mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-11-13 00:28:14 +00:00
471ef91d1c
PyTorch chose to make it Any because they expect its users' code to be "highly dynamic": https://github.com/pytorch/pytorch/pull/104321 It is not the case for us, in Refiners having untyped code goes contrary to one of our core principles. Note that there is currently an open PR in PyTorch to return `Module | Tensor`, but in practice this is not always correct either: https://github.com/pytorch/pytorch/pull/115074 I also moved Residuals-related code from SD1 to latent_diffusion because SDXL should not depend on SD1.
424 lines
16 KiB
Python
424 lines
16 KiB
Python
from math import isclose
|
|
from pathlib import Path
|
|
from typing import cast
|
|
from warnings import warn
|
|
|
|
import numpy as np
|
|
import pytest
|
|
import torch
|
|
import torch.nn as nn
|
|
from PIL import Image
|
|
from tests.foundationals.segment_anything.utils import (
|
|
FacebookSAM,
|
|
FacebookSAMPredictor,
|
|
SAMPrompt,
|
|
intersection_over_union,
|
|
)
|
|
from torch import Tensor
|
|
|
|
import refiners.fluxion.layers as fl
|
|
from refiners.fluxion import manual_seed
|
|
from refiners.fluxion.model_converter import ModelConverter
|
|
from refiners.fluxion.utils import image_to_tensor, load_tensors, no_grad
|
|
from refiners.foundationals.segment_anything.image_encoder import FusedSelfAttention, RelativePositionAttention
|
|
from refiners.foundationals.segment_anything.model import SegmentAnythingH
|
|
from refiners.foundationals.segment_anything.transformer import TwoWayTransformerLayer
|
|
|
|
# See predictor_example.ipynb official notebook
|
|
PROMPTS: list[SAMPrompt] = [
|
|
SAMPrompt(foreground_points=((500, 375),)),
|
|
SAMPrompt(background_points=((500, 375),)),
|
|
SAMPrompt(foreground_points=((500, 375), (1125, 625))),
|
|
SAMPrompt(foreground_points=((500, 375),), background_points=((1125, 625),)),
|
|
SAMPrompt(box_points=[[(425, 600), (700, 875)]]),
|
|
SAMPrompt(box_points=[[(425, 600), (700, 875)]], background_points=((575, 750),)),
|
|
]
|
|
|
|
|
|
@pytest.fixture(params=PROMPTS)
|
|
def prompt(request: pytest.FixtureRequest) -> SAMPrompt:
|
|
return request.param
|
|
|
|
|
|
@pytest.fixture
|
|
def one_prompt() -> SAMPrompt:
|
|
# Using the third prompt of the PROMPTS list in order to strictly do the same test as the official notebook in the
|
|
# test_predictor_dense_mask test.
|
|
return PROMPTS[2]
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def facebook_sam_h_weights(test_weights_path: Path) -> Path:
|
|
sam_h_weights = test_weights_path / "sam_vit_h_4b8939.pth"
|
|
if not sam_h_weights.is_file():
|
|
warn(f"could not find weights at {sam_h_weights}, skipping")
|
|
pytest.skip(allow_module_level=True)
|
|
return sam_h_weights
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def sam_h_weights(test_weights_path: Path) -> Path:
|
|
sam_h_weights = test_weights_path / "segment-anything-h.safetensors"
|
|
if not sam_h_weights.is_file():
|
|
warn(f"could not find weights at {sam_h_weights}, skipping")
|
|
pytest.skip(allow_module_level=True)
|
|
return sam_h_weights
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def facebook_sam_h(facebook_sam_h_weights: Path, test_device: torch.device) -> FacebookSAM:
|
|
from segment_anything import build_sam_vit_h # type: ignore
|
|
|
|
sam_h = cast(FacebookSAM, build_sam_vit_h())
|
|
sam_h.load_state_dict(state_dict=load_tensors(facebook_sam_h_weights))
|
|
return sam_h.to(device=test_device)
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def facebook_sam_h_predictor(facebook_sam_h: FacebookSAM) -> FacebookSAMPredictor:
|
|
from segment_anything import SamPredictor # type: ignore
|
|
from segment_anything.modeling import Sam # type: ignore
|
|
|
|
predictor = SamPredictor(cast(Sam, facebook_sam_h)) # type: ignore
|
|
return cast(FacebookSAMPredictor, predictor)
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def sam_h(sam_h_weights: Path, test_device: torch.device) -> SegmentAnythingH:
|
|
sam_h = SegmentAnythingH(device=test_device)
|
|
sam_h.load_from_safetensors(tensors_path=sam_h_weights)
|
|
return sam_h
|
|
|
|
|
|
@pytest.fixture(scope="module")
|
|
def ref_path(test_sam_path: Path) -> Path:
|
|
return test_sam_path / "test_sam_ref"
|
|
|
|
|
|
@pytest.fixture
|
|
def truck(ref_path: Path) -> Image.Image:
|
|
return Image.open(ref_path / "truck.jpg").convert("RGB")
|
|
|
|
|
|
@no_grad()
|
|
def test_fused_self_attention(facebook_sam_h: FacebookSAM) -> None:
|
|
manual_seed(seed=0)
|
|
x = torch.randn(25, 14, 14, 1280, device=facebook_sam_h.device)
|
|
|
|
attention = cast(nn.Module, facebook_sam_h.image_encoder.blocks[0].attn)
|
|
|
|
refiners_attention = FusedSelfAttention(
|
|
embedding_dim=1280, num_heads=16, spatial_size=(14, 14), device=facebook_sam_h.device
|
|
)
|
|
|
|
rpa = refiners_attention.layer("RelativePositionAttention", RelativePositionAttention)
|
|
linear_1 = refiners_attention.layer("Linear_1", fl.Linear)
|
|
linear_2 = refiners_attention.layer("Linear_2", fl.Linear)
|
|
|
|
linear_1.weight = attention.qkv.weight
|
|
linear_1.bias = attention.qkv.bias
|
|
linear_2.weight = attention.proj.weight
|
|
linear_2.bias = attention.proj.bias
|
|
rpa.horizontal_embedding = attention.rel_pos_w
|
|
rpa.vertical_embedding = attention.rel_pos_h
|
|
|
|
y_1 = attention(x)
|
|
assert y_1.shape == x.shape
|
|
|
|
y_2 = refiners_attention(x)
|
|
assert y_2.shape == x.shape
|
|
|
|
assert torch.equal(input=y_1, other=y_2)
|
|
|
|
|
|
@no_grad()
|
|
def test_image_encoder(sam_h: SegmentAnythingH, facebook_sam_h: FacebookSAM, truck: Image.Image) -> None:
|
|
image_tensor = image_to_tensor(image=truck.resize(size=(1024, 1024)), device=facebook_sam_h.device)
|
|
y_1 = facebook_sam_h.image_encoder(image_tensor)
|
|
y_2 = sam_h.image_encoder(image_tensor)
|
|
|
|
assert torch.allclose(input=y_1, other=y_2, atol=1e-4)
|
|
|
|
|
|
@no_grad()
|
|
def test_prompt_encoder_dense_positional_embedding(facebook_sam_h: FacebookSAM, sam_h: SegmentAnythingH) -> None:
|
|
facebook_prompt_encoder = facebook_sam_h.prompt_encoder
|
|
refiners_prompt_encoder = sam_h.point_encoder
|
|
|
|
facebook_dense_pe: Tensor = cast(Tensor, facebook_prompt_encoder.get_dense_pe()) # type: ignore
|
|
refiners_dense_pe = refiners_prompt_encoder.get_dense_positional_embedding(image_embedding_size=(64, 64))
|
|
|
|
assert torch.equal(input=refiners_dense_pe, other=facebook_dense_pe)
|
|
|
|
|
|
@no_grad()
|
|
def test_prompt_encoder_no_mask_dense_embedding(facebook_sam_h: FacebookSAM, sam_h: SegmentAnythingH) -> None:
|
|
facebook_prompt_encoder = facebook_sam_h.prompt_encoder
|
|
refiners_prompt_encoder = sam_h.mask_encoder
|
|
|
|
_, facebook_dense_pe = facebook_prompt_encoder(points=None, boxes=None, masks=None)
|
|
refiners_dense_pe = refiners_prompt_encoder.get_no_mask_dense_embedding(image_embedding_size=(64, 64))
|
|
|
|
assert torch.equal(input=refiners_dense_pe, other=facebook_dense_pe)
|
|
|
|
|
|
@no_grad()
|
|
def test_point_encoder(facebook_sam_h: FacebookSAM, sam_h: SegmentAnythingH, prompt: SAMPrompt) -> None:
|
|
facebook_prompt_encoder = facebook_sam_h.prompt_encoder
|
|
refiners_prompt_encoder = sam_h.point_encoder
|
|
|
|
facebook_sparse_pe, _ = facebook_prompt_encoder(
|
|
**prompt.facebook_prompt_encoder_kwargs(device=facebook_sam_h.device)
|
|
)
|
|
|
|
prompt_dict = prompt.__dict__
|
|
# Skip mask prompt, if any, since the point encoder only consumes points and boxes
|
|
# TODO: split `SAMPrompt` and introduce a dedicated one for dense prompts
|
|
prompt_dict.pop("low_res_mask", None)
|
|
|
|
assert prompt_dict is not None, "`test_point_encoder` cannot be called with just a `low_res_mask`"
|
|
|
|
coordinates, type_mask = refiners_prompt_encoder.points_to_tensor(**prompt_dict)
|
|
# Shift to center of pixel + normalize in [0, 1] (see `_embed_points` in segment-anything official repo)
|
|
coordinates[:, :, 0] = (coordinates[:, :, 0] + 0.5) / 1024.0
|
|
coordinates[:, :, 1] = (coordinates[:, :, 1] + 0.5) / 1024.0
|
|
refiners_prompt_encoder.set_type_mask(type_mask=type_mask)
|
|
refiners_sparse_pe = refiners_prompt_encoder(coordinates)
|
|
|
|
assert torch.equal(input=refiners_sparse_pe, other=facebook_sparse_pe)
|
|
|
|
|
|
@no_grad()
|
|
def test_two_way_transformer(facebook_sam_h: FacebookSAM) -> None:
|
|
dense_embedding = torch.randn(1, 64 * 64, 256, device=facebook_sam_h.device)
|
|
dense_positional_embedding = torch.randn(1, 64 * 64, 256, device=facebook_sam_h.device)
|
|
sparse_embedding = torch.randn(1, 3, 256, device=facebook_sam_h.device)
|
|
|
|
refiners_layer = TwoWayTransformerLayer(
|
|
embedding_dim=256, feed_forward_dim=2048, num_heads=8, device=facebook_sam_h.device
|
|
)
|
|
facebook_layer = facebook_sam_h.mask_decoder.transformer.layers[1] # type: ignore
|
|
assert isinstance(facebook_layer, nn.Module)
|
|
|
|
refiners_layer.set_context(
|
|
context="mask_decoder",
|
|
value={
|
|
"dense_embedding": dense_embedding,
|
|
"dense_positional_embedding": dense_positional_embedding,
|
|
"sparse_embedding": sparse_embedding,
|
|
},
|
|
)
|
|
facebook_inputs = {
|
|
"queries": sparse_embedding,
|
|
"keys": dense_embedding,
|
|
"query_pe": sparse_embedding,
|
|
"key_pe": dense_positional_embedding,
|
|
}
|
|
|
|
converter = ModelConverter(
|
|
source_model=facebook_layer,
|
|
target_model=refiners_layer,
|
|
skip_output_check=True, # done below, manually
|
|
)
|
|
|
|
assert converter.run(source_args=facebook_inputs, target_args=(sparse_embedding,))
|
|
|
|
refiners_layer.set_context(
|
|
context="mask_decoder",
|
|
value={
|
|
"dense_embedding": dense_embedding,
|
|
"dense_positional_embedding": dense_positional_embedding,
|
|
"sparse_embedding": sparse_embedding,
|
|
},
|
|
)
|
|
y_1 = facebook_layer(**facebook_inputs)[0]
|
|
y_2 = refiners_layer(sparse_embedding)[0]
|
|
|
|
assert torch.equal(input=y_1, other=y_2)
|
|
|
|
|
|
@no_grad()
|
|
def test_mask_decoder(facebook_sam_h: FacebookSAM, sam_h: SegmentAnythingH) -> None:
|
|
manual_seed(seed=0)
|
|
facebook_mask_decoder = facebook_sam_h.mask_decoder
|
|
refiners_mask_decoder = sam_h.mask_decoder
|
|
|
|
image_embedding = torch.randn(1, 256, 64, 64, device=facebook_sam_h.device)
|
|
dense_positional_embedding = torch.randn(1, 256, 64, 64, device=facebook_sam_h.device)
|
|
point_embedding = torch.randn(1, 3, 256, device=facebook_sam_h.device)
|
|
mask_embedding = torch.randn(1, 256, 64, 64, device=facebook_sam_h.device)
|
|
|
|
from segment_anything.modeling.common import LayerNorm2d # type: ignore
|
|
|
|
import refiners.fluxion.layers as fl
|
|
|
|
assert issubclass(LayerNorm2d, nn.Module)
|
|
custom_layers = {LayerNorm2d: fl.LayerNorm2d}
|
|
|
|
converter = ModelConverter(
|
|
source_model=facebook_mask_decoder,
|
|
target_model=refiners_mask_decoder,
|
|
custom_layer_mapping=custom_layers, # type: ignore
|
|
)
|
|
|
|
inputs = {
|
|
"image_embeddings": image_embedding,
|
|
"image_pe": dense_positional_embedding,
|
|
"sparse_prompt_embeddings": point_embedding,
|
|
"dense_prompt_embeddings": mask_embedding,
|
|
"multimask_output": True,
|
|
}
|
|
|
|
refiners_mask_decoder.set_image_embedding(image_embedding)
|
|
refiners_mask_decoder.set_point_embedding(point_embedding)
|
|
refiners_mask_decoder.set_mask_embedding(mask_embedding)
|
|
refiners_mask_decoder.set_dense_positional_embedding(dense_positional_embedding)
|
|
|
|
mapping = converter.map_state_dicts(source_args=inputs, target_args={})
|
|
assert mapping is not None
|
|
mapping["IOUMaskEncoder"] = "iou_token"
|
|
|
|
state_dict = converter._convert_state_dict( # type: ignore
|
|
source_state_dict=facebook_mask_decoder.state_dict(),
|
|
target_state_dict=refiners_mask_decoder.state_dict(),
|
|
state_dict_mapping=mapping,
|
|
)
|
|
state_dict["IOUMaskEncoder.weight"] = torch.cat(
|
|
[facebook_mask_decoder.iou_token.weight, facebook_mask_decoder.mask_tokens.weight], dim=0
|
|
) # type: ignore
|
|
refiners_mask_decoder.load_state_dict(state_dict=state_dict)
|
|
|
|
facebook_output = facebook_mask_decoder(**inputs)
|
|
|
|
refiners_mask_decoder.set_image_embedding(image_embedding)
|
|
refiners_mask_decoder.set_point_embedding(point_embedding)
|
|
refiners_mask_decoder.set_mask_embedding(mask_embedding)
|
|
refiners_mask_decoder.set_dense_positional_embedding(dense_positional_embedding)
|
|
mask_prediction, iou_prediction = refiners_mask_decoder()
|
|
|
|
facebook_masks = facebook_output[0]
|
|
facebook_prediction = facebook_output[1]
|
|
|
|
assert torch.equal(input=mask_prediction, other=facebook_masks)
|
|
assert torch.equal(input=iou_prediction, other=facebook_prediction)
|
|
|
|
|
|
def test_predictor(
|
|
facebook_sam_h_predictor: FacebookSAMPredictor, sam_h: SegmentAnythingH, truck: Image.Image, prompt: SAMPrompt
|
|
) -> None:
|
|
predictor = facebook_sam_h_predictor
|
|
predictor.set_image(np.array(truck))
|
|
facebook_masks, facebook_scores, _ = predictor.predict(**prompt.facebook_predict_kwargs()) # type: ignore
|
|
|
|
assert len(facebook_masks) == 3
|
|
|
|
masks, scores, _ = sam_h.predict(truck, **prompt.__dict__)
|
|
masks = masks.squeeze(0)
|
|
scores = scores.squeeze(0)
|
|
|
|
assert len(masks) == 3
|
|
|
|
for i in range(3):
|
|
mask_prediction = masks[i].cpu()
|
|
facebook_mask = torch.as_tensor(facebook_masks[i])
|
|
assert isclose(intersection_over_union(mask_prediction, facebook_mask), 1.0, rel_tol=5e-05)
|
|
assert isclose(scores[i].item(), facebook_scores[i].item(), rel_tol=1e-05)
|
|
|
|
|
|
def test_predictor_image_embedding(sam_h: SegmentAnythingH, truck: Image.Image, one_prompt: SAMPrompt) -> None:
|
|
masks_ref, scores_ref, _ = sam_h.predict(truck, **one_prompt.__dict__)
|
|
|
|
image_embedding = sam_h.compute_image_embedding(truck)
|
|
masks, scores, _ = sam_h.predict(image_embedding, **one_prompt.__dict__)
|
|
|
|
assert torch.equal(masks, masks_ref)
|
|
assert torch.equal(scores_ref, scores)
|
|
|
|
|
|
def test_predictor_dense_mask(
|
|
facebook_sam_h_predictor: FacebookSAMPredictor, sam_h: SegmentAnythingH, truck: Image.Image, one_prompt: SAMPrompt
|
|
) -> None:
|
|
"""
|
|
NOTE : Binarizing intermediate masks isn't necessary, as per SamPredictor.predict_torch docstring:
|
|
> mask_input (np.ndarray): A low resolution mask input to the model, typically
|
|
> coming from a previous prediction iteration. Has form Bx1xHxW, where
|
|
> for SAM, H=W=256. Masks returned by a previous iteration of the
|
|
> predict method do not need further transformation.
|
|
"""
|
|
predictor = facebook_sam_h_predictor
|
|
predictor.set_image(np.array(truck))
|
|
facebook_masks, facebook_scores, facebook_logits = predictor.predict(
|
|
**one_prompt.facebook_predict_kwargs(), # type: ignore
|
|
multimask_output=True,
|
|
)
|
|
|
|
assert len(facebook_masks) == 3
|
|
|
|
facebook_mask_input = facebook_logits[np.argmax(facebook_scores)] # shape: HxW
|
|
|
|
# Using the same mask coordinates inputs as the official notebook
|
|
facebook_prompt = SAMPrompt(
|
|
foreground_points=((500, 375),), background_points=((1125, 625),), low_res_mask=facebook_mask_input[None, ...]
|
|
)
|
|
facebook_dense_masks, _, _ = predictor.predict(**facebook_prompt.facebook_predict_kwargs(), multimask_output=True) # type: ignore
|
|
|
|
assert len(facebook_dense_masks) == 3
|
|
|
|
masks, scores, logits = sam_h.predict(truck, **one_prompt.__dict__)
|
|
masks = masks.squeeze(0)
|
|
scores = scores.squeeze(0)
|
|
|
|
assert len(masks) == 3
|
|
|
|
mask_input = logits[:, scores.max(dim=0).indices, ...] # shape: 1xHxW
|
|
|
|
assert np.allclose(
|
|
mask_input.cpu().numpy(), facebook_mask_input, atol=1e-1
|
|
) # Lower doesn't pass, but it's close enough for logits
|
|
|
|
refiners_prompt = SAMPrompt(
|
|
foreground_points=((500, 375),), background_points=((1125, 625),), low_res_mask=mask_input.unsqueeze(0)
|
|
)
|
|
dense_masks, _, _ = sam_h.predict(truck, **refiners_prompt.__dict__)
|
|
dense_masks = dense_masks.squeeze(0)
|
|
|
|
assert len(dense_masks) == 3
|
|
|
|
for i in range(3):
|
|
dense_mask_prediction = dense_masks[i].cpu()
|
|
facebook_dense_mask = torch.as_tensor(facebook_dense_masks[i])
|
|
assert dense_mask_prediction.shape == facebook_dense_mask.shape
|
|
assert isclose(intersection_over_union(dense_mask_prediction, facebook_dense_mask), 1.0, rel_tol=5e-05)
|
|
|
|
|
|
def test_mask_encoder(
|
|
facebook_sam_h_predictor: FacebookSAMPredictor, sam_h: SegmentAnythingH, truck: Image.Image, one_prompt: SAMPrompt
|
|
) -> None:
|
|
predictor = facebook_sam_h_predictor
|
|
predictor.set_image(np.array(truck))
|
|
_, facebook_scores, facebook_logits = predictor.predict(
|
|
**one_prompt.facebook_predict_kwargs(), # type: ignore
|
|
multimask_output=True,
|
|
)
|
|
facebook_mask_input = facebook_logits[np.argmax(facebook_scores)]
|
|
facebook_mask_input = (
|
|
torch.from_numpy(facebook_mask_input) # type: ignore
|
|
.to(device=predictor.model.device)
|
|
.unsqueeze(0)
|
|
.unsqueeze(0) # shape: 1x1xHxW
|
|
)
|
|
|
|
_, fb_dense_embeddings = predictor.model.prompt_encoder(
|
|
points=None,
|
|
boxes=None,
|
|
masks=facebook_mask_input,
|
|
)
|
|
|
|
_, scores, logits = sam_h.predict(truck, **one_prompt.__dict__)
|
|
scores = scores.squeeze(0)
|
|
mask_input = logits[:, scores.max(dim=0).indices, ...].unsqueeze(0) # shape: 1x1xHxW
|
|
dense_embeddings = sam_h.mask_encoder(mask_input)
|
|
|
|
assert facebook_mask_input.shape == mask_input.shape
|
|
assert torch.allclose(dense_embeddings, fb_dense_embeddings, atol=1e-4, rtol=1e-4)
|