Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Fix timm pretrained error #318

Merged
merged 1 commit into from
Dec 11, 2024
Merged

Fix timm pretrained error #318

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
22 changes: 12 additions & 10 deletions terratorch/models/backbones/prithvi_vit.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -51,7 +51,8 @@ def _cfg(**kwargs):
**kwargs
}

prithvi_default_cfgs = {

prithvi_cfgs = {
"prithvi_eo_tiny": _cfg(num_frames=1, embed_dim=256, depth=4, num_heads=4,
decoder_embed_dim=128, decoder_depth=4, decoder_num_heads=4),
"prithvi_eo_v1_100": _cfg(num_frames=3, mean=PRITHVI_V1_MEAN, std=PRITHVI_V1_STD),
Expand All @@ -64,8 +65,8 @@ def _cfg(**kwargs):
coords_encoding=["time", "location"], coords_scale_learn=True),
}


pretrained_cfgs = generate_default_cfgs(
# Timm pretrained configs
default_cfgs = generate_default_cfgs(
{
"prithvi_eo_v1_100": {
"hf_hub_id": "ibm-nasa-geospatial/Prithvi-EO-1.0-100M",
Expand Down Expand Up @@ -203,19 +204,20 @@ def checkpoint_filter_wrapper_fn(state_dict, model):
return checkpoint_filter_fn_mae(state_dict, model, pretrained_bands, model_bands)

if pretrained:
assert variant in pretrained_cfgs, (f"No pre-trained model found for variant {variant} "
f"(pretrained models: {pretrained_cfgs.keys()})")
assert variant in default_cfgs, (f"No pre-trained model found for variant {variant} "
f"(pretrained models: {default_cfgs.keys()})")
# Load pre-trained config from hf
try:
model_args, _ = load_model_config_from_hf(pretrained_cfgs[variant].default.hf_hub_id)
model_args, _ = load_model_config_from_hf(default_cfgs[variant].default.hf_hub_id)
model_args.update(kwargs)
except:
logger.warning(f"No pretrained configuration was found on HuggingFace for the model {variant}.")
model_args = prithvi_default_cfgs[variant].copy()
logger.warning(f"No pretrained configuration was found on HuggingFace for the model {variant}."
f"Using random initialization.")
model_args = prithvi_cfgs[variant].copy()
model_args.update(kwargs)
else:
# Load default config
model_args = prithvi_default_cfgs[variant].copy()
model_args = prithvi_cfgs[variant].copy()
model_args.update(kwargs)

# When the pretrained configuration is not available in HF, we shift to pretrained=False
Expand All @@ -229,7 +231,7 @@ def checkpoint_filter_wrapper_fn(state_dict, model):
**model_args,
)
except RuntimeError:
logger.warning(f"No pretrained configuration was found for the model {variant}.")
logger.warning(f"No pretrained configuration was found for the model {variant}. Using random initialization.")
model = build_model_with_cfg(
prithvi_model_class,
variant,
Expand Down
Loading