lots of improvements
data augmentation plotting of intermediate pics
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11640a6494
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c56e583f68
@ -15,11 +15,13 @@ def apply_model(filepath: str):
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model = load_model()
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model.to(device)
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pic = DataLoader.preprocess(img, precision=np.float32)
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pic = DataLoader.crop_image(img)
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pic = DataLoader.preprocess(pic, precision=np.float32)
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pic = ex4.ex4(pic, (5, 5), (4, 4))[0]
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Image.fromarray((np.transpose(pic * 255.0, (1, 2, 0)).astype(np.uint8))).save("filename_grid.jpg")
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Image.fromarray((np.transpose(DataLoader.postprocess(pic), (1, 2, 0)).astype(np.uint8))).save("filename_grid.jpg")
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out = model(torch.from_numpy(pic).to(device))
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out = DataLoader.postprocess(out.cpu().detach().numpy())
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out = out.cpu().detach().numpy()
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out = DataLoader.postprocess(out)
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out = np.transpose(out, (1, 2, 0))
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im = Image.fromarray(out)
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im.save("filename.jpg", format="jpeg")
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@ -7,6 +7,7 @@ from torch.utils.data import Dataset
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from torch.utils.data import DataLoader
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from torchvision import transforms
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from PIL import Image
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import random
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import ex4
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@ -14,21 +15,27 @@ IMG_SIZE = 100
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class ImageDataset(Dataset):
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def __init__(self, image_dir, precision: np.float32 or np.float64):
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def __init__(self, image_dir, offsetrange: (int, int), spacingrange: (int, int), transform_chain: transforms,
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precision: np.float32 or np.float64 = np.float32):
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self.image_files = sorted(glob.glob(os.path.join(image_dir, "**", "*.jpg"), recursive=True))
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self.precision = precision
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self.offsetrange = offsetrange
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self.spacingrange = spacingrange
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self.transform_chain = transform_chain
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def __getitem__(self, index):
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# Open image file, convert to numpy array and scale to [0, 1]
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target_image = Image.open(self.image_files[index])
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target_image = crop_image(target_image)
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target_image = self.transform_chain(target_image)
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target_image = preprocess(target_image, self.precision)
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# calculate image with black grid
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doomed_image = ex4.ex4(target_image, (5, 5), (4, 4))
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# convert image to grayscale
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# target_image = rgb2gray(target_image) # todo look if gray image makes sense
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offset = (random.randint(*self.offsetrange), random.randint(*self.offsetrange))
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spacing = (random.randint(*self.spacingrange), random.randint(*self.spacingrange))
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doomed_image = ex4.ex4(target_image, offset, spacing)
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return doomed_image[0], np.transpose(target_image, (2, 0, 1))
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@ -36,16 +43,20 @@ class ImageDataset(Dataset):
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return len(self.image_files)
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def preprocess(input: np.array, precision: np.float32 or np.float64) -> np.array:
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# image = np.array(Image.open(self.image_files[index]), dtype=np.float32) / 255
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def crop_image(image: Image) -> np.array:
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resize_transforms = transforms.Compose([
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transforms.Resize(size=IMG_SIZE),
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transforms.CenterCrop(size=(IMG_SIZE, IMG_SIZE)),
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])
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input = resize_transforms(input)
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return resize_transforms(image)
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# normalize image from 0-1
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target_image = np.array(input, dtype=precision) / 255.0
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def preprocess(input: np.array, precision: np.float32 or np.float64) -> np.array:
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# image = np.array(Image.open(self.image_files[index]), dtype=np.float32) / 255
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# https://www.geeksforgeeks.org/how-to-normalize-images-in-pytorch/
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# normalize image from -1 - 1
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target_image = np.array(input, dtype=precision)
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target_image = target_image / 255.0
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# Perform normalization for each channel
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# image = (image - self.norm_mean) / self.norm_std
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@ -55,28 +66,47 @@ def preprocess(input: np.array, precision: np.float32 or np.float64) -> np.array
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# postprecess should be the inverese function of preprocess!
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def postprocess(input: np.array) -> np.array:
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target_image = (input * 255.0).astype(np.uint8)
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# todo clipping here correct? some values are >1 because of model
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target = np.clip(input, 0, 1)
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target_image = (target * 255.0).astype(np.uint8)
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return target_image
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def get_image_loader(path: str, precision: np.float32 or np.float64):
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image_dataset = ImageDataset(path, precision)
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totlen = len(image_dataset)
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# ranges due to project spec
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image_dataset = ImageDataset(path,
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offsetrange=(0, 8),
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spacingrange=(2, 6),
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transform_chain=transforms.Compose([transforms.RandomHorizontalFlip(),
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transforms.RandomVerticalFlip()]),
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precision=precision)
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image_dataset_augmented = ImageDataset(path,
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offsetrange=(0, 8),
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spacingrange=(2, 6),
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transform_chain=transforms.Compose([transforms.RandomHorizontalFlip(),
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transforms.RandomVerticalFlip(),
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transforms.GaussianBlur(3, 4)]),
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precision=precision)
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# merge different datasets here!
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merged_dataset = torch.utils.data.ConcatDataset([image_dataset, image_dataset_augmented])
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totlen = len(merged_dataset)
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test_set_size = .1
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trains, tests = torch.utils.data.dataset.random_split(image_dataset, lengths=(totlen - int(totlen * test_set_size),
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int(totlen * test_set_size)),
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generator=torch.Generator().manual_seed(0))
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train_split, test_split = torch.utils.data.dataset.random_split(merged_dataset,
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lengths=(totlen - int(totlen * test_set_size),
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int(totlen * test_set_size)))
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train_loader = DataLoader(
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trains,
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train_split,
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shuffle=True, # shuffle the order of our samples
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batch_size=25, # stack 4 samples to a minibatch
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num_workers=4 # no background workers (see comment below)
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)
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test_loader = DataLoader(
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tests,
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test_split,
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shuffle=True, # shuffle the order of our samples
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batch_size=5, # stack 4 samples to a minibatch
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num_workers=0 # no background workers (see comment below)
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@ -1,13 +1,17 @@
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import os
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import sys
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import PIL
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import numpy as np
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import packaging
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import torch
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from PIL.Image import Image
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from matplotlib import pyplot as plt
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from packaging.version import Version
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import DataLoader
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from DataLoader import get_image_loader
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from Net import ImageNN
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# 01.05.22 -- 0.5h
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from netio import save_model, load_model, eval_evalset
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from netio import save_model, eval_evalset
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def get_train_device():
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@ -22,16 +26,20 @@ def train_model():
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torch.manual_seed(0)
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device = get_train_device()
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# Prepare a path to plot to
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plotpath = "plots/"
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os.makedirs(plotpath, exist_ok=True)
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# Load datasets
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train_loader, test_loader = get_image_loader("training/", precision=np.float32)
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nn = ImageNN(n_in_channels=3, precision=np.float32) # todo net params
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nn.train() # init with train mode
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nn.train() # init with train modeAdam
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nn.to(device) # send net to device available
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optimizer = torch.optim.AdamW(nn.parameters(), lr=0.1, weight_decay=1e-5) # todo adjust parameters and lr
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optimizer = torch.optim.AdamW(nn.parameters(), lr=1e-3, weight_decay=1e-5) # todo adjust parameters and lr
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loss_function = torch.nn.MSELoss()
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loss_function.to(device)
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n_epochs = 7 # todo epcchs here
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n_epochs = 5 # todo epcchs here
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train_sample_size = len(train_loader)
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losses = []
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@ -40,12 +48,15 @@ def train_model():
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print(f"Epoch {epoch}/{n_epochs}\n")
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i = 0
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for input_tensor, target_tensor in train_loader:
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optimizer.zero_grad() # reset gradients
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output = nn(input_tensor.to(device)) # get model output (forward pass)
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loss = loss_function(output.to(device), target_tensor.to(device)) # compute loss given model output and true target
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loss = loss_function(output.to(device),
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target_tensor.to(device)) # compute loss given model output and true target
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loss.backward() # compute gradients (backward pass)
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optimizer.step() # perform gradient descent update step
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optimizer.zero_grad() # reset gradients
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losses.append(loss.item())
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i += train_loader.batch_size
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@ -64,6 +75,12 @@ def train_model():
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nn.train()
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# Plot output
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if i % 100 == 0:
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plot(input_tensor.detach().cpu().numpy()[:1], target_tensor.detach().cpu().numpy()[:1],
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output.detach().cpu().numpy()[:1],
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plotpath, i, epoch)
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# evaluate model with submission pkl file
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eval_evalset()
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@ -89,6 +106,35 @@ def eval_model(model: torch.nn.Module, dataloader: torch.utils.data.DataLoader,
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return loss
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def plot(inputs, targets, predictions, path, update, epoch):
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"""Plotting the inputs, targets and predictions to file `path`"""
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os.makedirs(path, exist_ok=True)
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fig, axes = plt.subplots(ncols=3, figsize=(15, 5))
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for i in range(len(inputs)):
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for ax, data, title in zip(axes, [inputs, targets, predictions], ["Input", "Target", "Prediction"]):
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ax.clear()
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ax.set_title(title)
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# ax.imshow(DataLoader.postprocess(np.transpose(data[i], (1, 2, 0))), interpolation="none")
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ax.imshow(np.transpose((data[i]), (1, 2, 0)), interpolation="none")
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ax.set_axis_off()
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fig.savefig(os.path.join(path, f"{epoch:02d}_{update:07d}_{i:02d}.png"), dpi=100)
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plt.close(fig)
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def check_module_versions() -> None:
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python_check = '(\u2713)' if sys.version_info >= (3, 8) else '(\u2717)'
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numpy_check = '(\u2713)' if Version(np.__version__) >= Version('1.18') else '(\u2717)'
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torch_check = '(\u2713)' if Version(torch.__version__) >= Version('1.6.0') else '(\u2717)'
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pil_check = '(\u2713)' if Version(PIL.__version__) >= Version('6.0.0') else '(\u2717)'
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print(f'Installed Python version: {sys.version_info.major}.{sys.version_info.minor} {python_check}')
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print(f'Installed numpy version: {np.__version__} {numpy_check}')
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print(f'Installed PyTorch version: {torch.__version__} {torch_check}')
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print(f'Installed PIL version: {PIL.__version__} {pil_check}')
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assert any(x == '(\u2713)' for x in [python_check, numpy_check, torch_check, pil_check])
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if __name__ == '__main__':
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check_module_versions()
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train_model()
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@ -70,7 +70,7 @@ def scoring_file(prediction_file: str, target_file: str):
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"""Computes the mean RMSE loss on two lists of numpy arrays stored in pickle files prediction_file and targets_file
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Computation of mean RMSE loss, as used in the challenge for exercise 5. See files "example_testset.pkl" and
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"example_submission_random.pkl" for an example test set and example targets, respectively. The real test set
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"example_submission_random.pkl" for an example testing set and example targets, respectively. The real testing set
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(without targets) will be available as download (see assignment sheet 2).
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Parameters
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