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Small fixes for notebook to run without issue. #3

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Small fixes for notebook to run without issue. #3

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3 changes: 2 additions & 1 deletion environment.yml
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
name: pytorch
name: lensless_learning
channels:
- pytorch
- conda-forge
Expand Down Expand Up @@ -137,6 +137,7 @@ dependencies:
- pytorch=1.0.1=py3.7_cuda9.0.176_cudnn7.4.2_2
- torchvision=0.2.2=py_3
- pip:
- lpips==0.1.4
- dask==1.1.4
- torch==1.0.1.post2
prefix: /home/kristina/anaconda3/envs/pytorch
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92 changes: 55 additions & 37 deletions pre-trained reconstructions.ipynb
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17 changes: 5 additions & 12 deletions utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -232,15 +232,13 @@ def save_model_summary(model, admm, filename, device, description, test_loader):


import sys
sys.path.append('/home/kristina/PerceptualSimilarity')
from models import dist_model as dm
from lpips import LPIPS
from admm_helper_functions_torch import *


def test_training_images(model, model_admm, test_loader, device):

lpipsloss = dm.DistModel()
lpipsloss.initialize(model='net-lin',net='alex',use_gpu=True,version='0.1')

lpipsloss = LPIPS(net='alex', version='0.1')

mse_loss = torch.nn.MSELoss(size_average=None)

Expand Down Expand Up @@ -326,11 +324,8 @@ def test_training_images(model, model_admm, test_loader, device):
return loss_dict

def test_training_images2(model, test_loader, device):

#model = model.eval()

lpipsloss = dm.DistModel()
lpipsloss.initialize(model='net-lin',net='alex',use_gpu=True,version='0.1')
lpipsloss = LPIPS(net='alex', version='0.1')


mse_loss = torch.nn.MSELoss(size_average=None)
Expand Down Expand Up @@ -428,9 +423,7 @@ def run_timing_test(model, test_loader, device, num_trials = 100):
##### Plotting functions
def preplot(image):
image = np.transpose(image, (1,2,0))
image_color = np.zeros_like(image);
image_color[:,:,0] = image[:,:,2]; image_color[:,:,1] = image[:,:,1]
image_color[:,:,2] = image[:,:,0];
image_color = cv.cvtColor(image, cv.COLOR_BGR2RGB)
out_image = np.flipud(np.clip(image_color, 0,1))
return out_image[60:,62:-38,:]

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