Create model.yaml

model.yaml

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+
+type: pytorch
+args:
+  module_file: pretrained_model_reloaded_th.py
+  module_obj: model
+  weights:
+    md5: 4878981d84499eb575abd0f3b45570d3
+    url: https://zenodo.org/record/1466068/files/pretrained_model_reloaded_th.pth?download=1
+default_dataloader:
+  defined_as: kipoiseq.dataloaders.SeqIntervalDl
+  default_args:
+    alphabet_axis: 0
+    auto_resize_len: 600
+    dtype: np.float32
+    dummy_axis: 2
+dependencies:
+  conda:
+  - python=3.6
+  - h5py=2.10.0
+  - _pytorch_select=0.2=gpu_0
+  - pytorch=1.3.1=cuda100py36h53c1284_0
+  - pip=20.3.3
+  - pysam=0.15.3
+  - cython=0.29.23
+  pip:
+  - kipoiseq
+info:
+  authors:
+  - github: davek44
+    name: David R. Kelley
+  cite_as: https://doi.org/10.1101/gr.200535.115
+  contributors:
+  - github: krrome
+    name: Roman Kreuzhuber
+  trained_on: "From 2,071,886 total sites, 71,886 randomly reserved for testing and 70,000 for validation, leaving 1,930,000 for training."
+  doc: "This is the Basset model published by David Kelley converted to pytorch by\
+    \ Roman Kreuzhuber. It categorically predicts probabilities of accesible genomic\
+    \ regions in  164 cell types (ENCODE project and Roadmap Epigenomics Consortium). Data was generated using DNAse-seq. The sequence\
+    \ length the model uses as input is 600bp. The input of the tensor has to be (N,\
+    \ 4, 600, 1) for N samples, 600bp window size and 4 nucleotides. Per sample, 164\
+    \ probabilities of accessible chromatin will be predicted. \n"
+  license: MIT
+  name: Basset
+  tags:
+  - DNA accessibility
+  version: 0.1.0
+schema:
+  inputs:
+    associated_metadata: ranges
+    doc: DNA sequence
+    name: seq
+    shape: (4,600,1)
+    special_type: DNASeq
+  targets:
+    column_labels:
+    - target_labels.txt
+    doc: Probability of accessible chromatin in 164 cell types
+    name: DHS_probs
+    shape: (164, )
+
+test:
+  expect:
+    url: https://s3.eu-central-1.amazonaws.com/kipoi-models/predictions/14f9bf4b49e21c7b31e8f6d6b9fc69ed88e25f43/Basset/predictions.h5
+    md5: 9df59f9899b27e65ab95426cb9557ad3