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clean up ferret_arch.py #3

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clean up ferret_arch.py #3

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27 changes: 1 addition & 26 deletions ferret/model/ferret_arch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,6 @@ def rand_sample(x, max_len):
def rand_sample_repeat(x, max_len):
if x.shape[0] < max_len:
indices = torch.randint(0, x.shape[0], (max_len-x.shape[0],))
# pdb.set_trace()
return torch.cat((x, x[indices]), dim=0)
elif x.shape[0] == max_len:
return x
Expand Down Expand Up @@ -62,7 +61,6 @@ def point_sample(input, point_coords, return_dtype, **kwargs):
if point_coords.dim() == 3:
add_dim = True
point_coords = point_coords.unsqueeze(2)
# output = F.grid_sample(input, 2.0 * point_coords - 1.0, **kwargs)
output = F.grid_sample(input.float(), (2.0 * point_coords - 1.0).float(), **kwargs)
output = output.to(return_dtype)
if add_dim:
Expand Down Expand Up @@ -211,7 +209,6 @@ def __init__(self,

self.norm_init_weights()

# self.dtype = torch.float32
def norm_init_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
Expand All @@ -235,7 +232,6 @@ def forward(self,
# (w, h)
ori_image_wh = torch.tensor([region_masks_list_i[0].shape[0], region_masks_list_i[0].shape[1]], device=region_masks_list_i[0].device)[None,]
# list of elements of shape [num_sample_point, 2]
# pdb.set_trace()
cur_non_zero_pos = [rand_sample_repeat((m.nonzero()/ori_image_wh), self.num_init_point) for m in region_masks_list_i]
# list -> [num_mask, num_sample_point, 2]
cur_non_zero_pos = torch.stack(cur_non_zero_pos)
Expand All @@ -252,7 +248,6 @@ def forward(self,
return_dtype,
align_corners=True,
)
# region_feature_i = region_feature_i.to(dup_region_feature_map_i.dtype)
region_feature_i = region_feature_i.transpose(-2, -1)

cur_img_ids = [img_idx] * len(cur_non_zero_pos)
Expand All @@ -261,15 +256,13 @@ def forward(self,
all_points_fea.append(region_feature_i)
all_points_img_ids.extend(cur_img_ids)

# pdb.set_trace()
# No region found, return list of None.
if len(all_points) == 0:
return [None] * len(region_masks)

all_points = torch.cat(all_points, dim=0).to(return_dtype) # [B*num_mask, num_sample_point, 2]
all_points_fea = torch.cat(all_points_fea, dim=0) # [B*num_mask, num_sample_point, C]
all_points_img_ids = torch.tensor(all_points_img_ids, device=all_points_fea.device)
# pdb.set_trace()
assert all_points_fea.shape[:-1] == all_points_fea.shape[:-1]

# Processing.
Expand All @@ -287,45 +280,36 @@ def forward(self,
grouped_points = index_points(all_points, idx) # [B, npoint, k, 2]
grouped_points_fea = index_points(all_points_fea, idx) # [B, npoint, k, d]

# pdb.set_trace()
local_points_fea = torch.cat([grouped_points_fea, grouped_points],dim=-1) # [B, npoint, k, d+2]
anchor_points_fea = torch.cat([new_points_fea, new_points],dim=-1).unsqueeze(-2)
diff_points_fea = local_points_fea-anchor_points_fea

diff_points_fea = self.diff_projector_list[stage_i](diff_points_fea)
gather_points_fea = torch.cat([diff_points_fea, anchor_points_fea.repeat(1, 1, cur_num_neighbor, 1)], dim=-1) # [B, npoint, k, 2(d+2)]

# pdb.set_trace()
b, n, s, d = gather_points_fea.size()
gather_points_fea = gather_points_fea.permute(0, 1, 3, 2) # [B, npoint, 2(d+2), k]
gather_points_fea = gather_points_fea.reshape(-1, d, s) # [B*npoint, 2(d+2), k]
gather_points_fea = self.agg_projector_list[stage_i](gather_points_fea) # [B*npoint, d, k]
# pdb.set_trace()
batch_size, new_dim, _ = gather_points_fea.size()
gather_points_fea = self.pooler_list[stage_i](gather_points_fea).view(batch_size, new_dim) # [B*npoint, d]
# gather_points_fea = F.adaptive_max_pool1d(gather_points_fea, 1).view(batch_size, -1) # [B*npoint, d]
# pdb.set_trace()
gather_points_fea = gather_points_fea.reshape(b, n, -1) # [B, npoint, d]
# pdb.set_trace()

all_points = new_points
all_points_fea = gather_points_fea

# pdb.set_trace()
x = all_points_fea.flatten(1, -1) # [B, npoint x d]
x = self.flatten_projector(x)
all_region_fea = self.dim_projector(x) # [B, d]

output_region_fea = []
for img_idx in range(len(region_masks)):
cur_mask = all_points_img_ids == img_idx
# pdb.set_trace()
if not cur_mask.any():
output_region_fea.append(None)
else:
output_region_fea.append(all_region_fea[cur_mask])

# pdb.set_trace()
return output_region_fea


Expand All @@ -344,7 +328,6 @@ def __init__(self, config):
self.region_fea_adapter = nn.Linear(config.mm_hidden_size, config.hidden_size)

if hasattr(config, "region_geo_sampler"):
# pdb.set_trace()
self.region_geo_sampler = GeoRegionSampler(input_dim=config.mm_hidden_size,
output_dim=config.hidden_size,
num_init_point=self.max_sample_point,
Expand Down Expand Up @@ -386,7 +369,6 @@ def initialize_vision_modules(self, model_args, fsdp=None, add_region_feature=Fa
if region_geo_sampler:
self.config.region_geo_sampler = True
self.config.sampler_pooler_mode = sampler_pooler_mode
# pdb.set_trace()
if not hasattr(self, 'region_geo_sampler'):
self.region_geo_sampler = GeoRegionSampler(input_dim=self.config.mm_hidden_size,
output_dim=self.config.hidden_size,
Expand Down Expand Up @@ -454,7 +436,6 @@ def extract_region_feature(self, region_feature_map, region_masks, original_dtyp
# [num_mask, C, H, W] x [num_mask, num_sample_point(padded), 2] -> [num_mask, C, num_sample_point(padded)]
# F.grid_sample doesn't support BF16. Need to tranform into float32 then transform back.
dup_region_feature_map_i_ori_type = dup_region_feature_map_i.to(original_dtype)
# pdb.set_trace()
region_feature_i = point_sample(dup_region_feature_map_i_ori_type,
non_zero_pos.flip(dims=(2,)).type(original_dtype),
return_dtype,
Expand Down Expand Up @@ -487,7 +468,6 @@ def prepare_inputs_labels_for_multimodal(
assert region_flag == False
concat_images = torch.cat([image for image in images], dim=0)
raw_image_features, image_features, region_feature_map = self.encode_images(concat_images, region_flag, region_geo_sampler)
# image_features = self.encode_images(concat_images)
split_sizes = [image.shape[0] for image in images]
image_features = torch.split(image_features, split_sizes, dim=0)
image_features = [x.flatten(0, 1) for x in image_features]
Expand All @@ -496,7 +476,6 @@ def prepare_inputs_labels_for_multimodal(

if region_flag:
if region_geo_sampler:
# pdb.set_trace()
region_features = self.get_model().region_geo_sampler(region_feature_map, region_masks,
original_dtype=raw_image_features.dtype,
return_dtype=image_features.dtype)
Expand Down Expand Up @@ -567,12 +546,8 @@ def prepare_inputs_labels_for_multimodal(
if region_flag and region_features[batch_idx] is not None:
region_embs = torch.zeros_like(text_input_embeds)
region_replace_mask = (cur_input_ids == self.config.im_region_fea_token)
# pdb.set_trace()
region_embs[region_replace_mask] = region_features[batch_idx].to(text_input_embeds.dtype)
text_input_embeds = text_input_embeds * (~region_replace_mask).to(text_input_embeds.dtype)[:, None] + region_embs
# print('region_embs[..., 0].nonzero()', region_embs[..., 0].nonzero())
# raise NotImplementedError()
# pdb.set_trace()
text_input_embeds = text_input_embeds * (~region_replace_mask).to(text_input_embeds.dtype)[:, None] + region_embs
else:
if hasattr(self.config, 'im_region_fea_token'):
assert (cur_input_ids == self.config.im_region_fea_token).sum() == 0
Expand Down