feat:enable cpu mode,set local read token

inference_resnet.py

@@ -1,6 +1,10 @@
 import tensorflow as tf
 gpu_devices = tf.config.experimental.list_physical_devices('GPU')
-tf.config.experimental.set_memory_growth(gpu_devices[0], True)
+if gpu_devices:
+    tf.config.experimental.set_memory_growth(gpu_devices[0], True)
+else:
+    print(f"TensorFlow device: {gpu_devices}")
+
 from keras.applications import resnet
 import tensorflow.keras.layers as L
 import os 
@@ -15,7 +19,10 @@ import numpy as np
 if not os.path.exists('model_classification'):
 
     REPO_ID='Serrelab/fossil_classification_models'
-    snapshot_download(repo_id=REPO_ID, token=os.environ.get('READ_TOKEN'),repo_type='model',local_dir='model_classification')
+    token = os.environ.get('READ_TOKEN')
+    if token is None:
+        print("warning! A read token in env variables is needed for authentication.")
+    snapshot_download(repo_id=REPO_ID, token=token,repo_type='model',local_dir='model_classification')
 
 
 def get_model(base_arch='Nasnet',weights='imagenet',input_shape=(600,600,3),classes=64500):

inference_sam.py

@@ -1,8 +1,19 @@
 import torch
-torch.cuda.set_per_process_memory_fraction(0.3, device=0)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+if torch.cuda.is_available():
+    device = "cuda"
+    torch.cuda.set_per_process_memory_fraction(0.3, device=device)
+else:
+   device = "cpu"
+print(f"Torch device: {device}")
+
 import tensorflow as tf
 gpu_devices = tf.config.experimental.list_physical_devices('GPU')
-tf.config.experimental.set_memory_growth(gpu_devices[0], True)
+if gpu_devices:
+    tf.config.experimental.set_memory_growth(gpu_devices[0], True)
+else:
+    print(f"TensorFlow device: {gpu_devices}")
 
 from segment_anything import SamPredictor, sam_model_registry
 import matplotlib.pyplot as plt
@@ -14,10 +25,13 @@ from huggingface_hub import snapshot_download
 
 if not os.path.exists('model'):
   REPO_ID='Serrelab/SAM_Leaves'
-  snapshot_download(repo_id=REPO_ID, token=os.environ.get('READ_TOKEN'),repo_type='model',local_dir='model')
+  token = os.environ.get('READ_TOKEN')
+  if token is None:
+     print("warning! A read token in env variables is needed for authentication.")
+  snapshot_download(repo_id=REPO_ID, token=token,repo_type='model',local_dir='model')
 
-sam = sam_model_registry["default"]("/home/irodri15/Documents/Projects/Fossils/fossil_app/model/sam_02-06_dice_mse_0.pth")
-sam.cuda()
+sam = sam_model_registry["default"]("/model/sam_02-06_dice_mse_0.pth")
+sam.to(device) #sam.cuda()
 predictor = SamPredictor(sam)
 
 
@@ -40,7 +54,7 @@ def preprocess(img):
   img_preprocess = predictor.transform.apply_image(img)
   intermediate_shape = img_preprocess.shape
 
-  img_preprocess = torch.as_tensor(img_preprocess).cuda()
+  img_preprocess = torch.as_tensor(img_preprocess).to(device) #torch.as_tensor(img_preprocess).cuda()
   img_preprocess = img_preprocess.permute(2, 0, 1).contiguous()[None, :, :, :]
 
   img_preprocess = sam.preprocess(img_preprocess)
@@ -155,7 +169,7 @@ def one_step_inference(x):
     mask = F.interpolate(low_res_masks, (1024, 1024))[:, :, :intermediate_shape[0], :intermediate_shape[1]]
     mask = F.interpolate(mask, (original_size[0], original_size[1]))
 
-  return mask 
+  return mask.to(device) #mask
 
 def segmentation_sam(x,SIZE=384):
     

model/.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
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+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
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+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

model/README.md

@@ -0,0 +1,37 @@
+---
+license: bigscience-openrail-m
+language:
+- en
+pipeline_tag: image-segmentation
+---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
+
+## Model Details
+
+SAM trained with MSE and finetuned for leaves segmentation
+
+### Model Description
+
+
+
+
+- **Developed by:** Thomas Fel and I . Rodriguez
+
+- **Model type:** Semantic Segmentation 
+- **Language(s) (NLP):** [More Information Needed]
+
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+
+

model/sam_02-06_dice_mse_0.pth

@@ -0,0 +1 @@
+../../../../.cache/huggingface/hub/models--Serrelab--SAM_Leaves/blobs/920cc0f6b6d80b8dcd771cb39bdec6c6be9c44cc6fc95314ef20025b71e55a73