Project dependencies may have API risk issues #24
Description
Hi, In minDALL-E, inappropriate dependency versioning constraints can cause risks.
Below are the dependencies and version constraints that the project is using
torch==1.8.0
torchvision>=0.8.2
tokenizers>=0.10.2
pyflakes>=2.2.0
tqdm>=4.46.0
pytorch-lightning>=1.5
einops
omegaconf
git+https://github.com/openai/CLIP.git
matplotlib
The version constraint == will introduce the risk of dependency conflicts because the scope of dependencies is too strict.
The version constraint No Upper Bound and * will introduce the risk of the missing API Error because the latest version of the dependencies may remove some APIs.
After further analysis, in this project,
The version constraint of dependency tqdm can be changed to >=4.36.0,<=4.64.0.
The above modification suggestions can reduce the dependency conflicts as much as possible,
and introduce the latest version as much as possible without calling Error in the projects.
The invocation of the current project includes all the following methods.
The calling methods from the tqdm
tqdm.tqdm.set_description tqdm.tqdm
The calling methods from the all methods
self.resid_drop torch.cuda.manual_seed_all PIL.Image.fromarray PIL.Image.fromarray.save ExpConfig self.key hashlib.md5 module.weight.data.normal_ self.head pytorch_lightning.loggers.TensorBoardLogger self.lr_schedulers.get_last_lr text_features.image_features.F.cosine_similarity.squeeze W.B.device.H.torch.arange.repeat.transpose numpy.transpose min argparse.ArgumentParser.add_argument self.quantize.get_codebook_entry self.v sorted_idx_remove_cond.scatter self.quant_conv RuntimeError self.apply ImageNetDataModule self.sos.repeat pytorch_lightning.Trainer.fit torchvision.transforms.Compose self.stage2.sos AttnBlock model.stage1.from_ckpt from_file reversed get_positional_encoding datetime.datetime.now tokens.to.unsqueeze torch.nn.functional.cosine_similarity probs.torch.multinomial.clone self.encode pl_module.stage1 self.down.append Normalize self.mid.block_1 download self.conv1 Downsample z_q.permute.contiguous self.conv OptConfig torch.nn.functional.pad Stage1Hparams self.embedding super w_.permute.permute i.images.astype source.info.get from_file.enable_truncation self.norm2 random.seed numpy.random.seed os.path.expanduser x.self.query.view codes.device.T.torch.arange.repeat layers.Block device.args.num_candidates.args.softmax_temperature.args.top_p.args.top_k.args.prompt.model.sampling.cpu self.conv_in device.H.torch.arange.repeat self.mlp.transpose cutoff_topp_probs.masked_fill self.norm1 k.reshape.reshape torch.cuda.amp.autocast x.contiguous.contiguous loop.update argparse.ArgumentParser.parse_args prompt.clip.tokenize.to self.tok_emb_txt device.args.num_candidates.args.softmax_temperature.args.top_p.args.top_k.args.prompt.model.sampling.cpu.numpy Stage2Hparams os.path.dirname torch.tril self.ln1 pytorch_lightning.callbacks.ModelCheckpoint cnt.code_.unsqueeze model_clip.encode_text y.transpose.contiguous.view ImageNetDataModule.setup tuple enumerate torch.nn.Linear self.resid_drop.transpose tokenizer.build_tokenizer i_block.i_level.self.down.attn self.register_buffer self.dropout torchvision.utils.make_grid self.mid.attn_1 x.self.value.view torch.randn output.write self.pos_emb_img self.n_heads.C.self.n_heads.B.T.x.self.key.view.transpose self.ln2 self.nin_shortcut self.stage2.eval self.lr_schedulers.step self.blocks os.path.abspath model.stage2.from_ckpt torch.multinomial self.encoder quant.permute.permute min_encoding_indices.self.embedding.view torch.nn.functional.interpolate labels.self.sos.unsqueeze print torchvision.transforms.Normalize sys.path.append self.decoder torch.einsum self.norm_out torch.optim.AdamW images.self.stage1.get_codes.detach.view MultiHeadSelfAttention einops.rearrange urllib.parse.urlparse stage2.transformer.Transformer1d self.stage1.get_codes DataConfig self.drop omegaconf.OmegaConf.structured dalle.models.Dalle.from_pretrained.sampling preprocess_clip images.torch.stack.to tqdm.tqdm.set_description utils.config.get_base_config tqdm.tqdm x.self.key.view self.n_heads.C.self.n_heads.B.T.x.self.query.view.transpose torch.cat.clone self.decode self.stage2 self.query i_level.self.up.upsample urllib.request.urlopen torch.nn.ModuleList.append self.conv2 source.info self.n_heads.C.self.n_heads.B.T.x.self.value.view.transpose self.lr_schedulers layers.Encoder tarfile.open images.self.stage1.get_codes.detach model_clip.encode_image cutoff_topk_logits utils.sampling.sampling torch.nn.Sequential torch.nn.ModuleList setup_callbacks self.value tokens.to.to self.log math.sqrt isinstance omegaconf.OmegaConf.merge open torch.cat torch.ones torch.topk self.proj_out.reshape torch.argmin self.q self.stage1.parameters os.path.join os.path.exists torch.utils.data.DataLoader self.embedding.weight.data.uniform_ scores.torch.argsort.cpu torch.nn.Module cutoff_topk_logits.to dalle.utils.utils.clip_score int cutoff_topk_logits.clone N.x.contiguous f.extract torch.stack torch.sort self.attn_drop.masked_fill torchvision.datasets.ImageNet torchvision.transforms.CenterCrop optimizer.step download_target.open.read cnt.pos_enc_code_.unsqueeze args.config_downstream.os.path.basename.split self torch.optim.lr_scheduler.CosineAnnealingLR stage1.vqgan.VQGAN ValueError torch.argsort Stage1Config range torch.nn.functional.avg_pool2d omegaconf.OmegaConf.load self.sos x.transpose.contiguous torch.manual_seed os.path.isfile image.astype present.torch.stack.clone pl_module.logger.experiment.add_image os.path.basename ImageLogger self.stage1.eval pytorch_lightning.seed_everything torch.cat.size v.reshape.reshape sos.self.stage2.sos.unsqueeze torchvision.transforms.Resize url.split clip.tokenize datetime.datetime.now.strftime device.W.torch.arange.repeat torch.nn.Conv2d torch.nn.LayerNorm dalle.utils.utils.set_seed cls_idx.torch.LongTensor.to torch.nn.functional.softmax i_block.i_level.self.up.attn ResnetBlock torch.nn.functional.cross_entropy probs.torch.multinomial.clone.detach float images.texts.torch.cat.contiguous f.getmembers z_q.permute.contiguous.view dalle.models.Dalle.from_pretrained source.read VectorQuantizer pytorch_lightning.Trainer torch.sigmoid self.tok_emb_img i_block.i_level.self.down.block torch.clamp self.tokenizer.encode h.self.quantize.view self.conv_out nonlinearity model_clip.to self.ln_f q.permute.reshape torch.arange self.load_state_dict q.permute.permute self.k functools.partial torch.sum self.stage2.sos.repeat self.norm self.mid.block_2 self.head_txt cls utils.realpath_url_or_path torch.load torch.no_grad format past.append torchvision.transforms.ToTensor device.N.torch.arange.repeat presents.append self.stage1.decode_code self.quantize from_file.token_to_id os.makedirs self.pos_emb_txt torch.nn.Embedding utils.sampling.sampling_igpt code.clone.detach dalle.models.ImageGPT.from_pretrained z_q.permute.contiguous.permute torchvision.transforms.RandomCrop self.attn Upsample stage2.transformer.iGPT self.post_quant_conv torch.cumsum super.__init__ download_target.open.read.hashlib.md5.hexdigest self.proj_out i_level.self.down.downsample h.sos.torch.cat.contiguous ImageNetDataModule.train_dataloader self.stage2.view self.head_img self.proj ImageNetDataModule.valid_dataloader self.parameters len z.rearrange.contiguous torch.clip torch.nn.GroupNorm torch.nn.Parameter model.sampling argparse.ArgumentParser torch.nn.Dropout sorted_idx_remove_cond.clone block.sample torch.LongTensor self.log_img from_file.enable_padding torch.bmm self.mlp self.conv_shortcut y.transpose.contiguous recons.cpu.cpu module.bias.data.zero_ GELU self.up.insert dataclasses.field module.weight.data.fill_ clip.load torch.nn.functional.gelu i_block.i_level.self.up.block present.torch.stack.clone.detach from_file.add_special_tokens Stage2Config torch.repeat_interleave dalle.models.Dalle.from_pretrained.to layers.Decoder scores.torch.argsort.cpu.numpy cutoff_topp_probs self.mask.torch.tril.view sos.self.stage2.sos.unsqueeze.repeat torch.cat.transpose images.cpu.cpu self.attn_drop quant.rearrange.contiguous z.rearrange.contiguous.view
@developer
Could please help me check this issue?
May I pull a request to fix it?
Thank you very much.