diff --git a/.gitignore b/.gitignore index fcda494..e67edc1 100644 --- a/.gitignore +++ b/.gitignore @@ -124,12 +124,15 @@ dmypy.json # Model weights and checkpoints *.pth *.pt +*.pt2 *.bin *.ckpt *.safetensors weights/ checkpoints/ sam3_logs/ +artifacts/ +tests/export/export_logs/ # Data files *.h5 diff --git a/assets/images/cat_dog.jpg b/assets/images/cat_dog.jpg new file mode 100644 index 0000000..0368769 Binary files /dev/null and b/assets/images/cat_dog.jpg differ diff --git a/pyproject.toml b/pyproject.toml index 9df1b67..4bb315a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ name = "sam3" dynamic = ["version"] description = "SAM3 (Segment Anything Model 3) implementation" readme = "README.md" -requires-python = ">=3.8" +requires-python = ">=3.9,<3.13" license = {file = "LICENSE"} authors = [ {name = "Meta AI Research"} @@ -33,6 +33,8 @@ dependencies = [ "iopath>=0.1.10", "typing_extensions", "huggingface_hub", + "einops", + "psutil", ] [project.optional-dependencies] @@ -92,6 +94,23 @@ sam3 = ["assets/*.txt.gz"] [tool.setuptools.dynamic] version = {attr = "sam3.__version__"} +[dependency-groups] +dev = [ + "pytest", + "pytest-cov", + "black==24.2.0", + "ufmt==2.8.0", + "ruff-api==0.1.0", + "usort==1.0.2", + "gitpython==3.1.31", + "yt-dlp", + "pandas", + "opencv-python", + "pycocotools", + "numba", + "python-rapidjson", +] + [tool.black] line-length = 88 target-version = ['py38', 'py39', 'py310', 'py311', 'py312'] @@ -133,3 +152,4 @@ testpaths = ["tests"] python_files = "test_*.py" python_classes = "Test*" python_functions = "test_*" +markers = ["slow: long-running export and artifact tests"] diff --git a/sam3/model/decoder.py b/sam3/model/decoder.py index 7a204be..7555397 100644 --- a/sam3/model/decoder.py +++ b/sam3/model/decoder.py @@ -10,6 +10,7 @@ import numpy as np import torch +import torch.nn.functional as F from sam3.sam.transformer import RoPEAttention from torch import nn, Tensor from torchvision.ops.roi_align import RoIAlign @@ -151,24 +152,40 @@ def forward( tgt = tgt + self.catext_dropout(tgt2) tgt = self.catext_norm(tgt) - if presence_token is not None: - presence_token_mask = torch.zeros_like(cross_attn_mask[:, :1, :]) - cross_attn_mask = torch.cat( - [presence_token_mask, cross_attn_mask], dim=1 - ) # (bs*nheads, 1+nq, hw) + if presence_token is not None and cross_attn_mask is not None: + if cross_attn_mask.dim() == 4: + presence_token_mask = torch.zeros_like(cross_attn_mask[:, :, :1, :]) + cross_attn_mask = torch.cat( + [presence_token_mask, cross_attn_mask], dim=2 + ) # (bs, nheads, 1+nq, hw) + else: + presence_token_mask = torch.zeros_like(cross_attn_mask[:, :1, :]) + cross_attn_mask = torch.cat( + [presence_token_mask, cross_attn_mask], dim=1 + ) # (bs*nheads, 1+nq, hw) # Cross attention to image - tgt2 = self.cross_attn( - query=self.with_pos_embed(tgt, tgt_query_pos), - key=self.with_pos_embed(memory, memory_pos), - value=memory, - attn_mask=cross_attn_mask, - key_padding_mask=( - memory_key_padding_mask.transpose(0, 1) - if memory_key_padding_mask is not None - else None - ), - )[0] + key_padding_mask = ( + memory_key_padding_mask.transpose(0, 1) + if memory_key_padding_mask is not None + else None + ) + if cross_attn_mask is not None and cross_attn_mask.dim() == 4: + tgt2 = self._cross_attn_with_rpb( + query=self.with_pos_embed(tgt, tgt_query_pos), + key=self.with_pos_embed(memory, memory_pos), + value=memory, + attn_bias=cross_attn_mask, + key_padding_mask=key_padding_mask, + ) + else: + tgt2 = self.cross_attn( + query=self.with_pos_embed(tgt, tgt_query_pos), + key=self.with_pos_embed(memory, memory_pos), + value=memory, + attn_mask=cross_attn_mask, + key_padding_mask=key_padding_mask, + )[0] tgt = tgt + self.dropout1(tgt2) tgt = self.norm1(tgt) @@ -183,6 +200,44 @@ def forward( return tgt, presence_token_out + def _cross_attn_with_rpb( + self, + query: Tensor, + key: Tensor, + value: Tensor, + attn_bias: Tensor, + key_padding_mask: Optional[Tensor], + ) -> Tensor: + mha = self.cross_attn + assert isinstance(mha, nn.MultiheadAttention) + q, k, v = F._in_projection_packed( + query, key, value, mha.in_proj_weight, mha.in_proj_bias + ) + tgt_len, bsz, _ = q.shape + num_heads = mha.num_heads + head_dim = mha.head_dim + q = q.contiguous().view(tgt_len, bsz, num_heads, head_dim).permute(1, 2, 0, 3) + k = k.contiguous().view(-1, bsz, num_heads, head_dim).permute(1, 2, 0, 3) + v = v.contiguous().view(-1, bsz, num_heads, head_dim).permute(1, 2, 0, 3) + src_len = k.shape[2] + bias = attn_bias + if bias.dim() == 3: + bias = bias.view(bsz, num_heads, tgt_len, src_len) + if key_padding_mask is not None: + pad = key_padding_mask[:, None, None, :].to(dtype=q.dtype) + pad = pad.masked_fill(pad > 0, float("-inf")) + bias = pad if bias is None else bias + pad + attn_output = F.scaled_dot_product_attention( + q, + k, + v, + attn_mask=bias, + dropout_p=mha.dropout if self.training else 0.0, + is_causal=False, + ) + attn_output = attn_output.permute(2, 0, 1, 3).reshape(tgt_len, bsz, -1) + return F.linear(attn_output, mha.out_proj.weight, mha.out_proj.bias) + class TransformerDecoder(nn.Module): def __init__( @@ -333,10 +388,7 @@ def _get_rpb_matrix(self, reference_boxes, feat_size): self.compilable_cord_cache = self._get_coords(H, W, reference_boxes.device) self.compilable_stored_size = (H, W) - if torch.compiler.is_dynamo_compiling() or self.compilable_stored_size == ( - H, - W, - ): + if torch.compiler.is_dynamo_compiling(): # good, hitting the cache, will be compilable coords_h, coords_w = self.compilable_cord_cache else: @@ -348,8 +400,6 @@ def _get_rpb_matrix(self, reference_boxes, feat_size): ) coords_h, coords_w = self.coord_cache[feat_size] - assert coords_h.shape == (H,) - assert coords_w.shape == (W,) deltas_y = coords_h.view(1, -1, 1) - boxes_xyxy.reshape(-1, 1, 4)[:, :, 1:4:2] deltas_y = deltas_y.view(bs, num_queries, -1, 2) @@ -388,20 +438,13 @@ def _get_rpb_matrix(self, reference_boxes, feat_size): act_ckpt_enable=self.training and self.use_act_checkpoint, ) # bs, num_queries, H, n_heads - if not torch.compiler.is_dynamo_compiling(): - assert deltas_x.shape[:3] == (bs, num_queries, W) - assert deltas_y.shape[:3] == (bs, num_queries, H) B = deltas_y.unsqueeze(3) + deltas_x.unsqueeze( 2 ) # bs, num_queries, H, W, n_heads - if not torch.compiler.is_dynamo_compiling(): - assert B.shape[:4] == (bs, num_queries, H, W) B = B.flatten(2, 3) # bs, num_queries, H*W, n_heads B = B.permute(0, 3, 1, 2) # bs, n_heads, num_queries, H*W B = B.contiguous() # memeff attn likes ordered strides - if not torch.compiler.is_dynamo_compiling(): - assert B.shape[2:] == (num_queries, H * W) return B def forward( @@ -510,6 +553,7 @@ def forward( # conditional query query_pos = self.ref_point_head(query_sine_embed) # nq, bs, d_model + memory_mask = None if self.boxRPB != "none" and reference_boxes is not None: assert spatial_shapes.shape[0] == 1, ( "only single scale support implemented" @@ -518,7 +562,6 @@ def forward( reference_boxes, (spatial_shapes[0, 0], spatial_shapes[0, 1]), ) - memory_mask = memory_mask.flatten(0, 1) # (bs*n_heads, nq, H*W) if self.training: assert self.use_act_checkpoint, ( "Activation checkpointing not enabled in the decoder" diff --git a/sam3/model/encoder.py b/sam3/model/encoder.py index 3fc9406..49b0219 100644 --- a/sam3/model/encoder.py +++ b/sam3/model/encoder.py @@ -538,7 +538,7 @@ def forward( else None ) else: - assert all(x.dim == 4 for x in src), ( + assert all(x.dim() == 4 for x in src), ( "expected list of (bs, c, h, w) tensors" ) diff --git a/sam3/model/geometry_encoders.py b/sam3/model/geometry_encoders.py index d60ee54..815d0d3 100644 --- a/sam3/model/geometry_encoders.py +++ b/sam3/model/geometry_encoders.py @@ -645,11 +645,28 @@ def _encode_boxes(self, boxes, boxes_mask, boxes_labels, img_feats): # We need to denormalize, and convert to [x, y, x, y] boxes_xyxy = box_cxcywh_to_xyxy(boxes) scale = torch.tensor([W, H, W, H], dtype=boxes_xyxy.dtype) - scale = scale.pin_memory().to(device=boxes_xyxy.device, non_blocking=True) + if ( + torch.is_tensor(scale) + and scale.device.type == "cpu" + and boxes_xyxy.device.type != "cpu" + and not torch._dynamo.is_compiling() + ): + scale = scale.pin_memory() + scale = scale.to( + device=boxes_xyxy.device, + non_blocking=boxes_xyxy.device.type != "cpu" and not torch._dynamo.is_compiling(), + ) scale = scale.view(1, 1, 4) boxes_xyxy = boxes_xyxy * scale + boxes_xyxy = boxes_xyxy.transpose(0, 1) + batch_idx = torch.arange( + bs, device=boxes_xyxy.device, dtype=boxes_xyxy.dtype + ) + batch_idx = batch_idx.view(bs, 1, 1).expand(bs, n_boxes, 1) + boxes_for_roi = torch.cat([batch_idx, boxes_xyxy], dim=-1) + boxes_for_roi = boxes_for_roi.reshape(-1, 5) sampled = torchvision.ops.roi_align( - img_feats, boxes_xyxy.float().transpose(0, 1).unbind(0), self.roi_size + img_feats, boxes_for_roi.float(), self.roi_size ) assert list(sampled.shape) == [ bs * n_boxes, diff --git a/sam3/model/position_encoding.py b/sam3/model/position_encoding.py index a6a1266..e112447 100644 --- a/sam3/model/position_encoding.py +++ b/sam3/model/position_encoding.py @@ -53,7 +53,7 @@ def __init__( def _encode_xy(self, x, y): # The positions are expected to be normalized - assert len(x) == len(y) and x.ndim == y.ndim == 1 + # torch._check(len(x) == len(y) and x.ndim == y.ndim == 1) x_embed = x * self.scale y_embed = y * self.scale @@ -62,12 +62,8 @@ def _encode_xy(self, x, y): pos_x = x_embed[:, None] / dim_t pos_y = y_embed[:, None] / dim_t - pos_x = torch.stack( - (pos_x[:, 0::2].sin(), pos_x[:, 1::2].cos()), dim=2 - ).flatten(1) - pos_y = torch.stack( - (pos_y[:, 0::2].sin(), pos_y[:, 1::2].cos()), dim=2 - ).flatten(1) + pos_x = torch.stack((pos_x[:, 0::2].sin(), pos_x[:, 1::2].cos()), dim=2).flatten(1) + pos_y = torch.stack((pos_y[:, 0::2].sin(), pos_y[:, 1::2].cos()), dim=2).flatten(1) return pos_x, pos_y @torch.no_grad() @@ -89,9 +85,9 @@ def encode_points(self, x, y, labels): @torch.no_grad() def forward(self, x): - cache_key = None cache_key = (x.shape[-2], x.shape[-1]) - if cache_key in self.cache: + use_cache = all(isinstance(dim, int) for dim in cache_key) + if use_cache and cache_key in self.cache: return self.cache[cache_key][None].repeat(x.shape[0], 1, 1, 1) y_embed = ( torch.arange(1, x.shape[-2] + 1, dtype=torch.float32, device=x.device) @@ -121,6 +117,6 @@ def forward(self, x): (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4 ).flatten(3) pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) - if cache_key is not None: + if use_cache: self.cache[cache_key] = pos[0] return pos diff --git a/sam3/model_builder.py b/sam3/model_builder.py index 103b324..4548234 100644 --- a/sam3/model_builder.py +++ b/sam3/model_builder.py @@ -115,7 +115,9 @@ def _create_vl_backbone(vit_neck, text_encoder): return SAM3VLBackbone(visual=vit_neck, text=text_encoder, scalp=1) -def _create_transformer_encoder() -> TransformerEncoderFusion: +def _create_transformer_encoder( + num_feature_levels: int = 1, +) -> TransformerEncoderFusion: """Create transformer encoder with its layer.""" encoder_layer = TransformerEncoderLayer( activation="relu", @@ -144,7 +146,7 @@ def _create_transformer_encoder() -> TransformerEncoderFusion: layer=encoder_layer, num_layers=6, d_model=256, - num_feature_levels=1, + num_feature_levels=num_feature_levels, frozen=False, use_act_checkpoint=True, add_pooled_text_to_img_feat=False, @@ -296,6 +298,7 @@ def _create_sam3_model( dot_prod_scoring, inst_interactive_predictor, eval_mode, + num_feature_levels: int = 1, ): """Create the SAM3 image model.""" common_params = { @@ -303,7 +306,7 @@ def _create_sam3_model( "transformer": transformer, "input_geometry_encoder": input_geometry_encoder, "segmentation_head": segmentation_head, - "num_feature_levels": 1, + "num_feature_levels": num_feature_levels, "o2m_mask_predict": True, "dot_prod_scoring": dot_prod_scoring, "use_instance_query": False, @@ -515,9 +518,14 @@ def _create_vision_backbone( return vit_neck -def _create_sam3_transformer(has_presence_token: bool = True) -> TransformerWrapper: +def _create_sam3_transformer( + has_presence_token: bool = True, + num_feature_levels: int = 1, +) -> TransformerWrapper: """Create SAM3 transformer encoder and decoder.""" - encoder: TransformerEncoderFusion = _create_transformer_encoder() + encoder: TransformerEncoderFusion = _create_transformer_encoder( + num_feature_levels=num_feature_levels + ) decoder: TransformerDecoder = _create_transformer_decoder() return TransformerWrapper(encoder=encoder, decoder=decoder, d_model=256) @@ -566,6 +574,7 @@ def build_sam3_image_model( enable_segmentation=True, enable_inst_interactivity=False, compile=False, + num_feature_levels: int = 1, ): """ Build SAM3 image model @@ -600,7 +609,7 @@ def build_sam3_image_model( backbone = _create_vl_backbone(vision_encoder, text_encoder) # Create transformer components - transformer = _create_sam3_transformer() + transformer = _create_sam3_transformer(num_feature_levels=num_feature_levels) # Create dot product scoring dot_prod_scoring = _create_dot_product_scoring() @@ -628,6 +637,7 @@ def build_sam3_image_model( dot_prod_scoring, inst_predictor, eval_mode, + num_feature_levels=num_feature_levels, ) if load_from_HF and checkpoint_path is None: checkpoint_path = download_ckpt_from_hf() @@ -683,7 +693,9 @@ def build_sam3_video_model( visual_neck = _create_vision_backbone() text_encoder = _create_text_encoder(bpe_path) backbone = SAM3VLBackbone(scalp=1, visual=visual_neck, text=text_encoder) - transformer = _create_sam3_transformer(has_presence_token=has_presence_token) + transformer = _create_sam3_transformer( + has_presence_token=has_presence_token, num_feature_levels=1 + ) segmentation_head: UniversalSegmentationHead = _create_segmentation_head() input_geometry_encoder = _create_geometry_encoder() diff --git a/sam3/train/data/collator.py b/sam3/train/data/collator.py index 4a0f2e8..38d031d 100644 --- a/sam3/train/data/collator.py +++ b/sam3/train/data/collator.py @@ -194,7 +194,7 @@ def collate_fn_api( offset_img_id = 0 offset_query_id = [0 for _ in range(num_stages)] - for i, data in enumerate(batch): + for data in batch: img_batch.extend([img.data for img in data.images]) if data.raw_images is not None: @@ -209,7 +209,7 @@ def collate_fn_api( datapoint_query_id_2_stage_query_id.append(offset_query_id[stage_id]) offset_query_id[stage_id] += 1 - for j, q in enumerate(data.find_queries): + for q in data.find_queries: stage_id = q.query_processing_order stages[stage_id].img_ids.append(q.image_id + offset_img_id) if q.query_text not in text_batch: diff --git a/sam3/train/data/sam3_image_dataset.py b/sam3/train/data/sam3_image_dataset.py index c5a1c83..c956d7b 100644 --- a/sam3/train/data/sam3_image_dataset.py +++ b/sam3/train/data/sam3_image_dataset.py @@ -328,7 +328,7 @@ def load_queries(self, pil_images, annotations, queries, img_metadata): f"Number of queries in stage {stage} is {num_queries}, expected {num_queries_per_stage}" ) - for query_id, query in enumerate(queries): + for query in queries: h, w = id2imsize[query["image_id"]] if ( "input_box" in query diff --git a/scripts/benchmark_sam3_artifacts.py b/scripts/benchmark_sam3_artifacts.py new file mode 100644 index 0000000..f0ea143 --- /dev/null +++ b/scripts/benchmark_sam3_artifacts.py @@ -0,0 +1,351 @@ +import argparse +import sys +import time +from pathlib import Path + +import numpy as np +import torch +from PIL import Image + +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO_ROOT)) + +from sam3.model_builder import build_sam3_image_model +from sam3.model.data_misc import FindStage +from sam3.model.geometry_encoders import Prompt + + +def _load_image(path: Path, device: torch.device) -> torch.Tensor: + image = Image.open(path).convert("RGB") + np_image = np.array(image, dtype=np.float32) / 255.0 + tensor = torch.from_numpy(np_image).permute(2, 0, 1).unsqueeze(0) + return tensor.to(device) + + +def _prepare_image(image: torch.Tensor, size: int) -> torch.Tensor: + image = image.clamp(0, 1) + image = torch.nn.functional.interpolate( + image, size=(size, size), mode="bilinear", align_corners=False + ) + mean = torch.tensor([0.5, 0.5, 0.5], device=image.device).view(1, 3, 1, 1) + std = torch.tensor([0.5, 0.5, 0.5], device=image.device).view(1, 3, 1, 1) + return (image - mean) / std + + +def _make_inputs(model, image: torch.Tensor, prompts): + device = image.device + + tokenizer = model.backbone.language_backbone.tokenizer + token_ids = tokenizer(prompts, context_length=32).to(device) + + return ( + image, + token_ids, + ) + + +def _run_full_model(model, inputs): + images, token_ids = inputs + num_images = images.shape[0] + num_prompts = token_ids.shape[0] + device = images.device + bs = num_images * num_prompts + + img_ids = torch.arange(num_images, device=device, dtype=torch.long) + img_ids = img_ids.repeat_interleave(num_prompts) + text_ids = torch.arange(num_prompts, device=device, dtype=torch.long) + text_ids = text_ids.repeat(num_images) + + box_embeddings = torch.zeros(1, bs, 4, device=device) + box_mask = torch.zeros(bs, 1, device=device, dtype=torch.bool) + box_labels = torch.zeros(1, bs, device=device, dtype=torch.long) + backbone_out = model.backbone.forward_image(images) + text_encoder = model.backbone.language_backbone + _, text_tokens = text_encoder.encoder(token_ids) + text_tokens = text_tokens.transpose(0, 1) + text_memory = text_encoder.resizer(text_tokens) + text_attention_mask = token_ids.ne(0) + text_attention_mask = text_attention_mask.ne(1) + backbone_out["language_features"] = text_memory + backbone_out["language_mask"] = text_attention_mask + + find_input = FindStage( + img_ids=img_ids, + text_ids=text_ids, + input_boxes=box_embeddings, + input_boxes_mask=box_mask, + input_boxes_label=box_labels, + input_points=torch.zeros(0, int(token_ids.shape[0]), 2, device=images.device), + input_points_mask=torch.zeros( + int(token_ids.shape[0]), 0, device=images.device, dtype=torch.bool + ), + ) + geometric_prompt = Prompt( + box_embeddings=box_embeddings, + box_mask=box_mask, + box_labels=box_labels, + ) + out = model.forward_grounding( + backbone_out=backbone_out, + find_input=find_input, + find_target=None, + geometric_prompt=geometric_prompt, + ) + return out["pred_masks"], out["pred_boxes"], out["pred_logits"] + + +def _make_decoder_only_inputs_from_model( + model, + backbone_fpn, + vision_pos_enc, + text_memory, + text_attention_mask, + inputs, +): + images, token_ids = inputs + num_images = images.shape[0] + num_prompts = token_ids.shape[0] + device = images.device + bs = num_images * num_prompts + + img_ids = torch.arange(num_images, device=device, dtype=torch.long) + img_ids = img_ids.repeat_interleave(num_prompts) + text_ids = torch.arange(num_prompts, device=device, dtype=torch.long) + text_ids = text_ids.repeat(num_images) + + box_embeddings = torch.zeros(1, bs, 4, device=device) + box_mask = torch.zeros(bs, 1, device=device, dtype=torch.bool) + box_labels = torch.zeros(1, bs, device=device, dtype=torch.long) + backbone_out = { + "backbone_fpn": backbone_fpn, + "vision_pos_enc": vision_pos_enc, + "language_features": text_memory, + "language_mask": text_attention_mask, + } + find_input = FindStage( + img_ids=img_ids, + text_ids=text_ids, + input_boxes=box_embeddings, + input_boxes_mask=box_mask, + input_boxes_label=box_labels, + input_points=torch.zeros(0, int(token_ids.shape[0]), 2, device=images.device), + input_points_mask=torch.zeros( + int(token_ids.shape[0]), 0, device=images.device, dtype=torch.bool + ), + ) + geometric_prompt = Prompt( + box_embeddings=box_embeddings, + box_mask=box_mask, + box_labels=box_labels, + ) + prompt, prompt_mask, backbone_out = model._encode_prompt( + backbone_out, find_input, geometric_prompt + ) + backbone_out, encoder_out, _ = model._run_encoder(backbone_out, find_input, prompt, prompt_mask) + return ( + backbone_out["backbone_fpn"], + img_ids, + encoder_out["encoder_hidden_states"], + encoder_out["pos_embed"], + prompt, + prompt_mask, + encoder_out["level_start_index"], + encoder_out["spatial_shapes"], + encoder_out["valid_ratios"], + ) + + +def _load_export(path: Path): + exported = torch.export.load(str(path)) + return exported.module() + + +def _timeit(fn, iters: int, device: torch.device): + if device.type == "cuda": + torch.cuda.synchronize() + start = time.perf_counter() + for _ in range(iters): + fn() + if device.type == "cuda": + torch.cuda.synchronize() + end = time.perf_counter() + return (end - start) / iters + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument( + "--image", + type=Path, + default=Path("assets/images/cat_dog.jpg"), + help="Path to input image", + ) + parser.add_argument( + "--prompts", + type=str, + default="cat,dog", + help="Comma-separated text prompts", + ) + parser.add_argument( + "--device", + type=str, + default="cuda" if torch.cuda.is_available() else "cpu", + ) + parser.add_argument( + "--artifact-dir", + type=Path, + default=Path("artifacts/export"), + help="Directory with exported artifacts", + ) + parser.add_argument( + "--num-feature-levels", + type=int, + default=1, + help="Number of feature levels to use", + ) + parser.add_argument("--warmup", type=int, default=3) + parser.add_argument("--iters", type=int, default=10) + args = parser.parse_args() + + prompts = [p.strip() for p in args.prompts.split(",") if p.strip()] + if not prompts: + raise ValueError("Provide at least one prompt") + + model = build_sam3_image_model( + device=args.device, + eval_mode=True, + enable_segmentation=True, + num_feature_levels=args.num_feature_levels, + ) + model.eval() + + device = torch.device(args.device) + image = _load_image(args.image, device) + image = _prepare_image(image, size=1008) + inputs = _make_inputs(model, image, prompts) + + print("Device (eager):", next(model.parameters()).device) + + def eager_fn(): + _run_full_model(model, inputs) + + with torch.inference_mode(): + for _ in range(args.warmup): + eager_fn() + eager_ms = _timeit(eager_fn, args.iters, device) * 1000 + + if device.type == "cuda": + torch.cuda.empty_cache() + + image_module = _load_export(args.artifact_dir / "image_encoder.pt2") + text_module = _load_export(args.artifact_dir / "text_encoder.pt2") + encoder_module = _load_export(args.artifact_dir / "encoder_fusion.pt2") + pipeline_module = _load_export(args.artifact_dir / "full_sam3_pipeline.pt2") + decoder_module = _load_export(args.artifact_dir / "decoder_only.pt2") + print("Device (export):", inputs[0].device) + + def image_fn(): + return image_module(inputs[0]) + + def text_fn(): + return text_module(inputs[1]) + + def encoder_from_outputs(image_out, text_out): + vision_pos_enc = image_out[1] + backbone_fpn = image_out[2] + text_attention_mask, text_memory = text_out + img_feats = backbone_fpn[-1] + img_pos = vision_pos_enc[-1] + prompt_batch = text_attention_mask.shape[0] + if img_feats.shape[0] != prompt_batch: + if img_feats.shape[0] != 1: + raise ValueError("Image batch does not match prompt batch") + img_feats = img_feats.repeat(prompt_batch, 1, 1, 1) + img_pos = img_pos.repeat(prompt_batch, 1, 1, 1) + img_mask = torch.zeros( + img_feats.shape[0], + img_feats.shape[2], + img_feats.shape[3], + device=img_feats.device, + dtype=torch.bool, + ) + encoder_module(img_feats, img_pos, img_mask, text_memory, text_attention_mask) + + with torch.inference_mode(): + cached_image_out = image_fn() + cached_text_out = text_fn() + + def encoder_fn(): + encoder_from_outputs(cached_image_out, cached_text_out) + + pipeline_inputs = inputs + + def pipeline_fn(): + pipeline_module(*pipeline_inputs) + + with torch.inference_mode(): + cached_image_out = image_fn() + cached_text_out = text_fn() + decoder_only_inputs = _make_decoder_only_inputs_from_model( + model, + cached_image_out[2], + cached_image_out[1], + cached_text_out[1], + cached_text_out[0], + inputs, + ) + ( + decoder_backbone_fpn, + decoder_img_ids, + decoder_memory, + decoder_pos_embed, + decoder_prompt, + decoder_prompt_mask, + decoder_level_start_index, + decoder_spatial_shapes, + decoder_valid_ratios, + ) = decoder_only_inputs + if decoder_img_ids.shape[0] < 2: + repeat = 2 // decoder_img_ids.shape[0] + decoder_img_ids = decoder_img_ids.repeat(repeat) + decoder_memory = decoder_memory.repeat(1, repeat, 1) + decoder_pos_embed = decoder_pos_embed.repeat(1, repeat, 1) + decoder_prompt = decoder_prompt.repeat(1, repeat, 1) + decoder_prompt_mask = decoder_prompt_mask.repeat(repeat, 1) + decoder_valid_ratios = decoder_valid_ratios.repeat(repeat, 1, 1) + decoder_backbone_fpn = [feat.repeat(repeat, 1, 1, 1) for feat in decoder_backbone_fpn] + decoder_only_inputs = ( + decoder_backbone_fpn, + decoder_img_ids, + decoder_memory, + decoder_pos_embed, + decoder_prompt, + decoder_prompt_mask, + decoder_level_start_index, + decoder_spatial_shapes, + decoder_valid_ratios, + ) + + def decoder_only_fn(): + decoder_module(*decoder_only_inputs) + + with torch.inference_mode(): + for _ in range(args.warmup): + pipeline_fn() + image_ms = _timeit(image_fn, args.iters, device) * 1000 + text_ms = _timeit(text_fn, args.iters, device) * 1000 + encoder_ms = _timeit(encoder_fn, args.iters, device) * 1000 + pipeline_ms = _timeit(pipeline_fn, args.iters, device) * 1000 + decoder_only_ms = _timeit(decoder_only_fn, args.iters, device) * 1000 + + print("Eager total (ms):", round(eager_ms, 2)) + print("Export full pipeline total (ms):", round(pipeline_ms, 2)) + print("Export image encoder (ms):", round(image_ms, 2)) + print("Export text encoder (ms):", round(text_ms, 2)) + print("Export encoder fusion (ms):", round(encoder_ms, 2)) + print("Export decoder only (ms):", round(decoder_only_ms, 2)) + del model + + +if __name__ == "__main__": + main() diff --git a/scripts/benchmark_sam3_export_times.py b/scripts/benchmark_sam3_export_times.py new file mode 100644 index 0000000..b45b6a8 --- /dev/null +++ b/scripts/benchmark_sam3_export_times.py @@ -0,0 +1,170 @@ +import argparse +import sys +import time +from pathlib import Path + +import numpy as np +import torch +from PIL import Image + +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO_ROOT)) + +from sam3.model_builder import build_sam3_image_model +from tests.export.test_decoder_export import ( + _export_decoder_only, + _export_full_sam3_pipeline, + _make_decoder_only_inputs, + _make_inputs, +) +from tests.export.test_encoder_export import EncoderFusionWrapper +from tests.export.test_image_encoder_export import _export_image_encoder +from tests.export.test_text_encoder_export import _export_text_encoder + + +def _load_image(path: Path, device: torch.device) -> torch.Tensor: + image = Image.open(path).convert("RGB") + np_image = np.array(image, dtype=np.float32) / 255.0 + return torch.from_numpy(np_image).permute(2, 0, 1).unsqueeze(0).to(device) + + +def _prepare_image(image: torch.Tensor, size: int) -> torch.Tensor: + image = image.clamp(0, 1) + image = torch.nn.functional.interpolate( + image, size=(size, size), mode="bilinear", align_corners=False + ) + mean = torch.tensor([0.5, 0.5, 0.5], device=image.device).view(1, 3, 1, 1) + std = torch.tensor([0.5, 0.5, 0.5], device=image.device).view(1, 3, 1, 1) + return (image - mean) / std + + +def _time(label: str, fn) -> float: + start = time.perf_counter() + fn() + elapsed = time.perf_counter() - start + print(f"{label}: {elapsed:.3f}s") + return elapsed + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument( + "--image", + type=Path, + default=Path("assets/images/cat_dog.jpg"), + help="Path to input image", + ) + parser.add_argument( + "--device", + type=str, + default="cuda" if torch.cuda.is_available() else "cpu", + ) + parser.add_argument( + "--num-feature-levels", + type=int, + default=1, + help="Number of feature levels to use", + ) + args = parser.parse_args() + + device = torch.device(args.device) + model = build_sam3_image_model( + device=args.device, + eval_mode=True, + enable_segmentation=True, + num_feature_levels=args.num_feature_levels, + ) + model.eval() + + image = _prepare_image(_load_image(args.image, device), size=1008) + inputs = _make_inputs(1, 1008, 1008, str(device)) + + decoder_inputs = None + decoder_inputs_error = None + with torch.no_grad(): + backbone_out = model.backbone.forward_image(image) + text_encoder = model.backbone.language_backbone + _, text_tokens = text_encoder.encoder(inputs[1]) + text_tokens = text_tokens.transpose(0, 1) + text_memory = text_encoder.resizer(text_tokens) + text_attention_mask = inputs[1].ne(0) + text_attention_mask = text_attention_mask.ne(1) + img_feats = backbone_out["backbone_fpn"][-1] + img_pos = backbone_out["vision_pos_enc"][-1] + if img_feats.shape[0] < 2: + repeat = 2 // img_feats.shape[0] + img_feats = img_feats.repeat(repeat, 1, 1, 1) + img_pos = img_pos.repeat(repeat, 1, 1, 1) + text_memory = text_memory.repeat(1, repeat, 1) + text_attention_mask = text_attention_mask.repeat(repeat, 1) + img_mask = torch.zeros( + img_feats.shape[0], + img_feats.shape[2], + img_feats.shape[3], + device=img_feats.device, + dtype=torch.bool, + ) + try: + decoder_inputs = _make_decoder_only_inputs(model, inputs) + except Exception as exc: + decoder_inputs_error = exc + + def export_image_encoder(): + _export_image_encoder(model, image) + + def export_text_encoder(): + _export_text_encoder(model, inputs[1]) + + def export_encoder_fusion(): + encoder_wrapper = ( + EncoderFusionWrapper(model.transformer.encoder).to(img_feats.device).eval() + ) + if args.num_feature_levels != 1: + raise RuntimeError("encoder_fusion export currently expects num_feature_levels=1") + torch.export.export( + encoder_wrapper, + (img_feats, img_pos, img_mask, text_memory, text_attention_mask), + dynamic_shapes={ + "img_feats": {0: torch.export.Dim.AUTO}, + "img_pos": {0: torch.export.Dim.AUTO}, + "img_mask": {0: torch.export.Dim.AUTO}, + "prompt": { + 0: torch.export.Dim("seq", min=1, max=64), + 1: torch.export.Dim.AUTO, + }, + "prompt_mask": { + 0: torch.export.Dim.AUTO, + 1: torch.export.Dim("seq", min=1, max=64), + }, + }, + strict=False, + prefer_deferred_runtime_asserts_over_guards=True, + ) + + def export_full_pipeline(): + _export_full_sam3_pipeline(model, inputs) + + def export_decoder_only(): + if decoder_inputs_error is not None: + raise decoder_inputs_error + _export_decoder_only(model, decoder_inputs) + + with torch.no_grad(): + _time("Export image encoder", export_image_encoder) + _time("Export text encoder", export_text_encoder) + if args.num_feature_levels == 1: + _time("Export encoder fusion", export_encoder_fusion) + else: + print("Export encoder fusion: skipped (num_feature_levels != 1)") + try: + _time("Export full pipeline", export_full_pipeline) + except Exception as exc: + print(f"Export full pipeline: failed ({type(exc).__name__}: {exc})") + try: + _time("Export decoder only", export_decoder_only) + except Exception as exc: + print(f"Export decoder only: failed ({type(exc).__name__}: {exc})") + + +if __name__ == "__main__": + main() diff --git a/scripts/benchmark_sam3_load_time.py b/scripts/benchmark_sam3_load_time.py new file mode 100644 index 0000000..47f5586 --- /dev/null +++ b/scripts/benchmark_sam3_load_time.py @@ -0,0 +1,46 @@ +import argparse +import sys +import time +from pathlib import Path + +import torch +import torchvision.ops # noqa: F401 + +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO_ROOT)) + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument( + "--artifact", + type=Path, + default=Path("artifacts/export/full_sam3_pipeline.pt2"), + help="Path to exported full pipeline artifact", + ) + parser.add_argument( + "--device", + type=str, + default="cuda" if torch.cuda.is_available() else "cpu", + ) + parser.add_argument( + "--num-feature-levels", + type=int, + default=1, + help="Unused; kept for parity with other benchmarks", + ) + args = parser.parse_args() + + if args.device.startswith("cuda"): + torch.cuda.synchronize() + start = time.perf_counter() + module = torch.export.load(str(args.artifact)).module() + module.to(args.device) + if args.device.startswith("cuda"): + torch.cuda.synchronize() + elapsed = time.perf_counter() - start + print(f"Load full pipeline to {args.device}: {elapsed:.3f}s") + + +if __name__ == "__main__": + main() diff --git a/scripts/export_sam3_artifacts.py b/scripts/export_sam3_artifacts.py new file mode 100644 index 0000000..7cc0426 --- /dev/null +++ b/scripts/export_sam3_artifacts.py @@ -0,0 +1,185 @@ +import argparse +import sys +from pathlib import Path + +import numpy as np +import torch +from PIL import Image + +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO_ROOT)) + +from sam3.model_builder import build_sam3_image_model +from tests.export.test_decoder_export import ( + _export_decoder_only, + _export_full_sam3_pipeline, + _make_decoder_only_inputs, +) +from tests.export.test_encoder_export import EncoderFusionWrapper +from tests.export.test_image_encoder_export import _export_image_encoder +from tests.export.test_text_encoder_export import _export_text_encoder + + +def _load_image(path: Path, device: torch.device) -> torch.Tensor: + image = Image.open(path).convert("RGB") + np_image = np.array(image, dtype=np.float32) / 255.0 + tensor = torch.from_numpy(np_image).permute(2, 0, 1).unsqueeze(0) + return tensor.to(device) + + +def _prepare_image(image: torch.Tensor, size: int) -> torch.Tensor: + image = image.clamp(0, 1) + image = torch.nn.functional.interpolate( + image, size=(size, size), mode="bilinear", align_corners=False + ) + mean = torch.tensor([0.5, 0.5, 0.5], device=image.device).view(1, 3, 1, 1) + std = torch.tensor([0.5, 0.5, 0.5], device=image.device).view(1, 3, 1, 1) + return (image - mean) / std + + +def _make_inputs(model, image: torch.Tensor, prompts): + device = image.device + + tokenizer = model.backbone.language_backbone.tokenizer + token_ids = tokenizer(prompts, context_length=32).to(device) + + return ( + image, + token_ids, + ) + + +def _save_export(exported, path: Path) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + torch.export.save(exported, str(path)) + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument( + "--image", + type=Path, + default=Path("assets/images/cat_dog.jpg"), + help="Path to input image", + ) + parser.add_argument( + "--prompts", + type=str, + default="cat,dog", + help="Comma-separated text prompts", + ) + parser.add_argument( + "--device", + type=str, + default="cuda" if torch.cuda.is_available() else "cpu", + ) + parser.add_argument( + "--out-dir", + type=Path, + default=Path("artifacts/export"), + help="Directory to write exported artifacts", + ) + args = parser.parse_args() + + prompts = [p.strip() for p in args.prompts.split(",") if p.strip()] + if not prompts: + raise ValueError("Provide at least one prompt") + + model = build_sam3_image_model(device=args.device, eval_mode=True, enable_segmentation=True) + model.eval() + + image = _load_image(args.image, torch.device(args.device)) + image = _prepare_image(image, size=1008) + inputs = _make_inputs(model, image, prompts) + + print("Exporting image encoder...") + image_encoder = _export_image_encoder(model, inputs[0]) + print("Exporting text encoder...") + text_encoder = _export_text_encoder(model, inputs[1]) + print("Exporting encoder fusion...") + with torch.no_grad(): + image_module = image_encoder.module() + text_module = text_encoder.module() + _, vision_pos_enc, backbone_fpn = image_module(inputs[0]) + text_attention_mask, text_memory = text_module(inputs[1]) + prompt = text_memory + prompt_mask = text_attention_mask + img_feats = backbone_fpn[-1] + img_pos = vision_pos_enc[-1] + img_mask = torch.zeros( + img_feats.shape[0], + img_feats.shape[2], + img_feats.shape[3], + device=img_feats.device, + dtype=torch.bool, + ) + prompt_batch = prompt.shape[1] + if img_feats.shape[0] != prompt_batch: + if img_feats.shape[0] != 1: + raise ValueError("Image batch does not match prompt batch") + img_feats = img_feats.repeat(prompt_batch, 1, 1, 1) + img_pos = img_pos.repeat(prompt_batch, 1, 1, 1) + img_mask = img_mask.repeat(prompt_batch, 1, 1) + + encoder_wrapper = EncoderFusionWrapper(model.transformer.encoder).to(img_feats.device).eval() + encoder = torch.export.export( + encoder_wrapper, + (img_feats, img_pos, img_mask, prompt, prompt_mask), + dynamic_shapes={ + "img_feats": {0: torch.export.Dim("batch", min=1, max=4)}, + "img_pos": {0: torch.export.Dim("batch", min=1, max=4)}, + "img_mask": {0: torch.export.Dim("batch", min=1, max=4)}, + "prompt": {0: 32, 1: torch.export.Dim("batch", min=1, max=4)}, + "prompt_mask": {0: torch.export.Dim("batch", min=1, max=4), 1: 32}, + }, + strict=False, + prefer_deferred_runtime_asserts_over_guards=True, + ) + print("Exporting full pipeline...") + pipeline_inputs = _make_inputs(model, image, prompts[:1]) + full_pipeline = _export_full_sam3_pipeline(model, pipeline_inputs) + print("Exporting decoder only...") + decoder_inputs = _make_decoder_only_inputs(model, pipeline_inputs) + ( + backbone_fpn, + img_ids, + memory, + pos_embed, + prompt, + prompt_mask, + level_start_index, + spatial_shapes, + valid_ratios, + ) = decoder_inputs + if img_ids.shape[0] < 2: + repeat = 2 // img_ids.shape[0] + img_ids = img_ids.repeat(repeat) + memory = memory.repeat(1, repeat, 1) + pos_embed = pos_embed.repeat(1, repeat, 1) + prompt = prompt.repeat(1, repeat, 1) + prompt_mask = prompt_mask.repeat(repeat, 1) + valid_ratios = valid_ratios.repeat(repeat, 1, 1) + backbone_fpn = [feat.repeat(repeat, 1, 1, 1) for feat in backbone_fpn] + decoder_inputs = ( + backbone_fpn, + img_ids, + memory, + pos_embed, + prompt, + prompt_mask, + level_start_index, + spatial_shapes, + valid_ratios, + ) + decoder_only = _export_decoder_only(model, decoder_inputs) + + _save_export(image_encoder, args.out_dir / "image_encoder.pt2") + _save_export(text_encoder, args.out_dir / "text_encoder.pt2") + _save_export(encoder, args.out_dir / "encoder_fusion.pt2") + _save_export(full_pipeline, args.out_dir / "full_sam3_pipeline.pt2") + _save_export(decoder_only, args.out_dir / "decoder_only.pt2") + print("Saved exports to", args.out_dir) + + +if __name__ == "__main__": + main() diff --git a/scripts/export_sam3_full_pipeline.py b/scripts/export_sam3_full_pipeline.py new file mode 100644 index 0000000..9f50f3e --- /dev/null +++ b/scripts/export_sam3_full_pipeline.py @@ -0,0 +1,188 @@ +import argparse +import sys +from pathlib import Path + +import numpy as np +import torch +from PIL import Image + +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO_ROOT)) + +from typing import Any, cast + +from sam3.model_builder import build_sam3_image_model +from sam3.model.data_misc import FindStage +from sam3.model.geometry_encoders import Prompt + + +class FullSam3PipelineWrapper(torch.nn.Module): + def __init__(self, model: torch.nn.Module): + super().__init__() + self.model = model + + def forward( + self, + images: torch.Tensor, + token_ids: torch.Tensor, + ): + model = cast(Any, self.model) + num_images = images.shape[0] + num_prompts = token_ids.shape[0] + device = images.device + bs = num_images * num_prompts + + img_ids = torch.arange(num_images, device=device, dtype=torch.long) + img_ids = img_ids.repeat_interleave(num_prompts) + text_ids = torch.arange(num_prompts, device=device, dtype=torch.long) + text_ids = text_ids.repeat(num_images) + + box_embeddings = torch.zeros(1, bs, 4, device=device) + box_mask = torch.zeros(bs, 1, device=device, dtype=torch.bool) + box_labels = torch.zeros(1, bs, device=device, dtype=torch.long) + + backbone_out = model.backbone.forward_image(images) + text_encoder = model.backbone.language_backbone + _, text_tokens = text_encoder.encoder(token_ids) + text_tokens = text_tokens.transpose(0, 1) + text_memory = text_encoder.resizer(text_tokens) + text_attention_mask = token_ids.ne(0) + text_attention_mask = text_attention_mask.ne(1) + backbone_out["language_features"] = text_memory + backbone_out["language_mask"] = text_attention_mask + + find_input = FindStage( + img_ids=img_ids, + text_ids=text_ids, + input_boxes=box_embeddings, + input_boxes_mask=box_mask, + input_boxes_label=box_labels, + input_points=torch.zeros(0, bs, 2, device=device), + input_points_mask=torch.zeros(bs, 0, device=device, dtype=torch.bool), + ) + geometric_prompt = Prompt( + box_embeddings=box_embeddings, + box_mask=box_mask, + box_labels=box_labels, + ) + out = model.forward_grounding( + backbone_out=backbone_out, + find_input=find_input, + find_target=None, + geometric_prompt=geometric_prompt, + ) + return ( + out["pred_logits"], + out["pred_boxes"], + out["pred_masks"], + out.get("presence_logit_dec"), + ) + + +def _load_image(path: Path, device: torch.device) -> torch.Tensor: + image = Image.open(path).convert("RGB") + np_image = np.array(image, dtype=np.float32) / 255.0 + return torch.from_numpy(np_image).permute(2, 0, 1).unsqueeze(0).to(device) + + +def _prepare_image(image: torch.Tensor, size: int) -> torch.Tensor: + image = image.clamp(0, 1) + image = torch.nn.functional.interpolate( + image, size=(size, size), mode="bilinear", align_corners=False + ) + mean = torch.tensor([0.5, 0.5, 0.5], device=image.device).view(1, 3, 1, 1) + std = torch.tensor([0.5, 0.5, 0.5], device=image.device).view(1, 3, 1, 1) + return (image - mean) / std + + +def _make_inputs(model, image: torch.Tensor, prompts): + device = image.device + token_ids = model.backbone.language_backbone.tokenizer(prompts, context_length=32).to(device) + return ( + image, + token_ids, + ) + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument( + "--image", + type=Path, + default=Path("assets/images/cat_dog.jpg"), + help="Path to input image", + ) + parser.add_argument( + "--prompts", + type=str, + default="cat,dog", + help="Comma-separated text prompts", + ) + parser.add_argument( + "--device", + type=str, + default="cuda" if torch.cuda.is_available() else "cpu", + ) + parser.add_argument( + "--num-feature-levels", + type=int, + default=1, + help="Number of feature levels to use", + ) + parser.add_argument( + "--out-dir", + type=Path, + default=Path("artifacts/export"), + help="Directory to write exported artifact", + ) + args = parser.parse_args() + + prompts = [p.strip() for p in args.prompts.split(",") if p.strip()] + if not prompts: + raise ValueError("Provide at least one prompt") + + model = build_sam3_image_model( + device=args.device, + eval_mode=True, + enable_segmentation=True, + num_feature_levels=args.num_feature_levels, + ) + model.eval() + + image = _prepare_image(_load_image(args.image, torch.device(args.device)), size=1008) + inputs = _make_inputs(model, image, prompts) + wrapper = FullSam3PipelineWrapper(model).to(image.device).eval() + if image.shape[0] < 2: + repeat = 2 // image.shape[0] + export_inputs = ( + image.repeat(repeat, 1, 1, 1), + inputs[1].repeat(repeat, 1), + ) + else: + export_inputs = inputs + with torch.no_grad(): + exported = torch.export.export( + wrapper, + export_inputs, + dynamic_shapes={ + "images": { + 0: torch.export.Dim.AUTO, + 2: 1008, + 3: 1008, + }, + "token_ids": { + 0: torch.export.Dim("num_prompts", min=1), + 1: 32, + }, + }, + strict=False, + prefer_deferred_runtime_asserts_over_guards=True, + ) + out_path = args.out_dir / "full_sam3_pipeline.pt2" + out_path.parent.mkdir(parents=True, exist_ok=True) + torch.export.save(exported, str(out_path)) + print("Saved export to", out_path) + + +if __name__ == "__main__": + main() diff --git a/scripts/test_sam3_artifacts.py b/scripts/test_sam3_artifacts.py new file mode 100644 index 0000000..5d6c678 --- /dev/null +++ b/scripts/test_sam3_artifacts.py @@ -0,0 +1,447 @@ +import argparse +import sys +from pathlib import Path + +import numpy as np +import torch +from PIL import Image +from PIL import ImageDraw + +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO_ROOT)) + +from sam3.model_builder import build_sam3_image_model +from sam3.model.data_misc import FindStage +from sam3.model.geometry_encoders import Prompt + + +def _load_pil_image(path: Path) -> Image.Image: + return Image.open(path).convert("RGB") + + +def _pil_to_tensor(image: Image.Image, device: torch.device) -> torch.Tensor: + np_image = np.array(image, dtype=np.float32) / 255.0 + tensor = torch.from_numpy(np_image).permute(2, 0, 1).unsqueeze(0) + return tensor.to(device) + + +def _load_image(path: Path, device: torch.device) -> torch.Tensor: + return _pil_to_tensor(_load_pil_image(path), device) + + +def _prepare_image(image: torch.Tensor, size: int) -> torch.Tensor: + image = image.clamp(0, 1) + image = torch.nn.functional.interpolate( + image, size=(size, size), mode="bilinear", align_corners=False + ) + mean = torch.tensor([0.5, 0.5, 0.5], device=image.device).view(1, 3, 1, 1) + std = torch.tensor([0.5, 0.5, 0.5], device=image.device).view(1, 3, 1, 1) + return (image - mean) / std + + +def _make_inputs(model, image: torch.Tensor, prompts): + device = image.device + + tokenizer = model.backbone.language_backbone.tokenizer + token_ids = tokenizer(prompts, context_length=32).to(device) + + return ( + image, + token_ids, + ) + + +def _run_full_model(model, inputs): + images, token_ids = inputs + num_images = images.shape[0] + num_prompts = token_ids.shape[0] + device = images.device + bs = num_images * num_prompts + + img_ids = torch.arange(num_images, device=device, dtype=torch.long) + img_ids = img_ids.repeat_interleave(num_prompts) + text_ids = torch.arange(num_prompts, device=device, dtype=torch.long) + text_ids = text_ids.repeat(num_images) + + box_embeddings = torch.zeros(1, bs, 4, device=device) + box_mask = torch.zeros(bs, 1, device=device, dtype=torch.bool) + box_labels = torch.zeros(1, bs, device=device, dtype=torch.long) + backbone_out = model.backbone.forward_image(images) + text_encoder = model.backbone.language_backbone + _, text_tokens = text_encoder.encoder(token_ids) + text_tokens = text_tokens.transpose(0, 1) + text_memory = text_encoder.resizer(text_tokens) + text_attention_mask = token_ids.ne(0) + text_attention_mask = text_attention_mask.ne(1) + backbone_out["language_features"] = text_memory + backbone_out["language_mask"] = text_attention_mask + + find_input = FindStage( + img_ids=img_ids, + text_ids=text_ids, + input_boxes=box_embeddings, + input_boxes_mask=box_mask, + input_boxes_label=box_labels, + input_points=torch.zeros(0, int(token_ids.shape[0]), 2, device=images.device), + input_points_mask=torch.zeros( + int(token_ids.shape[0]), 0, device=images.device, dtype=torch.bool + ), + ) + geometric_prompt = Prompt( + box_embeddings=box_embeddings, + box_mask=box_mask, + box_labels=box_labels, + ) + out = model.forward_grounding( + backbone_out=backbone_out, + find_input=find_input, + find_target=None, + geometric_prompt=geometric_prompt, + ) + return ( + out["pred_masks"], + out["pred_boxes"], + out["pred_logits"], + out["pred_boxes_xyxy"], + ) + + +def _make_decoder_only_inputs_from_model( + model, + backbone_fpn, + vision_pos_enc, + text_memory, + text_attention_mask, + inputs, +): + images, token_ids = inputs + num_images = images.shape[0] + num_prompts = token_ids.shape[0] + device = images.device + bs = num_images * num_prompts + + img_ids = torch.arange(num_images, device=device, dtype=torch.long) + img_ids = img_ids.repeat_interleave(num_prompts) + text_ids = torch.arange(num_prompts, device=device, dtype=torch.long) + text_ids = text_ids.repeat(num_images) + + box_embeddings = torch.zeros(1, bs, 4, device=device) + box_mask = torch.zeros(bs, 1, device=device, dtype=torch.bool) + box_labels = torch.zeros(1, bs, device=device, dtype=torch.long) + backbone_out = { + "backbone_fpn": backbone_fpn, + "vision_pos_enc": vision_pos_enc, + "language_features": text_memory, + "language_mask": text_attention_mask, + } + find_input = FindStage( + img_ids=img_ids, + text_ids=text_ids, + input_boxes=box_embeddings, + input_boxes_mask=box_mask, + input_boxes_label=box_labels, + input_points=torch.zeros(0, int(token_ids.shape[0]), 2, device=images.device), + input_points_mask=torch.zeros( + int(token_ids.shape[0]), 0, device=images.device, dtype=torch.bool + ), + ) + geometric_prompt = Prompt( + box_embeddings=box_embeddings, + box_mask=box_mask, + box_labels=box_labels, + ) + prompt, prompt_mask, backbone_out = model._encode_prompt( + backbone_out, find_input, geometric_prompt + ) + backbone_out, encoder_out, _ = model._run_encoder(backbone_out, find_input, prompt, prompt_mask) + return ( + backbone_out["backbone_fpn"], + img_ids, + encoder_out["encoder_hidden_states"], + encoder_out["pos_embed"], + prompt, + prompt_mask, + encoder_out["level_start_index"], + encoder_out["spatial_shapes"], + encoder_out["valid_ratios"], + ) + + +def _load_export(path: Path): + exported = torch.export.load(str(path)) + return exported.module() + + +def _to_pil_image(image: torch.Tensor) -> Image.Image: + image = image.detach().cpu().clamp(0, 1) + np_image = (image.permute(1, 2, 0).numpy() * 255.0).astype(np.uint8) + return Image.fromarray(np_image) + + +def _color_palette(num_colors: int): + base = [ + (255, 99, 71), + (65, 105, 225), + (60, 179, 113), + (238, 130, 238), + (255, 215, 0), + (255, 165, 0), + ] + return [base[i % len(base)] for i in range(num_colors)] + + +def _overlay_masks(image: Image.Image, masks: torch.Tensor, scores: torch.Tensor, out_path: Path): + num_prompts, num_queries = scores.shape[:2] + best_idx = scores.squeeze(-1).argmax(dim=1) + colors = _color_palette(num_prompts) + base = image.copy().convert("RGBA") + overlay = Image.new("RGBA", base.size, (0, 0, 0, 0)) + for i in range(num_prompts): + mask = masks[i, best_idx[i]].detach().cpu() + mask = mask > 0 + mask_img = Image.fromarray((mask.numpy() * 255).astype(np.uint8), mode="L") + if mask_img.size != base.size: + mask_img = mask_img.resize(base.size, resample=Image.Resampling.NEAREST) + color = colors[i] + color_img = Image.new("RGBA", base.size, (*color, 120)) + overlay = Image.composite(color_img, overlay, mask_img) + blended = Image.alpha_composite(base, overlay) + blended.convert("RGB").save(out_path) + + +def _draw_boxes(image: Image.Image, boxes_xyxy: torch.Tensor, scores: torch.Tensor, out_path: Path): + num_prompts, num_queries = scores.shape[:2] + best_idx = scores.squeeze(-1).argmax(dim=1).clamp(max=boxes_xyxy.shape[1] - 1) + colors = _color_palette(num_prompts) + draw = ImageDraw.Draw(image) + for i in range(num_prompts): + box_tensor = boxes_xyxy[i, best_idx[i]].detach().cpu().flatten() + if box_tensor.numel() != 4: + continue + box = box_tensor.tolist() + width, height = image.size + if max(box) <= 1.0: + box = [ + box[0] * width, + box[1] * height, + box[2] * width, + box[3] * height, + ] + box = [ + max(0.0, min(box[0], width)), + max(0.0, min(box[1], height)), + max(0.0, min(box[2], width)), + max(0.0, min(box[3], height)), + ] + color = colors[i] + draw.rectangle(box, outline=color, width=3) + image.save(out_path) + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument( + "--image", + type=Path, + default=Path("assets/images/cat_dog.jpg"), + help="Path to input image", + ) + parser.add_argument( + "--prompts", + type=str, + default="cat,dog", + help="Comma-separated text prompts", + ) + parser.add_argument( + "--device", + type=str, + default="cuda" if torch.cuda.is_available() else "cpu", + ) + parser.add_argument( + "--artifact-dir", + type=Path, + default=Path("artifacts/export"), + help="Directory with exported artifacts", + ) + args = parser.parse_args() + + prompts = [p.strip() for p in args.prompts.split(",") if p.strip()] + if not prompts: + raise ValueError("Provide at least one prompt") + prompt_count = len(prompts) + + model = build_sam3_image_model(device=args.device, eval_mode=True, enable_segmentation=True) + model.eval() + + pil_image = _load_pil_image(args.image) + image = _pil_to_tensor(pil_image, torch.device(args.device)) + image = _prepare_image(image, size=1008) + inputs = _make_inputs(model, image, prompts) + + with torch.no_grad(): + eager_masks, eager_boxes, eager_logits, eager_boxes_xyxy = _run_full_model(model, inputs) + + image_module = _load_export(args.artifact_dir / "image_encoder.pt2") + text_module = _load_export(args.artifact_dir / "text_encoder.pt2") + encoder_module = _load_export(args.artifact_dir / "encoder_fusion.pt2") + pipeline_module = _load_export(args.artifact_dir / "full_sam3_pipeline.pt2") + decoder_module = _load_export(args.artifact_dir / "decoder_only.pt2") + + with torch.no_grad(): + _, vision_pos_enc, backbone_fpn = image_module(inputs[0]) + text_attention_mask, text_memory = text_module(inputs[1]) + img_feats = backbone_fpn[-1] + img_pos = vision_pos_enc[-1] + prompt_batch = text_attention_mask.shape[0] + if img_feats.shape[0] != prompt_batch: + if img_feats.shape[0] != 1: + raise ValueError("Image batch does not match prompt batch") + img_feats = img_feats.repeat(prompt_batch, 1, 1, 1) + img_pos = img_pos.repeat(prompt_batch, 1, 1, 1) + img_mask = torch.zeros( + img_feats.shape[0], + img_feats.shape[2], + img_feats.shape[3], + device=img_feats.device, + dtype=torch.bool, + ) + enc_out = encoder_module(img_feats, img_pos, img_mask, text_memory, text_attention_mask) + assert isinstance(enc_out, tuple) + pipeline_logits, pipeline_boxes, pipeline_masks, pipeline_boxes_xyxy = pipeline_module( + *inputs + ) + ( + decoder_backbone_fpn, + decoder_img_ids, + decoder_memory, + decoder_pos_embed, + decoder_prompt, + decoder_prompt_mask, + decoder_level_start_index, + decoder_spatial_shapes, + decoder_valid_ratios, + ) = _make_decoder_only_inputs_from_model( + model, + backbone_fpn, + vision_pos_enc, + text_memory, + text_attention_mask, + inputs, + ) + batch_target = decoder_img_ids.shape[0] + if batch_target < 2: + repeat = 2 // batch_target + decoder_img_ids = decoder_img_ids.repeat(repeat) + decoder_memory = decoder_memory.repeat(1, repeat, 1) + decoder_pos_embed = decoder_pos_embed.repeat(1, repeat, 1) + decoder_prompt = decoder_prompt.repeat(1, repeat, 1) + decoder_prompt_mask = decoder_prompt_mask.repeat(repeat, 1) + decoder_valid_ratios = decoder_valid_ratios.repeat(repeat, 1, 1) + decoder_backbone_fpn = [feat.repeat(repeat, 1, 1, 1) for feat in decoder_backbone_fpn] + pred_logits, pred_boxes, pred_masks, pred_boxes_xyxy = decoder_module( + decoder_backbone_fpn, + decoder_img_ids, + decoder_memory, + decoder_pos_embed, + decoder_prompt, + decoder_prompt_mask, + decoder_level_start_index, + decoder_spatial_shapes, + decoder_valid_ratios, + ) + eager_ref_masks, eager_ref_boxes, eager_ref_logits, eager_ref_boxes_xyxy = _run_full_model( + model, inputs + ) + + pred_logits = pred_logits[:prompt_count] + pred_boxes = pred_boxes[:prompt_count] + pred_masks = pred_masks[:prompt_count] + pred_boxes_xyxy = pred_boxes_xyxy[:prompt_count] + pipeline_logits = pipeline_logits[:prompt_count] + pipeline_masks = pipeline_masks[:prompt_count] + pipeline_boxes_xyxy = pipeline_boxes_xyxy[:prompt_count] + eager_ref_logits = eager_ref_logits[:prompt_count] + + print("Prompt count:", prompt_count) + print("Pred logits shape:", pred_logits.shape) + print("Pred boxes shape:", pred_boxes.shape) + print("Pred masks shape:", pred_masks.shape) + pred_scores = pred_logits.squeeze(-1) + eager_scores = eager_ref_logits.squeeze(-1) + pred_max = pred_scores.max(dim=1).values + eager_max = eager_scores.max(dim=1).values + pred_best_idx = pred_scores.argmax(dim=1) + eager_best_idx = eager_scores.argmax(dim=1) + for idx, prompt_text in enumerate(prompts): + print( + f"Prompt '{prompt_text}' max logit: " + f"export={pred_max[idx].item():.4f} (idx {pred_best_idx[idx].item()}), " + f"eager={eager_max[idx].item():.4f} (idx {eager_best_idx[idx].item()})" + ) + torch.testing.assert_close(pred_logits, eager_ref_logits, rtol=1e-3, atol=1e-3) + torch.testing.assert_close(pipeline_logits, eager_ref_logits, rtol=1e-3, atol=1e-3) + print("Eager vs export logits match") + + out_dir = args.artifact_dir + out_dir.mkdir(parents=True, exist_ok=True) + base_image = pil_image.copy() + _overlay_masks( + base_image.copy(), + eager_masks, + eager_logits, + out_dir / "eager_masks_overlay.jpg", + ) + _overlay_masks( + base_image.copy(), + pipeline_masks, + pipeline_logits, + out_dir / "pipeline_masks_overlay.jpg", + ) + _overlay_masks( + base_image.copy(), + pred_masks, + pred_logits, + out_dir / "decoder_only_masks_overlay.jpg", + ) + for idx, prompt_text in enumerate(prompts): + _overlay_masks( + base_image.copy(), + eager_masks[idx : idx + 1], + eager_logits[idx : idx + 1], + out_dir / f"eager_mask_overlay_{idx}.jpg", + ) + _overlay_masks( + base_image.copy(), + pipeline_masks[idx : idx + 1], + pipeline_logits[idx : idx + 1], + out_dir / f"pipeline_mask_overlay_{idx}.jpg", + ) + _overlay_masks( + base_image.copy(), + pred_masks[idx : idx + 1], + pred_logits[idx : idx + 1], + out_dir / f"decoder_only_mask_overlay_{idx}.jpg", + ) + _draw_boxes( + base_image.copy(), + eager_boxes_xyxy, + eager_logits, + out_dir / "eager_boxes_overlay.jpg", + ) + _draw_boxes( + base_image.copy(), + pipeline_boxes_xyxy, + pipeline_logits, + out_dir / "pipeline_boxes_overlay.jpg", + ) + _draw_boxes( + base_image.copy(), + pred_boxes_xyxy, + pred_logits, + out_dir / "decoder_only_boxes_overlay.jpg", + ) + print("Saved overlays to", out_dir) + + +if __name__ == "__main__": + main() diff --git a/scripts/test_sam3_full_pipeline.py b/scripts/test_sam3_full_pipeline.py new file mode 100644 index 0000000..6118e01 --- /dev/null +++ b/scripts/test_sam3_full_pipeline.py @@ -0,0 +1,121 @@ +import argparse +import sys +from pathlib import Path + +import numpy as np +import torch +from PIL import Image + +REPO_ROOT = Path(__file__).resolve().parents[1] +sys.path.insert(0, str(REPO_ROOT)) + +from sam3.model_builder import build_sam3_image_model + + +def _load_image(path: Path, device: torch.device) -> torch.Tensor: + image = Image.open(path).convert("RGB") + np_image = np.array(image, dtype=np.float32) / 255.0 + return torch.from_numpy(np_image).permute(2, 0, 1).unsqueeze(0).to(device) + + +def _prepare_image(image: torch.Tensor, size: int) -> torch.Tensor: + image = image.clamp(0, 1) + image = torch.nn.functional.interpolate( + image, size=(size, size), mode="bilinear", align_corners=False + ) + mean = torch.tensor([0.5, 0.5, 0.5], device=image.device).view(1, 3, 1, 1) + std = torch.tensor([0.5, 0.5, 0.5], device=image.device).view(1, 3, 1, 1) + return (image - mean) / std + + +def _make_inputs(model, image: torch.Tensor, prompts): + device = image.device + num_prompts = len(prompts) + num_images = int(image.shape[0]) + token_ids = model.backbone.language_backbone.tokenizer( + prompts, context_length=32 + ).to(device) + img_ids = torch.arange(num_images, device=device, dtype=torch.long) + img_ids = img_ids.repeat_interleave(num_prompts) + text_ids = torch.arange(num_prompts, device=device, dtype=torch.long) + text_ids = text_ids.repeat(num_images) + return ( + image, + token_ids, + img_ids, + text_ids, + torch.zeros(1, num_prompts, 4, device=device), + torch.zeros(num_prompts, 1, device=device, dtype=torch.bool), + torch.zeros(1, num_prompts, device=device, dtype=torch.long), + ) + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument( + "--image", + type=Path, + default=Path("assets/images/cat_dog.jpg"), + help="Path to input image", + ) + parser.add_argument( + "--prompts", + type=str, + default="cat,dog", + help="Comma-separated text prompts", + ) + parser.add_argument( + "--device", + type=str, + default="cuda" if torch.cuda.is_available() else "cpu", + ) + parser.add_argument( + "--num-feature-levels", + type=int, + default=1, + help="Number of feature levels to use", + ) + parser.add_argument( + "--artifact", + type=Path, + default=Path("artifacts/export/full_sam3_pipeline.pt2"), + help="Path to exported full pipeline artifact", + ) + args = parser.parse_args() + + prompts = [p.strip() for p in args.prompts.split(",") if p.strip()] + if not prompts: + raise ValueError("Provide at least one prompt") + + model = build_sam3_image_model( + device=args.device, + eval_mode=True, + enable_segmentation=True, + num_feature_levels=args.num_feature_levels, + ) + model.eval() + + image = _prepare_image( + _load_image(args.image, torch.device(args.device)), size=1008 + ) + inputs = _make_inputs(model, image, prompts) + + module = torch.export.load(str(args.artifact)).module() + with torch.no_grad(): + pred_logits, pred_boxes, pred_masks, pred_presence = module(*inputs) + + pred_scores = pred_logits.squeeze(-1) + print("Prompt count:", len(prompts)) + print("Pred logits shape:", pred_logits.shape) + print("Pred boxes shape:", pred_boxes.shape) + print("Pred masks shape:", pred_masks.shape) + if pred_presence is not None: + print("Presence logits shape:", pred_presence.shape) + for idx, prompt_text in enumerate(prompts): + max_val = pred_scores[idx].max().item() + max_idx = pred_scores[idx].argmax().item() + print(f"Prompt '{prompt_text}' max logit: {max_val:.4f} (idx {max_idx})") + + +if __name__ == "__main__": + main() diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/export/__init__.py b/tests/export/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/export/conftest.py b/tests/export/conftest.py new file mode 100644 index 0000000..00ab475 --- /dev/null +++ b/tests/export/conftest.py @@ -0,0 +1,31 @@ +from __future__ import annotations + +import os +import sys +from pathlib import Path + +import pytest +import torch + +REPO_ROOT = Path(__file__).resolve().parents[2] +sys.path.insert(0, str(REPO_ROOT)) + +from sam3.model_builder import build_sam3_image_model + + +@pytest.fixture(scope="session") +def sam3_model() -> torch.nn.Module: + force_cpu = os.getenv("SAM3_EXPORT_FORCE_CPU", "0") == "1" + device = os.getenv("SAM3_EXPORT_DEVICE") + if device is None: + device = "cuda" if torch.cuda.is_available() and not force_cpu else "cpu" + try: + model = build_sam3_image_model( + device=device, eval_mode=True, enable_segmentation=True + ) + except torch.OutOfMemoryError: + if device == "cuda": + torch.cuda.empty_cache() + pytest.skip("CUDA OOM while loading SAM3 model; free GPU memory or set SAM3_EXPORT_FORCE_CPU=1") + model.eval() + return model diff --git a/tests/export/test_decoder_export.py b/tests/export/test_decoder_export.py new file mode 100644 index 0000000..998cf6f --- /dev/null +++ b/tests/export/test_decoder_export.py @@ -0,0 +1,370 @@ +from __future__ import annotations + +from typing import Any, cast + +import pytest +import torch + +from sam3.model.data_misc import FindStage +from sam3.model.geometry_encoders import Prompt +from tests.export.utils import capture_stderr_on_fail, get_device, save_output_shapes + + +class FullSam3PipelineWrapper(torch.nn.Module): + def __init__(self, model: Any): + super().__init__() + self.model = model + + def forward( + self, + images: torch.Tensor, + token_ids: torch.Tensor, + ): + model = cast(Any, self.model) + num_images = images.shape[0] + num_prompts = token_ids.shape[0] + device = images.device + bs = num_images * num_prompts + + img_ids = torch.arange(num_images, device=device, dtype=torch.long) + img_ids = img_ids.repeat_interleave(num_prompts) + text_ids = torch.arange(num_prompts, device=device, dtype=torch.long) + text_ids = text_ids.repeat(num_images) + + box_embeddings = torch.zeros(1, bs, 4, device=device) + box_mask = torch.zeros(bs, 1, device=device, dtype=torch.bool) + box_labels = torch.zeros(1, bs, device=device, dtype=torch.long) + + backbone_out = model.backbone.forward_image(images) + text_encoder = model.backbone.language_backbone + _, text_tokens = text_encoder.encoder(token_ids) + text_tokens = text_tokens.transpose(0, 1) + text_memory = text_encoder.resizer(text_tokens) + text_attention_mask = token_ids.ne(0) + text_attention_mask = text_attention_mask.ne(1) + backbone_out["language_features"] = text_memory + backbone_out["language_mask"] = text_attention_mask + + find_input = FindStage( + img_ids=img_ids, + text_ids=text_ids, + input_boxes=box_embeddings, + input_boxes_mask=box_mask, + input_boxes_label=box_labels, + input_points=torch.zeros(0, bs, 2, device=device), + input_points_mask=torch.zeros(bs, 0, device=device, dtype=torch.bool), + ) + + geometric_prompt = Prompt( + box_embeddings=box_embeddings, + box_mask=box_mask, + box_labels=box_labels, + ) + + out = model.forward_grounding( + backbone_out=backbone_out, + find_input=find_input, + find_target=None, + geometric_prompt=geometric_prompt, + ) + + return ( + out["pred_logits"], + out["pred_boxes"], + out["pred_masks"], + out.get("presence_logit_dec"), + ) + + +def _make_inputs(batch: int, height: int, width: int, device: str): + images = torch.randn(batch, 3, height, width, device=device) + token_ids = torch.ones(batch, 32, device=device, dtype=torch.long) + token_ids[:, -1] = 0 + return images, token_ids + + +def _make_decoder_only_inputs(model: Any, inputs): + images, token_ids = inputs + model_any = cast(Any, model) + model_any = model_any.eval() + num_images = images.shape[0] + num_prompts = token_ids.shape[0] + device = images.device + bs = num_images * num_prompts + + img_ids = torch.arange(num_images, device=device, dtype=torch.long) + img_ids = img_ids.repeat_interleave(num_prompts) + text_ids = torch.arange(num_prompts, device=device, dtype=torch.long) + text_ids = text_ids.repeat(num_images) + + box_embeddings = torch.zeros(1, bs, 4, device=device) + box_mask = torch.zeros(bs, 1, device=device, dtype=torch.bool) + box_labels = torch.zeros(1, bs, device=device, dtype=torch.long) + + backbone_out = model_any.backbone.forward_image(images) + text_encoder = model_any.backbone.language_backbone + _, text_tokens = text_encoder.encoder(token_ids) + text_tokens = text_tokens.transpose(0, 1) + text_memory = text_encoder.resizer(text_tokens) + text_attention_mask = token_ids.ne(0) + text_attention_mask = text_attention_mask.ne(1) + backbone_out["language_features"] = text_memory + backbone_out["language_mask"] = text_attention_mask + + find_input = FindStage( + img_ids=img_ids, + text_ids=text_ids, + input_boxes=box_embeddings, + input_boxes_mask=box_mask, + input_boxes_label=box_labels, + input_points=torch.zeros(0, bs, 2, device=device), + input_points_mask=torch.zeros(bs, 0, device=device, dtype=torch.bool), + ) + geometric_prompt = Prompt( + box_embeddings=box_embeddings, + box_mask=box_mask, + box_labels=box_labels, + ) + + prompt, prompt_mask, backbone_out = model_any._encode_prompt( + backbone_out, find_input, geometric_prompt + ) + backbone_out, encoder_out, _ = model_any._run_encoder( + backbone_out, find_input, prompt, prompt_mask + ) + return ( + backbone_out["backbone_fpn"], + img_ids, + encoder_out["encoder_hidden_states"], + encoder_out["pos_embed"], + prompt, + prompt_mask, + encoder_out["level_start_index"], + encoder_out["spatial_shapes"], + encoder_out["valid_ratios"], + ) + + +def _export_full_sam3_pipeline(model: Any, inputs): + images, token_ids = inputs + device = images.device + wrapper = FullSam3PipelineWrapper(model).to(device).eval() # type: ignore[arg-type] + if images.shape[0] < 2: + repeat = 2 // images.shape[0] + export_inputs = ( + images.repeat(repeat, 1, 1, 1), + token_ids.repeat(repeat, 1), + ) + else: + export_inputs = inputs + with torch.no_grad(): + return torch.export.export( + wrapper, + export_inputs, + dynamic_shapes={ + "images": { + 0: torch.export.Dim.AUTO, + 2: 1008, + 3: 1008, + }, + "token_ids": { + 0: torch.export.Dim("num_prompts", min=1), + 1: 32, + }, + }, + strict=False, + prefer_deferred_runtime_asserts_over_guards=True, + ) + + +class DecoderOnlyWrapper(torch.nn.Module): + def __init__(self, model: torch.nn.Module): + super().__init__() + self.model = model + + def forward( + self, + backbone_fpn, + img_ids: torch.Tensor, + memory: torch.Tensor, + pos_embed: torch.Tensor, + prompt: torch.Tensor, + prompt_mask: torch.Tensor, + level_start_index: torch.Tensor, + spatial_shapes: torch.Tensor, + valid_ratios: torch.Tensor, + ): + model = cast(Any, self.model) + vis_feat_sizes = [(feat.shape[-2], feat.shape[-1]) for feat in backbone_fpn] + encoder_out = { + "pos_embed": pos_embed, + "padding_mask": None, + "level_start_index": level_start_index, + "spatial_shapes": spatial_shapes, + "valid_ratios": valid_ratios, + "vis_feat_sizes": vis_feat_sizes, + } + out = {"encoder_hidden_states": memory} + out, hs = model._run_decoder( + memory=memory, + pos_embed=pos_embed, + src_mask=None, + out=out, + prompt=prompt, + prompt_mask=prompt_mask, + encoder_out=encoder_out, + ) + backbone_out = {"backbone_fpn": backbone_fpn} + model._run_segmentation_heads( + out=out, + backbone_out=backbone_out, + img_ids=img_ids, + vis_feat_sizes=vis_feat_sizes, + encoder_hidden_states=out["encoder_hidden_states"], + prompt=prompt, + prompt_mask=prompt_mask, + hs=hs, + ) + return ( + out["pred_logits"], + out["pred_boxes"], + out["pred_masks"], + out["pred_boxes_xyxy"], + ) + + +def _export_decoder_only(model: Any, inputs): + ( + backbone_fpn, + img_ids, + memory, + pos_embed, + prompt, + prompt_mask, + level_start_index, + spatial_shapes, + valid_ratios, + ) = inputs + device = memory.device + wrapper = DecoderOnlyWrapper(model).to(device).eval() # type: ignore[arg-type] + dynamic_shapes = [ + [{0: torch.export.Dim.AUTO} for _ in backbone_fpn], + {0: torch.export.Dim.AUTO}, + {1: torch.export.Dim.AUTO}, + {1: torch.export.Dim.AUTO}, + {1: torch.export.Dim.AUTO}, + {0: torch.export.Dim.AUTO}, + {}, + {}, + {0: torch.export.Dim.AUTO}, + ] + with torch.no_grad(): + exported = torch.export.export( + wrapper, + ( + backbone_fpn, + img_ids, + memory, + pos_embed, + prompt, + prompt_mask, + level_start_index, + spatial_shapes, + valid_ratios, + ), + dynamic_shapes=dynamic_shapes, + strict=False, + prefer_deferred_runtime_asserts_over_guards=True, + ) + return exported + + +@pytest.mark.slow +def test_decoder_export_static(sam3_model): + device = get_device() + inputs = _make_inputs(1, 1008, 1008, device) + with capture_stderr_on_fail("export_static"): + exported = _export_full_sam3_pipeline(sam3_model, inputs) + assert exported is not None + + +@pytest.mark.slow +def test_decoder_export_loads(sam3_model): + device = get_device() + inputs = _make_inputs(1, 1008, 1008, device) + with capture_stderr_on_fail("export_loads"): + exported = _export_full_sam3_pipeline(sam3_model, inputs) + module = exported.module() + with torch.no_grad(): + out = module(*inputs) + assert isinstance(out, tuple) + assert len(out) == 4 + + +@pytest.mark.slow +def test_decoder_export_matches_eager(sam3_model): + device = get_device() + inputs = _make_inputs(1, 1008, 1008, device) + wrapper = FullSam3PipelineWrapper(sam3_model).to(device).eval() + with torch.no_grad(): + eager_out = wrapper(*inputs) + with capture_stderr_on_fail("export_match"): + exported = _export_full_sam3_pipeline(sam3_model, inputs) + module = exported.module() + with torch.no_grad(): + export_out = module(*inputs) + save_output_shapes("full_pipeline_eager", inputs, eager_out) + save_output_shapes("full_pipeline_export", inputs, export_out) + for eager, compiled in zip(eager_out, export_out): + if eager is None: + assert compiled is None + else: + torch.testing.assert_close(eager, compiled, rtol=1e-3, atol=1e-3) + + +@pytest.mark.slow +@pytest.mark.parametrize("batch", [1, 2]) +def test_full_sam3_pipeline_export_inference_shapes(sam3_model, batch: int): + device = get_device() + inputs = _make_inputs(1, 1008, 1008, device) + with capture_stderr_on_fail("export_inference_shapes"): + exported = _export_full_sam3_pipeline(sam3_model, inputs) + module = exported.module() + new_inputs = _make_inputs(batch, 1008, 1008, device) + with torch.no_grad(): + out = module(*new_inputs) + save_output_shapes(f"full_pipeline_export_batch_{batch}", new_inputs, out) + assert isinstance(out, tuple) + + +@pytest.mark.slow +def test_decoder_only_export_loads(sam3_model): + device = get_device() + inputs = _make_inputs(1, 1008, 1008, device) + decoder_inputs = _make_decoder_only_inputs(sam3_model, inputs) + with capture_stderr_on_fail("export_decoder_only_loads"): + exported = _export_decoder_only(sam3_model, decoder_inputs) + module = exported.module() + with torch.no_grad(): + out = module(*decoder_inputs) + assert isinstance(out, tuple) + assert len(out) == 4 + + +@pytest.mark.slow +def test_decoder_only_export_matches_eager(sam3_model): + device = get_device() + inputs = _make_inputs(1, 1008, 1008, device) + decoder_inputs = _make_decoder_only_inputs(sam3_model, inputs) + wrapper = DecoderOnlyWrapper(sam3_model).to(device).eval() + with torch.no_grad(): + eager_out = wrapper(*decoder_inputs) + with capture_stderr_on_fail("export_decoder_only_match"): + exported = _export_decoder_only(sam3_model, decoder_inputs) + module = exported.module() + with torch.no_grad(): + export_out = module(*decoder_inputs) + save_output_shapes("decoder_only_eager", None, eager_out) + save_output_shapes("decoder_only_export", None, export_out) + for eager, compiled in zip(eager_out, export_out): + torch.testing.assert_close(eager, compiled, rtol=1e-3, atol=1e-3) diff --git a/tests/export/test_encoder_export.py b/tests/export/test_encoder_export.py new file mode 100644 index 0000000..3285628 --- /dev/null +++ b/tests/export/test_encoder_export.py @@ -0,0 +1,156 @@ +from __future__ import annotations + +import pytest +import torch + +from sam3.model.encoder import TransformerEncoderFusion +from tests.export.utils import capture_stderr_on_fail, get_device + + +class EncoderFusionWrapper(torch.nn.Module): + def __init__(self, encoder: TransformerEncoderFusion): + super().__init__() + self.encoder = encoder + + def forward( + self, + img_feats: torch.Tensor, + img_pos: torch.Tensor, + img_mask: torch.Tensor, + prompt: torch.Tensor, + prompt_mask: torch.Tensor, + ): + out = self.encoder( + src=[img_feats], + src_pos=[img_pos], + src_key_padding_mask=[img_mask], + prompt=prompt, + prompt_key_padding_mask=prompt_mask, + ) + return ( + out["memory"], + out["pos_embed"], + out["padding_mask"], + out["level_start_index"], + out["spatial_shapes"], + out["valid_ratios"], + ) + + +def _make_image_tokens(batch: int, height: int, width: int, device: str): + channels = 256 + img_feats = torch.randn(batch, channels, height, width, device=device) + img_pos = torch.randn(batch, channels, height, width, device=device) + img_mask = torch.zeros(batch, height, width, dtype=torch.bool, device=device) + return img_feats, img_pos, img_mask + + +def _make_prompt(batch: int, seq_len: int, device: str): + prompt = torch.randn(seq_len, batch, 256, device=device) + prompt_mask = torch.zeros(batch, seq_len, dtype=torch.bool, device=device) + return prompt, prompt_mask + + +def _export_encoder(model: torch.nn.Module, img_feats, img_pos, prompt, prompt_mask): + device = img_feats.device + wrapper = EncoderFusionWrapper(model.transformer.encoder).to(device).eval() # type: ignore[arg-type] + if img_feats.shape[0] == 1: + img_feats = img_feats.repeat(2, 1, 1, 1) + img_pos = img_pos.repeat(2, 1, 1, 1) + img_mask = torch.zeros( + 2, img_feats.shape[2], img_feats.shape[3], dtype=torch.bool, device=device + ) + prompt = prompt.repeat(1, 2, 1) + prompt_mask = prompt_mask.repeat(2, 1) + else: + img_mask = torch.zeros( + img_feats.shape[0], + img_feats.shape[2], + img_feats.shape[3], + dtype=torch.bool, + device=device, + ) + with torch.no_grad(): + exported = torch.export.export( + wrapper, + (img_feats, img_pos, img_mask, prompt, prompt_mask), + dynamic_shapes={ + "img_feats": { + 0: torch.export.Dim.AUTO, + }, + "img_pos": { + 0: torch.export.Dim.AUTO, + }, + "img_mask": { + 0: torch.export.Dim.AUTO, + }, + "prompt": { + 0: torch.export.Dim("seq", min=1, max=64), + 1: torch.export.Dim.AUTO, + }, + "prompt_mask": { + 0: torch.export.Dim.AUTO, + 1: torch.export.Dim("seq", min=1, max=64), + }, + }, + strict=False, + prefer_deferred_runtime_asserts_over_guards=True, + ) + return exported + + +@pytest.mark.slow +def test_encoder_export_static(sam3_model): + device = get_device() + img_feats, img_pos, img_mask = _make_image_tokens(1, 72, 72, device) + prompt, prompt_mask = _make_prompt(1, 4, device) + with capture_stderr_on_fail("export_static"): + exported = _export_encoder(sam3_model, img_feats, img_pos, prompt, prompt_mask) + assert exported is not None + + +@pytest.mark.slow +def test_encoder_export_loads(sam3_model): + device = get_device() + img_feats, img_pos, img_mask = _make_image_tokens(1, 72, 72, device) + prompt, prompt_mask = _make_prompt(1, 4, device) + with capture_stderr_on_fail("export_loads"): + exported = _export_encoder(sam3_model, img_feats, img_pos, prompt, prompt_mask) + module = exported.module() + with torch.no_grad(): + out = module(img_feats, img_pos, img_mask, prompt, prompt_mask) + assert isinstance(out, tuple) + assert len(out) == 6 + + +@pytest.mark.slow +def test_encoder_export_matches_eager(sam3_model): + device = get_device() + img_feats, img_pos, img_mask = _make_image_tokens(1, 72, 72, device) + prompt, prompt_mask = _make_prompt(1, 4, device) + wrapper = EncoderFusionWrapper(sam3_model.transformer.encoder).to(device).eval() # type: ignore[arg-type] + with torch.no_grad(): + eager_out = wrapper(img_feats, img_pos, img_mask, prompt, prompt_mask) + with capture_stderr_on_fail("export_match"): + exported = _export_encoder(sam3_model, img_feats, img_pos, prompt, prompt_mask) + module = exported.module() + with torch.no_grad(): + export_out = module(img_feats, img_pos, img_mask, prompt, prompt_mask) + for eager, compiled in zip(eager_out, export_out): + torch.testing.assert_close(eager, compiled, rtol=1e-3, atol=1e-3) + + +@pytest.mark.slow +@pytest.mark.parametrize("batch,seq_len", [(1, 4), (2, 8)]) +def test_encoder_export_inference_shapes(sam3_model, batch: int, seq_len: int): + device = get_device() + img_feats, img_pos, img_mask = _make_image_tokens(1, 72, 72, device) + prompt, prompt_mask = _make_prompt(1, 4, device) + with capture_stderr_on_fail("export_inference_shapes"): + exported = _export_encoder(sam3_model, img_feats, img_pos, prompt, prompt_mask) + module = exported.module() + img_feats2, img_pos2, img_mask2 = _make_image_tokens(batch, 72, 72, device) + prompt2, prompt_mask2 = _make_prompt(batch, seq_len, device) + with torch.no_grad(): + out = module(img_feats2, img_pos2, img_mask2, prompt2, prompt_mask2) + assert isinstance(out, tuple) diff --git a/tests/export/test_image_encoder_export.py b/tests/export/test_image_encoder_export.py new file mode 100644 index 0000000..9597691 --- /dev/null +++ b/tests/export/test_image_encoder_export.py @@ -0,0 +1,105 @@ +from __future__ import annotations + +import pytest +import torch + +from sam3.model.vl_combiner import SAM3VLBackbone +from tests.export.utils import capture_stderr_on_fail, get_device + + +class ImageEncoderWrapper(torch.nn.Module): + def __init__(self, backbone: SAM3VLBackbone): + super().__init__() + self.backbone = backbone + + def forward(self, images: torch.Tensor): + out = self.backbone._forward_image_no_act_ckpt(images) + return ( + out["vision_features"], + out["vision_pos_enc"], + out["backbone_fpn"], + ) + + +def _make_images(batch: int, height: int, width: int, device: str) -> torch.Tensor: + return torch.randn(batch, 3, height, width, device=device, dtype=torch.float32) + + +def _export_image_encoder(model: torch.nn.Module, images: torch.Tensor): + device = images.device + wrapper = ImageEncoderWrapper(model.backbone).to(device).eval() # type: ignore[arg-type] + export_images = images + if images.shape[0] == 1: + export_images = images.repeat(2, 1, 1, 1) + with torch.no_grad(): + exported = torch.export.export( + wrapper, + (export_images,), + dynamic_shapes={ + "images": { + 0: torch.export.Dim("batch", min=1, max=4), + 2: 1008, + 3: 1008, + } + }, + strict=False, + prefer_deferred_runtime_asserts_over_guards=True, + ) + return exported + + +@pytest.mark.slow +def test_image_encoder_export_static(sam3_model): + device = get_device() + images = _make_images(1, 1008, 1008, device) + with capture_stderr_on_fail("export_static"): + exported = _export_image_encoder(sam3_model, images) + assert exported is not None + + +@pytest.mark.slow +def test_image_encoder_export_loads(sam3_model): + device = get_device() + images = _make_images(1, 1008, 1008, device) + with capture_stderr_on_fail("export_loads"): + exported = _export_image_encoder(sam3_model, images) + module = exported.module() + with torch.no_grad(): + out = module(images) + assert isinstance(out, tuple) + assert len(out) == 3 + + +@pytest.mark.slow +def test_image_encoder_export_matches_eager(sam3_model): + device = get_device() + images = _make_images(1, 1008, 1008, device) + wrapper = ImageEncoderWrapper(sam3_model.backbone).to(device).eval() + with torch.no_grad(): + eager_out = wrapper(images) + with capture_stderr_on_fail("export_match"): + exported = _export_image_encoder(sam3_model, images) + module = exported.module() + with torch.no_grad(): + export_out = module(images) + for eager, compiled in zip(eager_out, export_out): + if isinstance(eager, (list, tuple)): + for e_item, c_item in zip(eager, compiled): + torch.testing.assert_close(e_item, c_item, rtol=1e-3, atol=1e-3) + else: + torch.testing.assert_close(eager, compiled, rtol=1e-3, atol=1e-3) + + +@pytest.mark.slow +@pytest.mark.parametrize("batch,height,width", [(2, 1008, 1008)]) +def test_image_encoder_export_inference_shapes( + sam3_model, batch: int, height: int, width: int +): + device = get_device() + images = _make_images(1, 1008, 1008, device) + with capture_stderr_on_fail("export_inference_shapes"): + exported = _export_image_encoder(sam3_model, images) + module = exported.module() + with torch.no_grad(): + out = module(_make_images(batch, height, width, device)) + assert isinstance(out, tuple) diff --git a/tests/export/test_text_encoder_export.py b/tests/export/test_text_encoder_export.py new file mode 100644 index 0000000..79ec249 --- /dev/null +++ b/tests/export/test_text_encoder_export.py @@ -0,0 +1,111 @@ +from __future__ import annotations + +from typing import Any, cast + +import pytest +import torch + +from sam3.model.text_encoder_ve import VETextEncoder +from tests.export.utils import capture_stderr_on_fail, get_device + + +class TextEncoderWrapper(torch.nn.Module): + def __init__(self, text_encoder: VETextEncoder): + super().__init__() + self.text_encoder = text_encoder + + def forward(self, token_ids: torch.Tensor): + _, text_tokens = self.text_encoder.encoder(token_ids) + text_tokens = text_tokens.transpose(0, 1) + text_memory = self.text_encoder.resizer(text_tokens) + text_attention_mask = token_ids.ne(0) + text_attention_mask = text_attention_mask.ne(1) + return text_attention_mask, text_memory + + +def _make_tokens( + batch: int, seq_len: int, vocab_size: int, device: str +) -> torch.Tensor: + token_ids = torch.randint(0, vocab_size, (batch, seq_len), device=device) + token_ids[:, -1] = 1 + return token_ids + + +def _export_text_encoder(model: Any, token_ids: torch.Tensor): + device = token_ids.device + model_any = cast(Any, model) + text_encoder = cast(VETextEncoder, model_any.backbone.language_backbone) + wrapper = TextEncoderWrapper(text_encoder).to(device).eval() + export_tokens = token_ids + if token_ids.shape[0] == 1: + export_tokens = token_ids.repeat(2, 1) + with torch.no_grad(): + exported = torch.export.export( + wrapper, + (export_tokens,), + dynamic_shapes={ + "token_ids": { + 0: torch.export.Dim("batch", min=1, max=4), + 1: torch.export.Dim.AUTO, + } + }, + strict=False, + prefer_deferred_runtime_asserts_over_guards=True, + ) + return exported + + +@pytest.mark.slow +def test_text_encoder_export_static(sam3_model): + device = get_device() + vocab_size = sam3_model.backbone.language_backbone.encoder.vocab_size + token_ids = _make_tokens(1, 32, vocab_size, device) + with capture_stderr_on_fail("export_static"): + exported = _export_text_encoder(sam3_model, token_ids) + assert exported is not None + + +@pytest.mark.slow +def test_text_encoder_export_loads(sam3_model): + device = get_device() + vocab_size = sam3_model.backbone.language_backbone.encoder.vocab_size + token_ids = _make_tokens(1, 32, vocab_size, device) + with capture_stderr_on_fail("export_loads"): + exported = _export_text_encoder(sam3_model, token_ids) + module = exported.module() + with torch.no_grad(): + out = module(token_ids) + assert isinstance(out, tuple) + assert len(out) == 2 + + +@pytest.mark.slow +def test_text_encoder_export_matches_eager(sam3_model): + device = get_device() + vocab_size = sam3_model.backbone.language_backbone.encoder.vocab_size + token_ids = _make_tokens(1, 32, vocab_size, device) + text_encoder = cast(VETextEncoder, sam3_model.backbone.language_backbone) + wrapper = TextEncoderWrapper(text_encoder).to(device).eval() + with torch.no_grad(): + eager_out = wrapper(token_ids) + with capture_stderr_on_fail("export_match"): + exported = _export_text_encoder(sam3_model, token_ids) + module = exported.module() + with torch.no_grad(): + export_out = module(token_ids) + torch.testing.assert_close(eager_out[0], export_out[0]) + torch.testing.assert_close(eager_out[1], export_out[1], rtol=1e-3, atol=1e-3) + + +@pytest.mark.slow +@pytest.mark.parametrize("batch,seq_len", [(1, 32), (2, 32)]) +def test_text_encoder_export_inference_shapes(sam3_model, batch: int, seq_len: int): + device = get_device() + vocab_size = sam3_model.backbone.language_backbone.encoder.vocab_size + token_ids = _make_tokens(1, 32, vocab_size, device) + with capture_stderr_on_fail("export_inference_shapes"): + exported = _export_text_encoder(sam3_model, token_ids) + module = exported.module() + with torch.no_grad(): + out = module(_make_tokens(batch, seq_len, vocab_size, device)) + assert isinstance(out, tuple) diff --git a/tests/export/utils.py b/tests/export/utils.py new file mode 100644 index 0000000..bddf172 --- /dev/null +++ b/tests/export/utils.py @@ -0,0 +1,67 @@ +from __future__ import annotations + +import io +import os +import re +from contextlib import contextmanager, redirect_stderr +from typing import Generator +from pathlib import Path + +import torch + +LOG_DIR = Path(__file__).resolve().parent / "export_logs" + + +def _sanitize_test_name(name: str) -> str: + return re.sub(r"[^A-Za-z0-9_.-]+", "_", name) + + +def _current_test_name() -> str: + raw = os.getenv("PYTEST_CURRENT_TEST", "unknown") + return _sanitize_test_name(raw.split(" ")[0]) + + +@contextmanager +def capture_stderr_on_fail(suffix: str) -> Generator[None, None, None]: + LOG_DIR.mkdir(parents=True, exist_ok=True) + buffer = io.StringIO() + with redirect_stderr(buffer): + try: + yield + except Exception: + log_path = LOG_DIR / f"{_current_test_name()}-{suffix}.stderr.txt" + with log_path.open("w", encoding="utf-8") as handle: + handle.write(buffer.getvalue()) + raise + + +def get_device() -> str: + force_cpu = os.getenv("SAM3_EXPORT_FORCE_CPU", "0") == "1" + device = os.getenv("SAM3_EXPORT_DEVICE") + if device is None: + device = "cuda" if torch.cuda.is_available() and not force_cpu else "cpu" + return device + + +def save_output_shapes( + suffix: str, + inputs: tuple[torch.Tensor, ...] | None, + outputs: tuple[torch.Tensor | None, ...], +) -> None: + LOG_DIR.mkdir(parents=True, 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