = {})".format(
device, os.environ["CUDA_VISIBLE_DEVICES"]))
if ckpt_file is not None:
model_update, _, _ = load_checkpoint(ckpt_file)
model_config.update(model_update)
del model_update, _ # So sacred doesn't collect them.
def cfg(data_config):
model_config = { # Load pretrained model for testing
"model_name": "DenseDepthHistogramMatchingWasserstein",
"model_params": {
"sgd_iters": 100,
"sinkhorn_iters": 40,
"sigma": 0.5,
"lam": 1e1,
"kde_eps": 1e-4,
"sinkhorn_eps": 1e-7,
"dc_eps": 1e-5,
"lr": 1e5,
"min_depth": 0.,
"max_depth": 10.,
"sid_bins": 68,
"offset": 0.,
"alpha": 0.6569154266167957,
"beta": 9.972175646365525,
"existing": os.path.join("models", "nyu.h5"),
},
"model_state_dict_fn": None
}
ckpt_file = None # Keep as None
save_outputs = True
seed = 95290421 # changing seed does not impact evaluation
small_run = 0
dataset_type = "test"
entry = None
# print(data_config.keys())
output_dir = os.path.join(
"results",
data_config["data_name"], # e.g. nyu_depth_v2
"{}_{}".format(dataset_type, small_run),
model_config["model_name"]) # e.g. DORN_nyu_nohints
safe_makedir(output_dir)
ex.observers.append(
FileStorageObserver.create(os.path.join(output_dir, "runs")))
cuda_device = "0,1" # The visible gpus. First one is the tensorflow, second one is pytorch.
os.environ["CUDA_VISIBLE_DEVICES"] = cuda_device
# print("after: {}".format(os.environ["CUDA_VISIBLE_DEVICES"]))
device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
# print("after: {}".format(os.environ["CUDA_VISIBLE_DEVICES"]))
if ckpt_file is not None:
model_update, _, _ = load_checkpoint(ckpt_file)
model_config.update(model_update)
del model_update, _ # So sacred doesn't collect them.
def cfg(data_config):
model_config = { # Load pretrained model for testing
"model_name": "DORN_nyu_nohints",
"model_params": {
"in_channels":
3,
"in_height":
257,
"in_width":
353,
"frozen":
True,
"pretrained":
True,
"state_dict_file":
os.path.join("models", "torch_params_nyuv2_BGR.pth.tar"),
},
"model_state_dict_fn": None
}
ckpt_file = None # Keep as None
save_outputs = True
seed = 95290421
small_run = 0
entry = None
# hyperparams = ["sgd_iters", "sinkhorn_iters", "sigma", "lam", "kde_eps", "sinkhorn_eps"]
pdict = model_config["model_params"]
del pdict
# print(data_config.keys())
output_dir = os.path.join(
"results",
data_config["data_name"], # e.g. nyu_depth_v2
"{}_{}".format("test", small_run),
model_config["model_name"]) # e.g. DORN_nyu_nohints
safe_makedir(output_dir)
ex.observers.append(
FileStorageObserver.create(os.path.join(output_dir, "runs")))
cuda_device = "0" # The gpu index to run on. Should be a string
os.environ["CUDA_VISIBLE_DEVICES"] = cuda_device
# print("after: {}".format(os.environ["CUDA_VISIBLE_DEVICES"]))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("using device: {} (CUDA_VISIBLE_DEVICES = {})".format(
device, os.environ["CUDA_VISIBLE_DEVICES"]))
if ckpt_file is not None:
model_update, _, _ = load_checkpoint(ckpt_file)
model_config.update(model_update)
del model_update, _ # So sacred doesn't collect them.
def cfg(data_config):
model_config = { # Load pretrained model for testing
"model_name": "DORN_median_matching",
"model_params": {
"in_channels": 3,
"in_height": 257,
"in_width": 353,
"sid_bins": 68,
"offset": 0.,
"min_depth": 0.,
"max_depth": 10.,
"alpha": 0.6569154266167957,
"beta": 9.972175646365525,
"frozen": True,
"pretrained": True,
"state_dict_file": os.path.join("models", "torch_params_nyuv2_BGR.pth.tar"),
},
"model_state_dict_fn": None
}
ckpt_file = None # Keep as None
dataset_type = "test"
save_outputs = True
seed = 95290421
small_run = 0
# print(data_config.keys())
output_dir = os.path.join("results",
data_config["data_name"], # e.g. nyu_depth_v2
"{}_{}".format(dataset_type, small_run),
model_config["model_name"]) # e.g. DORN_nyu_nohints
safe_makedir(output_dir)
ex.observers.append(FileStorageObserver.create(os.path.join(output_dir, "runs")))
cuda_device = "0" # The gpu index to run on. Should be a string
os.environ["CUDA_VISIBLE_DEVICES"] = cuda_device
# print("after: {}".format(os.environ["CUDA_VISIBLE_DEVICES"]))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("using device: {} (CUDA_VISIBLE_DEVICES = {})".format(device,
os.environ["CUDA_VISIBLE_DEVICES"]))
if ckpt_file is not None:
model_update, _, _ = load_checkpoint(ckpt_file)
model_config.update(model_update)
del model_update, _ # So sacred doesn't collect them.
def cfg(data_config):
model_config = {} # To be loaded from the checkpoint file.
ckpt_file = "checkpoints/Mar07/02-18-30_DenoisingUnetModel_cifar10/checkpoint_epoch_0.pth.tar"
eval_config = {
"dataset": "val", # {val, test}
"mode": "save_outputs", # {save_outputs, evaluate_metrics}
"output_dir": "cifar10_eval"
}
seed = 95290421
cuda_device = "0" # The gpu index to run on. Should be a string
os.environ["CUDA_VISIBLE_DEVICES"] = cuda_device
# print("after: {}".format(os.environ["CUDA_VISIBLE_DEVICES"]))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("using device: {} (CUDA_VISIBLE_DEVICES = {})".format(
device, os.environ["CUDA_VISIBLE_DEVICES"]))
if ckpt_file is not None:
model_update, _, _ = load_checkpoint(ckpt_file)
model_config.update(model_update)
del model_update, _ # So sacred doesn't collect them.
def cfg(data_config):
model_config = { # Load pretrained model for testing
"model_name": "DORN_bayesian_opt",
"model_params": {
"sgd_iters":
20,
"lr":
1e-3,
"hints_len":
68,
"spad_weight":
1.,
"in_channels":
3,
"in_height":
257,
"in_width":
353,
"sid_bins":
data_config["sid_bins"],
"offset":
data_config["offset"],
"min_depth":
data_config["min_depth"],
"max_depth":
data_config["max_depth"],
"alpha":
data_config["alpha"],
"beta":
data_config["beta"],
"frozen":
True,
"pretrained":
True,
"state_dict_file":
os.path.join("models", "torch_params_nyuv2_BGR.pth.tar"),
},
"model_state_dict_fn": None # Keep as None
}
ckpt_file = None # Keep as None
dataset_type = "val"
eval_config = {
"save_outputs":
True,
"evaluate_metrics":
True,
"output_dir":
os.path.join("data", "results", model_config["model_name"],
dataset_type),
"entry":
None # If we want to evaluate on a single entry
}
seed = 95290421
small_run = False
cuda_device = "0" # The gpu index to run on. Should be a string
os.environ["CUDA_VISIBLE_DEVICES"] = cuda_device
# print("after: {}".format(os.environ["CUDA_VISIBLE_DEVICES"]))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("using device: {} (CUDA_VISIBLE_DEVICES = {})".format(
device, os.environ["CUDA_VISIBLE_DEVICES"]))
if ckpt_file is not None:
model_update, _, _ = load_checkpoint(ckpt_file)
model_config.update(model_update)
del model_update, _ # So sacred doesn't collect them.
