diff --git a/.gitignore b/.gitignore index b794d98..f97d6ef 100644 --- a/.gitignore +++ b/.gitignore @@ -2,7 +2,7 @@ third_party/detectron2 ./models configs-experimental experiments -# output dir +output dir index.html data/* slurm/ diff --git a/datasets/metadata/lvis-21k_clip_a+cname.npy b/datasets/metadata/lvis-21k_clip_a+cname.npy new file mode 100644 index 0000000..a98f10e Binary files /dev/null and b/datasets/metadata/lvis-21k_clip_a+cname.npy differ diff --git a/demo.py b/demo.py index 9221af2..343d22c 100755 --- a/demo.py +++ b/demo.py @@ -139,6 +139,9 @@ def test_opencv_video_format(codec, file_ext): demo = VisualizationDemo(cfg, args) + if args.output: + if not os.path.exists(args.output): + os.makedirs(args.output) if args.input: if len(args.input) == 1: args.input = glob.glob(os.path.expanduser(args.input[0])) @@ -147,23 +150,8 @@ def test_opencv_video_format(codec, file_ext): img = read_image(path, format="BGR") start_time = time.time() predictions, visualized_output = demo.run_on_image(img) - logger.info( - "{}: {} in {:.2f}s".format( - path, - "detected {} instances".format(len(predictions["instances"])) - if "instances" in predictions - else "finished", - time.time() - start_time, - ) - ) - if args.output: - if os.path.isdir(args.output): - assert os.path.isdir(args.output), args.output - out_filename = os.path.join(args.output, os.path.basename(path)) - else: - assert len(args.input) == 1, "Please specify a directory with args.output" - out_filename = args.output + out_filename = os.path.join(args.output, os.path.basename(path)) visualized_output.save(out_filename) else: cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL) diff --git a/demo_box.py b/demo_box.py new file mode 100644 index 0000000..f36e6ae --- /dev/null +++ b/demo_box.py @@ -0,0 +1,249 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +import argparse +import glob +import multiprocessing as mp +import numpy as np +import os +import tempfile +import time +import warnings +import cv2 +import tqdm +import sys +import mss + + + +from pascal_voc_writer import Writer + +from detectron2.config import get_cfg +from detectron2.data.detection_utils import read_image +from detectron2.utils.logger import setup_logger + +# os.chdir('/home/zeyu/Detic/') +sys.path.insert(0, 'third_party/CenterNet2/') +# sys.path.append(os.path.dirname('/home/zeyu/Autodet2/Detic/third_party/CenterNet2/')) + +from centernet.config import add_centernet_config +from detic.config import add_detic_config + +from detic.predictor import VisualizationDemo + +# Fake a video capture object OpenCV style - half width, half height of first screen using MSS +class ScreenGrab: + def __init__(self): + self.sct = mss.mss() + m0 = self.sct.monitors[0] + self.monitor = {'top': 0, 'left': 0, 'width': m0['width'] / 2, 'height': m0['height'] / 2} + + def read(self): + img = np.array(self.sct.grab(self.monitor)) + nf = cv2.cvtColor(img, cv2.COLOR_BGRA2BGR) + return (True, nf) + + def isOpened(self): + return True + def release(self): + return True + + +# constants +WINDOW_NAME = "Detic" + +def setup_cfg(args): + cfg = get_cfg() + if args.cpu: + cfg.MODEL.DEVICE="cpu" + add_centernet_config(cfg) + add_detic_config(cfg) + cfg.merge_from_file(args.config_file) + cfg.merge_from_list(args.opts) + # Set score_threshold for builtin models + cfg.MODEL.RETINANET.SCORE_THRESH_TEST = args.confidence_threshold + cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = args.confidence_threshold + cfg.MODEL.PANOPTIC_FPN.COMBINE.INSTANCES_CONFIDENCE_THRESH = args.confidence_threshold + cfg.MODEL.ROI_BOX_HEAD.ZEROSHOT_WEIGHT_PATH = 'rand' # load later + if not args.pred_all_class: + cfg.MODEL.ROI_HEADS.ONE_CLASS_PER_PROPOSAL = True + cfg.freeze() + return cfg + + +def get_parser(): + parser = argparse.ArgumentParser(description="Detectron2 demo for builtin configs") + parser.add_argument( + "--config-file", + default="configs/quick_schedules/mask_rcnn_R_50_FPN_inference_acc_test.yaml", + metavar="FILE", + help="path to config file", + ) + parser.add_argument("--webcam", help="Take inputs from webcam.") + parser.add_argument("--cpu", action='store_true', help="Use CPU only.") + parser.add_argument("--video-input", help="Path to video file.") + parser.add_argument( + "--input", + nargs="+", + help="A list of space separated input images; " + "or a single glob pattern such as 'directory/*.jpg'", + ) + parser.add_argument( + "--output", + help="A file or directory to save output visualizations. " + "If not given, will show output in an OpenCV window.", + ) + parser.add_argument( + "--vocabulary", + default="lvis", + choices=['lvis', 'openimages', 'objects365', 'coco', 'custom'], + help="", + ) + parser.add_argument( + "--custom_vocabulary", + default="", + help="", + ) + parser.add_argument("--pred_all_class", action='store_true') + parser.add_argument( + "--confidence-threshold", + type=float, + default=0.5, + help="Minimum score for instance predictions to be shown", + ) + parser.add_argument( + "--opts", + help="Modify config options using the command-line 'KEY VALUE' pairs", + default=[], + nargs=argparse.REMAINDER, + ) + return parser + + +def test_opencv_video_format(codec, file_ext): + with tempfile.TemporaryDirectory(prefix="video_format_test") as dir: + filename = os.path.join(dir, "test_file" + file_ext) + writer = cv2.VideoWriter( + filename=filename, + fourcc=cv2.VideoWriter_fourcc(*codec), + fps=float(30), + frameSize=(10, 10), + isColor=True, + ) + [writer.write(np.zeros((10, 10, 3), np.uint8)) for _ in range(30)] + writer.release() + if os.path.isfile(filename): + return True + return False + + +if __name__ == "__main__": + mp.set_start_method("spawn", force=True) + args = get_parser().parse_args() + setup_logger(name="fvcore") + logger = setup_logger() + logger.info("Arguments: " + str(args)) + + cfg = setup_cfg(args) + + demo = VisualizationDemo(cfg, args) + if args.output: + if not os.path.exists(args.output): + os.makedirs(args.output) + + if args.input: + if len(args.input) == 1: + args.input = glob.glob(os.path.expanduser(args.input[0])) + assert args.input, "The input path(s) was not found" + for path in tqdm.tqdm(args.input, disable=not args.output): + img = read_image(path, format="BGR") + start_time = time.time() + predictions, visualized_output = demo.run_on_image(img) + + boxes = predictions['instances'].get_fields()['pred_boxes'].tensor.cpu().numpy() + classes = predictions['instances'].get_fields()['pred_classes'].cpu().numpy() +# ind = np.concatenate((np.where(classes == 134)+np.where(classes == 383))) +# boxes = boxes[ind] + writer = Writer(path, img.shape[0], img.shape[1]) + for i,box in enumerate(boxes): +# print(demo.metadata.thing_classes[classes[i]]) + writer.addObject(demo.metadata.thing_classes[classes[i]], box[0], box[1], box[2], box[3]) + #folder_name = path.split('.')[0].split("/")[-2] + #writer.save(path.split('.')[0].replace(folder_name, folder_name+"_xml")+".xml") +# print(writer) +# writer.save(path.replace('.jpg', '.xml')) + + +# logger.info( +# "{}: {} in {:.2f}s".format( +# path, +# "detected {} instances".format(len(predictions["instances"])) +# if "instances" in predictions +# else "finished", +# time.time() - start_time, +# ) +# ) + + if args.output: + out_filename = os.path.join(args.output, os.path.basename(path)) +# visualized_output.save(out_filename) + writer.save(out_filename.replace('.jpg', '.xml')) + else: + cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL) + cv2.imshow(WINDOW_NAME, visualized_output.get_image()[:, :, ::-1]) + if cv2.waitKey(0) == 27: + break # esc to quit + elif args.webcam: + assert args.input is None, "Cannot have both --input and --webcam!" + assert args.output is None, "output not yet supported with --webcam!" + if args.webcam == "screen": + cam = ScreenGrab() + else: + cam = cv2.VideoCapture(int(args.webcam)) + for vis in tqdm.tqdm(demo.run_on_video(cam)): + cv2.namedWindow(WINDOW_NAME, cv2.WINDOW_NORMAL) + cv2.imshow(WINDOW_NAME, vis) + if cv2.waitKey(1) == 27: + break # esc to quit + cam.release() + cv2.destroyAllWindows() + elif args.video_input: + video = cv2.VideoCapture(args.video_input) + width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH)) + height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT)) + frames_per_second = video.get(cv2.CAP_PROP_FPS) + num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT)) + basename = os.path.basename(args.video_input) + codec, file_ext = ( + ("x264", ".mkv") if test_opencv_video_format("x264", ".mkv") else ("mp4v", ".mp4") + ) + if codec == ".mp4v": + warnings.warn("x264 codec not available, switching to mp4v") + if args.output: + if os.path.isdir(args.output): + output_fname = os.path.join(args.output, basename) + output_fname = os.path.splitext(output_fname)[0] + file_ext + else: + output_fname = args.output + assert not os.path.isfile(output_fname), output_fname + output_file = cv2.VideoWriter( + filename=output_fname, + # some installation of opencv may not support x264 (due to its license), + # you can try other format (e.g. MPEG) + fourcc=cv2.VideoWriter_fourcc(*codec), + fps=float(frames_per_second), + frameSize=(width, height), + isColor=True, + ) + assert os.path.isfile(args.video_input) + for vis_frame in tqdm.tqdm(demo.run_on_video(video), total=num_frames): + if args.output: + output_file.write(vis_frame) + else: + cv2.namedWindow(basename, cv2.WINDOW_NORMAL) + cv2.imshow(basename, vis_frame) + if cv2.waitKey(1) == 27: + break # esc to quit + video.release() + if args.output: + output_file.release() + else: + cv2.destroyAllWindows() diff --git a/embedding_matrix_imagenet.pt b/embedding_matrix_imagenet.pt new file mode 100644 index 0000000..5aeab9f Binary files /dev/null and b/embedding_matrix_imagenet.pt differ diff --git a/embedding_matrix_lvis.pt b/embedding_matrix_lvis.pt new file mode 100644 index 0000000..d87d87a Binary files /dev/null and b/embedding_matrix_lvis.pt differ diff --git a/imagenet_class_info.json b/imagenet_class_info.json new file mode 100644 index 0000000..8b90e04 --- /dev/null +++ b/imagenet_class_info.json @@ -0,0 +1 @@ +{"n00004475": {"img_url_count": 8, "flickr_img_url_count": 6, "class_name": "organism"}, "n00005787": {"img_url_count": 1264, "flickr_img_url_count": 626, "class_name": "benthos"}, "n00006024": {"img_url_count": 1, "flickr_img_url_count": 0, "class_name": "heterotroph"}, "n00006484": {"img_url_count": 1251, "flickr_img_url_count": 628, 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"n15093049": {"img_url_count": 55, "flickr_img_url_count": 0, "class_name": "vitamin K3"}, "n15093137": {"img_url_count": 1233, "flickr_img_url_count": 27, "class_name": "vitamin P"}, "n15093298": {"img_url_count": 1268, "flickr_img_url_count": 824, "class_name": "vitamin C"}, "n15102359": {"img_url_count": 1, "flickr_img_url_count": 1, "class_name": "planking"}, "n15102455": {"img_url_count": 1316, "flickr_img_url_count": 474, "class_name": "chipboard"}, "n15102894": {"img_url_count": 928, "flickr_img_url_count": 680, "class_name": "knothole"}} \ No newline at end of file diff --git a/lvis_cat_id_to_imgdir.npy b/lvis_cat_id_to_imgdir.npy new file mode 100644 index 0000000..fc79269 Binary files /dev/null and b/lvis_cat_id_to_imgdir.npy differ diff --git a/match_word.py b/match_word.py new file mode 100644 index 0000000..37818f5 --- /dev/null +++ b/match_word.py @@ -0,0 +1,241 @@ +import argparse +import json +# This code is modified from https://github.com/openai/CLIP/blob/main/clip/clip.py +# Modified by Xingyi Zhou +# The original code is under MIT license +# Copyright (c) Facebook, Inc. and its affiliates. +from typing import Union, List +from collections import OrderedDict +import torch +from torch import nn +import torch +import numpy as np +from nltk.corpus import wordnet as wn + +from clip.simple_tokenizer import SimpleTokenizer as _Tokenizer + +__all__ = ["tokenize"] + +count = 0 + +class LayerNorm(nn.LayerNorm): + """Subclass torch's LayerNorm to handle fp16.""" + + def forward(self, x: torch.Tensor): + orig_type = x.dtype + ret = super().forward(x.type(torch.float32)) + return ret.type(orig_type) + + +class QuickGELU(nn.Module): + def forward(self, x: torch.Tensor): + return x * torch.sigmoid(1.702 * x) + + +class ResidualAttentionBlock(nn.Module): + def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None): + super().__init__() + + self.attn = nn.MultiheadAttention(d_model, n_head) + self.ln_1 = LayerNorm(d_model) + self.mlp = nn.Sequential(OrderedDict([ + ("c_fc", nn.Linear(d_model, d_model * 4)), + ("gelu", QuickGELU()), + ("c_proj", nn.Linear(d_model * 4, d_model)) + ])) + self.ln_2 = LayerNorm(d_model) + self.attn_mask = attn_mask + + def attention(self, x: torch.Tensor): + self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None + return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0] + + def forward(self, x: torch.Tensor): + x = x + self.attention(self.ln_1(x)) + x = x + self.mlp(self.ln_2(x)) + return x + + +class Transformer(nn.Module): + def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None): + super().__init__() + self.width = width + self.layers = layers + self.resblocks = nn.Sequential( + *[ResidualAttentionBlock(width, heads, attn_mask) \ + for _ in range(layers)]) + + def forward(self, x: torch.Tensor): + return self.resblocks(x) + +class CLIPTEXT(nn.Module): + def __init__(self, + embed_dim=512, + # text + context_length=77, + vocab_size=49408, + transformer_width=512, + transformer_heads=8, + transformer_layers=12 + ): + super().__init__() + + self._tokenizer = _Tokenizer() + self.context_length = context_length + + self.transformer = Transformer( + width=transformer_width, + layers=transformer_layers, + heads=transformer_heads, + attn_mask=self.build_attention_mask() + ) + + self.vocab_size = vocab_size + self.token_embedding = nn.Embedding(vocab_size, transformer_width) + self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width)) + self.ln_final = LayerNorm(transformer_width) + + self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim)) + # self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07)) + + self.initialize_parameters() + + def initialize_parameters(self): + nn.init.normal_(self.token_embedding.weight, std=0.02) + nn.init.normal_(self.positional_embedding, std=0.01) + + proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5) + attn_std = self.transformer.width ** -0.5 + fc_std = (2 * self.transformer.width) ** -0.5 + for block in self.transformer.resblocks: + nn.init.normal_(block.attn.in_proj_weight, std=attn_std) + nn.init.normal_(block.attn.out_proj.weight, std=proj_std) + nn.init.normal_(block.mlp.c_fc.weight, std=fc_std) + nn.init.normal_(block.mlp.c_proj.weight, std=proj_std) + + if self.text_projection is not None: + nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5) + + def build_attention_mask(self): + # lazily create causal attention mask, with full attention between the vision tokens + # pytorch uses additive attention mask; fill with -inf + mask = torch.empty(self.context_length, self.context_length) + mask.fill_(float("-inf")) + mask.triu_(1) # zero out the lower diagonal + return mask + + @property + def device(self): + return self.text_projection.device + + @property + def dtype(self): + return self.text_projection.dtype + + def tokenize(self, + texts: Union[str, List[str]], \ + context_length: int = 77) -> torch.LongTensor: + """ + """ + if isinstance(texts, str): + texts = [texts] + + sot_token = self._tokenizer.encoder["<|startoftext|>"] + eot_token = self._tokenizer.encoder["<|endoftext|>"] + all_tokens = [[sot_token] + self._tokenizer.encode(text) + [eot_token] for text in texts] + result = torch.zeros(len(all_tokens), context_length, dtype=torch.long) + + for i, tokens in enumerate(all_tokens): + if len(tokens) > context_length: + st = torch.randint( + len(tokens) - context_length + 1, (1,))[0].item() + tokens = tokens[st: st + context_length] + # raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}") + result[i, :len(tokens)] = torch.tensor(tokens) + + return result + + def encode_text(self, text): + x = self.token_embedding(text).type(self.dtype) # [batch_size, n_ctx, d_model] + x = x + self.positional_embedding.type(self.dtype) + x = x.permute(1, 0, 2) # NLD -> LND + x = self.transformer(x) + x = x.permute(1, 0, 2) # LND -> NLD + x = self.ln_final(x).type(self.dtype) + # take features from the eot embedding (eot_token is the highest number in each sequence) + x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection + return x + + def forward(self, captions): + ''' + captions: list of strings + ''' + text = self.tokenize(captions).to(self.device) # B x L x D + features = self.encode_text(text) # B x D + return features + + +def build_text_encoder(pretrain=True): + text_encoder = CLIPTEXT() + if pretrain: + import clip + pretrained_model, _ = clip.load("ViT-B/32", device='cpu') + state_dict = pretrained_model.state_dict() + to_delete_keys = ["logit_scale", "input_resolution", \ + "context_length", "vocab_size"] + \ + [k for k in state_dict.keys() if k.startswith('visual.')] + for k in to_delete_keys: + if k in state_dict: + del state_dict[k] + print('Loading pretrained CLIP') + text_encoder.load_state_dict(state_dict) + # import pdb; pdb.set_trace() + return text_encoder + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description='Auto search') + parser.add_argument("--path") + parser.add_argument( + "--words", + default="", + help="", + ) + args = parser.parse_args() + +embedding_matrix_imagenet = torch.load("embedding_matrix_imagenet.pt") +embedding_matrix_lvis = torch.load("embedding_matrix_lvis.pt") +# in_data_imagenet = json.load(open('./imagenet_class_info.json', 'r')) +# vocab_names_imagenet = np.array([val['class_name'] for val in in_data_imagenet.values()]) +# vocab_id_imagenet = np.array([key for key in in_data_imagenet.keys()]) +wn_dict = np.load("wn_dict.npy",allow_pickle=True)[None][0] +vocab_names_imagenet = np.array(list(wn_dict.values())) +vocab_id_imagenet = np.array(list(wn_dict.keys())) + +vocab_names_lvis = np.load('vocab_names_lvis.npy')[None][0] +text_encoder = build_text_encoder(pretrain=True) +text_encoder.eval() + +prompt = " a" +new_word = args.words.split(',') +texts = [prompt + x for x in new_word] +new_emb = text_encoder(texts).detach().cpu() +print("imagenet") +arg_max_idices = torch.cdist(new_emb,embedding_matrix_imagenet).sort(dim=1).indices[:,:5] +for i,voc in enumerate(arg_max_idices): + print("desired: 「{}」, words: {} ,\n id: {}".format(new_word[i], "'"+"', '".join(vocab_names_imagenet[voc])+"'","'"+ "', '".join(vocab_id_imagenet[voc])+"'")) + + +print("lvis") +arg_max_idices = torch.cdist(new_emb,embedding_matrix_lvis).sort(dim=1).indices[:,:5] +for i,voc in enumerate(arg_max_idices): + print("desired: 「{}」, words: {} ".format(new_word[i], "'"+"', '".join(vocab_names_lvis[voc])+"'" )) + + +# parser = argparse.ArgumentParser(description='Auto search') +# parser.add_argument("--path") +# parser.add_argument("--download_expand_ratio",default=6,type =int ) +# parser.add_argument("--memory_bank_expand_ratio",default=2,type =int ) +# parser.add_argument("--K_nn",default=16,type =int ) +# parser.add_argument("--deduplicate_threshold",default=0.92,type =float ) +# args = parser.parse_args() \ No newline at end of file diff --git a/ml_model.py b/ml_model.py new file mode 100644 index 0000000..e69de29 diff --git a/parallel.py b/parallel.py new file mode 100644 index 0000000..c791751 --- /dev/null +++ b/parallel.py @@ -0,0 +1,14 @@ +from subprocess import Popen +import sys +import os + +procs = [] +for i in range(6): + script = "demo_box.py" + my_env = os.environ.copy() + my_env["CUDA_VISIBLE_DEVICES"] = str(i) + procs.append(Popen(['python', script, '--config-file', 'configs/Detic_LCOCOI21k_CLIP_SwinB_896b32_4x_ft4x_max-size.yaml','--input','/home/zeyu/brute_force/agg_{}/*/*.jpg'.format(i), '--output','/home/zeyu/brute_force/detection_result_images/download_data{}/'.format(i), '--vocabulary','custom','--custom_vocabulary','box,envelope,package,person,monitor,car,tree,','--opts','MODEL.WEIGHTS','models/Detic_LCOCOI21k_CLIP_SwinB_896b32_4x_ft4x_max-size.pth' ],env = my_env)) + +for proc in procs: + proc.wait() + diff --git a/visualize_result.tar.gz b/visualize_result.tar.gz new file mode 100644 index 0000000..4b958df Binary files /dev/null and b/visualize_result.tar.gz differ diff --git a/vocab_names_lvis.npy b/vocab_names_lvis.npy new file mode 100644 index 0000000..eecdf9f Binary files /dev/null and b/vocab_names_lvis.npy differ diff --git a/wn_dict.npy b/wn_dict.npy new file mode 100644 index 0000000..5b6c350 Binary files /dev/null and b/wn_dict.npy differ